text:
July 2005
Library and Archives Canada Cataloguing in Publication Data
Main entry under title:
Guidelines for air quality dispersion modelling in British
Columbia. --
Copies of this document are available from the Water, Air and Climate Change Branch website
at wlapwww.gov.bc.ca/air.
2.1.1 Classes of Models: Screening, Refined and Advanced ..........................................4
2.1.2 Guideline Model Types: Core Models ...................................................................5
2.1.3 Guideline Model Types: Special Circumstance Models ........................................5
2.1.4 Guideline Model Types: Alternate Models ............................................................6
2.1.5 Modifications to Models ........................................................................................6
2.2.1 Level 1 Assessment................................................................................................7
2.2.2 Level 2 Assessment................................................................................................8
2.2.3 Level 3 Assessment................................................................................................8
2.3.1 Screening Models.................................................................................................10
2.3.2 Refined Models ....................................................................................................11
188.8.131.52. CALPUFF and CALPUFF (ISC) ............................................................13
List of Figures:
Figure 2.1 - Section 2 Outline: Air Quality Dispersion Models and Their Application in BC. .. 3
Figure 3.1 - The 12 Steps to Good Modelling Practice.............................................................. 21
List of Tables:
These guidelines are the result of contributions from modelling and atmospheric-science experts
who are experienced with regulatory processes in British Columbia. The guideline development
processes started with a review of an internal ministry document by SENES Consultants, the
development of a first draft for peer review by consultants from RWDI Air, and subsequent
discussions and contributions from the following: P. Sagert, R. Nogami (Cirrus Consultants); J.
Young (SENES Consultants); R. Humphries, A. Schutte, C. Koscher, M. Furberg (Levelton
Consultants); M. Davies (RWDI Air); K. Reid (Greater Vancouver Regional District); and
D. Fudge, E. Plain, W. McCormick, R. Bennett, S. Meyn and S. Sakiyama (Ministry of
Environment). Technical editing services were provided by A. Dolan and Associates. Funding
support for this project includes the following agencies: Environment Canada, BC Ministry of
Energy and Mines, BC Oil and Gas Commission, and BC Ministry of Environment.
For expert practitioners experienced in applying models in BC, the portions of interest would be
Sections 2.3 and 2.4 (Recommended Models for Use in BC), Section 4 (Model Plan) and
Sections 5-11 (Model Inputs, Model Specific Information, Model Output, and Special Topics).
Very detailed recommendations provided in these sections are to guide model selection and
proper model application.
For those who are relative newcomers to dispersion modelling in BC, a review of the complete
document is recommended with particular focus on The 12 Steps to Good Modelling Practice
(Section 3), the Model Plan (Section 4) and the discussion on Model Quality Assurance and
Control (QA/QC) (Section 10).
For managers who use model output to inform decision making, the recommended portions are
related to introductory material on dispersion models, their use, the basic model types and their
application in terms of Levels of Assessment (Sections 1, 2), the importance of the Model Plan
(Section 4) and the discussion on Model Output (Section 10) as it relates to uncertainty.
Air quality management has rapidly evolved from the control of a few contaminants emitted
from industrial stacks to a complicated web of activities to address a host of new, inter-related
issues. These issues include extremes in space (local to global) and time (instantaneous to
decades) and involve multiple contaminants emitted from a wide variety of sources. In order to
sort out these complexities, decision makers have increasingly relied on air quality dispersion
models as they provide a way to evaluate different emission control policy scenarios or design
prototypes that would be expensive, difficult, or destructive to do in the real world. Dispersion
models are often the only cost-effective method to derive an understanding of the interaction of
existing or future emission sources, with meteorology, topography, and existing air quality. They
generate quantitative answers to "what if" questions and provide the foundation for decisions that
govern many day-to-day activities. These answers are needed to support prudent, environmentally
sound, and scientifically based air management decisions.
Dispersion models are commonly used to determine permit emission limits and conditions where
models are used to assess the air quality impacts of an existing or proposed source with respect
to federal, provincial and regional ambient air quality objectives.
However, models are often used as a decision-making tool for a wide variety of air management
issues, which include:
w identifying the main contributors to existing air contaminant problems
w evaluating potential mitigation strategies
w managing existing emissions in an airshed
w planning new sources of emissions
w optimizing the placement of emission sources to reduce their air quality impacts
w designing ambient air monitoring networks
w forecasting potential air quality episodes
w assessing risk and developing response plans in the event of an accidental air emission
The increased use of dispersion models and the confusion created by new and more complicated
models has led to the need for regulatory agencies to provide guidance on model selection and
application. Lack of such guidance can result in professional disagreement on technical details,
model misapplication, the absence of modelling in situations where it would provide valuable
information, disparity in model selection and application, and ultimately poor decisions that may
have societal and environmental consequences.
The Ministry of Environment has produced these guidelines to insure that dispersion modelling,
conducted for the ministry are:
w appropriate for the needs of the application
w applied correctly and consistently using accepted scientific techniques
w used to reliably inform air quality management decisions
From a broad ministry perspective, the guidelines are intended to further progress toward one of the
ministry's goals: "Provide effective and efficient planning and support for ministry programs"
through two identified strategies (Ministry Service Plan 2004):
w continue to guide and support the integration of science and risk management in ministry
decision making
w effectively communicate expectations and results to staff, clients and the public
1.2 Scope
To the extent possible, these guidelines are intended to provide explicit recommendations for
applying air quality dispersion models. However, given the diversity of models and their
application to different source, topography, and meteorological combinations, it is impossible to
provide detailed recipes for every situation. The professional judgment of technical experts and
managers will always be involved in any model application.
Other types of models, which are not included in these guidelines, can be used in situations where
dispersion models have limited application or where more than one tool is needed to provide a
weight of evidence. These include:
w Receptor Models: the measured chemical species concentrations in an air sample are
used to identify sources that contributed to the sample (for example PM source attribution
studies where PM sources are difficult to characterize)
w Physical Models: scale models in a wind tunnel or water flume allow for an
understanding of the flow and dispersion characteristics in very complex flow settings
(for example the behaviour of contaminants in an urban setting with multiple buildings
and street canyons)
w Statistical Models: statistical relationships between air quality and meteorology, based
on historical data, are applied to forecast future air quality conditions (for example PM
and O3)
The guidance provided in the document is intended for modelling applications submitted to the
Greater Vancouver Regional District (GVRD), as well as the ministry. As such, the term "GVRD"
can be used synonymously wherever the term "ministry" is used in the document.
This section provides an overview on models, identifies the models that are recommended in
British Columbia, and outlines different ways they can be applied. This sequence is illustrated in
the schematic below.
Figure 2.1 - Section 2 Outline: Air Quality Dispersion Models and Their Application in BC.
Air quality dispersion models are mathematical descriptions of the behaviour of air contaminants
in the atmosphere. A vast array of air dispersion models has been developed in different
jurisdictions around the world to handle a wide range of modelling conditions.
The geophysical setting of British Columbia presents unique challenges for dispersion
modelling. There are extremes in terrain — referred to as "complex" terrain (rolling hills,
valleys, bowls, coastal areas, mountains) and surfaces (water, open plains, forests, snow/ice,
urban zones) that all affect the dispersion of air contaminants.
The most common air dispersion models are steady-state, straight-line, Gaussian plume models.
They calculate contaminant concentrations for each hour assuming meteorological conditions are
uniform across the modelling domain. Because of the steady-state, straight-line nature of these
models, they do not explicitly account for curvilinear plume trajectories and variable wind
conditions that occur in complex terrain areas. In addition, these models have a limited ability to
represent low wind speeds, a frequent feature in the bowls and deep valleys in BC. For example,
in the Prince George basin winds less than 1 m/s nearly 20% of the time (Ministry 2004).
For these situations, models that treat emissions as a series of puffs have been developed. These
models require more computing power as they track puffs that represent discrete amounts of
contaminants over time. However, they have the advantage of allowing meteorological
conditions (winds, turbulence, vertical temperature structure) to vary across the modelling
domain. In addition, they can address the accumulation of contaminants during calm conditions,
the curved paths of plumes, and the effects of causality (where the previous position of the plume
is accounted for in determining the current plume position).
The straight-line (Gaussian) and curvilinear (puff) models described above are only two
examples of model types that exist in the models "supermarket." Even with these two model
types, there are several variants that are especially designed to account for unique situations,
such as chemical transformations and heavier than air emissions.
In order for these guidelines to simplify the choice and application of models in BC, broad
classes of models have been defined on the basis of both model complexity and model
Dispersion models can be very simple (a look-up table or graph) or very sophisticated (require
large computer resources). The simple models make many assumptions on the underlying
physics and require little effort and expertise to use them. The more sophisticated models include
formulations that describe such processes in a more comprehensive manner. They require more
resources as well as expertise in the set-up, running and interpretation of the output. The wide
range of model complexity can be broken into three broad classes: screening, refined and
Screening models are a simple and quick way to estimate a "worst case" concentration that may
possibly occur for a given emission. They do not require hourly, site-specific meteorological data
as input, but rather have a built-in, meteorological data matrix that represents a spectrum of
different combinations of meteorological conditions that could possibly occur. A screening
model calculates concentrations under these different combinations of meteorological conditions.
From this output, the highest (worst-case) concentration can be selected. Screening models are
generally limited to providing the worst-case one-hour concentrations at a receptor and cannot be
applied to long-term (eight-hour, daily, seasonal, annual) average concentrations unless a
correction factor is applied. Screening models are considered to provide conservative
concentration estimates, and as such are used as a flagging device that indicates the potential for
unacceptable air quality.
Refined models include a more detailed treatment of the atmospheric processes, require more
detailed input data, and provide time and space concentration (or deposition) distributions. Refined
models include more realism (physics and meteorological input) in order to make predictions that
are specific to the site and closer to reality (in theory) than screening model predictions. Model input
consists of geophysical data such as terrain and surface roughness, user-defined receptors, and a
sequential, hourly time series of meteorological data that is representative of the conditions at the
location of the source. This level of detail of model input allows for a sequential hour-by-hour
contaminants simulation for an array of receptor locations. Output can include both short-term (one
hour) and long-term (multiple-hour, daily, seasonal, annual) average concentrations at every
receptor location. Thus, it is possible to obtain the maximum concentration as well as details on the
space and time variation of the concentrations — something that may be of importance to decision
Advanced models include comprehensive treatments of meteorology, emissions and chemistry and
require considerable expertise and computer resources to set-up, run and interpret the results.
Examples include the regional scale photochemistry models such as CMAQ, where simulations of
secondary contaminants (O3 and PM) over large domains are conducted. Input files are largely
generated by other models that in turn are resource intensive (i.e., emissions models and mesoscale
meteorological models). Due to the resources required to run such models, they are typically applied
for select episode situations (i.e., hour-by-hour over a few days).
The ministry recommends a number of core models to be used for most air dispersion modelling
in British Columbia given that no one single model is appropriate for all geophysical, source and
application situations. In general, core models have been recommended for use by the United
States Environmental Protection Agency (US EPA). The BC guidelines are not bound by the
EPA (2003) guidelines in terms of model choice and application, but the US EPA process has the
advantage of involving numerous technical and regular stakeholder reviews and continuous
evaluation. Other advantages of adopting some of the recommended US EPA models include:
w open source code that can be evaluated and technically critiqued by anyone
w detailed user documentation
w free availability through the Internet
w extensive testing for coding errors and unrealistic behaviour
w performance evaluations with observations
w wide user community and experience in regulatory decision making throughout North
America and other parts of the world
w familiar to BC modellers in government, consulting and industry
There are "special circumstances" where the recommended core models listed in Section 2.3 may
not be the model of choice. Special circumstance models outlined in Section 2.4 can be
considered for a particular application if:
w a core model (because of its technical limitations for the application) cannot reasonably
perform the job for the situation
w the special circumstance model is simpler than a core model, but provides the required
information (i.e., if it is easier to apply a special circumstance model and still meet the
information needs, then a special circumstance model can be applied)
The special circumstance models listed here are those also listed as preferred models in EPA
For models that are not listed here, the use of alternate models can be considered only if:
w the core or special circumstance model (as listed here) do not have the capability to
handle a particular application
w the alternate model performs better than the core or special circumstance model
Although the guideline models will handle the vast majority of applications, one example of an
alternate model would be a CFD (Computational Flow Dynamics) model for flow and dispersion
around building structures. The models listed in these guidelines do not have the capability to
provide details of such flow situations. However, use of an alternate model is subject to ministry
approval based on the acceptance criteria listed in Section 2.1.2. Priority will be given to peer
reviewed evidence that demonstrates better performance where the model has been applied in a
similar situation using performance indicators relevant for this application (maximum hourly
concentrations comparisons, exceedance threshold comparisons, spatial pattern comparisons, etc.)
Any modifications to the recommended models in Section 2.3 that could affect the model results
are subject to ministry approval. All changes are to be documented and provided in the
Modelling Plan (Section 4).
Models are applied to assess the air quality implications of an emission (an "Air Quality
Assessment"). Assessments can vary in their level of detail and scope, which in turn is
determined by the objectives of the modelling effort, technical factors and the level of risk
associated with the project emissions.
Which model provides the necessary information to the decision maker? What level of detail and
accuracy is required for the analysis? The answer to these fundamental questions provides
direction on the selection of a specific model. For example, if the key questions can be answered
by a screening model that produces a conservative estimate of the worst-case concentration, then
a screening model listed here is the appropriate selection. If more detail is needed, such as air
quality impacts that vary in space and time, then models that consider hour-by-hour changes over
a grid of receptors is required.
Technical factors considered in model selection are those associated with the unique source,
emissions and meteorological setting. For example, if the formation of secondary contaminants
such as O3 or PM2.5 is important, then appropriate models that handle the transformation
chemistry are recommended. If curved trajectories are important (i.e., an examination of the
impacts around the bend in a valley), then a model that handles curved trajectories is
recommended. In addition, if the detail and accuracy of the input data required by a model is
unavailable, then either such data must be collected prior to the application of a model, or
another choice of model appropriate for the quality of the input data should be considered.
Finally, the level of risk associated with a project can determine the level of detail and accuracy
required from a model. The following factors help guide a distinction between high and low risk
w contaminant type
w amounts emitted
w current levels of air quality in the area (pristine or already degraded)
w sensitivity of the surrounding area (environmental and human)
w physical setting (sheltered valley vs. open area)
w environmental footprint of the facility
w future situation (land use, changes in sensitivity due to anticipated growth)
w public interest/concern
w consequences of decisions that will be made based on model output
Given the factors listed above, a variety of modelling solutions are possible. In order to help
clarify the choices, these guidelines provide three assessment levels with appropriate model
applications and example situations for each of these levels.
A Level 1 assessment provides an estimate of the worst-case air quality impacts. Therefore,
screening models are appropriate for this level. The primary purpose of a Level 1 assessment is
to identify those sources that need further analysis if they contribute to ambient concentrations in
excess of short-term air quality objectives. However, Level 1 assessments also have the ability to
help address a number of other related needs, some of which are:
w "go, no-go" evaluations (a critical acceptance/rejection criteria is exceeded or not
exceeded: typically for low-risk sources)
w permit/approval decisions for single sources (typically for low-risk sources)
w preliminary identification of air quality issues associated with proposed new sources or
modifications to existing sources
w planning purposes (internal resources required to conduct assessment, need to consider
other studies to support the decision-making process, need to contact other agencies)
w identification of the need for more detailed modelling using Level 2 or 3 assessment
approaches (if exceedances of short-term objectives are predicted)
w confirmation of refined model results that appear unusually high or low
w defining the Modelling Plan (Section 4) for Level 2 or 3 assessments
Although Level 1 assessments are useful in many situations, often the complete distribution of
concentrations in time and space are of interest. This distribution provides the spatial pattern of
maximum concentrations at different time averages surrounding the source area, and/or the
identification of areas where certain specified concentration thresholds are exceeded as well as
their exceedance frequencies.
Models used for Level 2 assessments are of the refined straight-line, steady-state Gaussian models
that require a time series of sequential, hourly meteorological data that represents the dispersion
conditions at the source.
Level 3 assessments require more sophisticated models and corresponding input data, resources and
model operator expertise to properly account for these factors. These more powerful models require
detailed meteorological, geophysical and source input that may include:
w one or more years of site-specific meteorological data (e.g., wind speed, wind direction,
temperature, turbulence, and mixing height) at a number of sites in the domain of interest
w detailed emission inventories for point, line, area and volume sources in an airshed that
has a mix of urban, industrial and natural sources
w the speciation, emissions and time variation of different contaminants (e.g., volatile
organic compounds from vegetation)
Gaussian puff trajectory models and regional-scale, photochemical grid models are examples of
models used for a Level 3 assessment. The latter type of model is commonly used to examine
episode situations due to emissions from a large urban area over a large distance (500 km). This
model is appropriate for forecasting or planning purposes or to determine the effectiveness of large-
scale source control measures. These situations demand modelling that is comprehensive and
thoroughly tested, so users have confidence in their predictive capabilities.
Several core models recommended for use in British Columbia are listed in Table 2.1 along with
recommendations on the situations for their application. The core screening models include
SCREEN3 and ISC-PRIME/S; whereas the core refined models include ISC-PRIME, AERMOD,
RTDM3.2, and CALPUFF. Only a sketch of each model is listed here. More complete
descriptions of these models are found in the corresponding model documentation (found on the
EPA website – Section 12 under Model Resources).
SCREEN3 is a single-source Gaussian plume model that provides maximum ground-level
concentration predictions for point, area, flare, and volume sources. SCREEN3 is able to account
for a variety of effects including: building downwash for both the near-wake and far-wake
regions, cavity recirculation, and flare releases. Simple-area sources can be modelled with
SCREEN3. This model can incorporate the effects of terrain below stack height on maximum
concentrations and can also estimate 24-hour average concentrations due to point-source plume
impaction for terrain above stack height.
Recommended Use:
w single point, area, volume sources
w single building effects on point source
w building wake cavity concentrations
w flare releases
w transport distances of less than 50 km
For multiple point sources, a representative combined source method could be used (using the
method described in Section 5.3). A better approach is to use ISC-PRIME/S where individual
sources are treated explicitly. For multiple area and volume sources ISC-PRIME/S is the only
For terrain that is greater than stack height, SCREEN3 produces a 24-hour average maximum
concentration. To obtain a one-hour average, multiply the 24- hour average by four.
For terrain areas at elevations less than the stack height, SCREEN3 produces a maximum one-
hour concentration. Averaging time adjustment factors can be applied to estimate concentrations
for longer averaging times. These are listed in Table 11.2 (Section 11.5).
ISC-PRIME/S is the application of the Industrial Source Complex, Short-Term model (ISCST)
with the PRIME algorithm (Plume Rise Model Enhancements) run in a screening mode (S). The
PRIME algorithm refers to the building downwash treatment.
The model is a steady-state Gaussian plume model that is capable of handling multiple sources,
including point, volume, area and open-pit source types. Line sources may also be modelled as a
string of volume sources or as elongated area sources. The emission rates may be treated as
constant throughout the modelling period, or may be varied by month, season, hour-of-day, or
other optional periods of variation. The model includes algorithms for modelling the effects of
settling and removal of large particulates (dry deposition). In addition, there are algorithms to
determine wet deposition of gases and particulates. The effects due to building downwash are
also accounted for (through the PRIME downwash algorithm). There is limited treatment for the
effects of elevated terrain.
In the screening mode (S), the model uses a matrix of pre-determined combinations of
meteorological conditions (similar to those in the SCREEN3 model) to find the maximum hourly
concentration. The preparation for an appropriate screening meteorological data set is discussed
in Appendix A.
Recommended Use:
w sources in an industrial complex (single or multiple point, area, line, volume sources)
with either no buildings or single or multiple buildings
w transport distances less than 50 km (depends on terrain)
w hourly concentration estimates for terrain heights below stack height except in lee areas
w particle depositions
Averaging time adjustment factors can be applied to estimate longer time-average concentrations
from the hourly values produced by the model. These factors are listed in Table 11.2 (Special
Topics, Section 11.5).
There is no technical difference between the ISC-PRIME model and ISC-PRIME/S. However
ISC-PRIME uses measured hour-by-hour meteorological data that represents the conditions
experienced by the source emissions in contrast to the generic screening meteorological data
matrix used for ISC-PRIME/S.
With hourly sequential meteorological input, ISC-PRIME can be used to estimate ambient
concentrations for differing sequential averaging periods (e.g., a multiple of one hour — 2, 3, 4,
6, 8, and 12 — daily, and annual) for an array of user-specified receptors. In addition, source
emission rates can be treated as constant throughout the modelling period, or may vary by
month, season, hour-of-day, or other time period. These variable emission rate factors may be
specified for a single source or for a group of sources. For further guidance on ISC-PRIME, see
Section 9.1
ISC-PRIME is not listed as an EPA preferred model (EPA 2003) as it is based on an earlier
version of ISCST3 (which is an EPA preferred model). However, the PRIME algorithm is
recognized by the EPA to be an improvement over of the current treatment of building
downwash in ISCST3. As such, the ministry has listed ISC-PRIME as a core model.
Recommended Use:
w sources in an industrial complex (single or multiple point, area, line, volume sources)
with either no buildings or with single or multiple buildings with building downwash
w constant or time-varying emissions
w one-hour to annual averaging times at specified receptors
w transport distances less than 50 km (depends on terrain)
w concentration estimates for terrain below stack height, except in lee areas (for terrain
areas above stack height see RTDM3.2 for guidance)
w particle depositions (however, model recommended for volume sources as unrealistic
deposition estimates have been reported).
The Rough Terrain Diffusion Model (RTDM3.2) is a straight-line, steady-state Gaussian plume
model designed to estimate ground-level concentrations in areas where the terrain is above the
stack height in the vicinity of one or more co-located point sources.
RTDM has features that are especially suited for simulating plume dispersion in terrain above
stack height (Paine and Egan, 1987). It provides concentrations at a grid of receptors in rural
areas using a time series of hourly meteorological input data. The terrain treatment involves a
calculation to determine whether a plume will impinge on a hill or go over it depending on
whether it is above or below the "critical dividing streamline" height. The approach is
technically superior to the terrain treatment in ISC-PRIME as demonstrated in performance
evaluations. No building effects are accounted for, and area and volume sources are not treated.
The model can account for hourly variations in source emissions.
The output consists of hourly concentrations at each receptor for the whole period of
meteorological data input. The output can be processed using different post-processors
(TOPVAL, CUMFREQ, PEAK, AVERAGES, SEQADD) to obtain maximum concentrations,
frequencies of exceedance above any specified ambient objective, concentrations for any
averaging time and contributions of multiple sources. SEQADD can be used to combine the
concentrations from individual runs of non co-located sources to produce a combined result.
The EPA (2003) recommends RTDM3.2 as a screening type model for terrain areas that are at
plume height or higher. The program is available from the EPA website although the user's
guide is only available through NTIS (see Paine and Egan, 1987). The ministry has retained the
use of RTDM3.2 because it has a superior treatment of terrain effects on plume behaviour than
ISC-PRIME for areas with elevations above stack height (ISC-PRIME generally produces more
conservative results for these areas). It is also used for well-test flaring situations (Special
Topics, Section 11.1). For situations other than well-test flaring, if more realistic estimates of
concentrations for areas above stack height elevation are desired, then RTDM3.2 can be applied.
For further guidance on running RTDM3.2, see Section 9.2.
Recommended Use:
w single or co-located point source with no building downwash effects (although non-co-
located point sources can be handled through SEQADD)
As part of the EPA acceptance process, AERMOD performance was evaluated using a variety of
comparison statistics against a variety of different data sets representing a range of source and
terrain types (Paine et al. 1998). The results show that AERMOD is nearly unbiased in predicting
the higher concentrations, on average, across all averaging times. For all averaging times and in
general and in most cases, AERMOD's model performance was better (closer to the observed)
than that of ISC-PRIME.
AERMOD is a modelling system that includes two preprocessors (AERMAP for terrain data, and
AERMET for meteorological data). AERMOD is designed to handle the same source types
currently addressed with ISC-PRIME (multiple point, area, line and volume sources, buildings,
concentrations, depositions). Line sources can be modelled as a string of volume sources or as
elongated area sources. For further guidance on running AERMOD, see Section 9.3.
Although AERMOD is not presently a recommended EPA Guidelines model, formal approval is
imminent. Once approved, it is expected to supersede ISC-PRIME and RTDM3.2.
Recommended Use:
w sources in an industrial complex (single or multiple point, area, line, volume sources)
with no buildings or single or multiple buildings with building downwash
w gas and particle depositions
w constant or time-varying emissions
w rural or urban areas
w transport distances less than 50 km (depends on terrain)
w concentration estimates for all terrain locations, except in lee areas
chemical reactions) and visibility effects of particulates. It can be applied to model small-scale
effects (in the order of tens of metres) to transport distances of hundreds of kilometres.
CALPUFF is a modelling system comprised of three component submodels: CALMET
(meteorological model), CALPUFF (calculates output), CALPOST (analysis and display of
output). The meteorological fields used by CALPUFF are produced by CALMET — a
meteorological model that includes a diagnostic wind field model. This model contains
treatments of slope flows, valley flows, terrain blocking effects, kinematic terrain effects (i.e.,
speed up over hills), lake and sea breeze circulations, and a procedure to insure mass is
conserved in the domain. CALMET inputs include surface and upper-air meteorological data as
well as the option to use the gridded meteorological fields produced by mesoscale
meteorological models.
CALPUFF uses the CALMET produced fields to generate a variety of outputs including
concentrations, wet and dry depositions, and visibility parameters (extinction coefficients). It
describes a continuous plume as a series of puffs, which in turn allows a better description of
how a plume can curve, deform (squeeze as it passes through a narrowing in the valley) and even
split apart in complex wind areas. Furthermore, unlike other straight-line Gaussian plume
models, it can be applied under no-wind conditions.
Currently building downwash is accounted for using the downwash algorithm in ISCST3 (the
predecessor to ISC-PRIME). Although PRIME is now part of CALPUFF, this version of the
model is still in its Beta testing stage and is not yet officially released by the EPA. CALPUFF
with PRIME is currently not recommended in this guideline until EPA approval of this latest
The proper application of CALPUFF requires more care and expertise than the other straight-
line, steady-state Gaussian models recommended here. There are far more options available to
the user and adjustable parameters, and establishing the right combination of these parameters
requires expert understanding of large-scale and small-scale meteorological effects, especially
when applied in complex flow situations.
CALPUFF can be used in short-range (<50 km) air quality estimates on a case-by-case basis
when approved by the ministry for complex, non-steady-state meteorological conditions where
steady-state, straight-line, Gaussian models are not appropriate. If long range transport (LRT) is
important (>50 km), CALPUFF is approved for use in estimating deposition and visibility
CALPUFF (ISC) is CALPUFF run in ISC mode. The transport and dispersion of the contaminant
is modelled using the straight-line, steady-state Gaussian plume ISCST3 model within the
CALPUFF framework. This option allows the effects of causality to be included in the straight-
line Gaussian plume framework. CALPUFF (ISC) uses the older downwash algorithm (as in
ISCST3) so it can only be used as a substitute for ISC-PRIME in situations where building
downwash is not affecting the behaviour of the emissions.
Recommended Use:
w long-range transport distances between 50 and 200 km
w for complex, non-steady-state meteorological conditions where transport distances are
less than 50 km, on a case-by-case basis, as approved by the ministry
w no buildings or single or multiple buildings
w availability of detailed meteorological and geophysical inputs
w deposition and light extinction where long-range transport distances are greater than 50
w PM secondary formation where long-range transport distances are greater than 50 km
w multiple source (point, area, volume) and buildings
w situations where there is no building downwash, use CALPUFF (ISC), as an alternative
to ISC-PRIME where a straight-line, plume trajectory treatment is acceptable.
The most common technical reason for considering CALPUFF over straight-line, Gaussian
models is related to its ability to handle complex flow situations. Determining whether a straight-
line Gaussian model of CALPUFF is appropriate for complex flow applications depends on the
following considerations based on the CALPUFF FAQ's (www.scr.com/calpuff/FAQ-
w the degree in which the flow is "complex" (wind and turbulence patterns change over
short distance, prolonged stagnations)
w the area of interest (i.e., transport distance) — shorter transport distances are less
influenced by complex flow and can be approximated by a straight line trajectory
w the objectives of the application — even in a very complex flow situation, if only
maximum concentrations are desired, then a straight line model may be appropriate,
however if the distribution of concentrations experienced at receptors around the bend of
a valley is required for example, then CALPUFF is the better choice.
There are some applications where the core models are not well suited to handle. As such, there are
several special circumstance models recommended for use in British Columbia that can be used
to deal with these situations. These models are listed in Table 2.1 along with recommendations on
the situations for their application. Only a sketch of each model is listed here. More complete
descriptions of these models are found in the corresponding model documentation (found on the
EPA website — see Section 12 under Model Resources).
CALINE3 is a Gaussian-dispersion, line-source, air quality model used to predict a single one-
hour average of a non-reactive air contaminant near highways and arterial streets due to
emissions from free-flowing motor vehicles. It can be applied to determine air contaminant
concentrations at receptor locations downwind of "at-grade," "fill" "bridge" and "cut section"
highways located in relatively uncomplicated terrain. The model has adjustments for averaging
time and surface roughness and can handle particle depositions. Given source strength,
meteorology, site geometry, and site characteristics, the model can reliably predict contaminant
concentrations for receptors located within 150 m of the roadway, although distances up to 500
m are acceptable (Bensen et al. 1989). The model produces a concentration given a user-
specified meteorological condition. There are no options for different dispersion treatments
(switch settings) that are common in other models.
Recommended Use:
w inert contaminants from free-flow highway (line) sources
w urban or rural areas
w meteorology spatially uniform (no terrain influences)
w transport distances less than 500 m of roadway
w one-hour averaging times under user-specified meteorology
CAL3QHC includes the CALINE-3 line-source dispersion model for free-flowing traffic with an
additional traffic algorithm for estimating vehicular queue lengths at signal intersections. This
model is able to estimate the total air contaminant concentrations from both moving and idling
vehicles. It is a reliable tool for predicting concentrations of inert air contaminants near signal
intersections. Because idle emissions account for a substantial portion of the total emissions at an
intersection, the model is relatively insensitive to traffic speed, a parameter difficult to predict
with a high degree of accuracy on congested urban roadways without a substantial data
collection effort.
CAL3QHC uses all the inputs required for CALINE-3 including: roadway geometries, receptor
locations, meteorological conditions, and vehicular emission rates. In addition, several other
parameters are necessary, including signal timing data and information describing the
configuration of the intersection.
CAL3QHCR is an enhanced version of CAL3QHC. The most significant enhancements provide the
model with the capability of 1) processing a year of hourly meteorological, carbon monoxide (CO) or
particulate matter (PM) emissions, traffic, and signalization data, 2) varying traffic-related input
variables by hour of the week, and 3) producing a variety of concentration time averages.
The recommended use for CAL3QHCR is the same as CAL3QHC although the following
additional features are offered with CAL3QHC:
w hourly variations in emissions, traffic and signalization
w time and space concentration distributions
w one-hour to annual average concentrations
VISCREEN calculates the potential visibility reduction due to primary emissions from a plume
for specific transport and dispersion conditions. The model is designed to calculate the contrast
against the sky and terrain for Class 1 areas (National Parks and Monuments in the US) under
worst-case meteorological conditions. The VISCREEN model can be run as a screening analysis
(Level 1 assessment) in which default values for stability class and particulate size are used. It
can also be run as a Level 2 assessment where a time series of site-specific meteorology and
particulate size information (primary particulates, soot, and sulphate particles) can be input.
VISCREEN determines the visibility degradation due to the plume relative to a baseline
visibility condition. The VISCREEN manual provides a map of these baseline conditions (in
terms of visual range) for the continental US. Although the map can be extended so that baseline
conditions can be established for BC border areas, establishing baseline conditions for the rest of
the province is problematic and will involve assumptions. Such uncertainty in the baseline
condition will lead to an additional level of uncertainty in the model predictions.
Recommended Use:
w visibility degradation due to a plume primary emissions for the near field (<10 km)
w baseline visibility can be reasonably estimated for BC locations
w default options and switches
Cautionary Note: Although there is no formally recognized visibility criteria in BC, visibility
effects of an emission source are of interest and thus the model is included in the guidelines.
Although VISCREEN provides a screening level calculation of maximum light extinction
associated with the plume, the reduction in visibility associated with this extinction depends on a
variety of factors such as the existing background visibility, the observer eye, the background
vista, etc. Thus, the interpretation of the VISCREEN results as they relate to visibility impacts
must be viewed with considerable uncertainty.
The Buoyant Line and Point Source Mode (BLP) is a Gaussian plume dispersion model designed
to handle unique modelling problems associated with industrial sources (specifically aluminum
smelters) where plume rise and downwash effects from stationary line sources are important. The
model can also account for building downwash effects, buoyancy associated with line-source
plume rise, plume rise enhancement due to multiple line sources, and vertical wind shear in
plume rise for both point and line sources. Terrain effects are incorporated along with a time-
dependent pollutant decay to account for pollutant removal.
The model requires hourly meteorological data, and will produce time- and space-varying
concentrations for a receptor grid surrounding the facility.
Recommended Use:
w aluminum reduction plants that contain buoyant, elevated line sources
w rural areas
The Offshore and Coastal Dispersion Model (OCD) is a straight-line, Gaussian model for flat terrain
developed to determine the impact of offshore emissions from point, area or line sources on the air
quality of coastal regions. OCD incorporates over-water plume transport and dispersion as well as
changes that occur as the plume crosses the shoreline. Hourly meteorological data is required for
both over-water and overland locations. The over-water measurements include wind direction and
speed, mixing height, over-water air temperature and relative humidity, and the sea surface
requirements. This model incorporates over-water and overland turbulence intensities.
The model features drilling platform building downwash, partial plume penetration into elevated
inversions, direct use of turbulence intensities for plume dispersions, interaction with overland
internal boundary layer, and continuous shoreline fumigation.
Recommended Use:
w over-water sources where onshore receptors are below the lowest stack height
w flat terrain
w hourly meteorological data over land and water is available
w transport distances less than 50 km
w default switch and options
w a simple alternative to CALPUFF where a straight-line, steady state Gaussian model can
be applied
The SLAB model was developed to simulate the atmospheric dispersion of denser-than-air
releases over flat terrain. The model treats continuous, finite duration, and instantaneous releases
from four types of sources: an evaporating pool, an elevated horizontal jet, a stack or vertical jet,
and an instantaneous-volume source. While SLAB is designed to treat denser-than-air releases, it
will also simulate cloud dispersion of neutrally buoyant releases. Consequently, a typical SLAB
simulation covers both the near-field, dense-gas phase and the far-field, passive-gas phase.
SLAB can also be used to model multi-phase releases. Calculations are performed for a user-
specified meteorological condition and concentrations represent time averages as specified by
the user. Although considerable detailed information is required regarding the source properties
and spill parameters, there are no options for different dispersion treatments available.
Recommended Use:
w denser-than-air releases (source types: evaporating pool, elevated horizontal jet, a
stack/vertical jet, instantaneous volume source)
w flat terrain
w concentration estimates for a user-specified meteorological condition
Chemical Transformation
Roadway Emissions
Building Downwash
Deposition (Gases /
Area, Volume, Line
Regional Airshed
Shoreline Effects
Acid Deposition
Plume Visibility
Multiple Stacks
Heavy Gases
(>50 km)
CORE MODELS Screening SCREEN3 √ √* √ √
√ √ √
Refined ISC-PRIME √ +
√ √ √ √ √**
√ √ ++
√ √ √++ √ √ √
√ √
SPECIAL Screening VISCREEN √
MODELS Refined CALINE3 √ √
* no line-source treatment,
** deposition of particles only,
for ISC-PRIME/S and ISC-PRIME, terrain below stack height only: for RTDM3.2, terrain above stack height only,
for distances >50 km,
single or co-located multiple stacks in single run but can use SEQADD to account for multiple non co-located point sources.
Defining the purpose and objectives of the air dispersion modelling under consideration is
critical. Objectives that are determined at an early stage will define the scope of the study and
identify modelling requirements before allocating resources. At this stage, schedules and the
roles of all parties can be identified and reviewed by the ministry (if required) in a timeframe that
is acceptable to all parties.
Based on the objectives of the study identified in Step 1, the modeller must gather information
on the sources of emissions to conduct the air dispersion modelling and provide the ministry with
sufficient knowledge of the project for its review and assessment. Section 5 provides detailed
guidance that can be used to characterize sources, including methods to determine appropriate
emission rates for modelling.
Modelling assessments require the identification of the domain where air quality impacts will be
assessed. The size of the modelling domain is dependent on the source of emissions and any
overlapping effects from other sources. For Level 1 assessments in flat terrain, it may be
sufficient to define the domain on the basis of the distance from the sources of concern to the
receptors of interest. Level 2 and Level 3 assessments normally use a grid of receptors over a
wide domain. Guidelines for determining the size of the modelling domain and the density of
receptors are provided in Section 6.
The geophysical characteristics (buildings, terrain and land use) in the model domain and the
attendant atmospheric behaviour (flow deflection, thermally driven flows, stagnations and wind
direction variability) must be assessed in terms of how these characteristics affect flow
complexity. This can determine the model selection especially for Level 2 and 3 assessments.
The information gathered in Steps 1-3 are needed to determine the assessment level required to
meet the objectives of the modelling study. The considerations discussed in Section 2.2
(information requirements, technical factors, and level of risk) help determine the level of effort
required and the corresponding level of assessment.
If the modelling results are to be submitted to the ministry for review, it is important to confirm
that the model choice and approach are acceptable prior to initiating any modelling. Once Steps
1-5 are completed, it is recommended that the modeller provide a "conceptual" model plan to the
ministry for review. A conceptual plan provides a general overview of the intended modelling
approach and will facilitate further discussions between the ministry and those conducting the
assessment. This is especially important when an extensive air quality assessment is planned and
considerable resources are required for collecting the input data, running the models, and
organizing the output in a way that will be meaningful to both the public and decision makers.
After the ministry has agreed to the conceptual approach, further details can be provided in a
detailed model plan once the input data is under development. Model plans are discussed in
Section 4.
For source data, the parameters for the different sources must be defined (point, area, line or
volume), their locations and emission situation (elevated, sideways, etc.) must be determined,
and the emission rates need to be estimated. Characterizing the emissions is a critical part of
modelling (Section 5).
The level of assessment and model selected determine the requirements for meteorological data.
Level 1 assessments do not require representative or site-specific meteorological data, but use
The level of assessment and model selected determine the requirements for geophysical data.
Such data can include terrain information, building dimensions as well as surface conditions,
roughness heights, Albedo and Bowen ratios, and distinctions between rural and urban land use.
For Level 2 and 3 assessments, the geophysical data requirements can be quite detailed and
require some manipulation to prepare the data for the specific models. The data preparation
process, as well as the sources of geophysical data for BC, is discussed in Section 8.
All levels of assessments must consider the background concentrations of air contaminants. The
intent is to compare the ambient air quality to the cumulative impact of new emissions and
existing baseline conditions. A process to select the background concentrations is provided in
Section 10.1.
3.9 Step 9: Submit Detailed Model Plan to the Ministry for Review
When sources of input data and background air quality have been determined, prior to preparing
input files, it is recommended that a detailed model plan be submitted to the ministry for review
and approval. Such a plan provides detailed information on the different sources, meteorological
and geophysical data, the corresponding data treatments, and the different outputs (tables and
figures) that address the information requirements for the level of assessment. Model plans are
discussed in Section 4.
Each model has options for specific features or treatments. In order to provide consistency,
recommended options for each model are provided in Section 9.
Each model has its own data input and formatting requirements. Models that are based on US
formats will require considerable data processing and reformatting. Sections 7 and 8 address the
meteorological and geophysical data sources that can be used for model inputs as well as
different treatments of data that are recommended for model applications in BC.
3.11 Step 11: Exercise Quality Assurance and Quality Control (QA/QC)
The preparation of input files involves the manipulation of thousands of pieces of information.
For all levels of assessment, modellers must exercise a QA/QC procedure to confirm the
accuracy of the input source, receptor, and meteorological data and the proper behaviour of the
models. A general approach on this is provided in Section 10.2.1. Specific attention is given to
CALPUFF, given the number of user-defined physics options, adjustable parameters, and data
input files required to run the model.
The model output should be presented and interpreted in a way that addresses the objectives and
information needs of the modelling effort as identified in Step 1. In order for the ministry and
other interested parties to conduct a thorough review of the air quality assessment, various input
and output files may be required. Recommendations on documentation are in Section 10.4.
Submission of the model plan for review and approval by the ministry is recommended for:
Level 1 assessments
w only when the modelling involves a deviation from the guidelines (i.e., alternate model,
non-regulatory model settings, model modifications, application outside of recommended
Level 2 assessments
w only when the modelling involves a deviation from the guidelines (i.e., alternate model,
non-regulatory model settings, model modifications, application outside of recommended
uses) OR
w on a case-by-case basis as determined by the ministry. Factors involved in this decision
are the same factors listed in Section 2.2 that guide the determination of the level of risk
associated with a project.
Level 3 assessments
w always
It is recognized that the details of the modelling approach including data sources, user defined
options/switches and model outputs may not be known in the early stages of an air quality
assessment. As such, development of a detailed model plan for ministry review and acceptance
may be premature. The guidelines recommend a two-stage process that involves:
w a conceptual plan outlines the modelling approach and direction in broad terms
w a detailed plan is submitted at a later date that specifies all the components involved in
the modelling assessment
The steps in the development of a conceptual or detailed model plan involve the same steps
outlined in the 12 Steps to Good Modelling Practice. A template for conceptual and detailed
model plans is found in Appendix B.
Source parameters, such as exit temperature, exit velocity and emission rate tend to vary over time.
These variations may be due to normal process changes or abnormal conditions such as start-up,
shutdown and upset conditions. Although the latter situation may occur very infrequently, the
resulting emission rates could lead to serious air quality degradation. These variations, whether
under normal or abnormal operations, must be considered as part of the assessment of the air quality
consequences of a source. The following sections provide guidance on choosing the source
parameters and emission rates that are required as air dispersion model inputs.
Given the importance of the emission rate used in the modelling assessment, the method to establish
the source conditions must be provided in the detailed modelling plan (Section 4).
Emission limits are often specified in permits for sources of common criteria air contaminants
and are often used to establish the emission rates for modelling purposes. A permit issued for an
air emission source may indicate a maximum permissible emission limit for a specific time
period that could be short-term (i.e., seconds, minutes, hours) or long-term (i.e., annual). For new
emission sources the determination of a permitted emission limit may be based on considerations
related to site location, overlapping sources, local meteorology, terrain, existing air quality,
control technology options and other air management considerations.
Emission sources can be simplified into four types based on geometry: point, area, volume and
line sources. All of the core air dispersion models listed in Section 2.3 can be used for point,
area, and volume source types with the exception of RTDM3.2, which does not treat volume and
area sources. With some core models (SCREEN3 and RTDM3.2), there are some limitations on
whether multiple sources are handled or whether they can be handled in a single model run.
A point source is a stationary, specific point of origin (or concentrated location) where
contaminants are emitted into the atmosphere (such as a stack, exhaust vent, etc.) The source
parameters normally required for point sources include the UTM or grid coordinates, release
height (i.e., stack height), exit velocity, stack diameter, exit temperature and mass emission rates
of the contaminants of concern. A flare is considered to be a point source, but special treatment
is required as discussed in Section 11.1.
An area source is an emission into the atmosphere that is distributed over a stationary spatial
area. Examples of area sources include settling ponds, feedlots, and even urban regions that
include multiple point sources (which combined together act as an area source) and smaller area
sources. Parameters normally required for area sources include the coordinates of the area
perimeter, the release height, and the mass emission flux rate of the pollutants of concern (i.e.,
mass emission rate per unit of area, g/s⋅m2).
A volume source is an emission to the atmosphere that has an initial width and depth at a
stationary release point. An example of a volume source is dust emission from an aggregate
storage pile. Parameters normally required for volume sources include the coordinates of the
volume dimensions and the mass emission rates.
A line sources is an emission to the atmosphere that is distributed over a line. Examples of line
sources include conveyor belts, roadways and rail lines. Specialized roadway/traffic models
(special circumstance models CAL3QHC and CAL3QHCR, see Section 2.4.2) address
continuous traffic on roadways as line sources explicitly, although ISC-PRIME (and ISC-
PRIME/S) models can be used to simulate roads by treating this type of source as a long, thin
area source and adjusting for initial mixing.
If the facility under study has more than one source of the contaminant to be modelled, then it is
a multi-source situation. There may also be other sources (Section 10.1) nearby that may
contribute to the contaminant load in the area. Deciding which sources should be included is
largely dictated by the source size (i.e., a large source has a large area of influence). For
example, the evaluation of NOx impacts from a five-metre-high exhaust, where the impacts are
expected to be within a 50 m, may not have to consider the influence of an adjacent 100 m stack.
Decisions regarding the inclusion of nearby sources in the modelling effort may involve an
iterative process, where subsequent model runs will indicate which sources are important.
For Level 1 screening assessments with multiple sources, ISC-PRIME/S is the preferred
screening model as it can explicitly handle multiple sources. As SCREEN3 can only account for
one source at a time, for multiple-source situations, estimating the concentration by direct
addition of the predicted worst-case concentrations for each source will produce a conservative
estimate. Alternatively, a group of closely spaced stacks can be modeled with SCREEN3 as a
single source. Stacks located less than one-stack diameter apart and with the same release height
and similar exit velocities and flow rates can be treated as a single source. A pseudo diameter for
the single source is calculated based on the total volume flow rate and exit velocity of the stacks.
Section 2.2 of EPA (1992) provides an acceptable method to merge multiple stacks.
For Level 2 and 3 assessments, the core refined models are all capable of handling multiple
sources although RTDM3.2 requires either co-location for point sources, or separate runs for
each source which are subsequently combined for a cumulative assessment using the SEQADD
post processor.
Selecting an emission rate depends on the purpose of the modelling application. If it is to assess
the air quality consequences of a new or modified existing source, then the emission rate can be
established on the basis of:
w approved/proposed emission limits
w continuous stack monitoring
w manufacturer specifications
w published emission factors
w stack sampling survey (limited use)
Most sources of air emissions will exhibit some degree of variability in the emission rates either
over short-term or long-term periods. The emission rates may vary when a process is not
operated continuously 24 hours per day or when the process rate (i.e., loading capacity, fuel
consumption) varies with time. Emission rates may also be dependent on meteorological factors
such as wind speed, temperature, humidity or season. This variability means that the maximum
short-term emission rate experienced over a period of a year can be much greater than the annual
average emission rate.
Often, it is desirable to simulate the maximum emissions under normal operating conditions in
order to determine compliance with ambient air quality objectives. Given that many ambient air
quality objectives are one-hour averages, this will typically involve a simulation with the
maximum expected one-hour emission. If the maximum short-term emission rate is used for
longer-term average simulations (8 hour, 24 hour, annual), the predictions for these longer-term
averages will be biased high. Thus, it is acceptable to use the maximum emission rates (expected
under normal operations) that correspond to the averaging time of the ambient predictions. For
example, the maximum 24-hour average emission rate can be used to simulate 24-hour average
concentrations. Similarly, the annual emission rate can be used for the annual average
If the model is used to reconstruct a past event, then actual emissions should be used. An hourly
emissions file can be input into the core models listed here, and variations in the emissions can
be accounted for explicitly if the data is available. In this case, continuous emission monitoring
data is the preferred data source. Alternatively, equipment manufacturer emission specifications
can be used (although the confidence in these values is reduced depending on a number of
factors in Section 5.4.3) and if specific events are of interest, then stack sampling data can be
used if it corresponds to the time of the event.
Maximum emissions limits for authorized discharges are often set in permits under the provision
of the Environmental Management Act. Emission mass-rate limits and volume-flow limits may
be set for sources of emissions that include particulate matter, sulphur oxides, nitrogen oxides or
other air contaminants of concern. The averaging time associated with these approved emission
rates may not be specified in the permit, so it is important to establish with the ministry whether
the permit levels correspond to a one-hour emission rate. If not, appropriate adjustments for other
time averages are required based on the other methods listed here to establish emission rates.
Large stacks at industrial facilities that emit a significant amount of air contaminants are often
equipped with continuous emission monitoring systems (CEMS) that measure exit temperature,
exit flow rate and mass emission rates. The data can be analyzed to develop appropriate
maximum, average or upper percentile (e.g., 95th or 98th) operating conditions of the stack.
Furthermore, the sequential time series of hourly average exit velocity, exit temperature, and
emission rate data can be used by many of the core models to assess the effect of variations in
emission parameters on predicted ambient air quality.
For new facilities or in cases where measurements are not available, manufacturer specifications
of emission rates can be used. The emission rates may be provided for different load capacities
and operating conditions. Confidence in manufacturer's ratings is reduced if the equipment:
w is old
w has been retrofitted or modified
w is not operating under optimum conditions
w is being operated in substantially different conditions than the emission test (i.e., climatic
conditions, altitude, process changes)
In such cases, the original manufacturer's specifications can be modified if there is evidence
(i.e., stack samples, data from other studies) that support a different value.
When no appropriate measured emission rates are available, published emission factors can be
used. The predominant source of emission data is the US EPA's AP-42 emission factors
(www.epa.gov/ttn/chief/). These emission factors provide the mass of contaminants discharged
per mass of fuel consumed, product processed or activity rate and are ranked according to data
quality (i.e., from A to E) to reflect their accuracy. The accuracy rating and the applicability of
the AP-42 emission factors to conditions in British Columbia (for example, ambient temperature)
must be given consideration before emission factors are used.
Stack sampling survey data provides a snapshot in time of the emissions and is normally
conducted for permit compliance reasons, rather than to characterize the emissions for modelling
purposes. Unless sufficient survey data is available to determine the range of possible emissions
from a source, they should only be used in situations where the air quality at the time of the
survey is of interest or when there is no or little reliable data from other sources of information
on emissions listed here. Stack survey information may also be useful to estimate emission rates
under reduced operation/production rates (discussed in the following section), if the
corresponding production rate was recorded when the survey was conducted.
If modelling is conducted to assess air quality that results from a future emission scenario,
typically a maximum one-hour emission rate (100% capacity operation) is used and assumed to
apply for the total simulation period. Although it is recognized that the source will not operate at
100% capacity for the entire period, this assumption provides a measure of conservatism in the
longer time average predicted concentrations.
In certain situations the maximum short-term concentration may occur under reduced capacity
operations so the air quality consequences of other operation scenarios are important to
investigate with a model. As well, the extreme (or abnormal) emissions situations of start-ups
and by-pass situations (due to equipment downtime for maintenance) are rare occurrences, but
the air quality consequences are of interest to decision makers. The following sections provide
guidance for the range of emissions associated with these situations.
Maximum predicted ambient concentrations may not necessarily occur under maximum normal
operating conditions. Even though the contaminant emission rates may be lower at a reduced
capacity, there is also a corresponding reduction in plume rise due to the lower gas effluent volumes
and temperatures. If the terrain is elevated, higher concentrations could result under the reduced
operation scenario than under maximum capacity emissions. Furthermore, some contaminants, such
as NOx, may have higher emission rates when a combustion source is not operating at full load due
to lower operating efficiencies. Therefore, for elevated sources it is recommended to examine the air
quality consequences of the 25, 50 and 75% capacity scenarios to insure that the source conditions
causing the highest concentration are captured. Using a screening model is a quick way to assess
whether these lower capacity scenarios are in fact important to consider in the modelling scenarios.
Ambient concentrations should also be predicted for abnormal operating conditions, since it is these
situations that can result in the poorest air quality. "Abnormal operation" conditions are distinct
from upset conditions in that the former are predictable based on operational history, anticipated
scheduled maintenance of control equipment, by-pass operations for plan safety, and start-up/shut-
down situations. Emissions under these circumstances can be characterized and modelled to
examine the resulting air quality implications.
Abnormal emissions should not be confused with "upset conditions." These are unanticipated
emissions (i.e., accidents) that result from unexpected equipment/process failure. Modelling the air
quality impacts for these accident scenarios is not part of these guidelines.
Abnormal emissions are anticipated, but it is not known whether they will occur when the
dispersion conditions are good or poor. Although the probability of abnormal emissions occurring at
the same time as poor dispersion conditions may be very small, decision makers are often interested
in the worst-case air quality scenario. The guidelines recommend the following:
1. Establish the realistic emission scenarios associated with abnormal emissions (based on the
physics/chemistry of the industrial process)
3. Apply a screening model to determine the potential worst-case air quality under these
5.6 PM Emissions
Particulate matter originating from both natural and human-caused sources includes a wide range
of sizes varying from greater than 100 µm to less than 0.1 µm in diameter. Particles greater than 10
µm in diameter typically consist of dust, grit, fly ash and visible smoke. Because of their weight,
they tend to settle to the ground close to the source. Particles with aerodynamic diameters less than
10 µm (PM10) remain suspended for longer periods before they are deposited whereas particles less
than 1 µm may remain in the air for several days. Particles with aerodynamic diameters less than or
equal to 2.5 µm (PM2.5) typically consist of primary particles formed during combustion and
secondary particles transformed in nature from gases while in the atmosphere.
Although CALPUFF includes a rudimentary treatment for secondary PM, it is only for purposes of
sulphate and nitrate deposition for long-range transport (> 50 km). Models that include a
comprehensive treatment of secondary aerosol formation (e.g., CMAQ) are not included in these
Stack sampling for particulate matter can include a filterable portion of various sizes (directly
emitted "primary" solid and liquid particles) and a condensable portion (gases or vapours). The
latter portion is assumed to be a measure of PM2.5 emissions, since these gases and vapours are
assumed to ultimately transform into PM2.5. For a few types of sources (i.e., veneer dryers), the
condensables may be a significant fraction of the total PM emissions.
Although cascade impactors can be used in stack sampling to distinguish particulate size
distributions (i.e., Total PM, PM10, PM2.5), the information available on PM emissions to the
modeller may not be size specific (i.e., may only provide total filterable PM) and may not
include the condensable fraction. For example, if emission factors or manufacturer's
specifications are used, the distinction between sizes and/or condensables may not be available.
w If the filterable and condensable fractions of the PM emissions are specified, then use
only the filterable portion as the PM emission for the models recommended in these
w If there is no information available on whether the emission rates include the condensable
portion, then assume the rate represents only the filterable fraction.
Fugitive sources can be defined as: "emissions that do not pass through a stack or functionally
equivalent opening where the source can be collected or reduced by emission control
equipment." Examples of common sources of fugitive emissions include bulk material transport
terminals, in-transit coal cars, cement batch plants and odorous gases from sewage treatment
plants and sour gas processing.
Fugitive sources are often difficult to characterize since their emissions may vary with wind
speed and time of day (for particle emissions) or process changes (gases), and the control
efficiency of mitigation measures applied to reduce emissions can be only crudely estimated with
attendant large uncertainties. Any industrial operation has fugitive emissions, which typically are
near the ground and have their greatest impact in the area near the source. Whether they need to
be considered in the assessment starts with an identification of sources of fugitive emissions
associated with a facility, the type of emissions and the potential for any sensitive receptors to be
in the affected area.
The core models ISC-PRIME, AERMOD and CALPUFF are suitable for modelling fugitive
emissions as they can handle various source configurations often associated with such emissions.
ISC-PRIME and CALPUFF have algorithms to account for the deposition of fugitive dust (with
particle-size distributions and settling velocities) from areas such as gravel parking lots.
Due to the large uncertainties associated with establishing fugitive emission characteristics and
the difficulties in modelling them, consider:
w the use of ambient monitoring data to establish the fugitive emission contribution
w physical modelling
w modelling to assess the effectiveness of mitigation plans (relative changes) with less
emphasis on the absolute results
The above are for general guidance the final receptor spacing will depend on a number of factors
such as plume release characteristics, buildings and terrain. An iterative approach is recommended
as the location of the maximum concentrations may dictate a finer grid resolution in order to ensure
the maximum is resolved. For example, terrain elevations can have a large impact on the dispersion
modelling results and must be considered when selecting receptor locations. A mountain a few
kilometres away may experience very high concentrations in a limited area and would need a higher
density of receptors to insure that the maximum concentration is captured.
For SCREEN3, the automated distances beyond 100 m are generally sufficient. If the maximum
concentration is predicted to occur at 100 m, the user is recommended to rerun the model with a
receptor spacing of 10 m for distances less than 100 m.
It is preferred that locations of emission sources, receptors and grid boundaries be provided in
Universal Transverse Mercator (UTM) coordinates (NAD83 datum) to allow a precise definition of
location. If the need arises, local coordinate systems can be used if enough information is provided
to identify exact locations.
The plant boundary will not be defined as the plant property unless it meets the preceding criteria.
Refined models require a time series of hourly sequential meteorological data. The ideal
meteorological input data would come from a specially designed and sited meteorological
collection program and would represent the conditions that determine the behaviour of the
modelled emissions. However, such data is not always available, and sometimes not practical to
collect depending on the needs of the modelling assessment. Thus, other sources of data must be
relied upon.
The best source of meteorological data for Level 2 and 3 assessments is from a meteorological
station that has been designed specifically for air quality assessment purposes for the emission
source(s) modelled. The term "site-specific" refers to measurements that represent the
meteorological conditions of the source being modelled. The site may or may not be within the legal
boundary of the facility.
The location of the site and the vertical level(s) of measurements require consideration of how
representative the measurements would be of the conditions that determine the behaviour of the
emissions. In some cases, depending on the model used and the purposes of the modelling study,
The ministry and GVRD meteorological stations are specifically designed for air quality data
interpretation and for dispersion modelling purposes. Locations and descriptions of these
monitoring programs throughout BC are provided in Appendix C, Tables C.2 to C.4.
If there is no site-specific data, then other data sources can be used under certain conditions as
discussed in the following sections.
The use of data from a program at a different location as a substitute for a site-specific program
depends on the judgment of experienced dispersion meteorologists. An assessment to determine
whether this data is adequately representative will involve an examination of the surrounding
terrain, surface characteristics, and the height of the source versus the height of the measurements.
Meteorological data should be considered representative only when the monitoring site and the
facility site are in climatologically similar regimes. For example, observations from a coastal
location will not be applicable for an inland mountain valley.
Before meteorological data from another site is used, the following considerations are
recommended (EPA 2000):
w determine whether the data collected at an alternate site characterizes the meteorological
conditions at the site under consideration. In British Columbia, the meteorological
conditions can significantly change over a small distance, thus data from another location
can be limited in its use for modelling at different locations.
w determine whether the geophysical situation at an alternative site characterizes the
geophysical conditions at the site under consideration. The following terrain features
should be considered: (a) aspect ratio of terrain (height of valley walls to width of valley,
height of ridge to length of ridge, and height of isolated hill to width of hill at base); (b)
slope of terrain; (c) ratio of terrain height to stack/plume height; (d) distance of source
from terrain; and (e) correlation of terrain feature to prevailing meteorological conditions
(e.g., the orientation of the valleys).
w determine the purpose of the alternate site collection program and the measured
parameters, the type of instrumentation and data collection, and the height of the
measurements. Certain parameters may be missing or not suitable for modelling. For
example, the Ministry of Forests operates a number of weather stations throughout the
province but the instrumentation type, data treatment, and site locations are suited for fire
weather/forest management purposes, and may not provide measurements suited for air
quality models.
w determine the instrument specifications, siting criteria and data treatment (response
thresholds, data collection and handling protocols, special data entry flags). This will
indicate the limitations and quality of the data. It is recognized that background
documentation on these alternate programs is often not available. In such cases,
modellers must be vigilant in determining whether such data is appropriate for modelling
purposes by scrutinizing the hourly data files for low wind speed thresholds, consistency
with expected behaviour for the given location, range checks, and hour-to-hour changes.
w when CALPUFF is applied in a complicated geophysical setting, it is better to have data
from a number of meteorological stations within the modelling domain if the stations are
individually representative of the specific surface areas. Thus, the determination of
adequately representative involves an assessment of whether CALMET is able to
adequately represent the complete meteorological field using the available data.
w assess the critical nature of the air quality assessment as part of the decision-making
process. Approval decisions that have large societal and environmental consequences
may require the establishment of a well designed and operated meteorological program
that characterizes the conditions governing the behaviour of the plumes emitted from the
modelled source(s).
Given that the meteorological monitoring network in BC is sparse with limited spatial
applicability due to the complex terrain, there is growing interest in the use of MM models as a
means to produce meteorological data in areas where no observations exist. Such models include
sophisticated physics packages and can produce meteorological fields at horizontal grid
resolutions in the order of a kilometre and in multiple vertical levels over a large domain.
w provides details of the space and time variability of the meteorology in three dimensions
within a modelling domain, especially important for BC given the complex geophysical
While MM models offer many advantages, they require considerable expertise and
computational resources (processing, storage) which escalate rapidly with domain size, grid
resolution and simulation duration. Thus, it is impractical to apply these models for dispersion
modelling in situations where it is required to simulate one or multiple years of hourly
meteorology at a fine enough grid scale (on the order of 100 m) and vertical levels in order to
resolve the effects of small-scale terrain features.
An alternative approach to the generation of detailed meteorological fields is through the use of a
diagnostic model. This type of model contains simplified physics (e.g., no ability to forecast and
the treatments of different processes are parameterized) so that the computational resource
demands are greatly reduced. However, even with these simplifications, they can efficiently
account for very fine scale terrain effects in a dynamically consistent framework.
Finally, there is a combined MM and diagnostic model approach, where coarse spatial resolution
MM model output is used as an initial guess field for a fine spatial resolution diagnostic model
(such as CALMET). The diagnostic model further applies dynamically consistent diagnostic
algorithms in concert with available observed data to develop terrain effects adjustments on a
fine scale. This hybrid approach uses the strengths of both models and makes it practical to
produce fine-scale meteorological fields for simulation periods of a year. CALMET also has an
option to use MM output without any surface or upper-air measurements (i.e., the "no obs"
Given that air quality assessments typically use historical meteorological data, MM models can
be run in hindcast mode. In theory, this approach should produce more accurate meteorological
fields than the forecast mode approach since actual observations are used to "nudge" the model
in its solutions. As an early attempt to develop experience with mesoscale model output for
CALPUFF applications, the provincial governments of Alberta and British Columbia jointly
funded the production of CALMET-ready, MM5 meteorological fields for western Canada.
These hindcast fields were generated for every hour of 1995 at a 20-kilometre grid resolution and
18 vertical levels. The data is available on CD from the British Columbia Queen's Printer at
The concern with using these models to simulate hour-by-hour meteorology on a fine spatial
scale is related to performance (i.e., how well do these models simulate the boundary layer,
especially in geophysically demanding conditions?). Performance evaluation is challenging as
comparing model grid output for a parameter to observations (that represent a value at a single
point) could seem like comparing "apples" with "oranges" and may lead to false conclusions
about the model accuracy. As well, different evaluation approaches can lead to differing views
on model accuracy. Paired in space and time comparisons (specific hour-by-hour comparisons to
observations taken at a specific location) may be poor even though the long-term statistics (i.e.,
wind rose) could be reproduced quite well. The reasons for this could be simply due to an offset
in timing of changes in the meteorological fields (i.e., frontal passage offset an hour in the model
simulations vs. the observations), rather than a fundamental problem in the modelling system.
In order to assess the value of the hybrid MM/diagnostic model approach for fine-scale
meteorological fields, Schutte and Lundgren (2002) conducted a test in northeast BC using the
20-kilometre grid resolution MM5 data and CALMET to generate fine-scale (1 km)
meteorological data. The study concluded that this combination "significantly improves
resolution of the local scale wind system in complex terrain." Furthermore, the study found that
this combination of models is an objective means to produce meteorological data at a site for
straight-line, Gaussian models that is at least as representative for that site as applying and
adapting data from other locales.
McEwan and Murphy (2004) tested two MM models (RAMS and MC2) run in forecast mode at
one- and two-kilometre grid resolution over the Kamloops area. The testing involved the
effectiveness of the hybrid approach (with CALMET) to produce meteorological fields at a grid
resolution of 250 m. The study concluded that: "numerical simulations can be a highly useful
source of data for regulatory dispersion modelling, although modelling outcomes may
substantially differ depending on the mesoscale model (and choice of mesoscale model options)
used. Some initial analysis of the boundary layer characteristics or a mesoscale simulation would
help select a model, or modelling configuration that adequately characterizes a region of
The ministry accesses and archives hourly meteorological data at various stations across the
province, including the GVRD. Some sites are operated by the Ministry or GVRD, and some are
operated by an industry under permit to these agencies. The sites typically consist of a 10 m
tower where various meteorological variables are measured and the programs are designed to
provide input to air quality models as well as to support analysis of the dispersion conditions.
Some parameters such as the standard deviation of the wind direction fluctuations (sigma theta,
s ?) are specifically for dispersion analysis and the determination of stability class. The
instrumentation reflects performance standards (i.e., low starting threshold of 0.5 m/s) that are
suited for air quality purposes. The data quality is maintained to ministry standard through a
maintenance/siting program, numerous data quality checks, and secure storage. The parameters
available vary for each site and specific measures are designated by the different codes. Full
descriptions of each site and the locations are provided in Appendix C.
Data are available online for current and historical sites from:
Access to the data via this website requires a password that is available from the Ministry
Quality Assurance Scientist (250-387-9940).
MSC has archives of meteorological data collected throughout the province. These include data
collected at reporting airports, climate stations, buoys, lighthouses, and special studies.
Typically, airport data is the most complete for dispersion modelling purposes, having detailed
information and key modelling parameters (cloud cover and ceiling height). Data from non-
airport stations can also be of use, providing temperatures and winds within the modelling
domain that can be used to assess space and time variability of the meteorological fields and as
important input into models such as CALPUFF. Data is available to download from:
The web may not provide all the necessary data (i.e., cloud cover). In such cases, for locations in
British Columbia, contact Environment Canada for customized requests at: (604) 664-9067,
The use of hourly airport observation data for modelling purposes must consider the following:
w MSC observations are primarily designed to generate long-term statistics and to satisfy
weather forecast requirements. In particular, it is important to note that:
a) wind measurement is not a true hourly average, but typically an observer estimated
average over only a few minutes
b) wind directions are reported to the nearest 10°
c) the wind direction is assigned a zero when the wind speed is zero
Using this quasi-instantaneous measure of wind speed/direction to predict the hourly
average concentrations at a specific location increases the uncertainty of the individual
hourly predictions.
w the anemometers used at reporting airports have a starting threshold of 2 knots (or
approximately 1 m/s). If there are frequent periods where the wind speeds are less than
this starting threshold (which commonly occurs in valleys/basins), the airport data will
report an unrealistically high frequency of calms (zero wind speed).
w the location of the airport may not adequately represent the range of meteorological
conditions at the site under consideration
Despite these issues, airport data is valuable for modelling purposes. Cloud amounts and heights
— parameters that unattended site-specific programs cannot measure — are required for
Turner's method to determine stability classes. Models using airport data have been shown to
perform well in providing the maximum concentration, independent of where and when it
occurs. In many cases, this is all the information decision-makers need.
The EPA has developed meteorological data processors (MPRM and PCRAMMET) that prepare
and process airport data for different models (Section 7.8). For example, these processors treat
the coarse wind direction reporting (nearest 10°) by randomizing the wind direction within a 10-
degree sector. Low wind speed situations (calms processing) are treated according to EPA
protocols and stability classes are determined based on the cloud information.
Given the different steps required to process airport data into formats and data suitable for
dispersion modelling, the guidelines recommend:
w using MPRM or PCRAMMET (Section 7.8) for the processing and preparation of airport
data for ISC-PRIME and RTDM3.2
Finally, some private consulting companies provide the same MSC data with additional
customization to address the user's needs (i.e., formatted for specific models).
Other sources of data include the Ministry of Forests (fire weather network program), the
Ministry of Transportation and Highways (road maintenance and avalanche forecast programs),
university research programs, and programs operated within national parks. In data-sparse areas,
or areas where a network of meteorological data is needed to characterize non-uniform
meteorological fields (for areas where CALMET is applied), such data can be useful. The
instrument siting, type, maintenance and data type are very important given that these stations
are set up for purposes other than air quality. For example, many Ministry of Transportation and
Highways stations are located along roadways and wind directions may be severely influenced
by tree canopies that border the roads. Other factors to consider include the units of
measurement. For example, wind speed data may be in km/h whereas the model may require
wind speeds in m/s. Often information on the siting, instrumentation and data treatments may be
lacking, so considerable care is required before using the data from these sources for dispersion
modelling purposes. Data can be obtained by contacting the respective agencies.
For any meteorological data that is not from ministry, GVRD or Environment Canada stations,
the following is recommended:
w review siting to determine whether the data is influenced by obstacles or terrain features
w review instrument types for thresholds
w review maintenance and data quality assurance protocols
w review sampling frequencies
w assess the validity of the data for the purposes of modelling through the generation of
wind roses, frequency of calms
w if no documentation on the data is available, do not use the data or use it with caution
Upper-air data is required in some models to determine mixing heights (which in turn are input into
the models) or directly to specify the profiles or wind and temperature (as required in AERMOD
and CALPUFF). In British Columbia, there are few upper-air stations and the applicability of using
this data (especially at lower levels) for different locations (i.e., different mountain valleys) is
questionable, unless the site in question is very near the sounding site and at approximately the same
elevation. A list of upper-air stations that could have some applicability for modelling applications
in British Columbia is provided in Table 7.1.
Upper-air (Radiosonde Ascent) data for upper-air sites in North America (including those
locations in BC and the surrounding areas as indicated in Table 7.1) is available on a CD for
historical data (National Climatic Data Center called "Radiosonde Data of North America" see
www5.ncdc.noaa.gov/cgi-bin/script/webcat.pl?action=ALL) , and online for more recent data in
FSL format. (http://raob.fsl.noaa.gov/)
Predicting the air quality impacts of a yet-to-be-built emission source raises a fundamental question:
what will the atmosphere be like in the future? Nobody can be absolutely certain. It is possible,
however, to get a good idea of the future conditions, given measurements taken over a number of
years in the past.
The measurement period required to obtain a stable distribution of conditions depends on the
variable being measured. Landsberg and Jacobs (1951) indicate that greater than 10 years may be
required to achieve stability in the frequency distributions of some meteorological variables. Burton
et al. (1983) indicate that five years of data collection would be a minimum period to obtain a
representative long-term average. In many situations, there are no site-specific data of five years'
duration available.
The refined models recommended in these guidelines require an hour-by-hour sequential time
series. Some, such as CALPUFF, will not run with gaps in the data series, while others
(AERMOD, ISC-PRIME) skip over missing data periods.
Based on EPA (2000), protocols to "fill" missing data gaps are provided. These rely on
substitution, interpolation and/or persistence approaches. When missing values arise the
following is recommended:
w use other site-specific data
w if the missing period is a few hours or less, use linear interpolation or data from the
previous time period. Caution is needed for transition periods (e.g., sunrise/sunset).
w for periods greater than one hour, use data from a nearby representative site (Section
7.1.2), although the relevancy of another data set to the data in question must be
evaluated on a case-by-case determination. Mountainous terrain may severely limit the
spatial applicability of the data from nearby sites. In these situations, interpolation
techniques based on typical conditions during this period are required.
w apply profiling to a multi-level database (multiple measurements at different levels on a
tower, or upper-air sounding data) where missing data for one level is replaced by an
estimate based on data from an alternative level or levels in the same database. If the data
is available, this method is the preferred approach to fill longer missing data periods.
w flag any substituted data due to missing periods. This will assist in the interpretation of
model output and the uncertainty associated with the concentrations predicted during
periods of substituted data. The greater the missing data period, the greater the potential
for error in the filled data.
Other data filling approaches with greater potential for error include:
w substitution from measurements at nearby locations with dissimilar meteorological
w substitution of a climatological value for a particular time period
w substitution of simulated meteorology using a mesoscale weather (MM) model,
depending on its performance (Section 7.1.3)
The "data completeness" recommendation is 90% (i.e., if more than 10% of data is missing, more
data should be collected before running the model). If the data completeness recommendation of
90% cannot be met, the consequences of a lesser amount of data need to be described within the
context of the purposes of the modelling and outlined in the modelling plan.
7.4 Ministry Data: Wind speed and Direction for Use in Models
The list of parameters collected at ministry meteorological stations include a variety of different
types of wind speeds, directions and measures of wind direction fluctuations. These parameters
are available by virtue of the built-in processing capability in the dataloggers and each provides
some insight into the characteristics of the meteorological conditions as they relate to air quality.
For more information on how each of these parameters is calculated and why they are important,
see EPA (2000).
Light wind conditions can lead to worst-case concentrations as emissions can build up in an area
or transport slowly downwind with very little mixing and impact on the side of a hill, resulting in
high concentrations in a localized zone. Light wind conditions pose a challenge to models as the
wind direction can become highly variable and difficult to specify. Furthermore, steady-state
Gaussian models have an inverse dependency on wind speed — a zero wind speed will result in an
infinitely large concentration. Thus, steady-state Gaussian models have a low wind speed limit (or
threshold) that is intended to avoid this problem.
The term "calm" can be used in different ways, and can refer to three types of light wind
w a "meteorological" calm which refers to the condition of no movement of air (no wind)
w an "instrument" calm, where there may be wind, but it is below the starting threshold of
the anemometer
w a "model" calm, which occurs when wind speeds are less than a certain speed threshold
and the model will skip the concentration calculation (otherwise an unrealistically high
concentration can be calculated)
The term "calm" used in these guidelines refers to a "model" calm. With respect to these "calms"
the different core models use different methodologies to identify and treat these periods. In some
cases, the meteorological data file is pre-processed in a way to first identify calms, and then the
wind speeds and directions for these periods are adjusted (the wind speed is set equal 1 m/s and a
wind direction equal to the last valid "non calm" wind direction).
The 1 m/s threshold was based in part on the historical anemometer starting thresholds (AST) of
airport data. However, there are some cases where the airport anemometers and those used by other
agencies (ministry and GVRD in particular) have an AST that is less than 1 m/s. In these situations,
when wind speeds are less than 1 m/s but greater than or equal to the AST, then for some steady-state
Gaussian models, the wind speed is used as is, or adjusted to 1 m/s and the corresponding wind
direction used for that hour.
ISC-PRIME has a calms processing option (a regulatory default option) where any calm hour is
skipped. A calm can be flagged in different ways, depending on whether an ASCII file of
meteorological data is used, or whether certain preprocessors are used (for example, PCRAMMET
using the Binary output processing option) to develop the meteorological file. For ISC-PRIME a
multiple-hour average concentration that includes a calm (or missing hour) is flagged. While this is
an acceptable treatment for occasional model calm periods, it is inappropriate if model calms occur
frequently (especially when they occur for long durations, which frequently occur in BC valleys). As
a result, the maximum hourly concentrations may be missed and multiple-hour average
concentrations (e.g., 24-hour average) may be biased. For example, if there are fewer than 18 valid
hours in a 24-hour period, ISC-PRIME will calculate the 24-hour average by dividing by 18 (EPA
calms protocol correction technique for multiple-hour averages used in ISC-PRIME).
For RTDM3.2, any wind speed below 1 m/s is set to 1 m/s and the corresponding wind direction for
that hour is used to calculate the concentration. Given that the calm period could last for several
hours, the wind direction would remain the same for the calm period and the pollutant
concentrations can be overestimated for multiple-hour averaging times.
For AERMOD, the meteorological data processor (AERMET) will assign the reference wind
speed as 0.0 m/s in the surface meteorological file if the wind speeds are less than a user-
specified AST (typically around 0.5 m/s for ministry and GVRD stations). AERMET treats these
calm hours in the same way as ISC-PRIME (skip any model calm periods). AERMET flags those
multiple-hour average concentrations that include a model calm period in the output file.
CALPUFF does not have these low wind speed limitations, as it is able to handle meteorological
calms that occur over prolonged periods of time.
For British Columbia conditions, where light wind conditions (either a true calm condition, or
instrument calm conditions) frequently occur and can persist for prolonged periods, application of a
calm treatment in various steady-state Gaussian models may eliminate or inappropriately treat many
hours that may contribute to poor air quality events. Given the different ways model calms are
identified in model specific input files, and the different ways models treat calms, Table 7.2
provides guidance on calm treatments and meteorological data preparation for different models.
Determine the frequency of hours where wind speeds were <AST and assess
modelling results based on this frequency.
RTDM3.2 If the AST is <1 m/s, and
w if wind speed > = AST but <1 m/s, then set wind speed = 1 m/s and use the
wind direction as is for that hour
w if hourly wind speed <AST, then set wind speed = 1 m/s and use the wind
direction from the last valid non-calm hour. RTDM3.2 does not skip this hour
Determine the frequency of hours where winds speeds were <AST and assess
modelling results based on this frequency.
AERMOD Specify the AST in the AERMET input file. No special calms treatment of the
meteorological data is required. AERMET will identify the calm periods (those hours
where the wind speed is <AST) and skip these hours.
Determine the frequency of hours where wind speeds were <AST and assess
modelling results based on this frequency.
CALPUFF No special calms treatment of the meteorological data is required. CALPUFF is able to
treat conditions when wind speeds = 0.0
Special Circumstance Refer to the model documentation regarding calms processing to insure that the
Models meteorological data is treated correctly to accommodate any limitations regarding
model calm periods.
MPRM If MPRM is used to develop input files from site-specific meteorological data for ISC-
(Meteorological Data PRIME and RTDM3.2, specify the AST. MPRM treats the data according to the rules
Processor utility) specified above for ISC-PRIME and RTDM3.2 respectively when the AST is <1 m/s.
If MPRM is used to develop input files from airport data for ISC-PRIME and
RTDM3.2, the 1 m/s threshold is applied to the data to define calms. MPRM will set
wind speeds = 0.0 for calm periods when it generates a meteorological input file
suitable for ISC-PRIME. MPRM will set wind speeds = 1.0 and the wind direction
equal to the direction of the last non-calm hour when it generates a meteorological
input files suitable for RTDM3.2.
For ISC-PRIME and RTDM3.2, given the light wind treatments recommended here it is possible
that there will be a high frequency of periods where the winds are less than the AST (especially
with airport data), resulting in unreliable model results for certain critical periods. Alternate data
treatments to those outlined here can be considered to include more light wind conditions,
however these adjustments must be outlined in the Model Plan. As well, consider the use of
either AERMOD or CALPUFF, rather than relying on adjustments to many hours of wind data.
The transport dispersion of an airborne substance depends on the wind flow and atmospheric
mixing, or turbulence. Although turbulence is a continuum, some Gaussian type models require
the definition of discrete classes of turbulent "states" which are referred to as "P-G" stability
classes. They are a key input requirement into ISC-PRIME and RTDM3.2. The following
sections provide guidance on how to determine P-G classes.
The most frequently used method to determine P-G stability was developed by Turner (1964).
This method was developed in the early years of regulatory model application, and it is used in
the performance evaluations of many Gaussian-based models (including ISC-PRIME and
In the early days of modelling, direct turbulence measurements were very difficult to obtain, so
Turner used routine airport observations of hourly wind speed, cloud cover, time of day (EPA
1995). Since the method requires hourly cloud observations, this technique cannot be applied
using data from unattended, data collection stations. Other P-G stability class approaches were
developed as instrumentation and data logging capability increased (SRDT and turbulent
fluctuation methods).
A modified Turner's method may also be used to determine stability where a combination of
site-specific and airport data can be used. A wind speed measured at a site-specific station and
the cloud amount and height data as estimated at the nearest hourly reporting airport can be
combined using Turner's method to determine stability. This assumes that the cloud cover as
observed at another location applies to the site. This may be an acceptable assumption given a
number of factors including distance away from the reporting airport and whether the site under
consideration is subject to localized cloud conditions.
Finally, recent developments in instrumentation make automated hourly cloud cover and ceiling
height observations possible. These instruments can be operated in an unattended fashion and
included as part of a meteorological monitoring program.
EPA (2000) describes a stability classification method based on solar radiation and temperature
gradient (the SRDT method). The method was developed as an alternative to the Turner method
to determine P-G stability class, eliminating the need for an observer to estimate cloud amounts.
The SRDT method has not been tested in high latitude areas. There may be issues associated
with frequent low sun angles and the applicability of stability class categories as well as the
measurement of temperature gradients. It is recommended that if SRDT is to be used, until more
familiarity with this approach is attained, then the direct measures of turbulence (σ? and σE: see
next section) should also be obtained, as these parameters are built-in functions in dataloggers.
If σ? is not available, the standard deviation of the vertical wind direction fluctuations, σE, can
also be used in combination with the scalar wind speed to determine stability based on the
methodology given in EPA (2000).
By virtue of its historic precedence, widespread use and performance evaluations of Gaussian-
based models such as ISC-PRIME, based on EPA (2003), these guidelines recommend the
w for the core models ISC-PRIME and RTDM3.2, Turner's method is the preferred method
used in determining P-G stability if the hourly airport observations used to generate
Turner stability are applicable to the site. For principal station data from MSC, see
Section 7.2.2 or for the modified Turner's method, see Section 7.6.1.
w if these methods cannot be applied, then SRDT or the σ methods should be used.
Evaluations performed on the SRDT method indicate that it can identify the same P-G
stability category as the Turner method about 60% of the time and is within one category
about 90% of the time. The σ methods are expected to estimate the same Turner stability
category about 50% of the time and will be within one category about 90% of the time.
This performance is based on site-specific tuning.
Although AERMOD does not use discrete stability classes, it still requires hourly cloud
observations. This data is used by its component meteorological preprocessor program,
AERMET, to calculate various meteorological parameters.
For CALPUFF, the specification of stability depends on the dispersion option chosen in
CALMET. These guidelines recommend the same treatment as used in AERMOD (Section
184.108.40.206) and thus the calculation of P-G stability classes is not required.
The mixing height is defined by the layer adjacent to the surface through which mixing due to
turbulent motion occurs. Above this height turbulent motion is suppressed due to a capping
stable layer.
During a classic day-night cycle (clear skies and light winds), the mixing height is typically very
low at night (when there is a ground-based temperature inversion) and then increases throughout
the day as turbulence due to surface heating and wind moving over surface objects erodes the
bottom of the temperature inversion. The mixing height reaches its maximum in the afternoon,
and then collapses near sunset, as surface heating turns to surface cooling, the winds become
light, and the inversion reforms at the surface.
From an air quality perspective, the mixing height is important as it defines a layer where
emissions can be trapped. If the mixed layer is shallow, the trapped emissions have nowhere to
go and high concentrations throughout the layer can occur. Conversely, if the emissions are above
the mixed layer then very little material will reach the ground.
Refined models require hourly measures of mixing height or a sounding (in the case of AERMOD
and CALPUFF). Unfortunately data of this nature is difficult to obtain. Since the mixing layer
depths are frequently above 100 m, tall towers are not useful for this purpose. Hourly balloon
releases to establish a temperature profile would provide the data, but these programs are expensive.
The US EPA (2000) recommends use of the nearest upper-air site in conjunction with an hourly dry
bulb temperature measured on site. Hourly mixing heights can be generated by using a
meteorological preprocessor program and raw sounding data or extracted twice-daily mixing
heights corresponding to each sounding. For US upper-air stations, the extracted twice-daily mixing
heights using the Holzworth method (1972) are available from SCRAM for any upper-air station in
the US. For Canadian upper-air sites, the US National Climatic Data Center
(www.ncdc.noaa.gov/oa/ncdc.html) and some private Canadian air quality consultants can provide
this service. Once the twice-daily mixing heights are obtained, hourly values (required by refined
models) can be generated using the utility programs: PCRAMMET and MPRM (US EPA
meteorological processing programs, discussed in Section 7.8).
Unfortunately, in British Columbia the density of upper-air stations is very coarse and applicability
of this technique to different mountain valleys is questionable unless the site in question is very near
the sounding site. A list of upper-air stations that could have some applicability for modelling
applications in British Columbia is given in Table 7.1.
For most cases in BC, the nearest upper-air station data is not applicable to the site in question. The
following approaches are recommended:
w assess the importance of mixing height using SCREEN3 or ISC-PRIME/S. If the hourly
maximum concentrations are far below the ambient air quality objectives, then hourly
mixing height may not be important from the perspective of determining short-term
maximum concentrations and the level of effort to obtain them could be limited to
simpler approaches, such as the screening mixing height model described below.
The core models listed in this guide, as well as many special circumstance models require a
sequential time series of hourly meteorological data. Creation of these meteorological input files is
not a trivial undertaking as each model requires different formats. The process may be further
complicated by the availability, or rather non-availability, of suitable site-specific meteorology,
resulting in the need to merge data from differing locales into one file in order to supply all
necessary parameters. Finally, Canadian airport data is formats and units that require processing
before they can be used with the models.
The US EPA SCRAM (Support Center for Regulatory Air modelling) website
(www.epa.gov/scram001) contains both data and an array of utility programs for meteorological
data processing. Meteorological data includes twice-daily mixing height and hourly surface
weather data from numerous airport-reporting stations for US stations only. Some Canadian data
in formats suitable for the core models is also freely available from private contractors.
For preparing meteorological data for ISC-PRIME and RTDM3.2, the following utility programs
(available from the SCRAM website) are recommended:
w MPRM for both airport observation data and site-specific data
w PCRAMMET for airport observation data
These utilities also include QA/QC tests on the data and are described more fully in the
following sections.
MPRM was created as a general-purpose meteorological processor and data QA/QC utility with the
capability for processing site-specific meteorological data as well as US National Weather Service
format surface and upper-air data (EPA 1996). Output files provided by MPRM are produced in the
unique formats specific to each EPA dispersion model.
MPRM has features related both to the calculation and QA of hourly variables and can
accommodate time intervals of less than one hour. For example, it can average four 15-minute
measurements into an hourly value. QA checks are accomplished by using either user-specified
or default upper and lower bounds defined for each variable. At each step in the processing, error
and report files are written. These files record hours with missing data, the number of records
extracted, the specific variables flagged and any action MPRM took to correct missing data or
data problems. These reports are invaluable for data quality assurance.
With respect to upper-air sounding data, gradients of wind and temperature are checked against
either user-defined or default bounding values. Corrections are made automatically, and/or
questionable outputs are flagged for the user to review manually.
For wind and temperature data, the user can specify the specific height(s) at which data was
collected. If data is available for more than one height, the user specifies the height relevant to
the modelling exercise. Missing data at this height is substituted with data from alternate heights
or from alternate data sets. The wind is also checked for model calms and applies the EPA calms
protocol (EPA 2003) for wind speed and direction under these conditions. For airport wind
direction data, MPRM randomizes the wind directions within a 10-degree wind sector, given that
the data is reported to the nearest 10°.
MPRM will determine hourly stability class depending on the specifics of the input data and the
preferred method, as specified by the user. If there are periods where data for the preferred
method is not available, then alternate methods are used, depending on data availability. MPRM
allows the user to specify a direction dependent, surface roughness length for up to 12 wind
direction sectors. These roughness length values can also be varied by month of year. The
roughness lengths are used to determine stability, if the σθ ?or σE methods are employed.
MPRM determines hourly mixing heights by two methods — via upper-air data and pre-extracted
twice-daily values. The twice-daily approach uses an ASCII time sequential file containing mixing
heights that have already been extracted from the corresponding twice-daily upper-air data. The user
can specify the column format of the file using a FORTRAN-style format statement.
MPRM will also determine wet and dry particle deposition parameters. These are required inputs
to ISC-PRIME for particle deposition calculations.
In order to use MPRM with Canadian airport data, the data must be transformed into the CD-144
format and corresponding units (see Section 7.10).
The input requirements for PCRAMMET depend on the dispersion model and the model options
for which the data is being prepared. The minimum input requirements are the twice-daily
mixing heights and hourly surface observations of wind speed, wind direction, dry bulb
temperature, opaque cloud cover and ceiling height. For dry deposition estimates, station
pressure is recommended, and for wet deposition estimates, the precipitation type and the
amount are required for those periods during which precipitation was observed.
If these utilities are not used in the preparation of meteorological input data for RTDM3.2 and ISC-
PRIME, the modellers must insure the data files unique to these models are in the proper formats and
units. Furthermore, for airport wind direction data, randomizing the wind direction within the 10-
degree sector is recommended, otherwise the plume positions will be in 10-degree increments
(resulting in over-predictions along these increments, and under-predictions between them).
In order for MPRM and/or PCRAMMET and the meteorological preprocessors for AERMOD
(AERMET) and CALPUFF (METSCAN and SMERGE) to process Canadian hourly airport
observation data, the file must be in US National Weather Service CD-144 format. The CD-144
format is composed of one record/hour, with all weather elements reported in an 80-column card
image. MPRM and PCRAMMET can also accommodate SCRAM data format, which is similar
to CD-144 except that the data columns are compressed (they do not cover the full 80 columns).
Hourly, sequential surface observation data for British Columbia contact the Regional climate
data group at firstname.lastname@example.com (604) 664-9067. Note that the data must be transformed
into CD144 or SCRAM format. MSC data from reporting airports in British Columbia (some are
full 24-hour reporting stations and others operate only 12–16 hours/day) is identified by elements
71 (ceiling height – 30's of m), 76 (wind speed – km/h), 77 (surface pressure - kPa), 78 (dry bulb
temp - °C), 80 (RH - %), 81 (cloud opacity - tenths), 82 (cloud amount - tenths), and 156 (wind
direction – 10's deg). Each line in the element file has 24 values of that element (representing 24
hourly observations). In other words, each line is one day of observations for that parameter.
Request that the GRP118 (V3.1) utility be applied to the archived data so that the data is
provided in column oriented format (where each line represents an hour and the various
parameters provided in specific columns). This will ease conversion of the MSC data to the CD-
144 formats (and units).
There are private consultants that can access this data and provide a conversion the format and
parameter units that are consistent with the CD-144 format.
The AERMOD terrain processor (AERMAP) requires terrain data exclusively in USGS DEM
(one-degree and 7.5-minute resolution). CALMET preprocessor (TERREL) also accepts terrain
data in other formats including the GTOPO30, SRTM30 and the ARM3 digital terrain data. The
USGS DEM data is not available for Canada, although the GTOPO30 and SRTM30 format data
is. Unfortunately, this data (and the ARM3 data) provides terrain at around a 1 km grid
resolution, likely too coarse for many CALPUFF and AERMOD applications in BC.
For more highly resolved terrain data, SRTM3 terrain data is available for North America (as
well as other parts of the world) at a resolution of three arc-seconds (~90 m) up to 60° N. It is
available free of charge from: www.src.com/calpuff/calpuff1.htm . Go to the Datasets link and
download the terrain file that corresponds to the area of interest. TERREL can read this data
format directly.
Natural Resources Canada has digital terrain data (Canadian Digital Elevation Data – CDED)
based on a 1:50,000 and a 1:250,000 map scale (NAD83). The 1:50,000 map scale data has a
grid resolution range of 0.75 to 3 arc-seconds. For data south of 68° N latitude, the grid
resolution is roughly 20 m, depending on latitude. The 250,000 map scale data has a grid
resolution range of 3 to 12 arc-seconds, depending on latitude. For data south of 68° N latitude,
the grid resolution is roughly 90 m, depending on latitude. Natural Resources Canada indicates
that this data should be compatible with all programs designed to read and process USGS Digital
Terrain Elevation Data. To order the data, see:
Highly resolved, gridded terrain data (NAD83) for BC at approximately 25-metre resolution is
also available from LandData BC. For more information and instructions on ordering the data,
see: srmwww.gov.bc.ca/bmgs/trim/index.html. Request the MOEP "d" layer map in MOEP
binary compressed format. In order to convert the binary format to ASCII MOEP format, a
utility (EXPAND2) can be downloaded from: srmwww.gov.bc.ca/bmgs/freestuff.html.
Using the EXPAND2 utility only provides a means to convert the binary file into an ASCII file.
This file must be further converted into the required format for the specific models.
Finally, terrain heights extracted from a 1:50,000 contour map are acceptable, but care must be
taken to interpret the contour lines properly and determine the heights at specific grid points.
Again, the data must be formatted according to the requirements of the specific model.
Refined models, such as ISC-PRIME and AERMOD, require the specification of surface
roughness (either as a rural or urban designation, or specific roughness height values that
correspond to different land use types). Furthermore, for CALPUFF, the processing utility
program CTGPROG requires land use data.
Land use data for BC is available form at Geographic Data BC (BTM – Baseline Thematic Mapping)
and can be ordered online at: http://srmwww.gov.bc.ca/dss/initiatives/ias/btm/index.htm.
The BC data for land use classifications and formats are different from the US formats that are
required for the CALMET land use processor CTGPROG. The BTM is in polygonized format
(with areas of minimum size of about 15 hectares). The polygonized land use files can be
translated into a raster image, which would provide an x, y, and land use code as required by
CTGPROG. Such a raster file would need to be further manipulated to provide a format that
corresponds to the GEO.DAT requirements. Furthermore, if the BTM data is used with
CTGPROG, the BC land use codes must be translated into the USGS land use codes used in
GEO.DAT. A translation matrix is provided in Section 9.4.5 regarding CALPUFF specific
A less elegant approach, but one which avoids the complexities of file acquisition and
reformatting/extraction is to extract land use by hand using either 1:50,000 contour maps or the
BTM land use maps. The land use portion of the GEO.DAT file for CALPUFF purposes or
surface roughness determination for ISC-PRIME and AERMOD purposes is manually
developed. Although this is time consuming, it may take less time than manipulating all the
electronic files as previously described.
There are some source/building and stack configurations where the plume behaviour is affected
by flow distortion and enhanced turbulence due to a building. The plume (or part of it) may be
caught in the building cavity (a low pressure zone of re-circulation in the lee of the building)
and/or may be brought to the surface rapidly due the enhanced turbulence in the downwind
building wake (building downwash). These conditions can result in high ground-level
concentrations near an emission source.
Core models that include an explicit treatment of building wake effects are the screening models
SCREEN3 (includes cavity concentrations as well) and ISC-PRIME/S, and the refined models
ISC-PRIME, AERMOD and CALPUFF.
In order to determine whether building effects need to be accounted for in the modelling
assessment, the following criteria based on the EPA (2003) are to be applied. A simple test to
assess whether a plume is high enough above a building to avoid building effects is given by:
hs ≥ hb + 1.5 Lb
If the above equation holds, then the building will not affect the plume behaviour and the
building effects do not have to be accounted for by the model. In the case where the stack and
building are separated, hb includes any building where the distance between the stack and part of
the building which is nearest the stack is less than or equal to 5 x Lb but not greater than 0.8 km.
If there is more than one stack at a given facility, the above 1.5 rule must be successively applied
to each stack. If more than one building is involved, the rule must be successively applied to
each building. The parameter Lb (used to determine the vertical and horizontal extent of building
wake effects), can be tedious to determine by hand for complicated building structures (for
example, "L" shaped buildings with multiple tiers). Such effort can be avoided through the use
of the BPIPPRM or BPIP building preprocessor, a US EPA utility program. Based on building
and stack information, BPIPPRM or BPIP accounts for multiple buildings of various shapes,
calculates the relevant scaling parameters that are required as model input and identifies those
sources affected by buildings for all wind directions.
There are three main types of building downwash algorithms commonly used in air dispersion
models: Huber-Snyder, Schulman-Scire, and Plume Rise Model Enhancements (PRIME)
algorithms. The PRIME algorithm is recognized to be the superior algorithm for estimating
building downwash.
Building downwash information for the core models can be generated manually or automatically
by running building input utility programs BPIP (Building Profile Input Program) or BPIPPRM
(differs from BPIP in that it produces the parameters required by the PRIME building downwash
algorithm which is built into ISC-PRIME and AERMOD). Using building dimensions and the
stack height information BPIP and BPIPPRM can determine whether a stack is subjected to
wake effects from a structure(s), and calculate building heights (BH) and projected building
widths (PBW) for cases when the plume is affected by building wakes. The output is formatted to
be consistent with the dispersion model input requirements.
Often, more than one building affects a given stack plume. BPIPPRM and BPIP determine
building separation distances and will fill in the gap between the buildings under specific
circumstances if they are "sufficiently close." With the addition of more buildings and stacks, a
maze of influence zones results. BPPPRM and BPIP were developed to automate the calculations
for these complicated situations.
In the future, CALPUFF with PRIME will be available. The current version uses the original
building downwash treatments and as such, BPIP is the appropriate utility for the preparation of
stack/building information required by CALPUFF to account for downwash effects.
As a cautionary note, it is recommended that users of BPIPPRM examine the results closely for
unrealistic behaviour when processing a complex arrangement of buildings with multiple tiers. If
such behaviour is evident, merging tiers and combining buildings may be required.
For ISC-PRIME/S, screening meteorological data is required in order to determine the "worst-
case" meteorology and the resulting "worst-case concentration." The approach used to generate
this screening meteorological data is based on the meteorological matrix in SCREEN3 where
combinations of P-G stability and wind speed are applied through a sweep of all compass
directions. That is, for every wind direction from 0° to 360° by a user specified increment (2.5°
recommended) each combination of wind speed and stability class is applied to generate a
screening data matrix. The direction sweep is important in situations of multiple sources or with
buildings where it is not clear which direction is associated with the maximum concentration.
The last two parameters in the list are not explicitly required in the hourly time series. Default
values, as recommended in the ISC-PRIME user's manual, can be substituted.
The "flow vector" wind direction is not a real wind vector as defined in the mathematical sense,
but is a term for direction as wind blowing to rather than the standard meteorological convention
of wind blowing from. The user can modify all of the wind directions in the input file by 180° or
can specify the optional WDROTATE keyword and input 180°. This will allow the user to retain
the standard direction convention in the meteorological input file but will internally change the
input to the required flow vector wind direction (or "winds to").
The urban and rural mixing heights refer to the output automatically produced by the
meteorological processor programs MPRM and PCRAMMET for ISC-PRIME. The programs
generate hourly mixing heights that correspond to urban and rural areas. The choice of mixing
height depends on whether the surrounding area is classed as rural or urban (Section 9.1.3). If
mixing heights are determined by methods other than MPRM or PCRAMMET (Section 7.7),
then use the same hourly value for both rural and urban mixing heights.
Although ISC-PRIME has a default format for the meteorological data file, there are a number of
other format options available such as a user-specified format (specified columns or variables
separated by blanks or commas), an unformatted file format (as generated by PCRAMMET or
MPRM), and a format that includes hourly potential temperature profiles and wind profile exponents.
Finally, there is a long-term version of the model, where seasonal and annual averages are
calculated directly. If long-term concentrations/depositions are all that are required, then
meteorological input requirements consist of corresponding annual or seasonal averages of the
different meteorological variables. Thus it is not necessary to specify the parameters listed above
for every hour. Typically users desire finer time-resolved estimates, so the long-term version of
the model is rarely used in BC.
There is some flexibility in the way ISC-PRIME can be run. The following are the recommended
settings (regulatory default settings):
w stack tip downwash
w buoyancy-induced dispersion
w final plume rise
w calms processing (Section 7.5)
w default winds speed profile exponents
w default vertical potential temperature gradients
If the parameter DFAULT is specified in the input file runstream, the above settings are
automatically selected. If it is not specified, the regulatory default settings will still be used
unless a non-default option is specified in the input runstream.
Finally, the user must specify whether the dispersion is occurring over a rural or urban area. In
order to determine this in situations of a multi-land use situation within the modelling domain,
refer to Table 9.1. Classify the land use within the total area, Ao, circumscribed by a 3 km radius
circle around the source using the meteorological land use typing scheme as shown in Table 9.1
(Auer 1979). If land use types I1, I2, C1, R2, and R3 account for 50% or more of Ao, it is classed
as urban; otherwise it is rural.
Table 9.1 - Identification and Classification to Land Use Types (Auer 1979)
The last six parameters in the above list are optional. RTDM3.2 will use default potential
temperature gradients and wind speed profile exponents if none are determined from the
meteorological database. Direct measures of σ? and σE can be used as an alternate to Pasquill
stability category; however, the US EPA does not recommend the use of this option for
regulatory modelling.
Data formats required for RTDM3.2 are found in the RTDM3.2 users' guide. There is no
flexibility in the format or the order of the variables in the hourly sequential meteorological data
As a minimum, AERMOD requires hourly airport observation and upper-air sounding data.
Site-specific tower data can also be input if available. Cimorelli et al. (1998) studied changes in
AERMOD performance (i.e., how well the predictions compared to observations) by
systematically degrading the input meteorological data. This lead to the following
recommendations on the minimum meteorological data requirements for AERMOD applications:
w data must be horizontally and vertically representative. This should be judged
independently for each variable.
w surface characteristics around the meteorological site must be similar to the surface
characteristics within the modelling domain
w surface characteristics around the meteorological site should be used as input for
Adequately representative data for each of the following variables constitutes the minimum set of
meteorological variables that AERMOD requires:
w wind speed (at least one level between 7 times the surface roughness as defined in
Section 9.3.2 and 100 m, scalar is acceptable)
w wind direction (scalar, but unit vector is acceptable) – for airport observation data,
AERMET randomizes the direction within the 10 degree reporting sector
w ambient temperature (between the surface roughness height and 100 m)
w cloud cover
w morning upper-air sounding (upper air profile data is required in TD6201 format, but
only the 12 UTC sounding for each day is used).
For geophysical data, in addition to terrain data, the following surface data information is
required. A single value for the following variables can be chosen to represent the conditions
over the modelling domain. However, the user can specify direction and season-dependent
surface data.
w surface roughness
w Bowen ratio
w Albedo
As wind moves past objects (ground, rocks, trees, etc) complex turbulence patterns result and
effectively slow the wind down (i.e. they create drag caused by the roughness of the surface).
This surface roughness can be characterized by an "aerodynamic roughness length" which in
turn is a function of the height and arrangement of the surface roughness elements. The greater
the roughness length, the larger the turbulent eddies generated by the surface objects. These
surface generated eddies can be critical in determining the extent of the contaminant dispersion
and many dispersion models require the roughness length as an input.
AERMOD has the capability to account for seasonal variations in surface roughness, as well as
sectoral variations (different surface roughness for different wind sectors). Although AERMOD
has a default value, care is required to specify roughness (surface conditions at the site).
Characterize the land use type as described in Table 9.3 within a three-kilometre radius of the
source and select the corresponding roughness values for the different seasons (Paine 1987).
Table 9.3 - Surface Roughness Heights (m), For Typical Land Use Types and Seasons
Water (fresh water and sea water) 0.0001 0.0001 0.0001 0.0001
Deciduous Forest 1.00 1.30 0.80 0.50
Coniferous Forest 1.30 1.30 1.30 1.30
Swamp 0.20 0.20 0.20 0.05
Cultivated Land 0.03 0.20 0.05 0.01
Grassland 0.05 0.10 0.01 0.001
Urban 1.00 1.00 1.00 1.00
Desert Shrubland 0.30 0.30 0.30 0.15
"Spring" refers to periods when vegetation is emerging or partially green. This is a transitional
situation that applies for 1–2 months after the last killing frost in spring.
"Summer" applies to the period when vegetation is lush and healthy, typical of midsummer, but
also of other seasons where frost is less common.
"Autumn" refers to a period when freezing conditions are common, deciduous trees are leafless,
crops are not yet planted or are already harvested (bare soil exposed), grass surfaces are brown,
and no snow is present.
In addition to surface roughness, AERMOD and ISC-PRIME (using the deposition option)
requires Albedo and Bowen ratios. Although there are defaults specified for these values, they
should be selected carefully so they represent the local conditions. Further guidance on this can
be found in Paine (1987).
The regulatory default option is controlled from the MODELOPT keyword on the CO pathway.
As its name implies, this keyword controls the selection of modelling options. Unless specified
otherwise through the available keyword options, the AERMOD model implements the
following default options:
w use the elevated terrain algorithms requiring input of terrain height data
w use stack-tip downwash (except for building downwash cases)
w use the calms processing routines
w use the missing data processing routines
w use a four-hour half-life for exponential decay of SO2 for urban sources
The parameters used to specify options on the MODELOPT keyword are character strings, called
"secondary keywords," that are descriptive of the option being selected. For example, to ensure
that the regulatory default options are used for a particular run, the user would include the
secondary keyword "DFAULT" on the MODELOPT input. The presence of this secondary
keyword tells the model to override any attempt to use a non-regulatory default option. The
model will warn the user if a non-regulatory option is selected along with the DFAULT option,
but will not halt processing. For regulatory modelling applications, it is strongly suggested that
the DFAULT switch be set, even though the model defaults to the regulatory options without it.
An evaluation of the applicability of cloud cover and especially upper-air sounding data to the
site is recommended. If in an area of complex terrain, this may limit the spatial applicability of
cloud cover and upper-air soundings.
There can be a sensitivity of results to the surface characteristics, notably surface roughness.
Care is required in selecting an appropriate surface roughness, and if there is any doubt regarding
the value a sensitivity analysis of the results could to assess the reasonableness of the results.
There are commercially available software that are user-friendly interfaces for preparing the
input data and running AERMOD. Sometimes these packages bundle models together, so the
user must be careful to specify the particular model used when model results are submitted for
While it is generally agreed that CALPUFF offers superior treatment of transport and
transformation than the straight-line Gaussian models, this increased capability brings additional
complexity. Such complexity involves larger data input files and a higher degree of professional
judgment in selecting the various options (for example, the dispersion and computation control
parameters including divergence criterion, sampling steps during a time step, etc.).
At present, the EPA has not provided a list a regulatory default options. This is in part due to the
newness of the model, but largely due to the various options available in the model and the
difficulty in applying a single set of options and user-defined factors for every circumstance. For
this reason, the modeller's understanding of atmospheric behaviour is essential to the proper
application of CALPUFF.
Although there are a number of parameters that can be adjusted in the model, in most cases
default values are provided and model behaviour is satisfactory with these values. However,
depending on the application, there may be a need to deviate from the default selections under
certain scenarios, such as:
w puff vs. slug option
w puff splitting
w dispersion coefficients (see Section 188.8.131.52 for specifics)
w subgrid-scale complex terrain
w chemical transformation
A good resource for guidance for a number of issues including the topics listed above is provided
in the CALPUFF FAQ's (www.src.com/calpuff/FAQ-answers.htm). A few of the areas that have
been problematic in the past are highlighted at this website.
CALMET can generate gridded meteorological fields with a minimum of input data. In addition
to the required geophysical data, hourly surface observations from a reporting airport or site-
specific meteorological station and upper-air data will provide CALMET with enough
information to produce meteorological fields, although there is no guarantee that these fields will
be reasonable. The following sections provide guidance on producing reasonable meteorological
fields with CALMET.
Users must convert Canadian airport observation data into a format (and units) readable by the
CALMET meteorological surface data processors, METSCAN, which only accepts CD-144 data
and SMERGE which accepts CD-144, SAMSON, HUSWO and DATSAV3 (TD-9956) format
(although the latter format needs to be converted to CD-144 format).
It is possible to reformat the Canadian MSC airport hourly observation data and site-specific
tower data (along with converting the units) into the surface data (SURF.DAT) file that is read
directly by CALMET. The downside to this approach is that METSCAN (a valuable quality
assurance data screening program) is bypassed. Furthermore, if multiple surface data sets are
used, then users must combine data from multiple sites together — something easily done by
SMERGE (a utility that combines files from individual stations into one file) if the data is in the
above formats.
Table 9.4 – CD-144 Precipitation Code Based on Canadian Hourly Weather Code
For some databases (such as Ministry of Forest data), no weather codes are available, thus
equivalent precipitation codes can be assigned based more commonly available dry bulb
temperature and precipitation observations. Equivalent CD-144 precipitation codes can be
assigned as indicated in Table 9.5
Table 9.5 – CD-144 Precipitation Code based on Dry Bulb Temp and Precipitation Rate
Precipitation rates equivalencies were based on the Federal Meteorological Handbook of the
USA (US National Weather Service, 1995) with categories given in the Table above.
Hourly precipitation data (in the NCDC TD-3240 or PRECIP.DAT) format is also required if
wet processes are modelled. At least one station is required but more would provide a better
observational data set for CALMET simulations of precipitation fields. Environment Canada
data include hourly precipitation, although at some Canadian stations only daily precipitation
amounts are available. Thus assumptions must be made on how the total precipitation is
partitioned for each hour. In this case, the hourly weather code can be used (elements 086 to 098
that indicate rain, snow, drizzle, etc) to determine when precipitation may be occurring during
the period. Partitioning to the total daily precipitation equally over the precipitation hours is
Although the substitution of daily data and estimation of precipitation codes are not ideal when
compared with hourly measurements, errors in the hour-to-hour specification of the code and
precipitation amounts are not as critical for long-term estimates (i.e. annual wet-deposition).
When running a full year of data, it is recommended that the range of hourly data include Day
1/Hour 0 through Day 365/Hour 23 to Day 1/hour 0 of the next year.
Since AERMOD is intended to replace ISC-PRIME because of its superior performance, the
recommended option for short-range, complex flow applications of CALPUFF (<50 km) is
MDISP=2 (AERMOD-type dispersion).
For upper-air data, at a minimum, data for the twice-daily soundings released at 00 Greenwich
Mean Time (GMT) and 12 GMT for one or more stations are required in TD-6201 or FSL
formats. READ62 is the upper-air data preprocessor that conducts a number of QA checks
(missing data, out of range values, etc.). The CALPUFF FAQ website
(www.src.com/calpuff/FAQ-answers.htm) provides guidance on replacing missing data.
There are seven CALMET settings that are user-defined and are set on the basis of the
modeller's understanding of meteorology, the observational data, and the behaviour of the
model. These are BIAS, IEXTRP, TERRAD, R1 and R2, RMAX1 and RMAX2. Users are
referred to the model documentation and the model FAQ website for guidance on each of these
settings. It is critical that modellers carefully review the role of each of these factors, and conduct
several tests before the final settings are established. A number of these tests are outlined in
Section 10.2.1.
CALMET generates wind fields through a two-step process that involves the establishment of an
initial-guess wind field, which is subsequently adjusted for terrain and surface effects to generate
a Step 1 wind field. This wind field is further refined by observational data to create the final
wind field.
There are different options available for initializing the wind field in CALMET:
w gridded field from a prognostic mesoscale model (i.e. MM data)
w objective analysis of all available observations where CALMET interpolates horizontally
and vertically between the different observation data sites to produce a spatially varying
wind field
w spatially-uniform guess field (not recommended unless for local scale applications where
there is good representative, site-specific data)
w DIAG.DAT file (not recommended)
The first option is preferred because it provides a spatially varying meteorological field and takes
into account the effect geophysical features have on these fields. The use of such data, when
blended together with hourly surface observations, has provided at least as good, or often better
results than use of surface observations alone (CALPUFF FAQ website). Some tests conducted
by Schutte and Lundgren (2002) and McEwan and Murphy (2004) provide support for the value
of this approach when used with CALMET (see Section 7.1.3 for more on these studies).
CALMET also has a "no-obs" option, where MM data can be input and meteorological fields
produced without observations. Thus the final, fine-scale wind fields are created solely on the basis
of the MM data and the terrain and surface adjustments in CALMET. McEwan and Murphy (2004)
examined the behaviour of CALMET using MC2 (2 km grids) and RAMS (1 km grids) data as an
initial guess field, and in "no-obs" mode. The study, which was conducted over a limited time period,
found that CALMET in the "no-obs" mode, using these models, was successful at simulating both
upper-air and surface levels winds. Although the study was limited to a narrow time period (two-
week winter period), it does provide some encouraging results for the "no obs" approach.
If mesoscale model data is to be used in a CALPUFF application, the conditions for use of such
data are outlined in Section 7.1.3. The specifics of the approach must be included in the detailed
model plan and approved by the ministry.
9.4.5 Land Use Data – Code Translation from BC Land Use Code to CALMET Code
If the Baseline Thematic Mapping (BTM) land use data from BC Geographic Data is used, then the
land use codes used in the BC data must be converted to the USGS type land use codes required by
the CALMET land use processor, CTPROG. Table 9.6 below provides the conversions.
BC Land BC Land Use Category CALMET Code CALMET Land Use Category
Class Code
Although it is useful to know the predicted incremental impact due to a source, it is the
cumulative air quality that is also of importance. The cumulative air quality is given by:
"Background" is the concentration due to emissions from both natural and human-caused
sources. In other words, it is the result of the contribution from all sources except the source(s)
being modelled.
Choosing the appropriate background concentration can be critical in assessing overall air
quality. In order of priority, the information sources used to establish the background
concentration level are:
w a network of long-term ambient monitoring stations near the source under study
w long-term ambient monitoring at a different location that is adequately representative
w modelled background
Given the importance of the background level, the method to establish background conditions
must be included in the modelling plan (Section 4).
The background concentration can be determined through the use of existing ambient monitoring data
collected at the site. The process to determine this is discussed in Section 10.1.2. Current and historical
air quality monitoring data is available from various sites across the province (including the GVRD)
and is available on-line from: www.elp.gov.bc.ca:8000/pls/aqiis/archive.report.
Access to the data on this website requires a password that is available from the Ministry Quality
Assurance Scientist (250-387-9940).
If data is not available in the vicinity of the source under consideration, data collected from another
location could be used. Thus, the use of such off-site data to establish an appropriate background
requires a careful review of whether the site is representative. Factors to be considered are:
w differences in geophysical characteristics (meteorology, topography, surface features)
w differences in emissions types (point sources, roads, etc.)
w differences in emission changes (rapid urban growth, forest fires, nearby temporary
Finally, a review of the instrumentation and the data collection protocols used in the off-site
program is recommended to insure data confidence.
The decision to include or exclude sources that may or may not contribute to the background
concentration depends in part on the scale of the modelling domain. If the concern is over regional
transport (i.e., acid deposition or photochemical ozone formation), where impacts a few hundred
kilometres downwind are relevant to the assessing impacts, then sources over a wide area need to be
included. If the concern is limited to a local area, sources outside the area may not be important.
The straight-line Gaussian models recommended here predict air quality to a maximum of 50 km
(depending on terrain). If there are no other sources within this radius, their contribution to the
air quality near the source under study can be deemed insignificant. If there are sources within 50
km, then run SCREEN3 to determine the maximum concentrations as a function of distance for
each source within this radius. A careful review of the results will help identify the sources that
need to be included. Of critical importance are the distances to the maximums predicted for each
source. Any source within these distances should be included. One must also consider the
surrounding terrain where a source may be close to another, but is in an adjacent valley separated
by high ridges. It is obvious that there is no need to include the adjacent valley source if local
impacts are of concern. Final decisions may require an iterative approach.
The guidance that is outlined in Section 5 regarding source emission rates for model inputs also
applies for those sources included in the modelling runs to estimate background.
Although a fully instrumented, continuous monitoring station operating for at least a year is ideal,
establishing background could be determined from a short-term study or passive monitoring. Details
of any new ambient monitoring program need to be approved by the ministry.
When monitoring data is available (whether it is from the area where the source(s) are modelled, or
from a distant area), the next step is to select an appropriate background level. Typically, a single
value is chosen as background, which is assumed to apply for every hour of the year and for every
location within the modelling domain. Choosing an appropriate single value from a year's worth of
monitoring data is problematic given the variation in concentrations that will occur in time and
space over the period.
The value selected for background depends on the purposes of the modelling assessment. For a
Level 1 assessment, the screening nature of the approach would mean a screening background level
should be applied (i.e., the maximum value or 100th percentile measured). For situations where a
conservative estimate of the impacts from the source are desired (when determining compliance
with ambient objectives/guidelines, worst-case analysis, potential risk exposure estimates), a
conservative value should be used to establish background levels. For situations where the models
are used for a retrospective analysis (source identification, episode analysis), matching the hourly
model prediction with the corresponding hour of monitoring data would be appropriate.
In situations where the prime interest is in determining compliance with ambient objectives/
guidelines for continuously emitting sources, regulatory agencies differ in their recommendations
on establishing background concentrations. Examples include: a specific percentile (100th, 99th or
98th highest hourly value), a regionally dependent background level, sequence matching the hourly
background concentration with the hour being modelled, or a specified process to establish a
background level, or no guidance at all. Historically in BC, percentile background levels used in air
quality assessments have ranged from the 100th to 98th percentile.
These guidelines, which adopt some elements of these approaches, recognize the large uncertainty
associated with the selection of a background level. A recommended process to establish the
background is outlined in the following steps:
w If the monitoring data is deemed representative of the area under consideration, the
background should be based on the most recent data available from the last year. The data
record must be 75% complete in each quarter of the year.
w If the modelled source already exists, then periods when the source is impacting the
monitoring location used to determine background can be removed from the data set.
This can be determined by defining a 45º sector downwind of the source. Wind direction
data will define whether the plume location is within or outside the sector. If an area is
subject to frequent light wind conditions, then this rule does not apply and professional
judgment is required to establish whether the existing source is contributing to the
monitored levels. If the source in question is a new source, then the whole data set is
w The monitoring data can be pre-screened to exclude any periods when local, intermittent
sources have an influence on the monitor, but have minimal influence on the wider area.
For example, for PM10 concentrations measured at a monitor adjacent to a construction
site, the period of record when the site was active should be eliminated from the data.
w For Level 1 assessments, select the maximum measured concentration (100th percentile)
from the screened or unscreened data set as the background level.
w For Level 2 and 3 assessments, from the screened data set, select a background value
between not lower than the 98th percentile. The specific percentile depends on the level of
uncertainty associated with establishing the background value. That is if there is large
uncertainty, a conservative approach would be to select a percentile in the upper range of
the measured concentration distribution. The uncertainty depends on the following
² the time representativeness (total period of record: the longer the period of record, the
less uncertainty associated with the background selection)
² the area representativeness of the monitoring location (wider representativeness
means less uncertainty associated with the background selection)
² the number of monitoring sites used (more measurement sites, less uncertainty)
w Select background levels for time averages that correspond to the modelled time averages
(24-hour average background level for 24-hour model predicted time-average
w If there is more than one representative monitoring site, follow the previous steps for
each site, then take the arithmetic average of the selected background level to determine
the background concentration that corresponds to each averaging period.
w In figures and/or tables that present the modelling results, provide the total air quality
(background plus the increment due to the source modelled) and provide the modelled
incremental air quality impacts (due to the source modelled).
The process used to justify the selection of the background must be outlined in the model plan.
Good modelling practice involves the examination of the input files to insure that specific data
treatments have been applied properly (missing data, calms, formats and units). However, even
with error-free input files, there is no assurance that the model output will be correct. For
example, models such as CALPUFF have user-adjustable parameters, where expert judgment is
required to set the right combination of these parameters in order to improve model realism. It is
possible for models to have coding errors or exhibit odd behaviour under applications (even
though they have been extensively tested) or computing platforms. A review of the output is
essential as it can indicate whether the model is behaving as expected under different
circumstances. Spotting odd model behaviour and errors through this type of analysis comes
from experience as well as expert training. The latter is available from many courses on models
that are offered throughout North America by private contractors.
The following sections provide some guidance on the application of QA/QC for CALPUFF
modelling as it can involve more preparation and professional judgment for its proper application
than the other recommended core models.
There is a wide variety of quality QA/QC procedures that should be conducted for CALPUFF
modelling. Much of the quality assurance depends on following the recommendations for
preparation of the input data and the appropriate selection of model parameters as outlined here
and the CALPUFF users' guide. Further efforts for quality control are recommended in the
following sections. They are not exhaustive, nor are all of them necessary for every CALPUFF
application. They focus on CALMET, as this component of the CALPUFF modelling system
involves the greatest amount of data preparation, option choices and user-defined parameters.
w Plot the frequency distribution of mixing heights for different locations. Check for
w Plot a time series of mixing heights for a 24-hour summer and winter period during a
light wind, and a clear sky period. Examine the diurnal behaviour for reasonableness.
w Although the guideline recommends the AERMOD type dispersion option it is a good
idea to also produce P-G stability class and plot the frequency distribution for the source
location as well as surface station location. Compare to the airport observation P-G class
frequency distribution (if available). Check for reasonableness for the given locations.
w If MM data is used, examine the windfields for a 24-hour period of light winds, and clear
skies at surface, mid and upper levels with and without MM data and check for
10.2.2 Model Output: More Than One Model for the Same Source
Given the wide variety of models with their attendant unique abilities, situations will arise where
there may be different models run for the same source(s). For example, one model may be used
to specifically look at the local scale (<10 km) air quality, and a different model may be used to
examine the regional scale impacts (>50 km). In such situations, it is possible to have the
different models covering the same geographical areas for the same source(s). It is likely that the
predictions for these common areas will be different, which in turn creates a problem in choosing
which model prediction is "better." In order to avoid this problem, modellers must not overlap
the geographic areas associated with different models for the same source(s).
The same considerations need to be made for situations where different models are applied to
treat specific phenomena. For example, although a model can predict both concentrations and
depositions, there may be another model used for the deposition estimates due to its specific
strengths in the treatment of depositions. In such cases, modellers must only use the deposition
output from the latter model (the model with the higher confidence in the deposition results).
There may be discontinuities in the concentration/deposition spatial fields that arise because
different model output is applied to different geographic areas. These cannot be avoided.
However, if the transition zones between these areas are critical areas of interest or the
concentrations are at levels of concern, the output needs to be examined closely. Further model
runs may be needed for these specific areas in order to determine the appropriate concentrations.
The accuracy of the model predictions is often a source of debate. Measurements and model
predictions can be compared in a variety of ways, each providing a different perspective on
model performance. A model may show good competency in certain predictions (e.g., maximum
concentrations) but poor in others (e.g., the frequency of concentrations above a certain
threshold). Although it is common to blame the model for poor performance, there are other
reasons that are often ignored, including:
w uncertainties in the input values of the known conditions (for example, poor quality or
unrepresentative meteorological, geophysical and source emission data)
w errors in the measured concentrations that are used to compare with model predictions
The sources of uncertainty in the model predictions listed above are considered "reducible" —
they can be controlled or minimized by collecting the proper input data, preparing the input files
correctly, checking and re-checking for errors, correcting for odd model behaviour, insuring that
the errors in the measured data are minimized and applying better model physics. However, even
if the reducible uncertainty is eliminated, there is something called "inherent uncertainty" in
model predictions.
Dispersion models use known conditions (as defined by wind speed, stability, emission rate, etc.)
and produce a concentration for this specific event. Even though much is known about the
processes involved in dispersion, there are unknown conditions (turbulent processes that are not
completely known and cannot be resolved by measurements and known science). These
unknown conditions may vary even under seemingly identical repetitions of an event (under
what seems to be exactly the same wind speed, stability, emission rate, etc.) and can result in
differences between the model prediction and observations for that event. The differences will
occur even with a "perfect" model and the source of these differences is referred to as "inherent"
uncertainty. See EPA (2003) for a more complete rendering of this concept.
The EPA has conducted evaluations of many of the models listed here. Although every model
will perform differently under different circumstances, the EPA has developed the following
general statements on model performance (EPA 2003). Such statements must be considered in
their totality (i.e. quoting one of the statements in isolation of the others does not provide a
complete picture).
w Models are more reliable for estimating longer time-averaged concentrations than for
estimating short-term concentrations at specific locations.
w The models are reasonably reliable in estimating the magnitude of highest concentrations
occurring sometime, somewhere in the area. For example, errors in highest estimated
concentrations of ± 10 to 40% are found to be typical.
w Estimates of concentrations that occur at a specific time and site are poorly correlated
with actual observed concentrations (paired in space and time) and are much less reliable.
w The above poor correlations between paired concentrations at fixed stations may be due
to reducible uncertainties (i.e., error in plume location due to input wind direction error
can result in large differences) or un-quantified inherent uncertainties. Such uncertainties
do not indicate that an estimated concentration does not occur, only that the precise time
and locations are in doubt.
The above statements apply to the models listed in this guideline although conclusions regarding
CALPUFF performance for local scale modelling applications are still evolving. Although they
are general in nature, they provide helpful insights into their tendencies. It is recognized that
model performance will vary depending on the application. For example, in some cases a model
may consistently over-predict, and in others, it may consistently under-predict. Although this
behaviour may change depending on the application specifics (location, meteorological
situations) it is not necessary to conduct a full model performance evaluation every time a model
is used. To do so properly involves considerable effort and regulatory agencies (including the
ministry) assume the results of carefully controlled tests conducted and/or reviewed by the EPA
provide insight on how well they will perform in a variety of other applications.
The goal of these guidelines is to minimize the reducible error by providing direction on input
data, the appropriate choice and application of models, and directions on preparing model files
and settings. There is considerable reliance on the training and experience of the modeller to
insure each of these steps is done properly.
If the guidance provided here is followed and there are no errors in the selection, set up and
application of the model, then model performance is expected to be similar to the performance
under EPA test situations. Under these conditions, the predictions from models can be viewed as
the "best estimate" available to inform decision making.
If the model output is to be submitted to the ministry for review and approval, the recommended
information should include sufficient information (tables, figures) that address the objectives of
the study, and other supporting information to prove that the model has been applied properly
and the output can be used to inform decision makers. Specifically recommended documentation
for Level 1, 2 and 3 assessments are defined below.
Site Description
w a site plan showing location and elevation of emission sources and buildings
w description of topography in model domain and geophysical data used in assessment
w background concentration of the contaminants of concern
Emissions Description
w exhaust parameters that characterize the emissions
w emission rates used in assessment
Model-Related Information
w identification of model used for assessment, and assumptions, modifications and settings
w receptor grid resolution and size
w description of QA/QC procedures used to insure model input is correct, and model
behaviour is reasonable
Specific Output Formats (not all required, but depends on objectives of modelling study)
w background concentration
w predicted concentration contours (separately for source only contribution, then
cumulative) for all averaging times of concern overlaid on a map of model domain
w exceedance frequencies above a specified threshold concentration
w persistence of such exceedances (number of consecutive periods where a threshold is
w maximum concentrations (incremental and cumulative, locations)
w concentrations based on different emission scenarios (permitted, normal operation and
other operating conditions as specified)
Submit electronic copies of input and critical output files as identified by the ministry.
This section provides direction to industry for submitting applications to flare natural gas during
well testing operations. The primary air quality concern with flaring is the SO2 formation when
flaring sour gas streams (i.e., gas streams with H2S). Well-test flaring can introduce considerable
SO2 loadings into the environment over a short period of time. Thus, this situation warrants
special treatment (in contrast to a controlled source emission from a facility) and as such a
number of requirements are listed here. Flaring associated with drilling operations, such as under
balanced drilling and drill stem tests, are not covered, as they are dealt with through the well
drilling regulatory process.
The SCREEN3 model assumes that the radiation heat loss from the flame is 55% based on a
study by Leahey and Davies (1984), and that the remaining energy is available for plume rise.
Although (Guigard et al 2000) indicate this value is very conservative, the ministry is currently
reviewing the studies on radiation loss associated with flaring. The more conservative 55% factor
is currently adopted and used in the calculation of "pseudo parameters" to represent the flare.
The point-source approach in dispersion models is designed for conventional stacks with a
known height, diameter, exit velocity, and exit temperature. These parameters are not applicable
to flare stacks. Pseudo-stack parameters have to be calculated to allow the model to simulate
dispersion from flare stacks. The pseudo parameters are selected to ensure the correct plume rise
is calculated. The pseudo parameters are given by:
w effective flare height = HSTK + (0.00456)(THR0.478)
w pseudo flare diameter = 0.000663 THR
w pseudo flare exit velocity = 20 m/s
w pseudo flare exit temperature = 1000 ºC = 1273 K
In addition, the ambient temperature when evaluating flares must be set to 15 ºC (= 288 K). Additional
information on the evaluating flare stacks can be found in Chapter 11 of Beychok (1995).
For most well-test flaring, the duration of the flaring period is greater than one-hour and the
models can be used to predict maximum hourly averages. When the flaring period is greater than
24 hours, maximum daily average concentrations can be calculated as well. For upset or
maintenance flaring, the duration can be less than one hour in duration. As the models are
designed to predict one-hour concentrations, the user can adjust the model prediction to account
for flaring periods less than one hour using the following approach:
w use the actual SO2 emission rate that is expected for n minutes (where n is less than 60
w multiply the predicted one-hour average concentration by n/60 to arrive at the adjusted
one-hour concentration
In some cases, a flare stack may be used to depressurize a pipeline and the resulting flow rate of
gas to the flare will be transient (the flaring period may or may not be less than one hour,
depending on the pipeline volume and the flare diameter). In these cases, a pipeline flow
simulation program can be used to calculate the temporal blowdown profile. From the blowdown
profile, hourly average emission parameters can be determined for multi-hour blowdowns and n-
minute average emission parameters can be derived for blowdown periods less than one hour.
The operator must conduct and submit an assessment for flaring sour gas if the H2S
concentration in the gas is between 1 and 5% through a flare stack that is less than 12 m in height
and for flaring sour gas with an H2S concentration equal to or greater than 5% (OGC, 2000).
Depending on the maximum predicted concentration, the operator must submit a Level 1 or
Level 2 assessment.
The dispersion models recommended here, including SCREEN3, must be run with all switches
set to default values (see Section 9). Each modelling study will either have to use real time
meteorological data from the most relevant nearby meteorological site, or if such a site is not
available, data from an approved screening meteorological database. The ministry must be
consulted to determine the appropriate meteorological database.
The area to be modelled should have a minimum radius of 10 km from the flare site. Either polar
or Cartesian coordinate systems can be used.
w If the Cartesian coordinate system is used, gridded receptors should be placed at ground
level and spaced at least 100 m apart for the first 4 km from the flare, and at least 500 m
apart for distances from 4 to 10 km from the flare site.
w For the polar coordinate system, gridded receptors should be placed at ten or more degree
intervals and at least 100 m apart for the first 4 km, and at least 250 m apart for distances
from four to 10 km from the flare site. The source of terrain information must be
If at this point, predicted SO2 levels exceed the Level A objective, the operator may wish to
redesign the test parameters (e.g., modify the flare rates, increase the stack height, wait until
winter season).
For well-test flares, if the maximum predicted SO2 concentration exceeds the Level A ambient
air quality objective between April and September and the Level B ambient air quality objective
between October and March, then a vegetation effects assessment will be required as set out in
Section 22.214.171.124.
In order to address the risk to human health, if the maximum predicted SO2 concentration
exceeds the Level B SO2 air quality objective of 900 µg/m3 at any residences, then the applicant
must submit a plan to continuously monitor ambient SO2 levels at the affected residences
throughout the flaring operation. The operator must indicate that flaring will be suspended if
ambient hourly levels exceed the Level B objective at the residential monitoring location.
The vegetation sensitivity to SO2 is dependent on the season and time of day that influences
when vegetation is susceptible. Table 11.1 shows the calculated hourly concentration (µg/m3)
required for each group of consecutive hours (e.g., for the daytime period in May, hourly
concentrations over 459 µg/m3 for five consecutive hours can result in visible foliar injury).
For application of the foliar damage assessment, receptors should be set at the height of the
vegetation canopy in the region of the flare. Typically, this value would be a flagpole height of
15 m .
Along with this analysis, the operator must submit frequency distributions of predicted hourly
SO2 ambient levels greater than 450, 900, 2000, and 5000 µg/m3, and additional SO2 increments
of 5000 µg/m3 up to the maximum predicted concentration. The predicted maximum number of
hours of exceedance of each of these levels must be included. For each flare scenario assessed
isopleths of the frequency distributions, maximum predicted concentrations, and of any areas
where visible foliar injury is predicted to occur must also be provided with the analysis. The
operator must also submit a map showing the elevation contours and location of any residence or
areas that may have people (i.e., camp sites, coal mines), the elevation and location (either
lat./long. or UTM coordinates) of the flare site, the expected flare date, flow rate, stack height,
emissions rate, gas composition, wind rose of the meteorological data used, proposed flare date,
and any emission parameters used in the modelling. If there is a potential of acute visible foliar
injury, then the operator must provide the recommended meteorological conditions to avoid
during flaring, which may cause the acute visible foliar injury.
Table 11.1 - The Legge SO2 Concentration (µg/m ) Criteria
for the Onset of Acute Visible Foliar Injury
Number of
Apr – Jun Jul – Sep Apr – Sep Oct – Mar
hours with
(daytime) (daytime) (nighttime) (all hours)
above given level
1 1306 1741 4724 7086
2 832 1110 3025 4538
3 639 852 2331 3496
4 530 707 1937 2906
5 459 612 1678 2517
6 408 543 1493 2239
7 369 491 1352 2028
8 338 451 1241 1861
9 313 417 1150 1725
10 292 390 1075 1612
11 275 366 1011 1516
12 260 346 956 1434
At the application stage, the operator must provide monitoring plans for tests that are predicted to
cause vegetation damage. Such monitoring will include ambient SO2 sampling in the vicinity of
the flare and collection of on-site meteorological data, including air temperature, continuous
wind speed and direction, and standard deviation of wind direction or measurements of cloud
cover and cloud ceiling, for the duration of the flaring operation. Any observation of
precipitation should also be recorded.
Where potential for significant vegetation damage has been predicted prior to the test,
subsequent remodelling of the well test using the on-site meteorological and well-test data is
required. Within 60 days of completion of the well test, the operator must submit to the Oil and
Gas Commission and to WLAP all the meteorological data collected at the site, ambient SO2 data
collected in the area, and the remodelling results with an indication of associated vegetation
effects. The operator must also provide similar data as stated in Section 220.127.116.11.
If the remodelling shows that visible foliar damage has potentially occurred as a result of the
flaring operation, then a visual monitoring plan as stated in the "Natural Gas Flaring During Well
Testing" interim guidelines #OGC 00-01 will be required. Otherwise, if the remodelling
indicates that the visible foliar injury threshold was not exceeded, no visual monitoring is
Stagnation occurs when a light wind or no-wind condition (meteorological calm) persists for many
hours or days. This commonly occurs in British Columbia valleys, especially under wintertime,
Arctic air mass conditions. With stagnation, contaminant dispersion is minimal due to limited
turbulence and transport of the emissions from the area (accumulation), as well as pollutant
If a site is subject to frequent and prolonged stagnations, straight-line Gaussian models have limited
value as they do not have the ability to treat these conditions. (as discussed in Section 7.5).
If the air quality issues of interest are associated with stagnation conditions, including
meteorological calms, these guidelines recommend the use of CALPUFF as the puffs are able to
diffuse and grow without advection, and can explicitly treat recirculation.
Near the shore of a large body of water, or along the coast of British Columbia, complex winds
and turbulence are generated as a result of the difference in surface roughness and differential
heating between land and water. A sea breeze (onshore breeze) develops during the day when the
air over the land is warmer than air over the sea. At night, the opposite occurs and a land breeze
(offshore breeze) develops. The localized wind flow and turbulence can have significant effects
on the dispersion of emission sources.
Coastal fumigation can occur when a tall stack located on a shoreline emits a plume towards the
land into a stable (or neutral) air regime and is then intercepted by a thermal boundary layer or an
internal boundary layer over the land. These boundary layers over the land create considerable
convective or mechanical mixing and can generate high ground-level concentrations as the
plume is pulled towards the ground.
The Offshore and Coastal Dispersion Model (OCD), a special circumstance model (see Section
2.4.5), is a straight-line Gaussian model developed to determine the impact of land and offshore
emissions from point, area or line sources on the air quality of coastal regions. Coastal
fumigation is treated explicitly in this model. Alternatively, CALPUFF is able to handle multiple
point, area and line sources in coastal areas and includes a coastal fumigation treatment of the
Total oxides of nitrogen (NOx) are comprised of nitric oxide (NO) and nitrogen dioxide (NO2). The
concentration of NO2 in the exhaust stream of typical combustion sources is generally in the order
of five to 10% of the NOx concentration. Transformation of NO to NO2 continues in the atmosphere
due to rapid reaction with atmospheric ozone. The BC ambient air quality objectives refer to NO2
(not NO). Dispersion models do not typically account for this NO to NO2 conversion and need to
resort to a conversion factor. The following provides guidance on establishing such a factor. These
should be considered in order of priority listed here:
w Assume the total conversion of all NO to NO2 (i.e., 100% conversion). That is, NOx
emissions are assumed to be NO2 in the dispersion model used to predict the NO2
w If these values exceed the ambient objectives for NO2 or if a more "realistic" estimate of
NO2 is desired, then alternate methods of estimating NOx to NO2 conversion can be
considered. If representative ambient NO and NO2 monitoring data is available, and the
data is considered to represent a quasi equilibrium condition where further significant
changes of the NO/NO2 ratio is not expected, then NO/NO2 ratios based on the
monitoring data can be applied to the modelled NOx (as NO2). The details of such an
approach must be detailed in the Model Plan (Section 4).
w If adequate monitoring data to establish representative NO/NO2 ratios is not available, the
Ozone Limiting Method can be applied (Cole and Summerhays 1979) where:
NO2 = 0.1* NOx + the lesser of (O3 or 0.9 NOx) + background NO2
The initial 10% conversion of NO to NO2 (the first term in the equation) corresponds to a
power plant plume and may not be appropriate for other sources of NOx such as mobile
emissions. In this case, it would be acceptable with suitable proof to use an initial conversion
value that better corresponds to the source type.
The above equation assumes the concentrations for all constituents are in vol/vol units. If the
concentrations are in mass/vol units, then the O3 concentration must be multiplied by a factor of
46/48. If vol/vol ratio concentrations are preferred, and the model output is in µg/m3, then
multiply the modelled NOx concentration (as NO2) by 0.531 to convert µg/m3 to ppb. If O3
concentrations are provided in µg/m3, multiply by 0.51 to convert µg/m3 to ppb. Note that for
modelling purposes, NOx is the predicted concentration as NO2 since the NOx emission rate is
expressed as NO2 equivalent.
If there are multiple plumes that have merged together, apply OLM to the combined plume NOx
concentration. Otherwise for plumes that have retained their identify, apply OLM to each
plume separately.
This method requires a representative O3 value that must be assigned based on other nearby
monitoring data or from other representative areas. If data from other monitoring sites is used,
then they should correspond to the setting (whether rural or urban) of the plume being modelled.
The maximum O3 value (100th percentile) from this data set should be applied.
Since screening models only produce a short-term (one-hour) concentration, EPA (1992)
recommended a method to convert the one-hour average produced by a screening estimate into
longer time-average concentrations so comparisons to 3-, 8-, and 24-hour and annual air quality
objectives can be made. The recommended factors are listed in Table 11.2 below.
The numbers in the parentheses are recommended limits where one may diverge from the
multiplying factors representing the general case. The degree of divergence depends on the local
situation, and any monitoring data that may provide direction on this. For example, if the plume
is constrained within a valley, then the multiplying factors for longer time averages will be on
the lower end of the range indicated. The multiplying factors are based upon general experience
with elevated point sources. The factors are only intended as a rough guide for estimating
maximum concentrations for averaging times greater than one hour. A degree of conservatism is
incorporated in the factors to provide reasonable assurance that maximum concentrations for 3-,
8-, and 24-hour and annual values will not be underestimated.
These factors are to be used only for screening estimates of concentrations (the maximum
predicted concentration) resulting from point-source emissions. They should not be applied to
ambient data or ambient objectives or concentrations generated from other non-screening
If the source is an area source, then these factors may not apply depending on the area-source
plume width relative to the receptor distance away from the source. If the plume width of the
area source is large relative to the downwind distance to the receptor, then these multiplying
factors will be biased low. Concentrations at these receptors will not vary as much as those for
point sources in response to varying wind directions, and the meteorological conditions that are
likely to give maximum one-hour concentrations can persist for several hours. A conservative
assumption is to assume the maximum 24-hour average concentration is the same as the one-
hour concentration in these situations.
If the source is a line source, these factors will be biased low for reasons cited above if the plume
width is large relative to the downwind distance. If the plume is aligned with the line source (i.e.,
wind in the same orientation as the line source), then the time-average factors in Table 11.2 can
be applied.
Throughout these guidelines, there are references made to the need for ministry approval on
certain proposed choices and actions. The following is a summary of when an approval is
2. modifications to a guideline model that can change the results (Section 2.1.5)
4. submission and approval of a Conceptual and Detailed Model Plan to the ministry
(Section 4):
w Level 1 assessments only if there is a deviation from these guidelines
w Level 2 assessments only if there is deviation from these guidelines OR on a case-by-case
basis (see Section 3.1). It is recommended that any Level 2 assessment should be
discussed with the ministry in order to determine the need for a modelling plan.
w Level 3 assessments
5. detailed modelling plan (if needed), should include (see Section 4):
w model selected, switch settings, general approach (Section 2.3, 2.4)
w method used to establish source emission rates (Section 5.4)
w method used to establish background concentrations (Section 10.1.2)
w meteorological data input sources, including weather forecast models if applicable, and
data treatments (Section 7 )
w the source of the geophysical data, receptor grid and data treatments (Section 8)
w method used to determine NO2 concentrations if not OLM or the 100% conversion
approach (Section 11.4)
6. any new monitoring program, ambient (Section 10.1.1.3) or meteorological (Section 7.1.1),
7. any well-test flaring application has specific approval requirements (Section 11.1)
Dispersion models recommended in these guidelines are based on the US EPA recommendations
described in the US EPA Guidelines on Air Quality Models (EPA 2003). The US EPA provides
model support primarily through the Support Center for Regulatory Air Models (SCRAM)
website at: www.epa.gov/scram001. The user community can access the latest developments on
models and model guidance, email questions to support staff and enter discussion threads on
specific model topics.
As a way to understand air management in BC, there are a number of air quality policy and
technical studies supported by the ministry and available from the Water, Air and Climate
Change Branch website at: wlapwww.gov.bc.ca/air.
There are a number of air quality specialists within the BC agencies (Ministry of Environment
and the Greater Vancouver Regional District) that issue permits, orders and other regulatory
instruments. These specialists will define the requirements for modelling that are part of an air
quality assessment, and will review the results and provide feedback on any modelling submitted
for review. If a Model Plan is needed, then early contact with the appropriate staff (Table 12.1) is
critical to the development and acceptance of the plan.
Air Quality Water, Air & Climate Change Branch Voice: (250) 953-3369
Science Specialist PO Box 9341, Stn Provincial Govt Fax: (250) 356-7197
Victoria, BC V8W 9M1
Air Quality Regional Operations: Thompson and Cariboo Voice: (250) 371-6279
Meteorologist 1259 Dalhousie Drive Fax: (250) 828-4000
Kamloops, BC V2C 5Z5
Air Quality Regional Operations: Kootenay and Okanagan Voice: (250) 489-8529
Meteorologist 205 Industrial Road G. Fax: (250) 489-8506
Cranbrook, BC V1C 7G5
Air Quality Regional Operations: Thompson and Cariboo Voice: (250) 398-4762
Meteorologist 540 Borland Street Fax: (250) 398-4214
Williams Lake, BC V2G 1R8
Ambient Air Greater Vancouver Regional District Policy and Voice: (604) 436-6878
Analyst Planning Department Fax: (604) 436-6707
4330 Kingsway
Burnaby, BC V58 4GH
American Society for Mechanical Engineers (ASME). 1979. Recommended Guide for the
Prediction of Airborne Effluents. Third Edition, New York, NY.
Auer, Jr., A.H. 1978. Correlation of Land Use and Cover with Meteorological Anomalies.
Journal of Applied Meteorology, 17(5): 636-643.
Burton, C.S., T.E. Stoeckenius and J. P. Nordin. 1983. The Temporal Representativeness of
Short-Term Meteorological Data Sets: Implications for Air Quality Impact Assessments.
Systems Applications, Inc. San Rafael, CA. (Docket No. A-80-46. II-G-11)
Cimorelli, A.J, S.G. Perry, A. Venkatram, J.C. Weil, R.J. Paine, R.B. Wilson, R.F. Lee, and
W.D. Peters. 1998. AERMOD: Description of Model. (www.epa.gov/scram001)
Cole, H.S. and J.E. Summerhays. 1979. A Review of Techniques Available for Estimation of Short-
Term NO2 Concentrations. Journal of the Air Pollution Control Association, 29(8): 812-817.
EPA. 1992. Screening Procedures for Estimating the Air Quality Impact of Stationary Sources.
Revised. EPA Publication No. EPA-454/R-92-019. Office of Air Quality Planning and
Standards. Research Triangle Park, NC. (NTIS No. PB 93-219095).
EPA. 1995. SCREEN3 User's Guide. EPA Publication No. EPA-454/B-95-004. Office of Air
Quality Planning and Standards. Research Triangle Park, NC. (NTIS No. PB 95-222766)
EPA. 2003. Guideline on Air Quality Models. U.S. Federal Register, Appendix W of 40 CFR
Ch. I (7-1-03 Edition). (www.epa.gov/scram001)
EPA. 2000. Meteorological Monitoring Guidance for Regulatory Modeling Applications. EPA
Publication No. EPA-454/R-99-005. Office of Air Quality Planning and Standards, Research
Triangle Park, NC. (PB 2001-103606) (www.epa.gov/scram001)
EPA. 1995. SCREEN3 User's Guide. EPA Publication No. EPA-454/B-95-004. Office of Air
Quality Planning and Standards. Research Triangle Park, NC. (NTIS No. PB 95-222766)
Holzworth, G.C. 1972. Mixing Heights, Wind Speeds and Potential for Urban Air Pollution
Throughout the Contiguous United States. Publication No. Ap-101, U.S. Environmental
Protection Agency, Research Triangle Park, NC.
Guigard, S.E., W.B.Kindzierski, and N. Harper. 2002. Heat Radiation from Flares. Report Prepared for
the Science and Technology Branch, Alberta Environment, ISBN 0-7785-1188-X, Edmonton, Alberta.
Leahey, D.M. and M.J.E. Davies. 1984. Observations of Plume Rise from Sour Gas Flares.
Atmospheric Environment, 18: 917-922.
Legge, A. 1995. An Environmentally Significant Flare Event from a Sour Gas Processing Plant:
Proposed Quantitative Definitions. Prepared for Westcoast Energy Inc. Biosphere Solutions.
Calgary, Alberta. February 11, 1995.
McEwan B. and B. Murphy. 2004. Use of High Resolution Numerical Meteorological Fields
with the CALPUFF Modelling System: An Analysis of RAMS and MC2 Fields over Kamloops
B.C. Prepared for the B.C. Ministry of Water, Land and Air Protection by SENES Consultants
Ltd. (wlapwww.gov.bc.ca/air/airquality/index.html#5)
Ministry. 2004 Annual Air Quality Report for Prince George. Environmental Protection
Division, Omenica-Peace Region, Ministry of Water, Land and Air Protection.
Ministry Service Plan. 2004. 2004/05 – 2006/07 Service Plan, Ministry of Water, Land and Air
Protection. (www.bcbudget.gov.bc.ca/bgt2004/sp2004/wlap/wlap_goals.htm)
Oil and Gas Commission. 2000. Natural Gas Flaring During Well Testing. Interim Guidance
#OGC 00-01 (www.ogc.gov.bc.ca/documents/guidelines/welltestflaring.pdf)
Paine, R.J. 1997. User's Guide to the CTDM Meteorological Preprocessor Program. EPA
Publication No. EPA-600/8-88-004. Office of Research and Development, Research Triangle
Park, NC. (NTIS No. PB 88-162102). (www.epa.gov/scram001)
Paine, R.J. and B.A.Egan. 1987. User's Guide to the Rough Terrain Diffusion Model (RTDM)
(Rev 3.20) ERT Document No. P-D535-585. Environmental Research and Technology, Inc.
Concord. MA. (available from the U.S. National Technical Information Service, Document No.
PB 88-171467, order via email: email@example.com or 1-800-553-6847 or 703-605-6000)
Radonjic, Z.R. and J.W.S. Young. 1997. A Mixing Height Study for North America (1987-
1991). Prepared for the Atmospheric and Climate Science Directorate, Meteorological Service of
Canada, Environment Canada by SENES Consultants Ltd. Report available from the Air Quality
Research Branch.
Schutte A. and J. Lundgren. 2002. Research on the Applicability of Modelled Site Specific
Meteorological Data to Well Test Flaring Assessments in B.C. Prepared for the BC Oil and Gas
Commission by Levelton Consultants Ltd. OGC File:2000-19.
Turner, D.B. 1964. A Diffusion Model for an Urban Area. Journal of Applied Meteorology, 3(1):
United States National Weather Service. 1995. Federal Meteorological Handbook. No. 1.
A DOS utility program, BCMETISC.EXE, is available to download from the ministry ftp
website at ftp://ftp.env.gov.bc.ca/pub/outgoing/BCMETISC It is based on a version produced by the
Oregon Dept of Environmental Quality and will generate a screening meteorological file ready
for ISC-PRIME/S using inputs of : source emission parameters (exit temperature, flow rate, exit
diameter, stack height), average ambient temperature, latitude and wind sector increments. A
rural mixing height is calculated and the urban mixing height is set equal to this value. The
ministry version of the utility program includes the ability to account for latitude in the
calculation of mechanically mixed heights.
The combinations of P-G stability and wind speed as given in Table A.1 are applied through a
sweep of all compass directions. That is, for every direction from 0 to 360º by increments of at
least 15º (a smaller increment of 2.5º is recommended), each combination of wind speed and
stability class as given in Table A.1 is applied. The increment needs to be fine enough so the
maximum concentration is resolved. Selecting an increment less than 3° produces more than one
meteorological file (as the number of combinations of wind speed, direction and stability exceeds
8760 - the number of hours in a year)
The wind direction sweep is needed when there are multiple sources and/or buildings. In these
situations it is not obvious which direction will be associated with the maximum concentration so
the screening data must include wind direction.
Zm =
U10 is the 10-metre-level wind speed (m/s) and f is the Coriolis parameter that depends
on latitude (φ). Consistent with SCREEN3, the above equation is based on a surface
July 2005
Guidelines for Air Quality Dispersion Modelling in British Columbia (Draft)
Plume heights are determined on an hourly basis using the plume rise algorithms in the ISC-
PRIME model and the hourly wind speeds, ambient temperature and stability class data. The
plume height is based on the Brigg's plume rise formula for non-stable conditions where the
stack buoyancy flux is first calculated as:
For Ta, choose the annual average value unless a specific season or time period is under
consideration (in this case, choose the average temperature for the time period).
For stable conditions, the corresponding Briggs plume rise formulas for PG stabilities E and F
are applied.
Table A.1 - Wind Speed and Stability Class Combinations Used by SCREEN3
Stability Wind Speed at 10 m above ground level (m/s)
1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 8.0 10.0 15.0 20.0
July 2005
Guidelines for Air Quality Dispersion Modelling in British Columbia (Draft)
Model Domain and Receptors (preliminary domain dimension, receptor grid/locations, sensitive
Geophysical Characteristics within Domain (flat terrain or terrain effects on dispersion required,
general description of land use)
Existing Air Quality Situation (pristine, industrial, urban: sources contributing to background):
General Information
July 2005
Guidelines for Air Quality Dispersion Modelling in British Columbia (Draft)
Emission Sources
___other (specify)
___other (specify)
___other (specify)
___other (specify)
___other (specify)
___other (specify)
Surface Met Data Source Period of Missing Data Filling (none, Freq Wind
Data WLAP, GVRD, Record data (%) interpolation, climate Speed (%)
(location) MSC, Site (start/end average, (< anemometer
Specific, other date) substitution) starting
(specify) threshold)
July 2005
Guidelines for Air Quality Dispersion Modelling in British Columbia (Draft)
____other (specify):
___special study
Dispersion Model
___max concentration/deposition
___worst-case meteorological conditions
___time and space distribution
___depositions (___particle,____gaseous)
___time ave (____short term ___long term)
___area coverage (___local ___regional)
___building cavity
___max concentration/deposition
___worst-case meteorological conditions
___time and space distribution
___depositions (___particle,____gaseous)
___time ave (____short term ___long term)
___area coverage (___local ___regional)
___building cavity
___max concentration/deposition
___worst-case meteorological conditions
___time and space distribution
___depositions (___particle,____gaseous)
___time ave (____short term ___long term)
___area coverage (___local ___regional)
___building cavity
Background Concentrations: Describe monitoring data and methodology used to establish the
background concentration for each modelled contaminant.
NO to NO2 Conversion: If OLM applied provide rationale on parameters (initial conversion, O3).
If applying monitoring data to establish NO2/NOx ratios, describe methodology.
July 2005
Guidelines for Air Quality Dispersion Modelling in British Columbia (Draft)
Southern E244775 Osoyoos 6601 89th Street 49 02 15 297 01/08 02/09 1,4,6,7,14,15,20,22,23,
Interior Shaw 119 26 18 24,25,33
Southern E249492 Vernon 2704 Highway #6 50 14 00 500 03/11 - 1,4,6,7,14,15,20,22,23,
Interior Science 119 17 00 24,25,33
Kootenay M114017 Brilliant 17-metre tower 49 19 18 465 93/05 96/11 5,12
117 38 25 93/05 02/07 1,4,6,7
96/11 02/07 14,15,20,22,23,24,25
Kootenay M114217 Castlegar seven-metre tower 49 18 50 451 85/10 93/04 1,4,5,6,7,12
on penthouse: roof 117 39 28
of Castlegar
Kootenay M114009 Celgar Pulp 10-metre tower on 49 20 00 449 99/11 - 1,4,6,7,14,15,19,20,22,
the foreshore 117 43 08 23,24,25,
development E of
the pulp mill
Kootenay M114008 Celgar RB 20-metre tower on 49 20 10 530 94/05 95/06 1,4,6,7,14,15,20,22,
the recovery boiler 117 43 20 23,24,25
Kootenay E242577 Cranbrook forest 49 31 55 00/08 00/09 1,4,7,33
Forest Fire 115 45 13
Kootenay M114018 Creston PC roof of conference 49 05 40 609 95/01 96/11 1,4,6,7,14,15,20,22,23,
School centre by playing 116 30 45 24
field 95/05 95/07 5,12
95/09 96/11 25
Kootenay E243003 Golden 670 9th Street S 51 17 44 773 00/11 - 1,4,6,7,14,15,19,20,22,
Lady Grey 116 58 18 23,24,25,33
Kootenay M114216 Nelson 10-metre tower on 49 29 34 538 85/10 87/09 1,4,5,6,7,12
roof of airport 117 17 49
hanger at the
Nelson airport
Kootenay E248021 Revelstoke 50 59 14 1500 02/09 - 1,4,6,7,14,15,20,22,
Mt. Begbie 118 11 18 23,24,25,33
Kootenay M115024 Skookum- 10-metre tower at 49 50 13 783 87/11 96/11 5,12
chuck Skookumchuck 115 44 45 87/11 - 1,4,6,7
Prairie on 96/11 - 14,15,20,22,23,24,25
Walkey's property
W of highway 95.
Omenica- E248774 Kwoen Gas Kwoen Gas Plant 55 22 40 665 02/09 - 1,4,6,7,14,15,20,22,
Peace Plant PO Box 906 121 42 17 23,24,25,33
Omenica- M109940 PRG valley bottom 53 56 00 578 85/01 89/09 1,4,5,6,7,12
Peace Foreman station W of 122 40 58
Flats Foreman Flats Rd
in an open field on
a 10-metre tower.
Omenica- E224014 PRG 7310 Cluff 53 59 44 750 96/12 98/03 5,12
Peace Glenview 122 46 32 96/12 - 1,4,6,7,14,15,20,22,23,
School 24,25
Omenica- M109915 PRG Jail SE of 2nd Avenue 53 54 25 623 84/12 87/06 1,4,5,6,7,12
Peace Fraser River 122 42 48
bridge. W of the
P.G. jail complex
on a 10-metre
Omenica- M109943 PRG seven-metre tower 53 54 55 584 84/12 87/06 1,4,5,6,7,12
Peace Lakewood on 7.9-metre-high 122 47 46
roof of Lakewood
school one half
block W of Ospika
Bld on Rainbow.
Omenica- M109913 PG 10-metre tower on 53 58 03 577 87/06 96/11 5,12
Peace Northwood 3.6-metre-high 122 41 27 97/06 - 1,4,6,7
building housing
96/11 - 14,15,20,22,23,24,25
pump controls
beside aeration
ponds. **See
Omenica- M109912 PG Plaza- seven-metre tower 53 54 48 570 84/12 96/11 5,12
Peace 400 on penthouse on 122 44 28 84/12 - 1,4,6,7
the NE corner of
96/11 - 14,15,20,22,23,24,25
the 25-metre-high
Plaza-400 building. 00/05 - 33
02/03 - 8
Omenica- M109914 PRG Pulp 10-metre tower on 53 55 28 600 87/06 96/11 5,12
Peace N end of 13-metre- 122 41 10 87/06 - 1,4,6,7
high pulp/paper
96/11 14,15,20,22,23,24,25
**Elevation is at
tower base.
Omenica- M109946 PRG Van seven-metre tower 53 53 21 594 85/01 89/08 5,12
Peace Bien on the 6.1-metre- 122 45 37 85/01 89/09 1,4,6,7
high gym roof of
Van Bien
elementary school.
Omenica- 0770703 Pine River Kwoen Gas Plant 55 34 30 1113 01/08 - 4,7
Peace Gas Plant PO Box 906 121 55 15 02/12 - 1,6,14,15,20,22,23,
Omenica- E237631 Pine River Pine River Gas 55 36 20 610 01/08 - 1,4,6,7,14,15,20,22,23,
Peace Hasler Plant 121 58 20 24,25,33
Omenica- E234230 Taylor Pingle Creek Road 56 06 21 686 00/07 - 1,4,6,7,14,15,20,22,
Peace South Hill 120 39 48 23,24,25,33
Omenica- 0770708 Taylor McMahon 56 09 03 480 00/07 1,4,6,7,14,15,20,22,23,
Peace Townsite Complex, Alaska 120 41 10 24,25,33
Skeena E225267 Burns Lake #8 4th Avenue 54 13 51 652 97/03 - 1,4,6,7,14,15,20,22,23,
Fire Centre 125 45 52 24,25
Skeena E227290 Endako Endako 54 01 48 97/07 - 1,4,6,7,14,15,20,22,23,
125 04 48 24,25,33
Skeena M120001 Golden 10-metre tower on 58 12 20 980 93/10 96/11 5,12
Bear roof above office; 132 17 15 93/10 00/03 1,4,6,7
datalogger in office 96/11 00/03 14,15,20,22,23,24,25
Skeena M107004 Houston 12-metre tower on 54 23 50 594 94/11 96/11 5,12
Firehall roof of building 126 38 42 94/11 - 1,4,6,7
96/11 - 14,15,20,22,23,24,25
Skeena M106008 Kitimat 53 59 42 10 96/10 - 1,4,6,7,14,15,20,22,23,
Eurocan 128 40 42 24,25
Skeena M106009 Kitimat Haulage Road 54 01 47 2 96/10 - 1,4,6,7,14,15,20,22,23,
Haul Road 128 42 06 24,25
Skeena M106010 Kitimat 1332 Lahakas Blvd N 54 04 03 119 97/01 - 1,4,6,7,14,15,20,22,23,
Whitesail 128 38 25 24,25
Skeena E230577 Manalta Telkwa Coal Mine 54 38 42 98/01 00/08 18,19
Road 127 07 13 98/02 00/08 1,4,6,7,14,15,20,22,23,
Skeena E243949 Port Skeena Cellulose 54 14 01 01/05 - 1,4,6,7,14,15,20,22,23,
Edward Inc. 130 17 32 24,25,33
Table C. 3 – Greater Vancouver Regional District Meteorological Data - Station Inventory List