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Estimating Particulate Exposure from Modern Municipal Waste Incinerators in Great Britain

Municipal Waste Incineration (MWI) is regulated through the European Union Directive on Industrial Emissions (IED), but there is ongoing public concern regarding potential hazards to health. Using dispersion modeling, we estimated spatial variability in PM10 concentrations arising from MWIs at postcodes (average 12 households) within 10 km of MWIs in Great Britain (GB) in 2003–2010. We also investigated change points in PM10 emissions in relation to introduction of EU Waste Incineration Directive (EU-WID) (subsequently transposed into IED) and correlations of PM10 with SO2, NOx, heavy metals, polychlorinated dibenzo-p-dioxins/furan (PCDD/F), polycyclic aromatic hydrocarbon (PAH) and polychlorinated biphenyl (PCB) emissions. Yearly average modeled PM10 concentrations were 1.00 × 10–5 to 5.53 × 10–2 μg m–3, a small contribution to ambient background levels which were typically 6.59–2.68 × 101 μg m–3, 3–5 orders of magnitude higher. While low, concentration surfaces are likely to represent a spatial proxy of other relevant pollutants. There were statistically significant correlations between PM10 and heavy metal compounds (other heavy metals (r = 0.43, p = <0.001)), PAHs (r = 0.20, p = 0.050), and PCBs (r = 0.19, p = 0.022). No clear change points were detected following EU-WID implementation, possibly as incinerators were operating to EU-WID standards before the implementation date. Results will be used in an epidemiological analysis examining potential associations between MWIs and health outcomes.


■ INTRODUCTION
Incineration of domestic and commercial waste is increasing in Europe in response to European Union (EU) legislation to divert waste from landfill sites. Waste incinerator feedstock includes paper, food, plastics, glass, electrical appliances, and other nonhazardous materials and may vary day to day and from incinerator to incinerator. 1 Composition of combustion emissions depends on feedstock mix but potentially comprises particulate matter, sulfur dioxide (SO 2 ), nitrogen oxides (NOx), hydrogen chloride (HCl), carbon monoxide (CO), Volatile Organic Compounds (VOCs), Persistent Organic Pollutants (POPs) such as polychlorinated dibenzo-p-dioxins/ furans (PCDD/Fs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), and heavy metals. 1 3,4 While there is public concern regarding potential adverse health effects from MWI emissions, findings from epidemiological studies are inconsistent and inconclusive. 5 Most studies have focused on adult cancers 6−9 and to a lesser extent reproductive and child health outcomes. 5,10−14 Exposure assessment has often used simple proxies, adopting proximity to incinerator as the exposure measure. 15 −18 There have been three recent studies of incinerators conducted in Italy. One used a modified risk-assessment model to estimate lung cancer risk. 20 The remaining two used a dispersion model to assess exposure to particulate matter with a diameter <10 μm (PM 10 ) and found a higher risk of miscarriage 13 and preterm delivery 14 with increasing PM 10 exposure (but no associations with sex ratio, multiple births, or frequency of small for gestational age 14 ), where estimated PM 10 levels from incinerators were consistent with those estimated near two British incinerators. 19 Particulate matter/total dust emissions are monitored continuously and reported as daily means as part of the EU-WID regulations, so that dispersion of these emissions can be modeled in areas near incinerators on a daily basis (whereas heavy metals, PCDD/Fs, PAHs, and PCBs are measured periodically 3 to check compliance). We previously reported 19 methods for dispersion modeling around two MWIs. In the present study, our main aim was to model the spatial distribution of PM 10 concentrations within 10 km of GB MWIs in operation 2003−2010 for the resident population. We were also able to look at whether there were the following: 1. Emissions above the EU-WID daily average particulate (total dust) limit value of 10 mg m −3 per flue. 4 2. Correlations and associations between PM 10 emissions and within-flue emission measurements of heavy metals, PCDD/Fs, PAHs, and PCBs (to provide information about the chemical composition of PM 10 being emitted from flues).
3. Changes in levels of PM 10 emissions after the implementation of the EU-WID, which reduced daily average emission limit values from 30 mg m −3 to 10 mg m −3 for particulate matter/total dust. 21 We refer to PM 10 rather than total suspended particulates (total dust) throughout as size fraction studies have found all particulate incinerator emissions are <10 μm diameter. 22 ■ MATERIALS AND METHODS Study Area. We included all 22 MWIs in Great Britain ( Figure 1) 19 Incinerator Data. Information on the emissions, total annual licensed throughput, the number of flues, and whether an MWI opened to or adopted EU-WID specifications were provided by the Environment Agency (EA), Natural Resources Wales (NRW), and the Scottish Environment Protection Agency (SEPA) ( Table 1). Information on characteristics used in dispersion modeling including height and diameter of the MWI stack (m), exit temperature (°C), and exit velocity (m s −1 ) per MWI per flue are reported in Supporting Information (SI) A, Table S1. Daily measured PM 10 emissions per MWI, per flue, and per year (some originally in paper format, which were digitized and quality checked by a third party) were provided by the EA and SEPA. Non-numeric and negative values were recoded according to an algorithm agreed with the EA and SEPA (SI B, Table S2).
MWIs varied in size (Table 1, SI A) and location ( Figure 1). The licensed throughput varied from 3,500 (Porthmellon) to 750,000 (Edmonton) tonnes per annum (Table 1). Populations living within 10 km of each MWI varied from 2,203 (Porthmellon) to 2,726,145 (SELCHP) (information from census 2011 data). The majority of MWIs had multiple flues (15 MWIs; 12 with two flues, three with three flues). The number of nonoperational and missing days varied from MWI to MWI and from year to year and occurred sporadically for a few days or for longer periods (e.g., several months; Table 1 and SI C Table S3 ).
The availability of heavy metals, PCDD/Fs, PAHs, and PCB measurements varied. MWIs are required to complete at least two measurements per annum. 4 Typically in-flue measurements of 6−8 heavy metals and 3−4 PCDD/F, PAH, and PCBs are completed per year, per MWI, and per flue, but repeated measurements are taken if higher than limit values. Heavy metals, PCDD/Fs, PAHs, and PCBs were monitored in-flue, usually at the same time as each other over an 8 h period, using European committee standards (CEN).
Dispersion Modeling To Estimate Spatial Distribution of PM 10 Concentrations within 10 km of GB MWIs. The Atmospheric Dispersion Modeling System Urban (ADMS-Urban) (version 2.3), utilized in previous studies characterizing emissions from MWIs, 13,14,19,23,24 was used to model groundlevel PM 10 concentrations for postcode (average 12 household per postcode) area centroids within a 10 km radius of the MWIs. ADMS-Urban is a Gaussian based dispersion model that has been widely used and extensively validated. The model characterizes the atmospheric boundary layer using the Monin-Obukhov length and boundary layer depth. It is capable of simulating the effects of plume rise and the effects of buildings and complex topography on dispersion. 25

Article
The parametrization of the dispersion model is described in Ashworth et al. 19 In brief, all MWIs were modeled as point sources. Locations were verified using site addresses, grid references, and aerial photography. The EU-WID requires that average emission values are reported after subtraction of a fixed amount (taken as 30%) to account for measurement instrument uncertainty. 4 The EU-WID also allows up to ten measured daily average values to be discarded per year if there has been measurement instrument calibration or maintenance. For the purposes of the dispersion modeling, the emissions data were therefore increased by 30% from the reported values provided (except for Isle of Wight MWI, which did not subtract 30% to account for measurement instrument uncertainty). For the purposes of assessing emissions above the EU-WID daily average, emission values with 30% subtracted were used, as this is how compliance is assessed against the EU-WID limits. Missing emissions data were imputed using the median PM 10 value of the operational days for each year and each MWI (for justification of this approach see SI D; for counts of days with missing data per flue/year/MWI see SI C). For Coventry, Dudley, and Kirklees MWIs, data were missing for entire year(s), and therefore it was not possible to model PM 10 dispersion for these years. Hourly meteorological data from meteorological stations within 30 km of each MWI were

Environmental Science & Technology
Article obtained and selected for use based on land cover (to ensure that the land type surrounding the meteorological station was representative of the MWI) and completeness of data (for choice of meteorological station and justification see SI E). The Monin-Obukhov (MO) length (the height at which turbulence is driven by buoyancy instead of wind) and surface roughness (SR) at the dispersion site were computed by ADMS-Urban based on input meteorological variables and surface information extracted from CORINE land cover data from 2000 as per Ashworth et al. 19 (SI F, Table S5 contains MO and SR values). Complex terrain was included in the dispersion modeling if more than 5% of the area surrounding a MWI (within 10 km) contained slopes of 10% or higher, which was assessed using Ordnance Survey PANORAMA data. Daily average PM 10 concentrations were calculated at every postcode area centroid within 10 km of each MWI. Where the 10 km radius around MWIs overlapped (Edmonton and SELCHP in London; Tyseley, Dudley, and Wolverhampton in the Midlands) the modeled output concentrations were summed for each day. Our dispersion modeling was specific for MWIs and did not account for sources of PM 10 around the incinerators. It was not practical to include the effects of buildings within the ADMS-Urban model as the study area was large (10 km radius from each MWI). Moreover the MWI stack height was high (median = 72.5 m, max = 100 m), taller than surrounding buildings, and thus nearby buildings would not affect dispersion patterns. Instead different MO and SR lengths for each MWI were used to represent different land-uses around each MWI and effects on pollutant turbulence and dispersion (see SI F). It was not considered possible to model spatial distributions of PCDD/F, PCB, PAH, or heavy metals using flue emissions data due to the sparseness and variability of the measurements.
Correlations and Associations between PM 10 Emissions and Other Flue Emissions. Pairwise correlation was used to evaluate correlations of heavy metal compounds, PCDD/Fs, PAHs, and PCBs and daily averages of SO 2 , NOx, and PM 10 measured during the same time period. Measurements of heavy metals for most incinerators were reported as Cd and Tl and their compounds (CdTl), Hg and its compounds (HgComp), and grouped other heavy metals (OHMs) comprising Sb, As, Cr, Pb, Co, Cu, Mn, Ni, and V (see SI G, Table S6). As the data were not normally distributed, a nonparametric Spearman's rank correlation was used. This produces a coefficient, r, which ranges from −1 to 1. Values of −1 and 1 represent perfect negative or positive correlation, respectively, whereas a value of 0 represents no correlation. A Spearman correlation p-value <0.05 was considered statistically significant. As a Spearman's rank correlation will not account for differences in MWI operations, flues, and years of data, a linear multiple regression model was used to adjust for these factors; we considered one pollutant at a time and used PM 10 , year, flues, and MWI as predictors. Data were checked for normality (from Q-Q plots) and log transformed if necessary. We report the estimated coefficients with p-values, and the partial η 2 -which is the variance associated with an effect divided by that variance plus the error variance, to describe the proportion of variance accounted for by the variable.
Detecting Changes in Levels of PM 10 Emissions after the Implementation of the EU-WID. Data from MWI installations operating prior to the EU-WID were investigated to determine if emissions changed before or after the implementation date and when any change took place, as timings could be used to inform epidemiological analyses investigating changes in health outcomes rates before/after EU-WID implementation. First, we conducted a descriptive analysis

Environmental Science & Technology
Article of the daily average modeled concentrations (Table 2, SI H, Figure S3)

■ RESULTS
Dispersion Modeling To Predict Spatial Distribution of PM 10 Concentrations within 10 km of GB MWIs. Annual mean average modeled PM 10 concentrations (based on daily average modeled PM 10 concentrations) per MWI and per year ranged from 1.00 × 10 −5 to 5.53 × 10 −2 μg m −3 ( Table 2). Complex overlapping dispersion patterns were shown for those areas with overlapping fields from multiple MWIs ( Figure 2): surfaces (a) SELCHP and Edmonton and (b) Dudley, Tyseley, and Wolverhampton. In some instances (429 days total, 0−83 days per incinerator), a modeled output value was not calculated (across all postcodes) by ADMS-Urban even though the MWI was classified as being operational ("on" or "missing"). This may occur for a number of reasons but is commonly due to data processing error (e.g., when wind speed values are very low (<0.75 m s −1 when measured at 10 m above ground level) or due to missing meteorological data. 27 Emissions above the EU-WID Daily Average Particulate Limit Value. There were a small number of days with emissions above the EU-WID daily average particulate limit value in 14 of the 22 MWIs, the majority of which were <20 mg m −3 (Table 1; SI J Table S7). There was no distinct pattern that might indicate that there were fewer emissions above the EU-WID daily average particulate limit value after the implementation of the EU-WID or more instances of emissions >10 mg m −3 before it.
Detecting Changes in Levels of PM 10 Emissions Following the EU-WID. The descriptive analysis of the average modeled concentrations showed no clear pattern of a reduction in PM 10 concentrations after the implementation of the EU-WID in MWIs adopting EU-WID specifications ( Table  2, SI H, Figure S3), possibly as incinerators were already complying with EU-WID standards by the implementation date

Environmental Science & Technology
Article which required them to fit bag filters. These descriptive findings were supported by findings in the Batch Change Detection statistical analyses (Table 5 These contribute a small proportion of UK PM 10 background levels, which range between 6.59 and 2.68 × 10 1 μg m −3 (annual UK means per postcode in 2010, based on modeled data). 28 As all European incinerators operate to the EU WID, this suggests that MWIs also make a small contribution to European background concentrations within 10 km of incinerators across Europe (measured ambient mean concentrations, typically in the range 2.00 × 10 1 −5.00 × 10 1 μg m −3 ). 29 It is recognized that dispersion modeling is a simplification of reality. ADMS-Urban is a well validated, widely used dispersion model, and model errors were reduced as much as possible by using the most complete data available and by completing a series of sensitivity analyses to ensure that model inputs best represented actual conditions (see SI D, E, and F). Missing data were imputed using median values for the particular year, informed by results from a sensitivity analysis (SI D). For three incinerators in 2005, over two-thirds of the data were missing (Coventry, Stoke-on-Trent, Wolverhampton

Environmental Science & Technology
Article (SI C)), although annual mean modeled concentrations were still within similar ranges compared to other MWIs (Table 2). We do not know the reasons for missing data, but this may represent maintenance periods in 2005 to ensure the MWI complied with the WID implemented at the end of that year. All MWIs used moving grate technologies except for Allington, Dundee and Newlincs (Allington and Dundee used fluidized bed technology and Newlincs used rotary kiln technology). 30 Nixon et al. 30 found that plants using fluidized bed and rotary kiln technologies had higher emissions of HCl and CO. We found no differences in PM 10 emissions from Allington and Dundee (mean (standard deviation (SD) 2.30 (3.05), median 1.02) and Newlincs (mean (SD) 2.44 (1.27), median 2.30) compared with the remaining 19 MWIs (mean (SD) 2.10 (2.13), median 1.50).
Results for GB incinerators are consistent with two studies conducted in Italy (also operating to the EU-WID) by Candela et al. 13,14 and the previous study conducted in GB by Ashworth et al. 19 Concentration estimates were larger in Font et al. (3.00 × 10 −2 to 1.20 × 10 −1 μg m −3 ); 31 however, they did not measure PM 10 directly but used tracers of heavy metals to estimate maximum ambient PM 10 from two MWIs. The Font et al. 31 study used Cd measured during plume grounding as a quantitative tracer for PM 10 by multiplying measured groundlevel Cd concentrations by representative in-flue PM 10 to Cd emission ratios. This approach set out to find a maximum value by assuming that all Cd was from the MWI. 31 Our findings for PM 10 are in agreement with studies on ultrafine particles involving measurements within MWI flues and ambient air, showing that incinerators do not have significant impacts on ultrafine particles in localities near MWIs. 32 Additional work was undertaken to confirm the plausibility of the very low modeled PM 10 concentrations. MWI emissions were fingerprinted using daily in-flue PM 10 to NOx concentrations, and ratios were compared to data from 15 ambient monitoring sites within 10 km of four MWIs (Edmonton, SELCHP, Tyseley, and Wolverhampton) (SI L, Table S10). Results showed that while there was some evidence of NOx and PM 10 emissions from MWIs being detected at ground level, these were few and often could not be distinguished from other sources such as traffic (SI L, Figure  S5). This supports the very low contributions of MWI PM 10 to background concentrations in areas near MWIs in the present study.
Exposure surfaces for selected MWIs in Figure 2 that have been previously presented 19 show that incinerator-related PM 10 concentrations were not merely a function of distance from incinerator but showed complex spatial patterns including differences between years, largely relating to differences in emission rates (including off days) and meteorology.
Consideration of Other Pollutants Emitted from MWIs. While ambient PM 10 has been associated with adverse birth outcomes, 33 levels are much higher than those arising from MWIs emissions. Despite this, some recent epidemiological studies relating to MWIs operating to the EU-WID have found associations with adverse birth outcomes. 13,14 If these are causal associations, it is likely to be due to agents other than PM 10 that are also emitted from incinerators such as PCDD/Fs, PAHs, and heavy metals. We were unable to model spatial distribution of these other agents directly due to sparse emissions data. Other potential incinerator emissions including polybrominated or mixed polybrominated/polychlorinated dibenzo-p-dioxins/furans (PBDD/Fs and PXDD/Fs) were not measured. However, it is a reasonable assumption that modeled spatial distribution of PM 10 reflects exposure patterns of other MWI emissions. This assumption has been used in previous dispersion modeling studies, which found that heavy metals 14 had a similar deposition distribution to PM 10 . Ranzi et al. 24 measured various pollutants including sulfur oxides, nitrogen dioxide, and heavy metals in Italy at maximum and minimum fallout points estimated by dispersion models and considered heavy metals as the tracer pollutant from MWIs. We found some support for this as we detected significant correlations for in-flue measurements between PM 10 and heavy metals, PAHs, and PCBs, which provides some support for using PM 10 as a tracer. While statistically significant, the amount of variance accounted for (partial η 2 ) was modest, which is likely due to variability in incinerator feedstock, especially differing amounts of electrical equipment. Information on feedstock mix is not recorded by MWIs.
The level of population exposure to metals and other agents from MWIs is likely to be small. Font et al. 31 compared heavy metal emission ratios with those measured at nearby ambient metal monitoring sites around six MWIs in England and found limited evidence that emissions from MWIs reached ground level.
Emissions above the EU-WID Daily Average Particulate Limit Value. Although emissions greater than the EU-WID limit of 10 mg m −3 were found in 14 of the 22 MWIs, these were usually <20 mg m −3 (SI J Table S7). These may not all represent exceedances under the EU-WID as in the event of temporary abatement failure MWIs are allowed to operate for up to 4 h at a time (maximum 60 h per flue per year) at an elevated half-hourly particulate limit value of 150 mg m −3 (normally 30 mg m −3 ). If there are less than 43 half-hourly monitoring results available in a day, the daily average can be disregarded. Daily average emissions >20 mg m −3 were infrequent, and there were only rare occurrences >30 mg m −3 , which may have occurred due to "one off" changes in feedstock or failure of abatement systems. We were not provided with information on reasons for emissions greater than the EU-WID limit. However, given that mean PM 10 concentrations estimated by the dispersion model were small (1.00 × 10 −5 to 5.53 × 10 −2 μg m −3 , a small contribution to ambient background levels which were typically 6.59−2.68 × 10 1 μg m −3 ), these infrequent emissions above EU-WID limits would still be expected to result in very low population exposures.
Detecting Changes in Emissions Following the EU-WID. We conducted the change point analysis for existing incinerators using the Crameŕ-von Mises method to account for the ordered data structure. A simpler test (e.g. a two sample t test to compare PM 10 emissions before and after the EU-WID implementation) may have introduced bias due to the number of nonoperational, missing days and non-Gaussian distributed data. We assumed that a fall in emissions would be detected in existing incinerators within one year (prior or posterior) of the EU-WID implementation date, but this was only seen for five of 11 incinerators in the change point analysis. A possible explanation is that many existing MWIs may have already met (or been modified to meet) the EU-WID requirements. However, information as to whether and when each MWI adopted a new abatement system was not available. In three of the six MWIs where a change point was detected within a year prior or posterior to the EU-WID implementation date (28 December 2005), a higher mean level of PM 10 was detected

Environmental Science & Technology
Article after the change point date in at least one flue (Edmonton, Sheffield, and Tyseley), though the increases after the change point date were small, and remained below EU-WID limits. This could be related to a number of factors including differences in the feedstock or changes in the amount of waste processed over time. Since we could not identify a clear date after which emissions fell in relation to the EU-WID in preexisting MWIs, we conclude it is not possible to conduct before/after epidemiological studies examining the impact of the EU-WID on rates of adverse health outcomes in preexisting MWIs. However, in MWIs opening after 28 December 2002 (n = 8 in 2003−2010) that have always operated to the EU-WID standards, it is possible to use the opening date of the incinerator as the before/after change point date.
Overall this study suggests that PM 10 exposures related to MWI emissions in Great Britain are extremely low (annual means ranging from 1.00 × 10 −5 to 5.53 × 10 −2 μg m −3 ) especially when compared to annual mean background concentrations (typically ranging between 2.00 × 10 1 and 5.00 × 10 1 μg m −3 in Europe). 29 The results of the modeling will be used in an epidemiological analysis examining associations between MWIs and potential reproductive and other health effects.