{'fraction_non_alphanumeric': 0.033548073416798344, 'fraction_numerical': 0.01932660751185122, 'mean_word_length': 4.802538787023978, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Source-specific fine particulate air pollution and systemic inflammation in ischaemic heart disease patients

Objective To compare short-term effects of fine particles (PM2.5; aerodynamic diameter <2.5 µm) from different sources on the blood levels of markers of systemic inflammation. Methods We followed a panel of 52 ischaemic heart disease patients from 15 November 2005 to 21 April 2006 with clinic visits in every second week in the city of Kotka, Finland, and determined nine inflammatory markers from blood samples. In addition, we monitored outdoor air pollution at a fixed site during the study period and conducted a source apportionment of PM2.5 using the Environmental Protection Agency's model EPA PMF 3.0. We then analysed associations between levels of source-specific PM2.5 and markers of systemic inflammation using linear mixed models. Results We identified five source categories: regional and long-range transport (LRT), traffic, biomass combustion, sea salt, and pulp industry. We found most evidence for the relation of air pollution and inflammation in LRT, traffic and biomass combustion; the most relevant inflammation markers were C-reactive protein, interleukin-12 and myeloperoxidase. Sea salt was not positively associated with any of the inflammatory markers. Conclusions Results suggest that PM2.5 from several sources, such as biomass combustion and traffic, are promoters of systemic inflammation, a risk factor for cardiovascular diseases.


Source Apportionment of PM 2.5 using EPA PMF 3.0
We determined the sources of fine particles (PM 2.5 ; particles with aerodynamic diameter less than 2.5 µm) by using U.S. Environmental Protection Agency's model EPA PMF 3.0. PMF (Positive Matrix Factorization) is an advanced multivariate receptor modeling technique that calculates site-specific source profiles and source contributions (Paatero 1997). Investigators comparing results of several source apportionment methods, including PMF, concluded that the results were consistent across users and methods (Hopke et al. 2006). One benefit of PMF compared to other methods is point-by-point scaling of the data that enables PMF to handle missing and below-detection-limit data that commonly occur during environmental measurements. The U.S. EPA's Office of Research and Development has developed a standalone graphical user interface (EPA PMF 3.0) that is freely distributed (Norris et al. 2008).
EPA PMF 3.0 solves the general receptor model using constrained, weighted least-squares as implemented in the program ME2 (Multilinear Engine) (Paatero 1999 where x ij is the j th species concentration measured in the i th sample, g ip is the particulate mass concentration from the p th source contributing to the i th sample, f pj is the j th species mass fraction from the p th source, e ij is residual associated with the j th species concentration measured in the i th sample, and P is the total number of independent sources. 3 The task of EPA PMF is to minimize the sum of squares The value σ ij is the uncertainty of the measured value x ij . We constrained all sources to have non-negative species concentration, and allowed no sample to have negative source contribution.
We operated the model in a robust mode so that for any data point for which the residual exceeded 4 times the error estimate, the value was processed as an extreme value and its weight was decreased.
The use of point-by-point error estimates as the weight of the data points improves the fit since more accurate values get more weight than less accurate values. Thus, the accuracy depends on the analyzed species as well as on its concentration level. We determined the uncertainty as where MDL was the method detection limit. For ions and levoglucosan we used MDLs based on the chemical analysis. For other composition data, we estimated the values from the lowest reported concentrations. The percentage uncertainty consists of the analytical reproducibility and the modeling uncertainty. Modeling uncertainty is included, because data do not exactly meet the modeling assumptions, namely the assumption that the ratios of species in each factor do not vary through time (Norris et al. 2008). We used 20 percent as the modeling uncertainty, which produced reasonable relative uncertainties for each dataset.
We measured and collected PM 2.5 samples at urban background station between 14 Nov 2005 and 21 Apr 2006. In the model, we used daily average concentrations of PM 2.5 , absorption coefficient (ABS), particle number concentration (PNC), as well as sodium (Na + ), ammonium ) and oxalate ions, and levoglucosan. In addition, we utilized NO concentration measured at a nearby (450 m) municipal measurement site. Number of data points, geometric mean, geometric standard deviation, and MDL are presented in Table S1. In the data, we substituted missing values with geometric mean and weighted them down by four (σ i =4σ i ).

Source Apportionment Results
We run the model with 4 to 10 factors from 100 random starting points. We regarded the five factor model as the best solution to interpret the likely sources. Correlation coefficients between source categories and the individual pollutants are presented in Table S2 (Supplemental Material, Table S2).
The first factor contained high percentages of SO 4 2and NH 4 + concentration (Supplemental Material, Figure S2). Also oxalate was associated with this factor. SO 4 2and NH 4 + ions are common tracers of secondary aerosol PM, which is formed from inorganic and organic gaseous emissions during regional transport within hours or maybe one day or during long range transport (LRT) of air masses for hundreds of kilometers within some days. Oxalate can originate from primary emissions of biomass burning (Yamasoe et al. 2000) and/or be formed as a secondary product by the oxidation of gaseous organic compounds (Kawamura and Ikushima 1993). The average source contribution of LRT to PM 2.5 was 4.5 µg m -3 or 56% (Supplemental Material, Table S3). The highest concentrations took place between the end of February and mid-March (Supplemental Material, Figure S3).
The factor describing traffic emissions was characterized by NO and PNC, and to lesser extend by Ca 2+ and ABS (Supplemental Material, Figure S2). Emissions from gasoline and diesel powered vehicles could not be separated from each other on the daily level. Ca 2+ indicates that also traffic induced road dust emission was included in this factor, in addition to the tailpipe exhaust emissions. Traffic emissions explained, on average, 8% of the PM 2.5 mass (Supplemental Material, Table S3). Contribution of traffic emissions was higher during weekdays than weekends.
The third factor explained 96% of the variation in levoglucosan concentration.
Levoglucosan is a commonly used, specific and relatively stable organic chemical tracer for biomass combustion that is exclusively produced by thermal breakdown of cellulose and diverse hemicelluloses (Simoneit 2002). Other, non-specific tracers of biomass burning include black carbon and ions such as K + from inorganic ash, and oxalate. In our data, also ABS and K + were associated with this factor. Thus, this factor described PM 2.5 emissions from biomass combustion. The average source contribution was 1.6 µg m -3 (20%) (Supplemental Material, Table S3).
The fourth factor described the sea spray aerosols. It explained 90% of the variation in Clconcentration and the ratios of the sea salt components, i.e. Cl -, Na + , SO 4 2-, Mg 2+ , K + and Ca 2+ , were close to that in sea water. Sea salt particles were observed in Kotka when the air masses had passed over the Northern Atlantic before arriving to the measurement site (data not shown). 6 Almost 70% of the Na + concentration and 20-30% of Ca 2+ , K + and Mg 2+ concentrations were associated with the fifth factor. The highest concentrations (Supplemental Material, Figure   S3) were observed during northern winds and back trajectories arriving via south-east border of  Figure S2. Factor profiles in Kotka. Columns = concentrations of species (units in table S1); * = relative contribution of resolved factors to chemical species (%).
(LRT = regional and long range transport)