PM source apportionment and health effects: 1. Intercomparison of source apportionment results

Comparison of three source apportionment methods based on observed and initial HCHO in Taiyuan, China

Identifying the sources of formaldehyde (HCHO) is key to reducing the pollution of HCHO and ozone (O3) on the ground level. Using the same datasets applied to the positive matrix factorization (PMF) model by (Hua et al., 2023), the initial concentrations of HCHO were estimated using the photochemical age and the sources of observed and initial HCHO were apportioned based on multiple linear regression (MLR) and photochemical age-based parameterization (PCAP) methods. These results suggest that the source of the initial HCHO can better reflect its contribution. The secondary formation contributed to 49.3-69.1 % of initial HCHO at four sites in Taiyuan based on MLR, which was higher (7.4-36.2 %) than the contributions of secondary formation from observed HCHO. The HCHO was mainly affected by anthropogenic secondary (10.8-34.4 %) and background sources (17.4-78.7 %) based on the PCAP method. We compared the results of the HCHO sources from the MLR, PCAP, and PMF models under photochemical loss. There was good agreement among the emission ratios of acetylene-based HCHO obtained by the different methods at the four sites. The correlation analysis of different source apportionment methods illustrated that primary emissions from the PCAP and the MLR model had the greatest correlation (0.22-0.60). Secondary formations from the PMF and MLR models showed good correlations at all four sites, with R values ranging from 0.42 to 0.83. The HCHO peak of diurnal variation simulated by MLR appeared late compared to the other methods, and the difference in daily variation of HCHO from the PMF model was significantly higher than that of PCAP and MLR. The overlapping conclusions of different source apportionment methods should be considered and used to guide efforts to improve air quality.

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region (NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban site of Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81 µg m-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10 were entrenched to the receptor models to comprehend the possible sources of PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

Wintertime investigation of PM10 concentrations, sources, and relationship with different meteorological parameters

Meteorological factors play a crucial role in affecting air quality in the urban environment. Peshawar is the capital city of the Khyber Pakhtunkhwa province in Pakistan and is a pollution hotspot. Sources of PM10 and the influence of meteorological factors on PM10 in this megacity have yet to be studied. The current study aims to investigate PM10 mass concentration levels and composition, identify PM10 sources, and quantify links between PM10 and various meteorological parameters like temperature, relative humidity (RH), wind speed (WS), and rainfall (RF) during the winter months from December 2017 to February 2018. PM10 mass concentrations vary from 180 - 1071 µg m-3, with a mean value of 586 ± 217 µg m-3. The highest concentration is observed in December, followed by January and February. The average values of the mass concentration of carbonaceous species (i.e., total carbon, organic carbon, and elemental carbon) are 102.41, 91.56, and 6.72 μgm-3, respectively. Water-soluble ions adhere to the following concentration order: Ca2+  > Na+  > K+  > NH4+  > Mg2+. Twenty-four elements (Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Co, Zn, Ga, Ge, As, Se, Kr, Ag, Pb, Cu, and Cd) are detected in the current study by PIXE analysis. Five sources based on Positive Matrix Factorization (PMF) modeling include industrial emissions, soil and re-suspended dust, household combustion, metallurgic industries, and vehicular emission. A positive relationship of PM10 with temperature and relative humidity is observed (r = 0.46 and r = 0.56, respectively). A negative correlation of PM10 is recorded with WS (r =  - 0.27) and RF (r =  - 0.46). This study's results motivate routine air quality monitoring owing to the high levels of pollution in this region. For this purpose, the establishment of air monitoring stations is highly suggested for both PM and meteorology. Air quality standards and legislation need to be revised and implemented. Moreover, the development of effective control strategies for air pollution is highly suggested.

Characterization and source apportionment for light absorption amplification of black carbon at an urban site in eastern China

The mass absorption efficiency (MAE) of black carbon (BC) could be amplified by both internal mixing and the lensing effect from non-absorbing coating, which could intensify the global warming effect of BC. In this study, a two-year-long continuous campaign with measurements of aerosol optical properties and chemical composition were conducted in Nanjing, a typical polluted city in the Yangtze River Delta (YRD) region. Relatively large MAE values were observed in 2016, and the high BC internal mixing level could be the main cause. The strong positive correlation between the ratio of non-absorbing particulate matter (NAPM) over elemental carbon (EC) and the MAE value indicated that the coating thickness of BC largely promotes its light absorption ability. The impacts of chemical component coating on MAE amplification in autumn and winter were greater than in other seasons. Multiple linear regression was performed to estimate the MAE amplification effect by internal mixing and the coating of different chemical components. Nitrate coating had the strongest impact on MAE amplification, followed by organic matter. The effects of organic matter and nitrate coatings on MAE amplification increased with the internal mixing index (IMI). Based on the positive matrix factorization (PMF) model, it was found that large decrease in the contribution of industrial emissions and coal combustion to PM_2.5 from 2016 to 2017 was the main cause for MAE reduction. The novel statistical model developed in this study could be a useful tool to separate the impacts of internal mixing and non-absorbing coating.

Dynamic harmonization of source-oriented and receptor models for source apportionment

Millions of premature mortalities are caused by the air pollution of fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) globally per year. To effectively control the dominant emission sources and abate air pollution, source apportionment of PM_2.5 is normally conducted to quantify the contributions of various sources, but the results of different methods might be inconsistent. In this study, we dynamically harmonized the results from the two dominant source apportionment methods, the source-oriented and receptor models, by updating the emission inventories of primary PM_2.5 from the major sectors based on the Bayesian Inference. An adjoint model was developed to efficiently construct the source-receptor sensitivity matrix, which was the critical information for the updates, and depicted the response of measurements to the changes in the emissions of various sources in different regions. The harmonized method was applied to a measurement campaign in Beijing from January to February 2021. The results suggested a significant reduction of primary PM_2.5 emissions in Beijing. Compared with the baseline emission inventory of 2017, the primary PM_2.5 emissions from the local residential combustion and industry in Beijing had significantly declined by about 90 % during the investigated period of the year, and the traffic emission decreased by about 50 %. The proposed methods successfully identified the temporally dynamic changes in the emissions induced by the Spring Festival. The methods could be a promising pathway for the harmonization of source-oriented and receptor source apportionment models.

Evidence of non-tailpipe emission contributions to PM2.5 and PM10 near southern California highways

Particulate Matter (PM) concentrations near highways are influenced by vehicle tailpipe and non-tailpipe emissions, other emission sources, and urban background aerosols. This study collected PM_2.5 and PM₁₀ filter samples near two southern California highways (I-5 and I-710) over two weeks in winter 2020. Samples were analyzed for chemical source markers. Mean PM_2.5 and PM₁₀ concentrations were approximately 10-15 and 30 μg/m³, respectively. Organic matter, mineral dust, and elemental carbon (EC) were the most abundant PM components. EC and polycyclic aromatic hydrocarbons at I-710 were 19-26% and 47% higher than those at the I-5 sites, respectively, likely due to a larger proportion of diesel vehicles. High correlations were found for elements with common sources, such as markers for brake wear (e.g., Fe, Ba, Cu, and Zr) and road dust (e.g., Al, Si, Ca, and Mn). Based on rubber abundances, the contributions of tire tread particles to PM_2.5 and PM₁₀ mass were approximately 8.0% at I-5 and 5.5% at I-710. Two different tire brands showed significantly different Si, Zn, carbon, and natural rubber abundances.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM_2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM_2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

Long-Term Exposure to Source-Specific Fine Particles and Mortality─A Pooled Analysis of 14 European Cohorts within the ELAPSE Project

We assessed mortality risks associated with source-specific fine particles (PM_2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM_2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM_2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m³ increase) across five identified sources. On a 1 μg/m³ basis, the residual oil-related PM_2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM_2.5 mass, suggesting that past estimates using the generic PM_2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents

Although PM_2.5 mass varies in source and composition over time and space, most health effects assessment have made the inherent assumption that all PM_2.5 mass has the same health implications, irrespective of composition. Nationwide estimates of source-specific PM_2.5 mass and constituents at local-scale would allow for epidemiological studies and health effects assessments that consider the variability in PM_2.5 characteristics in their health impact assessments. In response, we developed US models of annual exposures at the census tract level for five major PM_2.5 sources (traffic, soil, coal, oil, and biomass combustion) and six trace elements (elemental carbon, sulfur, silicon, selenium, nickel, and non-soil potassium) for 2001 through 2014. We employed Absolute Factor Analysis (APCA) to derive the source-specific PM_2.5 impacts at monitoring stations. Random forest algorithms that incorporated predictors derived from satellite, chemical transport model, and census tract resolution land-use data on traffic, meteorology, and emissions, which were rigorously tested by 10-fold cross-validation (CV), were then employed to estimate elemental and source-specific PM_2.5 levels at non-monitoring site census-tracts over the study years. Model performances were moderate to good, with CV R² ranging from 0.41 to 0.95. For PM_2.5 sources, the highest CV R² was attained for traffic PM_2.5 (CV R² = 0.73), followed by coal (CV R² = 0.65), oil (CV R² = 0.62), soil (CV R² = 0.60), and biomass (CV R² = 0.41). Among constituents, the CV was highest for sulfur (CV R² = 0.95). Our analyses provided highly resolved spatial estimates of annual elemental and source-specific PM_2.5 concentrations at the census-tract level, for 2001 through 2014. This dataset offers exposure estimates in support of future nationwide long-term health effects studies of source-specific PM_2.5 mass and constituents, enabling epidemiological research that addresses the fact that not all particles are the same.

Impact of different sources on the oxidative potential of ambient particulate matter PM10 in Riyadh, Saudi Arabia: A focus on dust emissions

In this study, we employed Principal Component Analysis (PCA) and Multi-Linear Regression (MLR) to identify the most significant sources contributing to the toxicity of PM10 in the city center of Riyadh. PM10 samples were collected using a medium-volume air sampler during cool (December 2019-March 2020) and warm (May 2020-August 2020) seasons, including dust and non-dust events. The collected filters were analyzed for their chemical components (i.e., water-soluble ions, metals, and trace elements) as well as oxidative potential and elemental and organic carbon (EC/OC) contents. Our measurements revealed comparable extrinsic oxidative potential (P-value = 0.30) during the warm (1.2 ± 0.1 nmol/min-m3) and cool (1.1 ± 0.1 nmol/min-m3) periods. Moreover, we observed higher extrinsic oxidative potential of PM10 samples collected during dust events (~30% increase) compared to non-dust samples. Our PCA-MLR analysis identified soil and resuspended dust, secondary aerosol (SA), local industrial activities and petroleum refineries, and traffic emissions as the four sources contributing to the ambient PM10 oxidative potential in central Riyadh. Soil and resuspended dust were the major source contributing to the oxidative potential of ambient PM10, accounting for 31% of the total oxidative potential. Secondary aerosols (SA) were the next important source of PM10 toxicity in the area as they contributed to about 20% of the PM10 oxidative potential. Results of this study revealed the major role of soil and resuspended road dust on PM10 toxicity and can be helpful in adopting targeted air quality policies to reduce the population exposure to PM10.

Source apportionment based on the comparative approach of two receptor models in a large-scale region in China

Soil heavy metal(loid) (HM) source apportionment is the prerequisite to develop suitable mitigation measures and make pollution control and prevention regulations. The selection of appropriate tools (models) for source analysis is crucial, that is especially true for large-scale regions, as the Pearl River Delta (PRD), due to the high spatial variability in soil parent materials, soil topographical feature, and wide range of anthropogenic activities. The objective of this study is to evaluate the potential applications of receptor models (positive matrix factorization [PMF] and Unmix) which have been widely used in air pollution research to quantitatively apportion sources of heavy metal(loid)s in the soils. To assist the interpretation of the derived factors (sources) of the receptor models, enrichment factors and GIS mapping were used to identify the potential relationships between the factor contributions and human activities in the study area. As the models are built on completely different algorithms, a comparative approach was adopted in addition to evaluate the impact of sample size on the model results. Factor profiles generated by different receptor models were quite similar as well as their corresponding factor contributions spatial distribution. Though the stability of their results decreases with a reduced sample size, the results of PMF were less significantly influenced by the sample size than those of Unmix. Due to the difficulty (time consuming and expensive) of soil sample collection in large-scale regions, the PMF model appears to be practically more effective than Unmix. In addition, further investigation is needed for Unmix model to understand the reason for its high sensitivity and determine an appropriate sample size.

Commuter types identified using clustering and their associations with source-specific PM2.5

Traffic-related fine particulate matter air pollution (tr-PM_2.5) has been associated with adverse health outcomes such as cardiopulmonary morbidity and mortality, with in-vehicle tr-PM_2.5 exposure contributing to total personal pollution exposure. Trip characteristics, including time of day, day of the week, and traffic congestion, are associated with in-vehicle PM_2.5 exposures. We hypothesized that some commuter characteristics, such as whether commuters travel primarily during rush hour, would also be associated with increased tr-PM_2.5 exposures. The commute data consisted of unscripted personal vehicle trips of 46 commuters in the Washington, D.C. metro area over 48-h, with a total of 320 trips. We identified commuter types using sparse K-means clustering, which identifies the hours throughout the day important for clustering commuters. Source-specific PM_2.5 over 48 h was estimated using Positive Matrix Factorization. Linear regression was used to estimate differences in source-specific PM_2.5 by commuter cluster. Two commuter clusters were identified using the clustering approach: rush hour commuters, who primarily travelled during rush hour, and sporadic commuters, who travelled throughout the day. The hours given the largest weights by sparse K-means were 7-8 a.m. and 6-7 p.m., corresponding to peak travel times. Integrated black carbon (BC) was higher for rush hour commuters (median = 3.1 μg/m³ (IQR = 1.5)) compared to sporadic commuters (2.0 μg/m³ (IQR = 1.9)). Mobile PM_2.5, consisting primarily of tailpipe emissions and brake/tire wear, was also higher for rush hour commuters (2.9 μg/m³ (IQR = 1.6)) compared to sporadic commuters (2.1 μg/m³ (IQR = 2.4)), though this difference was not statistically significant in regression models. Estimated differences between commuter types for secondary/mixed PM_2.5 and road salt PM_2.5 were smaller. Further research may elucidate whether commuter characteristics are an efficient way to identify individuals with highest tr-PM_2.5 exposures associated with commuting and to develop effective mitigation strategies.

The Role of Fossil Fuel Combustion Metals in PM2.5 Air Pollution Health Associations

In this review, we elucidate the central role played by fossil fuel combustion in the health-related effects that have been associated with inhalation of ambient fine particulate matter (PM2.5). We especially focus on individual properties and concentrations of metals commonly found in PM air pollution, as well as their sources and their adverse health effects, based on both epidemiologic and toxicological evidence. It is known that transition metals, such as Ni, V, Fe, and Cu, are highly capable of participating in redox reactions that produce oxidative stress. Therefore, particles that are enriched, per unit mass, in these metals, such as those from fossil fuel combustion, can have greater potential to produce health effects than other ambient particulate matter. Moreover, fossil fuel combustion particles also contain varying amounts of sulfur, and the acidic nature of the resulting sulfur compounds in particulate matter (e.g., as ammonium sulfate, ammonium bisulfate, or sulfuric acid) makes transition metals in particles more bioavailable, greatly enhancing the potential of fossil fuel combustion PM2.5 to cause oxidative stress and systemic health effects in the human body. In general, there is a need to further recognize particulate matter air pollution mass as a complex source-driven mixture, in order to more effectively quantify and regulate particle air pollution exposure health risks.

Public health impact of coal-fired power plants: a critical systematic review of the epidemiological literature

Coal-based energy production is the most utilized method of electricity production worldwide and releases the highest concentration of gaseous, particulate, and metallic pollutants. Toxicological research has shown that coal combustion by-products are carcinogens, endocrine disruptors, and cardiorespiratory toxins. This article aims to systematically review the epidemiological literature on the impact emissions from coal-based power production has on morbidity and mortality worldwide. Two thousand one hundred and fifty-two articles were retrieved based on search criteria. Word search of abstract and article text filtered the results to 95 articles. Forty articles were included after screening. The literature indicates a significant adverse effect from particulate matter and polyaromatic hydrocarbon emissions on morbidity and mortality. There is a lack of consistency of exposure assessment and inadequate control of significant potential confounders such as social economic status. Future research should focus on improving exposure assessment models, specifically source-apportionment and geographic information system methods to model power plant-specific emissions.

Global review of recent source apportionments for airborne particulate matter

Source apportionments have become increasingly performed to determine the origins of ambient particulate pollution. The results can be helpful in designing mitigation strategies to improve air quality. Source specific particulate matter (PM) concentrations are also being used in health effects studies to be able to focus attention on those sources most likely to be responsible for the observed adverse health effects. In 2015, the World Health Organization (WHO) released its initial compilation of source apportionment studies published through August 2014. This initial database was described by Karagulian et al. (Atmospheric Environment120 (2015) 475-483). In the present report, a new compilation has been prepared of those apportionments published since 2014 through December 2019. In addition, the database has been expanded to include apportionments of heavy metals, water-soluble components, and carbonaceous components in ambient PM. As a result of this work, we have developed and presented some perspectives on source apportionment going forward. We also have made a series of recommendations for source apportionment studies and reporting them. It is essential for papers to provide a minimum set of information so that the study can be adequately assessed, and the results utilized by others in making policy decisions or as part of other scientific studies.

Assessing the PM2.5 impact of biomass combustion in megacity Dhaka, Bangladesh

In Dhaka, Bangladesh, fine particulate matter (PM_2.5) air pollution shows strong seasonal trends, with significantly higher mean concentrations during winter than during the monsoon (winter = 178.1 μg/m³ vs. monsoon = 30.2 μg/m³). Large-scale open burning of post-harvest agricultural waste across the Indo-Gangetic Plain is a major source of PM_2.5 air pollution in northern India during the non-monsoon period. This study evaluates the extent to which the seasonal differences in PM_2.5 pollution concentrations in Dhaka are accounted for by biomass-burning vs. fossil-fuel combustion sources. To assess this, an index was developed based on elemental potassium (K) as a marker for biomass particulate matter, after adjusting for soil-associated K contributions. Alternatively, particulate sulfur was employed as a tracer index for fossil-fuel combustion PM_2.5. By simultaneously regressing total PM_2.5 on S and adjusted K, the PM_2.5 mass for each day was apportioned into: 1) fossil-fuels combustion associated PM_2.5; 2) biomass-burning associated PM_2.5; and, 3) all other PM_2.5. The results indicated that fossil-fuel combustion contributed 21.6% (19.5 μg/m³), while biomass contributed 40.2% (36.3 μg/m³) of overall average PM_2.5 from September 2013 to December 2017. However, the mean source contributions varied by season: PM_2.5 in Dhaka during the monsoon season was dominated by fossil-fuels sources (44.3%), whereas PM_2.5 mass was dominated by biomass-burning (41.4%) during the remainder of the year. The contribution to PM_2.5 and each of its source components by transport of pollution into Dhaka during non-monsoon time was also evaluated by: 1) Conditional bivariate (CBPF) and pollution rose plots; 2) Concentration weighted trajectories (CWT), and; 3) NASA satellite photos to identify aerosol loading and fire locations on high pollution days. The collective evidence indicates that, while the air pollution in Dhaka is contributed to by both local and transboundary sources, the highest pollution days were dominated by biomass-related PM_2.5, during periods of crop-burning in the Indo-Gangetic Plain.

Estimating exposure to traffic-related PM2.5 for women commuters using vehicle and personal monitoring

Exposure to traffic-related fine particulate matter air pollution (tr-PM_2.5) has been associated with adverse health outcomes including preterm birth and low birthweight. In-vehicle exposure to tr-PM_2.5 can contribute substantially to total tr-PM_2.5 exposure. Because average commuting habits of women differ from men, a research gap is estimating in-vehicle tr-PM_2.5 exposures for women commuters. For 46 women commuters in the Washington, D.C. metro area, we measured personal exposure to PM_2.5 during all vehicle trips taken in a 48-h sampling period. We also measured 48-h integrated PM_2.5 chemical constituents including black carbon and zinc. We identified trip times using vehicle monitors, specifically on-board diagnostics data loggers and dashboard cameras. For 386 trips, we estimated associations between PM_2.5 exposure and trip characteristics using linear mixed models accounting for participant, day, and time of day. Additionally, we estimated associations between rush hour trip PM_2.5 and 48-h integrated PM_2.5 chemical constituents using linear models. Exposure to PM_2.5 during trips was 1.9 μg/m³ (95% confidence interval (CI): 0.9, 2.9) higher than non-trip exposures and rush hour trip exposures were 3.2 μg/m³ (95% CI: 1.8, 4.6) higher than non-trip exposures on average. We did not find differences in PM_2.5 exposure by trip length. Although concentrations of tr-PM_2.5 chemical constituents were generally positively associated with rush hour trip PM_2.5, associations were weak indicating that other settings contribute to total tr-PM_2.5 exposure. Our study demonstrates the utility of combining vehicle monitors and personal PM_2.5 monitors for estimating personal exposure to tr-PM_2.5. Future work will investigate whether additional data collected by vehicle monitors, such as traffic and speed, can be leveraged to better understand tr-PM_2.5 exposure among commuters.

Associations between Source-Specific Particulate Matter and Respiratory Infections in New York State Adults

The response of respiratory infections to source-specific particulate matter (PM) is an area of active research. Using source-specific PM_2.5 concentrations at six urban sites in New York State, a case-crossover design, and conditional logistic regression, we examined the association between source-specific PM and the rate of hospitalizations and emergency department (ED) visits for influenza or culture-negative pneumonia from 2005 to 2016. There were at most N = 14 764 influenza hospitalizations, N = 57 522 influenza ED visits, N = 274 226 culture-negative pneumonia hospitalizations, and N = 113 997 culture-negative pneumonia ED visits included in our analyses. We separately estimated the rate of respiratory infection associated with increased concentrations of source-specific PM_2.5, including secondary sulfate (SS), secondary nitrate (SN), biomass burning (BB), pyrolyzed organic carbon (OP), road dust (RD), residual oil (RO), diesel (DIE), and spark ignition vehicle emissions (GAS). Increased rates of ED visits for influenza were associated with interquartile range increases in concentrations of GAS (excess rate [ER] = 9.2%; 95% CI: 4.3%, 14.3%) and DIE (ER = 3.9%; 95% CI: 1.1%, 6.8%) for lag days 0-3. There were similar associations between BB, SS, OP, and RO, and ED visits or hospitalizations for influenza, but not culture-negative pneumonia hospitalizations or ED visits. Short-term increases in PM_2.5 from traffic and other combustion sources appear to be a potential risk factor for increased rates of influenza hospitalizations and ED visits.

Dynamic Correlation Analysis Method of Air Pollutants in Spatio-Temporal Analysis

Pollutant analysis and pollution source tracing are critical issues in air quality management, in which correlation analysis is important for pollutant relation modeling. A dynamic correlation analysis method was proposed to meet the real-time requirement in atmospheric management. Firstly, the spatio-temporal analysis framework was designed, in which the process of data monitoring, correlation calculation, and result presentation were defined. Secondly, the core correlation calculation method was improved with an adaptive data truncation and grey relational analysis. Thirdly, based on the general framework and correlation calculation, the whole algorithm was proposed for various analysis tasks in time and space, providing the data basis for ranking and decision on pollutant effects. Finally, experiments were conducted with the practical data monitored in an industrial park of Hebei Province, China. The different pollutants in multiple monitoring stations were analyzed crosswise. The dynamic features of the results were obtained to present the variational correlation degrees from the proposed and contrast methods. The results proved that the proposed dynamic correlation analysis could quickly acquire atmospheric pollution information. Moreover, it can help to deduce the influence relation of pollutants in multiple locations.

Exploring the characteristics and sources of carbonaceous aerosols in the agro-pastoral transitional zone of Northern China

Carbonaceous aerosols are linked to severe haze and health effects, while its origins remain still unclear over China. PM_2.5 samples covering four seasons from Jan. 2016 to Jan. 2017 were collected at six sites in Chifeng, a representative agro-pastoral transitional zone of North China focusing on the characteristics and sources of organic carbon (OC) and elemental carbon (EC). The annual averages of OC, EC were 9.00 ± 7.24 μg m^-3, 1.06 ± 0.79 μg m^-3 with site Songshan in coal mining region exhibited significantly enhanced levels. The residential heating emissions, air stagnation, and secondary organic formation all contributed the higher OC, EC levels in winter. Meanwhile, the impacts from open biomass burning were most intensive in spring. The retroplumes via Lagrangian model highlighted a strong seasonality of regional sources which had more impacts on EC increases. The Positive Matrix Factorization (PMF) model resolved six primary sources, namely, coal combustion, biomass burning, industrial processes, oil combustion, fugitive dust, and fireworks. Coal combustion and biomass burning comprised large fractions of OC (30.57%, 30.40%) and EC (23.26%, 38.47%) across the sites, while contributions of industrial processes and oil combustion clearly increased in the sites near industrial sources as smelters. PMF and EC tracer method gave well correlated (r=0.65) estimates of Secondary OC (SOC). The proportion of coal combustion and SOC were more enhanced along with PM_2.5 elevation compared to other sources, suggesting their importances during the pollution events.

Triggering of cardiovascular hospital admissions by source specific fine particle concentrations in urban centers of New York State

BACKGROUND

Previous work reported increased rates of acute cardiovascular hospitalizations associated with increased PM2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference.

OBJECTIVES

To estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM2.5 concentrations.

METHODS

Using source apportioned PM2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM2.5 concentrations in the previous 1, 4, and 7 days.

RESULTS

Interquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources.

CONCLUSIONS

These findings suggest a role of several sources of PM2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events.

Similarities and differences in PM10 and PM2.5 concentrations, chemical compositions and sources in Hefei City, China

Atmospheric particulates were sampled in Hefei City, China from October 2016 to January 2017 to compare chemical compositions and sources of PM_2.5 (particle size smaller than 2.5 μm) and PM₁₀ (particle size smaller than 10 μm). The mean levels of PM_2.5 and PM₁₀ were 81 and 109 μg/m³, which are higher than the health threshold levels regulated by national and international standards. During the sampling period, AQI (Air Quality Index) was strongly correlated with PM_2.5 (Pearson's coefficient r = 0.94) rather than PM₁₀ concentrations. The PM_2.5/PM₁₀ ratios were approximately 0.7, revealing the characteristics of fine particle pollution. Pollution elements (S, Zn, Cu and Pb) took up a large proportion of the composition and had high enrichment factors of 437, 385, 20 and 53, respectively, in PM₁₀. Coal combustion and high-tech manufacture industry discharges were suggested to be the main pollution sources of both PM_2.5 and PM₁₀. The PM_2.5/PM₁₀ ratios of anthropogenic element concentrations were much higher than ratios of earth crust element. As compared to PM₁₀, S and Pb in PM_2.5 had larger EFs, indicating that pollution elements were predominantly enriched in PM_2.5. Furthermore, a paired sample t-test confirmed similar sources of PM_2.5 and PM₁₀. Our study provides basic database to evaluate the heavy metal pollution status of atmospheric particulates in Chinese cities.

High level of source-specific particulate matter air pollution associated with cardiac arrhythmias

Epidemiological evidence linking source-specific ambient particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) and cardiac arrhythmias is limited. In this study, we investigated the impact of source-specific PM_2.5 on cardiac arrhythmias in a panel of forty-five healthy adults living in Beijing, China, between 2015 and 2016. Repeated measures of 24-hour electrocardiograms were conducted during clinical visits, and daily counts of four arrhythmia events including supraventricular premature beat (SVPB), atrial tachycardia (AT), premature ventricular contraction (PVC) and ventricular tachycardia (VT) were recorded. One hundred forty-seven constituents in PM_2.5 were analyzed for collected particulate samples, in which fifty-six of them above laboratory detection limits were selected for source apportionment analysis using positive matrix factorization. The average contributions of identified five major sources to PM_2.5 were 45.9% from secondary nitrate/sulfate, 18.0% from coal combustion, 16.9% from crustal soil, 13.8% from biomass burning, and 5.4% from cooking. Generalized estimating equation models were used to estimate relative risks (RR) of arrhythmias in association with interquartile-range (IQR) increases in PM_2.5 constituents and specific sources. Total PM_2.5 mass as well as several combustion related constituents were found of significant impacts on increased risks of arrhythmia events. Among the identified sources of PM_2.5, coal burning has been found the major source that associated with increased risks of SVPB, PVC and VT with RR of 1.19 [95% confidence intervals (CI): 1.04, 1.36] to 1.64 (95% CI: 1.35, 2.00). PM_2.5 from combustion related secondary nitrate/sulfate was also found of significant impact on SVPB and AT, followed by PM_2.5 from biomass burning and crustal soil. Our results indicated that PM_2.5 from anthropogenic activity related sources were most responsible for increased risks of arrhythmia events. Our findings enhance the understanding of increased risks of arrhythmias from exposure to PM_2.5, and provide evidence on source-specific PM control priorities.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM₁₀ (particulate matter with aerodynamic diameter ≤ 10 μm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM₁₀ sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM₁₀. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM₁₀ concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 μg m^-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM₁₀ mass concentration over the IGP region of India.

Contamination, health risk, mineralogical and morphological status of street dusts- case study: Erbil metropolis, Kurdistan Region-Iraq

Street dusts contamination by heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs), their mineralogical and morphological properties were investigated in Erbil metropolis. A total of 43 soil and street dust samples were collected and analyzed, and SPSS, ImageJ, X-powder and positive matrix factorization (PMF) software were used for data analyzing. Results showed the role of geology in mineralogical status of dusts. Based on ImageJ software processing, dust particles with diameters below 10 μm and low circularity and roundness were dominant. The enrichments for Hg, Cu, Pb and Zn contents were observed in compare with their calculated local geochemical baselines and geoaccumulation index, while, Ni, Co and Cr were categorized as particularly unpolluted. However, Hg, Cu and Pb showed the highest ecological risk (E_jⁱ) values among the studied elements, and 25.80%, 25.80% and 12.90% of the samples were classified as very high, moderate and considerable potential ecological risks, respectively. Hazard index (HI) followed the decreasing order of Pb > Cr > Cu > Ni > Hg > Zn > Co, and ingestion was the main exposure route particularly for children. The concentrations of individual PAHs ranged from 1.06 to 1000 ng/g, with the dominance of 3 and 4-ring compounds. Also, non-carcinogenic PAHs concentrations were higher than carcinogenic compounds and toxic equivalents (TEQs) ranged from 22.30 to 246.92 ng/g, with a max value in Northern industrial zone. Finally, source identification using multivariate statistics and PMF introduced three main PAHs and HMs sources in the study area including geogenic, traffic and industries, and incinerators (mainly for medical wastes).

Speciation of organic fractions does matter for aerosol source apportionment. Part 2: Intensive short-term campaign in the Paris area (France)

The present study aimed at performing PM₁₀ source apportionment, using positive matrix factorization (PMF), based on filter samples collected every 4h at a sub-urban station in the Paris region (France) during a PM pollution event in March 2015 (PM₁₀>50μgm^-3 for several consecutive days). The PMF model allowed to deconvolve 11 source factors. The use of specific primary and secondary organic molecular markers favoured the determination of common sources such as biomass burning and primary traffic emissions, as well as 2 specific biogenic SOA (marine+isoprene) and 3 anthropogenic SOA (nitro-PAHs+oxy-PAHs+phenolic compounds oxidation) factors. This study is probably the first one to report the use of methylnitrocatechol isomers as well as 1-nitropyrene to apportion secondary OA linked to biomass burning emissions and primary traffic emissions, respectively. Secondary organic carbon (SOC) fractions were found to account for 47% of the total OC. The use of organic molecular markers allowed the identification of 41% of the total SOC composed of anthropogenic SOA (namely, oxy-PAHs, nitro-PAHs and phenolic compounds oxidation, representing 15%, 9%, 11% of the total OC, respectively) and biogenic SOA (marine+isoprene) (6% in total). Results obtained also showed that 35% of the total SOC originated from anthropogenic sources and especially PAH SOA (oxy-PAHs+nitro-PAHs), accounting for 24% of the total SOC, highlighting its significant contribution in urban influenced environments. Anthropogenic SOA related to nitro-PAHs and phenolic compounds exhibited a clear diurnal pattern with high concentrations during the night indicating the prominent role of night-time chemistry but with different chemical processes involved.

Chemical characterisation and source identification of atmospheric aerosols in the Snowy Mountains, south-eastern Australia

Characterisation of atmospheric aerosols is of major importance for: climate, the hydrological cycle, human health and policymaking, biogeochemical and palaeo-climatological studies. In this study, the chemical composition and source apportionment of PM_2.5 (particulate matter with aerodynamic diameters less than 2.5μm) at Yarrangobilly, in the Snowy Mountains, SE Australia are examined and quantified. A new aerosol monitoring network was deployed in June 2013 and aerosol samples collected during the period July 2013 to July 2017 were analysed for 22 trace elements and black carbon by ion beam analysis techniques. Positive matrix factorisation and back trajectory analysis and trajectory clustering methods were employed for source apportionment and to isolate source areas and air mass travel pathways, respectively. This study identified the mean atmospheric PM_2.5 mass concentration for the study period was (3.3±2.5)μgm^-3. It is shown that automobile (44.9±0.8)%, secondary sulfate (21.4±0.9)%, smoke (12.3±0.6)%, soil (11.3±0.5)% and aged sea salt (10.1±0.4)% were the five PM_2.5 source types, each with its own distinctive trends. The automobile and smoke sources were ascribed to a significant local influence from the road network and bushfire and hazard reduction burns, respectively. Long-range transport are the dominant sources for secondary sulfate from coal-fired power stations, windblown soil from the inland saline regions of the Lake Eyre and Murray-Darling Basins, and aged sea salt from the Southern Ocean to the remote alpine study site. The impact of recent climate change was recognised, as elevated smoke and windblown soil events correlated with drought and El Niño periods. Finally, the overall implications including potential aerosol derived proxies for interpreting palaeo-archives are discussed. To our knowledge, this is the first long-term detailed temporal and spatial characterisation of PM_2.5 aerosols for the region and provides a crucial dataset for a range of multidisciplinary research.

Source-specific pollution exposure and associations with pulmonary response in the Atlanta Commuters Exposure Studies

Concentrations of traffic-related air pollutants are frequently higher within commuting vehicles than in ambient air. Pollutants found within vehicles may include those generated by tailpipe exhaust, brake wear, and road dust sources, as well as pollutants from in-cabin sources. Source-specific pollution, compared to total pollution, may represent regulation targets that can better protect human health. We estimated source-specific pollution exposures and corresponding pulmonary response in a panel study of commuters. We used constrained positive matrix factorization to estimate source-specific pollution factors and, subsequently, mixed effects models to estimate associations between source-specific pollution and pulmonary response. We identified four pollution factors that we named: crustal, primary tailpipe traffic, non-tailpipe traffic, and secondary. Among asthmatic subjects (N = 48), interquartile range increases in crustal and secondary pollution were associated with changes in lung function of -1.33% (95% confidence interval (CI): -2.45, -0.22) and -2.19% (95% CI: -3.46, -0.92) relative to baseline, respectively. Among non-asthmatic subjects (N = 51), non-tailpipe pollution was associated with pulmonary response only at 2.5 h post-commute. We found no significant associations between pulmonary response and primary tailpipe pollution. Health effects associated with traffic-related pollution may vary by source, and therefore some traffic pollution sources may require targeted interventions to protect health.

Metal composition of ambient PM2.5 influences the pulmonary function of schoolchildren: A case study of school located nearby of an electric arc furnace factory

The present study combined air sampling with pulmonary function tests (PFTs) to determine both the extent of air pollution proximal to an electric arc furnace (EAF) and its impact on human health. The mass concentrations of particulate matter with aerodynamic diameters less than 2.5 µm (PM_2.5) in exposure areas were not significantly higher than the samples taken at a control area. However, the concentrations of five metal elements, Cd, Cr, Cu, Ni, and Zn in PM_2.5 were significantly higher in the exposure area than that of the control area. PFTs showed that the average forced vital capacity (FVC) of boys was decreased with decreasing distance from the EAF factory. With normalization of pulmonary function by age, height, and weight, we found that the FVC became more negative with a decrease in distance from the EAF. Lastly, regression analysis was performed to analyze the impact of the concentrations of the five metals in PM_2.5 on the performance of pulmonary function. The results showed that the metals can be ranked from the highest to the lowest in terms of impact on the FVC of boys as follows: Cr, Cd, Ni, Cu, and Zn. This finding is consistent with the ranking of metal toxicity reported in the literature for a rat lung epithelial cell line. The results of this study showed that only measuring PM_2.5 mass concentrations may not provide a full explanation of its toxicity and health effects. The chemical composition of the PM_2.5 can be an important factor that determined the health impact of PM_2.5.

Recent Approaches to Estimate Associations Between Source-Specific Air Pollution and Health

PURPOSE OF REVIEW

Estimating health effects associated with source-specific exposure is important for better understanding how pollution impacts health and for developing policies to better protect public health. Although epidemiologic studies of sources can be informative, these studies are challenging to conduct because source-specific exposures (e.g., particulate matter from vehicles) often are not directly observed and must be estimated. We reviewed recent studies that estimated associations between pollution sources and health to identify methodological developments designed to address important challenges.

RECENT FINDINGS

Notable advances in epidemiologic studies of sources include approaches for (1) propagating uncertainty in source estimation into health effect estimates, (2) assessing regional and seasonal variability in emissions sources and source-specific health effects, and (3) addressing potential confounding in estimated health effects. Novel methodological approaches to address challenges in studies of pollution sources, particularly evaluation of source-specific health effects, are important for determining how source-specific exposure impacts health.

Involvement of oxidative stress and calcium signaling in airborne particulate matter - induced damages in human pulmonary artery endothelial cells

Recent studies have revealed that particulate matter (PM) exert deleterious effects on vascular function. Pulmonary artery endothelial cells (HPAEC), which are involved in the vasomotricity regulation, can be a direct target of inhaled particles. Modifications in calcium homeostasis and oxidative stress are critical events involved in the physiopathology of vascular diseases. The objectives of this study were to assess the effects of PM_2.5 on oxidative stress and calcium signaling in HPAEC. Different endpoints were studied, (i) intrinsic and intracellular production of reactive oxygen species (ROS) by the H₂DCF-DA probe, (ii) intrinsic, intracellular and mitochondrial production of superoxide anion (O₂^-) by electronic paramagnetic resonance spectroscopy and MitoSOX probe, (iii) reactive nitrosative species (RNS) production by Griess reaction, and (vi) calcium signaling by the Fluo-4 probe. In acellular conditions, PM_2.5 leads to an intrinsic free radical production (ROS, O₂^-) and a 4h-exposure to PM_2.5 (5-15μg/cm²), induced, in HPAEC, an increase of RNS, of global ROS and of cytoplasmic and mitochondrial O₂^- levels. The basal intracellular calcium ion level [Ca²⁺]i was also increased after 4h-exposure to PM_2.5 and a pre-treatment with superoxide dismutase and catalase significantly reduced this response. This study provides evidence that the alteration of intracellular calcium homeostasis induced by PM_2.5 is closely correlated to an increase of oxidative stress.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^-3 (range 25.1-429.8 μg m^-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.

A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources

Exposure to particulate matter (PM) air pollution has been associated with a range of adverse health outcomes, including cardiovascular disease hospitalizations and other clinical parameters. Determining which sources of PM, such as traffic or industry, are most associated with adverse health outcomes could help guide future recommendations aimed at reducing harmful pollution exposure for susceptible individuals. Information obtained from multisite studies, which is generally more precise than information from a single location, is critical to understanding how PM impacts health and to informing local strategies for reducing individual-level PM exposure. However, few methods exist to perform multisite studies of PM sources, which are not generally directly observed, and adverse health outcomes. We developed SHared Across a REgion (SHARE), a hierarchical modeling approach that facilitates reproducible, multisite epidemiologic studies of PM sources. SHARE is a two-stage approach that first summarizes information about PM sources across multiple sites. Then, this information is used to determine how community-level (i.e., county-level or city-level) health effects of PM sources should be pooled to estimate regional-level health effects. SHARE is a type of population value decomposition that aims to separate out regional-level features from site-level data. Unlike previous approaches for multisite epidemiologic studies of PM sources, the SHARE approach allows the specific PM sources identified to vary by site. Using data from 2000 to 2010 for 63 northeastern US counties, we estimated regional-level health effects associated with short-term exposure to major types of PM sources. We found that PM from secondary sulfate, traffic, and metals sources was most associated with cardiovascular disease hospitalizations. Copyright © 2017 John Wiley & Sons, Ltd.

Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model

Fine particulates (PM_2.5) constitute dominant proportion of airborne particulates and have been often associated with human health disorders, changes in regional climate, hydrological cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM_2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concentrated over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, positive matrix factorization and principal component analysis (62% of studies) were the primary choices, followed by chemical mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of organic molecular markers and gas-to-particle conversion were minimum. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM_2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM_2.5>100 μgm^-3, n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was found single dominating source over southern part while over Bangladesh, both vehicular, biomass burning and industrial sources were significant.

Associations between Source-Specific Fine Particulate Matter and Emergency Department Visits for Respiratory Disease in Four U.S. Cities

BACKGROUND

Short-term exposure to ambient fine particulate matter (PM2.5) concentrations has been associated with increased mortality and morbidity. Determining which sources of PM2.5 are most toxic can help guide targeted reduction of PM2.5. However, conducting multicity epidemiologic studies of sources is difficult because source-specific PM2.5 is not directly measured, and source chemical compositions can vary between cities.

OBJECTIVES

We determined how the chemical composition of primary ambient PM2.5 sources varies across cities. We estimated associations between source-specific PM2.5 and respiratory disease emergency department (ED) visits and examined between-city heterogeneity in estimated associations.

METHODS

We used source apportionment to estimate daily concentrations of primary source-specific PM2.5 for four U.S. cities. For sources with similar chemical compositions between cities, we applied Poisson time-series regression models to estimate associations between source-specific PM2.5 and respiratory disease ED visits.

RESULTS

We found that PM2.5 from biomass burning, diesel vehicle, gasoline vehicle, and dust sources was similar in chemical composition between cities, but PM2.5 from coal combustion and metal sources varied across cities. We found some evidence of positive associations of respiratory disease ED visits with biomass burning PM2.5; associations with diesel and gasoline PM2.5 were frequently imprecise or consistent with the null. We found little evidence of associations with dust PM2.5.

CONCLUSIONS

We introduced an approach for comparing the chemical compositions of PM2.5 sources across cities and conducted one of the first multicity studies of source-specific PM2.5 and ED visits. Across four U.S. cities, among the primary PM2.5 sources assessed, biomass burning PM2.5 was most strongly associated with respiratory health. Citation: Krall JR, Mulholland JA, Russell AG, Balachandran S, Winquist A, Tolbert PE, Waller LA, Sarnat SE. 2017. Associations between source-specific fine particulate matter and emergency department visits for respiratory disease in four U.S. cities. Environ Health Perspect 125:97-103; http://dx.doi.org/10.1289/EHP271.

Assessment of PM2.5 sources and their corresponding level of uncertainty in a coastal urban area using EPA PMF 5.0 enhanced diagnostics

Datasets that include only the PM elemental composition and no other important constituents such as ions and OC, should be treated carefully when used for source apportionment. This work is demonstrating how a source apportionment study utilizing PMF 5.0 enhanced diagnostic tools can achieve an improved solution with documented levels of uncertainty for such a dataset. The uncertainty of the solution is rarely reported in source apportionment studies or it is reported partially. Reporting the uncertainty of the solution is very important especially in the case of small datasets. PM2.5 samples collected in Patras during the year 2011 were used. The concentrations of 22 elements (Z=11-33) were determined using PIXE. Source apportionment analysis revealed that PM2.5 emission sources were biomass burning (11%), sea salt (8%), shipping emissions (10%), vehicle emissions (33%), mineral dust (2%) and secondary sulfates (33%) while unaccounted mass was 3%. Although Patras city center is located in a very close proximity to the city's harbor, the contribution of shipping originating emissions was never before quantified. As rotational stability is hard to be achieved when a small dataset is used the rotational stability of the solution was thoroughly evaluated. A number of constraints were applied to the solution in order to reduce rotational ambiguity.

Identification and elucidation of anthropogenic source contribution in PM10 pollutant: Insight gain from dispersion and receptor models

Source apportionment study of PM₁₀ (Particulate Matter) in a critically polluted area of Jharia coalfield, India has been carried out using Dispersion model, Principle Component Analysis (PCA) and Chemical Mass Balance (CMB) techniques. Dispersion model Atmospheric Dispersion Model (AERMOD) was introduced to simplify the complexity of sources in Jharia coalfield. PCA and CMB analysis indicates that monitoring stations near the mining area were mainly affected by the emission from open coal mining and its associated activities such as coal transportation, loading and unloading of coal. Mine fire emission also contributed a considerable amount of particulate matters in monitoring stations. Locations in the city area were mostly affected by vehicular, Liquid Petroleum Gas (LPG) & Diesel Generator (DG) set emissions, residential, and commercial activities. The experimental data sampling and their analysis could aid understanding how dispersion based model technique along with receptor model based concept can be strategically used for quantitative analysis of Natural and Anthropogenic sources of PM₁₀.

Association Between Health Symptoms and Particulate Matter from Traffic and Residential Heating - Results from RHINE III in Tartu

Background:

Traffic and residential heating are the main sources of particulate matter (PM) in Northern Europe. Wood is widely used for residential heating and vehicle numbers are increasing. Besides traffic exhaust, studded tires produce road dust that is the main source of traffic-related PM₁₀. Several studies have associated total PM mass with health symptoms; however there has been little research on the effects of PM from specific sources.

Objective:

To study the health effects resulting from traffic and local heating PM.

Methods:

Data on respiratory and cardiac diseases were collected within the framework of RHINE III (2011/2012) in Tartu, Estonia. Respondents’ geocoded home addresses were mapped in ArcGIS and linked with local heating-related PM_2.5, traffic-related PM₁₀ and total PM_2.5 concentrations. Association between self-reported health and PM was assessed using multiple logistic regression analysis.

Results:

The annual mean modelled exposure for local heating PM_2.5 was 2.3 μg/m³, for traffic PM₁₀ 3.3 μg/m³ and for all sources PM_2.5 5.6 μg/m³. We found relationship between traffic induced PM₁₀ as well as all sources induced PM_2.5 with cardiac disease, OR=1.45 (95% CI 1.06−1.93) and 1.42 (95% CI 1.02−1.95), respectively. However, we did not find any significant association between residential heating induced particles and self-reported health symptoms. People with longer and better confirmed exposure period were also significantly associated with traffic induced PM₁₀, all sources induced PM_2.5 and cardiac diseases.

Conclusion:

Traffic-related PM₁₀ and all sources induced PM_2.5 associated with cardiac disease; whereas residential heating induced particles did not.

Associations of Source-Specific Fine Particulate Matter With Emergency Department Visits in California

While many studies have investigated the health effects associated with acute exposure to fine particulate matter (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)), very few have considered the risks of specific sources of PM2.5 We used city-specific source apportionment in 8 major metropolitan areas in California from 2005-2009 to examine the associations of source-specific PM2.5 exposures from vehicular emissions, biomass burning, soil, and secondary nitrate and sulfate sources with emergency department visits (EDVs) for cardiovascular and respiratory diseases, including 7 subclasses. Using a case-crossover analysis, we observed associations of vehicular emissions with all cardiovascular EDVs (excess risk = 1.6%, 95% confidence interval: 0.9, 2.4 for an interquartile-range increment of 2.8 µg/m(3)) and with several subclasses of disease. In addition, vehicular emissions, biomass burning, and soil sources were associated with all respiratory EDVs and with EDVs for asthma. The soil source, which includes resuspended road dust, generated the highest risk estimate for asthma (excess risk = 4.5%, 95% confidence interval: 1.1, 8.0). Overall, our results provide additional evidence of the public health consequences of exposure to specific sources of PM2.5 and indicate that some sources of PM2.5 may pose higher risks than the overall PM2.5 mass.

An inter-comparison of PM10 source apportionment using PCA and PMF receptor models in three European sites

Source apportionment of aerosol is an important approach to investigate aerosol formation and transformation processes as well as to assess appropriate mitigation strategies and to investigate causes of non-compliance with air quality standards (Directive 2008/50/CE). Receptor models (RMs) based on chemical composition of aerosol measured at specific sites are a useful, and widely used, tool to perform source apportionment. However, an analysis of available studies in the scientific literature reveals heterogeneities in the approaches used, in terms of "working variables" such as the number of samples in the dataset and the number of chemical species used as well as in the modeling tools used. In this work, an inter-comparison of PM10 source apportionment results obtained at three European measurement sites is presented, using two receptor models: principal component analysis coupled with multi-linear regression analysis (PCA-MLRA) and positive matrix factorization (PMF). The inter-comparison focuses on source identification, quantification of source contribution to PM10, robustness of the results, and how these are influenced by the number of chemical species available in the datasets. Results show very similar component/factor profiles identified by PCA and PMF, with some discrepancies in the number of factors. The PMF model appears to be more suitable to separate secondary sulfate and secondary nitrate with respect to PCA at least in the datasets analyzed. Further, some difficulties have been observed with PCA in separating industrial and heavy oil combustion contributions. Commonly at all sites, the crustal contributions found with PCA were larger than those found with PMF, and the secondary inorganic aerosol contributions found by PCA were lower than those found by PMF. Site-dependent differences were also observed for traffic and marine contributions. The inter-comparison of source apportionment performed on complete datasets (using the full range of available chemical species) and incomplete datasets (with reduced number of chemical species) allowed to investigate the sensitivity of source apportionment (SA) results to the working variables used in the RMs. Results show that, at both sites, the profiles and the contributions of the different sources calculated with PMF are comparable within the estimated uncertainties indicating a good stability and robustness of PMF results. In contrast, PCA outputs are more sensitive to the chemical species present in the datasets. In PCA, the crustal contributions are higher in the incomplete datasets and the traffic contributions are significantly lower for incomplete datasets.

PM2.5 and survival among older adults: effect modification by particulate composition

BACKGROUND

Fine particulate (PM2.5) air pollution has been consistently linked to survival, but reported effect estimates are geographically heterogeneous. Exposure to different types of particle mixtures may explain some of this variation.

METHODS

We used k-means cluster analyses to identify cities with similar pollution profiles, (ie, PM2.5 composition) across the United States. We examined the impact of PM2.5 on survival, and its variation across clusters of cities with similar PM2.5 composition, among Medicare enrollees in 81 US cities (2000-2010). We used time-varying annual PM2.5 averages, measured at ambient central monitoring sites, as the exposure of interest. We ran by-city Cox models, adjusting for individual data on previous cardiopulmonary-related hospitalizations and stratifying by follow-up time, age, gender, and race. This eliminates confounding by factors varying across cities and long-term trends, focusing on year-to-year variations of air pollution around its city-specific mean and trend. We then pooled the city-specific effects using a random effects meta-regression. In this second stage, we also assessed effect modification by cluster membership and estimated cluster-specific PM2.5 effects.

RESULTS

We followed more than 19 million subjects and observed more than 6 million deaths. We found a harmful impact of annual PM2.5 concentrations on survival (hazard ratio = 1.11 [95% confidence interval = 1.01, 1.23] per 10 μg/m). This effect was modified by particulate composition, with higher effects observed in clusters containing high concentrations of nickel, vanadium, and sulfate. For instance, our highest effect estimate was observed in cities with harbors in the Northwest, characterized by high nickel, vanadium, and elemental carbon concentrations (1.9 [1.1, 3.3]). We observed null or negative associations in clusters with high oceanic and crustal particles.

CONCLUSIONS

To the best of our knowledge, this is the first study to examine the association between PM2.5 composition and survival. Our findings indicate that long-term exposure to fuel oil combustion and power plant emissions have the highest impact on survival.

Use of the Adaptive LASSO Method to Identify PM2.5 Components Associated with Blood Pressure in Elderly Men: The Veterans Affairs Normative Aging Study

BACKGROUND

PM2.5 (particulate matter ≤ 2.5 μm) has been associated with adverse cardiovascular outcomes, but it is unclear whether specific PM2.5 components, particularly metals, may be responsible for cardiovascular effects.

OBJECTIVES

We aimed to determine which PM2.5 components are associated with blood pressure in a longitudinal cohort.

METHODS

We fit linear mixed-effects models with the adaptive LASSO penalty to longitudinal data from 718 elderly men in the Veterans Affairs Normative Aging Study, 1999-2010. We controlled for PM2.5 mass, age, body mass index, use of antihypertensive medication (ACE inhibitors, non-ophthalmic beta blockers, calcium channel blockers, diuretics, and angiotensin receptor antagonists), smoking status, alcohol intake, years of education, temperature, and season as fixed effects in the models, and additionally applied the adaptive LASSO method to select PM2.5 components associated with blood pressure. Final models were identified by the Bayesian Information Criterion (BIC).

RESULTS

For systolic blood pressure (SBP), nickel (Ni) and sodium (Na) were selected by the adaptive LASSO, whereas only Ni was selected for diastolic blood pressure (DBP). An interquartile range increase (2.5 ng/m3) in 7-day moving-average Ni was associated with 2.48-mmHg (95% CI: 1.45, 3.50 mmHg) increase in SBP and 2.22-mmHg (95% CI: 1.69, 2.75 mmHg) increase in DBP, respectively. Associations were comparable when the analysis was restricted to study visits with PM2.5 below the 75th percentile of the distribution (12 μg/m3).

CONCLUSIONS

Our study suggested that exposure to ambient Ni was associated with increased blood pressure independent of PM2.5 mass in our study population of elderly men. Further research is needed to confirm our findings, assess generalizability to other populations, and identify potential mechanisms for Ni effects.

CITATION

Dai L, Koutrakis P, Coull BA, Sparrow D, Vokonas PS, Schwartz JD. 2016. Use of the adaptive LASSO method to identify PM2.5 components associated with blood pressure in elderly men: the Veterans Affairs Normative Aging Study. Environ Health Perspect 124:120-125; http://dx.doi.org/10.1289/ehp.1409021.

A model-based approach for imputing censored data in source apportionment studies

Sources of particulate matter (PM) air pollution are generally inferred from PM chemical constituent concentrations using source apportionment models. Concentrations of PM constituents are often censored below minimum detection limits (MDL) and most source apportionment models cannot handle these censored data. Frequently, censored data are first substituted by a constant proportion of the MDL or are removed to create a truncated dataset before sources are estimated. When estimating the complete data distribution, these commonly applied methods to adjust censored data perform poorly compared with model-based imputation methods. Model-based imputation has not been used in source apportionment and may lead to better source estimation. However if the censored chemical constituents are not important for estimating sources, censoring adjustment methods may have little impact on source estimation. We focus on two source apportionment models applied in the literature and provide a comprehensive assessment of how censoring adjustment methods, including model-based imputation, impact source estimation. A review of censoring adjustment methods critically informs how censored data should be handled in these source apportionment models. In a simulation study, we demonstrated that model-based multiple imputation frequently leads to better source estimation compared with commonly used censoring adjustment methods. We estimated sources of PM in New York City and found estimated source distributions differed by censoring adjustment method. In this study, we provide guidance for adjusting censored PM constituent data in common source apportionment models, which is necessary for estimation of PM sources and their subsequent health effects.

Forecasting of VOC emissions from traffic and industry using classification and regression multivariate methods

In this study, advanced multivariate methods were applied for VOC source apportionment and subsequent short-term forecast of industrial- and vehicle exhaust-related contributions in Belgrade urban area (Serbia). The VOC concentrations were measured using PTR-MS, together with inorganic gaseous pollutants (NOx, NO, NO2, SO2, and CO), PM10, and meteorological parameters. US EPA Positive Matrix Factorization and Unmix receptor models were applied to the obtained dataset both resolving six source profiles. For the purpose of forecasting industrial- and vehicle exhaust-related source contributions, different multivariate methods were employed in two separate cases, relying on meteorological data, and on meteorological data and concentrations of inorganic gaseous pollutants, respectively. The results indicate that Boosted Decision Trees and Multi-Layer Perceptrons were the best performing methods. According to the results, forecasting accuracy was high (lowest relative error of only 6%), in particular when the forecast was based on both meteorological parameters and concentrations of inorganic gaseous pollutants.

Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications

BACKGROUND

Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

OBJECTIVE

Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

METHODS

First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

RESULTS

Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null.

CONCLUSIONS

We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

The relationship between daily cardiovascular mortality and daily ambient concentrations of particulate pollutants (sulfur, arsenic, selenium, and mercury) and daily source contributions from coal power plants and smelters (individually, combined, and with interaction) in Phoenix, AZ, 1995-1998: A multipollutant approach to acute, time-series air pollution epidemiology: I

The objective of this paper is to estimate the increase in risk of daily cardiovascular mortality due to an increase in the daily ambient concentration of the individual particulate pollutants sulfur (S), arsenic (As), selenium (Se), and mercury (Hg) using single-pollutant models (SPMs) and to compare this risk to the combined increase in risk due to an increase in all four pollutants by including all four pollutants in the same model (multipollutant model, MPM) and to the risks from source contributions from power plants and smelters. Individual betas in a multipollutant model (MPM) were summed to give a combined beta. Interaction was investigated with a pollutant product term. SPMs (controlling for time trends, temperature, and relative humidity), for an interquartile range (IQR) increase in the pollutant concentration on lag day 0, gave these percent excess risks (±95% confidence levels): S, 6.9% (1.3-12%); As, 2.9% (0.4-5.5%); Se, 1.4% (-1.7 to 4.6); Hg, 9.6% (4.8-14.6%). The SPM beta for S (as sulfate) was higher than found in other studies. The SPM beta for Hg gave the largest t-statistic and beta per unit mass of any pollutant studied. An (IQR) increase in all four pollutants gave an excess risk of 15.4% (7.5-23.8%), slightly smaller than the combination of S and Hg, 16.7% (9.1-24.9%). The combined beta was 71% of the sum of the four individual SPM betas, indicating a reduction in confounding among pollutants in the combined model. As and Se were shown to be noncausal; their SPM betas could be explained as confounding by S.

IMPLICATIONS

The combined effect of several pollutants can be estimated by including the appropriate pollutants in the same statistical model, summing their individual betas to give a combined beta, and using a variance-covariance matrix to obtain the standard error. This approach identifies and reduces confounding among the species in the multipollutant model and can be used to identify confounded species that have no independent relationship with mortality. The effect of several pollutants acting together may be higher than that of one pollutant. Further work is needed to understand the strong relationship of mortality with particulate mercury and sulfate.