EPA Positive Matrix Factorization Research Articles

Sources of personal PM2.5 exposure during pregnancy in the MADRES cohort.

Personal exposure to fine particulate matter (PM2.5) is impacted by different sources each with different chemical composition. Determining these sources is important for reducing personal exposure and its health risks especially during pregnancy. Identify main sources and their contributions to the personal PM2.5 exposure in 213 women in the 3rd trimester of pregnancy in Los Angeles, CA. We measured 48-hr integrated personal PM2.5 exposure and analyzed filters for PM2.5 mass, elemental composition, and optical carbon fractions. We used the EPA Positive Matrix Factorization (PMF) model to resolve and quantify the major sources of personal PM2.5 exposure. We then investigated bivariate relationships between sources, time-activity patterns, and environmental exposures in activity spaces and residential neighborhoods to further understand sources. Mean personal PM2.5 mass concentration was 22.3 (SD = 16.6) μg/m3. Twenty-five species and PM2.5 mass were used in PMF with a final R2 of 0.48. We identified six sources (with major species in profiles and % contribution to PM2.5 mass) as follows: secondhand smoking (SHS) (brown carbon, environmental tobacco smoke; 65.3%), fuel oil (nickel, vanadium; 11.7%), crustal (aluminum, calcium, silicon; 11.5%), fresh sea salt (sodium, chlorine; 4.7%), aged sea salt (sodium, magnesium, sulfur; 4.3%), and traffic (black carbon, zinc; 2.6%). SHS was significantly greater in apartments compared to houses. Crustal source was correlated with more occupants in the household. Aged sea salt increased with temperature and outdoor ozone, while fresh sea salt was highest on days with westerly winds from the Pacific Ocean. Traffic was positively correlated with ambient NO2 and traffic-related NOx at residence. Overall, 76.8% of personal PM2.5 mass came from indoor or personal compared to outdoor sources. We conducted source apportionment of personal PM2.5 samples in pregnancy in Los Angeles, CA. Among identified sources, secondhand smoking contributed the most to the personal exposure. In addition, traffic, crustal, fuel oil, fresh and aged sea salt sources were also identified as main sources. Traffic sources contained markers of combustion and non-exhaust wear emissions. Crustal source was correlated with more occupants in the household. Aged sea salt source increased with temperature and outdoor ozone and fresh sea salt source was highest on days with westerly winds from the Pacific Ocean.

Effects of in-utero personal exposure to PM2.5 sources and components on birthweight

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-h personal PM2.5 exposure monitoring sub-study. The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions evaluated the association between personal PM2.5 sources/components and birthweight, adjusting for gestational age, maternal age, race, infant sex, parity, diabetes status, temperature, maternal education, and smoking history. Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3295.8 g (484.1) and mean PM2.5 exposure was 21.3 (14.4) µg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2 g decrease in birthweight (95% CI − 197.7, − 0.6), and aged sea salt was associated with a 70.1 g decrease in birthweight (95% CI − 141.7, 1.4). Magnesium, sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass. This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Indoor residential and outdoor sources of PM2.5 and PM10 in Nicosia, Cyprus

Cyprus is a typical eastern Mediterranean country that suffers from local emissions, transported anthropogenic pollution, and dust storms all year round. Therefore, exposures to PM in ambient and residential micro-environments are of great public health concern. Our study collected indoor and outdoor PM2.5 and PM10 samples simultaneously in 22 houses in Nicosia, Cyprus, during warm seasons and cold seasons from February 2019 to May 2021. Samples were analyzed for mass and constituents’ concentrations. To determine indoor and outdoor sources of PM in residential environments, we used the EPA positive matrix factorization (PMF) model to conduct source apportionment analyses for both indoor and outdoor PM2.5 and PM10 particles. Generally, six types of residential-level PM sources were resolved: biomass burning, traffic, local or regional secondary sulfate pollution, Ca-rich particles, sea salt, and soil dust. In the source apportionment of PM2.5, the main contribution to outdoor levels (33.1%) was associated with sulfate-rich transported pollution. The predominant contribution to indoor levels (48.0%) was attributed to secondary sulfate pollution as a mixture of local- and regional-scale pollutants. Biomass burning and traffic sources constituted the main outdoor sources of indoor PM2.5, while the Ca-rich particles were identified to almost originate from indoors. By contrast, the largest fraction (29.3%) of the ambient PM10 and a smaller proportion (10.2%) of indoor PM10 were attributed to Ca-rich particles. Indoor PM10 was associated mainly with outdoor sources, except for the soil dust which originated from indoor activities.

Compositional Difference and Health Risk Assessment of Polycyclic Aromatic Hydrocarbons over the Coal Spontaneous Combustion Zone

In this study, the U.S. Environmental Protection Agency prioritized polycyclic aromatic hydrocarbons (PAHs), associated pollution level, and health risks were assessed in a typical coal spontaneous combustion zone in the Rujigou coal mine in Northwestern China. This study used gas chromatography-mass spectrometry (GC-MS) to detect the chemical composition, spatial variation, distribution profiles, impact of coal spontaneous combustion, and health risks of PAHs. The entire study area is divided into three zones according to different features: the spontaneous combustion zone (C-zone), the living zone (L-zone), and the non-spontaneous combustion zone (N-zone). The results showed that: (1) the highest concentrations were measured in the C-zone, and the average concentrations of PAHs in the C-zone, N-zone, and L-zone were 13.28 ng·m−3, 9.56 ng·m−3, and 7.67 ng·m−3, respectively. (2) The PAHs of the study area were mainly composed of three ring to five ring PAHs. (3) EPA positive matrix factorization (PMF) analysis of qualitative source apportionment of PAHs showed that chemical production was the major source of atmospheric PAHs in all three zones, followed by coal combustion. (4) The inhalation of PAHs showed higher potential cancer risk for children than for adults, and the impact of coal combustion in the C-zone was much greater than the other zone. The adverse health impacts associated with PAH exposure indicates the need for mitigation measures of pollution control in this region.

Continuous Measurements and Source Apportionment of Ambient PM2.5-Bound Elements in Windsor, Canada

Ambient fine particulate matter (PM2.5) levels in Windsor, Ontario, Canada, are impacted by local emissions and regional/transboundary transport input and also attributable to secondary formation. PM2.5-bound elements were monitored hourly in Windsor from April to October 2021. Observed concentrations of the elements were generally comparable to historical measurements at urban sites in Ontario. A clear diurnal pattern was observed for most of the elements, i.e., high in the morning and low in the afternoon, mostly related to evolution of atmospheric mixing heights and local anthropogenic activities. Conversely, sulfur showed elevated levels in the afternoon, suggesting conversion of gaseous sulfur dioxide to particulate sulphate was enhanced by increased ambient temperatures. Five source factors were resolved using the US EPA positive matrix factorization model, including three traffic-related sources (i.e., vehicular exhaust, crustal dust, and vehicle tire and brake wear factors) and two industrial sources (i.e., coal/heavy oil burning and metal processing factors). Overall, the three traffic-related sources were mostly local and contributed to 47% of the total elemental concentrations, while the two industrial sources may originate from regional/transboundary sources and contributed to 53%. Measures to control both local traffic emissions and regional/transboundary industrial sources would help reduce levels of PM2.5-bound elements in Windsor.

Positive matrix factorization-based receptor modelling of particulate matter in northwest India

India is facing a dearth in the area of particulate matter (PM) source apportionment studies. Frequent exceedances and unusual high concentrations of PM observed in North India further presses the identification and quantification of particulate pollution sources to plan mitigation measures. But most of the PM source apportionment studies performed here are concentrated in and around Delhi, so this paper sets to explore the sources of PM in an understudied region of North India. The present study employed positive matrix factorization (PMF) 5.0 model developed by the United States Environmental Protection Agency (US EPA) for source characterisation of particulate matter quantified at Mohali in May 2012. Application of US EPA PMF 5.0 to a hybrid dataset of particulate matter and volatile organic compounds identified seven source factors, namingly, dust, wheat residue burning, biofuel use and waste disposal, mixed daytime, cars, two-wheelers) and industrial emissions and solvent use. The contributions of these individual source factors to PM10 and PM2.5 mass loadings were found to be 48.8%, 20.6%, 15.8%, 7.79%, 2.89%, 2.86%, 1.37% and 33.6%, 22.6%, 29.6%, 4.97%, 2.05%, 4.07% and 3.07% respectively. Dust and biomass burning were the major sources of PM in Mohali that contributed relatively highest average concentrations of PM10 (∼321 μg/m3) and PM2.5 (∼123 μg/m3) during morning hours (05:00–10:00 LT) in May 2012. This study reaffirmed the need to check anthropogenic activities and calls for strict air quality measures, especially in May to limit PM emissions in northwest India.

Contributions of primary sources to submicron organic aerosols in Delhi, India

Abstract. Delhi, India, experiences extremely high concentrations of primary organic aerosol (POA). Few prior source apportionment studies on Delhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol (COA) on POA. In a companion paper, we develop a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements of this approach over traditional PMF. Here, we quantify the contributions of BBOA, COA, and hydrocarbon-like organic aerosol (HOA) by applying positive matrix factorization (PMF) resolved by time of day on two seasons (winter and monsoon seasons of 2017) using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. We also conduct detailed uncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition to HOA, COA is found to be a major constituent of POA in the monsoon, and BBOA is found to be a major constituent of POA in the winter. Neither COA nor the different types of BBOA were resolved in the seasonal (not time-resolved) analysis. The COA mass spectra (MS) profiles are consistent with mass spectral profiles from Delhi and around the world, particularly resembling MS of heated cooking oils with a high m/z 41. The BBOA MS have a very prominent m/z 29 in addition to the characteristic peak at m/z 60, consistent with previous MS observed in Delhi and from wood burning sources. In addition to separating the POA, our technique also captures changes in MS profiles with the time of day, a unique feature among source apportionment approaches available. In addition to the primary factors, we separate two to three oxygenated organic aerosol (OOA) components. When all factors are recombined to total POA and OOA, our results are consistent with seasonal PMF analysis conducted using EPA PMF. Results from this work can be used to better design policies that target relevant primary sources of organic aerosols in Delhi.

Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution

Abstract. Present methodologies for source apportionment assume fixed source profiles. Since meteorology and human activity patterns change seasonally and diurnally, application of source apportionment techniques to shorter rather than longer time periods generates more representative mass spectra. Here, we present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements in this approach over traditional PMF. We report on source apportionment conducted on four example time periods in two seasons (winter and monsoon seasons of 2017), using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. Compared to the traditional seasonal PMF approach, we extract a larger number of factors as well as PMF factors that represent the expected sources of primary organic aerosol using time-of-day PMF. By capturing diurnal time series patterns of sources at a low computational cost, time-of-day PMF can utilize large datasets collected using long-term monitoring and improve the characterization of sources of organic aerosol compared to traditional PMF approaches that do not resolve by time of day.

Organophosphate esters in the mariculture ecosystem: Environmental occurrence and risk assessments

Most investigations on organophosphate esters (OPEs) are conducted predominantly in a separate biological or abiotic medium, and few joint analyses have been performed in the mariculture ecosystem based on yearly sampling. Herein, we investigated the occurrence, load estimation, phase distribution, source diagnostics, and risks of 20 OPEs in seawater, sediment, and aquaculture organisms from a typical mariculture area in China. The total of these OPEs (∑OPEs) ranged within 3.97–1068 ng/L, 0.39–65.5 ng/g (dw), and 4.09–16.3 ng/g (ww) in seawater, sediment and organisms, respectively. Chlorinated OPEs were the predominant congeners detected in seawater, whereas alkyl-OPEs were the leading contributors in sediment and biological samples. Seasonal variations of ∑OPEs in seawater were more distinct than those in sedimentary environments. Load estimation indicated that approximately 70% of the OPEs in the study area existed in the water bodies. Source identification performed using the U.S. EPA positive matrix factorization indicated that polyurethane foam/plastics and hydraulic oil made the greatest contributions in seawater, whereas chemical production was the predominant source in sediment. Indices of ecological and health risks of OPEs were lower than their risk threshold, indicating that the OPEs detected in this study posed a low risk to the aquatic environment and human health.

Characterization of Aerosol Pollution in Two Hungarian Cities in Winter 2009–2010

In this study, atmospheric particulate matter (APM) pollution was compared in urban background sites of two cities in Hungary—namely the capital Budapest and Debrecen—by analyzing daily aerosol samples collected between 8 December 2009 and 18 March 2010. Concentration, elemental composition, including BC, and sources of fine (PM2.5) and coarse (PM2.5–10) aerosol pollution, as well as their variation due to meteorological conditions and anthropogenic activities, were determined for both cities. The average PM2.5 concentrations were 22 μg/m3 and 17 μg/m3 in Budapest and Debrecen, respectively. In the case of PM10, the mean concentration was 32 μg/m3 in Budapest and 23 μg/m3 in Debrecen. The concentration of the coarse fraction decreased significantly over the weekends compared to working days. The number of exceedances of the WHO recommended limit value for PM2.5 (15 μg/m3) were 67 in Budapest and 46 in Debrecen, which corresponds to 73% and 50% of the sampling days, respectively. At the time of the exceedances the daily average temperature was below freezing. The average PM2.5/PM10 ratio was 70% and 75% for the two sites, indicating the dominance of the fine fraction aerosol particles during the study period. Elements of natural origin (Al, Si, Ca, Ti, Mn, Fe, Ba) and chlorine were found to be dominant in the coarse fraction, while elements of anthropogenic origin (S, K, Cu, Zn, Pb) were characteristic to the fine fraction. Similar concentrations were measured in the two cities in the case of S which originates from regional transport and K which serves as a tracer for biomass combustion. Traffic-related elements were present in 2–3 times higher concentrations in Budapest. The episodic peaks in the Cl time series could be attributed to salting after snowfalls. The following sources of APM pollution were identified by using the EPA Positive Matrix Factorization (PMF) 5.0 receptor model: soil, traffic, road dust, secondary sulfate, biomass burning, and de-icing of streets. On polluted days when the PM2.5 concentration exceeded the 25 μg/m3 value the contribution of secondary sulfate, domestic heating, and traffic increased significantly compared to the average. On weekends and holidays the contribution of soil and traffic decreased. The main pollution sources and their contributions were similar to the ones in other cities in the region. Comparing our findings to results from winter 2015 it can be concluded that while the PM2.5 pollution level remained almost the same, a significant increase in the contribution of biomass burning was observed in both cities from 2010 to 2015, indicating a change of heating habits.

Associations of Air Pollution and Pediatric Asthma in Cleveland, Ohio

Air pollution has been associated with poor health outcomes and continues to be a risk factor for respiratory health in children. While higher particulate matter (PM) levels are associated with increased frequency of symptoms, lower lung function, and increase airway inflammation from asthma, the precise composition of the particles that are more highly associated with poor health outcomes or healthcare utilization are not fully elucidated. PM is measured quantifiably by current air pollution monitoring systems. To better determine sources of PM and speciation of such sources, a particulate matter (PM) source apportionment study, the Cleveland Multiple Air Pollutant Study (CMAPS), was conducted in Cleveland, Ohio, in 2009–2010, which allowed more refined assessment of associations with health outcomes. This article presents an evaluation of short-term (daily) and long-term associations between motor vehicle and industrial air pollution components and pediatric asthma emergency department (ED) visits by evaluating two sets of air quality data with healthcare utilization for pediatric asthma. Exposure estimates were developed using land use regression models for long-term exposures for nitrogen dioxide (NO2) and coarse (i.e., with aerodynamic diameters between 2.5 and 10 μm) particulate matter (PM) and the US EPA Positive Matrix Factorization receptor model for short-term exposures to fine (<2.5 μm) and coarse PM components. Exposure metrics from these two approaches were used in asthma ED visit prevalence and time series analyses to investigate seasonal-averaged short- and long-term impacts of both motor vehicles and industry emissions. Increased pediatric asthma ED visits were found for LUR coarse PM and NO2 estimates, which were primarily contributed by motor vehicles. Consistent, statistically significant associations with pediatric asthma visits were observed, with short-term exposures to components of fine and coarse iron PM associated with steel production. Our study is the first to combine spatial and time series analysis of ED visits for asthma using the same periods and shows that PM related to motor vehicle emissions and iron/steel production are associated with increased pediatric asthma visits.

Effect-directed identification of novel aryl hydrocarbon receptor-active aromatic compounds in coastal sediments collected from a highly industrialized area

In this study, we identified major aryl hydrocarbon receptor (AhR) agonists in the sediments from Yeongil Bay (n = 6) using effect-directed analysis. Using the H4IIE-luc bioassays, great AhR-mediated potencies were found in aromatic fractions (F2) of sediment organic extracts from silica gel column chromatography and sub-fractions (F2.6–F2.8) from reverse phase-HPLC. Full-scan mass spectrometric analysis using GC-QTOFMS was conducted to identify novel AhR agonists in highly potent fractions, such as F2.6–F2.8 of S1 (Gumu Creek). Selection criteria for AhR-active compounds consisted of three steps, including matching factor of NIST library (≥70), aromatic structures, and the number of aromatic rings (≥4). Fifty-nine compounds were selected as tentative AhR agonist candidates, with the AhR-mediated activity being assessed for six compounds for which standard materials were available commercially. Of these compounds, 20-methylcholanthrene, 7-methylbenz[a]anthracene, 10-methylbenz[a]pyrene, and 7,12-dimethylbenz[a]anthracene exhibited significant AhR-mediated potency. Relative potency values of these compounds were determined relative to benzo[a]pyrene to be 3.2, 1.4, 1.2, and 0.2, respectively. EPA positive matrix factorization modeling indicated that the sedimentary AhR-active aromatic compounds primarily originated from coal combustion and vehicle emissions. Potency balance analysis indicated that four novel AhR agonists explained 0.007% to 1.7% of bioassay-derived AhR-mediated potencies in samples.

Wildfires and wood stoves: Woodsmoke toxicity and chemical characterization study in the north-western United States

PM2.5 is the most monitored air pollutant for which EPA has set national ambient air quality standards (NAAQS). As such, it is the pollutant on which the Air Quality Index (AQI) is most often based. PM2.5 and PM10 are the only criteria pollutant whose composition, and therefore toxicity varies. The PM2.5 AQI does not account for chemical composition. In areas where woodsmoke is the dominant PM2.5 source, coemitted hazardous air pollutants (HAPs) could impact community health differently than indicated by PM2.5 based AQI. PM2.5, carbon monoxide (CO), and particle light absorption were measured continuously from August 2018 through May 2019 in a suburban community where woodsmoke contributed the majority of ambient PM2.5. Air samples were also collected at least every 6 days at the site and analyzed for a suite of hazardous air pollutants (HAPs). EPA's positive matrix factorization (PMF) model 5.0 was used to identify the primary source contributors to local air pollution and apportion what fraction of each pollutant was from woodsmoke. Both measurements and the PMF model result showed significant chemical distinction between wood stove and wildfire smoke. Wildfire smoke contained significantly less CO and organic compounds than wood stove smoke relative to measured PM2.5. Comparing select HAPs to ambient CO concentration indicated atmospheric oxidation depleted PAHs in the wildfire smoke, although the relative amount of UV absorbing organic carbon did not change. HAPs most strongly associated with wood combustion were assessed relative to their individual cancerous and non-cancerous screening levels, as well as the PM2.5 and CO NAAQS.

Investigating of primary components and source apportionment of persistent organic pollutants of indoor dust

A number of studies have pointed out the occurrence of persistent organic pollutants (POPs) in indoor dust from different commercial and residential settings, but limited studies exist on the apportionment of their potential sources. This study aims to determine the levels and apportion of possible sources of 14 polybrominated diphenyl ethers (PBDEs) and also 16 polycyclic aromatic hydrocarbons (PAHs) which are the most abundant POPs found in indoor dusts. A total of 90 indoor dust samples were collected from the homes located in different districts of Kocaeli, Turkey, between February and April, 2016. The total concentrations of PBDEs (Σ14PBDEs) were measured in the range of 11.74 ng g−1 and 2484 ng g−1 with a median value of 207.4 ng g−1, while the concentration of Σ16PAHs was between 21.6 and 11274 ng g−1, and the median value was 927.6 ng g−1. Possible sources of PAHs and PBDEs indoors were extracted with the EPA Positive Matrix Factorization 5 model and identified with the information gathered from both the applied questionnaires and literature. Coal/biomass combustion was found to be highest contributor to measured PAHs while the emission of commercial penta-BDE was the main source of PBDEs indoor dust in Kocaeli. The findings from this study obviously indicate the mitigation of these main sources can be effective in improving the indoor air quality.

Application of Positive Matrix Factorization Receptor Model for Source Identification of PM10 in the City of Sofia, Bulgaria

The Positive Matrix Factorization (PMF) receptor model is used for identification of source contributions to PM10 sampled during the period January 2019–January 2020 in Sofia. More than 200 filters were analyzed by X-Ray Fluorescence (XRF), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and Ion chromatography for chemical elements and soluble ions. Seasonal patterns of PM10 mass and elements’ concentration are observed with minimum in the summer months and maximum in the cold period. The results from source apportionment (SAP) study showed that the resuspension factor is the main contributor to the total PM10 mass (25%), followed by Biomass burning (BB) (23%), Mixed SO42− (19%), Sec (16%), Traffic (TR) (9%), Industry (IND) (4%), Nitrate rich (4%), and Fuel oil burning (FUEL) (0.4%) in Sofia. There are some similarities in relative contribution of the main factors compared to the years 2012–2013. The differences are in identification of the new factor described as mixed sulphate as well as the decrease of the FUEL factor. The results of comparing SAP with EPA PMF 5.0 and chemical transport models (CTM), given by Copernicus Atmosphere Monitoring Service, are presented and discussed for the first time for Bulgaria.

Assessment of particulate toxic metals at an Environmental Justice community

Abstract Exposure to particulate matter containing toxic metal species can have negative health implications. The community of Eden Park, Delaware, is surrounded by numerous industrial facilities, the Port of Wilmington, and a highly trafficked freeway, all of which can be sources of toxic metals. The U.S. Environmental Protection Agency's EJ Screen ranks the community in the 88th percentile nationally for the Environmental Justice (EJ) index of cancer risk and the 90th percentile for the EJ index of PM2.5 exposure. To understand the ambient concentrations and sources of toxic metals and particulate matter, we collected hourly PM10 metals measurements using an Xact 625i at a temporary monitoring site in the community. Additionally, PM2.5, wind speed and direction, NOx, SO2, and black carbon were monitored. Conditional bivariate probability function maps and time variation maps were constructed using the OpenAir statistical analysis package, and EPA Positive Matrix Factorization was used to determine the sources of the measured metals. We found that transient concentration events, where arsenic and lead concentrations were more than an order of magnitude larger than the study average, occurred intermittently, but concentrations were generally below health benchmarks. Chromium and nickel had common sources, including a metal plating facility and a chemical additive facility, and unidentified sources were responsible for spikes in chromium levels up to 17 times above average. With Positive Matrix Factorization, three sources were identified: soil/dust, concrete manufacturing dust, and brake wear. During periods of moderate air quality (PM10 = 55–154 μg/m3) concrete dust contributes 32% to PM10 concentrations, while soil dust contributes 12%. Overall, the local industry and intermodal traffic emissions are large contributors to the ambient air pollution in the community, though transient high-concentration events from other sources are also present.

PM2.5 source apportionment during severe haze episodes in a Chinese megacity based on a 5-month period by using hourly species measurements: Explore how to better conduct PMF during haze episodes

To understand rapid physicochemical changes and to explore how to better conduct PMF for source apportionment during haze episodes, EPA Positive Matrix Factorization (PMF) 5.0, including an assessment of uncertainties, was performed based on hourly measurements of PM2.5-bound species during a heating period in 2017–2018 in the Chinese megacity of Tianjin. Five haze episodes were the focus of this study. PMF was conducted using two modes (whole-based mode for the entire sampling period and episode-based mode for each episode), and some indicators, including bootstrap (BS), displacement of factor elements (DISP), E/M (ratios of estimated and measured concentrations), were then used to evaluate PMF performance. The contributions of secondary ions and secondary organic carbon (SOC) for episodes I and III were greater than 50% (which were defined as SPE: episodes strongly influenced by secondary particles). The coal combustion contribution in episode II (defined as coal combustion episode: CCE) was greater than in the other episodes, and fireworks burning were extracted in episode IV (defined as fireworks burning episode: FBE). The PMF solutions were poor for the regional transport episode (RTE), during which episode the species showed weak variations (low coefficients of variation) and similar patterns (high correlations). During SPE, E/M were 0.71–1.00 for episode-based mode and 0.65–1.28 for whole-based mode, indicating good performance for both modes; while during CCE and FBE, E/M were 0.53–0.99 for episode-based mode and 0.10–1.06 for whole-based mode, showing that estimation of some important markers were poor for the whole-based mode. Sensitivity tests were then conducted to systematically investigate the influence of heavy-pollution types on PMF and showed that PMF was insensitive to contribution variations but was strongly sensitive to variations in source profiles. Overall, episode-based mode of PMF is better for episodes with strong variations of primary sources (CCE and FBE), whereas whole-based mode can be used for SPE and RTE. This work will help us to accurately assess hourly source variations.

The impact of household air cleaners on the oxidative potential of PM2.5 and the role of metals and sources associated with indoor and outdoor exposure

The health effects associated with human exposure to airborne fine particulate matter (PM2.5) have been linked to the ability of PM2.5 to facilitate the production of excess cellular reactive oxygen species (oxidative potential). Concern about the adverse human health impacts of PM2.5 has led to the increased use of indoor air cleaners to improve indoor air quality, which can be an important environment for PM2.5 exposure. However, the degree to which the oxidative potential of indoor and personal PM2.5 can be influenced by an indoor air cleaner remains unclear. In this study we enrolled 43 children with physician diagnosed asthma in suburban Shanghai, China and collected two paired-sets of 48-h indoor, outdoor, and personal PM2.5 exposure samples. One set of samples was collected under “real filtration” during which a functioning air cleaner was installed in the child's bedroom, and the other (“false filtration”) with an air cleaner without internal filters. The PM2.5 samples were characterized by inductively coupled plasma mass spectroscopy for elements, and by an alveolar macrophage assay for oxidative potential. The sources of metals contributing to our samples were determined by the EPA Positive Matrix Factorization model. The oxidative potential was lower under real filtration compared to sham for indoor (median real/sham ratio: 0.260) and personal exposure (0.813) samples. Additionally, the sources of elements in PM2.5 that were reduced indoors and personal exposure samples by the air cleaner (e.g. regional aerosol and roadway emissions) were found by univariate multiple regression models to be among those contributing to the oxidative potential of the samples. An IQR increase in the regional aerosol and roadway emissions sources was associated with a 107% (95% CI: 80.1–138%) and 38.1% (17.6–62.1%) increase in measured oxidative potential respectively. Our results indicate that indoor air cleaners can reduce the oxidative potential of indoor and personal exposure to PM2.5, which may lead to improved human health.

Source apportionment analysis of winter 2016 Neil Armstrong Academy data (West Valley City, Utah)

A significant need exists to better characterize air pollution and its sources. This especially pertains to fine particulate matter (PM2.5). PM2.5 is chemically complex and its sources of emission and secondary production are highly variable. PM2.5 complexity is largely due to the organic fraction, which ranges from 10 to 90% of its total mass. However, the organic compounds in PM2.5 have generally not been monitored in the field due to limitations in available sampling techniques. A source apportionment study was conducted at the Neil Armstrong Academy campus, 5194 Highbury Pkwy, West Valley City, UT 84120, during January and February 2016. Among the objectives of the study was to identify winter-time sources of fine particulate material in West Valley City, Utah. Fine particulate mass and components, particulate organic marker and related gas phase species were all measured on an hourly average basis. The following hourly averaged data were used in the Positive Matrix Factorization (PMF) analysis, fine particulate mass, cations and anions, carbonaceous material, organic marker compounds, gas phase species including NOX, NO2 and CO and mass spectrometric measurements of gas phase methanol and C8 aromatics.A total of 557 hourly averaged data sets with 23 components were available for an EPA Positive Matrix Factorization (v5.0) analysis of the data set. The data were best described by a solution with 7 factors. Two of the factors were associated with emissions from mobile sources, diesel and automotive. One factor was associated with wood smoke emissions. Three factors were associated with the formation of secondary fine particulate material. The larger of the three was associated with the production of secondary nitrate and accounted for 70% of the PM2.5, while the other two were associated with secondary formation of particulate organic material from wood smoke associated gas phase organic compounds but accounted for only 3% of the total PM2.5 mass. The final factor was associated with fine particulate sulfate and SO2. Meteorological back-trajectory data clearly indicted the source was associated with emissions from a copper smelter to the west of the sampling site.

Distribution, Sources, and Ecological Risk Assessment of Polycyclic Aromatic Hydrocarbons in the Surface Waters of the Yinchuan Wetlands

In order to explore the composition, sources and ecological risks of polycyclic aromatic hydrocarbons (PAHs) in water from Yinchuan wetlands, water samples were collected in the dry season and plentiful season from 15 wetlands. Sixteen species of PAHs were analyzed by gas-mass spectrometry, and source identification of PAHs was investigated by PCA and EPA positive matrix factorization 5.0. Ecological risk was assessed using the risk entropy method based on the neglected concentrations (NCs) and the maximum permissible concentrations (MPCs). The results showed that:① in the dry season, eight kinds of PAHs were detected, the concentrations of which ranged from 1455.38 ng·L-1to 2538.84 ng·L-1. In the plentiful season, 12 kinds of PAHs were detected, the concentrations of which ranged from 818.69 ng·L-1 to 1582.14 ng·L-1. The concentrations of PAHs in the dry season in Yinchuan were higher than that during the plentiful season. Compared with other domestic and overseas surface waters, PAH pollution was high; ② in the dry season, PAHs were mainly composed of 3-5 rings, and 2-3 and 4-6rings accounted for 35.6%-59.2% and 40.8%-59.7%, respectively. In the plentiful season, PAHs were mainly composed of 4-5 rings, and 2-3 and 4-6rings accounted for 10.2%-45.07% and 54.92%-89.76%, respectively; ③ the source analysis showed that in both the dry season and in the plentiful season, the main source were combustion and automobile emissions; ④ the ecological risk assessment indicated that the RQMPCs of BaA, BbF, InP, DBA, and BghiP during both the dry and plentiful seasons, and RQMPCs of Phe during the dry season, were higher than 1.0, indicating that attention needs to be paid to pollution levels. The RQNCs of Nap, Ace, Fla, Pyr, and BaP during the plentiful season and the RQNCs of Nap during the dry season were higher than 1.0, indicating the pollution risk was moderate and control and prevision of pollution from PAHs are required in the region.