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Select volatile organic compounds (VOCs) were measured in the vicinity of chemical facilities and other operations in the Rubbertown industrial area of Louisville, Kentucky (USA) using modified EPA Methods 325A/B passive sampler tubes. Two-week, time-integrated passive samplers were deployed at ten sites which were aggregated into three site groups of varying distances from the Rubbertown area facilities. In comparison to canister data from 2001 to 2005, two of the sites suggested generally lower current VOC levels. Good precision was obtained from the duplicate tubes (≤ 12%) for benzene, toluene, ethylbenzene, and xylene isomers (BTEX), styrene, 1,3-butadiene, perchloroethylene, and other trace VOCs. BTEX, styrene, and 1,3-butadiene concentrations were statistically significantly higher at two site groups near Rubbertown sources than the site group farther away. As found in a similar study in South Philadelphia, BTEX concentrations were also lower for sites farther from a source, though the decline was less pronounced on a percentage basis in Rubbertown. These results suggest that EPA Methods 325A/B can be useful to assess VOC gradients for emissions from chemical facilities besides fenceline benzene levels from refineries.

The United States Environmental Protection Agency held an international two-day workshop in June 2018 to deliberate possible performance targets for non-regulatory fine particulate matter (PM2.5) and ozone (O3) air sensors. The need for a workshop arose from the lack of any market-wide manufacturer requirement for documented sensor performance evaluations, the lack of any independent third party or government-based sensor performance certification program, and uncertainty among all users as to the general usability of air sensor data. A multi-sector subject matter expert panel was assembled to facilitate an open discussion on these issues with multiple stakeholders. This summary provides an overview of the workshop purpose, key findings from the deliberations, and considerations for future actions specific to sensors. Important findings concerning PM2.5 and O3 sensors included the lack of consistent performance indicators and statistical metrics as well as highly variable data quality requirements depending on the intended use. While the workshop did not attempt to yield consensus on any topic, a key message was that a number of possible future actions would be beneficial to all stakeholders regarding sensor technologies. These included documentation of best practices, sharing quality assurance results along with sensor data, and the development of a common performance target lexicon, performance targets, and test protocols. Keywords: Low-cost air quality sensors, Performance targets, PM2.5, Ozone

The Cleveland airshed comprises a complex mixture of industrial source emissions that contribute to periods of non-attainment for fine particulate matter (PM2.5) and are associated with increased adverse health outcomes in the exposed population. Specific PM sources responsible for health effects however are not fully understood. Size-fractionated PM (coarse, fine, and ultrafine) samples were collected using a ChemVol sampler at an urban site (G.T. Craig (GTC)) and rural site (Chippewa Lake (CLM)) from July 2009 to June 2010, and then chemically analyzed. The resulting speciated PM data were apportioned by EPA positive matrix factorization to identify emission sources for each size fraction and location. For comparisons with the ChemVol results, PM samples were also collected with sequential dichotomous and passive samplers, and evaluated for source contributions to each sampling site. The ChemVol results showed that annual average concentrations of PM, elemental carbon, and inorganic elements in the coarse fraction at GTC were ∼2, ∼7, and ∼3 times higher than those at CLM, respectively, while the smaller size fractions at both sites showed similar annual average concentrations. Seasonal variations of secondary aerosols (e.g., high NO3− level in winter and high SO42− level in summer) were observed at both sites. Source apportionment results demonstrated that the PM samples at GTC and CLM were enriched with local industrial sources (e.g., steel plant and coal-fired power plant) but their contributions were influenced by meteorological conditions and the emission source's operation conditions. Taken together the year-long PM collection and data analysis provides valuable insights into the characteristics and sources of PM impacting the Cleveland airshed in both the urban center and the rural upwind background locations. These data will be used to classify the PM samples for toxicology studies to determine which PM sources, species, and size fractions are of greatest health concern.

Understanding the drivers for high ozone (O(3)) and atmospheric particulate matter (PM) concentrations is a pressing issue in urban air quality, as this understanding informs decisions for control and mitigation of these key pollutants. The Houston, TX metropolitan area is an ideal location for studying the intersection between O(3) and atmospheric secondary organic carbon (SOC) production due to the diversity of source types (urban, industrial, and biogenic) and the on- and off-shore cycling of air masses over Galveston Bay, TX. Detailed characterization of filter-based samples collected during Deriving Information on Surface Conditions from Column and VERtically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Houston field experiment in September 2013 were used to investigate sources and composition of organic carbon (OC) and potential relationships between daily maximum 8 h average O(3) and PM. The current study employed a novel combination of chemical mass balance modeling defining primary (i.e. POC) versus secondary (i.e. SOC) organic carbon and radiocarbon ((14)C) for apportionment of contemporary and fossil carbon. The apportioned sources include contemporary POC (biomass burning [BB], vegetative detritus), fossil POC (motor vehicle exhaust), biogenic SOC and fossil SOC. The filter-based results were then compared with real-time measurements by aerosol mass spectrometry. With these methods, a consistent urban background of contemporary carbon and motor vehicle exhaust was observed in the Houston metropolitan area. Real-time and filter-based characterization both showed that carbonaceous aerosols in Houston was highly impacted by SOC or oxidized OC, with much higher contributions from biogenic than fossil sources. However, fossil SOC concentration and fractional contribution had a stronger correlation with daily maximum 8 h average O(3), peaking during high PM and O(3) events. The results indicate that point source emissions processed by on- and off-shore wind cycles likely contribute to peak events for both PM and O(3) in the greater Houston metropolitan area.

A variety of single pollutant and multipollutant metrics can be used to represent exposure to traffic pollutant mixtures and evaluate their health effects. Integrated mobile source indicators (IMSIs) that combine air quality concentration and emissions data have recently been developed and evaluated using data from Atlanta, Georgia. IMSIs were found to track trends in traffic-related pollutants and have similar or stronger associations with health outcomes. In the current work, we apply IMSIs for gasoline, diesel and total (gasoline + diesel) vehicles to two other cities (Denver, Colorado and Houston, Texas) with different emissions profiles as well as to a different dataset from Atlanta. We compare spatial and temporal variability of IMSIs to single-pollutant indicators (carbon monoxide (CO), nitrogen oxides (NOx) and elemental carbon (EC)) and multipollutant source apportionment factors produced by Positive Matrix Factorization (PMF). Across cities, PMF-derived and IMSI gasoline metrics were most strongly correlated with CO (r = 0.31–0.98), while multipollutant diesel metrics were most strongly correlated with EC (r = 0.80–0.98). NOx correlations with PMF factors varied across cities (r = 0.29–0.67), while correlations with IMSIs were relatively consistent (r = 0.61–0.94). In general, single-pollutant metrics were more correlated with IMSIs (r = 0.58–0.98) than with PMF-derived factors (r = 0.07–0.99). A spatial analysis indicated that IMSIs were more strongly correlated (r &gt, 0.7) between two sites in each city than single pollutant and PMF factors. These findings provide confidence that IMSIs provide a transferable, simple approach to estimate mobile source air pollution in cities with differing topography and source profiles using readily available data.

Industrial facilities and other sources can emit air pollutants from fugitive leaks, process malfunctions and area sources that can be difficult to understand and to manage. Next generation emissions measurement (NGEM) approaches executed near facilities are enabling new ways to assess these sources and their impacts to nearby populations. This paper describes complementary uses of emerging NGEM systems in a Louisville, KY industrial district (Rubbertown), focusing on an important area air toxic, 1,3-butadiene. Over a one-year deployment starting in September 2017, two-week average passive samplers (PSs) at 11 sites showed both geospatial and temporal trends. At 0.24 ppbv annual average 1,3-butadiene concentration, a group of PSs located near facility fence lines was elevated compared to a PS group located in the community and upwind from facilities (0.07 ppbv average). Two elevated PS periods capturing emission events were examined using time-resolved NGEM approaches as case studies. In one event a 1.18 ppbv PS reading was found to be relatively localized and was caused by a multiday emission from a yet to be identified, non-facility source. In the other event, the airshed was more broadly impacted with PS concentrations ranging from 0.71 ppbv for the near-facility group to 0.46 ppbv for the community group. This case was likely influenced by a known emission event at an industrial facility. For both case studies, air pollutant and wind data from prototype NGEM systems were combined with source location models to inform the emission events. This research illustrates the power of applying NGEM approaches to improve both the understanding of emissions near sources and knowledge of impacts to near-source communities.

Emissions from transportation sources can impact local air quality and contribute to adverse health effects. The Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS), conducted over a 1-year period, researched emissions source characterization in the Argentine, Turner, and Armourdale, Kansas (KS) neighborhoods and the broader southeast Kansas City, KS area. This area is characterized as a near-source environment with impacts from large railyard operations, major roadways, and commercial and industrial facilities. The spatial and meteorological effects of particulate matter less than 2.5 &micro, m (PM2.5), and black carbon (BC) pollutants on potential population exposures were evaluated at multiple sites using a combination of regulatory grade methods and instrumentation, low-cost sensors, citizen science, and mobile monitoring. The initial analysis of a subset of these data showed that mean reference grade PM2.5 concentrations (gravimetric) across all sites ranged from 7.92 to 9.34 &micro, g/m3. Mean PM2.5 concentrations from low-cost sensors ranged from 3.30 to 5.94 &micro, g/m3 (raw, uncorrected data). Pollution wind rose plots suggest that the sites are impacted by higher PM2.5 and BC concentrations when the winds originate near known source locations. Initial data analysis indicated that the observed PM2.5 and BC concentrations are driven by multiple air pollutant sources and meteorological effects. The KC-TRAQS overview and preliminary data analysis presented will provide a framework for forthcoming papers that will further characterize emission source attributions and estimate near-source exposures. This information will ultimately inform and clarify the extent and impact of air pollutants in the Kansas City area.

This study reports on the performance of electrochemical-based low-cost sensors and their use in a community application. CairClip sensors were collocated with federal reference and equivalent methods and operated in a network of sites by citizen scientists (community members) in Houston, Texas and Denver, Colorado, under the umbrella of the NASA-led DISCOVER-AQ Earth Venture Mission. Measurements were focused on ozone (O3) and nitrogen dioxide (NO2). The performance evaluation showed that the CairClip O3/NO2 sensor provided a consistent measurement response to that of reference monitors (r2 = 0.79 in Houston; r2 = 0.72 in Denver) whereas the CairClip NO2 sensor measurements showed no agreement to reference measurements. The CairClip O3/NO2 sensor data from the citizen science sites compared favorably to measurements at nearby reference monitoring sites. This study provides important information on data quality from low-cost sensor technologies and is one of few studies that reports sensor data collected directly by citizen scientists.

Intra-urban variability in air pollution source impacts was investigated using receptor modeling of daily speciated PM2.5 measurements collected at residential outdoor locations across Detroit, MI (Wayne County) as part of the Detroit Exposure and Aerosol Research Study (DEARS) during summer and winter from 2004 to 2006. Six areas were selected for the residential monitoring in the DEARS to capture impacts from different sources including local industry, motor vehicles, and upwind regional sources. PM2.5 measurements were also collected at the Allen Park, MI Chemical Speciation Network (CSN) site for comparison with the residential outdoor sites. Sources impacting PM2.5 were quantified using the EPA Chemical Mass Balance Model (CMB 8.2). Published source profiles were used as input to CMB along with a mixed industrial profile and a steel manufacturing profile obtained by applying the EPA Positive Matrix Factorization Model (PMF 4.0) to CSN data from a Midwestern U.S. site with industrial sources similar to Detroit. Major PM2.5 sources impacting the Allen Park and residential monitoring areas during DEARS included motor vehicles (24–36% by mass), secondary sulfate/coal combustion (17–35%), secondary nitrate (16–37%) and organic matter (17–21%). Road dust, steel manufacturing, and mixed industrial sources contributed less than 11% by mass. CMB source contribution estimates for Allen Park during the DEARS generally compared well to CMB estimates from the collocated year-long CSN measurements using the same source profiles. CMB source contributions during DEARS showed similar contributions across the residential monitoring areas for secondary sulfate/coal combustion and secondary nitrate consistent with regional impacts for these sources. Contributions from motor vehicles, steel manufacturing, and mixed industrial sources varied across the DEARS monitoring areas, indicating impacts from local sources within the Detroit airshed that may not be well characterized by the Allen Park monitoring location.

This analysis investigated different possible strategies for source apportionment of airborne fine particulate matter (PM2.5) using data collected as part of the Pittsburgh Air Quality Study (PAQS). More specifically, we apportioned the organic fraction of the winter and summer season PM2.5 using two source–receptor models – the EPA Chemical Mass Balance 8.2 (CMB) and EPA Positive Matrix Factorization 1.1 (PMF) models – and tested several case scenarios with each model by varying either the chemical species or source profiles used as model input. Moreover, we added the constraint of selecting only individual molecular marker species with concentrations above their minimum quantitative limits. Model results suggest that the molecular marker and source profile selection can strongly affect the model, as reflected in the source contribution estimates determined by both CMB and PMF. Biomass burning and mobile emissions sources were identified by both models as being major source contributors in Pittsburgh. A third source was consistent with a meat cooking profile but was more likely a combination of cooking and secondary organic aerosol. As expected, the relative proportion of each source's contribution depended on both the season and on whether the CMB or PMF model was applied. Selecting fewer species in CMB resulted in less mass being apportioned, and an unrealistically large wood burning contribution estimate. Swapping a wildfire profile for one of the two wood burning profiles also resulted in less mass being apportioned in the winter. The results suggest that CMB can distinguish between fireplace burning and wildfire contributions when appropriate species are included. The gasoline/diesel split also varied by up to an order of magnitude, depending on which model was applied and which species were fit.

Concentrations of PM2.5, elemental carbon (EC), organic carbon (OC), 30 organic source markers (13 alkanes, 14 polycyclic aromatic hydrocarbons and ketones, and 3 hopanes) and 19 inorganic source markers are reported from residential indoor, residential outdoor, and ambient microenvironments from a nine home pilot study conducted in Tampa, Florida. Mean daily PM2.5 concentrations were 9.3 μg m−3 for residential indoor, 11.3 μg m−3 for residential outdoor, and 12.7 μg m−3 for ambient microenvironments. The EPA Chemical Mass Balance Model (CMB8.2) was used for source apportionment of PM2.5 residential outdoor samples. Four main sources of PM2.5 were identified: sulfate (55±6%) (average outdoor PM2.5±S.D.), gasoline-powered motor vehicles (32±4%), diesel-powered vehicles (8±2%), and road dust (5±1%).

Size-fractionated particulate matter (PM) samples were collected from six U.S. cities and chemically analyzed as part of the Multiple Air Pollutant Study. Particles were administered to cultured lung cells and the production of three different proinflammatory markers was measured to explore the association between the health effect markers and PM. Ultrafine, fine, and coarse PM samples were collected between December 2003 and May 2004 over a 4-wk period in each city. Filters were pooled for each city and the PM samples were extracted then analyzed for trace metals, ions, and elemental carbon. Particle extracts were applied to cultured human primary airway epithelial cells, and the secreted levels of interleukin-8 (IL-8), heme oxygenase-1, and cyclooxygenase-2 were measured 1 and 24 h following exposure. Fine PM sources were quantified by the chemical mass balance (CMB) model. The relationship between toxicological measures, PM sources, and individual species were evaluated using linear regression. Ultrafine and fine PM mass were associated with increases in IL-8 (r(2) = .80 for ultrafine and r(2) = .52 for fine). Sources of fine PM and their relative contributions varied across the sampling sites and a strong linear association was observed between IL-8 and secondary sulfate from coal combustion (r(2) = .79). Ultrafine vanadium, lead, copper, and sulfate were also associated with increases in IL-8. Increases in inflammatory markers were not observed for coarse PM mass and source markers. These findings suggest that certain PM size fractions and sources are associated with markers of lung injury or inflammation.

Practical and economical plans to reduce air pollution require models that relate the sources of airborne pollution to concentrations at sampling sites. In receptor models, samples of airborne pollution are subjected to extensive chemical analysis. Contributing sources leave chemical fingerprints in the sample. The amount of pollution coming from each source can be estimated if the chemical fingerprints of the sources are known. Thus, receptor models do not require any knowledge of emission rates or meteorological observations that are very important to atmospheric dispersion models. However, receptor models do rely on the observed concentrations of chemical species in the atmosphere. These species must be conserved during transport between the source and the receptor. Chemical elements are good candidates for receptor modeling since these are often immutable under ordinary conditions and relatively easily measured. Keywords: receptor models; hybrid model; source apportionment; sector apportionment

Multi-city population-based epidemiological studies have observed significant heterogeneity in both the magnitude and direction of city-specific risk estimates, but tended to focus on regional differences in PM2.5 mortality risk estimates. Interpreting differences in risk estimates is complicated by city-to-city heterogeneity observed within regions due to city-to-city variations in the PM2.5 composition and the concentration of gaseous pollutants. We evaluate whether variations in PM2.5 composition and gaseous pollutant concentrations have a role in explaining the heterogeneity in PM2.5 mortality risk estimates observed in 27 US cities from 1997 to 2002. Within each region, we select the two cities with the largest and smallest mortality risk estimate. We compare for each region the within- and between-city concentrations and correlations of PM2.5 constituents and gaseous pollutants. We also attempt to identify source factors through principal component analysis (PCA) for each city. The results of this analysis indicate that identifying a PM constituent(s) that explains the differences in the PM2.5 mortality risk estimates is not straightforward. The difference in risk estimates between cities in the same region may be attributed to a group of pollutants, possibly those related to local sources such as traffic.

Atmospheric dispersion of particles from mine waste is potentially an important route of human exposure to metals in communities close to active and abandoned mining areas. This study assessed sources of mass and metal concentrations in two size fractions of respirable particles using positive matrix factorization (U.S. Environmental Protection Agency [EPA] PMF 3.0). Weekly integrated samples of particulate matter (PM) 10 microm in aerodynamic diameter or less (PM10) and fine PM (PM2.5, or PM2.5 microm in aerodynamic diameter) were collected at three monitoring sites, varying distances (0.5-20 km) from mine waste piles, for 58 consecutive weeks in a former lead (Pb) and zinc (Zn) mining region. Mean mass concentrations varied significantly across sites for coarse PM (PM10-PM2.5) but not PM2.5 particles. Concentrations of Pb and Zn significantly decreased with increasing distance from the mine waste piles in PM10-PM2.5 (P0.0001) and PM2.5 (P0.0005) fractions. Source apportionment analyses deduced five sources contributing to PM2.5 (mobile source combustion, secondary sulfates, mine waste, crustal/soil, and a source rich in calcium [Ca]) and three sources for the coarse fraction (mine waste, crustal/soil, and a Ca-rich source). In the PM2.5 fraction, mine waste contributed 1-6% of the overall mass, 40% of Pb, and 63% of Zn. Mine waste impacts were more apparent in the PM10-PM2.5 fraction and contributed 4-39% of total mass, 88% of Pb, and 97% of Zn. Percent contribution of mine waste varied significantly across sites (P0.0001) for both size fractions, with highest contributions in the site closest to the mine waste piles. Seasonality, wind direction, and concentrations of the Ca-rich source were also associated with levels of ambient aerosols from the mine waste source. Scanning electron microscopy results indicated that the PMF-identified mine waste source is mainly composed of Zn-Pb agglomerates on crustal particles in the PM10-PM2.5 fraction. In conclusion, the differential impacts of mine waste on respirable particles by size fraction and location should be considered in future exposure evaluations.

Associations are well established between particulate matter (PM) and increased human mortality and morbidity. Fine particulate matter (particle diameter < 2.5 μm) is most strongly linked to adverse health impacts. The toxicity of PM may depend on the PM source and composition which will vary by location. While a number of epidemiological studies have shown that certain PM sources are associated with specific health outcomes, the underlying mechanisms are still unclear. To investigate these mechanisms, continuous weekly PM2.5 samples were collected for four consecutive weeks (24 hours a day for seven days) in six cities across the U.S. as part of the Multiple Air Pollutant Study (MAPS).

The Detroit Exposure and Aerosol Research Study (DEARS) was designed to assess the impacts of local industrial and mobile sources on human exposures to air pollutants in and around Detroit, Michigan. Daily integrated measurements were made of personal exposure, and residential indoor and outdoor concentrations in six neighborhoods throughout the Detroit area. Concurrent data were collected for comparison at a central community ambient monitoring location and a regional background site. These data collected in DEARS can be used to evaluate local air quality and explore the application of air quality models to assess human exposure in an urban area.

Identifying the mechanisms by which air pollution causes human health effects is a daunting task. Airsheds around the world are composed of pollution mixtures made up of hundreds of chemical and biological components with an extensive array of physicochemical properties. Current in vivo approaches are limited to the identification of associations between pollutants and health but do not allow for the identification of precise biological mechanisms of effect or the component(s) responsible for the effect. High-throughput in vitro methods using relevant cell culture systems and microarray technology allow researchers to evaluate the mechanisms by which air pollutants affect human health. Our studies have used human airway epithelial cells primarily to test the toxicological effects of particles of different sizes and of various particle components from several cities across the United States. Chemical mass balance analysis is also being used to analyze these samples to establish links between physicochemical properties of particulate matter (PM) and potential sources. The ultimate goal of this line of research is to link the mechanistic data to the PM source data in order to gain an understanding about how the components and sources of PM affect human health.

Hundreds of epidemiological studies have shown that exposure to ambient particulate matter (PM) is associated with dose-dependent increases in morbidity and mortality. While early reports focused on PM less than 10 microm (PM10), numerous studies have since shown that the effects can occur with PM stratified into ultrafine (UF), fine (FI), and coarse (CO) size modes despite the fact that these materials differ significantly in both evolution and chemistry. Furthermore the chemical makeup of these different size fractions can vary tremendously depending on location, meteorology, and source profile. For this reason, high-volume three-stage particle impactors with the capacity to collect UF, FI, and CO particles were deployed to four different locations in the United States (Seattle, WA; Salt Lake City, UT; Sterling Forest and South Bronx, NY), and weekly samples were collected for 1 mo in each place. The particles were extracted, assayed for a standardized battery of chemical components, and instilled into mouse lungs (female BALB/c) at doses of 25 and 100 microg. Eighteen hours later animals were euthanized and parameters of injury and inflammation were monitored in the bronchoalveolar lavage fluid and plasma. Of the four locations, the South Bronx coarse fraction was the most potent sample in both pulmonary and systemic biomarkers, with a strong increase in lung inflammatory cells as well as elevated levels of creatine kinase in the plasma. These effects did not correlate with lipopolysaccharide (LPS) or total zinc or sulfate content, but were associated with total iron. Receptor source modeling on the PM2.5 samples showed that the South Bronx sample was heavily influenced by emissions from coal fired power plants (31%) and mobile sources (22%). Further studies will assess how source profiles correlate with the observed effects for all locations and size fractions.