Urban Organic Aerosol Exposure: Spatial Variations in Composition and Source Impacts

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Development and performance evaluation of online monitors for near real-time measurement of total and water-soluble organic carbon in fine and coarse ambient PM

In this study, we developed two online monitors for total organic carbon (TOC) and water-soluble organic carbon (WSOC) measurements in fine (dp < 2.5μm) and coarse (2.5μm < dp < 10μm) particulate matter (PM), respectively. Their performance has been evaluated in laboratory and field tests to demonstrate the feasibility of using these monitors to measure near real-time concentrations, with consideration of their potential for being employed in long-term measurements. The fine PM collection setup was equipped with a versatile aerosol concentration enrichment system (VACES) connected to an aerosol-into-liquid-sampler (AILS), whereas two virtual impactors (VIs) in tandem with a modified BioSampler were used to collect coarse PM. These particle collection setups were in tandem with a Sievers M9 TOC analyzer to read TOC and WSOC concentrations in aqueous samples hourly. The average hourly TOC concentration measured by our developed monitors in fine and coarse PM were 5.17 ± 2.41 and 0.92 ± 0.29 μg/m3, respectively. In addition, our TOC readings showed good agreement and were comparable with those quantified using Sunset Lab EC/OC analyzer operating in parallel as a reference. Furthermore, we conducted field tests to produce diurnal profiles of fine PM-bound WSOC, which can show the effects of ambient temperature on maximum values in the nighttime chemistry of the winter, as well as on increased photochemical activities in afternoon peaks during the summer. According to our experimental campaign, WSOC mean values during the study period (3.07 μg/m3 for the winter and 2.7 μg/m3 for the summer) were in a comparable range with those of earlier studies in Los Angeles. Overall, our results corroborate the performance of our developed monitors in near real-time measurements of TOC and WSOC, which can be employed for future source apportionment studies in Los Angeles and other areas, aiding in understanding the health impacts of different pollution sources.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Air Pollutant Patterns and Human Health Risk following the East Palestine, Ohio, Train Derailment

On February 3, 2023, a train carrying numerous hazardous chemicals derailed in East Palestine, OH, spurring temporary evacuation of residents and a controlled burn of some of the hazardous cargo. Residents reported health symptoms, including headaches and respiratory, skin, and eye irritation. Initial data from U.S. Environmental Protection Agency (EPA) stationary air monitors indicated levels of potential concern for air toxics based on hazard quotient calculations. To provide complementary data, we conducted mobile air quality sampling on February 20 and 21 using proton transfer reaction-mass spectrometry. Measurements were taken at 1 s intervals along routes designed to sample both close to and farther from the derailment. Mobile air monitoring indicated that average concentrations of benzene, toluene, xylenes, and vinyl chloride were below minimal risk levels for intermediate and chronic exposures, similar to EPA stationary monitoring data. Levels of acrolein were high relative to those of other volatile organic compounds, with spatial analyses showing levels in East Palestine up to 6 times higher than the local rural background. Nontargeted analyses identified levels of additional unique compounds above background levels, some displaying spatiotemporal patterns similar to that of acrolein and others exhibiting distinct hot spots. These initial findings warrant follow-up mobile air quality monitoring to characterize longitudinal exposure and risk levels.

Comparison and implications of the carbonaceous fractions under different environments in polluted central plains in China: Insight from the lockdown of COVID-19 outbreak

Before and during the COVID-19 outbreak in the heated winter season of 2019, the carbonaceous fractions including organic carbon (OC), elemental carbon (EC), OC1-4, and EC1-5 were investigated between normal (November 1, 2019, to January 24, 2020) and lockdown (January 25, to February 29, 2020) periods in polluted regions of northern Henan Province. In comparison to urban site, four rural sites showed higher concentrations of carbonaceous components, especially secondary OC (SOC); the concentration of SOC in rural sites was 1.5-3.4 times that in the urban site. During the lockdown period, SOC in urban site decreased slightly, while it increased significantly in rural sites. NO₂ has a significant effect on SOC generation, particularly in normal period when NO₂ concentrations were high. Nevertheless, NO₂ significantly decreased, and the elevated O₃ (increased by 103-138%) contributed considerably to the generation of SOC during lockdown. Relative humidity (RH) promoted SOC production when RH was below 60%, but SOC was negatively correlated or uncorrelated with RH when RH exceeded 60%. Additionally, RH has a more pronounced effect on SOC during lockdown. The contribution of gasoline vehicle emissions decreases significantly in both urban and rural sites (3-12%) due to the significant reduction of anthropogenic activities during lockdown, although the urban site remained with the biggest contributions (37%). These results provide innovative insights into the variations in carbonaceous aerosols and SOC generation during the unique time when anthropogenic sources were significantly reduced and illustrate the differences in pollution characteristics and sources of carbonaceous fractions in different environments.

Identifying Patterns and Sources of Fine and Ultrafine Particulate Matter in London Using Mobile Measurements of Lung-Deposited Surface Area

We performed more than a year of mobile, 1 Hz measurements of lung-deposited surface area (LDSA, the surface area of 20-400 nm diameter particles, deposited in alveolar regions of lungs) and optically assessed fine particulate matter (PM_2.5), black carbon (BC), and nitrogen dioxide (NO₂) in central London. We spatially correlated these pollutants to two urban emission sources: major roadways and restaurants. We show that optical PM_2.5 is an ineffective indicator of tailpipe emissions on major roadways, where we do observe statistically higher LDSA, BC, and NO₂. Additionally, we find pollutant hot spots in commercial neighborhoods with more restaurants. A low LDSA (15 μm² cm^-3) occurs in areas with fewer major roadways and restaurants, while the highest LDSA (25 μm² cm^-3) occurs in areas with more of both sources. By isolating areas that are higher in one source than the other, we demonstrate the comparable impacts of traffic and restaurants on LDSA. Ratios of hyperlocal enhancements (ΔLDSA:ΔBC and ΔLDSA:ΔNO₂) are higher in commercial neighborhoods than on major roadways, further demonstrating the influence of restaurant emissions on LDSA. We demonstrate the added value of using particle surface in identifying hyperlocal patterns of health-relevant PM components, especially in areas with strong vehicular emissions where the high LDSA does not translate to high PM_2.5.

Investigation of organic carbon profiles and sources of coarse PM in Los Angeles

Source apportionment analyses are essential tools to determine sources of ambient coarse particles (2.5 <d_p < 10 μm) and to disentangle their association and contribution from other pollutants, particularly PM_2.5 (<2.5 μm). A semi-continuous sampling campaign was conducted using two virtual impactors/concentrators to enhance coarse particulate matter concentrations coupled with an online thermal-optical EC/OC monitor to quantify coarse PM-bound organic carbon volatility fractions (OC₁-OC₄) in central Los Angeles during the winter, spring, and summer of 2021. The total OC and its volatility fraction concentrations, meteorological parameters (i.e., wind speeds and relative humidity), vehicle miles traveled (VMT), and gaseous source tracers (i.e., O₃ and NO₂) were used as inputs to positive matrix factorization (PMF) model. A 3-factor solution identified vehicular emissions (accounting for 46% in the cold phase and 26% in the warm phase of total coarse OC concentrations), secondary organic carbon (27% and 37%), and re-suspended dust (27% and 37%) as the primary organic carbon sources of coarse PM. The re-suspended dust factor showed a higher contribution of more volatile organic carbons (i.e., OC₁ up to 77%) due to their re-distribution on dust particles, whereas the SOA factor was the dominant contributor to less volatile organic aerosols (i.e., OC₄ up to 54%), which are the product of reactions at high relative humidity (RH). Our findings revealed that the total OC concentrations in the coarse size range were comparable with those of previous studies in the area, underscoring the challenges in curtailing coarse PM-bound OC sources and the necessity of developing effective emission control regulations on coarse PM. The results from the current study provide insights into the seasonal and temporal variation of total OC and its volatility fractions in Los Angeles.

Impact of COVID-19 lockdown on carbonaceous aerosols in a polluted city: Composition characterization, source apportionment, influence factors of secondary formation

Carbonaceous fractions throughout the normal period and lockdown period (LP) before and during COVID-19 outbreak were analyzed in a polluted city, Zhengzhou, China. During LP, fine particulate matters, elemental carbon (EC), and secondary organic aerosol (SOC) concentrations fell significantly (29%, 32% and 21%), whereas organic carbon (OC) only decreased by 4%. Furthermore, the mean OC/EC ratio increased (from 3.8 to 5.4) and the EC fractions declined dramatically, indicating a reduction in vehicle emission contribution. The fact that OC1-3, EC, and EC1 had good correlations suggested that OC1-3 emanated from primary emissions. OC4 was partly from secondary generation, and increased correlations of OC4 with OC1-3 during LP indicated a decrease in the share of SOC. SOC was more impacted by NO₂ throughout the research phase, thereby the concentrations were lower during LP when NO₂ levels were lower. SOC and relative humidity (RH) were found to be positively associated only when RH was below 80% and 60% during the normal period (NP) and LP, respectively. SOC, Coal combustion, gasoline vehicles, biomass burning, diesel vehicles were identified as major sources by the Positive Matrix Factorization (PMF) model. Contribution of SOC apportioned by PMF was 3.4 and 3.0 μg/m³, comparable to the calculated findings (3.8 and 3.0 μg/m³) during the two periods. During LP, contributions from gasoline vehicles dropped the most, from 47% to 37% and from 7.1 to 4.3 μg/m³, contribution of biomass burning and diesel vehicles fell by 3% (0.6 μg/m³) and 1% (0.4 μg/m³), and coal combustion concentrations remained nearly constant. The findings of this study highlight the immense importance of anthropogenic source reduction in carbonaceous component variations and SOC generation, and provide significant insight into the temporal variations and sources of carbonaceous fractions in polluted cities.

Long-term trends in the contribution of PM2.5 sources to organic carbon (OC) in the Los Angeles basin and the effect of PM emission regulations

This study aimed to investigate the long-term variations in the contributions of emission sources to ambient PM_2.5 organic carbon (OC) in central Los Angeles (CELA) and Riverside using the Chemical Speciation Network (CSN) database in the 2005-2015 period, during which several federal and state PM-based regulations were implemented to reduce tailpipe emissions in the region. The measured concentrations of OC, OC volatility fractions (i.e., OC₁, OC₂, and OC₃), elemental carbon (EC), ozone (O₃), sulfate, the ratio of potassium ion to potassium (K⁺/K), and selected metal elements were used as the input to the positive matrix factorization (PMF) model. PMF resolved tailpipe emissions, non-tailpipe emissions, secondary organic aerosols (SOA), biomass burning, and local industrial activities as the main sources contributing to ambient OC at both sampling sites. Vehicular exhaust emissions, non-tailpipe emissions, and SOA were dominant sources of OC across our sampling sites, accounting cumulatively for more than 80% of total OC mass throughout the study period. Our findings showed a significant reduction in the absolute and relative contributions of tailpipe emissions to the ambient OC levels in CELA and Riverside over the time period of 2005-2015. The contribution of exhaust emissions to total OC in CELA decreased from 3.5 µg m^-3 (49%) in 2005 to 1.5 µg m^-3 (34%) in 2015, while similar trends were observed at Riverside during this period. These reductions are mainly attributed to the implementation of several federal, state, and local air quality regulations targeting tailpipe emissions in the area. The implementation of these regulations furthermore reduced the emissions of primary organic precursors of secondary aerosols, resulting in an overall decrease (although not statistically significant, P values ranging from 0.4 to 0.6) in SOA mass concentration in both locations over the study period. In contrast to the tailpipe emissions, we observed an increasing trend (by ∼4 to 14%) in the relative contribution of non-tailpipe emissions to OC over this time period at both sites. Our results demonstrated the effectiveness of air quality regulations in reducing direct tailpipe emissions in the area, but also underpinned the need to develop equally effective mitigation policies targeting non-tailpipe PM emissions.

Site Suitability Analysis for Low Cost Sensor Networks for Urban Spatially Dense Air Pollution Monitoring

Urban air pollution continues to represent a primary concern for human health, despite significant efforts by public authorities for mitigating its effects. Regulatory monitoring networks are essential tools for air pollution monitoring. However, they are sparse networks, unable to capture the spatial variability of the air pollutants. For addressing this issue, networks of low cost stations are deployed, supplementing the regulatory stations. Regarding this application, an important question is where these stations are installed The objective of this study was to generate a site suitability map for the development of a network of low cost multi-sensor stations across a city for a spatially dense urban air quality monitoring. To do that, a site suitability analysis was developed based on two geographical variables properly selected for representing the impact of urban pollutant sources and urban form on the pollutant concentrations. By processing information about emissions patterns and street canyon effects, we were able to identify air quality hotspot areas supposed to show high spatial variability. Low cost monitoring stations, there located, are able to provide that informative content, which is lacking for both regulatory monitoring networks and predictive modelling for high resolution air quality mapping.

Impacts of discriminated PM2.5 on global under-five and maternal mortality

Globally, it was estimated that maternal and under-five deaths were high in low-income countries than that of high-income countries. Most studies, however, have focused only on the clinical causes of maternal and under-five deaths, and yet there could be other factors such as ambient particulate matter (PM). The current global estimates indicate that exposure to ambient PM_2.5 (with ≤ 2.5 microns aerodynamic diameter) has caused about 7 million deaths and over 100 million disability-adjusted life-years. There are also several health risks that have been linked PM_2.5, including mortality, both regionally and globally; however, PM_2.5 is a mixture of many compounds from various sources. Globally, there is little evidence of the health effects of various types of PM_2.5, which may uniquely contribute to the global burden of disease. Currently, only two studies had estimated the effects of discriminated ambient PM_2.5, that is, anthropogenic, biomass and dust, on under-five and maternal mortality using satellite measurements, and this study found a positive association in Africa and Asia. However, the study area was conducted in only one region and may not reflect the spatial variations throughout the world. Therefore, in this study, we discriminated different ambient PM_2.5 and estimated the effects on a global scale. Using the generalized linear mixed-effects model (GLMM) with a random-effects model, we found that biomass PM_2.5 was associated with an 8.9% (95% confidence interval [CI] 4.1-13.9%) increased risk of under-five deaths, while dust PM_2.5 was marginally associated with 9.5% of under-five deaths. Nevertheless, our study found no association between PM_2.5 type and global maternal deaths. This result may be because the majority of maternal deaths could be associated with preventable deaths that would require clinical interventions. Identification of the mortality-related types of ambient PM_2.5 can enable the development of a focused intervention strategy of placing appropriate preventive measures for reducing the generation of source-specific PM_2.5 and subsequently diminishing PM_2.5-related mortality.

Gathering Pipeline Methane Emissions in Utica Shale Using an Unmanned Aerial Vehicle and Ground-Based Mobile Sampling

The United States Environmental Protection Agency Greenhouse Gas Inventory only recently updated the emission factors of natural gas gathering pipelines in April 2019 from the previous estimates based on a 1990s study of distribution pipelines. Additional measurements are needed from different basins for more accurate assessments of methane emissions from natural gas midstream industries and hence the overall climate implications of natural gas as the interim major energy source for the next decade. We conducted an unmanned aerial vehicle (UAV) survey and a ground-based vehicle sampling campaign targeting gathering pipeline systems in the Utica Shale from March to April in 2019. Out of 73 km of pipeline systems surveyed, we found no leaks on pipelines and two leaks on an accessory block valve with leak rates of 3.8 ± 0.4 and 7.6 ± 0.8 mg/s. The low leak frequency phenomenon was also observed in the only existing gathering pipeline study in Fayetteville Shale. The UAV sampling system facilitated ease of access, broadened the availability of pipelines for leak detection, and was estimated to detect methane leaks down to 0.07 g/s using Gaussian dispersion modeling. For future UAV surveys adopting similar instrument setup and dispersion models, we recommend arranging controlled release experiments first to understand the system’s detection limit and choosing sampling days with steady and low wind speeds (2 m/s).

Impact of secondary and primary particulate matter (PM) sources on the enhanced light absorption by brown carbon (BrC) particles in central Los Angeles

In this study, we investigated aerosol chemical composition, spectral properties of aerosol extracts, and source contributions to the aerosol light-absorbing brown carbon (BrC) in central Los Angeles from July 2018 to March 2019, during warm and cold seasons. Spectrophotometric measurements (water and methanol extracts; 200 < λ < 1100) and chemical analyses were performed on collected particulate matter (PM), and relationships of BrC light absorption (Abs₃₆₅) to source tracer chemical species were evaluated. Mass absorption efficiency (MAE) of both water and methanol extracted solutions exhibited an increasing trend from warm period to cold season, with an annual average value of 0.61 ± 0.22 m².g^-1 and 1.38 ± 0.89 m².g^-1, respectively. Principal component analysis (PCA) were coupled with multiple linear regression (MLR) to identify and quantify sources of BrC light absorption in each of the seasons. Our finding documented fossil fuel combustion as the dominant source of BrC light absorption during warm season, with relative contribution of 38% to total BrC light absorption, followed by (secondary organic aerosol) SOA (30%) and biomass burning (12%). In contrast, biomass burning was the major source of BrC during the cold season (53%), while fossil fuel combustion and SOA contributed to 18% and 12% of BrC, respectively. Significantly higher contribution of biomass burning to BrC during the cold season suggested that residential heating activities (wood burning) play a major role in increased BrC concentrations. Previously collected Aethalometer model data documented fossil fuel combustion as the dominant contributing source to >90% of BC throughout the year. Finally, the solar radiation absorption ratio of BrC to elemental carbon (EC) in the ultraviolet range (300-400 nm) was maximum during the cold season with the annual corresponding values of 13-25% and 17-29% for water- and methanol-soluble BrC, respectively; which provides further evidence of the important effect of BrC light absorption on atmospheric radiative balance.

Semi-volatile components of PM2.5 in an urban environment: volatility profiles and associated oxidative potential

The volatility profiles of PM2.5 semi-volatile compounds and relationships to the oxidative potential of urban airborne particles were investigated in central Los Angeles, CA. Ambient and thermodenuded fine (PM2.5) particles were collected during both warm and cold seasons by employing the Versatile Aerosol Concentration Enrichment System (VACES) combined with a thermodenuder. When operated at 50 °C and 100 °C, the VACES/thermodenuder system removed about 50% and 75% of the PM2.5 volume concentration, respectively. Most of the quantified PM2.5 semi-volatile species including organic carbon (OC), water soluble organic carbon (WSOC), polycyclic aromatic hydrocarbons (PAHs), organic acids, n-alkanes, and levoglucosan, as well as inorganic ions (i.e., nitrate, sulfate, and ammonium) exhibited concentration losses in the ranges of 40-66% and 67-92%, respectively, as the thermodenuder temperature increased to 50 °C and 100 °C. Species in the PM2.5 such as elemental carbon (EC) and inorganic elements (including trace metals) were minimally impacted by the heating process - thus can be considered refractory. On average, nearly half of the PM2.5 oxidative potential (as measured by the dichlorodihydrofluorescein (DCFH) alveolar macrophage in vitro assay) was associated with the semi-volatile species removed by heating the aerosols to only 50 °C, highlighting the importance of this quite volatile compartment to the ambient PM2.5 toxicity. The fraction of PM2.5 oxidative potential lost upon heating the aerosols to 100 °C further increased to around 75-85%. Furthermore, we document statistically significant correlations between the PM2.5 oxidative potential and different semi-volatile organic compounds originating from primary and secondary sources, including OC (Rwarm, and Rcold) (0.86, and 0.74), WSOC (0.60, and 0.98), PAHs (0.88, and 0.76), organic acids (0.76, and 0.88), and n-alkanes (0.67, and 0.83) in warm and cold seasons, respectively, while a strong correlation between oxidative potential and levoglucosan, a tracer of biomass burning, was observed only during the cold season (Rcold=0.81).

Land-Use Regression Modeling of Source-Resolved Fine Particulate Matter Components from Mobile Sampling

This study presents land-use regression (LUR) models for submicron particulate matter (PM₁) components from an urban area. Models are presented for mass concentrations of inorganic species (SO₄, NO₃, NH₄), organic aerosol (OA) factors, and total PM₁. OA is source-apportioned using positive matrix factorization (PMF) of data collected from aerosol mass spectrometry deployed on a mobile laboratory. PMF yielded a three-factor solution: cooking OA (COA), hydrocarbon-like OA (HOA), and less-oxidized oxygenated OA (LO-OOA). This study represents the first time that LUR has been applied to source-resolved OA factors. We sampled a roughly 20 km² area of West Oakland, California, USA, over 1 month (mid-July to mid-August, 2017). The road network of the sampling domain was comprehensively sampled each day using a randomized driving route to minimize temporal and spatial bias. Mobile measurements were aggregated both spatially and temporally for use as discrete spatial observations for LUR model building. LUR model performance was highest for those species with more spatial variability (primary OA factors: COA R² = 0.80, HOA R² = 0.67) and lowest for secondary inorganic species (SO₄ R² = 0.47, NH₄ R² = 0.43) that were more spatially homogeneous. Notably, the stepwise selective LUR algorithm largely selected predictors for primary OA factors that correspond to the associated land-use categories (e.g., cooking land-use variables were selected in cooking-related PM models). This finding appears to be robust, as we demonstrate the predictive link between land-use variables and the corresponding source-resolved PM₁ components through a subsampling analysis.

Restaurant Impacts on Outdoor Air Quality: Elevated Organic Aerosol Mass from Restaurant Cooking with Neighborhood-Scale Plume Extents

Organic aerosol (OA) is a major component of fine particulate matter (PM_2.5) in urban environments. We performed in-motion ambient sampling from a mobile platform with an aerosol mass spectrometer (AMS) to investigate the spatial variability and sources of OA concentrations in Pittsburgh, Pennsylvania, a midsize, largely postindustrial American city. To characterize the relative importance of cooking and traffic sources, we sampled in some of the most populated areas (∼18 km²) in and around Pittsburgh during afternoon rush hour and evening mealtime, including congested highways, major local roads, areas with high densities of restaurants, and urban background locations. We found greatly elevated OA concentrations (10s of μg m^-3) in the vicinity of numerous individual restaurants and commercial districts containing multiple restaurants. The AMS mass spectral information indicates that majority of the high concentration plumes (71%) were from cooking sources. Areas containing both busy roads and restaurants had systematically higher OA concentrations than areas with only busy roads and urban background locations. Elevated OA concentrations were measured hundreds of meters downwind of some restaurants, indicating that these sources can influence air quality on neighborhood scales. Approximately 20% of the population (∼250 000 people) in the Pittsburgh area lives within 200 m of a restaurant; therefore, restaurant emissions are potentially an important source of outdoor PM exposures for this large population.

Spatial Variability of Sources and Mixing State of Atmospheric Particles in a Metropolitan Area

Characterizing intracity variations of atmospheric particulate matter has mostly relied on fixed-site monitoring and quantifying variability in terms of different bulk aerosol species. In this study, we performed ground-based mobile measurements using a single-particle mass spectrometer to study spatial patterns of source-specific particles and the evolution of particle mixing state in 21 areas in the metropolitan area of Pittsburgh, PA. We selected sampling areas based on traffic density and restaurant density with each area ranging from 0.2 to 2 km². Organics dominate particle composition in all of the areas we sampled while the sources of organics differ. The contribution of particles from traffic and restaurant cooking varies greatly on the neighborhood scale. We also investigate how primary and aged components in particles mix across the urban scale. Lastly we quantify and map the particle mixing state for all areas we sampled and discuss the overall pattern of mixing state evolution and its implications. We find that in the upwind and downwind of the urban areas, particles are more internally mixed while in the city center, particle mixing state shows large spatial heterogeneity that is mostly driven by emissions. This study is to our knowledge, the first study to perform fine spatial scale mapping of particle mixing state using ground-based mobile measurement and single-particle mass spectrometry.