BTEX exposures in an area impacted by industrial and mobile sources: Source attribution and impact of averaging time

<p>The impacts of emissions plumes from major industrial sources on black carbon (BC) and BTEX (benzene, toluene, ethyl benzene, xylene isomers) exposures in communities located >10 km from the industrial source areas were identified with a combination of stationary measurements, source identification using positive matrix factorization (PMF), and dispersion modeling. The industrial emissions create multihour plume events of BC and BTEX at the measurement sites. PMF source apportionment, along with wind patterns, indicates that the observed pollutant plumes are the result of transport of industrial emissions under conditions of low boundary layer height. PMF indicates that industrial emissions contribute >50% of outdoor exposures of BC and BTEX species at the receptor sites. Dispersion modeling of BTEX emissions from known industrial sources predicts numerous overnight plumes and overall qualitative agreement with PMF analysis, but predicts industrial impacts at the measurement sites a factor of 10 lower than PMF. Nonetheless, exposures associated with pollutant plumes occur mostly at night, when residents are expected to be home but are perhaps unaware of the elevated exposure. Averaging data samples over long times typical of public health interventions (e.g., weekly or biweekly passive sampling) misapportions the exposure, reducing the impact of industrial plumes at the expense of traffic emissions, because the longer samples cannot resolve subdaily plumes. Suggestions are made for ways for future distributed pollutant mapping or intervention studies to incorporate high time resolution tools to better understand the potential impacts of industrial plumes.</p> <p><i>Implications</i>: Emissions from industrial or other stationary sources can dominate air toxics exposures in communities both near the source and in downwind areas in the form of multihour plume events. Common measurement strategies that use highly aggregated samples, such as weekly or biweekly averages, are insensitive to such plume events and can lead to significant under apportionment of exposures from these sources.</p

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

We conducted a mobile sampling campaign in a historically industrialized terrain (Pittsburgh, PA) targeting spatial heterogeneity of organic aerosol. Thirty-six sampling sites were chosen based on stratification of traffic, industrial source density, and elevation. We collected organic carbon (OC) on quartz filters, quantified different OC components with thermal-optical analysis, and grouped them based on volatility in decreasing order (OC1, OC2, OC3, OC4, and pyrolyzed carbon (PC)). We compared our ambient OC concentrations (both gas and particle phase) to similar measurements from vehicle dynamometer tests, cooking emissions, biomass burning emissions, and a highway traffic tunnel. OC2 and OC3 loading on ambient filters showed a strong correlation with primary emissions while OC4 and PC were more spatially homogeneous. While we tested our hypothesis of OC2 and OC3 as markers of fresh source exposure for Pittsburgh, the relationship seemed to hold at a national level. Land use regression (LUR) models were developed for the OC fractions, and models had an average <i>R</i>² of 0.64 (SD = 0.09). The paper demonstrates that OC2 and OC3 can be useful markers for fresh emissions, OC4 is a secondary OC indicator, and PC represents both biomass burning and secondary aerosol. People with higher OC exposure are likely inhaling more fresh OC2 and OC3, since secondary OC4 and PC varies much less drastically in space or with local primary sources

Estimating ambient particulate organic carbon concentrations and partitioning using thermal optical measurements and the volatility basis set

<p>We introduce a new method to estimate the mass concentration of particulate organic carbon (POC) collected on quartz filters, demonstrating it using quartz-filter samples collected in greater Pittsburgh. This method combines thermal-optical organic carbon and elemental carbon (OC/EC) analysis and the volatility basis set (VBS) to quantify the mass concentration of semi-volatile POC on the filters. The dataset includes ambient samples collected at a number of sites in both summer and winter as well as samples from a highway tunnel. As a reference we use the two-filter bare-Quartz minus Quartz-Behind-Teflon (Q-QBT) approach to estimate the adsorbed gaseous fraction of organic carbon (OC), finding a substantial fraction in both the gas and particle phases under all conditions. In the new method we use OC fractions measured during different temperature stages of the OC/EC analysis for the single bare-quartz (BQ) filter in combination with partitioning theory to predict the volatility distributions of the measured OC, which we describe with the VBS. The effective volatility bins are consistent for data from both ambient samples and primary organic aerosol (POA)-enriched tunnel samples. Consequently, with the VBS model and total OC fractions measured over different heating stages, particulate OC can be determined by using the BQ filter alone. This method can thus be applied to all quartz filter-based OC/EC analyses to estimate the POC concentration without using backup filters.</p> <p>© 2016 American Association for Aerosol Research</p

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