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Sampling strategies in the collection of ultrafine particle (UFP) data to develop land-use regression (LUR) models can strongly influence the resulting exposure estimates. Here, we systematically examine how much sampling is needed to develop robust and stable UFP LUR models. To address this question, we collected 3-6 weeks of continuous measurements of UFP concentrations at 32 sites in Pittsburgh, Pennsylvania covering a wide range of urban land-use attributes. Through systematic subsampling of this data set, we evaluate the performance of hundreds of LUR models with varying numbers of sampling days and daily sampling durations. Our base LUR model derived from wintertime average concentrations explained about 80% of the spatial variability in the data (adjusted R 2 ∼ 0.8). The performance of the LUR models degrades with decreasing number of sampling days and sampling duration per day. For our data set, 1-3 h of sampling per day for 10-15 days provided UFP concentration estimates comparable to models derived from the entire data set. Small numbers of repeated sampling per site (1-3 days) at short duration (∼15-60 min per day) result in poor performance ( R 2 < 0.5), similar to previous UFP LUR models. This study provides guidelines for the design of future measurement campaigns and monitoring networks to generate robust UFP LUR models for exposure assessments. Further study in other locations with more sites is needed to evaluate these guidelines over a broader range of conditions.

Volatile organic compounds (VOCs) are important atmospheric constituents because they contribute to formation of ozone and secondary aerosols, and because some VOCs are toxic air pollutants. We measured concentrations of a suite of anthropogenic VOCs during summer and winter at 70 locations representing different microenvironments around Pittsburgh, PA. The sampling sites were classified both by land use (e.g., high versus low traffic) and grouped based on geographic similarity and proximity. There was roughly a factor of two variation in both total VOC and single-ring aromatic VOC concentrations across the site groups. Concentrations were roughly 25% higher in winter than summer. Source apportionment with positive matrix factorization reveals that the major VOC sources are gasoline vehicles, solvent evaporation, diesel vehicles, and two factors attributed to industrial emissions. While we expected to observe significant spatial variability in the source impacts across the sampling domain, we instead found that source impacts were relatively homogeneous.

Localized primary emissions of carbonaceous aerosol are the major drivers of intracity variability of submicron particulate matter (PM 1 ) concentrations. We investigated spatial variations in PM 1 composition with mobile sampling in Pittsburgh, Pennsylvania, United States and performed source-apportionment analysis to attribute primary organic aerosol (OA) to traffic (HOA) and cooking OA (COA). In high-source-impact locations, the PM 1 concentration is, on average, 2 μg m -3 (40%) higher than urban background locations. Traffic emissions are the largest source contributing to population-weighted exposures to primary PM. Vehicle-miles traveled (VMT) can be used to reliably predict the concentration of HOA and localized black carbon (BC) in air pollutant spatial models. Restaurant count is a useful but imperfect predictor for COA concentration, likely due to highly variable emissions from individual restaurants. Near-road cooking emissions can be falsely attributed to traffic sources in the absence of PM source apportionment. In Pittsburgh, 28% and 9% of the total population are exposed to >1 μg m -3 of traffic- and cooking-related primary emissions, with some populations impacted by both sources. The source mix in many U.S. cities is similar; thus, we expect similar PM spatial patterns and increased exposure in high-source areas in other cities.

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 km 2 ) 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.