Source Apportionment and Spatial Distributions of Coarse Particles During the Regional Air Pollution Study

To identify the coarse particle sources and to estimate the variability in their contributions to coarse particle mass (CPM) concentrations across the St. Louis metropolitan area, positive matrix factorization (PMF) was applied to historic ambient coarse particle compositional data from 10 Regional Air Pollution Study/Regional Air Monitoring System (RAPS/RAMS) monitoring sites in St. Louis. Coarse particles in this study had aerodynamic sizes between 2.4 and 20 µm. The sources were qualitatively identified, and the source contributions were quantitatively estimated. Nine sources were identified for 8 of the 10 sampling sites (except rural sites 122 and 124) including soil, cement kiln/quarry, iron and steel, motor vehicle, incinerator, pigment plant, primary/secondary lead smelter, zinc smelter, and copper production, respectively. At site 122, five sources were identified as soil, cement kiln/quarry, motor vehicle, incinerator, and zinc smelter. At site 124, six sources were identified as soil, cement kiln/quarry, motor vehicle, incinerator, primary/secondary lead smelter, and zinc smelter. Soil was the largest coarse particle source across the study area (6.15 µg/m3, 29.3%). Cement kiln/quarry, iron and steel, and motor vehicle sources were the other large contributions to the coarse particles mass (5.27 µg/m3, 25.1%; 3.53 µg/m3, 16.8%; 2.72 µg/m3, 12.9%). The results of this study suggest there can be significant potential for exposure misclassification in time-series epidemiologic studies when regressing health outcomes against source contributions if they were to be estimated at a single central monitoring site

Spatial Variability of Fine Particle Mass, Components, and Source Contributions during the Regional Air Pollution Study in St. Louis

Community time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentra tions to human health effects. If the day-to-day variations in 24-hour integrated concentrations differ substantially across an urban area (i.e., daily measurements at monitors at different locations are not highly correlated), then there is a significant potential for exposure misclassification in community time-series epidemiology. If the annual average concentration differs across an urban area, then there is a potential for exposure misclassification in epidemiologic studies that use annual averages (or multi-year averages) as an index of exposure across different cities. The spatial variability in PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), its elemental components, and the contributions from each source category at 10 monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study (RAPS) based on the Regional Air Monitoring System (RAMS) conducted between 1975 and 1977. Positive matrix factorization (PMF) was applied to each ambient PM2.5 compositional data set to estimate the contributions from the source categories. The spatial distributions of components and source contributions to PM2.5 at the 10 sites were characterized using Pearson correlation coefficients and coefficients of divergence. Sulfur and PM2.5 are highly correlated elements between all of the site pairs Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of 1.7 difference in secondary sulfate contributions between the highest and lowest site on average. Motor vehicles represent the next most highly correlated source component. However, there is a factor of 3.6 difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much more variable. For example, the contributions from incinerators show a difference of a factor of 12.5 between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and contributions from source categories can be highly heterogeneous within a given airshed and thus, there is the potential for exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures

Light Absorption Properties and Radiative Effects of Primary Organic Aerosol Emissions

Organic aerosols (OAs) in the atmosphere affect Earth’s energy budget by not only scattering but also absorbing solar radiation due to the presence of the so-called “brown carbon” (BrC) component. However, the absorptivities of OAs are not represented or are poorly represented in current climate and chemical transport models. In this study, we provide a method to constrain the BrC absorptivity at the emission inventory level using recent laboratory and field observations. We review available measurements of the light-absorbing primary OA (POA), and quantify the wavelength-dependent imaginary refractive indices (<i>k</i>_OA, the fundamental optical parameter determining the particle’s absorptivity) and their uncertainties for the bulk POA emitted from biomass/biofuel, lignite, propane, and oil combustion sources. In particular, we parametrize the <i>k</i>_OA of biomass/biofuel combustion sources as a function of the black carbon (BC)-to-OA ratio, indicating that the absorptive properties of POA depend strongly on burning conditions. The derived fuel-type-based <i>k</i>_OA profiles are incorporated into a global carbonaceous aerosol emission inventory, and the integrated <i>k</i>_OA values of sectoral and total POA emissions are presented. Results of a simple radiative transfer model show that the POA absorptivity warms the atmosphere significantly and leads to ∼27% reduction in the amount of the net global average POA cooling compared to results from the nonabsorbing assumption

Toward verifying fossil fuel CO₂ emissions with the CMAQ model: Motivation, model description and initial simulation

<div><p>Motivated by the question of whether and how a state-of-the-art regional chemical transport model (CTM) can facilitate characterization of CO₂ spatiotemporal variability and verify CO₂ fossil-fuel emissions, we for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate CO₂. This paper presents methods, input data, and initial results for CO₂ simulation using CMAQ over the contiguous United States in October 2007. Modeling experiments have been performed to understand the roles of fossil-fuel emissions, biosphere–atmosphere exchange, and meteorology in regulating the spatial distribution of CO₂ near the surface over the contiguous United States. Three sets of net ecosystem exchange (NEE) fluxes were used as input to assess the impact of uncertainty of NEE on CO₂ concentrations simulated by CMAQ. Observational data from six tall tower sites across the country were used to evaluate model performance. In particular, at the Boulder Atmospheric Observatory (BAO), a tall tower site that receives urban emissions from Denver, CO, the CMAQ model using hourly varying, high-resolution CO₂ fossil-fuel emissions from the Vulcan inventory and CarbonTracker optimized NEE reproduced the observed diurnal profile of CO₂ reasonably well but with a low bias in the early morning. The spatial distribution of CO₂ was found to correlate with NOx, SO₂, and CO, because of their similar fossil-fuel emission sources and common transport processes. These initial results from CMAQ demonstrate the potential of using a regional CTM to help interpret CO₂ observations and understand CO₂ variability in space and time. The ability to simulate a full suite of air pollutants in CMAQ will also facilitate investigations of their use as tracers for CO₂ source attribution. This work serves as a proof of concept and the foundation for more comprehensive examinations of CO₂ spatiotemporal variability and various uncertainties in the future. </p><p></p><p>Implications: </p><p>Atmospheric CO₂ has long been modeled and studied on continental to global scales to understand the global carbon cycle. This work demonstrates the potential of modeling and studying CO₂ variability at fine spatiotemporal scales with CMAQ, which has been applied extensively, to study traditionally regulated air pollutants. The abundant observational records of these air pollutants and successful experience in studying and reducing their emissions may be useful for verifying CO₂ emissions. Although there remains much more to further investigate, this work opens up a discussion on whether and how to study CO₂ as an air pollutant.</p><p></p><p></p></div