Bayesian Spatial Multivariate Receptor Modeling for Multisite Multipollutant Data

Predicting latent source-specific PM2.5 pollution from regional sources at unmonitored sites by Bayesian spatial multivariate receptor modeling

Fine particulate matter (PM_2.5) has been a pollutant of main interest globally for more than two decades, owing to its well-known adverse health effects. For developing effective management strategies for PM_2.5, it is vital to identify its major sources and quantify how much they contribute to ambient PM_2.5 concentrations. With the expanded monitoring efforts established during recent decades in Korea, speciated PM_2.5 data needed for source apportionment of PM_2.5 are now available for multiple sites (cities). However, many cities in Korea still do not have any speciated PM_2.5 monitoring station, although quantification of source contributions for those cities is in great need. While there have been many PM_2.5 source apportionment studies throughout the world for several decades based on monitoring data collected from receptor site(s), none of those receptor-oriented studies could predict unobserved source contributions at unmonitored sites. This study predicts source contributions of PM_2.5 at unmonitored locations using a recently developed novel spatial multivariate receptor modeling (BSMRM) approach, which incorporates spatial correlation in data into modeling and estimation for spatial prediction of latent source contributions. The validity of BSMRM results is also assessed based on the data from a test site (city), not used in model development and estimation.

Health risk assessment and source apportionment of PM2.5-bound toxic elements in the industrial city of Siheung, Korea

The emission sources and their health risks of fine particulate matter (PM_2.5) in Siheung, Republic of Korea, were investigated as a middle-sized industrial city. To identify the PM_2.5 sources with error estimation, a positive matrix factorization model was conducted using daily mean speciated data from November 16, 2019, to October 2, 2020 (95 samples, 22 chemical species). As a result, 10 sources were identified: secondary nitrate (24.3%), secondary sulfate (18.8%), traffic (18.8%), combustion for heating (12.6%), biomass burning (11.8%), coal combustion (3.6%), heavy oil industry (1.8%), smelting industry (4.0%), sea salts (2.7%), and soil (1.7%). Based on the source apportionment results, health risks by inhalation of PM_2.5 were assessed for each source using the concentration of toxic elements portioned. The estimated cumulative carcinogenic health risks from the coal combustion, heavy oil industry, and traffic sources exceeded the benchmark, 1E-06. Similarly, carcinogenic health risks from exposure to As and Cr exceeded 1E-05 and 1E-06, respectively, needing a risk reduction plan. The non-carcinogenic risk was smaller than the hazard index of one, implying low potential for adverse health effects. The probable locations of sources with relatively higher carcinogenic risks were tracked. In this study, health risk assessment was performed on the elements for which mass concentration and toxicity information were available; however, future research needs to reflect the toxicity of organic compounds, elemental carbon, and PM_2.5 itself.

Global review of recent source apportionments for airborne particulate matter

Source apportionments have become increasingly performed to determine the origins of ambient particulate pollution. The results can be helpful in designing mitigation strategies to improve air quality. Source specific particulate matter (PM) concentrations are also being used in health effects studies to be able to focus attention on those sources most likely to be responsible for the observed adverse health effects. In 2015, the World Health Organization (WHO) released its initial compilation of source apportionment studies published through August 2014. This initial database was described by Karagulian et al. (Atmospheric Environment120 (2015) 475-483). In the present report, a new compilation has been prepared of those apportionments published since 2014 through December 2019. In addition, the database has been expanded to include apportionments of heavy metals, water-soluble components, and carbonaceous components in ambient PM. As a result of this work, we have developed and presented some perspectives on source apportionment going forward. We also have made a series of recommendations for source apportionment studies and reporting them. It is essential for papers to provide a minimum set of information so that the study can be adequately assessed, and the results utilized by others in making policy decisions or as part of other scientific studies.

Assessment of mobile source contributions in El Paso by PMF receptor modeling coupled with wind direction analysis

It is well-known that El Paso is the only border area in Texas that has violated national air quality standards. Mobile source emissions (including vehicle exhaust) contribute significantly to air pollution, along with other sources including industrial, residential, and cross-border. This study aims at separating unobserved vehicle emissions from air-pollution mixtures indicated by ambient air quality data. The level of contributions from vehicle emissions to air pollution cannot be determined by simply comparing ambient air quality data with traffic levels because of the various other contributors to overall air pollution. To estimate contributions from vehicle emissions, researchers employed advanced multivariate receptor modeling called positive matrix factorization (PMF) to analyze hydrocarbon data consisting of hourly concentrations measured from the Chamizal air pollution monitoring station in El Paso. The analysis of hydrocarbon data collected at the Chamizal site in 2008 showed that approximately 25% of measured Total Non-Methane Hydrocarbons (TNMHC) was apportioned to motor vehicle exhaust. Using wind direction analysis, researchers also showed that the motor vehicle exhaust contributions to hydrocarbons were significantly higher when winds blow from the south (Mexico) than those when winds blow from other directions. The results from this research can be used to improve understanding source apportionment of pollutants measured in El Paso and can also potentially inform transportation planning strategies aimed at reducing emissions across the region.

Characterizing population exposure to coal emissions sources in the United States using the HyADS model

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 withSO 4 2 - and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.