Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization.

Vlad Isakov,Saravanan Arunachalam,Sue Kimbrough,Marc Serre,Stephen Krabbe,Brian Naess,Michael Breen,Parikshit Deshmukh,Richard Baldauf,Andy Hawkins,Alejandro Valencia

doi:10.3390/atmos10100610

Abstract

Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigation strategies. Monitoring-based assessments can characterize local air quality when enough monitors are deployed. However, modeling plays a vital role in furthering the understanding of the relative contributions of emissions sources impacting the community. In this study, we combine dispersion modeling and measurements from the Kansas City TRansportation local-scale Air Quality Study (KC-TRAQS) and use data fusion methods to characterize air quality. The KC-TRAQS study produced a rich dataset using both traditional and emerging measurement technologies. We used dispersion modeling to support field study design and analysis. In the study design phase, the presumptive placement of fixed monitoring sites and mobile monitoring routes have been corroborated using a research screening tool C-PORT to assess the spatial and temporal coverage relative to the entire study area extent. In the analysis phase, dispersion modeling was used in combination with observations to help interpret the KC-TRAQS data. We extended this work to use data fusion methods to combine observations from stationary, mobile measurements, and dispersion model estimates.

Highlights

An increasing number of health studies have revealed associations between proximity to major transportation hubs and adverse public health effects [1]
The initial KC-TRAQS field study design included various monitoring instruments with the sampling frequency ranging from seconds to daily averages
This study demonstrates the use of dispersion modeling to support the design of air monitoring field studies

Summary

Introduction

An increasing number of health studies have revealed associations between proximity to major transportation hubs and adverse public health effects [1]. Many communities located near major transportation sources such as freeways, rail yards, distribution centers, ports, and airports, typically have higher levels of air pollution than other. Spatially and temporally resolved air quality characterization is critical for community-scale exposure studies and for developing future air quality mitigations. Spatially resolved air quality characterization is needed to identify areas with elevated levels of pollutant concentrations. Understanding relative contributions of emissions sources impacting the community is necessary for developing mitigation strategies. There is a lack of accessible tools that can be applied to study near-source pollution and identify contributing sources and develop strategies for reducing emissions and exposure. Understanding the relative contributions of emissions sources impacting the community would require modeling. A combination of dispersion modeling and monitoring could be a practical approach for community-scale air quality characterization

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