Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection

Abstract

The minute-scale variations of fine particulate matter (PM_2.5) and carbon monoxide (CO) concentrations near a road intersection in Shanghai, China were investigated to identify the influencing factors at three traffic periods. Measurement results demonstrate a synchronous variation of pollutant concentrations at the roadside and setbacks, and the average concentration of PM_2.5 at the roadside is 7% (44% for CO) higher than that of setbacks within 500 m of the intersection. The pollution level at traffic peak periods is found to be higher than that of off-peak periods, and the morning peak period is found to be the most polluted due to a large amount of diesel vehicles and unfavorable dispersion conditions. Partial least square regressions were constructed for influencing factors and setback pollutant concentrations, and results indicate that meteorological factors are the most significant, followed by setback distance from the intersection and traffic factors. CO is found to be sensitive to distance from the traffic source and vehicle type, and highly dependent on local traffic conditions, whereas PM_2.5 originates more from other sources and background levels. These findings demonstrate the importance of localized factors in understanding spatiotemporal patterns of air pollution at intersections, and support decision makers in roadside pollution management and control.

Keywords

traffic-related pollutants / fine-scale variation / distance gradient / meteorology / road intersection

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Acknowledgments

This work was sponsored by the Peking University-Lincoln Institute (DS20120901), the Shanghai Environmental Protection Bureau (No. 2014-8) and the State Key Laboratory of Ocean Engineering (GKZD 010059) at Shanghai Jiao Tong University, and the National Natural Science Foundation of China (11302125). We would like to thank members from the Shanghai Environmental Monitoring Center for their assistance in the instrumental calibration, and a special appreciation is expressed to colleagues from the Center for ITS and UAV Applications Research at Shanghai Jiao Tong University for their hard work in data collection and processing. We also acknowledge Wina Meyer and Alissa Meyer from the International Friendship of the University of Florida and Trina Burgess from the Department of Geography at the University of Lethbridge for their proofreading on our manuscript. Finally, we appreciate the anonymous reviewers’ insightful comments on our work.

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg