Prof. Hezhong Tian

Beijing Normal University, China

Invited Speech: Evolution of air pollution in China and the impacts of COVID-19 pandemic

Biography:

School of Environment / Center of Atmospheric Environmental Studies, Beijing Normal University, China

Prof. Hezhong Tian got his Ph.D. on Environmental Science and Engineering from the School of Environment at Tsinghua University. His main research interests include development and application on emission inventory of hazardous air pollutants (covering conventional air pollutants like SO2, NOx, PM; and non-conventional or emerging air pollutants such as heavy metals and trace elements like Hg, Pb, As, Cd, Cr, Ni, etc.) on multiple temporal-spatial scales and sectoral dimensions (global, continental, country, city clusters, urban); Regional air quality simulation with chemical models like WRF-CMAQ/Chem and GEOS-Chem; Source apportionment of regional PM2.5 and O3 pollution; and Exposure risks of airborne toxic pollutants. As the first or corresponding authors, Prof. Tian has published over 100 peer reviewed papers on international SCI-TOP journals, including ES&T, ACP, JGR-Atmosphere, Journal of Hazardous Materials, and Science of the Total Environment. His papers have been cited by over 6700 times, with a Google h-index of 44 and i10-index of 80. Therein, 4 of papers are listed as ESI Highly Cited Papers.

Abstract:

Airborne trace elements (TEs) pose a notable threat to human health due to their toxicity and carcinogenicity, whereas their exposures and associated health risks in China remain unclear. Here, we present a nationwide assessment of spatiotemporal exposure to 11 airborne trace elements (TEs) in China by coupling a bottom-up comprehensive emission inventory with a modified CMAQ model capable of TEs simulation, and the associated exposure health risks of 11 TEs are evaluated by using a set of risk assessment models. In addition, to prevent the spread of COVID-19 and protect public health, governments throughout the world have imposed strict lockdown measures, and the unprecedented decline in human activities caused by the COVID-19 lockdown provides a unique natural-triggered experimental chance to explore the possible impacts of anthropogenic emissions change on air quality. Here, we applied a machine learning algorithm (random forest model) and Theil–Sen regression technique to differentiate meteorological and long-term trends effects on several air pollutants concentrations in North China and attempted to precisely identify changes in their concentrations ascribed to lockdown measures.

Abstract

Prof. Hezhong Tian

Airborne trace elements (TEs) pose a notable threat to human health due to their toxicity and carcinogenicity, whereas their exposures and associated health risks in China remain unclear. Here, we present a nationwide assessment of spatiotemporal exposure to 11 airborne trace elements (TEs) in China by coupling a bottom-up comprehensive emission inventory with a modified CMAQ model capable of TEs simulation, and the associated exposure health risks of 11 TEs are evaluated by using a set of risk assessment models. In addition, to prevent the spread of COVID-19 and protect public health, governments throughout the world have imposed strict lockdown measures, and the unprecedented decline in human activities caused by the COVID-19 lockdown provides a unique natural-triggered experimental chance to explore the possible impacts of anthropogenic emissions change on air quality. Here, we applied a machine learning algorithm (random forest model) and Theil–Sen regression technique to differentiate meteorological and long-term trends effects on several air pollutants concentrations in North China and attempted to precisely identify changes in their concentrations ascribed to lockdown measures.