Spatiotemporal Variations of Ambient Concentrations of Trace Elements in a Highly Polluted Region of China. Issue 7 (3rd April 2019)

• Record Type: Journal Article
• Authors: Liu, Shuhan; Zhu, Chuanyong; Tian, Hezhong; Wang, Yuxuan; Zhang, Kai; Wu, Bobo; Liu, Xiangyang; Hao, Yan; Liu, Wei; Bai, Xiaoxuan; Lin, Shumin; Wu, Yiming; Shao, Panyang; Liu, Huanjia
• Is Part Of: Journal of geophysical research. Volume 124:Issue 7(2019)
• Page Start: 4186
• Page End: 4202
• Publication Date: 2019-04-03
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2018JD029562
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Anthropogenic emissions of trace elements (TEs) into the atmosphere have warranted global concern due to their adverse effects on human health and the ecosystem. We adapted the Weather Research and Forecasting (WRF) and Models‐3/Community Multiscale Air Quality (CMAQ) modeling system to predict the spatial and temporal concentrations of 11 TEs (arsenic, selenium, lead, cadmium, chromium, nickel, antimony, manganese, cobalt, copper, and zinc) in fine particulate matter (PM2.5 ) in January, April, July, and October of 2012 over Beijing, Tianjin, and Hebei (BTH) region of Northern China, one of the most polluted areas in China with frequent occurrences of haze episodes. A localized emission inventory that includes both conventional air pollutants and TEs established by our previous study was used in the model. Predicted concentrations of TEs were compared with observations collected at a site located in Beijing Normal University. The results show that arsenic, selenium, lead, copper, nickel, and zinc predictions better agree with observations made in both months. Manganese, antimony, and cobalt simulations display different abilities to reproduce observations between months. Chromium predictions have lower correlation coefficients. Cadmium predictions, similar to previous findings, show the least ability to reproduce observations. According to the model predictions, higher concentrations of TEs are primarily identified in the central and southern areas of the BTH region, and seasonally January sees the highest TEs. Potential causes for high concentrations of TEs were examined by evaluating emissions and contributions from major sources. Finally, specific countermeasures for emissions reduction of TEs were proposed. Key Points: Simulation capabilities of 11 TEs were developed in WRF‐CMAQ for China Spatiotemporal variations of 11 TEs in BTH region were predicted using localized inventory Beijing had higher concentrations of Sb and Cu, while other TEs are higher in the central and south of BTH