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Liu K, Wu Q, Wang L, Wang S, Liu T, Ding D, Tang Y, Li G, Tian H, Duan L, Wang X, Fu X, Feng X, Hao J. Measure-Specific Effectiveness of Air Pollution Control on China's Atmospheric Mercury Concentration and Deposition during 2013-2017. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:8938-8946. [PMID: 31242727 DOI: 10.1021/acs.est.9b02428] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
China took aggressive air pollution control measures from 2013 to 2017, leading to the mitigation of atmospheric mercury pollution as a cobenefit. This study is the first to systematically evaluate the effect of five major air pollution control measures in reducing mercury emissions, the total gaseous mercury (TGM) concentration and mercury deposition flux (FLX) for unit emissions reduction. From 2013 to 2017, China's mercury emissions decreased from 571 to 444 tons, resulting in a 0.29 ng m-3 decrease in the TGM concentration, on average, and in a 17 μg m-2 yr-1 decrease in FLX. Ultralow emission renovations of coal-fired power plants are identified as the most effective emission abatement measure. As a result of this successful measure, coal-fired power plants are no longer the main mercury emitters. In 2017, the cement clinker sector became the largest emitter due to the use of less effective mercury removal measures. However, in terms of the mitigated TGM concentration and FLX levels per unit emission abatement, newly built wet flue gas desulfurization (WFGD) systems in coal-fired industrial boilers have become particularly effective in decreasing FLX levels. Therefore, to effectively reduce atmospheric mercury pollution in China, prioritizing mercury emissions control of cement clinkers and coal-fired industrial boilers is recommended.
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Affiliation(s)
- Kaiyun Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
| | - Qingru Wu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
| | - Long Wang
- School of Environment and Energy , South China University of Technology , Guangzhou 510006 , China
| | - Shuxiao Wang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex , Beijing 100084 , China
| | - Tonghao Liu
- China National Environmental Monitoring Centre , Beijing 100012 , China
| | - Dian Ding
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
| | - Yi Tang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
| | - Guoliang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment , Beijing Normal University , Beijing 100875 , China
| | - Lei Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex , Beijing 100084 , China
| | - Xun Wang
- State Key Laboratory of Environmental Geochemistry , Institute of Geochemistry, Chinese Academy of Sciences , Guiyang 550081 , China
| | - Xuewu Fu
- State Key Laboratory of Environmental Geochemistry , Institute of Geochemistry, Chinese Academy of Sciences , Guiyang 550081 , China
| | - Xinbin Feng
- State Key Laboratory of Environmental Geochemistry , Institute of Geochemistry, Chinese Academy of Sciences , Guiyang 550081 , China
| | - Jiming Hao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment , Tsinghua University , Beijing 100084 , China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex , Beijing 100084 , China
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Bastien LAJ, McDonald BC, Brown NJ, Harley RA. High-resolution mapping of sources contributing to urban air pollution using adjoint sensitivity analysis: benzene and diesel black carbon. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:7276-7284. [PMID: 26001097 DOI: 10.1021/acs.est.5b00686] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The adjoint of the Community Multiscale Air Quality (CMAQ) model at 1 km horizontal resolution is used to map emissions that contribute to ambient concentrations of benzene and diesel black carbon (BC) in the San Francisco Bay area. Model responses of interest include population-weighted average concentrations for three highly polluted receptor areas and the entire air basin. We consider both summer (July) and winter (December) conditions. We introduce a novel approach to evaluate adjoint sensitivity calculations that complements existing methods. Adjoint sensitivities to emissions are found to be accurate to within a few percent, except at some locations associated with large sensitivities to emissions. Sensitivity of model responses to emissions is larger in winter, reflecting weaker atmospheric transport and mixing. The contribution of sources located within each receptor area to the same receptor's air pollution burden increases from 38-74% in summer to 56-85% in winter. The contribution of local sources is higher for diesel BC (62-85%) than for benzene (38-71%), reflecting the difference in these pollutants' atmospheric lifetimes. Morning (6-9am) and afternoon (4-7 pm) commuting-related emissions dominate region-wide benzene levels in winter (14 and 25% of the total response, respectively). In contrast, afternoon rush hour emissions do not contribute significantly in summer. Similar morning and afternoon peaks in sensitivity to emissions are observed for the BC response; these peaks are shifted toward midday because most diesel truck traffic occurs during off-peak hours.
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Affiliation(s)
- Lucas A J Bastien
- †University of California at Berkeley, Berkeley, California 94720, United States
- ‡Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Brian C McDonald
- †University of California at Berkeley, Berkeley, California 94720, United States
| | - Nancy J Brown
- †University of California at Berkeley, Berkeley, California 94720, United States
- ‡Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Robert A Harley
- †University of California at Berkeley, Berkeley, California 94720, United States
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