Trends of source apportioned PM2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions

Qili Dai; Jiajia Chen; Xuehan Wang; Tianjiao Dai; Yingze Tian; Xiaohui Bi; Guoliang Shi; Jianhui Wu; Baoshuang Liu; Yufen Zhang; Beizhan Yan; Patrick L Kinney; Yinchang Feng; Philip K Hopke

doi:10.1016/j.envpol.2023.121344

Trends of source apportioned PM_2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions

Environ Pollut. 2023 May 15:325:121344. doi: 10.1016/j.envpol.2023.121344. Epub 2023 Mar 4.

Authors

Affiliations

¹ State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.
² Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, 10964, USA.
³ Department of Environmental Health, Boston University School of Public Health, Boston, MA, 02118, USA.
⁴ State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; China Meteorological Administration-Nankai University (CMA-NKU) Cooperative Laboratory for Atmospheric Environment-Health Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China. Electronic address: fengyc@nankai.edu.cn.
⁵ Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA; Institute for a Sustainable Environment, Clarkson University, Potsdam, NY, 13699, USA.

PMID: 36878277
DOI: 10.1016/j.envpol.2023.121344

Abstract

A long-term (2013-2019) PM_2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM_2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM_2.5 air quality was observed in Tianjin with decreases in PM_2.5 at an annual rate of 6.6%/y. PM_2.5 from CC decreased by 4.1%/y. The reductions in SO₂ concentration, PM_2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO₂, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NO_X emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NO_X controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Keywords: Accountability; Control measure; PM(2.5); Policy evaluation; Source apportionment.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
China
Coal / analysis
Dust / analysis
Environmental Monitoring
Nitrates
Particulate Matter / analysis
Seasons
Vehicle Emissions / analysis

Substances

Air Pollutants
Vehicle Emissions
Particulate Matter
Nitrates
Dust
Coal