Source apportionment of particulate matter based on numerical simulation during a severe pollution period in Tangshan, North China☆
Graphical abstract
Introduction
With the acceleration of industrialization and urbanization as well as the rapid growth of the number of motor vehicles, air pollution incidents occur frequently in China. From 1980 to 2014, the observed winter haze days in eastern China significantly increased (Yang et al., 2016). The air quality problems have aroused widespread concern of the government and public. Particle matter with aerodynamic diameter of 2.5 μm or less (PM2.5) is the primary pollutant in China, and the Beijing–Tianjin–Hebei region is one of the most polluted areas in China (He et al., 2017b). Although the concentration of PM2.5 in China has decreased slightly in recent years (Song et al., 2017b), it is much higher than the standard values of the ambient air quality standard (GB 3095-2012) and World Health Organization (Chai et al., 2014). Serious air pollution has adverse effects on human health (An et al., 2015), and a previous study revealed that PM2.5 pollution contributed as much as 15.5% to all causes of death in 2015 over China (Song et al., 2017a). Since 2012, a series of air quality standards, control measures, and laws have been promulgated in China. This reflects the tremendous attention of the government to atmospheric environmental issues. Moreover, new requirements and urgent needs have been proposed for the scientific prevention and control of air pollution.
Understanding of the source of air pollution is a key factor for its prevention and control. The methods of source apportionment of PM mainly include the emission inventory method, diffusion model method, receptor model method, and ensemble model method (Shi et al., 2018; Zhang et al., 2015a). The diffusion model method is based on the pollutant emission inventory and meteorological field. The processes of pollutant transport, diffusion, chemical transformation, and deposition in the atmosphere are simulated by a numerical model. The contribution of different pollutant sources to pollutant concentration at the acceptor point is estimated. Based on the Lagrange particle transport diffusion model, the pollutant transport path, backward trajectory, potential source regions, and potential source contribution function are widely investigated (Ding et al., 2013; Dordevic et al., 2019; He et al., 2017c; Liu et al., 2013; Mulder et al., 2019). Based on the Eulerian air quality numerical model, the brute force method, decoupled direct method, adjoint method, tagged species source apportionment method, and Gaussian process emulation method are broadly used for source apportionment (Chen et al., 2020; Dunker et al., 2002; He et al., 2017c; Koo et al., 2009; Wang et al., 2009; Zhai et al., 2018; Zhang et al., 2015b). With linear relationships between concentration and emissions, source apportionment results using different methods are similar or equivalent (Clappier et al., 2017). Source apportionment based on the diffusion model method is not limited to the observation points, and can obtain the spatial distributions of the emission contribution and regional transport. The uncertainties in the diffusion model method originate from the emission inventory, boundary layer meteorological process, and complex atmospheric chemical process (Koo et al., 2009; Zhang et al., 2015a).
Tangshan is located in Hebei province, one of the worst air pollution regions in China (He et al., 2017b). The total resident population is 7.9 million. Tangshan’s Gross Domestic Product (GDP) ranked first in Hebei Province, with $97 billion in 2019 (https://xw.qq.com/cmsid/20200304 A0V6VA00). Numerous pollution sources, adverse diffusion of the meteorological conditions, and distinct topography are the important reasons for the serious air pollution in the Beijing–Tianjin–Hebei region (Gao et al., 2011; He et al., 2017a; Zhang et al., 2018). According to “Brief Situation of National Eco-environmental Quality in 2018″, Tangshan ranked fourth in the cities with poor air quality in China in 2018 (https://baijiahao.baidu.com/s?id=1628312479084586136&wfr=spider& for = pc). Sulfur dioxide and nitrogen oxides emissions in Tangshan reached 159 × 103 and 204 × 103 tons in 2017 (http://www.tangshan.gov.cn/zhuzhan/tjxxnb/20190115/669214.html). Numerous studies on the causes of air pollution focus on large cities, such as Beijing, Tianjin, and Shijiazhuang. There is little research investigating the sources of the atmospheric pollutants and influences of the meteorological conditions over Tangshan. Using the Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) and FLEXible PARTicle (FLEXPART) dispersion model, this study analyses the contributions of the local emissions on PM2.5 concentration, backward trajectory, and potential source regions in Tangshan. The impact of atmospheric circulation on the source apportionment is studied. The results are of tremendous significance for understanding the causes of air pollution and adopting air pollution prevention and control measures in Tangshan and similar cities.
Section snippets
WRF-Chem simulation setting
WRF-Chem is an online coupled air quality numerical model. It was developed and supported by the National Oceanic and Atmospheric Administration (NOAA), National Center for Atmospheric Research (NCAR) and other organizations or institutions. WRF-Chem V3.9 is used to investigate the contribution of total local emissions and emissions of different sectors to the near-surface PM2.5 concentration.
WRF-Chem is configured to have two nested domains with horizontal resolutions of 25 km (140 × 100
Model evaluation
The good performance of the meteorological simulation is an important basis for pollutant diffusion simulation by FLEXPART and air quality simulation by WRF-Chem. The statistical performances of T2, RH2, and WS10 are listed in Table 1. WRF underestimates T2 and RH2 with an MB of −1.97 K and −5.21% and overestimates WS10 with an MB of 0.68 m s−1. The IOA and R for T2 are 0.95 and 0.94, respectively, whereas they are 0.84 and 0.73 for RH2. This suggests that WRF well reproduces the change
Conclusions
Air pollution problems have attracted the attention of the government and public of China. Understanding the sources of the pollutants is helpful in the prevention and control of air pollution. Using the WRF-Chem and WRF-FLEXPART models, this study analyses the contributions of the local emissions to the PM2.5 concentration, backward trajectory, and potential source regions in Tangshan.
Generally, the WRF-Chem can well reproduce the spatial–temporal variations of the meteorological and pollutant
CRediT authorship contribution statement
Jianjun He: Writing - original draft, Writing - review & editing, Conceptualization, Methodology, Software, Investigation, Visualization. Lei Zhang: Writing - review & editing. Zhanyu Yao: Writing - review & editing. Huizheng Che: Supervision, Writing - review & editing. Sunling Gong: Supervision, Writing - review & editing. Min Wang: Software, Investigation, Visualization. Mengxue Zhao: Software, Investigation, Visualization. Boyu Jing: Writing - review & editing.
Declaration of competing interest
I declare on behalf of my co-authors that the work described is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 91744209, 41975131, 41705080, 41775139), Key Project of Strategic International Scientific and Technological Innovation Cooperation of the Ministry of Science and Technology (No. 2016YFE0201900), the Opening Research Foundation of State Environmental Protection Key Laboratory of Odor Pollution Control, P.R. China (No. 201903102), and the CMA Innovation Team for Haze-fog Observation and Forecasts.
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This paper has been recommended for acceptance by Pavlos Kassomenos.