Source apportionment of particulate matter based on numerical simulation during a severe pollution period in Tangshan, North China

Highlights

• The contribution of local and different sectors emissions on PM_2.5 concentration.

• Backward trajectory and potential source regions of PM_2.5.

• The impact of multi-scale meteorological conditions on source apportionment.

• Local emissions accounted for 46.0% of the near-surface PM_2.5 concentration.

• Static weather significantly enhanced the contribution of regional transport.

Abstract

Facing serious air pollution problems, the Chinese government has taken numerous measures to prevent and control air pollution. Understanding the sources of pollutants is crucial to the prevention of air pollution. Using numerical simulation method, this study analysed the contributions of the total local emissions and local emissions from different sectors (such as industrial, traffic, resident, agricultural, and power plant emissions) to PM_2.5 concentration, backward trajectory, and potential source regions in Tangshan, a typical heavy industrial city in north China. The impact of multi-scale meteorological conditions on source apportionment was investigated. From October 2016 to March 2017, total local emissions accounted for 46.0% of the near-surface PM_2.5 concentration. In terms of emissions from different sectors, local industrial emissions which accounted for 23.1% of the near-surface PM_2.5 concentration in Tangshan, were the most important pollutant source. Agricultural emissions were the second most important source, accounting for 10.3% of the near-surface PM_2.5 concentration. The contributions of emissions from power plants, traffic, residential sources were 2.0%, 3.0%, and 7.2%, respectively. The contributions of total local emissions and emissions from different sectors depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport to the near-surface PM_2.5 concentration. Eight cluster backward trajectories were identified for Tangshan. The PM_2.5 concentration for the 8 cluster trajectories significantly differed. The near-surface PM_2.5 in urban Tangshan (receptor point) was mainly from the local emissions, and another important potential source region was Tianjin. The results of the source apportionment suggested the importance of joint prevention and control of air pollution in some areas where cities or industrial regions are densely distributed.

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 (PM_2.5) 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.