Quantifying the impacts of inter-city transport on air quality in the Yangtze River Delta urban agglomeration, China: Implications for regional cooperative controls of PM2.5 and O3
Graphical abstract
Introduction
The Yangtze River Delta (YRD) region is one of the most populous and most developed urban agglomerations in China. With rapid economic development and fast industrialization and urbanization, the air pollution in this region has become very serious, and it is characterized by high concentrations of fine particulate matter (PM2.5) and ozone (O3) (Hu et al., 2014; Ma et al., 2019; Tie et al., 2006; Wang et al., 2001; Wang et al., 2014b; Xiao et al., 2011). Wang et al. (2014b) reported that the annual average PM2.5 values were 56, 64, 75, and 86 μg/m3 in Shanghai, Hanghzou, Nanjing, and Hefei in 2013, respectively. The annual average PM2.5 concentrations in the YRD significantly decreased by about 30%, while the 90th percentile maximum daily average (MDA8) O3 concentrations increased by 22% from 2013 to 2017 (Wang et al., 2020b).
A number of studies (Chang et al., 2019; Li et al., 2017; Wang et al., 2017) have pointed out that regional transport plays a crucial role in the formation of PM2.5 pollution, accounting for about 30–80% of the total PM2.5 concentrations. Streets (Streets et al., 2007) used the Community Multi-scale Air Quality (CMAQ) model to simulate the contributions of the surrounding provinces and cities to Beijing's air pollution during the 2008 Beijing Olympic Games. The results showed that under the effect of a stable southerly wind, the pollution emissions from Hebei Province greatly affected the air quality in Beijing, and transport contributed 50–70% and 20–30% to the concentrations of PM2.5 and O3, respectively. Jie and Li (2014) used the Granger Causality test to explore the characteristics of air quality spillovers among the cities in the Pearl River Delta, and they showed that the emissions in Guangzhou and Foshan had significant impacts on the air quality in Shenzhen and Zhuhai. Due to the proximity of cities, the intensive emissions, and the relatively flat terrain in the YRD region, the mutual transport of air pollution among cities is significant (Cheng et al., 2011). Hu et al. (2018) revealed that the transport of O3 and its precursors from Wuxi, Suzhou, and Shanghai plays an important role in the high downwind O3 pollution in Nanjing and the western part of the YRD region. Wang et al. (2020a) found that the local, Zhejiang and Jiangsu emissions account for 53%, 19% and 14% of the non-background MDA8 O3 in Shanghai in August 2013.
It is necessary to understand the impacts of transport on the local air quality so that regional cooperative prevention and control measures can be designed to effectively reduce PM2.5 and O3 pollution. Several methods have been developed and used to estimate the impacts of regional transport on local air quality. The Air Resources Laboratory's HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the Potential Source Contribution Function (PSCF), and the Concentration Weighted Trajectory (CWT) method have been widely used to identify major transport trajectories and high emission regions (Dimitriou et al., 2015; Wang et al., 2010; Zhang et al., 2018; Zong et al., 2018). However, these methods only take the atmospheric dynamics into account and do not involve any chemical reactions. Compared with the above models, the Chemical Transport Model (CTM) is more suitable for the quantitative estimation of the transport contributions because it contains a full description of the physical and chemical atmospheric processes (Li et al., 2014; Wang et al., 2014a).
This study aims to quantitatively estimate the transport among the cities in the YRD region. A source-oriented CMAQ model was used to quantify the inter-city transport of PM2.5 and O3 among 41 cities in the YRD region and to provide insights for effective regional cooperative emission control strategies.
Section snippets
Model description
We used source-oriented CMAQ model v5.0.2 to track the emissions of the precursors of PM2.5 and O3 from different regions. The source-oriented CMAQ model has been continuously developed in our previous studies (Hu et al., 2017b; Hu et al., 2015; Shi et al., 2017; Wang et al., 2019b; Wang et al., 2018; Zhang et al., 2012), and the details of the algorithms are documented in these studies. Therefore only a brief description is given here. To develop a source-oriented treatment in the CMAQ model,
Inter-city transport matrix for PM2.5 and O3
Table 1 shows the transport matrix for PM2.5 among the 41 cities. The emissions from the local city itself make the largest contribution in general, which is indicated by the diagonal in the table. The contributions from the other cities in the YRD normally do not exceed 10% individually, except for a few cases, such as Nanjing's contribution in Chuzhou, Hefei's contribution to Huainan, and Ningbo's contribution to Shaoxing. Fig. 2 further summarizes the results and groups the contributions
Conclusions
The impacts of inter-city transport on PM2.5 and O3 pollution in all of the 41 cities in the YRD region were estimated using a source-oriented CMAQ model during the EXPLORE-YRD campaign. The results show that inter-city transport is very significant in the YRD region. On average, the emissions from the local city, the other YRD cities, and the regions outside of the YRD contributed 25.3% (min-max: 8.8–43.2%), 49.9% (min-max: 10.5–67.1%), and 24.8% (min-max: 12.8–49.4%) to the PM2.5,
CRediT authorship contribution statement
KG, and JH designed research. KG, LL, and XW conducted the simulations, JL, MQ, QY and JH contributed to model development and configuration. KG, LL, XW, and JH analyzed the data. HL, SG, MH, YZ discussed the results. KG prepared the manuscript and all coauthors helped improve the manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This study was supported by the National Key R&D Program of China (2018YFC0213800), the National Natural Science Foundation of China (41975162, 41675125 and 41705102), and the Jiangsu Environmental Protection Research Project (2016015).
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