Quantifying the seasonal variations and regional transport of PM2.5 in the Yangtze River Delta region, China: Characteristics, sources, and health risks
Abstract. Given the increasing complexity of the chemical composition of PM2.5, identifying and quantitatively assessing the contributions of pollution sources has played an important role in formulating policies to control particle pollution. This study provides a comprehensive assessment between PM2.5 chemical characteristics, sources, and health risks based on sampling data conducted over one year (March 2018 to February 2019) in Nanjing. Results show that PM2.5 exhibits a distinct variation across different seasons, which is primarily driven by emissions, meteorological conditions, and chemical conversion of gaseous pollutants. First, the chemical mass reconstruction shows that secondary inorganic aerosols (SIA, 62.5 %) and carbonaceous aerosols (21.3 %) contributed most to the PM2.5 mass. The increasing oxidation rates of SO2 and NO2 from summer to winter indicate that the secondary transformation of gaseous pollutants is strongly positively correlated with relative humidity. Second, the positive matrix factorization (PMF) method shows that identified PM2.5 sources include SIA (42.5 %), coal combustion (CC, 22.4 %), industry source (IS, 17.3 %), vehicle emission (VE, 10.7 %), fugitive dust (FD, 5.8 %) and other sources (1.3 %). The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the concentration-weighted trajectory (CWT) analysis are used to further explore different spatial distributions and regional transport of sources. High emissions (10-11 μg·m−3) of SIA and CC distribute in Nanjing and central China in winter. Moderate emissions (8-9 μg·m−3) of IS and VE are potentially located in the north of Jiangsu, Anhui, and Jiangxi. The PM2.5 pollution from long-range transport is attenuated by meteorological conditions and ocean air masses. Finally, the health risk assessment indicates that the carcinogenic and non-carcinogenic risks of toxic elements (Cr, As, Ni, Mn, V, and Pb) mainly come from IS, VE, and CC, which are within the tolerance or acceptable level. Although the main source of pollution in Nanjing is SIA at present, we should pay more attention to the health burden of vehicle emissions, coal combustion, and industrial processes.
Yangzhihao Zhan et al.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2023-489', Anonymous Referee #1, 25 Apr 2023
- RC2: 'Comment on egusphere-2023-489', Anonymous Referee #2, 27 Apr 2023
Yangzhihao Zhan et al.
Yangzhihao Zhan et al.
Viewed (geographical distribution)
Particle pollution is of great concern in many areas of East Asia. The Yangtze River Delta region in China is one of these typical highly polluted areas. This paper investigates the source apportionment of PM2.5 by applying positive matrix factorization based on a regional background site in the Yangtze River Delta region and pays attention to the health burden of PM2.5 pollution. The topic and some findings are fascinating. This manuscript not only provides some phenomena but also is a good job of explaining the mechanism. In all, the manuscript can be considered to be published after minor revision. The specific comments are listed below:
(1) The English should be checked and polished.
(2) In Introduction
On lines 52-53, “Air exposure models have been widely used to further assess the non-carcinogenic and carcinogenic health risks of toxic elements in PM2.5”. The source apportionment and health risk of PM2.5 pollution is the focus of this study, and the health risk assessment of this study should conduct a brief description.
In the second and third paragraphs of the introduction section, the authors introduce the chemical components of PM2.5 and the source apportionment methods in a scientific way. The authors summarize and analyze the shortcomings and advantages of relevant literature researches worldwide. However, more papers about air pollution in China should be added to support the ideas in the introduction. For example,
(3) In Data and Methodology
In Section 2.1, the author introduces detailed information on the sampling and measuring instruments. Whether there are parallel and blank sampling in this study. How about the distribution of pollution sources around the sampling site?
On lines 132-133, “Second, we calculated the uncertainty (Unc) for each species based on the concentration fraction and MDL.” How to set the uncertainty of different data? Detailed formulas on the Unc and MDL are lacking.
On lines 156-157, “The CWT method divided the research area into small equal grids, set a standard value for the research object, and defined the trajectory exceeding the standard value as the pollution trajectory.” It is necessary to present the standard value of the research object in this study. Why use this standard value?
(4) In Results and discussions
On lines 247-248, “meteorological conditions provided favorable conditions for the transformation of gaseous precursors.” There are many factors affecting the oxidation rate of gaseous precursors of PM2.5. In addition to relative humidity, meteorological factors such as temperature, radiation intensity, and boundary layer height all have a significant impact on oxidation rates. Please further explain the reason. For example, comparing the correlation coefficients of PM2.5 with the main meteorological factors in different seasons.
On Line 261, please add the meaning of “C”, “MP”, and “HP” in Fig. 2.
On Lines 338-339, “However, the industrial pollution derived from long-range transport was attenuated by meteorological conditions.” Which meteorological factors had the impacts? I think this needs to be explained in the text.
In Section 3.4, the author investigated the non-carcinogenic and the carcinogenic risks in PM2.5 and their total health risk in each source in Nanjing. The conclusion in this section should be compared with similar studies in other regions. Please briefly describe or cite some papers.
(5) In Conclusions
On lines 375-377, “Based on values of NO3−/SO42− and OC/EC ratio, dominant vehicle emission occurred during the heavily polluted period, and the contribution of coal combustion increased in winter.” The specific rate data should be given.