Dataset Information

Spatiotemporal characteristics of PM2.5 and PM10 at urban and corresponding background sites in 23 cities in China.

ABSTRACT: Air pollution episodes in China are frequent and a more comprehensive understanding of pollution sources and impacts is needed to design appropriate strategies and set emission reduction targets. This study analyzes PM2.5 and PM10 concentrations measured in 23 cities at 178 urban sites and at 23 corresponding "urban contrast" sites in China with the goals of understanding spatial and temporal trends and quantifying the regional component of PM pollution. The contrast sites, located an average of 29km from cities in the upwind direction, are intended to represent "background" levels. Using daily measurements from April 2013 to March 2014, we assess compliance with air quality standards, PM2.5/PM10 ratios and urban "increments," defined as the increase in PM levels in the city compared to the contrast site. Spatial and temporal patterns at daily, monthly and annual levels are shown using distributions, correlations, spatial autocorrelation, and factor analyses. At the contrast sites, PM2.5 and PM10 concentrations averaged 56±26 and 91±44?gm-3, respectively, and China's daily and annual average air quality standards were frequently exceeded. PM2.5 and PM10 concentrations in most cities exceeded levels at the corresponding contrast sites, but by an average of only 14±14 and 26±27?gm-3, respectively. Seasonal changes in PM2.5 and PM10 concentrations and urban increments were striking, e.g., levels increased 2 to 3-fold in winter at several sites. The significance of exurban and regional sources of PM2.5 is demonstrated by the small urban increments, the strong correlations across broad regions, and the correlation between daily levels at city and contrast sites. These sources will require control to achieve air quality goals, in particular, the PM10 and PM2.5 targets announced by the Chinese government in 2013.

SUBMITTER: Xu L

PROVIDER: S-EPMC5975381 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatiotemporal characteristics of PM2.5 and PM10 at urban and corresponding background sites in 23 cities in China.

Xu Lizhong L Batterman Stuart S Chen Fang F Li Jiabing J Zhong Xuefen X Feng Yongjie Y Rao Qinghua Q Chen Feng F

The Science of the total environment 20170525

Air pollution episodes in China are frequent and a more comprehensive understanding of pollution sources and impacts is needed to design appropriate strategies and set emission reduction targets. This study analyzes PM2.5 and PM10 concentrations measured in 23 cities at 178 urban sites and at 23 corresponding "urban contrast" sites in China with the goals of understanding spatial and temporal trends and quantifying the regional component of PM pollution. The contrast sites, ...[more]

PMID: 28558430

Similar Datasets

Project description:From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018-2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021-2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25-35 μg/m3 increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing-Tianjin-Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Project description:Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

Dataset Information

Spatiotemporal characteristics of PM2.5 and PM10 at urban and corresponding background sites in 23 cities in China.

Publications

Spatiotemporal characteristics of PM<sub>2.5</sub> and PM<sub>10</sub> at urban and corresponding background sites in 23 cities in China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets