Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China

中国 地理 北京 人口 比例(比率) 自然地理学 环境科学 社会经济学 环境卫生 地图学 医学 考古 社会学
作者
Yu Bai,Menghang Liu
出处
期刊:Environmental Pollution [Elsevier BV]
卷期号:340 (Pt 2): 122857-122857 被引量:15
标识
DOI:10.1016/j.envpol.2023.122857
摘要

Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
yzl科研爱我完成签到,获得积分10
刚刚
尊敬寒松完成签到 ,获得积分10
刚刚
无极微光应助Hear采纳,获得20
1秒前
野猪亨利完成签到,获得积分10
2秒前
magicyang发布了新的文献求助10
3秒前
swslyg发布了新的文献求助10
3秒前
清蒸鱼吖完成签到,获得积分20
3秒前
小二郎应助背后艳采纳,获得10
3秒前
5秒前
5秒前
麦田发布了新的文献求助30
6秒前
Jiali完成签到,获得积分10
6秒前
xm发布了新的文献求助10
7秒前
迅速金毛完成签到,获得积分10
8秒前
梧柚子完成签到,获得积分10
8秒前
简单妖妖发布了新的文献求助10
9秒前
9秒前
ssjc发布了新的文献求助10
9秒前
9秒前
9秒前
10秒前
離c完成签到 ,获得积分10
11秒前
magicyang完成签到,获得积分10
11秒前
科研狗应助swslyg采纳,获得80
13秒前
ZAL完成签到,获得积分10
13秒前
隋阳完成签到 ,获得积分10
13秒前
医学事业发布了新的文献求助10
15秒前
田様应助霸道恒天采纳,获得10
17秒前
Hony132完成签到,获得积分10
17秒前
小土豆完成签到,获得积分10
18秒前
温婉完成签到,获得积分10
19秒前
科研小子完成签到,获得积分10
22秒前
呆萌冷雪完成签到 ,获得积分10
22秒前
nqterysc完成签到,获得积分10
22秒前
南风知我意完成签到,获得积分10
22秒前
顾羽完成签到,获得积分10
23秒前
24秒前
歪比巴卜完成签到,获得积分20
24秒前
Hear发布了新的文献求助20
26秒前
yin完成签到,获得积分10
26秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6359136
求助须知:如何正确求助?哪些是违规求助? 8173192
关于积分的说明 17212908
捐赠科研通 5414217
什么是DOI,文献DOI怎么找? 2865393
邀请新用户注册赠送积分活动 1842789
关于科研通互助平台的介绍 1690934