Disease specific air quality health index (AQHI) for spatiotemporal health risk assessment of multi-air pollutants

环境卫生 空气污染 空气质量指数 污染物 风险评估 健康风险评估 公共卫生 医学 疾病 空气污染物 人口 健康风险 环境科学 地理 气象学 病理 生物 计算机科学 生态学 计算机安全
作者
Xun Deng,Bin Zou,Shenxin Li,Jian Wu,Chenjiao Yao,Minxue Shen,Jun Chen,Sha Li
出处
期刊:Environmental Research [Elsevier BV]
卷期号:231: 115943-115943 被引量:8
标识
DOI:10.1016/j.envres.2023.115943
摘要

While significant reductions in certain air pollutant concentrations did not induce obvious mitigations of health risks, a shift from air quality management to health risk prevention and control might be necessary to protect public health. This study thus constructed an Air Quality Health Index (AQHI) for respiratory (Res-AQHI), cardiovascular (Car-AQHI), and allergic (Aller-AQHI) risk groups using mixed exposure under multi-air pollutants and portrayed their distribution and variation at multiple spatiotemporal scales using spatial analysis in GIS with the medical big data and air pollution remote sensing data by taking Hunan Province in China as a case. Results showed that the AQHIs constructed for specific health-risk groups could better express their risks than common AQHI and AQI. Moreover, based on the spatiotemporal association of health and environmental information, the allergic risk group in Hunan provided the highest health risk mainly affected by O3. The following cardiovascular and respiratory risk groups can be significantly attributed to NO2. Moreover, the spatiotemporal heterogeneity of AQHIs within regions was also evident. On the annual scale, the population in the air health risk hotspots for respiratory and cardiovascular risk decreased, while allergic risks increased. Meanwhile, on seasonal scale, the hotspots for respiratory and cardiovascular risks expanded significantly in winter while completely disappearing for allergic risk. These findings suggest that disease specific AQHIs effectively disclose the health effects of multi-air pollutants and their subsequently varied spatiotemporal distribution patterns.
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
zlx发布了新的文献求助10
刚刚
1秒前
科研天才完成签到,获得积分10
1秒前
温柔的芸完成签到 ,获得积分10
1秒前
杜凯敏完成签到,获得积分20
1秒前
小白完成签到,获得积分10
2秒前
nainomi发布了新的文献求助10
2秒前
只喝怡宝发布了新的文献求助10
2秒前
王木木发布了新的文献求助10
3秒前
cdercder应助琪凯定理采纳,获得20
3秒前
3秒前
huang应助TingWan采纳,获得10
4秒前
BUTTOND完成签到 ,获得积分10
4秒前
英姑应助lilizi采纳,获得10
5秒前
5秒前
钟馗完成签到,获得积分10
5秒前
mei发布了新的文献求助10
6秒前
77完成签到,获得积分10
6秒前
6秒前
平淡的宛凝应助peng采纳,获得30
6秒前
白日梦梦梦想家完成签到,获得积分10
7秒前
脑洞疼应助南木亦枫采纳,获得10
7秒前
zlx完成签到,获得积分10
7秒前
7秒前
yang发布了新的文献求助10
7秒前
俭朴苑博应助12345采纳,获得10
7秒前
8秒前
8秒前
杜富豪完成签到 ,获得积分10
8秒前
8秒前
林家小弟完成签到 ,获得积分10
9秒前
丛雨完成签到,获得积分10
9秒前
10秒前
烟花应助百芜芽采纳,获得10
10秒前
10秒前
10秒前
辛勤静珊发布了新的文献求助10
10秒前
JorgeSwift完成签到 ,获得积分10
11秒前
11秒前
连敏锐发布了新的文献求助10
11秒前
高分求助中
Markov Chain Monte Carlo 10000
(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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Advanced Weaponeering Fourth Edition, Volume 2 1000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7558241
求助须知:如何正确求助?哪些是违规求助? 9140147
关于积分的说明 19537383
捐赠科研通 7147691
什么是DOI,文献DOI怎么找? 3261323
关于科研通互助平台的介绍 2427802
邀请新用户注册赠送积分活动 2250664