A seriously air pollution area affected by anthropogenic in the central China: temporal–spatial distribution and potential sources

2020 ◽  
Vol 42 (10) ◽  
pp. 3199-3211 ◽  
Author(s):  
Hao Yu ◽  
Jinglan Feng ◽  
Xianfa Su ◽  
Yi Li ◽  
Jianhui Sun
2021 ◽  
Vol 193 (5) ◽  
Author(s):  
Lei Liu ◽  
Xin Ma ◽  
Wei Wen ◽  
Chang Sun ◽  
Jiao Jiao

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhan Ren ◽  
Xingyuan Liu ◽  
Tianyu Liu ◽  
Dieyi Chen ◽  
Kuizhuang Jiao ◽  
...  

Abstract Background Positive associations between ambient PM2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error. Methods This study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM2.5. We also conducted stratification analyses by age, sex, and season. Results A total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 μg/m3 increase in PM2.5 (lag0–2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01–1.45%) and 1.95% (95% CI 1.63–2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season. Conclusions This study contributes evidence of short-term effects of PM2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan.


Chemosphere ◽  
2016 ◽  
Vol 145 ◽  
pp. 495-507 ◽  
Author(s):  
Elio Padoan ◽  
Mery Malandrino ◽  
Agnese Giacomino ◽  
Mauro M. Grosa ◽  
Francesco Lollobrigida ◽  
...  

2021 ◽  
Vol 940 (1) ◽  
pp. 012018
Author(s):  
K I Solihah ◽  
D N Martono ◽  
B Haryanto

Abstract Particulate matter is one of the threatening pollutants harmful to health. Currently, many researchers focus on the problem of PM2.5 concentrations in urban areas. This study aims to estimate the spatial distribution of PM2.5, and identify human behavior on air pollution in Jakarta. The method used were Spline with Tension to build the PM2.5 models, and multiple linear regression models to analyze human behavior on air pollution. The results showed that the annual average of PM2.5 in the last two years tends to be high in western, southern, and eastern parts of Jakarta. In addition, there was a decrease of PM2.5 concentration in 2020 compared to 2019 assumed as a result of Covid-19 Pandemic restrictions. Besides, analysis results showed a significant association between knowledge and attitude aspects on the action aspect. Based on descriptive analysis, people have good knowledge of air pollution and also concern to reduce air pollution. However, the actions for air pollution control are still not maximized which may cause high PM2.5 concentrations in Jakarta. We conclude that to reduce air pollution, the government should focus on the border areas of Jakarta and it can be done by increasing public knowledge and raising awareness for air pollution.


Sign in / Sign up

Export Citation Format

Share Document