Comparative Analysis of Air Pollution Characteristics of Typical Cities in Central China from 2015 to 2018

: World Health Organization (WHO) office in China received the information of pneumonia cases of unknown aetiology from Wuhan, central China on 31st December 2019, subsequently this disease spreading in china and rest of world. Till the March 2020 end, more than 2 lakhs confirmed cases with more than 70000 deaths were reported worldwide, very soon researchers identified it as novel beta Corona virus (virus SARS-CoV-2) and its infection coined as COVID-19. Health ministries of various countries and WHO together fighting to this health emergency, which not only affects public health, but also started affecting various economic sectors as well. The main aim of the current article is to explore the various pandemic situations (SARS, MERS) in past, life cycle of COVID-19, diagnosis procedures, prevention and comparative analysis of COVID-19 with other epidemic situations.

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Data mining-based air pollution characteristics and real-time monitoring of college students’ physical and mental health

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07926-2 ◽

2021 ◽

Vol 14 (15) ◽

Author(s):

Xiaomei Wu ◽

Xuejun Ma

Keyword(s):

Mental Health ◽

College Students ◽

Data Mining ◽

Air Pollution ◽

Real Time ◽

Physical And Mental Health ◽

Real Time Monitoring ◽

Pollution Characteristics

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Effect of ambient fine particulates (PM2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China

Respiratory Research ◽

10.1186/s12931-021-01731-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhan Ren ◽

Xingyuan Liu ◽

Tianyu Liu ◽

Dieyi Chen ◽

Kuizhuang Jiao ◽

...

Keyword(s):

Air Pollution ◽

Hospital Admission ◽

Hospital Admissions ◽

Conditional Logistic Regression ◽

Warm Season ◽

Central China ◽

Air Pollution Control ◽

Short Term ◽

Short Term Exposure ◽

Increased Risk

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.

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Prediction and Comparative Analysis of Air Pollution in Major cities of India using Deep Learning Techniques

10.1109/icesc51422.2021.9532860 ◽

2021 ◽

Author(s):

Battula Bheemeswara Gopi Reddy ◽

Chinthada Praveen ◽

Marri Venkata Sai Kumar ◽

Idamakanti Mani Raghavendra Reddy ◽

Deepthi L. R

Keyword(s):

Air Pollution ◽

Deep Learning ◽

Comparative Analysis ◽

Learning Techniques

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How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17176274 ◽

2020 ◽

Vol 17 (17) ◽

pp. 6274 ◽

Cited By ~ 2

Author(s):

Zhiyu Fan ◽

Qingming Zhan ◽

Chen Yang ◽

Huimin Liu ◽

Meng Zhan

Keyword(s):

Air Pollution ◽

Regression Analysis ◽

Particulate Matter ◽

Socioeconomic Factors ◽

Influencing Factors ◽

Geographically Weighted Regression ◽

Meteorological Factors ◽

Central China ◽

Weighted Regression ◽

Pm2.5 And Pm10

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.

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