Spatio‐temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan

2021 ◽  
Author(s):  
Ayesha Mariam ◽  
Salman Tariq ◽  
Zia ul‐Haq ◽  
Usman Mehmood
Keyword(s):  
Particulate Matter ◽  
Health Risk ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Fine Particulate ◽  
Spatio Temporal

scholarly journals Comments on: “Meteorological correlates and AirQ+ health risk assessment of ambient fine particulate matter in Tehran, Iran”

2019 ◽  
Vol 174 ◽  
pp. 122-124
Author(s):  
Sasan Faridi ◽  
Sadegh Niazi ◽  
Mansour Shamsipour ◽  
Mohammad Sadegh Hassanvand
Keyword(s):  
Risk Assessment ◽  
Particulate Matter ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Fine Particulate Matter ◽  
Fine Particulate

scholarly journals Analysis of Spatio-Temporal Heterogeneity and Socioeconomic Driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1324
Author(s):  
Ju Wang ◽  
Ran Li ◽  
Kexin Xue ◽  
Chunsheng Fang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Shandong Province ◽  
Temporal Heterogeneity ◽  
Pollution Levels ◽  
Fine Particulate ◽  
Spatio Temporal ◽  
Surrounding Areas ◽  
Pm2.5 Concentration

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

scholarly journals Examining the Spatio-temporal Dynamics of PM2.5 in Saudi Arabia Using Satellite-derived Data: A Cluster Study

2016 ◽  
Author(s):  
Yusuf Aina ◽  
Elhadi Adam ◽  
Fethi Ahmed
Keyword(s):  
Cluster Analysis ◽  
Saudi Arabia ◽  
Particulate Matter ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
World Health ◽  
Self Organizing Map ◽  
Fine Particulate

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

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