A Multivariate Geostatistical Framework to Assess the Spatio-Temporal Dynamics of Air Pollution and Land Surface Temperature in Bangladesh

被引:4
作者
Rahaman, Sk Nafiz [1 ]
Nelson, Jake [1 ]
Ali, Al Artat Bin [1 ]
Shermin, Nishat [2 ]
Pricope, Narcisa G. [2 ]
Al Kafy, Abdulla [3 ]
Sabuj, Md Shahaduzzaman [4 ]
Toa, Sharmin Sultana [5 ]
机构
[1] Auburn Univ, Dept Geosci, Auburn, AL 36849 USA
[2] Mississippi State Univ, Dept Geosci, Mississippi, MS 39762 USA
[3] Univ Texas Austin, Dept Geog & Environm, Austin, TX 78712 USA
[4] Khulna Univ, Urban & Rural Planning Discipline, Khulna 9208, Bangladesh
[5] Auburn Univ, Coll Forestry Wildlife & Environm, Auburn, AL 36849 USA
关键词
Air pollutants; Land surface temperature; Spatial-temporal analysis; Geostatistical analysis; Remote sensing; URBAN HEAT-ISLAND; WEIGHTED REGRESSION; EMISSIVITY PRODUCTS; QUALITY; EXPOSURE; ASTER; EMISSIONS; AEROSOLS; HEALTH; IMPACT;
D O I
10.1007/s41748-024-00418-9
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the context of escalating urban heat dynamics, the effect of air pollutants on Land Surface Temperature (LST) is an urgent concern, especially in the Global South. These regions are experiencing rapid industrialization, leading to an increase in greenhouse gas concentrations. Although the heat-absorbing capacity of air pollutants is well-recognized, the spatiotemporal relationship between these pollutants and LST remains underexplored, particularly in densely populated and industrialized metropolitan areas. Moreover, studies examining multiple pollutants simultaneously to understand their cumulative impact on surface temperature anomalies are scarce. Our study addresses this research gap by developing a spatial-temporal framework using remote sensing data from Google Earth Engine (GEE). We assessed the levels of Nitrogen Dioxide (NO2), Carbon Monoxide (CO), Aerosol Optical Depth (AOD), Ozone (O3), Sulfur Dioxide (SO2), and Formaldehyde (HCHO) in Bangladesh. Utilizing Emerging Hotspot Analysis and Geographically Weighted Regression (GWR) and complementing these with Principal Component Analysis (PCA) to create a Pollutant Impact Index (PII), we provide a detailed understanding of pollutant's impact on LST. The results revealed a global R-squared value of 0.61 with maximum local R-squared value of 0.68. Over 30% of the areas studied exhibit high-high clusters for air pollutant coefficients, with notably alarming levels of NO2 and O3, affecting 48.53% and 54.67% of the area, respectively. The PCA underscored the significant role of these pollutants, with the first three principal components accounting for 75% of the variance. Notably, the spatial distribution of the PII across Bangladesh showed substantial regional variations. Urban areas, like Dhaka and Sylhet, exhibited much higher PII values compared to less industrialized regions. These insights highlight the need for targeted environmental strategies to mitigate the impact of air pollution on urban heat dynamics and public health. The study's findings underscore the urgency of addressing these environmental challenges, particularly in rapidly developing areas of the Global South.
引用
收藏
页码:71 / 91
页数:21
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