Spatial interpolation of regional PM2.5 concentrations in China during COVID-19 incorporating multivariate data

Wei, Pengzhi; Xie, Shaofeng; Huang, Liangke; Liu, Lilong; Cui, Lilu; Tang, Youbing; Zhang, Yabo; Meng, Chunyang; Zhang, Linxin

doi:10.1016/j.apr.2023.101688

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…In this study, the GWR model's R² was consistently high, even though it was higher compared to GWR and GWR-EBK and lower compared to GWRK and GWR-TSF in the previous study [23]. The GWR model in this study exhibited high performance, explaining more than 90% of the total data, even without combining with other models and functions.…”

Section: Spatiotemporal Heterogeneity Analysissupporting

confidence: 50%

“…The GWR model demonstrated varying R 2 values, with the highest (0.9748) observed in March and the lowest (0.9116) in August. Comparatively, the model exhibited strong performance compared to a previous study [23].…”

Section: Spatiotemporal Heterogeneity Analysismentioning

confidence: 65%

“…Considering the notable correlations of O3 in July (0.747), November (−0.777), and December (−0.734), multicollinearity issues in the GWR model regression analysis are potential concern. In previous studies, the correlation coefficient between PM2.5 and ozone was largely over 0.7, and NDVI also showed a high correlation [23,24]. While it i acknowledged that this correlation may vary over time and space, an overall strong cor relation is anticipated.…”

Section: Pearson's Correlation Analysismentioning

confidence: 88%

“…In Figure 9, the regression coefficient of O3 from the GWR model results is presented. The analysis compares and examines the regression coefficients of PM2.5 and O3 for June and December, representing months with the minimum and maximum monthly average To assess the model's accuracy, R² was compared with a previous study that evaluated the suitability of the GWR model [23]. The purpose of comparing our study with previous research is to assess the reliability of the coefficient of determination produced by the GWR model we employed.…”

Section: Spatiotemporal Heterogeneity Analysismentioning

confidence: 99%

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Spatial Distribution Characteristics and Analysis of PM2.5 in South Korea: A Geographically Weighted Regression Analysis

Lee,

Kim,

Kim

et al. 2024

Atmosphere

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PM2.5, a critical air pollutant, requires health-conscious management, with concentrations varying across regions due to diverse sources. This study, conducted in South Korea in 2021, employed the geographically weighted regression model to analyze the spatiotemporal correlations of PM2.5 with O3 and the normalized difference vegetation index (NDVI). Regional differences in the correlation between PM2.5 and O3 were observed, influenced by common precursors (SOx, NOx, and volatile organic compounds (VOCs)), seasonal temperature variations, and solar radiation differences. Notably, PM2.5 and O3 exhibited a heightened regression coefficient in summer, emphasizing the need for specific management targeting VOCs and NO2. The interplay between PM2.5 and NDVI revealed a negative overall impact but a positive effect in the central region of Korea, suggesting vegetation’s role in the PM2.5 concentration increase due to atmospheric stagnation caused by mountain ranges. These findings enhance our understanding of PM2.5 distribution mechanisms, highlighting the need for tailored policies in each region for effective concentration reductions.

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Section: Spatiotemporal Heterogeneity Analysissupporting

confidence: 50%

Section: Spatiotemporal Heterogeneity Analysismentioning

confidence: 65%

Section: Pearson's Correlation Analysismentioning

confidence: 88%

Section: Spatiotemporal Heterogeneity Analysismentioning

confidence: 99%

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