Evolution in disparity of PM2.5 pollution in China

Shi, Su; Wang, Weidong; Li, Xinyue; Xu, Chang; Lei, Jian; Jiang, Yixuan; Zhang, Lina; He, Cheng; Xue, Tao; Chen, Renjie; Kan, Haidong; Meng, Xia

doi:10.1016/j.eehl.2023.08.007

Cited by 10 publications

(2 citation statements)

References 36 publications

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“…MAIAC AOD values for cloud contamination or land covered by snow were cleaned based on quality assurance (QA) flags. For comparing long-term trend of UV radiation and air pollution, fine particulate matter (PM2.5) data was also included in this study, which was predicted using a random forest model with a daily level and spatial resolution of 1 km × 1 km in China (Meng et al, 2021;Shi et al, 115 2023a;Shi et al, 2023b). Elevation and SZA was spatially joined and averaged into 10 km grid cells.…”

Section: Other Predictor Variablesmentioning

confidence: 99%

Comment on essd-2024-111

2024

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Section: Other Predictor Variablesmentioning

confidence: 99%

Comment on essd-2024-111

2024

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“…As a fine particulate matter capable of penetrating deep into the lungs and entering the bloodstream, PM 2.5 poses a grave threat to human health [11]. Recent years have seen China achieve notable improvements in air quality [12]. Significant strides have been achieved in reducing major air pollutants and enhancing air quality, attributed to initiatives such as the Action Plan for Prevention and Control of Air Pollution and the Three-Year Action Plan for Winning the Battle for the Blue Sky.…”

Section: Introductionmentioning

confidence: 99%

Effects of Big Data on PM2.5: A Study Based on Double Machine Learning

Wei,

Cheng,

Duan

et al. 2024

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The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data’s suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies.

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