Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

Song, Junlei; Li, Chunlin; Liu, Miao; Hu, Ying-Li; Wu, Wen

doi:10.3390/rs14133173

Cited by 7 publications

(2 citation statements)

References 71 publications

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“…MERRA-2, CHAP, and the generated data in this study have been resampled to the exact resolution (1 km) of LGHAP and Landscan data. Additionally, we analyzed region exposure risk (RER) at a daily (24-h) level [60]. Details of Landscan, PWE, and RER can be found in Sections S3-S5.…”

Section: Particle Exposure Analysismentioning

confidence: 99%

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Chu,

Zhang,

2023

Remote Sensing

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Satellite-derived aerosol optical depth (AOD) has been extensively utilized for retrieving ground-level PM2.5 distributions. However, the presence of non-random missing data gaps in AOD poses a challenge to directly obtaining the gap-free AOD-derived PM2.5, thereby impeding accurate exposure risk assessment. Here, this study presents a novel and flexible framework that couples stacking and flexible spatiotemporal data fusion (FSDAF) approaches. By integrating multiple models and data sources, this framework aims to generate hourly (24-h) gap-free PM2.5 estimates for the Beijing–Tianjin–Hebei (BTH) region in 2018. This study effectively reconstructed data at least three times more effectively than the original AOD-derived PM2.5, achieving the Pearson coefficient (r), the coefficient determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) values of 0.91, 0.84, 19.38 µg/m3, and 12.17 µg/m3, respectively, based on entire samples. Such strong predictive performance was also exhibited in spatial-based (r: 0.92–0.93, R2: 0.85–0.87, RMSE: 18.13 µg/m3–20.18 µg/m3, and MAE: 11.21 µg/m3–12.52 µg/m3) and temporal-based (r: 0.91–0.98, R2: 0.82–0.96, RMSE: 3.8 µg/m3–21.89 µg/m3, and MAE: 2.71 µg/m3–14.00 µg/m3) validations, indicating the robustness of this framework. Additionally, this framework enables the assessment of annual and seasonal PM2.5 concentrations and distributions, revealing that higher levels are experienced in the southern region, while lower levels prevail in the northern part. Winter exhibits the most severe levels, followed by spring and autumn, with comparatively lower levels in summer. Notably, the proposed framework effectively mitigates bias in calculating population-weighted exposure risk by filling data gaps with calculated values of 51.04 µg/m3, 54.17 µg/m3, 56.24 µg/m3, and 55.00 µg/m3 in Beijing, Tianjin, Hebei, and the BTH region, respectively.

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Section: Particle Exposure Analysismentioning

confidence: 99%

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Chu,

Zhang,

2023

Remote Sensing

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“…Following the implementation of the Action Plan of Air Pollution Prevention and Control by the State Council on 10 September 2013, China saw promising results in air pollution prevention and control [9]. Between 2015 and 2020, the concentration of PM 2.5 in urban agglomerations significantly decreased [10]; however, O 3 concentrations increased. Ozone primarily forms in cities through the reaction of nitrogen oxides (NO x ), volatile organic compounds (VOCs), carbon monoxide (CO), and other precursors in the presence of light [11], with these substances consistently affected by human activities and industrial emissions [12,13].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Zhou

Liu

2023

Sustainability

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Tropospheric ozone (O3) pollution has emerged as a significant concern, as it can adversely influence human health, daily activities, and the surrounding environment(The following tropospheric O3 is referred to as O3). Research on the societal contribution to O3 primarily concentrates on the generation mechanisms and chemical processes, with limited studies examining the influence of social and economic activities on O3 at a national scale. In this investigation, spatial econometric models, random forest models, and geographically weighted regression (GWR) were adopted for assessing the effects of meteorological, natural, and socioeconomic factors on O3 concentration throughout the country. The spatial error model (SEM) revealed that precipitation, temperature, wind direction, per capita GDP, RD project funding, and SO2 were the primary factors influencing O3 concentration in China, among which precipitation had the strongest effect on O3, followed by temperature and SO2. Subsequently, the GWR model was utilized to demonstrate the regional differences in the impacts of precipitation, NOx, secondary industry proportion, and electricity consumption. In central and western regions, such as Jiangxi, Guangxi, and Guizhou, precipitation, NOx, and power consumption were the leading factors contributing to severe O3 pollution. The secondary industry proportion substantially affected O3 pollution in the Beijing-Tianjin-Hebei region, indicating that this sector played a crucial role in the region’s economic growth and contributed to elevated O3 concentrations. Meteorological, natural, and socioeconomic factors exhibited a lesser influence on O3 pollution in most eastern regions compared to central and western regions. This study’s findings identified the primary contributors to O3 pollution and provided a scientific basis for developing strategies to mitigate its impact.

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Comprehensively Assessing PM2.5 Exposure Across Mainland China with Estimated Concentrations Considering Spatial Aggregation

Li,

Huang,

Zhang

et al. 2024

Int J Environ Res

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Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

Cited by 7 publications

References 71 publications

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Bridging the Data Gap: Enhancing the Spatiotemporal Accuracy of Hourly PM2.5 Concentration through the Fusion of Satellite-Derived Estimations and Station Observations

Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China

Comprehensively Assessing PM2.5 Exposure Across Mainland China with Estimated Concentrations Considering Spatial Aggregation

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