Investigating the performance of satellite-based models in estimating the surface PM2.5 over China

Dong, Lechao; Li, Siwei; Yang, Jie; Shi, Wenxuan; Zhang, Lei

doi:10.1016/j.chemosphere.2020.127051

Cited by 25 publications

(14 citation statements)

References 36 publications

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“…The prediction accuracy in the by-location CV (R 2 of 0.71 and RMSE of 22.1 μg/m 3 ) and by-year CV (R 2 of 0.58 and RMSE of 27.5 μg/m 3 ) was lower than that in the five-fold CV, indicating unobserved temporal and spatial trends contributed to the PM2.5 prediction. The model performance was comparable to that reported in previous studies (Xiao et al, 2018;He and Huang, 2018;Dong et al, 2020).…”

Section: Evaluation Of the Two-stage Pm25 Prediction Modelsupporting

confidence: 86%

Separating emission and meteorological contribution to PM2.5 trends over East China during 2000–2018

Xiao¹,

Zheng²,

Geng³

et al. 2021

Preprint

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Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used a combination of machine learning model, statistical model and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods, a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals as well as the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modelling system, to examine the meteorological contribution to PM2.5. We found good agreements between the GAM model estimations and the CMAQ model estimations of meteorological contribution to PM2.5 on monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual trends in meteorology-associate PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution control across the North China Plain and central China, but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over East China peaked at 2006 and 2011, mainly driven by the emission peaks in PM2.5 and PM2.5 precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in East China estimated from the GAM model. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fen-wei Plain, −4.8 % to 4.3 % in the Yangtze River Delta and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the worsening meteorological conditions in the northern part of China where air pollution was severe and population was clustered, stricter clean air actions are needed to avoid haze events in the future.

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Section: Evaluation Of the Two-stage Pm25 Prediction Modelsupporting

confidence: 86%

Separating emission and meteorological contribution to PM2.5 trends over East China during 2000–2018

Xiao¹,

Zheng²,

Geng³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This algorithm enhanced the spatial resolution of environmental variables at a specific location by calculating a weighted average of the nearest 4 grids. 28 The closer air pollutant and meteorologic variables grids were to the location of the cases, the larger the associated weights associated with the grids. In locations where fewer than 4 grids were available (for example, islands or coastal cities), we used given available grids and recalculated the weights so that the weights sum up to unity.…”

Section: Matching Stroke Cases With Exposure Using Bilinear Interpola...mentioning

confidence: 99%

Association of Ambient Particulate Matter Pollution of Different Sizes With In-Hospital Case Fatality Among Stroke Patients in China

et al. 2022

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Objective:To characterize the association of ambient particulate matter (PM) pollution of different sizes (particulate matter ≤1 µm in aerodynamic diameter [PM1], PM2.5, and PM10) with in-hospital case fatality among stroke patients in China.Methods:We collected hospitalizations due to stroke in four provinces in China from 2013 to 2019. Seven-day and annual averages of PM prior to hospitalization were estimated using bilinear interpolation and residential addresses. Associations with in-hospital case fatality were estimated using random-effects logistic regression models. Potential reducible fraction and the number of fatalities attributed to PM were estimated using a counterfactual approach.Results:Among 3,109,634 stroke hospitalizations (mean age 67.23 [standard deviation 12.22]; 1,765,644 [56.78%] male), we identified 32,140 in-hospital stroke fatalities (case fatality rate 1.03%). Each 10 µg/m3 increase in 7-day average (short-term) exposure to PM was associated with increased in-hospital case fatality: odds ratios (ORs) were 1.058 (95% confidence interval [CI]: 1.047-1.068) for PM1, 1.037 (95% CI: 1.031-1.043) for PM2.5, and 1.025 (95% CI: 1.021-1.029) for PM10. Similar but larger ORs were observed for annual averages (long-term): 1.240 (95% CI: 1.217-1.265) for PM1, 1.105 (95% CI: 1.094-1.116) for PM2.5, and 1.090 (95% CI: 1.082-1.099) for PM10. In counterfactual analyses, PM10 was associated with the largest potential reducible fraction in in-hospital case fatality (10% [95% CI: 8.3%-11.7%] for short-term exposure and 21.1% [19.1%-23%] for long-term exposure), followed by PM1 and PM2.5.Conclusion:PM pollution is a risk factor for in-hospital stroke-related deaths. Strategies that target reducing PM pollution may improve the health outcomes of stroke patients.

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“…These meteorological parameters have been reported to be strongly associated with PM 2.5 concentrations in various regions in China (Z. Feng et al, 2020) and contributed significantly in previous PM 2.5 prediction models (She et al, 2020). Both the PM 2.5 data and the meteorology data followed the same processing protocol.…”

Section: Assessment Of the Meteorological Effects On Pm 25 Using Gammentioning

confidence: 99%

“…The interannual variations in the meteorology-associated PM 2.5 assessed in this study were consistent with those reported in previous studies . For example, Feng et al (2020) presented the long-term variations in air stagnation in northern China that characterized the circulation and diffusion in the boundary layer with fixed emissions to describe the temporal trend of haze-related weather conditions. The temporal pattern of the air stagnation index from 2000 to 2018 closely resembled the temporal trend of the estimated meteorological-associated PM 2.5 in this study.…”

Section: Interannual and Seasonal Variabilities Of Meteorology-associated Pm 25mentioning

confidence: 99%

“…Previous studies further analyzed the long-term trend of effects of meteorological systems and climate change on PM 2.5 pollution, especially in the context of global warming Wang and Chen, 2016;Yi et al, 2019). For example, Feng et al (2020) reported a trend of negative meteorological effects on air quality improvements in northern China during 1980-2018, but the effects dropped during 2013-2018. Xu et al (2020) also reported a trend of negative meteorological effects during 2000-2017 in Beijing but an overall trend of beneficial meteorological effects in some provinces in northern China.…”

Section: Introductionmentioning

confidence: 99%

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Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000–2018

et al. 2021

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Abstract. The contribution of meteorology and emissions to long-term PM2.5 trends is critical for air quality management but has not yet been fully analyzed. Here, we used the combination of a machine learning model, statistical method, and chemical transport model to quantify the meteorological impacts on PM2.5 pollution during 2000–2018. Specifically, we first developed a two-stage machine learning PM2.5 prediction model with a synthetic minority oversampling technique to improve the satellite-based PM2.5 estimates over highly polluted days, thus allowing us to better characterize the meteorological effects on haze events. Then we used two methods to examine the meteorological contribution to PM2.5: a generalized additive model (GAM) driven by the satellite-based full-coverage daily PM2.5 retrievals and the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. We found good agreements between GAM estimations and the CMAQ model estimations of the meteorological contribution to PM2.5 on a monthly scale (correlation coefficient between 0.53–0.72). Both methods revealed the dominant role of emission changes in the long-term trend of PM2.5 concentration in China during 2000–2018, with notable influence from the meteorological condition. The interannual variabilities in meteorology-associated PM2.5 were dominated by the fall and winter meteorological conditions, when regional stagnant and stable conditions were more likely to happen and when haze events frequently occurred. From 2000 to 2018, the meteorological contribution became more unfavorable to PM2.5 pollution across the North China Plain and central China but were more beneficial to pollution control across the southern part, e.g., the Yangtze River Delta. The meteorology-adjusted PM2.5 over eastern China (denoted East China in figures) peaked in 2006 and 2011, mainly driven by the emission peaks in primary PM2.5 and gas precursors in these years. Although emissions dominated the long-term PM2.5 trends, the meteorology-driven anomalies also contributed −3.9 % to 2.8 % of the annual mean PM2.5 concentrations in eastern China estimated from the GAM. The meteorological contributions were even higher regionally, e.g., −6.3 % to 4.9 % of the annual mean PM2.5 concentrations in the Beijing-Tianjin-Hebei region, −5.1 % to 4.3 % in the Fenwei Plain, −4.8 % to 4.3 % in the Yangtze River Delta, and −25.6 % to 12.3 % in the Pearl River Delta. Considering the remarkable meteorological effects on PM2.5 and the possible worsening trend of meteorological conditions in the northern part of China where air pollution is severe and population is clustered, stricter clean air actions are needed to avoid haze events in the future.

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Investigating the performance of satellite-based models in estimating the surface PM2.5 over China

Cited by 25 publications

References 36 publications

Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

Association of Ambient Particulate Matter Pollution of Different Sizes With In-Hospital Case Fatality Among Stroke Patients in China

Separating emission and meteorological contributions to long-term PM<sub>2.5</sub> trends over eastern China during 2000–2018

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Investigating the performance of satellite-based models in estimating the surface PM2.5 over China

Cited by 25 publications

References 36 publications

Separating emission and meteorological contribution to PM&lt;sub&gt;2.5&lt;/sub&gt; trends over East China during 2000–2018

Separating emission and meteorological contribution to PM&lt;sub&gt;2.5&lt;/sub&gt; trends over East China during 2000–2018

Association of Ambient Particulate Matter Pollution of Different Sizes With In-Hospital Case Fatality Among Stroke Patients in China

Separating emission and meteorological contributions to long-term PM&lt;sub&gt;2.5&lt;/sub&gt; trends over eastern China during 2000–2018

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Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

Separating emission and meteorological contribution to PM<sub>2.5</sub> trends over East China during 2000–2018

Separating emission and meteorological contributions to long-term PM<sub>2.5</sub> trends over eastern China during 2000–2018