The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China

Wei, Jing; Li, Zhanqing; Xue, Wenhao; Sun, Lin; Fan, Tianyi; Liu, Lei; Su, Tianning; Cribb, Maureen

doi:10.1016/j.envint.2020.106290

Cited by 211 publications

(80 citation statements)

References 80 publications

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“…There is a slight temporal dependence in that the PM 2.5 bias increases gradually with increasing standard deviation, reaching a maximum around 11:00 LT and subsequently decreasing. This seems to be closely related to the diurnal variation in PM 2.5 concen- 2019c) because machine learning is not sensitive to the systematic bias of aerosol retrievals (Wei et al, 2021c). Nevertheless, our model is generally robust and can accurately estimate PM 2.5 concentrations with small mean (median) biases of 0.05-0.08 (0.63-0.99) µg m −3 during different hours throughout the day.…”

Section: Temporal-scale Performancementioning

confidence: 72%

“…For near-surface concentrations, the networks provide high-quality PM 2.5 measurements every hour (even every few minutes) but with non-uniform coverage. In recent years, an increased effort has been made in estimating PM 2.5 with products generated from multiple instruments on sunsynchronous satellites, e.g., the Multi-angle Imaging Spec-troRadiometer (MISR) (Liu et al, 2005;van Donkelaar et al, 2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) (Liu et al, 2007;Ma et al, 2014;Wei et al, 2019aWei et al, , 2020Wei et al, , 2021a, and the Visible Infrared Imaging Radiometer Suite (VIIRS) (Wei et al, 2021c;Wu et al, 2016;Yao et al, 2019). However, due to their low revisit cycles (one or two overpasses per day), they are unable to monitor the diurnal variation in pollution.…”

Section: Introductionmentioning

confidence: 99%

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Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

et al. 2021

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Abstract. Fine particulate matter with a diameter of less than 2.5 µm (PM2.5) has been used as an important atmospheric environmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors that usually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation to it. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5. Given the huge volume of satellite data, based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving the spatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset for China (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additional environmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements in China (cross-validation coefficient of determination, CV-R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of 13.62 and 8.49 µg m−3, respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increases gradually in the morning, reaching a peak at about 10:00 LT (GMT+8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, making it most suitable for routine pollution monitoring.

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Section: Temporal-scale Performancementioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

et al. 2021

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“…Meanwhile, fine particles (PM 2.5 ) are primary pollutants of substantial public concern in urban cities (Wei et al, 2019;Wei et al, 2021b). Through wet precipitation, rainwater scavenges air pollutants from the air (Niu et al, 2014;Rao et al, 2016;Wei et al, 2020;Wei et al, 2021a).…”

Section: Recent-past Reviewmentioning

confidence: 99%

A critical review of the variation in rainwater acidity in 24 Chinese cities during 1982–2018

Han

2021

Elementa: Science of the Anthropocene

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Rainwater is an essential pathway to remove fine particulate matter and dissolved atmospheric pollutants (e.g., SO2, HNO3, and NH3). Acid rain (pH < 5.6) has been a severe environmental issue in China since the 1970s, adversely impacting ecosystem health. This study focuses on the influence of anthropogenically induced anions (SO42– and NO3–) and alkaline cations (Ca2+ and NH4+) on acid rain in Chinese cities. In this review, cities with high population density east of the Hu Huanyong Line that divides China geographically according to its uneven economic development were studied. Coastal and central areas of China to the east of the line are characterized by a much faster developing economy and rapid urbanization. The observed trends and spatial variability of acidity and chemical composition in rainwater are discussed in relation to industrialization and environmental changes in China. Over the past 3½ decades, the precipitation pH in the urban regions has exhibited reduced acidity. A mixed nitric–sulfuric acid rain type has become prominent due to the significant decrease in SO42– via desulfurization. Ca2+ levels have decreased, while NH4+ has increased slightly due to more vehicular transportation. In addition, the neutralization capacity of Ca2+ and NH4+ has decreased from north to south. Overall, the acid rain problem in Chinese cities has been alleviated in recent years.

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“…This procedure was repeated 10 times until all the data had been tested. The training and validation data were totally independent in the sample and spatial scales, which have been widely used to evaluate the overall accuracy and spatial prediction ability of the model [40][41][42]. In addition, four statistical indicators were employed: the regression line (slope and intercept), R 2 , RMSE, In the first stage, to describe the time information more accurately, a time-weighted matrix was established to reflect the temporal difference in PM 2.5 concentrations among different days in a year.…”

Section: Valuation Approachesmentioning

confidence: 99%

Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

et al. 2021

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Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China.

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The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China

Cited by 211 publications

References 80 publications

Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

A critical review of the variation in rainwater acidity in 24 Chinese cities during 1982–2018

Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

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The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China

Cited by 211 publications

References 80 publications

Himawari-8-derived diurnal variations in ground-level PM&lt;sub&gt;2.5&lt;/sub&gt; pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Himawari-8-derived diurnal variations in ground-level PM&lt;sub&gt;2.5&lt;/sub&gt; pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

A critical review of the variation in rainwater acidity in 24 Chinese cities during 1982–2018

Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

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Resources

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Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)

Himawari-8-derived diurnal variations in ground-level PM<sub>2.5</sub> pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)