Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China

Remote Sensing of Environment, Год журнала: 2021, Номер 270, С. 112775 - 112775

Опубликована: Ноя. 11, 2021

Язык: Английский

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Tracking Air Pollution in China: Near Real-Time PM_2.5 Retrievals from Multisource Data Fusion

Environmental Science & Technology, Год журнала: 2021, Номер 55(17), С. 12106 - 12115

Опубликована: Авг. 19, 2021

Air pollution has altered the Earth's radiation balance, disturbed ecosystem, and increased human morbidity mortality. Accordingly, a full-coverage high-resolution air pollutant data set with timely updates historical long-term records is essential to support both research environmental management. Here, for first time, we develop near real-time database known as Tracking Pollution in China (TAP, http://tapdata.org.cn/) that combines information from multiple sources, including ground observations, satellite aerosol optical depth (AOD), operational chemical transport model simulations, other ancillary such meteorological fields, land use data, population, elevation. Daily PM2.5 at spatial resolution of 10 km our product. The TAP estimated based on two-stage machine learning coupled synthetic minority oversampling technique tree-based gap-filling method. Our an averaged out-of-bag cross-validation R2 0.83 different years, which comparable those studies, but improves its performance high levels fills gaps missing AOD daily scale. full coverage allow us track day-to-day variations concentrations over manner. since 2000 will also policy assessments health impact studies. are publicly available through website sharing communities.

Язык: Английский

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

Environment International, Год журнала: 2020, Номер 146, С. 106290 - 106290

Опубликована: Дек. 11, 2020

Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets coarse spatial resolutions, limiting their applications, especially at city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as STET model), is designed to estimate near-surface concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, pollutant emissions, are used in model generate high-resolution (1 km) high-quality dataset China ChinaHighPM10) from 2015 2019. has an out-of-sample (out-of-station) cross-validation coefficient determination (CV-R2) 0.86 (0.82) a root-mean-square error (RMSE) 24.28 (27.07) μg/m3, outperforming most widely models previous related studies. High levels concentration occurred northwest (e.g., Tarim Basin) Northern Plain. Overall, concentrations had significant declining trend 5.81 μg/m3 per year (p < 0.001) over past five years China, three key urban agglomerations. ChinaHighPM10 potentially useful future small- medium-scale air pollution studies by virtue its higher overall accuracy.

Язык: Английский

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Ground-Level NO₂Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence

Environmental Science & Technology, Год журнала: 2022, Номер 56(14), С. 9988 - 9998

Опубликована: Июнь 29, 2022

Nitrogen dioxide (NO2) at the ground level poses a serious threat to environmental quality and public health. This study developed novel, artificial intelligence approach by integrating spatiotemporally weighted information into missing extra-trees deep forest models first fill satellite data gaps increase availability 49% then derive daily 1 km surface NO2 concentrations over mainland China with full spatial coverage (100%) for period 2019–2020 combining measurements, tropospheric columns derived from TROPOMI OMI, atmospheric reanalysis, model simulations. Our estimates have an average out-of-sample (out-of-city) cross-validation coefficient of determination 0.93 (0.71) root-mean-square error 4.89 (9.95) μg/m3. The seamless high-resolution high-quality dataset "ChinaHighNO2" allows us examine patterns fine scales such as urban–rural contrast. We observed systematic large differences between urban rural areas (28% on average) in NO2, especially provincial capitals. Strong holiday effects were found, declines 22 14% during Spring Festival National Day China, respectively. Unlike North America Europe, there is little difference weekdays weekends (within ±1 μg/m3). During COVID-19 pandemic, decreased considerably gradually returned normal levels around 72nd day after Lunar New Year which about 3 weeks longer than column, implying that former can better represent changes NOx emissions.

Язык: Английский

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Ground-level gaseous pollutants (NO₂, SO₂, and CO) in China: daily seamless mapping and spatiotemporal variations

Atmospheric chemistry and physics, Год журнала: 2023, Номер 23(2), С. 1511 - 1532

Опубликована: Янв. 26, 2023

Abstract. Gaseous pollutants at the ground level seriously threaten urban air quality environment and public health. There are few estimates of gaseous that spatially temporally resolved continuous across China. This study takes advantage big data artificial-intelligence technologies to generate seamless daily maps three major ambient pollutant gases, i.e., NO2, SO2, CO, China from 2013 2020 a uniform spatial resolution 10 km. Cross-validation between our observations illustrated high on basis for surface CO concentrations, with mean coefficients determination (root-mean-square errors) 0.84 (7.99 µg m−3), (10.7 0.80 (0.29 mg respectively. We found COVID-19 lockdown had sustained impacts pollutants, where recovered its normal in around 34th day after Lunar New Year, while SO2 NO2 rebounded more than 2 times slower due emissions residents' increased indoor cooking atmospheric oxidation capacity. Surface reached their peak annual concentrations 21.3 ± 8.8 m−3, 23.1 13.3 1.01 0.29 m−3 2013, then continuously declined over time by 12 %, 55 17 respectively, until 2020. The declining rates were prominent 2017 sharper reductions anthropogenic but have slowed down recent years. Nevertheless, people still suffer high-frequency risk exposure eastern China, almost World Health Organization (WHO) recommended short-term guidelines (AQG) since 2018, benefiting implemented stricter “ultra-low” emission standards. reconstructed dataset will benefit future (especially short-term) pollution environmental health-related studies.

Язык: Английский

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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)

Atmospheric chemistry and physics, Год журнала: 2021, Номер 21(10), С. 7863 - 7880

Опубликована: Май 25, 2021

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 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 air pollution, well our adaptation to it. Most existing products derived from polar-orbiting satellites. This study exploits use next-generation geostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) document variation in PM2.5. Given huge volume data, based idea gradient boosting, highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method involving spatiotemporal characteristics namely space-time LightGBM (STLG) model, developed. An hourly dataset for China (i.e., ChinaHighPM2.5) at 5 km spatial resolution aerosol additional variables. Hourly estimates (number data samples = 1 415 188) are correlated ground measurements (cross-validation coefficient determination, CV-R2 0.85), root-mean-square error (RMSE) mean absolute (MAE) 13.62 8.49 µg m−3, respectively. Our model captures variations showing pollution increases gradually morning, reaching peak about 10:00 LT (GMT+8), then decreases steadily until sunset. The proposed approach outperforms most traditional statistical regression machine-learning models much lower computational burden terms speed memory, making it suitable routine monitoring.

Язык: Английский

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Separating Daily 1 km PM_2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data

Environmental Science & Technology, Год журнала: 2023, Номер 57(46), С. 18282 - 18295

Опубликована: Апрель 28, 2023

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, health. These effects are still not well understood due to limited surface observations uncertainties in model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) estimate daily PM2.5 at spatial resolution of 1 km China since 2000 by integrating measurements species from high-density observation network, satellite retrievals, atmospheric reanalyses, Cross-validation results illustrate reliability sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), chloride (Cl-) estimates, with high coefficients determination (CV-R2) ground-based 0.74, 0.75, 0.71, 0.66, average root-mean-square errors (RMSE) 6.0, 6.6, 4.3, 2.3 μg/m3, respectively. The three components secondary inorganic aerosols (SIAs) account for 21% 20% 14% (NH4+) total mass eastern China; we observed significant reductions 40-43% between 2013 2020, slowing down 2018. Comparatively, ratio SIA increased 7% across except Beijing nearby areas, accelerating recent years. SO42- been dominant component China, although it was surpassed NO3- some e.g., Beijing-Tianjin-Hebei region 2016. SIA, accounting nearly half (∼46%) mass, drove explosive formation winter haze episodes North Plain. A sharp decline concentrations an increase SIA-to-PM2.5 ratios during COVID-19 lockdown were also revealed, reflecting enhanced oxidation capacity particles.

Язык: Английский

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LGHAP: the Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion

Earth system science data, Год журнала: 2022, Номер 14(2), С. 907 - 927

Опубликована: Фев. 24, 2022

Abstract. Developing a big data analytics framework for generating the Long-term Gap-free High-resolution Air Pollutant concentration dataset (abbreviated as LGHAP) is of great significance environmental management and Earth system science analysis. By synergistically integrating multimodal aerosol acquired from diverse sources via tensor-flow-based fusion method, gap-free optical depth (AOD) with daily 1 km resolution covering period 2000–2020 in China was generated. Specifically, gaps AOD imageries Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra were reconstructed based on set tensors satellites, numerical analysis, situ air quality measurements integrative efforts spatial pattern recognition high-dimensional gridded image analysis knowledge transfer statistical mining. To our knowledge, this first long-term high-resolution China, which spatially contiguous PM2.5 PM10 concentrations then estimated using an ensemble learning approach. Ground validation results indicate that LGHAP are good agreement observations Aerosol Robotic Network (AERONET), R 0.91 RMSE equaling 0.21. Meanwhile, estimations also agreed well ground measurements, values 0.95 0.94 RMSEs 12.03 19.56 µg m−3, respectively. The provides suite maps high to better examine changes over past 2 decades, three major variation periods haze pollution revealed. Additionally, proportion population exposed unhealthy increased 50.60 % 2000 63.81 2014 across reduced drastically 34.03 2020. Overall, generated has potential trigger multidisciplinary applications observations, climate change, public health, ecosystem assessment, management. AOD, PM2.5, datasets publicly available at https://doi.org/10.5281/zenodo.5652257 (Bai et al., 2021a), https://doi.org/10.5281/zenodo.5652265 2021b), https://doi.org/10.5281/zenodo.5652263 2021c), Monthly annual can be https://doi.org/10.5281/zenodo.5655797 2021d) https://doi.org/10.5281/zenodo.5655807 2021e), Python, MATLAB, R, IDL codes provided help users read visualize these data.

Язык: Английский

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Extreme Temperature Events, Fine Particulate Matter, and Myocardial Infarction Mortality

Circulation, Год журнала: 2023, Номер 148(4), С. 312 - 323

Опубликована: Июль 24, 2023

Extreme temperature events (ETEs), including heat wave and cold spell, have been linked to myocardial infarction (MI) morbidity; however, their effects on MI mortality are less clear. Although ambient fine particulate matter (PM

Язык: Английский

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Spatial–temporal evolution and driving force analysis of eco-quality in urban agglomerations in China

The Science of The Total Environment, Год журнала: 2023, Номер 866, С. 161465 - 161465

Опубликована: Янв. 7, 2023

Язык: Английский

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