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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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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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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Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

The Lancet Planetary Health, Год журнала: 2023, Номер 7(12), С. e963 - e975

Опубликована: Дек. 1, 2023

Long-term improvements in air quality and public health the continental USA were disrupted over past decade by increased fire emissions that potentially offset decrease anthropogenic emissions. This study aims to estimate trends black carbon PM

Язык: Английский

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First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Дек. 15, 2023

Abstract Here we retrieve global daily 1 km gapless PM 2.5 concentrations via machine learning and big data, revealing its spatiotemporal variability at an exceptionally detailed level everywhere every day from 2017 to 2022, valuable for air quality monitoring, climate change, public health studies. We find that 96%, 82%, 53% of Earth’s populated areas are exposed unhealthy least one day, week, month in respectively. Strong disparities exposure risks duration exhibited between developed developing countries, urban rural areas, different parts cities. Wave-like dramatic changes clearly seen around the world before, during, after COVID-19 lockdowns, as is mortality burden linked fluctuating pollution events. Encouragingly, only approximately one-third all countries return pre-pandemic levels. Many nature-induced episodes also revealed, such biomass burning.

Язык: Английский

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A review of machine learning for modeling air quality: Overlooked but important issues

Atmospheric Research, Год журнала: 2024, Номер 300, С. 107261 - 107261

Опубликована: Янв. 21, 2024

Язык: Английский

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Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data

Electronics, Год журнала: 2025, Номер 14(4), С. 696 - 696

Опубликована: Фев. 11, 2025

The integration of artificial intelligence (AI) agents with the Internet Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, more effective decision making. This comprehensive literature review explores AI IoT technologies within sciences, particular focus on applications related to water quality climate data. methodology involves systematic search selection relevant studies, followed by thematic, meta-, comparative analyses synthesize current research trends, benefits, challenges, gaps. highlights how enhances IoT’s collection capabilities through predictive modeling, real-time analytics, automated making, thereby improving accuracy, timeliness, efficiency systems. Key benefits identified include enhanced precision, cost efficiency, scalability, facilitation proactive management. Nevertheless, this encounters substantial obstacles, including issues quality, interoperability, security, technical constraints, ethical concerns. Future developments point toward enhancements technologies, incorporation innovations like blockchain edge computing, potential formation global systems, greater public involvement citizen science initiatives. Overcoming these challenges embracing new technological trends could enable play pivotal role strengthening sustainability resilience.

Язык: Английский

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Systemic inflammation accelerates the adverse effects of air pollution on metabolic syndrome: Findings from the China health and Retirement Longitudinal Study (CHARLS)

Environmental Research, Год журнала: 2022, Номер 215, С. 114340 - 114340

Опубликована: Сен. 12, 2022

Язык: Английский

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Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign

Environment International, Год журнала: 2022, Номер 170, С. 107606 - 107606

Опубликована: Ноя. 3, 2022

Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution limited coverage, uncertainties influential factors seriously restrain related epidemiology pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily concentrations fine grid scale (1 km × 1 km) from 2018 2020 across China based machine learning methods using hourly observed ground-level pollutant data, meteorological satellite auxiliary data including digital elevation model (DEM), land use (LUD), normalized difference vegetation index (NDVI), population (POP), nighttime light images (NTL), identify diverse urbanization topography conditions. Some findings were achieved. The correlation coefficients (R2) between surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 v-component (MVW), NDVI 0.80, 0.40, 0.35, 0.30, 0.20, respectively. random forest (RF) demonstrated highest validation R2 (0.86) lowest RMSE (13.74 μg/m3) in estimating concentrations, followed by support vector (SVM) (R2 = 0.75, 18.39 μg/m3), backpropagation neural network (BP) 0.74, 19.26 multiple linear regression (MLR) 0.52, 25.99 μg/m3). Our High-Resolution Dataset (CHROD) exhibited an acceptable accuracy at different spatial-temporal scales. Additionally, showed decreasing trend represented obviously heterogeneity 2020. Overall, was mainly affected activities higher regions, while controlled factors, lower regions. this study is useful valuable understanding mechanism formation improving quality dataset.

Язык: Английский

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Individual and joint associations of long-term exposure to air pollutants and cardiopulmonary mortality: a 22-year cohort study in Northern China

The Lancet Regional Health - Western Pacific, Год журнала: 2023, Номер 36, С. 100776 - 100776

Опубликована: Май 4, 2023

Evidence on the associations between long-term exposure to multiple air pollutants and cardiopulmonary mortality is limited, especially for developing regions with higher pollutant levels. We aimed characterise individual joint (multi-pollutant) of mortality, identify that primarily contributes risk.

Язык: Английский

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