Spatial–Temporal Characteristics, Source Apportionment, and Health Risks of Atmospheric Volatile Organic Compounds in China: A Comprehensive Review DOI Creative Commons

Yangbing Wei,

Xuexue Jing, Yaping Chen

et al.

Toxics, Journal Year: 2024, Volume and Issue: 12(11), P. 787 - 787

Published: Oct. 29, 2024

Volatile organic compounds (VOCs) are ubiquitous in the atmosphere, posing significant adverse impacts on air quality and human health. However, current research atmospheric VOCs mainly focuses specific regions or industries, without comprehensive national-level analysis. In this study, a total of 99 articles China published from 2015 to 2024 were screened, data their concentrations, source apportionment, health risks extracted summarized. The results revealed that annual average concentrations TVOCs groups generally increased then decreased between 2011 2022, peaking 2018-2019. A distinct seasonal pattern was observed, with highest occurring winter, followed by autumn, spring, summer. TVOC emissions highly concentrated northern eastern China, contributed alkanes alkenes. Source apportionment indicated vehicle sources (32.9% ± 14.3%), industrial (18.0% 12.8%), other combustion (13.0% 13.0%) primary China. There positive correlation (

Language: Английский

Two-decade surface ozone (O3) pollution in China: Enhanced fine-scale estimations and environmental health implications DOI
Zeyu Yang, Zhanqing Li,

Fan Cheng

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 317, P. 114459 - 114459

Published: Nov. 21, 2024

Language: Английский

Citations

8

Full-coverage estimation of PM2.5 in the Beijing-Tianjin-Hebei region by using a two-stage model DOI
Qiaolin Zeng,

Yeming Li,

Jinhua Tao

et al.

Atmospheric Environment, Journal Year: 2023, Volume and Issue: 309, P. 119956 - 119956

Published: July 13, 2023

Language: Английский

Citations

12

Spatiotemporal Patterns of Air Pollutants over the Epidemic Course: A National Study in China DOI Creative Commons
Kun Qin, Zhanpeng Wang, Shaoqing Dai

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(7), P. 1298 - 1298

Published: April 7, 2024

Air pollution has been standing as one of the most pressing global challenges. The changing patterns air pollutants at different spatial and temporal scales have substantially studied all over world, which, however, were intricately disturbed by COVID-19 subsequent containment measures. Understanding fine-scale stages epidemic’s course is necessary for better identifying region-specific drivers preparing environmental decision making during future epidemics. Taking China an example, this study developed a multi-output LightGBM approach to estimate monthly concentrations six major (i.e., PM2.5, PM10, NO2, SO2, O3, CO) in revealed distinct spatiotemporal each pollutant course. 5-year period 2019–2023 was selected observe changes from pre-COVID-19 era lifting performance our model, assessed cross-validation R2, demonstrated high accuracy with values 0.92 0.95 0.90 0.79 0.82 CO. Notably, there improvement particulate matter, particularly although PM10 exhibited rebound northern regions. SO2 CO consistently declined across country (p < 0.001 p 0.05, respectively), while O3 southern regions experienced notable increase. Concentrations Beijing–Tianjin–Hebei region effectively controlled mitigated. findings provide critical insights into trends quality public health emergencies, help guide development targeted interventions, inform policy aimed reducing disease burdens associated pollution.

Language: Английский

Citations

4

Time-series simulation of alpine grassland cover using transferable stacking deep learning and multisource remote sensing data in the Google Earth Engine DOI Creative Commons
Xingchen Lin, Jianjun Chen, Tonghua Wu

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 131, P. 103964 - 103964

Published: June 12, 2024

Language: Английский

Citations

4

Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning DOI
Qingqing He,

Jingru Cao,

Pablo E. Saide

et al.

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: 58(36), P. 15938 - 15948

Published: Aug. 28, 2024

Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying understanding a proxy that well-correlated with variation available high-resolution data. study introduces modeling method utilizing the XGBoost algorithm satellite-derived land surface temperature (LST) as primary predictor. Focusing on China in 2019, our model achieved cross-validation

Language: Английский

Citations

3

Spatial-and-local-aware deep learning approach for Ground-Level NO2 estimation in England with multisource data from satellite-based observations and chemical transport models DOI
Siying Wang, Shuangyin Zhang,

Dawei Wang

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 139, P. 104506 - 104506

Published: March 31, 2025

Language: Английский

Citations

0

Multi-routine-data driven spatio-temporal short-term predictions for surface ozone in China DOI

C. X. Zheng,

Hengqing Shen, Jianan Sun

et al.

Air Quality Atmosphere & Health, Journal Year: 2025, Volume and Issue: unknown

Published: May 14, 2025

Language: Английский

Citations

0

Neighborhood Ozone Estimation in Busan, South Korea: A Comparative Study of Proximity-Based Ensemble Clustering and Machine-Learning Models DOI
Ahmad Daudsyah Imami, Jurng‐Jae Yee

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102601 - 102601

Published: June 1, 2025

Language: Английский

Citations

0

Research on satellite data-driven algorithm for ground-level ozone concentration inversion: case of Yunnan, China DOI

Weiqiang Yu,

Tao Feng,

Xingwei Man

et al.

Earth Science Informatics, Journal Year: 2024, Volume and Issue: 17(2), P. 1053 - 1066

Published: Jan. 8, 2024

Language: Английский

Citations

2

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models DOI

Xingwei Man,

Rui Liu, Yu Zhang

et al.

Environmental Research, Journal Year: 2024, Volume and Issue: 251, P. 118609 - 118609

Published: March 3, 2024

Language: Английский

Citations

2