Comment on egusphere-2024-3310 DOI Creative Commons

Опубликована: Дек. 17, 2024

Abstract. Understanding the spatiotemporal characteristics of long- and short-term exposure to ground ozone is crucial for improving environmental management health studies. However, such studies have been constrained by availability high-resolution data. To address this, we characterized ground-level variations risks across multiple spatial (pixel, county, region, national) temporal (daily, monthly, seasonal, annual) scales using daily 1-km data from 2000 2020, derived satellite LST via a machine-learning method. The model provided reliable estimates, validated through rigorous cross-validation direct comparison with external measurements. Our long-term estimates revealed seasonal shifts in high-exposure centers: spring eastern China, summer North China Plain (NCP), autumn Pearl River Delta (PRD). A non-monotonous trend was observed, levels rising 2001–2007 at rate 0.47 μg/m3/year, declining after 2008 (-0.58 μg/m3/year), increasing significantly 2016–2020 (1.16 accompanied regional fluctuations. Notably, increased 0.63 μg/m3/year NCP during second phase, 6.38 PRD third phase. Exposure over 100 μg/m3 shifted June May, exceeding 160 were primarily seen NCP, showing an expanding trend. day-to-day analysis highlights influence meteorological factors on extreme events. These findings emphasize need stronger mitigation efforts.

Язык: Английский

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

Fan Cheng

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 317, С. 114459 - 114459

Опубликована: Ноя. 21, 2024

Язык: Английский

Процитировано

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

и другие.

Atmospheric Environment, Год журнала: 2023, Номер 309, С. 119956 - 119956

Опубликована: Июль 13, 2023

Язык: Английский

Процитировано

12

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

и другие.

Remote Sensing, Год журнала: 2024, Номер 16(7), С. 1298 - 1298

Опубликована: Апрель 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.

Язык: Английский

Процитировано

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

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 131, С. 103964 - 103964

Опубликована: Июнь 12, 2024

Язык: Английский

Процитировано

4

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

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 139, С. 104506 - 104506

Опубликована: Март 31, 2025

Язык: Английский

Процитировано

0

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

C. X. Zheng,

Hengqing Shen, Jianan Sun

и другие.

Air Quality Atmosphere & Health, Год журнала: 2025, Номер unknown

Опубликована: Май 14, 2025

Язык: Английский

Процитировано

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, Год журнала: 2025, Номер unknown, С. 102601 - 102601

Опубликована: Июнь 1, 2025

Язык: Английский

Процитировано

0

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

Weiqiang Yu,

Tao Feng,

Xingwei Man

и другие.

Earth Science Informatics, Год журнала: 2024, Номер 17(2), С. 1053 - 1066

Опубликована: Янв. 8, 2024

Язык: Английский

Процитировано

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

и другие.

Environmental Research, Год журнала: 2024, Номер 251, С. 118609 - 118609

Опубликована: Март 3, 2024

Язык: Английский

Процитировано

2

Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador DOI Creative Commons
Mohammad Amin Khalili, Silvio Coda, Domenico Calcaterra

и другие.

Geomatics Natural Hazards and Risk, Год журнала: 2024, Номер 15(1)

Опубликована: Июль 30, 2024

Among the most intense geological disasters, landslides frequently occur throughout world. These phenomena have been studied using space geodetic techniques, including Global Navigation Satellite Systems (GNSS) and Multi-Temporal Interferometric Synthetic Aperture Radars (MT-InSAR). Nevertheless, complete mapping analysis of landslides' surface deformation in areas can be complicated due to a large diversity kinematics, such as periods quiescence acceleration toe crown. One these is Cuenca Ecuador, where investigation revealed that was located urban areas, with more noticeable effects. In contrast, its crown mainly rural green land area. this study, we show potential synergistic use COSMO-SkyMed (CSK) Sentinel-1A (S1A) synthetic aperture radar (SAR) data for comprehensively monitoring landslides. To aim, used Long-Short Term Memory (LSTM) Convolutional Neural Networks (CNN) two different Deep Learning Algorithms (DLAs) integrate results temporal spatial domain, respectively. A cross-comparison made nine GPS-derived deformations visual effects (i.e. crack width pattern) on field. This validation against GPS observation reveals RMSEs final MT-InSAR-derived velocity after applying synergic double band SAR dataset decrease at than 73% stations.

Язык: Английский

Процитировано

2