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

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

и другие.

Environmental Science & Technology, Год журнала: 2024, Номер 58(36), С. 15938 - 15948

Опубликована: Авг. 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

Язык: Английский

Процитировано

2
Spatiotemporal Patterns and Quantitative Analysis of Factors Influencing Surface Ozone over East China DOI Open Access
Mingliang Ma,

Mengjiao Liu,

Mengnan Liu

и другие.

Sustainability, Год журнала: 2023, Номер 16(1), С. 123 - 123

Опубликована: Дек. 22, 2023

Surface ozone pollution in China has been persistently becoming worse recent years; therefore, it is of great importance to accurately estimate and explore the spatiotemporal variations surface East China. By using S5P-TROPOMI-observed NO2, HCHO data (7 km × 3.5 km), other surface-ozone-influencing factors, including VOCs, meteorological data, NOX emission inventory, NDVI, DEM, population, land use cover, hourly situ observations, an extreme gradient boosting model was used daily 0.05° gridded maximum average 8 h (MDA8) during 2019–2021. Four estimation models were established by combining NO2 from S5P-TROPOMI observations CAMS reanalysis data. The sample-based validation R2 values these four all larger than 0.92, while their site-based 0.82. results revealed that coverage ratio highest (100%), second (96.26%). Furthermore, MDA8 two averaged produce final dataset. It indicated O3 2019–2021 susceptible anthropogenic precursors such as VOCs (22.55%) (8.97%), well factors (27.35%) wind direction, temperature, speed. Subsequently, patterns analyzed. Ozone mainly concentrated North Plain (NCP), Pearl River Delta (PRD), Yangtze (YRD). Among three regions, NCP occurs June (summer), YRD May (spring), PRD April (spring) September (autumn). In addition, concentration decreased 3.74% 2020 compared 2019, which may have influenced COVID-19 epidemic implementation policy synergistic management PM2.5 pollution. regions mostly affected (−2~−8%), Middle Lower (−6~−10%), (−4~−10%). Overall, estimated 2019 2021 provides a promising source analysis basis for related researchers. Meanwhile, reveals spatial temporal main influencing good control pollution, also technical support sustainable development environment

Язык: Английский

Процитировано

5
High-resolution estimation of near-surface ozone concentration and population exposure risk in China DOI
Jinghu Pan,

Xuexia Li,

Shixin Zhu

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(3)

Опубликована: Фев. 10, 2024

Язык: Английский

Процитировано

1
Quantitative Analysis of Spatiotemporal Patterns and Factor Contributions of Surface Ozone in the North China Plain DOI Creative Commons
Yi Li, Mengjiao Liu,

Lingyue Lv

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(12), С. 5026 - 5026

Опубликована: Июнь 9, 2024

Over the past decade, surface ozone has emerged as a significant air pollutant in China, especially North China Plain (NCP). For effective management NCP, it is crucial to accurately estimate levels and identify primary influencing factors for pollution this region. This study utilized precursors such volatile organic compounds (VOCs) nitrogen oxides (NOX), meteorological data, land cover, normalized difference vegetation index (NDVI), terrain, population data build an extreme gradient boosting (XGBoost)-based estimation model NCP during 2019 2021. Four models were developed using different NO2 formaldehyde (HCHO) datasets from Sentinel-5 TROPOMI observations Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Site-based validation results of these four showed high accuracy with R2 values above 0.86. Among models, two higher spatial coverage ratio selected, their averaged produce final products. The indicated that VOCs NOX main pollutants causing relative contributions accounted more than 23.34% 10.23%, respectively, while HCHO also played role, contributing over 5.64%. Additionally, had notable impact, 28.63% pollution, each individual factor 2.38%. distribution identified Hebei–Shandong–Henan junction hotspot, peak occurring summer, particularly June. Therefore, hotspot region promoting reduction NOx can play important role mitigation O3 improvement quality

Язык: Английский

Процитировано

1
A multi-perspective assessment of satellite surface Ozone products in China: Spatiotemporal variability, land cover impacts and pollution monitoring capability DOI

Jian Wang,

Yuling Du,

Tianxiang Cui

и другие.

Remote Sensing Applications Society and Environment, Год журнала: 2024, Номер unknown, С. 101359 - 101359

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

1
High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model DOI
Mingyun Hu, Xingcheng Lu, Yiang Chen

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 371, С. 123096 - 123096

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1
Data imbalance causes underestimation of high ozone pollution in machine learning models: a weighted support vector regression solution DOI

Ling Zhen,

Baihua Chen, Lin Wang

и другие.

Atmospheric Environment, Год журнала: 2024, Номер unknown, С. 120952 - 120952

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1
High Spatiotemporal Resolution Estimation and Analysis of Global Surface Co Concentrations Using a Deep Learning Model DOI
Mingyun Hu, Xingcheng Lu, Yiang Chen

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Two-decade surface ozone (O3) pollution in China: enhanced fine-scale estimations and environmental health implications DOI
Zeyu Yang, Zhanqing Li, Cheng Fan

и другие.

Authorea (Authorea), Год журнала: 2024, Номер unknown

Опубликована: Июль 14, 2024

Surface ozone (O3) has become a primary pollutant affecting urban air quality and public health in China.To address this concern, we developed nation-wide surface maximum daily average 8-h (MDA8) O3 concentration dataset for China (ChinaHighO3) at 10-km resolution since 2013 which been widely employed wide range studies.To meet the increasing demand its usage, have made significant enhancements, including development of more advanced deep learning model incorporation major source updates, such as 1 km downward solar radiation temperature directly from satellite retrievals.Additionally, extend temporal coverage dating back to 2000, increase spatial km, most importantly, notably improve data (with 5% cross-validation coefficient determination an 11.2% decrease root-mean-square error compared previous dataset).Using substantially improved new product, analyzed found some dynamic diverse patterns national levels over past two decades.The annual mean shown relative stability 2000 2015, followed by sharp increase, reaching peak values 2019, subsequently declining.Additionally, observed large difference 13% peak-season concentrations between rural regions China.This disparity significantly increased particularly Beijing-Tianjin-Hebei Pearl River Delta regions.Notably, nearly all population across (> 99.5%) resided areas exposed pollution exceeding World Health Organization (WHO) recommended long-term guideline (AQG) level [peak-season MDA8 > 60 μg/m 3 ] 2000.Moreover, short-term population-risk exposure showed trend 1.19% (p < 0.001) days WHO's AQG (daily = 100 ) per year during 22-year period considered here.The overall upward (0.93 ± 0.19 /yr, p led exceptionally rate 964 (95% confidence interval: 492, 1303) premature deaths 2000-2021 China.Urgent action is required develop comprehensive strategies aimed mitigating enhanced future.

Язык: Английский

Процитировано

0
Nonparametric Spatio-Temporal Modeling: Contruction of a Geographically and Temporally Weighted Spline Regression DOI
Sifriyani Sifriyani,

Syaripuddin Syaripuddin,

M. Fathurahman

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0