Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data DOI Creative Commons
Chen Wang, Qianqian Sheng, Zunling Zhu

и другие.

Land, Год журнала: 2024, Номер 13(9), С. 1499 - 1499

Опубликована: Сен. 15, 2024

The interaction between the natural environmental and socioeconomic factors is crucial for assessing dynamics of plateau ecosystems. Therefore, remote sensing ecological index (RSEI) CatBoost-SHAP model were employed to investigate changes in quality their driving Diqing Tibetan Autonomous Prefecture, China, from 2001 2021. results showed an increase 0.44 0.71 2021 average RSEI indicating overall upward trend quality. Spatial analysis shows percentage area covered by different levels temporal changes. revealed that “good” accounted largest proportion study area, at 42.77%, followed “moderate” 21.93%, “excellent” 16.62%. “Fair” areas 16.11% “poor” only 2.57%. drivers based on framework also indicated climate have a greater impact than factors; however, this effect differed significantly with altitude. findings suggest that, addition strengthening monitoring, further advancements engineering are required ensure sustainable development ecosystem continuous improvement Prefecture.

Язык: Английский

Reactive Oxygen Species Drive Aging-associated Microplastic Release in Diverse Infusion Ingredients DOI Creative Commons
Xianghua Zhang, Neng Li, X. Li

и другие.

Journal of Hazardous Materials, Год журнала: 2025, Номер 490, С. 137728 - 137728

Опубликована: Фев. 25, 2025

Язык: Английский

Процитировано

1

Identifying ESG types of Chinese solid waste disposal companies based on machine learning methods DOI

Jianling Jiao,

Yana Shuai,

Jingjing Li

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 360, С. 121235 - 121235

Опубликована: Май 25, 2024

Язык: Английский

Процитировано

4

Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning DOI
Yan Lyu, Haonan Xu, Haonan Wu

и другие.

Environmental Pollution, Год журнала: 2024, Номер unknown, С. 125380 - 125380

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

3

Unprecedented impacts of meteorological and photolysis rates on ozone pollution in a coastal megacity of northern China DOI

Jianli Yang,

Chaolong Wang, Yisheng Zhang

и другие.

Atmospheric Pollution Research, Год журнала: 2025, Номер unknown, С. 102461 - 102461

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

An interpretable physics-informed deep learning model for estimating multiple air pollutants DOI Creative Commons
Binjie Chen,

Jiacong Hu,

Yumiao Wang

и другие.

GIScience & Remote Sensing, Год журнала: 2025, Номер 62(1)

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

0

Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China DOI Creative Commons
Yan Liu, Tingting Hu, Yusen Duan

и другие.

Atmosphere, Год журнала: 2025, Номер 16(4), С. 457 - 457

Опубликована: Апрель 15, 2025

Elevated O3 concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses investigating the key factors affecting formation in ecologically sensitive Dongtan Wetland (Chongming District, Shanghai, China) area. By comparing performance of concentration prediction multiple machine learning models, this found that random forest model achieved highest accuracy (R2 = 0.9, RMSE 11.5). Feature importance structure mining showed peroxyacetyl nitrate (PAN), nitrogen oxides (NOx), temperature, wind direction, relative humidity were main drivers formation. Specifically, PAN exceeding 0.1 ppb temperatures above 3 °C have impact levels, especially spring, summer, autumn. Trajectory analysis westward urban pollution emissions transported from ocean highlights need for targeted emission control strategies, precursors generated by ships NOx industries, providing important insights improving air quality areas.

Язык: Английский

Процитировано

0

Association of Ozone and Temperature with Ischemic Heart Disease Mortality Risk: Mediation and Interaction Analyses DOI

Xing Gong,

Fengxia Sun,

Wei Li

и другие.

Environmental Science & Technology, Год журнала: 2024, Номер 58(46), С. 20378 - 20388

Опубликована: Ноя. 7, 2024

Global warming and elevated ozone (O3) levels are gradually gaining widespread attention, exposure to which may cause many physiological changes associated with cardiovascular events such as hypertension, cardiomyocyte apoptosis, etc. In addition, ischemic heart disease (IHD) is the leading of death worldwide. However, contributions temperature O3, independently or in combination, IHD mortality not well understood. This study employs a two-stage analytical protocol (generalized additive model followed by meta-analysis) explore respective associations O3 mortality, determine their possible mediation interaction effects. Our results suggest that increases 10 μg/m3 1 °C at lag01 day increased risks 0.789% 0.686%, respectively. can mediate relationship between pooled estimate 0.140%, while association 0.162%. The multiplicative effects were significantly mortality. findings demonstrate higher concentrations increase human risk through effects, providing scientific basis for synergistic management interventions.

Язык: Английский

Процитировано

2

Exploring Ecological Quality and Its Driving Factors in Diqing Prefecture, China, Based on Annual Remote Sensing Ecological Index and Multi-Source Data DOI Creative Commons
Chen Wang, Qianqian Sheng, Zunling Zhu

и другие.

Land, Год журнала: 2024, Номер 13(9), С. 1499 - 1499

Опубликована: Сен. 15, 2024

The interaction between the natural environmental and socioeconomic factors is crucial for assessing dynamics of plateau ecosystems. Therefore, remote sensing ecological index (RSEI) CatBoost-SHAP model were employed to investigate changes in quality their driving Diqing Tibetan Autonomous Prefecture, China, from 2001 2021. results showed an increase 0.44 0.71 2021 average RSEI indicating overall upward trend quality. Spatial analysis shows percentage area covered by different levels temporal changes. revealed that “good” accounted largest proportion study area, at 42.77%, followed “moderate” 21.93%, “excellent” 16.62%. “Fair” areas 16.11% “poor” only 2.57%. drivers based on framework also indicated climate have a greater impact than factors; however, this effect differed significantly with altitude. findings suggest that, addition strengthening monitoring, further advancements engineering are required ensure sustainable development ecosystem continuous improvement Prefecture.

Язык: Английский

Процитировано

1