Spatiotemporal Variations and Socio-Economic Drivers of Major Air Pollutants: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration DOI
Weiqi Wang,

Yufeng He,

Jingran Gao

et al.

Published: Jan. 1, 2023

Air pollution greatly impacts economic development and is of common concern to all sectors society. However, the discussion on interrelationships between air pollutants effect socio-economic indicators remain lacking. This study systematically analyzes spatiotemporal characteristics drivers four major based a panel data 199 districts counties in Beijing–Tianjin–Hebei region from 2013 2020. The results showed that concentrations PM2.5, PM10 NO₂ decreased by 48.87%, 48.54% 29.33%, whereas O₃ increased 24.78%, making it concern. Moreover, demonstrated an overall positive spatial correlation. Among factors, GDP per capita total social retail goods mitigated pollution, secondary industry was biggest cause pollutant concentrations. increase electricity consumption unit alleviated south-central Beijing–Tianjin–Hebei. Furthermore, ecological conservation areas represented Zhangjiakou Chengde tended exacerbate as level increased. study's comprehensive analysis provides theoretical support for targeted control measures policies sustainable different regions.

Language: Английский

PM2.5 Concentration Prediction in Six Major Chinese Urban Agglomerations: A Comparative Study of Various Machine Learning Methods Based on Meteorological Data DOI Creative Commons
Min Duan,

Yufan Sun,

Binzhe Zhang

et al.

Atmosphere, Journal Year: 2023, Volume and Issue: 14(5), P. 903 - 903

Published: May 22, 2023

The escalating issue of air pollution in China’s rapidly developing urban areas has prompted increased attention to the role meteorological conditions PM2.5 pollution. This study examines spatiotemporal distribution concentrations and their relationship with factors six major Chinese agglomerations from 2017 2020, using daily average data. Statistical spatial analysis techniques are employed, alongside construction eight machine learning models for prediction purposes. also compares feature importance various impacting concentrations. Results reveal significant regional differences both levels influences. Multilayer Perceptron (MLP) model demonstrates highest accuracy According MLP model’s identification, temperature is most factor affecting across all agglomerations, while wind speed precipitation have least impact. Contributions pressure dew point temperature, however, vary among different agglomerations. research considers impact on offers valuable artificial intelligence-based insights into key influencing diverse regions, thereby informing development effective control policies.

Language: Английский

Citations

4
An exploration of urban air health navigation system based on dynamic exposure risk forecast of ambient PM2.5 DOI Creative Commons

Pei Jiang,

C. Y. Gao,

Junrui Zhao

et al.

Environment International, Journal Year: 2024, Volume and Issue: 190, P. 108793 - 108793

Published: June 3, 2024

Under international advocacy for a low-carbon and healthy lifestyle, ambient PM2.5 pollution poses dilemma urban residents who wish to engage in outdoor exercise adopt active commuting. In this study, an Urban Air Health Navigation System (UAHNS) was designed proposed assist users by recommending routes with the least exposure dynamically issuing early risk warnings based on topologized digital maps, application programming interface (API), eXtreme Gradient Boosting (XGBoost) model, two-step spatial interpolation. A test of UAHNS's functions applications carried out Wuhan city. The results showed that, compared trained random forest (RF), LightGBM, Adaboost models, etc., XGBoost model performed better, R

Language: Английский

Citations

1
Assessment on eco-environmental quality of the Yellow River Basin by considering desertification index DOI
Min An,

Fan Meng,

Weijun He

et al.

Journal of Mountain Science, Journal Year: 2024, Volume and Issue: 21(10), P. 3275 - 3292

Published: Oct. 1, 2024

Language: Английский

Citations

1
Improving the empirical sediment yield index and identifying the spatiotemporal heterogeneity of its driving factors DOI
Yanhu He,

Daoguo Xu,

Z. Wang

et al.

Hydrological Sciences Journal, Journal Year: 2024, Volume and Issue: 69(13), P. 1750 - 1764

Published: July 30, 2024

Soil erosion and sediment yield in basins are influenced by a combination of land use/land cover changes climatic factors. The existing empirical index (SYI) model does not consider the spatiotemporal non-stationarity parameters its application is limited data-deficient basins. To address these issues, novel framework was proposed to extend SYI model, identify heterogeneity driving factors using geographically temporally weighted regression (GTWR) it demonstrated Dongjiang River basin, South China. constructed multi-factor GTWR explains 87% variation SYI. Spatially, population density urban main SYI, there significant spatial differences their effects. Temporally, most factor for effect increased over time.

Language: Английский

Citations

0
Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends DOI Creative Commons

Tzu-Chi Lin,

Pei-Te Chiueh, Ta-Chih Hsiao

et al.

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 13, 2024

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, it challenging to separate these effects. This study utilized valuable long-term number and size distribution (PNSD) data from 2018 2023 develop tree-based machine learning model enhanced an interpretable component, incorporating temporal markers characterize background or time series residuals. Our results demonstrated that, differing PM

Language: Английский

Citations

0
Coordinated change of PM2.5 and multiple landscapes based on spatial coupling model: a comparison between inland and waterfront cities DOI Creative Commons

Zhen Shen,

Zhonghao Zhang,

Lihan Cui

et al.

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 16, 2023

Abstract Context Landscape heterogeneity is closely related to the spatial differentiation characteristics of PM 2.5 concentration in urbanized areas. Exploring changing coordination landscape evolution and change provides robust support for mitigating urban pollution. Previous studies mainly focused on a single specific area, lacking quantitative comparison multiple changes different types cities. Objectives This study aims quantify how landscapes could affect compare whether what kind differences exist among such effects across various regions. Methods Taking two typical inland waterfront cities China as examples, this uses exploratory data analysis coupling models analyze distribution its coordinated with (i.e., green, blue, gray), townships basic unit. Results The concentrations Hohhot Tianjin have evident concentration. Moreover, green regions show opposite trends owing effect natural background. other can increase concentration, maximum 2.04 µg/m 3 . However, may inhibit , particularly blue dominant, strong area. Conclusions By comparing caused by evolutions, managers take differentiated measures tailored local conditions provide information planning strategies air

Language: Английский

Citations

0
Quantitative Estimation of Urban PM2.5 Pollution Baseline and Meteorological Resource Endowment Using Machine Learning in Chinese Yangtze River Economic Belt DOI Creative Commons

Changhong Ou,

Fei Li, Jingdong Zhang

et al.

IETI Transactions on Data Analysis and Forecasting (iTDAF), Journal Year: 2023, Volume and Issue: 1(4), P. 68 - 78

Published: Dec. 21, 2023

Considering the influence of baseline values, meteorological conditions, and human activities on PM2.5, quantifying them will facilitate classification, control, management pollution. The machine learning model explained PM2.5-meteorological nonlinear relationship between PM2.5 factors in each city across Yangtze River Economic Belt, China. Meteorological resource endowments (MRE) are used to quantify variation concentration caused by conditions. Contamination (CB) is characterize lowest limit anthropogenic impact contamination without interference. According values MRE CB, cities economic belt can be divided into four categories (Q1-4). average value −0.41 μg/m3. CB 34.05 μg/m3, which lower than Chinese Grade II standard (GB 3095-2012). additional emissions humans resulted an increase 7 μg/m3 concentration, while led a decrease In terms Q1 concentrated midstream, most challenging pollutant control. Q2 downstream, with relatively high but favorable Q3 upstream, there surplus environmental capacity even limited Cites Q4 have suitable development potential exhibit discrete spatial distribution. research distinguished various pollution provided insights different characteristics around Belt. This information has helped government classify implement specific policies based their individual situations.

Language: Английский

Citations

0
Spatiotemporal Variations and Socio-Economic Drivers of Major Air Pollutants: A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration DOI
Weiqi Wang,

Yufeng He,

Jingran Gao

et al.

Published: Jan. 1, 2023

Air pollution greatly impacts economic development and is of common concern to all sectors society. However, the discussion on interrelationships between air pollutants effect socio-economic indicators remain lacking. This study systematically analyzes spatiotemporal characteristics drivers four major based a panel data 199 districts counties in Beijing–Tianjin–Hebei region from 2013 2020. The results showed that concentrations PM2.5, PM10 NO₂ decreased by 48.87%, 48.54% 29.33%, whereas O₃ increased 24.78%, making it concern. Moreover, demonstrated an overall positive spatial correlation. Among factors, GDP per capita total social retail goods mitigated pollution, secondary industry was biggest cause pollutant concentrations. increase electricity consumption unit alleviated south-central Beijing–Tianjin–Hebei. Furthermore, ecological conservation areas represented Zhangjiakou Chengde tended exacerbate as level increased. study's comprehensive analysis provides theoretical support for targeted control measures policies sustainable different regions.

Language: Английский

Citations

0