Principal component regression approach for measuring the impact of built environment variables on multiple air pollutants in Delhi

Discover Atmosphere, Год журнала: 2025, Номер 3(1)

Опубликована: Март 11, 2025

Язык: Английский

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High-resolution air quality maps for Bucharest using a mixed-effects modeling framework

Atmospheric chemistry and physics, Год журнала: 2025, Номер 25(9), С. 4639 - 4654

Опубликована: Май 5, 2025

Abstract. High-resolution mapping of pollutants based on mobile observation facilitates deep understanding air pollutant distributions within a city. This approach fosters science-based decisions to improve quality, by adding the existing but not optimally distributed permanent monitoring stations. In this study, we developed high-resolution concentration maps nitrogen dioxide (NO2), particulate matter (PM10) and ultrafine particles (UFP) for Bucharest, Romania, evaluate spatial variation across city during warm cold seasons. Maps were generated using mixed-effects method applied land-use regression (LUR) model. The relies multiple traffic predictor variables assimilation data collected measurements over 30 d in periods May–July 2022 January–February 2023. Cross-validation was done against situ extracted from same collection, while validation organized comparison with standard at fixed reference sites. Our study shows that combined has good performance all (R2>0.65), highest being observed season. PM10 indicate sources season, most important source traffic. During show more uniform distribution Bucharest. city's principal roads, particularly Bucharest ring road, are also highlighted NO2 maps, higher gradient period.

Язык: Английский

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Spatial-temporal analysis of urban air pollution related exposure and health impacts: Driving human-centered regulation and control

Urban Climate, Год журнала: 2024, Номер 58, С. 102161 - 102161

Опубликована: Окт. 12, 2024

Язык: Английский

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Long-term exposure to PM2.5 has significant adverse effects on childhood and adult asthma: A global meta-analysis and health impact assessment

One Earth, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

Язык: Английский

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Integrated land use regression (iLUR) model with road traffic characteristics for environmental noise prediction and mapping in urban regions with heterogenous traffic conditions

Applied Acoustics, Год журнала: 2025, Номер 239, С. 110830 - 110830

Опубликована: Май 24, 2025

Язык: Английский

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Comparison of Population-Weighted Exposure Estimates of Air Pollutants Based on Multiple Geostatistical Models in Beijing, China

Toxics, Год журнала: 2024, Номер 12(3), С. 197 - 197

Опубликована: Март 1, 2024

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies investigated the performance of different exposure on population-weighted estimates and resulting potential misclassification across various modeling approaches. This study developed for NO2 PM2.5 conducted assessment Beijing, China. It explored three approaches: variable dimension reduction, machine learning, conventional linear regression. compared their model by cross-validation (CV) estimates. Specifically, partial least square (PLS) regression, random forests (RF), supervised regression (SLR) were based an ordinary kriging (OK) framework The mean squared error-based R2 (R2mse) root error (RMSE) leave-one site-out (LOOCV) performance. These predict levels urban area estimate quartiles between them. results showed that PLS-OK PM2.5, with LOOCV R2mse 0.82 0.81, respectively, outperformed other models. estimated RF-OK exhibited lowest quartiles. For comparable indicated made choosing approaches should be carefully considered, bias needs evaluated studies.

Язык: Английский

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Development of land use regression models to characterise spatial patterns of particulate matter and ozone in urban areas of Lanzhou

Urban Climate, Год журнала: 2024, Номер 55, С. 101879 - 101879

Опубликована: Апрель 4, 2024

There are still many challenges in Land use regression (LUR) application cities China due to insufficient air pollutants data. In this study, the LUR models of TSP, PM10, PM4, PM2.5, PM1, and O3 developed by basing on mobile monitoring 2019 Lanzhou, China. Our results show that adjusted-R2 six best rang 0.45⁓0.87. Referring adjusted-R2, differences cross-validation-R2 (CV-R2) using training data less than 9% excluding CV-R2 test within 19% O3. Overall, more robust PM1. The model has a good fit. spatial patterns PMs exhibit high concentration west, center east area, being higher south north. predicted concentrations decrease from west east. All indicate there highest level largest area Xigu Distinct. These can provide scientific for urban planning, land regulation, prevention control pollution.

Язык: Английский

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Mapping the Spatiotemporal Variability of Particulate Matter Pollution in Delhi: Insights from Land Use Regression Modelling

Journal of the Indian Society of Remote Sensing, Год журнала: 2024, Номер 52(6), С. 1329 - 1346

Опубликована: Май 23, 2024

Язык: Английский

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Urban spatial structure and air quality in the United States: Evidence from a longitudinal approach

Environment International, Год журнала: 2024, Номер 190, С. 108871 - 108871

Опубликована: Июль 3, 2024

Previous studies on the relationship between urban form and air quality: (1) report mixed results among specific aspects of spatial structure (e.g., expansion, form, or shape) (2) use primarily cross-sectional approaches with a single year data. This study takes advantage multi-decade, longitudinal approach to investigate impact population-weighted concentrations PM

Язык: Английский

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Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning

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

Опубликована: Ноя. 1, 2024

Язык: Английский

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