Spatial Patterns and Determinants of PM2.5 Concentrations: A Land Use Regression Analysis in Shenyang Metropolitan Area, China DOI Open Access
Tuo Shi,

Yang Zhang,

Xuemei Yuan

и другие.

Sustainability, Год журнала: 2024, Номер 16(12), С. 5119 - 5119

Опубликована: Июнь 16, 2024

Identifying impact factors and spatial variability of pollutants is essential for understanding environmental exposure devising solutions. This research focused on PM2.5 as the target pollutant developed land use regression models specific to Shenyang metropolitan area in 2020. Utilizing Least Absolute Shrinkage Selection Operator approach, were all seasons annual average, explaining 62–70% concentrations. Among predictors, surface pressure exhibited a positive correlation with concentrations throughout most year. Conversely, both elevation tree cover had negative effects levels. At 2000 m scale, landscape aggregation decreased levels, while at larger scale (5000 m), splitting facilitated dispersion. According partial R2 results, vegetation-related types significant, shrubland proportion positively correlated local-scale spring. Bare vegetation areas primary factor autumn, whereas mitigating effect contrasted this trend, even winter. The NDVI, an index used assess growth, was not determined be influencing factor. findings reaffirm function reducing PM2.5. Based research, actionable strategies pollution control outlined promote sustainable development region.

Язык: Английский

How Does the Location of Power Plants Impact Air Quality in the Urban Area of Bucharest? DOI Creative Commons
Doina Nicolae, Camelia Talianu, Jeni Vasilescu

и другие.

Atmosphere, Год журнала: 2025, Номер 16(6), С. 636 - 636

Опубликована: Май 22, 2025

This study investigates the impact of a thermal power plant site on air quality in Bucharest, Romania. It emphasizes importance accurate pollutant inmission measurements urban areas by utilizing mobile low-cost sensors, Copernicus’ Copernicus Atmosphere Monitoring Service (CAMS) and Land (CLMS), satellite retrieval to better understand climate change drivers their potential near- surface concentrations column densities NO2, CO, PM (particulate matter). focuses attention need considering placement plants relation metropolitan while making this assessment. The research highlights limits typical mesoscale models effectively capturing pollution dispersion distribution using LUR (Land Use Regressions) retrievals. authors investigate variety ways areas, including observations, measurements, land use regression models. largely capital Romania, which has issues caused vehicle traffic, industrial activity, heating systems, plants. results indicate how may affects nearby residential areas.

Язык: Английский

Процитировано

0

Combustion-driven inorganic nitrogen in PM2.5 from a city in central China has the potential to enhance the nitrogen load of North China DOI
Hao Xiao, Hong‐Wei Xiao, Yu Xu

и другие.

Journal of Hazardous Materials, Год журнала: 2024, Номер 483, С. 136620 - 136620

Опубликована: Ноя. 22, 2024

Язык: Английский

Процитировано

2

Spatial Patterns and Determinants of PM2.5 Concentrations: A Land Use Regression Analysis in Shenyang Metropolitan Area, China DOI Open Access
Tuo Shi,

Yang Zhang,

Xuemei Yuan

и другие.

Sustainability, Год журнала: 2024, Номер 16(12), С. 5119 - 5119

Опубликована: Июнь 16, 2024

Identifying impact factors and spatial variability of pollutants is essential for understanding environmental exposure devising solutions. This research focused on PM2.5 as the target pollutant developed land use regression models specific to Shenyang metropolitan area in 2020. Utilizing Least Absolute Shrinkage Selection Operator approach, were all seasons annual average, explaining 62–70% concentrations. Among predictors, surface pressure exhibited a positive correlation with concentrations throughout most year. Conversely, both elevation tree cover had negative effects levels. At 2000 m scale, landscape aggregation decreased levels, while at larger scale (5000 m), splitting facilitated dispersion. According partial R2 results, vegetation-related types significant, shrubland proportion positively correlated local-scale spring. Bare vegetation areas primary factor autumn, whereas mitigating effect contrasted this trend, even winter. The NDVI, an index used assess growth, was not determined be influencing factor. findings reaffirm function reducing PM2.5. Based research, actionable strategies pollution control outlined promote sustainable development region.

Язык: Английский

Процитировано

1