Development and evaluation of rapid, national-scale outdoor air pollution modelling and exposure assessment: Hybrid air dispersion exposure system (HADES)

Environment International, Journal Year: 2025, Volume and Issue: unknown, P. 109304 - 109304

Published: Jan. 1, 2025

Improvements in computer processing power are facilitating the development of more detailed environmental models with greater geographical coverage. We developed a national-scale model outdoor air pollution (Hybrid Air Dispersion Exposure System - HADES) for rapid production concentration maps nitrogen dioxide (NO2) and ozone (O3) at very high spatial resolution (10m). The combines dispersion modelling satellite-derived estimates background concentrations, land cover, 3-D representation buildings, statistical calibration framework. an emissions inventory covering England Wales to implement tested its performance using data years 2018-2019 from fixed-site monitoring locations. In 10,000 Monte Carlo cross-validation iterations, hourly-annual average R2 values NO2 were 0.77-0.79 (RMSE: root mean squared error 5.3-5.7 µg/m3), 0.87-0.89 O3 (RMSE = 3.6-3.8 µg/m3) 95% confidence interval. annual was 0.80 4.9 0.86 3.2 aggregating estimates. surfaces freely available non-commercial use. these exposure assessment, all residential locations, neighbourhoods urban areas, unlikely be below 2021 World Health Organisation Quality Guidelines threshold (10 concentrations µg/m3). Rural suburban areas likely exceed peak-season 8-hour daily maximum (60

Language: Английский

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Traffic noise assessment in urban Bulgaria using explainable machine learning

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106169 - 106169

Published: Jan. 1, 2025

Language: Английский

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Machine learning-based analysis of workers' exposure and detection to volatile organic compounds (VOC)

International Journal of Environmental Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 25, 2025

Language: Английский

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Urban forests and public health: Analyzing the role of citizen perceptions in their conservation intentions

City and Environment Interactions, Journal Year: 2025, Volume and Issue: unknown, P. 100189 - 100189

Published: Feb. 1, 2025

Language: Английский

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Advancing air pollution exposure assessment model: challenges and future perspectives

Journal of Environmental Exposure Assessment, Journal Year: 2025, Volume and Issue: 4(1)

Published: March 3, 2025

In recent years, air pollution exposure assessment models have experienced significant advancements, particularly in integrating advanced technologies. However, the intrinsic deficiency of geostatistical model existing studies restricted further development model. this perspective, we summarized several emerging technologies that can overcome limitations and estimate exposures with high spatial temporal resolutions. As these evolve, they are expected to play an increasingly role improving public health managing environmental challenges.

Language: Английский

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Impact of land use characteristics on air pollutant concentrations considering the spatial range of influence

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102498 - 102498

Published: March 1, 2025

Language: Английский

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Tracking diurnal variation of NO2 at high spatial resolution in China using a time-constrained machine learning model

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 138, P. 104470 - 104470

Published: March 11, 2025

Language: Английский

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

Discover Atmosphere, Journal Year: 2025, Volume and Issue: 3(1)

Published: March 11, 2025

Language: Английский

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Evaluación de la exposición de largo plazo al material particulado fino (PM2.5) en el estudio de la cohorte MEDELLIN

Revista de la Escuela Nacional de Salud Pública, Journal Year: 2025, Volume and Issue: 43

Published: Jan. 1, 2025

Objetivo: Estimar la variabilidad del pm2.5 durante 2018-2019 en el área urbana de Medellín diferentes escalas geográficas. Métodos: Se aplicó metodología regresión usos suelo (lur), considerando como variable dependiente concentración promedio anual las estaciones monitoreo y cercanas; se definieron áreas influencia o buffers circulares con radios 100, 150, 200, 300 500 m, centro coordenadas cada sitio monitoreo; buffer construyeron modelos espaciales eligió mayor rendimiento. Resultados: Los seleccionados para los años 2018 2019 explican entre 40 46 % pm2.5, errores concentraciones previstas 1,64 2,18 µg/m3, respectivamente. La distribución contaminante fue heterogénea a nivel barrios manzanas. Las mayores anuales localizaron hacia franja central ciudad, circundantes río Medellín, marcadas al sur centro. Mientras que estimaron superiores 15 µg/m3 21 manzanas, 2019, total estimaciones estuvo por encima esta concentración. Conclusión: evalúa exposición medio año, predominaron variables explicativas uso tráfico. niveles inferiores 25 escalas, una baja permitió asignación exposiciones individuales largo plazo residencia participantes proyecto.

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Air pollution mapping and variability over five European cities

Environment International, Journal Year: 2025, Volume and Issue: 199, P. 109474 - 109474

Published: April 15, 2025

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High concentrations are due to the proximity of sources such as traffic or residential heating, density with presence buildings that reduce street ventilation. This complexity makes fine-scale mapping challenging, even regulated pollutants NO2 PM2.5. In this study we apply state-of-the-art empirical deterministic modeling approaches produce high-resolution (<100 m) maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city capturing intra-urban gradients concentrations. Depending on methodology, (NO2, PM2.5) and/or emerging (black carbon (BC) ultrafine particles (UFP characterized here by particulate number concentration PNC)) considered. For modelling, different presented: a multi-scale Eulerian modelling chain down scale chemistry/aerosol dynamics at all scales, hybrid models regional dispersion Gaussian subgrid dispersion, Gaussian-based model. Empirical land use regression were developed based upon mobile monitoring. To compare relative performance evaluate their limitations, results compared fixed measurement stations. We introduce standardized metric quantify spatial seasonal variability assess each method's capacity reproduce heterogeneity. also how data assimilation affects both accuracy representation-particularly relevant where sparse. confirm established patterns: more pronounced PNC, BC than PM2.5, higher during winter periods. observe reduced in PM2. 5 (linked heating) significant wood burning emissions. adds unique value evaluating these patterns using stations, quantifying them entire areas very fine resolution m). Furthermore, important methodological strengths limitations pointed out, providing practical guidance selection improvement methods, supporting implementation new EU Air Quality Directive.

Language: Английский

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