Landscape Metrics as Ecological Indicators for PM10 Prediction in European Cities DOI Creative Commons
Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2245 - 2245

Published: Dec. 21, 2024

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death Europe. Urban populations are particularly exposed to high concentrations pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding spatiotemporal variations PM10 is essential for developing effective control strategies. This study aimed enhance prediction models by integrating landscape metrics ecological indicators into our previous models, assessing their significance monthly average concentrations, and analyzing correlations with across European urban landscapes during heating (cold) non-heating (warm) seasons. In research, we only calculated proportion land uses (PLANDs), but according current research hypothesis, have a impact on quality. Therefore, expanded independent variables incorporating that capture compositional heterogeneity, including Shannon diversity index (SHDI), well reflect configurational heterogeneity landscapes, Mean Patch Area (MPA) Shape Index (SHI). Considering data from 1216 quality (AQ) stations, applied Random Forest model using cross-validation discover patterns complex relationships. Climatological factors, temperature, wind speed, precipitation, mean sea level pressure, emerged key predictors, season when temperature increased 5.80% 22.46% at 3 km. Landscape metrics, SHDI, MPA, SHI, were significantly related concentration. The SHDI was negatively correlated levels, suggesting heterogeneous could help mitigate pollution. Our enhanced achieved an R² 0.58 1000 m buffer zone 0.66 3000 zone, underscoring utility these improving predictions. findings suggest complexity, patch sizes, more fragmented associated sources built-up areas, along larger evenly distributed green spaces, can contribute reduction

Language: Английский

Landscape Metrics as Ecological Indicators for PM10 Prediction in European Cities DOI Creative Commons
Seyedehmehrmanzar Sohrab, Nándor Csikós, Péter Szilassi

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2245 - 2245

Published: Dec. 21, 2024

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death Europe. Urban populations are particularly exposed to high concentrations pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding spatiotemporal variations PM10 is essential for developing effective control strategies. This study aimed enhance prediction models by integrating landscape metrics ecological indicators into our previous models, assessing their significance monthly average concentrations, and analyzing correlations with across European urban landscapes during heating (cold) non-heating (warm) seasons. In research, we only calculated proportion land uses (PLANDs), but according current research hypothesis, have a impact on quality. Therefore, expanded independent variables incorporating that capture compositional heterogeneity, including Shannon diversity index (SHDI), well reflect configurational heterogeneity landscapes, Mean Patch Area (MPA) Shape Index (SHI). Considering data from 1216 quality (AQ) stations, applied Random Forest model using cross-validation discover patterns complex relationships. Climatological factors, temperature, wind speed, precipitation, mean sea level pressure, emerged key predictors, season when temperature increased 5.80% 22.46% at 3 km. Landscape metrics, SHDI, MPA, SHI, were significantly related concentration. The SHDI was negatively correlated levels, suggesting heterogeneous could help mitigate pollution. Our enhanced achieved an R² 0.58 1000 m buffer zone 0.66 3000 zone, underscoring utility these improving predictions. findings suggest complexity, patch sizes, more fragmented associated sources built-up areas, along larger evenly distributed green spaces, can contribute reduction

Language: Английский

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