TY - JOUR
T1 - Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019
AU - Shen, Youchen
AU - de Hoogh, Kees
AU - Schmitz, Oliver
AU - Clinton, Nick
AU - Tuxen-Bettman, Karin
AU - Brandt, Jørgen
AU - Christensen, Jesper H
AU - Frohn, Lise M
AU - Geels, Camilla
AU - Karssenberg, Derek
AU - Vermeulen, Roel
AU - Hoek, Gerard
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/3/25
Y1 - 2024/3/25
N2 - Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31–0.66 for NO2, 0.4–0.79 for O3, 0.4–0.78 for PM10, 0.46–0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.
AB - Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31–0.66 for NO2, 0.4–0.79 for O3, 0.4–0.78 for PM10, 0.46–0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.
KW - Air pollution
KW - Europe-wide
KW - Land-use regression
KW - Monthly variation
KW - Spatiotemporal variation
UR - http://www.scopus.com/inward/record.url?scp=85185557994&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2024.170550
DO - 10.1016/j.scitotenv.2024.170550
M3 - Article
C2 - 38320693
SN - 0048-9697
VL - 918
SP - 1
EP - 16
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 170550
ER -