Shen Youchen, de Hoogh Kees, Schmitz Oliver, Clinton Nick, Tuxen-Bettman Karin, Brandt Jørgen, Christensen Jesper H, Frohn Lise M, Geels Camilla, Karssenberg Derek, Vermeulen Roel, Hoek Gerard
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.
Sci Total Environ. 2024 Mar 25;918:170550. doi: 10.1016/j.scitotenv.2024.170550. Epub 2024 Feb 4.
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 (NO, O, PM and PM) 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 O, PM, and PM. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R: 0.31-0.66 for NO, 0.4-0.79 for O, 0.4-0.78 for PM, 0.46-0.87 for PM). 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.
在非常精细的空间分辨率（25米）下构建的月空气污染水平详细空间模型，有助于推动相关研究，以探索中期暴露的关键时间窗口。欧洲各地空气污染的季节性变化可能会影响空气污染的水平和空间格局。我们构建了全欧洲范围的土地利用回归（LUR）模型，以估算2000年至2019年期间受监管空气污染物（NO、O、PM 和 PM）的月浓度。月平均浓度数据来自常规监测站。我们纳入了月度固定和可变空间变量，使用监督线性回归（SLR）来选择预测变量，并使用地理加权回归（GWR）来估算每个月的空间变化回归系数。通过5折交叉验证（CV）评估模型性能。我们还将月度LUR模型的性能与月度调整浓度进行了比较。结果显示，估计值和模型结构均存在显著的月度变化，尤其是对于O、PM 和 PM。5折交叉验证表明，月度GWR模型在各月和各年的表现总体良好（5折交叉验证R：NO为0.31 - 0.66，O为0.4 - 0.79，PM为0.4 - 0.78，PM为0.46 - 0.87）。月度GWR模型略优于月度调整模型。月度GWR模型之间的相关性一般为中度到高度（皮尔逊相关>0.6）。总之，我们首次为欧洲的空气污染开发了稳健的月度LUR模型。这些空间分辨率为25米的月度LUR模型，有助于流行病学家更好地描述全欧洲范围内与空气污染相关的中期健康影响，便于在出生队列研究中调查关键暴露时间窗口。
