Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute , 4051, Basel, Switzerland.
Environ Sci Technol. 2013;47(23):13555-64. doi: 10.1021/es403089q. Epub 2013 Nov 11.
Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005-2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R(2)) is 0.48-0.58 for NO2 and 0.22-0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R(2)) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available.
土地利用回归 (LUR) 模型通常研究城市内部的空气污染变化。最近数据质量和可用性的提高，包括卫星衍生的污染物测量，支持了更大区域的精细尺度 LUR 建模。在这里，我们根据超过 1500 个 EuroAirnet 监测站点描述了西欧的 NO2 和 PM10 LUR 模型，这些监测站点涵盖了背景、工业和交通环境。预测变量包括土地利用特征、人口密度、主要和次要道路的长度（从 0.1 公里到 10 公里）、海拔和距海距离。我们探索了具有和不具有基于卫星的 NO2 和 PM2.5 作为预测变量的模型，并比较了两种可用的土地覆盖数据集（全球；欧洲）。NO2 的模型性能（调整 R(2)）为 0.48-0.58，PM10 的模型性能为 0.22-0.50。卫星数据的纳入平均提高了 0.05 的 NO2 和 0.11 的 PM10 模型性能（调整 R(2)）。这些模型适用于西欧的 100 米网格；为了支持未来的研究，这些数据集是公开可用的。
