Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands.
Global Geo Health Data Center (GGHDC), Utrecht University, Utrecht, The Netherlands.
Sci Data. 2019 Mar 12;6:190035. doi: 10.1038/sdata.2019.35.
Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO, NO, PM, PM and PM The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.
长期暴露在空气污染中被认为是一个主要的公共卫生问题,它与总死亡率和各种疾病(如呼吸道和心血管疾病)有关。由于空气污染浓度存在空间变异性,因此需要高分辨率的空间数据集来评估个体对空气污染的暴露程度。将详细的空气污染图与个人的流动性和活动模式相结合,可以进行更精确的暴露评估。我们提供了荷兰的高分辨率数据集,提供了 2009 年 NO、NO、PM、PM 和 PM 的平均环境空气污染物浓度值。这些栅格数据集以 5×5 米的网格覆盖整个荷兰,是使用源自欧洲空气污染效应队列研究(ESCAPE)项目的土地利用回归模型计算得出的。还使用了具有全国和区域测量值的其他数据集来评估生成的浓度图。所提供的数据集允许在不同尺度上进行空间聚合、全国范围内的个体暴露评估,以及将活动模式纳入个体暴露估计中。