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Spatial interpolation of air pollution measurements using CORINE land cover data

机译：使用CORINE土地覆盖数据对空气污染测量值进行空间插值

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Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost, this interpolation model is an efficient tool for an environment agency when performing real-time air quality assessment. Beside this, a reliable interpolation model can be used to produce analysed maps of historical data records as well. Such maps are essential for correctly checking compliance with population exposure limit values as foreseen by the new EU Air Quality Directive. RIO is an interpolation model that can be classified as a detrended Kriging model. In a first step, the local character of the air pollution sampling values is removed in a detrending procedure. Subsequently, the site-independent data is interpolated by an Ordinary Kriging scheme. Finally, in a re-trending step, a local bias is added to the Kriging interpolation results. As spatially resolved driving force in the detrending process, a land use indicator is developed based on the CORINE land cover data set. The indicator is optimized independently for the three pollutants O_3, NO_2 and PM_(10). As a result, the RIO model is able to account for the local character of the air pollution phenomenon at locations where no monitoring stations are available. Through a cross-validation procedure the superiority of the RIO model over standard interpolation techniques, such as the Ordinary Kriging is demonstrated. Air quality maps are presented for the three pollutants mentioned and compared to maps based on standard interpolation techniques.
机译：近年来，对环境空气质量的实时评估越来越引起人们的关注。为了支持这一发展，开发了统计空气污染插值模型RIO。由于计算成本非常低，因此该插值模型是环境机构在执行实时空气质量评估时的有效工具。除此之外，还可以使用可靠的插值模型来生成历史数据记录的分析图。此类地图对于正确检查是否符合新的《欧盟空气质量指令》所预期的人口暴露极限值至关重要。 RIO是一种插值模型，可以归类为去趋势的Kriging模型。第一步，在趋势消除过程中去除空气污染采样值的局部特征。随后，通过普通Kriging方案对与站点无关的数据进行插值。最后，在重新趋势步骤中，将局部偏差添加到Kriging插值结果中。作为空间趋势分解过程中的驱动力，基于CORINE土地覆盖数据集开发了土地利用指标。该指标针对三种污染物O_3，NO_2和PM_（10）进行了独立优化。结果，RIO模型能够解释没有监测站可用的地点的空气污染现象的局部特征。通过交叉验证过程，证明了RIO模型优于标准插值技术（例如普通克里金法）的优势。列出了上述三种污染物的空气质量图，并与基于标准插值技术的图进行了比较。

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