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Spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt

机译：埃及大开罗和三角洲空气污染物的时空插值

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摘要

This paper analyses the spatiotemporal variability of air pollutants in Egypt using monthly averages from the air quality monitoring network from 2011 to 2015. Particulate Matters (PM_(10)) Nitrogen Dioxide (NO_2) and Sulfur Dioxide (SO_2), measured by the monitoring stations network are studied. A log transformation is applied for the three pollutants to achieve normality. The sum-metric function is utilized for modelling the spatiotemporal variogram as it gave the smallest Mean Squared Error (MSE) compared to other forms namely separable, metric, and product sum models. Therefore, employing the gstat package in R together with the trans-Gaussian spatiotemporal kriging, the maps are generated for the interpolated surfaces for the monthly averages of 2015 and the corresponding standard error values. These maps will help the decision maker to understand and visualize the spatial and temporal variability of the measured pollutants and hence undertake the necessary policies and decisions. The results show that the down town area has the highest pollutants levels. As concerns the temporal dimension, the highest values are depicted during the month of February as compared to the rest of the year. Furthermore, Egypt is suffering from a serious PM_(10) problem for the area and period under study that extremely exceed the WHO and Egyptian guidelines.
机译：本文使用2011年至2015年空气质量监测网络的月平均值分析埃及的空气污染物的时空变化。监测站测量的颗粒物（PM_（10））二氧化氮（NO_2）和二氧化硫（SO_2）网络研究。对三种污染物进行对数转换以达到正态。和度量函数用于建模时空变异函数，因为与其他形式（即可分离，度量和乘积和模型）相比，它给出的最小均方误差（MSE）。因此，在R中使用gstat软件包以及跨高斯时空克里金法，将为插值曲面生成2015年月平均值和相应标准误差值的映射。这些地图将帮助决策者了解和可视化所测污染物的时空变化，从而采取必要的政策和决策。结果表明，下城区的污染物水平最高。关于时间维度，与一年中其余时间相比，在2月中显示了最大值。此外，在所研究的区域和时期，埃及正遭受严重的PM_（10）问题，其严重超出了WHO和埃及的准则。

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