Spatial estimation of urban air pollution with the use of artificial neural network models
Spatial estimation of urban air pollution with the use of artificial neural network models
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DOI：
10.1016/j.atmosenv.2018.07.058
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发表时间：
2018-10-01
影响因子：
5
通讯作者：
Deligiorgi, D.
中科院分区：
文献类型：
--
作者：
Alimissis, A.;Philippopoulos, K.;Deligiorgi, D.
城市空气质量的恶化被认为是世界范围内的主要环境问题之一，科学证据表明，暴露在环境空气污染中会严重影响健康。这一事实突显了生成准确的空气污染区域对于量化当前和未来与健康有关的风险的重要性。在空气污染模拟领域，用于点估计的插值法使得能够估计未监测地点的污染物浓度。这项研究的主要目的是评估两种内插方法，人工神经网络和多元线性回归，使用位于希腊雅典大都市更大区域的真实城市空气质量监测网的数据。采用一套相关性和差异性统计方法和残差分布，对5种大气污染物(二氧化氮、氮氧化物、臭氧、一氧化碳和二氧化硫)的监测结果进行了比较。人工神经网络在大多数情况下被发现明显优越，特别是在空气质量网络密度有限的情况下，导致监测点之间的空间相关性程度降低。
The deterioration of urban air quality is considered worldwide one of the primary environmental issues and scientific evidence associates the exposure to ambient air pollution with serious health effects. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. Interpolation methods for point estimations in the field of air pollution modelling enable the estimation of pollutant concentrations in unmonitored locations. The main objective of this study is to evaluate two interpolation methodologies, Artificial Neural Networks and Multiple Linear Regression, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece. The results for five regulated air pollutants (Nitrogen dioxide, Nitrogen monoxide, Ozone, Carbon monoxide and Sulphur dioxide) are compared through the use of a set of correlation and difference statistical measures and residuals distribution. Artificial neural networks are found in most cases to be significantly superior, especially where the air quality network density is limited, leading to a decreased degree of spatial correlations among the monitoring sites.