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A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological studies

机译:马德里(西班牙)低空气污染和居民空气污染的时空建模的数据科学方法:对流行病学研究的启示

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摘要

Model developments to assess different air pollution exposures within cities are still a key challenge in environmental epidemiology. Background air pollution is a long-term resident and low-level concentration pollution difficult to quantify, and to which population is chronically exposed. In this study, hourly time series of four key air pollutants were analysed using Hidden Markov Models to estimate the exposure to background pollution in Madrid, from 2001 to 2017. Using these estimates, its spatial distribution was later analysed after combining the interpolation results of ordinary kriging and inverse distance weighting. The ratio of ambient to background pollution differs according to the pollutant studied but is estimated to be on average about six to one. This methodology is proposed not only to describe the temporal and spatial variability of this complex exposure, but also to be used as input in new modelling approaches of air pollution in urban areas.
机译:在城市流行病学中,评估城市内不同空气污染暴露程度的模型开发仍然是一个关键挑战。背景空气污染是长期居民和低水平的浓度污染,难以量化,并且长期受到人口的污染。在这项研究中,使用隐马尔可夫模型对四种主要空气污染物的小时时间序列进行了分析,以估计马德里从2001年至2017年的背景污染物暴露。使用这些估计值,随后结合常规的插值结果来分析其空间分布。克里金法和距离反比加权法。环境污染与背景污染的比率因所研究的污染物而异,但估计平均约为六比一。提出该方法不仅用于描述这种复杂暴露的时间和空间变异性,而且还可以用作新的城市空气污染建模方法的输入。

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