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Prediction and spatio - Temporal analysis of ozone concentration in a metropolitan area

机译:预测和时空-大都市地区臭氧浓度的时间分析

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摘要

Increases in the concentrated amounts of ozone (O-3) on the earth's surface have become a major matter of concern for human health and may have dire consequences. The purpose of this study is to predict the ozone concentration and demonstrate its spatio-temporal changes in Tehran. A large amount on meteorology, concentration of other pollutants, traffic information, industrial agglomeration and remote sensing data were used for this purpose. Study parameters on ozone concentrations were initially analyzed in order to find meaningful relationships between the study parameters and ozone concentrations. Ozone concentration was then predicted using multivariate linear regression and spatio-temporal changes of ozone concentration were finally investigated. Results indicated that the proposed regression model was able to account for 89 percent of changes in 2015. Spatio-temporal analysis showed that the best performance of the model, in regards to ozone concentration, occurred in autumn (R-2 = 0.84) at Park Rose (R-2 = 0.89). Furthermore, the spatial distribution of estimated ozone concentrations at the park were consistent with the actual amounts observed. Finally, in accordance with spatial dispersion of ozone concentrations throughout the 22 districts of Tehran, areas 8, 13, and 20, located in the eastern and southern parts of Tehran were indicated as areas with the highest ozone concentration.
机译:地球表面臭氧(O-3)的浓缩量增加已成为人类健康关注的主要问题,并可能产生可怕的后果。这项研究的目的是预测臭氧浓度并证明其在德黑兰的时空变化。为此,大量使用了气象学,其他污染物的浓度,交通信息,工业集聚和遥感数据。最初对臭氧浓度的研究参数进行了分析,以发现研究参数与臭氧浓度之间的有意义的关系。然后使用多元线性回归预测臭氧浓度,并最终研究臭氧浓度的时空变化。结果表明,提出的回归模型能够解决2015年变化的89%。时空分析表明,就臭氧浓度而言,该模型的最佳性能发生在Park的秋季(R-2 = 0.84)罗斯(R-2 = 0.89)。此外,估计的公园内臭氧浓度的空间分布与观察到的实际数量一致。最后,根据整个德黑兰22个区中臭氧浓度的空间分布,位于德黑兰东部和南部的8、13和20区被指示为臭氧浓度最高的区域。

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