Air Quality Evaluation Method Based on Data Analysis

Haitao Ma, Shihong Yue, Jia Li
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Abstract

In the existing environmental air quality evaluation index system, the AQI depends on the concentration of the dominant pollutant in the six types of air pollutants. However, the interaction among different pollutants and the effect of the combination of pollutants on AQI is rarely studied. In this paper, concentrations of six types of air pollutants and corresponding AQI of Tianjin in 2018 were taken as samples, and trained by Choquet integral. The Shapley interaction index of six types of pollutants are obtained by nonadditive measure. Based on the Shapley interaction index, the weights of six air pollutants on AQI and the interaction among different pollutants were analyzed. The results show that the main air pollutants in Tianjin in 2018 are PM2.5 and O3, and there is a negative interaction between them. Finally, the trained model is used to recalculate AQI of Tianjin in the first three months of 2019, providing a reference for air quality evaluation.
基于数据分析的空气质量评价方法
在现有的环境空气质量评价指标体系中,AQI取决于六类大气污染物中优势污染物的浓度。然而,不同污染物之间的相互作用以及污染物组合对AQI的影响研究较少。本文以天津市2018年6种空气污染物浓度及其对应的AQI为样本,采用Choquet积分训练。通过非加性测量,得到了六种污染物的Shapley相互作用指数。基于Shapley相互作用指数,分析了6种空气污染物对AQI的权重以及不同污染物之间的相互作用。结果表明:2018年天津市主要大气污染物为PM2.5和O3,两者之间存在负交互作用;最后,利用训练好的模型重新计算天津市2019年前3个月的AQI,为空气质量评价提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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