Environmental Pollution Analysis and Impact Study—A Case Study for the Salton Sea in California

Atmosphere

Samiksha Pandey¹  Jerry Gao¹  Dian Yu¹  Rui Xu¹  Venkata Sai Kusuma Sindhoora Vankayala Siva¹  Jia Liu² 

[1] Department of Applied Data Science, San Jose State University, San Jose, CA 95192, USA;Department of Electrical and Information Engineering, Jilin Engineering Normal University, Changchun 130052, China;

关键词: air pollution;    PM concentrations;    Salton Sea;    asthma prevalence;   

DOI  :  10.3390/atmos13060914

来源: DOAJ

【 摘 要 】

A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in $P{M}_{2.5}$ concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality ($O_3$ and $\mathrm{CO}$) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for $O_3$ and $\mathrm{CO}$, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly $P{M}_{2.5}$ and $P{M}_{10}$ using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for $P{M}_{2.5}$ and $P{M}_{10}$ prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily $P{M}_{2.5}$ and $P{M}_{10}$ concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978.

【 授权许可】

Unknown