What is the spatiotemporal pattern of benzene concentration spread over susceptible area surrounding the Hartman Park community, Houston, Texas?

• Published in: Journal of hazardous materials Vol. 474; p. 134666
• Main Authors: Asri, Aji Kusumaning, Newman, Galen D., Tao, Zhihan, Zhu, Rui, Chen, Hsiu-Ling, Lung, Shih-Chun Candice, Wu, Chih-Da
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 05.08.2024
• Subjects: Benzene concentration; Ensemble learning model; Hazardous pollution zone; Machine-learning algorithms; Vulnerable community; Vulnerable community; Benzene concentration; Hazardous pollution zone; Ensemble learning model; Machine-learning algorithms
• ISSN: 0304-3894; 1873-3336; 1873-3336
• DOI: 10.1016/j.jhazmat.2024.134666

The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities. [Display omitted] •Estimating spatiotemporal patterns of benzene in environmentally disadvantaged area.•Utilizing ensemble learning model to accurately estimate benzene concentration.•Ensemble model depicted 98.7% benzene spatial variability in Hartman Park community.•Intensively developed areas and industry-related factors contribute to benzene.•High benzene levels were spread over industrial area to residential area nearby.