First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China

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

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.

A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.