Using Machine Learning and Google Earth Engine to Understand Land Use and Land Cover Classifications and NO₂ Levels in California

Anthropogenic activities release pollutants into the air, which can negatively affect human health and the environment. One such pollutant is nitrogen dioxide (NO ₂), which can contribute to smog formation, decreased crop growth and yield, and respiratory damage. This study aimed to find a relationship between land use/land cover (LULC) classifications and NO₂ levels in the air. We used Google Earth Engine (GEE) to collect LULC and air quality data using the Google Dynamic World and the Sentinel-5P NRTI NO₂ datasets. We focused on Pasadena, California, as it provided a good demonstration of an urban area surrounded by greenery, allowing for an adequate analysis of both forms of landscape and their impact on air quality. Random forest (RF) and decision tree (DT) classifiers were used on the provided datasets, with the estimated probability of complete coverage for each LULC type being the input features and the NO₂ density being the output label, measured in mol/m² . Our output labels were then discretized, classifying the categories into high and low NO₂ . The machine learning classifier found a correlative relationship between LULC and NO₂ levels, as signified by our modeled accuracy outputting a value of 85%, with an average f1 score of 86%. We performed 10-fold cross-validation to enhance the reliability of model evaluation. The results from this study suggest that machine learning models can be used to predict the changes in air quality based on changes in LULC from anthropogenic activities. With future studies confirming this relationship, inner-city green spaces may benefit mental and physical well-being.

Recommended citation: B. Yacoob, E. Scheys, E. Oladipo, A. Price and S. Banitaan. "Using Machine Learning and Google Earth Engine to Understand Land Use and Land Cover Classifications and NO₂ Levels in California." 2024 IEEE International Conference on Electro Information Technology (EIT), Eau Claire, WI, USA, 2024, pp. 410-417, doi: 10.1109/eIT60633.2024.10609851.
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