Affiliation:
1. School of Engineering, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
2. Mechanical Engineering Department, Technology Center, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil
3. Lakes Environmental, 170 Columbia St. W, Waterloo, ON N2L 3L3, Canada
Abstract
The presence of pollutants in our atmosphere has become one of humanity’s greatest challenges. These pollutants, produced primarily by burning fossil fuels, are detrimental to human health, our climate and agriculture. This work proposes the use of a spatiotemporal graph neural network, designed to forecast ozone concentration based on the GraphSAGE paradigm, to aid in our understanding of the dynamic nature of these pollutants’ production and proliferation in urban areas. This model was trained and tested using data from Houston, Texas, the United States, with varying numbers of time-lags, forecast horizons (1, 3, 6 h ahead), input data and nearby stations. The results show that the proposed GNN-SAGE model successfully recognized spatiotemporal patterns underlying these data, bolstering its forecasting performance when compared with a benchmarking persistence model by 33.7%, 48.7% and 57.1% for 1, 3 and 6 h forecast horizons, respectively. The proposed model produces error levels lower than we could find in the existing literature. The conclusions drawn from variable importance SHAP analysis also revealed that when predicting ozone, solar radiation becomes relevant as the forecast time horizon is raised. According to EPA regulation, the model also determined nonattainment conditions for the reference station.
Funder
Natural Sciences and Engineering Research Council of Canada (NSERC) Alliance
Conselho Nacional de Desenvolvimento Científico e Tecnológico—Brasil
Lakes Environmental Software Inc.
Subject
Atmospheric Science,Environmental Science (miscellaneous)
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