Thirty years of air quality change in North Dakota: Seasonal trends and machine-learning forecasts of NO₂ and SO₂

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Abstract

Long-term air quality patterns in the Northern Great Plains remain insufficiently characterized, particularly for gaseous pollutants that are sensitive to seasonal atmospheric processes and regional emission changes. This study provides a multidecadal analysis of nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) concentrations from 1995 to 2025 at two monitoring stations in Fargo and Bismarck, North Dakota, using seasonally averaged (summer and winter) observations. Historical trends show substantial declines in both pollutants over the 30-year period, with winter concentrations consistently exceeding summer values. To evaluate future air quality trajectories, four machine-learning models (i.e., random forest, extreme gradient boosting, support vector machine, and a feedforward neural network) were trained using engineered lag features and standardized predictors. Model validation with independent 2020–2024 data demonstrated that the neural network achieved the highest predictive accuracy across all scenarios, with coefficients of determination exceeding 0.96 and minimal forecast error. Using a recursive forecasting framework, pollutant levels were projected from 2025 to 2050. The forecasts indicate continued improvement in some cases, stabilization in others, and slight upward tendencies for specific pollutant-season combinations, particularly winter NO₂ in Bismarck and summer SO₂ in both cities. These results highlight that while historical emission reductions have been highly effective, future progress may not be uniform across pollutants or seasons. The findings underscore the importance of sustained monitoring and proactive regulatory planning to preserve North Dakota’s historically clean air.

Keywords

nitrogen dioxide / sulfur dioxide / machine learning / seasonal trends / long-term prediction / North Dakota

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