Identifying surface sulphur dioxide (SO2) monitoring gaps in Saint John, Canada with land use regression and hot spot mapping

Saint John experiences ambient sulphur dioxide (SO₂) pollution due to a high density of industrial activities. Despite recent reduction in SO₂ emissions, over 90% of the provincial exceedances of air pollutants were related to SO₂ or total reduced sulphur (TRS), and over 70% among which occurred in Saint John. Pinpointing intra-urban SO₂ hot spots is important for revealing the neighborhoods exposed to high health risk. However, this is challenging due to limited spatial coverage of monitoring. To fill the monitoring gap, we developed two-stage gradient boosting models combining a classifier that discerned between SO₂-free and SO₂-polluted days and a regressor that estimated daily SO₂ levels based on remote sensing data. With a 10-fold cross-validation, the classifier achieved 83% accuracy and the regressors attained R² of 0.46 and 0.44 for daily mean and maximum SO₂ respectively. Based on model outputs, we conducted spatial hot spot analysis and found high SO₂ levels spread to northeast, northwest, and southeast Saint John, where SO₂ monitoring was absent. Several existing monitoring sites in west Saint John coincided with the hot spots, where SO₂ was not regularly measured. Besides the spatiotemporal lags of nearby monitored SO₂, wind-related variables such as wind speed and direction had high importance in predicting surface SO₂, which might suggest potential impacts to remote unmonitored communities from the transport of SO₂. In summary, our findings suggest that certain unmonitored areas in Saint John may experience high SO₂ levels. Expansion of monitoring efforts would help inform where and when mitigation should be taken to minimize SO₂-related health impacts.