A Spatiotemporal Modeling Approach for Predicting Concentrations of Ammonia in an Agricultural Region.

The availability of data on ambient ammonia exposure is limited in both spatial and temporal resolutions, especially in rural areas, despite its potential health impacts and rapidly increasing emissions. In this study, a land-use regression (LUR)-based spatiotemporal modeling approach was applied to predict agricultural-related ammonia concentrations at a fine spatial scale, extending the method's application in terms of the species of pollutant and the type of region. In this approach, spatiotemporal variations are primarily explained by a linear combination of temporal basis functions, with weights determined by LUR. We developed the model using ammonia concentrations we collected in 2022 and 2023 from 10 monitoring sites within Urbana-Champaign, Illinois, along with geographic covariates focused on agricultural features. Analysis of the model structure showed that livestock farms and populated areas are associated with ammonia peaks in summer and winter, whereas croplands are more closely associated with peaks in spring and fall. Cross-validated predictions showed high accuracies at most monitoring sites (R 2 ≥ 0.69 at 90% of the sites), though accuracy varied by location, with lower accuracy linked to unique time trend in ammonia concentrations at a location. Finally, we mapped the seasonal predictions of ammonia concentrations across the study area at 30-m spatial resolution. In contrast, alternative models using simpler formulation or a smaller selection of covariates could not achieve an acceptable level of performance in identical evaluations. These results demonstrate the potential of LUR-based models as valuable tools for investigating agriculture-related air pollution exposure and highlight their broader applicability.