Bridging temporal gaps: AI-based temporal downscaling of biweekly NH₃ to daily scale with spatial transferability

Ammonia (NH₃) is a gaseous pollutant with significant environmental and health implications. Over recent decades, its increasing concentrations, driven by industrialization and agriculture, have necessitated high-resolution monitoring. However, limited daily ground-based observations hinder comprehensive analysis. This study developed machine learning-based frameworks-deep neural network (DNN), random forest, and light gradient boosting machine-to predict biweekly NH₃ concentrations and downscale them to daily estimates across the United States during 2017-2022. The models integrate NH₃ column concentrations, meteorological variables, land cover data, livestock information, and ground-based measurements. Among the models, DNN showed superior performance in both spatial cross-validation and independent testing, achieving a correlation coefficient of 0.79, a root mean square error of 0.98 µg/m³ , and an index of agreement of 0.83- effectively capturing fine-scale spatial variations at a 9 km resolution. Shapley additive explanations analysis identified temporal dynamic factors-such as day of year and meteorological variables-as key predictors, along with land cover and cattle density, highlighting the model's ability to support the temporal downscaling of NH₃ from biweekly to daily scale. When applied to the UK, the model demonstrated its potential for spatial transferability via the leave-one station-out and leave-one year-out cross validations. These findings highlight the ability of machine learning in bridging temporal gaps and generating high-resolution daily NH₃ estimates.