Ambient ammonia (NH(3)) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH(3)'s properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH(3) data monitored by the air quality stations. While there were other studies to predict NH(3) and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH(3) predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH(3) concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH(3) with domain knowledge. From 2016 to 2018, Taichung's hourly average NH(3) values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH(3) concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites.
Date:
2024-03
Relation:
Environment International. 2024 Mar;185:Article number 108520.
