The study involved data analysis from 51 monitoring stations across the Northern Hemisphere, revealing a marked decline in mercury levels. Researchers used two distinct modeling techniques to analyze the measurements and identify potential drivers of this trend, with both methods pointing to reduced human-driven emissions as the primary factor behind the decrease. Lead author Ari Feinberg from the MIT Institute for Data, Systems, and Society (IDSS) emphasized the importance of field data in refining emission models: "Our work shows that it is very important to learn from actual, on-the-ground data to try and improve our models and these emissions estimates. This is very relevant for policy because, if we are not able to accurately estimate past mercury emissions, how are we going to predict how mercury pollution will evolve in the future?"

Traditionally, global emissions inventories are created through estimates based on polluting activities, such as industrial production and the operation of coal-fired power plants. These models often rely on average emissions data and industry activity levels, which can miss nuanced trends captured by real-world measurements. Although inventories have often indicated a rise in mercury emissions, these projections have not aligned with the recent observed decline, highlighting a potential gap in how data is modeled and interpreted.