Digging Into Nitrogen Pollution: Analyzing Drivers of Groundwater Nitrate Concentration using the SENSE Nutrient Modeling Framework

Abstract:

Growing population and intensification of industrial agriculture since the mid 20th century have driven an explosion of nitrogen and phosphorus use globally, with nitrogen use increasing by nearly an order of magnitude. Today, policy-makers, managers, and farmers are tasked with changing how we use fertilizers and treat human waste to address water quality challenges, such as harmful algal blooms and hypoxia. To facilitate the next generation of research and decision support tools, we developed SENSEmap, the Spatially Explicit Nutrient Source Estimate map, to quantify nitrogen and phosphorus inputs from seven distinct sources at 30 meter resolution across the Laurentian Great Lakes Basin. SENSEmap synthesizes broadly available data from government databases, remote sensing products, and scientific literature to create nutrient input maps useful for hydrologic, ecological, and agricultural studies. Beyond the sciences, these maps are used by local land use planners via the Tipping Point Planner.

Recently, exposure to nitrate in drinking water has been linked to increased risk of certain cancers and birth defects, even at concentrations below currently regulated levels. Despite this rising concern, little is known about where groundwater nitrate concentrations are elevated and how nutrient use affects groundwater pollution. Here we combine nitrogen inputs to groundwater from SENSEmap and its nutrient fate and transport model counterpart, SENSEflux, with an unprecedented dataset of over 300,000 nitrate samples from drinking water wells in Michigan, USA. We then apply Classification and Regression Tree analysis to statistically untangle the relationship between a suite of potential driver variables of nitrate concentration. Intriguingly, for all but the highest nitrate levels, total nitrogen load to groundwater was not among the most significant variables, suggesting that nitrate contamination is more complex than high nitrogen inputs alone. Instead, a combination of vulnerable hydrogeologic properties combined with agricultural land uses best described high concentrations. These insights improve our ability to identify at-risk landscapes and, paired with source-specific estimates from SENSEmap and SENSEflux, can provide a foundation for making data-driven changes to nutrient management.