Spatial machine learning predictions of geogenic compounds in groundwater

Groundwater is a critical source of drinking water and as demand increases on a global scale under climate change and population growth, a suitable quantity of clean groundwater must be ensured. Groundwater chemistry depends on environmental factors such as geology, climate, groundwater table and residence time, land use, and recharge source and rate. Geogenic compounds, such as arsenic (As), manganese (Mn), phosphorus (P), ammonium (NH₄⁺), and iron (Fe), often occur in groundwater and are important determinants of groundwater quality. When exceeding recommended concentration limits in groundwater, these compounds can pose risks to human health and the environment, and cause problems in water treatment and distribution. In this study, we applied machine learning, i.e., classification algorithms and feature importance analysis, to investigate the spatial patterns of selected geogenic compounds and their governing factors. We used groundwater chemistry measurements from over 7,000 well intakes with mean depth of 47.8 m distributed across Denmark and 34 covariate maps including soil, geology, and hydrogeology information. Models are developed for As, Mn, total P, NH₄⁺ and Fe, and achieve a balanced accuracy between 76% and 88%. The main results are prediction maps of 100 m resolution showing the probability of the selected geogenic compounds exceeding the concentration limits in groundwater recommended by Danish legislation. Our analysis advocates that the spatial variability of all selected compounds depends mostly on geological factors such as the thickness of Quaternary, accumulated clay deposits above chalk, and the depth to chalk formations. High concentrations of all studied geogenic compounds are predicted in areas with thick Quaternary and clay deposits, while low Mn and P predictions occur in areas where chalk is present at lower depths. Overall, we found that groundwater exceeds recommended concentration limits for As, Mn, P, NH₄⁺ and Fe in 9.6%, 67.5%, 48.5%, 73.5% and 83% of Denmark’s area, respectively. Our results enhance the understanding of the processes driving groundwater quality in Denmark, which may be transferable to other domains with similar hydrogeological settings, e.g. northern America. The generated prediction maps can guide for identifying optimal locations for new wells and water treatment techniques, improving the overall groundwater resource management at national scale. Accordingly, this study shows how public high-quality databases can aid groundwater management.