Text mining-aided meta-research on nutrient dynamics in surface water and groundwater: Popular topics and perceived gaps

Nutrient (e.g., nitrogen and phosphorus) loss from point and non-point sources can cause critical environmental problems, deteriorating the quality of surface water and groundwater. Therefore, the protection of surface water and groundwater is imperative for maintaining a healthy environment. Although many review studies have investigated nutrient transport in surface water and groundwater, most of them concentrated on examining nutrient transport in one or two water systems, such as rivers, lakes, and groundwater without considering and including the research gaps in other systems. To date, there are no systematic studies that have determined the main insights and research gaps in the field of nutrient transport in surface and subsurface water using a text mining algorithm and topic identification through a quantitative and qualitative analysis. Therefore, metaresearch (research on research) was performed using the Latent Dirichlet Allocation (LDA) algorithm (a text mining algorithm) to identify the most frequent topics in the literature that covered the nutrient transport processes in surface water and groundwater using a quantitative analysis. Based on the results of the quantitative analysis, ten key topics were identified: nitrogen transport in groundwater; nitrogen transport in surface water; phosphorus transport in surface water; sediment transport in watersheds; nutrient loss in subsurface drainage; nutrient transport associated with lakes; nutrient transport in forest-dominated watersheds; modeling of nutrient transport; agricultural watershed management; and effect of soil-litter on nutrient transport. Furthermore, these topics were explained in a qualitative analysis to highlight the most important insights from literature and to identify the main knowledge gaps for further investigation in future studies. According to the most frequent topics in the literature, five research gaps were evident: developing integrated models for nutrient transport processes in the surface water-groundwater interactions; evaluating the effect of climate change and crop management practices on nutrient transport in surface water and groundwater; application of machine learning algorithms in nutrient transport; and describing the in-stream sediment and nutrient transport in fluvial environments such as rivers and creeks. The results of the current meta-research study are a steppingstone towards better identification of the common themes and trends in the previous publications in literature pertaining to nutrient transport in surface water and groundwater by applying an informative text mining algorithm where key knowledge gaps were determined based on the recommendations of these publications.