About

Professor Robert L. Hill is an expert in Environmental Science & Technology at the University of Maryland. His research focuses on the parameterization of tillage effects on soil hydraulic properties and agrichemical losses, as well as the development of nutrient management planning and environmental risk assessment software. His work aims to improve understanding of soil and environmental interactions related to agricultural practices, contributing to sustainable land use and environmental protection.

Research topics

• Environmental science
• Soil science
• Biology
• Agronomy
• Ecology
• Animal science
• Atmospheric sciences
• Geotechnical engineering
• Geology
• Meteorology

Selected publications

• Estimating phytoplankton group abundances in an agricultural pond from in situ sensed data with machine learning: use of the SHAP analysis for ecological assessments

  Frontiers in Environmental Science · 2026-02-27

  articleOpen access

  Phytoplankton are a crucial component of aquatic ecosystems and are closely tied to water quality. Direct counts of phytoplankton abundances are resource-demanding, but the indirect estimation of those abundances has proven to be beneficial when conducting ecological assessments of waterbodies. Agricultural ponds serve as important water sources for irrigation, recreation, processing harvested agricultural products, animal watering, and other purposes. This work examined the use of random forest (RF), coupled with a Shapley Additive exPlanations (SHAP) analysis, to estimate the abundances of phytoplankton groups in an agricultural pond in Maryland. In situ sensing (ISS) of water quality parameters on a permanent sampling grid during the produce growing season provided dissolved oxygen, pH, specific conductance, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity measurements. Phytoplankton abundance data was determined using a modified Utermöhl microscopy method. Values of the determination coefficient for training and testing datasets were on average 0.81 and 0.74, and varied from 0.50 to 0.88 for ISS predictors, respectively. The explanatory analysis using SHAP revealed that the most influential predictors, identified as the top three for each phytoplankton taxonomic group, were specific conductance, fluorescent dissolved organic matter, and chlorophyll a. The RF analysis provided good estimates of the abundance of the phytoplankton community in agricultural pond waters and the addition of the SHAP analysis allowed for an exploration of what factors were most critical in supporting the phytoplankton groups observed.

  PublisherOA PDFDOI

• Utilizing strontium isotopes to trace fertilizer-derived metal(loid) accumulation in the soil-wheat system

  2025-01-01

  article1st authorCorresponding
  PublisherDOI

• The fate and ecological risk of mefentrifluconazole in the water-sediment system: A systematic analysis at the enantiomer level

  Environmental Research · 2025-04-23 · 3 citations

  articleOpen access

  Triazole fungicides occupy a significant position in the global fungicidal market. Mefentrifluconazole (MFZ) is a new-generation chiral triazole fungicide with broad applications. It can effectively control several rice fungal diseases; thus, its wide application increases its risk of entering the water and sediment in ecosystems. In this study, the stereoselective fate and risk of MFZ in the water-sediment system were investigated. The results showed stereoselective differences in the acute toxicity and fate of MFZ enantiomers (S-MFZ and R-MFZ) in the water-sediment system, with S-MFZ being more toxic and persistent. Both R-MFZ and S-MFZ induced significant decreases in the oxidation-reduction potential (ORP) value and organic matter (OM) content of the sediment. Additionally, soil enzyme activity in the sediments changed in varying degrees during exposure. Further microbiome sequencing results showed that both R-MFZ and S-MFZ induced changes in the composition of sediment microbial communities and decreased species diversity, which, in turn, affected the metabolic processes of microorganisms and the function of glycosyltransferase (GT) enzymes, especially S-MFZ. Correlation analysis showed that stereoselectivity in the interaction between the MFZ enantiomers and GT enzymes induced a difference in the synthesis of lipopolysaccharides; thus, affecting the abundances of the relevant bacterial genera and carbohydrate metabolic pathways. Exposure to MFZ enantiomers induced an increase in the abundance of anaerobic bacteria, such as Methylophilus and Rhodoferax, which exacerbated the anaerobic environment of the sediment system, leading to acidification and accelerated nutrient decomposition.

  PublisherDOI

• Investigating the Relationship Between Microcystin Concentrations and Water Quality Parameters in Three Agricultural Irrigation Ponds Using Random Forest

  Water · 2025-08-08 · 2 citations

  articleOpen access

  Cyanotoxins in agricultural waters pose a human and animal health risk. These toxins can be transported to nearby crops and soil during irrigation practices; they can remain in the soil for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds, cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the random forest algorithm in estimating microcystin concentrations using eight in situ water quality measurements at one active livestock water pond and two working irrigation ponds in Georgia and Maryland, USA. Measurements of microcystin along with eight in situ-sensed water quality parameters were used to train and test the machine learning model. The models performed better at the Georgia ponds compared to the Maryland pond, and interior models performed better than nearshore or whole-pond models. The most important variables for microcystin prediction were water temperature and phytoplankton pigments. Overall, the random forest algorithm(RF), augmented with a ‘trainControl’ function to perform repeated cross validations, was able to explain 40% to 70% of the microcystin concentration variation in the three agricultural ponds. Water quality measurements showed potential to aid water monitoring/sampling design by predicting the microcystin concentrations in the studied ponds by using readily available and easy to collect in situ data.

  PublisherOA PDFDOI

• Transient Performance Simulation

  2025-01-01

  book-chapterSenior author
  PublisherDOI

• Water quality in a legacy lithium mining district of North Carolina

  2024-01-01

  articleOpen access
  PublisherOA PDFDOI

• Persistence of Microcystin in Three Agricultural Ponds in Georgia, USA

  Toxins · 2024-11-07 · 3 citations

  articleOpen access

  Cyanobacteria and their toxins can have multiple effects on agricultural productivity and water bodies. Cyanotoxins can be transported to nearby crops and fields during irrigation and may pose a risk to animal health through water sources. Spatial and temporal variations in cyanotoxin concentrations have been reported for large freshwater sources such as lakes and reservoirs, but there are fewer studies on smaller agricultural surface water bodies. To determine whether spatiotemporal patterns of the cyanotoxin microcystin occurred in agricultural waters used for crop irrigation and livestock watering, three agricultural ponds on working farms in Georgia, USA, were sampled monthly within a fixed spatial grid over a 17-month period. Microcystin concentrations, which ranged between 0.04 and 743.75 ppb, were determined using microcystin-ADDA ELISA kits. Temporal stability was assessed using mean relative differences between microcystin concentrations at each location and averaged concentrations across ponds on each sampling date. There were locations or zones in all three ponds that were consistently higher or lower than the average daily microcystin concentrations throughout the year, with the highest microcystin concentrations occurring in winter. Additionally, microcystin patterns were strongly correlated with the patterns of chlorophyll, phycocyanin, and turbidity. The results of this work showed that consistent spatiotemporal patterns in cyanotoxins can occur in produce irrigation and livestock watering ponds, and this should be accounted for when developing agricultural water monitoring programs.

  PublisherDOI

• Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland

  The Science of The Total Environment · 2024-09-20 · 13 citations

  article
  PublisherDOI

• Comparison of SWAT and a deep learning model in nitrate load simulation at the Tuckahoe creek watershed in the United States

  2024-03-08

  preprintOpen access

  Simulating nitrate fate and transport in freshwater is an essential part in water quality management. Numerical and data-driven models have been used for it. The numerical model SWAT simulates daily nitrate loads using simulated flow rate. Data-driven models are more flexible compared to SWAT as they can simulate nitrate load and flow rate independently. The objective of this work was evaluating the performance of SWAT and a deep learning model in terms of nutrient loads in cases when deep learning model is used in (a) simulating flow rate and nitrate concentration independently and (b) simulating both flow rate and nitrate concentration. The deep learning model was built using long-short-term-memory and three-dimensional convolutional networks. The input data, weather data and image data including leaf area index and land use, were acquired at the Tuckahoe Creek watershed in Maryland, United States. The SWAT model was calibrated with data over the training period (2014-2017) and validated with data over the testing period (2019) to simulate flow rate and nitrate load. The Nash-Sutcliffe efficiency was 0.31 and 0.40 for flow rate and -0.26 and -0.18 for the nitrate load over training and testing periods, respectively. Three data-driven modeling scenarios were generated for nitrate load. Scenario 1 included the flow rate observation and nitrate concentration simulation, scenario 2 included the flow rate simulation and nitrate concentration observation, and scenario 3 included the flow rate and nitrate concentration simulations. The deep learning model outperformed SWAT in all three scenarios with NSE from 0.49 to 0.58 over the training period and from 0.28 to 0.80 over the testing period. Scenario 1 showed the best results for nitrate load. The performance difference between SWAT and the deep learning model was most noticeable in fall and winter seasons. The deep learning modeling can be an efficient alternative to numerical watershed-scale models when the regular high frequency data collection is provided.

  PublisherDOI

• Corrigendum to “Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland” [Sci. Total Environ. 954 (2024) 176256]

  The Science of The Total Environment · 2024-11-12

  erratumOpen access
  PublisherDOI

Recent grants

• NIH Grant R37GM025766

  NIH · $4.9M · 1998

• NIH Grant R01GM025766

  NIH · $2.3M · 2003

Frequent coauthors

• Thomas Vanaman

  University of Kentucky

  64 shared

• J. Evan Sadler

  62 shared

• Thomas A. Beyer

  61 shared

• James C. Paulson

  Scripps Research Institute

  58 shared

• Jean‐Paul Prieels

  GlaxoSmithKline (Belgium)

  53 shared

• Keith Brew

  51 shared

• James I. Rearick

  A.T. Still University

  49 shared

• Ying Zhao

  University of Saskatchewan

  43 shared