Dan uses geospatial analytics to study complex environmental systems; his primary focus is on water quality dynamics in streams, lakes and coastal areas. He develops spatial and spatiotemporal geostatistical models to study hypoxia, harmful algal blooms and other aquatic phenomena. These models help characterize the extent of water quality impairments while also assessing key drivers of spatial and temporal variability. Other interests include environmental forecasting using mechanistic and empirical models with rigorous uncertainty quantification, so that policy makers and the public can be presented with the ranges of likely outcomes associated with different future scenarios, allowing for more informed decision-making.
- Relationship between total and bioaccessible lead on children's blood lead levels in urban residential Philadelphia soils (2017)
- Ensemble modeling informs hypoxia management in the northern Gulf of Mexico (2017)
- Tracking cyanobacteria blooms: Do different monitoring approaches tell the same story? (2017)
- Probabilistically assessing the role of nutrient loading in harmful algal bloom formation in western lake erie (2016)
- Non-point source evaluation of groundwater contamination from agriculture under geologic and hydrologic uncertainty (2016)
- Mapping the spatial distribution of the biomass and filter-feeding effect of invasive dreissenid mussels on the winter-spring phytoplankton bloom in Lake Michigan (2015)
- Independent data validation of an in vitro method for the prediction of the relative bioavailability of arsenic in contaminated soils (2015)