Qian Zhang
University of Maryland Center for Environmental Science / USEPA Chesapeake Bay Program Office, MD, United States
- This delegate is presenting an abstract at this event.
Qian Zhang is an Associate Research Scientist with University of Maryland Center for Environmental Science (UMCES) at the U.S. EPA Chesapeake Bay Program. His main responsibility is to collaborate with scientists and managers in the CBP Partnership to analyze monitoring data and explore the temporal and spatial patterns and trends of water quality in Chesapeake Bay and its watershed. He is interested in applying scientific principles and statistical approaches to examine nutrient and sediment loads from watersheds and better understand their drivers and impacts along the land-river-estuary continuum. Additionally, he holds a part-time appointment as an ORISE Faculty Research Fellow at the USEPA Office of Research and Development (ORD), where he works within the National Nutrient Inventory team on the development and application of nutrient inventories and predictive water quality models. He obtained his Ph.D. degree from the Department of Geography and Environmental Engineering at Johns Hopkins University in 2016. He also holds two Master of Science degrees from Johns Hopkins University, one in environmental engineering and the other in statistics.
Presentations this author is a contributor to:
An interactive framework for integrated visualization and analysis of monitored and model-expected load reductions for nitrogen, phosphorus, and sediment (#401)
12:15 PM
Qian Zhang
Special Session: Water quality patterns, trends, and drivers along land-river-estuary continuums: insights gained from large-scale monitoring and geospatial datasets 1.0
Regional patterns and drivers of nutrient trends across the Chesapeake Bay watershed: Machine-learning insights and management implications (#399)
11:45 AM
Qian Zhang
Special Session: Water quality patterns, trends, and drivers along land-river-estuary continuums: insights gained from large-scale monitoring and geospatial datasets 1.0
SFS 2025