John Bunoagurio
John E. Buonagurio, Jr.
Senior Associate
Ecological & Biological Sciences
  • Alexandria

Mr. Buonagurio specializes in geospatial data management and geostatistics as applied to environmental and ecological models, rich internet application development, and simulation programming for business analytics, finite element analysis, and compositional reservoir modeling.

Mr. Buonagurio has specific expertise in spatial data analysis with ArcGIS, engineering design with AutoCAD, ANSYS, LS-DYNA and SolidWorks, development of spatially-explicit, agent-based ecological simulations in C++/R, and numerical modeling of multi-phase fluid flow through porous media using CMG GEM, FLUENT and UTCHEM. He is also skilled in multidisciplinary data analysis and application development with SAS and the Microsoft .NET Framework.


  • B.S., Industrial Engineering, University of Pittsburgh, 2010

Professional Affiliations

Institute for Operations Research and the Management Sciences

Society of Petroleum Engineers

Project Experience

Developed a Monte Carlo simulation in SAS to predict future retiree healthcare expenses at a mining firm, using Medicare data and commercial claims databases.

Developed a Lagrangian particle tracking model using multibeam bathymetric surveys and tidal circulation data to predict spatial and temporal deposition patterns of an anti-parasitic drug at a Maine finfish farm, using SAS, MB-System, ArcGIS and DEPOMOD.

Developed a GIS plugin and data management software in C++ and C# for the spatially explicit exposure model (SEEM), an agent-based wildlife exposure model for the US Army. This model allows risk assessors to realistically evaluate terrestrial wildlife exposure to soil contaminants by incorporating species-specific foraging behaviors and habitat suitability.

Designed a complex 3D model in AutoCAD to estimate earthwork grading quantities at a large military facility, using construction bid documents and LiDAR topographic survey data.

Developed a GIS-based, site-specific share allocation model for a large Superfund site where dioxin-contaminated sediment was the primary risk driver. Analysis methods included conditional simulation and Voronoi diagrams.

For assessment of remedial options at an industrial facility, modeled infiltration of NAPL phase petroleum hydrocarbons in the vadose zone using UTCHEM.

For a litigation support case, developed a custom web-based mapping tool and web services framework in C# to query and visualize analytical chemistry data in a Gulf of Mexico embayment.

Performed a quantitative meta-analysis of cancer incidence rates in several ZIP codes attributable to chemical exposure using the EPA National Emissions Inventory, local cancer registries and US Census SF1 data.

Designed methods to correlate UK and German soil classification systems with the USDA soil taxonomy, to determine EPA acceptability of environmental fate studies of pesticides in foreign soils.

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