More Than a Powerful Visual: Using Statistical Modeling and Python to Efficiently Create Project Deliverables and Assess Remedial Options
Jim Depa, Senior Project Manager, Jacob and Hefner Associates, Inc.
While statistical modeling and 3D visualization technology have become widely accepted tools on large environmental remediation projects, they remain under-utilized at smaller sites. However, continuing advancements in both software technology and computing power have made the technology more accessible to projects of any size. Additionally, the integration of Python scripting language can help automate certain tasks to produce nearly any type of data deliverable more economically. Most importantly, analyses of statically modeled data can reduce both investigative and remedial costs by providing more accurate estimates the in-situ contaminant mass, simulating the size of an excavation, or by designing a more targeted subsurface injection strategy.
Mr. Depa is a geologist and senior project manager at Jacob Hefner and Associates. He is a 2005 graduate from the University of Illinois with degrees in Geology and Geographic Information Sciences (GIS). He has more than 16 years of experience in the environmental consulting field and specializes in creating 3D statistical models to assess complex sites. He has created data deliverables on over 230 environmental investigation projects - from routine data visualizations to multi-million-dollar design remedies. Mr. Depa has also been involved in four environmental litigation projects, including a case involving odor complaints from a landfill - recently settled in November 2021 for $28.5M, in what is thought to be the largest odor nuisance settlement in United States history.