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With Harrisburg University’s strengths in data science, computer science, information systems, engineering, and management, and biotechnology, the Center is uniquely positioned to focus on interdisciplinary research that that translates science into actionable policy solutions, business practice improvement, and the development of evidence-based decision support tools and systems.

Translating Science to Policy

The Center for E3 is developing into a premier interdisciplinary center for research on pressing energy and environmental challenges through a focus on pragmatic, evidence-based, actionable policy solutions and decision support tools. Located at HU’s main campus in the center of Pennsylvania’s policy-making process, E3 acts as an important conduit for developing high-level policy and business practice solutions to air, water, energy and environmental issues.

Combining the depth of rigorous scientific research with the power of cutting-edge data analytics and the development of evidence-based decision support tools and systems, HU’s Center for E3 stands out in the crowded energy space. It is difficult to translate valuable scientific insights into smart policy or business practice because regulators and businesses typically lack easy-to-use analytical tools to understand and implement the science. By focusing on this key science-policy ‘translation gap’ and a solutions-oriented approach, the Center for E3 is well positioned geographically and academically to fill this gap, and influence energy and environmental policy in Pennsylvania and beyond.

John-Quigley-Harrisburg-University

E3 Center Director John Quigley

John Quigley is the founding Director for the Center for Environment, Energy & Economy at the Harrisburg University. He is former Secretary of the Pennsylvania Department of Environmental Protection, and of the Pennsylvania Department of Conservation and Natural Resources.

Center for E3 Initiatives

Farm Monitoring Technologies

The Center for E3 and Leena Pattarkine, Ph.D., Professor of Biotechnology are collaborating with TeamAg to develop and demonstrate low-cost tools for monitoring of water quality on farms in Pennsylvania to improve their economic and environmental performance.

Virtual Environmental Monitoring Network

The Center for E3 is focusing initially on building a proof-of concept network in partnerships involving water quality in the Susquehanna River.  Initial project activities include building the network prototype by accessing available data sets.

Environmental Research
Investigative Unit

Harrisburg University’s High Performance Computing Research Laboratory (HPCRL), under the direction of Majid Shaalan, Ph.D., Professor of Computer Science & Program Lead for the Computer Science Graduate Program, is collaborating with the Center for E3 to launch an Environmental Research Investigative Unit (ERIU).  ERIU will provide computational and cloud services to support the Center’s projects.

The collaboration will support undergraduate and graduate students in computational sciences and faculty who conduct environmental-related research.

Smart and Connected Communities Lab

This laboratory is led by Iheb Abdellatif, Ph.D., Assistant Professor of Information Technology & Management. The lab focuses on using computer-aided planning to develop customized, integrated local solutions for disaster management (including COVID), resilience for Small/Medium Businesses and Towns, and services underserved populations in the US. The research currently includes:

  • Developing a LEAN Smart Community Platform to reduce pollution, climate footprint, and impacts on public health. The platform will collect, process, and analyze satellite imagery using Artificial Intelligence and combine resulting data with additional data from other sources like city sensors, crowdsourcing, governmental databases, etc. in order to create methodologies, tools, and systems to identify and minimize waste sources (in electricity, transportation, water, etc.).
  • Developing applications to predict wildfires and prevent vehicle-animal collisions on highways.

Susquehanna River Basin Commission Partnership

Faculty and students from Information Systems Engineering and Management, Environmental Science and Sustainability, Data Science, and Computational Science are involved in a multidisciplinary collaboration with the Susquehanna River Basin Commission pursuing various research projects:

  • Using machine learning to identify key environmental indicators that influence water quality in the watershed, and to determine the effects of environmental indicators on the watershed’s biotic community.
  • Combine data mining, deep learning techniques, and data visualization techniques to identify relationships among various environmental parameters/indicators in a Predictive Analytics Dashboard
  • Develop data science tools to predict and manage Harmful Algal Blooms
  • Develop data science tools to identify, predict, and manage the impacts of climate change.
  • Update/modernize analytical tools that are used to respond to water pollution incidents and protect public health and drinking water quality.

Predicting spatial/temporal trends in COVID-19 using wastewater surveillance

Sewage systems across USA are being sampled to provide estimates of the total number of COVID cases on a city or regional basis. This testing can be expensive, and results based on proprietary methodology provide only gross estimate of cases.

HU data science professors Kevin Purcell, Ph.D. and Alan Hitch, Ph.D. seek to demonstrate the effectiveness of a combination of less expensive laboratory analysis and carefully planned, wider scale geographic testing at regular time intervals.

The resulting data would be used to model virus presence and estimate case counts within communities over time – at a much greater level of detail than currently available.

This more detailed modelling would allow government and public health officials to design more targeted, limited policy interventions e.g. lockdowns, enable better public health responses, and minimize the economic impact of the virus.

This work could provide a model for an effective statewide public health surveillance system.

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