National Parks Conservation Association (NPCA) is requesting proposals for an independent consultant or graduate student with necessary GIS expertise to complete a Class I Areas Perimeter GIS Project described below as soon as practicable. Consultant or student is expected to have computer with relevant software. This project is to be completed remotely via communication with NPCA staff.
Please submit proposal including timeframe and cost estimate to Stephanie Kodish at email@example.com by May 1, 2019.
Class I Areas Perimeter GIS Project
The Environmental Protection Agency’s regional haze program seeks to improve the visibility at Clean Air Act designated “Class I” national park and wilderness areas, which has been degraded by air pollution sources. A common technique used in the regional haze program to quickly assess the potential of air pollution point sources (e.g., a power plant) to impact the visibility at Class I Areas is referred to as “Q/d.” Here, Q represents the amount of air pollution in tons, and d represents the distance measured from the latitude and longitude of the point source to the latitude and longitude of the closest edge of the Class I Area. Any pollution source with a Q/d value over 10.0 is typically analyzed in more detail by using air modeling. A listing of the 156 Class I Areas can be found at https://www.epa.gov/
Typically when analyzing only a few sources, d is found manually using GIS or Google Earth Pro. However, that is not practical when analyzing thousands of individual point sources, each one of which must have d calculated to the nearest edge of every one of the 156 Class I Areas. The biggest challenge to this problem is that there is no known dataset of perimeter lat/long points for the Class I Areas. As a consequence, a common approach uses the lat/long data for modeling receptors. Receptors are locations of imaginary points that have been located within each Class I Area with known lat/long information. They are used in photochemical modeling in order to provide modeling output of air emissions at specific points. Below is a representation of the receptor grid for Acadia National Park. Each diamond represents a receptor location. The distance, d is found via a brute force technique in which d from the point source to every receptor is calculated, and then the minimum of those distances is found and assigned to d. This is assumed to represent the distance to the closest edge of the Class I Area. This is very computationally intensive, as many larger Class I Areas have hundreds of receptor points (limited to 1,000). Calculating this for thousands of points sources can take days to weeks to accomplish on a typical laptop. A faster solution would be to start with lat/long data for just the perimeter of the Class I Areas. This would require developing a file that contains the perimeter-only lat/long data for each Class I Area.