Park Service Displays at 2007 ESRI International User Conference

A Modeling Route to Comprehensive Air Quality Reporting
From Raw Data to Policy Tool

The National Park Service (NPS) has developed a strategy to improve management of natural resources under its care. A major component of the strategy includes long-term monitoring of natural resources through the Vital Signs Monitoring Program and reporting the status and trends of those resources. Due to its important role in human and plant health, as well as park visitor enjoyment, air quality has been identified as one of these vital signs to be reported. Air quality parameters are evaluated through various nationwide monitoring networks. While thousands of monitoring sites across the country were used as input for the interpolations, air quality is measured directly at only fifty-one NPS Units across the country; it is a goal of the Vital Signs program to assess the conditions of all 270 NPS unites within the program. One of the primary benefites of using interpolated data is that it allows for the estimation of air quality in area were direct measurement is not possible. The interpolations used as a basis for this study are found in Air Atlas which includes an ArcIMS application which provides national maps and an associated look-up table with basline values of air quality parameters for most Vital Signs Monitoring Program park units (http://www2.nature.nps.gov/air/Maps/AirAtlas/index.htm). Estimates from 2001-2005 are used to rate the condition of parks for this report. This condition rating is based on several individual air quality parameters including ozone, atmospheric sulfur and nitrogen deposition, and visibility.

The Air Quality Condition (AQC) Scorecard represents a new tool for evaluating and reporting the air quality in our National Parks; and by providing a means for integrating several parameters into a single score it helps facilitate the distribution of information and the understanding of consequences of policy.

Input Data:
Interpolations of 5-year averages created for the Air Atlas proect were used as input. These interpolations were created using the inverse distance weighted method of interpolation. In addition to interpolations, input layers were created for supplemental risk factors. Parks with ecosystems especially sensitive to nitrogen or sulfur deposition, as well as parks located with EPA designated non-attainment counties for ozone or particulate matter were given an additional weighting after the reclassification.

Status: Interpolations derived from air quality monitors were used to determine air quality status.

Trend: The difference in interpolated values of two five-year averages were compared to determine values for the air quality trend.

Confidence: The straight line distance from air quality monitors used to create the interpolations was used to determine the confidence of the air quality estimate.

Models:
Using the model builder applicatoin within ArcMap allowed for the transformation from quantitative data in the form of interpolations to qualitative data in the form of a ranked grid.

By gathering specialists together and using their expertise to create cut points, the floating point values of the interpolations were reclassified into ordinal data. Once the interpolations had been reclassified it was then possible to combine the difference parameters in a meaningful way to create a composite score for each NPS unit.

An additional benefit to automating the scoring process through using model builder is that by substituting updated interpolations, future updates of AQC scores can easily be computed.

Final Product:

The three parameters of status, trend and confidence were then assembled into a single symbol for each NPS Unit within the vital signs program. The Air Quality Condition score determined for each unit provides a new and innovative way for evaluating progress towards meeting air quality goals as well as reporting conditions and trends were data is limited. The final status and trends are assembled into a table as well as a map for distribution.

While the final classification may be of a much more qualitative nature than the original input data due to the ranking process, the final symbols convey the information in a more undertsandable way which can be invaluable to policy and decision makers less familiar with air quality data.

Content by Drew Bingham NPS ARD.

Links of interest:

• NPS Spatial Odyssey 2006
• Twenty-Seventh Annual ESRI International User Conference Page
• National Park Service Website
• NPS GIS Website

Back to GIS Conference Page

For NPS go to inside for more information about the conference