Jump to main content.


Evaluating Lake Use Impairment Data in Nutrient Criteria Development

Scott A. Kishbaugh

New York State Department of Environmental Conservation (NYSDEC), Albany, New York

The NYSDEC recently completed a two year study for EPA Regions I, II and V involving the use of use impairment data linked with water quality data to identify reference conditions as part of the nutrient criteria development process. Data were evaluated from eight states and three EPA regions, all collected in a similar manner using standardized lake perception surveys, spread over eight aggregate EPA ecoregions, twenty-six level III EPA ecoregions, and 200,000 samples. Data were evaluated using a variety of methodologies to identify reference conditions, mostly consistent with historical methodologies used to identify intrastate ecoregions and the EPA CALM methodology used to identify support of designated uses. Reference conditions are defined as the 75th percentile of the reference dataset, consistent with the EPA recommendations. The first methodology presented defines reference waterbodies as those that are "slightly impaired" at a frequency of <10%, consistent with the CALM methodology (as adapted by several states) for "fully supporting" designated uses. The second methodology defines reference as corresponding to sampling conditions described as "could not be nicer" or (having) "very minor aesthetic problems". The third methodology assigns the percentage of lakes meeting the criteria in the first methodology to the entire EPA nutrient dataset. This presentation provides a summary of the resulting nutrient criteria based on these three methodologies in each of the major aggregate and level III ecoregions in EPA Region I, and provides an example of the use of these data in developing final nutrient criteria in one ecoregion.

Keywords: nutrients, criteria, lake, impairment, volunteer monitoring

EMAP Home | About EMAP | Components | Data | Documents | Bibliography | News | Site Map


Local Navigation


Jump to main content.