STATISTICAL TRENDS IN GROUNDWATER MONITORING DATA AT A LANDFILL SUPERFUND SITE - A CASE-STUDY

This paper describes the use of statistical regression models to characterize temporal trends in groundwater monitoring data collected between 1980 and 1990 on 15 wells and 13 parameters (195 cases in all) at the KL Avenue landfill site in Kalamazoo County, Michigan. This site was used as a municipal landfill prior to 1980, then was placed on the Superfund site list in 1982 after ground-water contamination was found. Six temporal regression trend models were defined using linear and quadratic regression models. These trends were used to classify each of the 195 cases as: improving, deteriorating, or stable over the 1980-1990 time period. Using these classifications it was determined that there were more than twice as many improving cases as deteriorating conditions at the KL site during this time period. These models provide a method for visualizing and interpreting trends in ground-water quality at individual well locations within the contaminant plume and for assessing the chemical trend behavior of the overall plume. The improving, deteriorating, and stable trend categories were developed for two purposes. The first purpose is to facilitate comprehension of information contained in large amounts of water quality data. The second is to assist communication among the many different groups of people who recommend actions, including remediation responsibilities at Superfund sites, like the KL site. A normal probability model was used in the trend classifications. This model contained provisions to accommodate nondetect data and other 'abnormal' laboratory determinations which can influence the trend selection process. The robustness of this classification procedure was examined using a lognormal probability model. The overall conclusions about the KL site using the lognormal model were similar to those obtained using the normal model. However, some individual trend indications were different using the lognormal model. The Shapiro-Wilk test was used to check the adequacy of both the normal and lognormal models. The lognormal model was found to be a somewhat more adequate model for fitting the KL site data, but was not found to be superior to the normal model for each case. The normal and lognormal models were both found to be suitable for determining overall trend conditions at this site. Both models are recommended for these purposes assuming an understanding of the statistical constraints and hydrochemical context. However, it is recommended that the search for more adequate trend models continues.