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Water‐quality data from 90 monitoring wells screened within 50 feet of the water table in the unconfined upper glacial aquifer beneath five areas of differing land use in Nassau and Suffolk Counties, Long Island, were compared to assess the effects of land use on ground‐water quality. The areas, which range from 22 to 44 square miles, represent suburban land sewered more than 22 years at the time of the study (long‐term sewered), suburban land sewered less than 8 years (recently sewered), suburban land without a regional sewer system, agricultural land, and undeveloped (forested) land. Comparison of water‐quality data from the 90 wells indicated that samples from the undeveloped area had the lowest and smallest range in concentrations of several human‐derived constituents, such as nitrate, alkalinity, boron, synthetic solvents, and pesticides. Concentrations of these constituents in samples from the three suburban areas and the agricultural area generally were intermediate to high and had the widest variation. Maximum‐likelihood logistic regression analysis of explanatory variables that characterize the type of land use and population density within a 1/2‐mile radius of each of the 90 wells was used to develop predictive equations for contaminant occurrence in ground water within 50 feet of the water table. Two logistic regression equations for the 90 monitoring wells were compared with equations developed independently from ground‐water quality data at more than 240 other wells throughout Nassau and Suffolk Counties to evaluate the predictive value of the land‐use variables at the larger two‐county scale. The results demonstrate that the population density and amount of agricultural, commercial, and high‐ and medium‐density residential land within specified areas around wells can be reliable predictors of contaminant presence. The strength of the correlations supports the premise that land use affects the quality of water in water‐table aquifers overlain by highly permeable material because land use commonly determines the types and amounts of chemicals introduced at land surface. When coupled with GIS technology and accurate, detailed land‐use and water‐quality information, the methods and results of this study can be useful to local planning boards in evaluation of potential effects of development on ground‐water quality. The methods can also be useful to hydrologists in the analysis and design of ground‐water‐monitoring networks.

Huckins, J. N.; Furlong, E. T.; Zaugg, S. D.; and Meyer, M. T., "Comparison of a novel passive sampler to standard water-column sampling for organic contaminants associated with wastewater effluents entering a New Jersey stream" (2005 AbstractFour water samples collected using standard depth and width water-column sampling methodology were compared to an innovative passive, in situ, sampler (the polar organic chemical integrative sampler or POCIS) for the detection of 96 organic wastewater-related contaminants (OWCs) in a stream that receives agricultural, municipal, and industrial wastewaters. Thirty-two OWCs were identified in POCIS extracts whereas 9-24 were identified in individual water-column samples demonstrating the utility of POCIS for identifying contaminants whose occurrence are transient or whose concentrations are below routine analytical detection limits. Overall, 10 OWCs were identified exclusively in the POCIS extracts and only six solely identified in the water-column samples, however, repetitive water samples taken using the standard method during the POCIS deployment period required multiple trips to the sampling site and an increased number of samples to store, process, and analyze. Due to the greater number of OWCs detected in the POCIS extracts as compared to individual water-column samples, the ease of performing a single deployment as compared to collecting and processing multiple water samples, the greater mass of chemical residues sequestered, and the ability to detect chemicals which dissipate quickly, the passive sampling technique offers an efficient and effective alternative for detecting OWCs in our waterways for wastewater contaminants. Published by Elsevier Ltd.