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This article in JEQ

  1. Vol. 35 No. 2, p. 421-432
    Received: Mar 31, 2005

    * Corresponding author(s): vbenson@upei.ca
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Spatial Analysis of Land Use Impact on Ground Water Nitrate Concentrations

  1. Victoria S. Benson *a,
  2. John A. VanLeeuwena,
  3. Javier Sancheza,
  4. Ian R. Dohooa and
  5. George H. Somersb
  1. a Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada
    b Water Management Division, PEI Department of Environment, Energy and Forestry, Charlottetown, PE, C1A 7N8, Canada


In spatial analyses of causes or health effects of environmental pollutants, small units of analyses are usually preferred for internal environmental homogeneity reasons but can only be done when fine resolution data are available for most units. Objectives of this study were to determine which land use practices were spatially associated with ground water nitrate concentrations across Prince Edward Island (PEI), Canada, and which spatial aggregation is the preferred unit of analyses. Nitrate concentrations were determined for 4855 samples from private wells. Validated field-by-field land use data were available. Average nitrate concentration and percentage of area for the 14 major land use categories in PEI were determined for each of three spatial aggregations: watersheds based on topography and hydrology; freeform polygon boundaries based on similar neighboring nitrate concentrations; and 500-m buffer zones around each well. Results showed that the percentages of potato, grain, and hay coverage were positive predictors of ground water nitrate concentrations. Percentage of blueberry was a marginally significant negative predictor in the watershed and freeform polygon models, and percentage of residential coverage was a positive predictor in the freeform polygon and buffer zone models. Spatial autocorrelation was present in the freeform polygon and buffer zone models even after land use was taken into account. In conclusion, analyses based on watersheds produced the best predictive model with the percentages of land cover of potato, hay, and grain being significantly associated with ground water nitrate concentrations, and the percentages of blueberry, clear-cut woodland, and other agriculture being marginally significant.

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