Escherichia coli (E.coli) is a widely used indicator of fecal contamination in water bodies. External contact and subsequent ingestion of bacteria coming from fecal contamination can lead to harmful health effects. Since E.coli data are sometimes limited, the objective of this study is to use secondary information in the form of turbidity to improve the assessment of E.coli at un-monitored locations. We obtained all E.coli and turbidity monitoring data available from existing monitoring networks for the 2000 -2006 time period for the Raritan River Basin, New Jersey. Using collocated measurements we developed a predictive model of E.coli from turbidity data. Using this model, soft data are constructed for E.coli given turbidity measurements at 739 space/time locations where only turbidity was measured. Finally, the Bayesian Maximum Entropy (BME) method of modern space/ time geostatistics was used for the data integration of monitored and predicted E.coli data to produce maps showing E.coli concentration estimated daily across the river basin. The addition of soft data in conjunction with the use of river distances reduced estimation error by about 30%. Furthermore, based on these maps, up to 35% of river miles in the Raritan Basin had a probability of E.coli impairment greater than 90% on the most polluted day of the study period.
Following passage of the New Jersey Private Well Testing Act, 50,800 domestic wells were tested between 2002 and 2007 for the presence of total coliform (TC) bacteria. Wells containing TC bacteria were further tested for either fecal coliform or Escherichia coli (FC/E. coli) bacteria. Analysis of the data, generated by 39 laboratories, revealed that the rate of coliform detections in groundwater (GW) was influenced by the laboratory and the method used, and also by geology. Based on one sample per well, TC and FC/E. coli were detected in wells located in bedrock 3 and 3.7 times more frequently, respectively, than in wells located in the unconsolidated strata of the Coastal Plain. In bedrock, detection rates were higher in sedimentary rock than in igneous or metamorphic rock. Ice-age glaciers also influenced detection rates, most likely by removing material in some areas and depositing thick layers of unconsolidated material in other areas. In bedrock, coliform bacteria were detected more often in wells with a pH of 3 to 6 than in wells with a pH of 7 to 10 whereas the reverse was true in the Coastal Plain. TC and FC/E. coli bacteria were detected in 33 and 9.5%, respectively, of sedimentary rock wells with pH 3 to 6. Conversely, for Coastal Plain wells with pH 3 to 6, detection rates were 4.4% for TC and 0.6% for FC/E. coli.
Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river metric into the space/time analysis of dissolved oxygen data for two impaired river basins. An efficient algorithm is developed to calculate river distances within the BMElib statistical package for space/time geostatistics. We find that using a river distance in a space/time context leads to an appreciable 10% reduction in the overall estimation error, and results in maps of DO that are more realistic than those obtained using a Euclidean distance. As a result river distance is used in the subsequent non-attainment assessment of DO for two impaired river basins in New Jersey.
The Kirkwood‐Cohansey aquifer has been identified as a critical source for meeting existing and expected water supply needs for southern New Jersey. Several contaminated sites exist in the region; their impact on the aquifer has to be evaluated using ground water flow and transport models. Ground water modeling depends on availability of measured hydrogeologic data (e.g., hydraulic conductivity, for parameterization of the modeling runs). However, field measurements of such critical data have inadequate spatial density, and their locations are often clustered. The goal of this study was to research, compile, and geocode existing data, then use geostatistics and advanced mapping methods to develop a map of horizontal hydraulic conductivity for the Kirkwood‐Cohansey aquifer. Spatial interpolation of horizontal hydraulic conductivity measurements was performed using the Bayesian Maximum Entropy (BME) Method implemented in the BMELib code library. This involved the integration of actual measurements with soft information on likely ranges of hydraulic conductivity at a given location to obtain estimate maps. The estimation error variance maps provide an insight into the uncertainty associated with the estimates, and indicate areas where more information on hydraulic conductivity is required.
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