Modern Space/Time Geostatistics Using River Distances: Data Integration of Turbidity andE. coliMeasurements to Assess Fecal Contamination Along the Raritan River in New Jersey

Abstract

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.

Modelling dendritic ecological networks in space: an integrated network perspective

“…; Money et al . ,b; Garreta et al . ), which are based on a branching, continuous spatial analogue to moving averages in time series.…”

“…; Money et al . ,b). These on ‐network methods are similar to traditional linear regression techniques commonly applied to point measurements in stream ecology; except that the assumption of independent errors is replaced with the notion that random errors co‐vary in both 2‐D and network space.…”

Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data

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