Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey

Money, Eric S.; Carter, Gail P.; Serre, Marc L.

doi:10.1016/j.watres.2009.01.034

Cited by 24 publications

(27 citation statements)

References 31 publications

(50 reference statements)

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“…The relatively long correlation distance estimates imply that NO 3 NO 2 -N concentration is correlated between locations far apart. This could be attributed to the impacts from nonpoint sources such as agriculture that could affect long stretches of rivers all at once (Money et al, 2009).…”

Section: Regression Resultsmentioning

confidence: 99%

“…Straight-line distance may not function well with stream monitoring sites because it fails to incorporate stream network connectivity and topology. Hydrological distance, which measures distance between sites along stream networks, has been proposed as an alternative for studying spatial correlation among stream monitoring sites (Gardner et al, 2003;Ganio et al, 2005;Cressie et al, 2006;Peterson and Urquhart, 2006;Money et al, 2009). In an Arc Hydro Geodatabase, hydrological distance can be easily calculated with a few python scripts utilizing the data model's HydroNetwork component that enables tracing water movement along stream networks.…”

Section: Categoriesmentioning

confidence: 99%

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GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

Yang

Jin

2010

Journal of Environmental Management

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Section: Regression Resultsmentioning

confidence: 99%

Section: Categoriesmentioning

confidence: 99%

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

Yang

Jin

2010

Journal of Environmental Management

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“…Details about the implementation of river distance calculations in BMElib are provided in Money et al . (28). The BME procedure consists of defining the general knowledge (i.e.…”

Section: Methodsmentioning

confidence: 99%

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

Money

Carter

Serre

2009

Environ. Sci. Technol.

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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.

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“…3) which allowed a complete stochastic description of those non-sampling areas (Money et al 2009). The maps showed gentle transitions between the different mapping units which reflected the normal behavior of continuous variables like water contaminants.…”

Section: Softwarementioning

confidence: 99%

Spatio-temporal distribution of pyrethroids in soil in Mediterranean paddy fields

et al. 2016

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Purpose The demand of rice by the increase in population in many countries has intensified the application of pesticides and the use of poor quality water to irrigate fields. The terrestrial environment is one compartment affected by these situations, where soil is working as a reservoir, retaining organic pollutants. Therefore, it is necessary to develop methods to determine insecticides in soil and monitor susceptible areas to be contaminated, applying adequate techniques to remediate them. Materials and methods This study investigates the occurrence of ten pyrethroid insecticides (PYs) and its spatio-temporal variance in soil at two different depths collected in two periods (before plow and during rice production), in a paddy field area located in the Mediterranean coast. Pyrethroids were quantified using gas chromatography-mass spectrometry (GC-MS) after ultrasound-assisted extraction with ethyl acetate. The results obtained were assessed statistically using non-parametric methods, and significant statistical differences (p < 0.05) in pyrethroids content with soil depth and proximity to wastewater treatment plants were evaluated. Moreover, a geographic information system (GIS) was used to monitor the occurrence of PYs in paddy fields and detect risk areas. Results and discussion Pyrethroids were detected at concentrations ≤57.0 ng g −1 before plow and ≤62.3 ng g −1 during rice production, being resmethrin and cyfluthrin the compounds found at higher concentrations in soil. Pyrethroids were detected mainly at the top soil, and a GIS program was used to depict the obtained results, showing that effluents from wastewater treatment plants (WWTPs) were the main sources of soil contamination. No toxic effects were expected to soil organisms, but it is of concern that PYs may affect aquatic organisms, which represents the worst case scenario. Conclusions A methodology to determine pyrethroids in soil was developed to monitor a paddy field area. The use of water from WWTPs to irrigate rice fields is one of the main pollution sources of pyrethroids. It is a matter of concern that PYs may present toxic effects on aquatic organisms, as they can be desorbed from soil. Phytoremediation may play an important role in this area, reducing the possible risk associated to PYs levels in soil.

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Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey

Cited by 24 publications

References 31 publications

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

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

Spatio-temporal distribution of pyrethroids in soil in Mediterranean paddy fields

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