River Nutrient and Chemical Transport Estimation

Verhoff, F. H.; Melfi, David A.; Yaksich, Stephen M.

doi:10.1061/jeegav.0001047

Cited by 57 publications

(6 citation statements)

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“…As this is difficult to perform, especially for trace elements such as mercury, total flux is generally estimated. Various calculation methods are available in the literature ( − ) which, together with the errors (precision and accuracy) associated with the estimation of fluxes, have been intensively discussed ( 19, 21, 22 ). Meybeck () recommends the use of a ratingcurve for both dissolved and particulate contaminants, provided that the relationship between solute or suspended particulate matter and discharge is well-known.…”

Section: Environmental Settingmentioning

confidence: 99%

Sources and Fluxes of Mercury in the St. Lawrence River

Quémerais

Cossa

Rondeau

et al. 1999

Environ. Sci. Technol.

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A mass balance approach, based essentially on the reconstruction of daily fluxes and circumscribed by strict error calculations, was designed to quantify the main mercury sources for the St. Lawrence and its tributaries, which constitute a large river system. High-frequency samplings were performed over an 18-month period (1995−1996) at the main water inputs and the mouth of the river. Minor tributaries and the Montreal effluent were also sampled. This strategy allowed models to be obtained that relate mercury concentrations in solution and in particles to the hydrological regime. The calculated budget was balanced relative to the calculated errors of the estimates. Gross mercury export from the river was found to be 5.9 kmol yr-1 (73% as particulate). Tributaries and internal erosion of the river contributed equally for a total of 75% of this gross load, whereas the Upper St. Lawrence River, which is almost exclusively composed of Lake Ontario waters, accounted for less than 10%, and inventoried anthropogenic point sources accounted for about 5%. Dissolved mercury was mainly from north shore tributaries, and particulate mercury was largely from erosion of the river bed and banks. On the basis of the present results as well as estimates of atmospheric deposition from the literature it can be inferred that at least 88% of deposited mercury was retained in the watersheds.

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Section: Environmental Settingmentioning

confidence: 99%

Sources and Fluxes of Mercury in the St. Lawrence River

Quémerais

Cossa

Rondeau

et al. 1999

Environ. Sci. Technol.

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“…The literature contains a number of references pertaining to computational methods for estimating fluvial sediment and nutrient loads using constituent concentration and river discharge measurements (3)(4)(5)(6)(7)(8)(9)(10)(11)(12). Preston et al (13) have provided an excellent summary of some of these methods with applications that include phosphorus, zinc, lead, and Aroclor 1242.…”

Section: Introductionmentioning

confidence: 99%

Estimated Annual Loads of Selected Organic Contaminants to Chesapeake Bay via a Major Tributary

Jt¹,

Foster²,

Ka³

1995

Environ. Sci. Technol.

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“…In FP sampling, L HT in Equation 5 is the most precise when l j is proportional to q j but remains unbiased regardless of the relationship between l j and q j . In addition, the load estimator, as a stratum-size weighted average of the stratum means with stratified sampling based on SRS (Thomas & Lewis, 1995;Verhoff et al, 1980), is also an HT estimator, although the ratio estimator (Beale, 1962) with stratified sampling employed in Great Lake studies (International Reference Group on Great Lakes Pollution from Land Use Activities & Whitt, 1977) was biased. The load estimates by the RCM become an HT estimator when p j in UPSWOR is proportional to the estimated loading rates by an RC (Tada & TADA AND TANAKAMARU 10.1029/2022WR031941 5 of 23 Tanakamaru, 2021).…”

Section: Unbiased Load Estimation With the Ht Estimatormentioning

confidence: 99%

“…Because the design of such a biased estimator is impossible without knowing the distribution of population elements, an unbiased estimator should be used in load estimation. Studies of unbiased load estimation have been based on either stratified sampling (Thomas & Lewis, 1993, 1995Verhoff et al, 1980) that used the weighted average of stratum means as a load estimate based on simple random sampling (SRS), or probability sampling methods (Thomas, 1985(Thomas, , 1988(Thomas, , 1989. In other studies, load calculation methods (LCMs) have been independently combined with sampling strategies.…”

mentioning

confidence: 99%

Unbiased Estimates and Confidence Intervals of Riverine Loads for Low‐Frequency Water Quality Monitoring Strategies

Tada

Tanakamaru

2022

Water Resources Research

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Eutrophication in semiclosed water bodies, such as lakes and estuaries, due to excessive nutrient loads has severely damaged the ecosystem via water pollution. The establishment of pollution prevention measures in ecosystems requires accurate estimates of the total mass of pollutants flowing into a water body during a certain period. Because rivers are the main transport paths of pollutants to a receiving water body, accurate load estimation methods for riverine constituent loads have been studied over the past four decades. These studies assumed that river discharge was observed at a high frequency, such as daily, hourly, or subhourly, but water quality (WQ) is generally observed at a low frequency, such as weekly or monthly, due to the high cost and labor requirements for sampling and analysis. This mismatch among observation frequencies can result in highly uncertain load estimates.The low-frequency sampling methods used for load estimation include autosampling, stratified sampling (Thomas

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River Nutrient and Chemical Transport Estimation

Cited by 57 publications

References 0 publications

Sources and Fluxes of Mercury in the St. Lawrence River

Sources and Fluxes of Mercury in the St. Lawrence River

Estimated Annual Loads of Selected Organic Contaminants to Chesapeake Bay via a Major Tributary

Unbiased Estimates and Confidence Intervals of Riverine Loads for Low‐Frequency Water Quality Monitoring Strategies

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