Modeling Water Quality in Watersheds: From Here to the Next Generation.

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

Sensitive method for nontransferrin-bound iron quantification by graphite furnace atomic absorption spectrometry.

OBJECTIVE: To establish a sensitive method for measuring nontransferrin-bound iron (NTBI) in serum samples using graphite furnace atomic absorption spectrometry (GFAAS). DESIGN AND METHODS: Nontransferrin-bound iron (NTBI) was chelated using nitrilotriacetic acid (NTA) and then ultrafiltered according to the method employed by Singh et al. [1]. Serum ultrafiltrates were diluted eightfold with distilled water. NTBI from the Fe-NTA complex present in the serum ultrafiltrate was measured using GFAAS. RESULTS: Nontransferrin-bound iron (NTBI) and other parameters were measured in seven patients diagnosed with hereditary hemochromatosis by liver biopsy. Total serum iron, NTBI and transferrin saturation values (ranging from 87% to 90%) were elevated for three of the seven hemochromatosis patients tested before therapeutic phlebotomy. Six of the seven hemochromatosis patients had undergone phlebotomy and revealed normal total serum iron, NTBI and transferrin saturation values. Nine test subjects (not diagnosed with hemochromatosis) with abnormally high total serum iron and/or ferritin concentrations exhibited normal NTBI values (< or =0.14 micromol/L to 0.29 micromol/L). The detection limit was 0.1 micromol/L for a 25 microL injection volume. CONCLUSIONS: The GFAAS method presented here provides a sensitive assay to quantitate NTBI in serum samples. The method developed is 4 to 5 times more sensitive than the only other GFAAS method [2] and more than an order of magnitude more sensitive than other colorimetric methods [1,3]. Improvement in sensitivity over the other GFAAS method [2] may be accounted for by differences in sample preparation between this method and that of Nielsen et al. [2]. Serum ultrafiltrates in this study were diluted eightfold with distilled water and mixed with a magnesium nitrate matrix modifier before GFAAS analysis. NTBI results obtained from this study indicate that the plasma iron pool in hemochromatosis patients awaiting phlebotomy increases to a level at which transferrin's ability to bind iron becomes exhausted and elevated NTBI levels appear in the serum. NTBI can mediate the production of reactive oxygen species and may cause organ damage associated with iron overload.

Statistical modeling of restricted pollutant data sets to assess compliance with air quality criteria.

Three statistical models are used to predict the upper percentiles of the distribution of air pollutant concentrations from restricted data sets recorded over yearly time intervals. The first is an empirical quantile-quantile model. It requires firstly that a more complete date set be available from a base site within the same airshed, and secondly that the base and restricted data sets are drawn from the same distributional form. A two-sided Kolmogorov-Smirnov two-sample test is applied to test the validity of the latter assumption, a test not requiring the assumption of a particular distributional form. The second model represents the a priori selection of a distributional model for the air quality data. To demonstrate this approach the two-parameter lognormal, gamma and Weibull models and the one-parameter exponential model were separately applied to all the restricted data sets. A third model employs a model identification procedure on each data set. It selects the 'best fit' model.

A hybrid model for predicting the distribution of pollutants dispersed from line sources.

This paper applies a hybrid modeling methodology to the problem of the dispersion of pollutants from line sources. The model combines a deterministic component, the GM model (Chock, 1978), with a statistical component, the two-parameter Weibull distribution, to produce estimates of the entire distribution of pollutant concentration. The approach is demonstrated using hourly average carbon monoxide data recorded in Melbourne, Australia. Using a second data set a model validation exercise is performed. The hybrid model has worked well, producing estimates of pollutant concentration with an accuracy better than a factor of two over all percentiles of the distribution of pollutant concentration.