| ARSENIC CYCLING IN NATURAL WATERS (Ph.D. Topic) |
| Recent insights into the toxicity of arsenic have prompted the U.S. Environmental Protection Agency to lower the drinking water standard from 50 to 10 ppb (USEPA, 2002). Although the most important exposure route is via groundwater, as in West Bengal and Bangladesh, surface waters can also have elevated concentrations (e.g. Lake Ohakuri, New Zealand, ~30 ppb, Aggett and O’Brien, 1985; Crowley Lake, California, ~50 ppb, Kneebone and Hering, 2000). For management purposes, it is important to understand how the arsenic concentration in surface waters responds to changes in forcing functions (e.g. reduced input, climatic variability). The goal of this research project is to construct a numerical model of arsenic cycling in natural surface waters. |
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Algae reduce and methylate arsenate. The product of the transformation reaction is a function of the growth rate of the algae. At high growth rates arsenite (As(III)) is produced. At lower growth rates dimethylarsinic acid (DMA) is produced. In lakes this leads to high As(III) concentrations during bloom conditions (e.g. spring and fall) and high DMA concentrations in the summer. This is evident in Lake Biwa, Japan as illustrated by the data of Sohrin et al. (1997). |
| This phenomenon is linked to the phosphorus luxury uptake of algae. Under P-limited conditions, which exist in the summer, algae take up As(V), reduce it to As(III), methylate it to MMA and DMA and then excrete it. However, under non P-limited conditions, which exist in the early stages of blooms, algae up-regulate their PO4 transport system to take up excess P (luxury uptake). Since As(V) is taken up by the PO4 transport system, large quantities of As(V) are also taken up at this time. Reduction of As(V) to As(III) is fast, but the methylation is slower causing As(III) to build up in the cell, which is excreted causing the increase in extracellular As(III). |  |
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A mathematical model was constructed and applied to laboratory batch data to present evidence for this hypothesis. The model builds on existing established algal nutrient models, which are simply extended to As. The figure on the right shows application of the model the data of Hasegawa et al. (2001). |
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| FATE AND TRANSPORT OF POLLUTANTS IN THE HUDSON |
| Understanding the transport characteristics of the Hudson Estuary is important for predicting the fate of contaminants discharged in the past (e.g., polychlorinated biphenyls (PCBs)), present (e.g., pathogens from combined sewer overflows (CSOs)) and future (e.g., accidental spills). Estuarine transport can be studied by observation as well as analytical and numerical modeling. Whereas either of these approaches can be used alone, the combination of data and model is the most effective approach because observational and modeling strategies complement each other. Data can be used to calibrate and validate a model and, at the same time, models help understand the physics governing natural systems and extrapolate data to areas and times with little or no coverage. This research project deals with using numerical modeling to study the fate of contaminants in the Hudson Estuary. |
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The effect of estuarine circulation and tidal trapping on mixing in the Hudson Estuary is investigated by a numerical model simulation of a tracer release. The data set used in this study was obtained from a large-scale SF6 tracer release experiment conducted during July/August 2001. It consists of over 2,000 measurements taken over a period of two weeks and distance of 110 km with a typical resolution of 400 meters. The model is based on the three-dimensional, time-variable, estuarine and coastal circulation modeling framework (ECOM), and consists of over 10,000 mass balance segments with a 600 m horizontal and 1 m vertical resolution in the study area. The ability of the model to reproduce the observed fate and transport of the tracer (model skill) is quantified using a new set of metrics. The model can reproduce the data over four orders of magnitude with a mean relative agreement (MRA) factor of 2.0. |
| The modeled and measured longitudinal tracer concentration profiles (plumes) differ from the ideal Gaussian shape in two ways: (1) On a large scale the plume is asymmetric, with the downstream end stretching out farther. (2) Small-scale (1-2 km) peaks are present at the upstream and downstream ends of the plume. |  |
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A sensitivity analysis is used to understand the processes responsible for these features. The model forcing functions (e.g., freshwater flow, boundary salinity, geometry) and process parameterizations (e.g., gas transfer velocity) are modified systematically and the resulting tracer profiles are compared to those from the unmodified model (base case). It is demonstrated that the large-scale asymmetry is due to salinity intrusion, which sets up an estuarine circulation. In the presence of an estuarine circulation, tracer in the surface layer is diluted to a larger degree with water from the lower layer causing increased longitudinal dispersion. Since the salinity intrusion is confined to the downstream end of the tracer plume, only that part of the plume is subject to a larger dispersion, which leads to the large-scale asymmetry. The small-scale peaks are due to tidal trapping. Small embayments along the estuary trap water and tracer as the plume passes by in the main channel. At a later time, when the plume in the main channel has passed, the tracer is released back to the main channel, causing a secondary peak in the longitudinal profile. |
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| SUPER-RIVERS PROJECT |
| Super-rivers, like the Amazon, discharge enormous amounts of water into the oceans. The sea surface salinity (SSS) distributions typically show the influence of rivers, as low salinity plumes stretching far into the oceans. Also, satellite color maps show plumes of high primary productivity near river mouths. The freshwater input effects the salinity stratification and adds nutrients to the oceans, which are important to biological activity. This research project deals with understanding the fate of water discharged by these super-rivers in the oceans. |
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Monthly Amazon River discharge is correlated to historical monthly sea surface salinity (SSS) in the western tropical Atlantic Ocean and the Caribbean Sea. At Barbados a very high inverse correlation (R2 = 0.92) exists if the discharge is lagged by two months, which corresponds to the travel time from the Amazon mouth to Barbados. The correlation is highest at Barbados and diminishes with distance downstream. Downstream of Barbados a subsurface maximum in correlation develops. The correlation is eroded more strongly at shallower depths due to more intense surface processes (e.g., evaporation and precipitation). |
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| REMOTE SENSING OF WATER QUALITY |
| The optical properties (i.e. reflectance) of water depend on the amount and character of suspended sediments, phytoplankton and dissolved organic matter (gelbstoff) it contains. Sensors aboard satellites can measure the amount of solar radiation at various wavelengths reflected by surface water, which can be correlated to water quality parameters (e.g. total suspended solids, TSS). This represents an alternative means of estimating water quality, which can be used to supplement sampling programs, reducing the number of ground samples required to characterize spatial and temporal trends. Besides reducing the ground sampling effort, satellite estimates offer several advantages over ground sampling. First, the near-continuous spatial coverage of satellite imagery allows for synoptic estimates over large areas. Second, the global coverage of satellites allows for the estimation of water quality in remote and inaccessible areas. Third, the relatively long record of archived imagery (e.g. Landsat since the early 1970’s) allows estimation of historical water quality, when no ground measurements can possibly be performed. The purpose of this research project is to investigate the utility of using remote sensing in water quality studies. |
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Landsat imagery can be useful in sediment transport studies. This figure shows the estuary turbidity maximum of the Hudson Estuary by Manhattan. (a) Landsat TM image for 4/15/96. The true color image is “stretched” to highlight the variability in the reflectance from water. Green points mark sampling stations. The tin white line outlines the high turbidity area. (b) Sidescan sonar mosaic for 6/23/98-6/25/98 from Woodruff et al. (2001). Areas of low side-scan backscatter appear dark in the figure while high side-scan backscatter areas appear light. The thin white line identifies the visual delineation between low and high side-scan backscatter environments. |
| Estimates of chlorophyll a, calculated from radiance measurements of modern sensors are available. These pictures highlight the spatial variability of the water quality parameters. However, for inland, coastal and near-shore waters ("Case 2" waters) a site-specific algorithm typically has to be developed. This figure shows MODIS chlorophyll a concentration (SeaWiFS analog algorithm) on 9/19/01. Lighter shading corresponds to higher concentration. Dark gray and black are land and “no data” masks, respectively. |  |
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Last Updated: 6/10/2004