Why is Quantifying Suspend Solids Important?
The concentration of total suspended solids (TSS) is important to both river and lake ecosystems for ecological and water quality reasons. Inorganic suspended solids attenuate light, primarily through the process of scattering. High concentrations of suspended solids degrade optical water quality by reducing water clarity and decreasing light available to support photosynthesis. Suspended solids have been shown to alter predator-prey relationships (for example turbid water might make it difficult for fish to see their prey (e.g., insects)). Suspended solids also influence metabolic activity and provide surface area for the sorption and transport of an array of constituents. Deposited solids alter streambed properties and aquatic habitat for fish, macrophytes, and benthic organisms. Deposited sediment may be available for resuspension and subsequent transport during periods of increased stream discharge Suspended solids in most freshwater systems originate from watershed sources, pollutant point sources, and sediment resuspension. More rarely other sources, such as hydrogeologic structures can be important. High stream total suspended solids can impact water quality and deposition in downstream lakes and reservoirs.
How Can Suspended Solids Be Measured Remotely?
Suspended solids concentrations are highly variable in most streams. Characterization and quantification of this variability is critical to accurately assess TSS impacts on aquatic systems, including the development of mass loading estimates. Large increases in TSS and TSS loading (TSSL) are widely observed in streams during runoff events. This often results in a large portion of the total TSSL being delivered during relatively brief intervals of high flow. Frequent measurements of flow are widely available from USGS (United States Department of the Interior U.S. Geological Survey) gauging stations, but practical limitations in sampling have generally limited the frequency of TSS data.
System-specific empirical relationships between TSS and flow have been widely developed and utilized to estimate TSSL as a function of flow. However, a number of authors have discussed the accuracy and limitations of TSS-flow relationships in predicting TSSL. The success of TSS-flow relationships in predicting TSSL depends strongly on the scatter around the best-fit regression line. Increased frequency of TSS sampling, with an emphasis on coverage of runoff events, has been shown to result in stronger SS-flow relationships and thereby increase the precision of TSSL estimates. However, manual event based sampling and the associated laboratory analyses are tedious and costly. Even with the aid of automated sampling equipment, laboratory demands continue to limit such a monitoring program.
An alternative approach to estimate stream TSSL is based on frequent monitoring of turbidity that is highly correlated to TSS. This may have distinct advantages if the associated temporal coverage benefits (e.g., deployed instrumentation for in situ measurements) more than compensate for the uncertainty in the relationship between TSS and turbidity (Tn). Relationships between TSS and Tn are expected to be imperfect and system-specific because of variations in composition and particle size distributions that influence mass concentration and light scattering differently. Yet the TSS-Tn relationship is stronger than the TSS-flow relationship in most cases. The approach of turbidity measurements as a surrogate of TSS has at least two advantages:
increased in situ measurement capabilities of deployable instrumentation can support resolution of patterns over short time scales that cannot reasonably be addressed by manual TSS-based programs. an important feature of the optical impact of TSS (i.e., light attenuation), and often a primary water quality concern, is measured.
Sampling Approach and Data Analysis
TSS-flow and TSS-Tn relationships were developed for the robotic monitoring platform (described here) at Dorwin Avenue (see map) utilizing USGS flow measurements made concurrent with the robotic station’s Tn and TSS measurements over the October 2003 through September 2004 period (USGS water year 2004).
Water samples were collected by an automated refrigerated sampler (ISCO® 6712). Samples were collected daily during baseline conditions. The sampling frequency increased substantially for nine runoff events, usually to once every two hours. Adjustments in sampling frequency were made occasionally during storms through remote commands. These samples were analyzed in the laboratory for Tn and TSS, and fixed (non-volatile) suspended solids (FSS). In situ measurements of Tn and temperature (T) were made with the probe package described here. Probe measurements were made every 15 minutes. A total of 23,905 Tn measurements were made by the robotic platform for the study period. Using data collected at the robotic platform, relationships between TSS (units of mg/L), flow (Q) and turbidity (Tn) were developed in the form:
TSS = A·Qb
and
TSS = C·Tnd
The coefficients were determined using least-square regression over paired measurements. The coefficients are:
A = 10.07, b = 1.12, C = 2.38, and d = 0.81.
TSSL (units of metric tons (1000 kg) per hour) is calculated from the product of flow and calculated TSS.
Results
Data collected at the robotic platform (including USGS flow data) are used to generate time-series plots of TSS and TSSL. Additionally, cumulative loading (summation of TSSL over time) is presented. Lastly, two plots are presented to illustrate in (perhaps) meaningful units the amount of TSS passing through the Onondaga Creek system. The first plot approximates the number of dump trucks (12 cubic yard) required to carry the TSS load. The second approximates the depth the sediment would measure if spread around the playing area of a football field. In these two analyses, the volume of the TSS is approximated by assuming density of the suspended solids were 2 g/cm3.
Most recent loading estimates
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