## Turbidity and Suspended Solids in Construction Site Runoff

#### February 2015 (volume 10 - issue 1)

*Contributed by Rebekah Perkins, Water Resources Engineer II at HNTB (former Graduate Research Assistant, Biosystems and Bioproducts Engineering, University of Minnesota, Advisors: Bruce N. Wilson and John S. Gulliver)*

**Funded by Minnesota Department of Transportation (Dwayne Stenlund, Technical Liaison)**

Stormwater runoff from construction sites (Figure 1) needs to be managed to avoid undesirable off-site impacts. This runoff contains eroded sediment from the exposed, barren ground, which is often transported to nearby water bodies causing water quality impairment, degrading their biotic communities and reducing their capacity to store water with sediment deposition. Reducing these negative impacts is dependent on determining the mass and concentration of eroded sediment in runoff. The quickest and a cost effective method of assessing these impacts is to measure the turbidity of the runoff.

*Figure 1: Runoff off of a local Twin Cities construction site. (Photo: Brad Hanson)*

Turbidity is an optical property of water associated with the light scattering properties of the particles suspended in water. This measurement can be used as a surrogate to determine the concentration of sediment or total suspended solids (TSS) in construction site runoff. A turbidity meter is a device that is composed of at least one light source and one photo-detector. The light source is beamed through a sample, and the light is scattered as it interacts with the particles in the water and the water itself. The photo-detector then reads how much light reaches it and at what angle, thus determining the turbidity of the sample. There are many kinds of turbidity meters with different Nephelometric Turbidity Units (NTUs) readings on the same water. Calibrations of each type of meter must therefore be conducted separately.

Construction sites by nature have the potential to create high turbidity values related to suspended sediment loads during storm events. For example, NCHRP (2012) estimated that using conventional best management practices would still result in turbidities of 500 to 1000 NTU’s leaving the site. This is substantially larger than the standard suggested by the Environment Protection Agency (EPA) in 2011 of 280 NTUs. Other states have considered implementing turbidity standards (California, 250 to 500 NTUs, Georgia 75 to 750 NTUs and reportable limits of 25 NTUs in Vermont and Washington). The usefulness of turbidity standards rests upon the validity of the turbidity data collected at construction sites.

Research has recently concluded at the University of Minnesota evaluating construction site turbidity both in the field and laboratory. An important goal of this research is to develop a protocol for turbidity monitoring that is easily understandable, repeatable and adaptable to a majority of construction sites. The research is also being done to gain insight into the factors that affect turbidity in construction site runoff. Both of these activities will be useful in establishing a reasonable turbidity standard limit for Minnesota. Sampling of turbidity on a highly mobile construction site is a challenge. We discovered that a new measurement apparatus was needed, one that can be put into place without generating scour and can facilitate the turbidity instrumentation, yet can be moved easily and placed in a new location when needed. The development and features of this apparatus are described in Perkins, et al. (2014). The discussion herein will utilize a Hach 2100N bench top turbidimeter, believed to be one of the more reliable measurements of turbidity.

### Measurements

While our field work has provided knowledge about the benefits and shortcomings of the current monitoring techniques, we realized that our ability to attain insight into the factors affecting turbidity was limited due to the unpredictable site conditions and limited quality of field data. We thus incorporated a laboratory experiment that allowed for a controlled setting and a repeatable process for many different soils. The laboratory experiment relied on the acquisition of 14 soils from all over Minnesota. Synthetic runoff was created using a rainfall simulator (Figure 2) that rained on the soil for 30 minutes at an intensity corresponding to the peak intensity of a 2 year, 24 hour storm. The simulator was also calibrated to replicate natural raindrop size. The runoff from these soils was collected and thoroughly examined so that trends in turbidity with soil characteristics can be determined and used to develop a predictive relationship between turbidity, total suspended solids and soil characteristics. Experimental details are given in Perkins, et al. (2014).

*Figure 2: Laboratory apparatus including rainfall simulator, soil box, and collection basin. (Photo taken by Rebekah Perkins)*

During the 30 minute rainfall, 50 mL samples of runoff were collected every 5 minutes and analyzed to understand how turbidity and concentration change over time during a rainfall. Each sample was then diluted, and concentration and turbidity values were recorded for each dilution to create time dependent concentration vs. turbidity curves, such as seen in Figure 3.

*Figure 3: Example of dilution curves. Regression equations are reported under the legend for each time sample.*

### Results

After performing a regression analysis with different possible functions, it was seen that a power fit best represents the data:

T = αTSS^{β} |
(1) |

where T is turbidity (NTU), TSS is the total suspended solids concentration (mg/L), and α and β are scaling and power coefficients, respectively.

All of the dilution curves for the 14 soils and several replicates are plotted in Figure 3. The soils followed a pattern from left to right: the most silty soil is on the left side of Figure 3. Moving to the right, the soil’s sand content increases and the silt content decreases. The axes are both logarithmic, so the slope of each set represents the value of β. It is seen that the value of β is close to a constant, equal to 7/5. The intercept at a sediment concentration of 1 mg/L provides the value of α, and it can be seen that α varies greatly between samples. This means that the conversion from turbidity to TSS concentration is not straight-forward, and equations, such as equation 1, to facilitate this conversion would be helpful.

Soil properties were used in a multiple linear regression to determine a relationship for α. The log transformed multiple linear regression was performed using the median α values when β was set at 7/5. Nearly 40 regression models were evaluated, and the most useful regression models are given herein. The most significant variables in the model are percent silt plus clay, percent silt, interrill erodibility (K_{i}), and maximum abstraction depth (S), a physical property of the site that is used in curve number runoff calculations. Percent silt represents the available particles on a site that can be easily eroded and transported in stormwater, maximum abstraction depth is a measurement of runoff potential on a site, and interrill erodibility is a value that quantifies the detachment and transport of soil by raindrops and overland flow. The equation for this model (called Model 1) is as follows:

α = 0.43 Silt^{1.19} S^{-0.31} K_{i}^{-0.56} |
(2) |

where Silt the content (percent), S is the maximum abstraction depth (inches), and K_{i} is the interrill erodibility (dimensionless).

The predicted and observed values of α are compared in Figure 4, where it is seen that equation 2 provides an acceptable fit of the observations, and explains 70% of the variation in α.

*Figure 4: Observed α values plotted against predicted α values for Model 1. Uncertainty lines represent the 95% confidence interval of turbidities taken at one TSS concentration.*

Although equation 2 is the preferred prediction model for α, the interrill erodibility and maximum abstraction may not be readily available for soils at construction sites. An alternative and simpler predictive model was obtained using only percent silt, provided in equation 3 (Model 2).

α = 1.94 x 10^{-4} Silt^{1.22} |
(3) |

This regression model explained 55% of the variability of α, and is compared to observations of α in figure 5.

*Figure 5: Observed α values plotted against predicted α values for Model 2. Uncertainty lines represent the 95% confidence interval of turbidities taken at one TSS concentration.*

### Normalization by a Standard

Turbidity-concentration data can be normalized with a chosen turbidity standard, T_{std}. Using Equation 1, T_{std} can be used to determine the corresponding standard concentration, C_{std}, with an appropriate estimate or known α. Equation 1 can then be normalized with these standard values as seen in Equation 4.

(4) |

Because α_{site} and α_{std} are both determined using the same site data, they are the same value and would cancel out in Equation 4. With the removal of α, the data collapses nicely on a single curve. A dimensionless plot of all of the laboratory data is shown in Figure 6. A single dimensionless curve was able to accurately represent the observed data.

*Figure 6: Laboratory turbidity and concentration data normalized by a 1000 NTU turbidity standard. T ^{*} is normalized turbidity, or T_{site}/T_{std}, and C^{*} is normalized TSS concentration, or TSS_{site}/TSS_{std}.*

The advantage in equation 4 is that a TSS concentration and turbidity can be measured for one soil, and then the conversion from turbidity to TSS for that soil can be made with equation 4 for all turbidity measurements.

### Application

To apply equation 4, one needs to measure turbidity and TSS for five or more samples taken at one time to result in good median values. Then, T_{std} is set equal to the median turbidity and TSS_{std} is set equal to the median TSS value. Equation 4 can then be rearranged into equation 5 in order to estimate a TSS_{site} for every T_{site}:

(5) |

### Conclusions

A power relationship between turbidity and TSS concentration was found to represent all of the time dependent runoff samples collected for each soil. The power value, β, was found to be 7/5, only varying slightly between soils. The intercept on a log-log graph, α, varied significantly between soils, but only varied slightly within soils. A relationship for α was determined through an extensive multiple linear regression using soil properties for each site. The results of this regression determined a relationship using percent silt, maximum abstraction, and interrill erodibility (Model 1) that explained nearly 65% of the variability in α. Because of the complexities involved in evaluating maximum abstraction and interrill erodibility, a simple relationship using only percent silt (Model 2) was also determined. Both models showed promise in determining α for the laboratory soils. Model 1 and Model 2 had R^{2} values of 0.70 and 0.55, respectively. Measurements of turbidity and TSS for one soil can be made to eliminate the need for estimating α, and used as a standard in applying the 7/5 exponent.

**References**

- Perkins, R. Hansen, B., Wilson, B.N., and Gulliver, J.S.. 2014. “Development and Evaluation of Effective Turbidity Monitoring Methods for Construction Projects,” Report no. MnDOT 2014-24, July 2014. http://www.cts.umn.edu/Publications/ResearchReports/reportdetail.html?id=2374
- United States Environmental Protection Agency. 2011. “Industry effluent guidelines: Construction and development.” Retrieved from water.epa.gov.

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