J.J. Iudicello et al.: Identification and Remediation of Water-Quality Hotspots in Havana, Cuba 72 ISSN 0511-5728 The West Indian Journal of Engineering Vol.35, No.2, January 2013, pp.72-82 Identification and Remediation of Water-Quality Hotspots in Havana, Cuba: Accounting for Limited Data and High Uncertainty Jeffrey J. Iudicelloa Ψ, Dylan A. Battermanb, Matthew M. Pollardc, Cameron Q. Scheidd, e and David A. Chin Department of Civil, Architectural, and Environmental Engineering, University of Miami, Coral Gables, FL, USA a E-mail: [email protected] b E-mail: [email protected] c E-mail: [email protected] d E-mail: [email protected] e E-mail: [email protected] Ψ Corresponding Author (Received 19 May 2012; Revised 26 November 2012; Accepted 01 January 2013) Abstract: A team at the University of Miami (UM) developed a water-quality model to link in-stream concentrations with land uses in the Almendares River watershed, Cuba. Since necessary data in Cuba is rare or nonexistent, water- quality standards, pollutant data, and stormwater management data from the state of Florida were used, an approach justified by the highly correlated meteorological patterns between South Florida and Havana. A GIS platform was used to delineate the watershed and sub-watersheds and breakdown the watershed into urban and non-urban land uses. The UM model provides a relative assessment of which river junctions were most likely to exceed water-quality standards, and can model water-quality improvements upon application of appropriate remediation strategies. The pollutants considered were TN, TP, BOD5, fecal coliform, Pb, Cu, Zn, and Cd. The key model result is that the river junctions most likely to exceed water-quality standards are at the intersections of upstream sub-watersheds, and the best way to reduce the concentrations is via better management of the runoff from the upstream sub-watersheds. Dilution and attenuation were significant factors in reducing the concentration at downstream river junctions. The model was conservative in that it did not consider point-sources or groundwater dynamics in the Almendares River, and was found to be comparable to an established USGS water-quality model. The UM model is a valuable tool in assessing the water quality in the Almendares River and can be applied similarly to other rivers in Cuba or in similar countries with water-quality problems and limited data availability. Keywords: Almendares River, water-quality model, infrastructure, Cuba 1. Introduction To address the problems of limited data and minimal An accurate assessment of the water quality in a stream regulations which are particularly prevalent in Cuba, a is of great benefit to society as it reflects the condition of water-quality model was developed to simulate the the surrounding natural and man-made environment and effects of stormwater runoff on the stream water quality can provide warning in case of risk to human health. in the Almendares River watershed, which is home to Normal riverine activities such as bathing and boating, or roughly 2.5 million people and includes the capital city using the river as a source for drinking water can expose of Havana. Upper and lower portions of the watershed communities to significant risk of illness or fatality if the were considered separately, where the runoff was routed waters have high toxic or pathogenic content. Likewise, from the upper region to the Ejército Rebelde reservoir the consumption of fish exposed to toxic substances in and from the lower region below the reservoir to the the water can greatly increase risk to human health. The Almendares River’s outfall to the sea. The model links conventional approach to managing the quality of water land use and runoff to predict in-stream concentrations in streams is to control the input of contaminants into the and assesses uncertainty in light of the significant data stream from point sources and non-point sources such limitations. The model identifies the locations in the that applicable water-quality standards are met. Almendares River where the greatest water-quality However, water-quality problems tend to be greater in problems are most likely to occur and can predict the lesser-developed countries like Cuba where regulatory water-quality improvements that would result from the standards, relevant scientific data, and enforcement of implementation of various levels of stormwater control. standards are rare or nonexistent. The development and application of the model as described in this report can serve as a protocol for J.J. Iudicello et al.: Identification and Remediation of Water-Quality Hotspots in Havana, Cuba 73 addressing water quality in other parts of Cuba or in ca = cu exp(−Kx) (5) similar countries with limited resources and data. The where ca is the attenuated concentration at the end of the model was developed by a team at the University of -3 lower stream segment [ML ], cu is the concentration at Miami (UM) specifically for application to conditions in the end of the upper stream segment [ML-3], K is the Cuba and is referred to in this report as the UM model. attenuation factor [L-1], and x [L] is the length of the downstream segment. The attenuation factor was 2. UM Model assumed to be 1000 m-1 based on flow rates for certain The UM model is formulated for watersheds that contain stream reaches in Havana as stated in Egues and Diaz a network of streams, with each stream segment (1997) and approximate stream dimensions. A sensitivity receiving direct runoff from a sub-area (sub-watershed) study and discussion of this assumed value for K are within a single overall watershed. Pollutant included in a later section. concentrations are predicted at sub-watershed pour- points from pollutant loads in sub-watershed runoff, where each sub-watershed is divided into “urban” and 3 “non-urban” land uses. The runoff, Qsw (m /yr), from each sub-watershed is determined using the relation (1) Qsw = Q1 + Q2 = R(C1A1 + C2 A2 ) where R is the annual rainfall (m), C (dimensionless) and A (m2) are the runoff coefficient and land area, respectively, and the subscripts 1 and 2 correspond to the urban and non-urban land uses, respectively. The pollutant load in the runoff, L (kg/yr), for each sub- watershed is determined using the relation L = Q1e1 + Q2e2 = R(C1A1e1 + C2A2e2) (2) where e (typically in mg/L) is the event-mean Figure 1a. Mass balances for a watershed concentration (EMC) per land use. The average-annual concentration at the pour-point of each sub-watershed, csw, as shown in Figure 1a is then given by L (3) csw = Qsw When stream segments exiting two neighboring sub- watersheds combine at a junction as illustrated in Figure 1b, the streamflows are summed and the average concentration at the junction, c, is determined using a flow-weighted average 2 Qc + Q c + (Q c ) 1 1 2 2 ∑i=1 ui ai (4) c = 2 Q + Q + Q 1 2 ∑i=1 ui Figure 1b. Mass balances for a junction where Qi and ci are the flow and concentration in segment i derived from the sub-watersheds contributing directly to segment i, Qui is the flow entering segment i 2.1 Uncertainty Analysis from an upstream segment, and cai is the corresponding An uncertainty analysis was conducted to quantify the attenuated concentration from the upstream segment. uncertainty in model predictions and provide 90%- Figures 1a and 1b show the mass balances for a typical confidence intervals around average concentrations watershed and a junction, respectively. predicted by the UM model. Due to the natural Attenuation represents the processes by which topography of the Almendares River watershed, the pollutant concentrations are reduced over the course of a mean and variance of both the flow and concentration stream segment. The primary attenuation mechanism is contributed by each sub-watershed were found first, then sedimentation, where pollutants sorbed to suspended these contributions were combined cumulatively at the sediment particles are removed as suspended particles downstream junctions where stream segments intersect. settle to the bottom of the stream, thereby reducing the Mean flow from each sub-watershed was found using in-stream concentration of the pollutants. The UM Equation 1 with the averaged value of each variable. The model accounted for the attenuation of concentrations corresponding variance in flow, 2 , at the exit of a from an upstream segment over the length of a σQ,sw downstream segment using the first-order relationship sub-watershed was determined by the first-order relation J.J. Iudicello et al.: Identification and Remediation of Water-Quality Hotspots in Havana, Cuba 74 σ2 = (RA )2σ2 + (RA )2σ2 (6) from upper stream segments depending on the Q,sw 1 C1 2 C2 watersheds intersecting at each junction. Similarly, σ 2 where the subscripts 1 and 2 correspond to urban and Q1 2 non-urban land use, respectively, and 2 and 2 are the and σ were determined from Equation 6. In cases σ σ Q 2 C1 C2 variances in the runoff coefficients considered in the where an upstream segment is present, the variance in model per land use. At each junction, the mean flow concentration (not flow) was attenuated according to leaving the junction is equal to the sum of the average Equation 5, where ca and cu were replaced with the appropriate 2 . flows entering the junction. The variance in flow leaving σ c the junction, σ 2 , is the sum of the variances of flows Qj 3. USGS Model entering the junction The UM model was compared with the USGS water- 2 2 2 2 2 (7) quality model as a reference point.