Matanza-Riachuelo is a highly polluted river that flows through Buenos Aires; the river banks are densely populated and large industries such as slaughterhouses and leather tanning as well as metal industries that historically as well as today flush their wastewater directly into the river. Buenos Aires being one of the largest cities in South America has a population of 2,776,138 inhabitants and only ~25% is connected to the sewage and fresh water system owned by AYSA (Agua y Saneamiento Argentino). AYSA has 3 water treatment plants connected to the river system of Matanza-Riachuelo (figure 1). In the basin lives 4,884,823 people in an area of 2,100 km2 meaning 87% of the basin is urban areas (Fernández Cirelli & Ojeda, 2006). The basin is monitored by the basin committee of Autoridad de Cuenca Matanza Riachuelo (ACUMAR). In 2010 ACUMAR started a large clean-up program with loans from the World Bank in order to restore the river banks. The program has 7 points of improvement; Providing Public information, Controlling Industrial Pollution Control, Closing Unsanitary Solid Waste Dumps, Constructing Water Supply, Drainage, and Sewerage Networks, Improving the River Banks, Attending to the Environmental Public Health Crisis, Enforcing and Monitoring the Implementation of the Court Ruling (The World Bank).
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Within the restoration program there is a part of controlling the Industry discharge this is done by developing pollution control plans for the industries. ACUMAR plans on helping the medium to small industries in order for them to meet their obligations. Therefor ACUMAR has plans on building a new water treatment plan for the industry discharge. To see how this will affect the water quality of the river will be evaluated by a Multi-Criteria Evaluation.
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The decision support model is based on one analytic model and one synthetic model. The analytic model consists of 3 parts; cross-impact matrix, cause-effect graph and system relationship graph. The synthetic model is based on multi-criteria evaluation with 3 scenarios.
the analytic model is based on 8 parameters; water chemistry, industry discharge, grain size(river), flow velocity(river), infiltration rate, time, organic content and groundwater level. They were then put into a matrix where they were evaluated against each other in order to create a vector, for each parameter, depending on 7 different parameters/dimensions the vector sum was den calculated to create 8 vectors that was transferred to a cause effect graph, showing the vectors relationship to each other.
A system relationship diagram was created to see the overall picture of the system and to get spatial view of the relationships between the parameters.
The Multi-Criteria evaluation (MCE) was done through 2 steps first a derivation of weights was created of the 8 vectors we used in the analytic model. Then 3 scenarios was created with utility values for each scenario and vector. The scenarios were;
The cause & effect diagram indicates which parameters are causing and which are the largely effected, see figure 3. As seen time is only a cause, industry discharge and grain size are mostly a cause. Flow velocity and infiltration rate are mostly a cause but are somewhat effected. Water chemistry and groundwater level are almost entirely effected but not causing change. Organic content are plotted on the graph and are a hence, both a cause and effect.
The system relationship graph is showing how the different parameters are influencing each other and to which magnitude. The continuous arrows are the high impact parameters. The dashed arrows are of medium impact and the dotted of low impact. This illustrates that the water chemistry is affected by multiple factors; flow velocity, organic content, industry discharge, grain size and time. Some parameters, grain size and time are influencing other parameters but are not themselves impacted. Whereas, organic content, flow velocity, infiltration rate and groundwater level are both inflators and influenced.
Decision Support Model
Scenario 1
Water Chemistry: very bad
Industry discharge: high
Grain size: clay
Flow velocity: 67
Infiltration rate: -
Organic content: high
Time: -
Groundwater level: -
Scenario 2
Water Chemistry: bad
Industry discharge: medium
Grain size: clay
Flow velocity: 67
Infiltration rate: -
Organic content: medium
Time: -
Groundwater level: -
Scenario 3
Water Chemistry: less bad
Industry discharge: low
Grain size: clay
Flow velocity: 67
Infiltration rate: -
Organic content: low
Time: -
Groundwater level: -
The result of the MCE shows that high industry discharge gives extremely bad water quality conditions in the river and that the magnitude difference between high- medium are less than medium-low. This indicates that the benefit is largest in the low discharge (scenario 3).
The MCE modelling shows that treatment of industry discharge will have a large impact on the water chemistry in the river we can also see that the grain size has a big impact on the pollution rate but the river is a clay based river and this parameter is hard to change. we kan as well see that if the water only is treated to a medium level the benefit we not be as large as if we treat it from a medium level to a good level. so by building a new plant to remediate the wastewater will have a big impact on the water quality and it will be beneficial to already from start have a heavy treatment in order to get to scenario 3 since the cost will be the same between going from scenario 1 to scenario 2 but the benefit will be less then going between scenario 2 and 3.