Water quality index and spatio-temporal perspective of a large Brazilian water reservoir

Abstract

The water spatio-temporal variability of the Irapé Hydroelectric Power Plant reservoir and its main tributaries was evaluated by analysing the temporal trend of the main parameters and applying the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI), using data from 2008 to 2018. This reservoir is in Minas Gerais, Brazil, and covers an area of approximately 143 km2 across seven municipalities. The dissolved iron (DFe) presented the highest percentage of standards violations (31.7% to 80.5%), most frequently in the reservoir tributaries. The Mann–Kendall tests indicated that the monitoring stations showed an increasing trend of 78.5% N–NH4+ and 64.1% DFe. During the evaluated period, the reservoir waters were classified as excellent (1.2%), good (61.3%), acceptable (29.5%), and poor (8.0%) according to the WQI for the proposed use. The poorest quality classes were more frequent in the tributaries, especially in the year 2009. The WQI seasonal assessment indicated a worsening during the rainy season in 57% of the stations, as a result of external material transport to the water bodies. The CCME WQI, in conjunction with temporal statistical analysis, contributed to the interpretation of the monitoring data, generating important information for reservoir water quality management.

Characteristics and Changes in Water Quality Based on Climate and Hydrology Effects in the Cirata Reservoir

“…This is similar to the patterns in the Kwan Phayao Reservoir, Thailand; the Adolfo López Mateos Reservoir, Mexico; and the Irapé Hydroelectric Power Plant Reservoir, Brazil. These areas also showed high nitrogen and PO 4 concentrations, which were related to agricultural runoff during the rainy season [1,2,6].…”

“…Agricultural activities affect the NO 3 -N concentrations in water [12]. The high rainfall can transport any material from agricultural land in the upstream watershed, which increased the nutrient levels in the reservoir water [1,2,6]. Conversely, the low rainfall decreased the NO 3 -N distribution to the Cirata Reservoir.…”

“…The prolonged dry season affects the water quality in terms of physical, chemical, and biological parameters [4,5]. In the rainy season, the reservoir water quality may deteriorate due to a combination of natural processes (rainfall intensity) and human activity [6]. In many cases, the deterioration of reservoir water quality is caused by complex pollution from both the watershed and the reservoir itself [7].…”

Analysis of the Physicochemical Quality of Water Within the Hydropower Plant on the Ślęza River in Wrocław, Poland

“…In these cases, better water quality was noted, expressed by the water quality index below the hydrotechnical structures. A different result was achieved for the Irapė hydroelectric power plant in Brazil, where the calculated result was the weakest below the damming structure (De Oliveira et al 2021). With regard to run-of-river hydroelectric power plants, such analyzes have hardly been carried out (however, physicochemical studies have been performed, e.g.…”

Impact of a small hydropower plant on water quality dynamics in a diversion and natural river channel

“…a Statistically significant correlation. Sources: De Oliveira et al (2021), Ling et al (2016), Luo et al (2019), study offer some recommendations for selecting the most appropriate index for this purpose, namely that the WQI should be characterized by flexibility in the selection of parameters, ease of comparing the calculated results, a simple point scale allowing for the standardization of variables, universality of use for various purposes (e.g., scientific, social, political), classification adapted to the needs of users, insensitivity to outliers, a large potential number of parameters that are taken into account, ease of calculation, and selection of values taking into account a wide range of parameter variability (e.g., due to the conditions of the climatic zone or the place where the water was collected) (Sutadian et al, 2016;Boyacioglu 2007). The UWQI seems to be the index that most faithfully reflects the impact of hydropower plants: the final classification is clear and easy to compare (five classes, scale from 0 to 100); calculations take into account the 90th percentile of parameter values, thus ensuring a wide range of variability, but also taking into account potential measurement errors and incidents of short-term, severe water pollution (e.g., uncontrolled leakage of sewage into water); the selection of the range of parameters is adjusted to the assessment of various sources of pollution, representative for a given type (e.g., BOD 5 : organic pollutant indicator; cadmium: industrial pollutants; NO 3 : agricultural pollutants); the method of calculations with the use of weights for parameters and subindices is intuitive, and the range of parameters taken into account can be modified.…”