Development and testing of a fast conceptual river water quality model

Evaluation and monitoring of the water quality of an Argentinian urban river applying multivariate statistics

In this work, we present the water quality assessment of an urban river, the San Luis River, located in San Luis Province, Argentina. The San Luis River flows through two developing cities; hence, urban anthropic activities affect its water quality. The river was sampled spatially and temporally, evaluating ten physicochemical variables on each water sample. These data were used to calculate a Simplified Index of Water Quality in order to estimate river water quality and infer possible contamination sources. Data were statistically analyzed with the opensource software R, 4.1.0 version. Principal component analysis, cluster analysis, correlation matrices, and heatmap analysis were performed. Results indicated that water quality decreases in areas where anthropogenic activities take place. Robust inferential statistical analysis was performed, employing an alternative of multivariate analysis of variance (MANOVA), MANOVA.wide function. The most statistically relevant physicochemical variables associated with water quality decrease were used to develop a multiple linear regression model to estimate organic matter, reducing the variables necessary for continuous monitoring of the river and, hence, reducing costs. Given the limited information available in the region about the characteristics and recovery of this specific river category, the model developed is of vital importance since it can quickly detect anthropic alterations and contribute to the environmental management of the rivers. This model was also used to estimate organic matter at sites located in other similar rivers, obtaining satisfactory results.

Water Environmental Capacity Calculation Based on Control of Contamination Zone for Water Environment Functional Zones in Jiangsu Section of Yangtze River, China

In recent years, due to unsustainable production methods and the demands of daily life, the water quality of the Yangtze River has deteriorated. In response to Yangtze River protection policy, and to protect and restore the ecological environment of the river, a two-dimensional model of the Jiangsu section was established to study the water environmental capacity (WEC) of 90 water environment functional zones. The WEC of the river in each city was calculated based on the results of the water environment functional zones. The results indicated that the total WECs of the study area for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total phosphorus (TP) were 251,198 t/year, 24,751 t/year, and 3251 t/year, respectively. Among the eight cities studied, Nanjing accounted for the largest proportion (25%) of pollutants discharged into the Yangtze River; Suzhou (11%) and Zhenjiang (12%) followed, and Wuxi contributed the least (0.4%). The results may help the government to control the discharge of pollutants by enterprises and sewage treatment plants, which would improve the water environment and effectively maintain the water ecological function. This research on the WEC of the Yangtze River may serve as a basis for pollution control and water quality management, and exemplifies WEC calculations of the world’s largest rivers.

Distributed Model Predictive Control Applied to a Sewer System

In this work, a Distributed Model Predictive Control (MPC) methodology with fuzzy negotiation among subsystems has been developed and applied to a simulated sewer network. The wastewater treatment plant (WWTP) receiving this wastewater has also been considered in the methodology by means of an additional objective for the problem. In order to decompose the system into interconnected local subsystems, sectorization techniques have been applied based on structural analysis. In addition, a dynamic setpoint generation method has been added to improve system performance. The results obtained with the proposed methodology are compared to those obtained with standard centralized and decentralized model predictive controllers.

Considering atmospheric N2O dynamic in SWAT model avoids the overestimation of N2O emissions in river networks

Modeling studies have focused on N₂O emissions in temperate rivers under static atmospheric N₂O (N₂O_airc), with cold temperate river networks under dynamic N₂O_airc receiving less attention. To address this knowledge and methodological gap, the dissolved N₂O concentration (N₂O_disc) and N₂O_airc algorithms were integrated with an air-water gas exchange model (F_N2O) into the SWAT (Soil and Water Assessment Tool). This new model (SWAT-F_N2O) allows users to simulate daily riverine N₂O emissions under dynamic atmospheric N₂O. The spatiotemporal fluctuations in the riverine N₂O emissions was simulated and its response to the static and dynamic atmospheric N₂O were analyzed in a middle-high latitude agricultural watershed in northeastern China. The results show that the SWAT-F_N2O model is a useful method for capturing the hotspots in riverine N₂O emissions. The model showed strong riverine N₂O absorption and weak N₂O emissions from September to February, which acted as a sink for atmospheric N₂O in this cold temperate area. High N₂O emissions occurred from April to July, which accounted for 83.34% of the yearly emissions. Spatial analysis indicated that the main stream and its tributary could contribute 302.3-1043.7 and 41.5-163.4 μg N₂O/(m²·d) to the total riverine N₂O emissions (15.02 t/a), respectively. The riverine N₂O emissions rates in the subbasins dominated by forests and paddy fields were lower than those in the subbasins dominated by arable and residential land. Riverine N₂O emissions can be overestimated under the static atmospheric N₂O rather than under the increasing atmospheric N₂O. This overestimation has increased from 1.52% to 23.97% from 1990 to 2016 under the static atmospheric N₂O. The results of this study are valuable for water quality and future climate change assessments that aim to protect aquatic and atmospheric environments.

Uncertainty analysis in a large-scale water quality integrated catchment modelling study

Receiving water quality simulation in highly urbanised areas requires the integration of several processes occurring at different space-time scales. These integrated catchment models deliver results with a significant uncertainty level associated. Still, uncertainty analysis is seldom applied in practice and the relative contribution of the individual model elements is poorly understood. Often the available methods are applied to relatively small systems or individual sub-systems, due to limitations in organisational and computational resources. Consequently this work presents an uncertainty propagation and decomposition scheme of an integrated water quality modelling study for the evaluation of dissolved oxygen dynamics in a large-scale urbanised river catchment in the Netherlands. Forward propagation of the measured and elicited uncertainty input-parametric distributions was proposed and contrasted with monitoring data series. Prior ranges for river water quality-quantity parameters lead to high uncertainty in dissolved oxygen predictions, thus the need for formal calibration to adapt to the local dynamics is highlighted. After inferring the river process parameters with system measurements of flow and dissolved oxygen, combined sewer overflow pollution loads became the dominant uncertainty source along with rainfall variability. As a result, insights gained in this paper can help in planning and directing further monitoring and modelling efforts in the system. When comparing these modelling results to existing national guidelines it is shown that the commonly used concentration-duration-frequency tables should not be the only metric used to select mitigation alternatives and may need to be adapted in order to cope with uncertainties.

Correlations between water quality and the structure and connectivity of the river network in the Southern Jiangsu Plain, Eastern China

Incorporating the structure and connectivity of the river network to seasonal variations and different land use patterns can help improve the understanding the complex relationship between water quality and environmental factors. The present study first employed the grey relational analysis (GRA) to examine any existing correlations between the water quality and the structure and connectivity of river networks in the Southern Jiangsu Plain in Eastern China. All grey relational degree results were greater than the distinguishing coefficient (ρ = 0.5), and their average value was 0.7551. The average grey relational degrees of the water quality parameters varied between 0.7389 and 0.7744, and those of the characteristic indicators of the river network ranged from 0.6874 to 0.8850. Seasonal variations and different land use patterns were then employed to further analyze these relationships. The average grey relational degrees in the urban, rural, and fringe regions were calculated to be 0.7231, 0.7530, and 0.7124 during the flood season, respectively, and 0.7331, 0.7432, and 0.7052 during the non-flood season. The results suggest strong correlations between the water quality and the structure and connectivity of the river network. The preponderance of the urban land weakened the original correlations more than that of the cultivated land, while the seasonal interactions of the cultivated and urban lands presented opposite. The GRA can be employed as an effective supplement for numerical modeling and statistical analysis of the incomplete data. In addition, the structure and connectivity of the river network should be taken in account to improve water quality.

The impact of socioeconomic system on the river system in a heavily disturbed basin

The quantitative assessment of the impact of socioeconomic development on river water environment is important to the scientific management of river basins. However, current methods have high data requirements or are difficult to deal with the impact between systems (which is defined by a collection of indicators). This paper first uses canonical correlation analysis (CCA) to understand the relationship between socialeconomic system (defined by a set of indicators reflecting socioeconomic development) and river system (defined by a set of indicators reflecting river water environment), and then proposes a method to assess the impact of socioeconomic system on river system by integrating CCA and the degrees of influence of river system indicators. The proposed method and framework are applied to the Shaying River Basin with the characteristics of multi-sluices, high pollution, and dense population based on data from 2000 to 2015. Results indicate that socioeconomic and river systems are highly related to each other with the average influence degree of greater than 0.9, indicating very close relationships between socioeconomic and river systems. The changes of influence degree vary between 0.19 and 0.79 with a turning point in 2010. Most of the influence levels are "moderate" (influence degree between 0.4 and 0.6) or "high" (influence degrees between 0.6 and 0.8) before 2010 but become to "low" (influence degrees between 0.2 and 0.4) since then. In addition, the influence degree shows a significant increase from upstream to downstream with Zhoukou Station as the turning point, meaning that the stronger the human activity is, the greater the impact of the socioeconomic system on the river system is. The main influential factors are population density and sewage treatment rate. The proposed method contributes to the research in river management with limited data availability and the results can serve as an important reference for basin management.