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Project Management

Integrated Risk Assessment for Water Quality

  • Introduction

Risk analysis is vital in water quality management and protection for the unknown and evolving pressures on water resource availability and quality from anthropogenic and natural sources. The specificity and importance of further analysis of the factors affecting the quality of water sources is the need for a more complex approach that can cover a wide range of factors and deliver an exhaustive explanation of the phenomenon. This paper seeks to systematically review the current literature on integrated risk assessment for water quality management at the DJKRB. The DJKRB is the water source for the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), the world’s longest inter-basin water diversion project, thus its water quality is vital for the wellbeing of over 100 million people’s health. This review will examine the prominent topics and approaches, shared positions, and identified discrepancies, the evolution of the area, and significant reflections on the issue at hand.

  • Main Themes in Integrated Risk Assessment for Water Quality

The main themes emerging from the literature on integrated risk assessment for water quality revolve around three key areas: the water quality and how it impacts the surface, the parameters of reason for change in water quality, and the methods employed on risk assessment.

  • Water Quality Assessment: WQI is one of the most important indicators used for assessing water quality. The WQI synthesizes all the physical, chemical, and biological characteristics of the water bodies, in a single estimate. The advantage with this approach is that it incorporates all the water quality data such that analysis and interpretation is relatively easy compared to the unitarian approaches that seek to determine the status of a single parameter against a given standard. This WQI method has been used since 1960 and has also been subsequently modified with regard to flexible weights.
  • Driving Factors of Water Quality Variation: Scholars indicate that man plays a leading role in the pollution of surface water. The sources of pollution within the basin are established to be the built-up land use and agriculture. In detail, water pollution features reveal that built-up land enhances the nutrient loading; agricultural water intake and industry contribute to contamination also. Non-climate properties like rainfall and exposure to sunshine affects water quality also, but man-influenced activities were discovered to have a stronger effect on the trends in water quality in the DJKRB.
  • Risk Assessment Frameworks: Scholars have indicated that there is a need for the integration of the analysis of internal and external sources of risk within regions and across borders. Transfer entropy (TE) is computed to investigate the entropy of directional info flow between several systems with application in getting the impact of tributaries to the reservoir that is being investigated. The other key method is the set-pair analysis (SPA), which forms a link between actual indicators and standards in order to create an objective approach to evaluating risk factors. SPA is more useful than quantitative method like AHP and the numerical models method (NM) because it avoids the difficulties in identifying model parameters in real conditions.

III. Methodologies Used in the Studies

The concepts applied in the analysis of the DJKRB contain the following: meticulous data collection, appropriate statistical analyses and the use of information theory as well as SPA.

  • Data Collection and Sampling: Monthly water samples were also taken from 47 monitoring sites representing the river systems in the DJKRB from the year 2020-2022. These sites were chosen to provide coverage of the full area of the basin and included all the water quality variables. Nine water quality indicators were monitored: water temperature, pH, dissolved oxygen, permanganate index, biochemical oxygen demand for five days, ammonia nitrogen, total phosphorus, total nitrogen, and fluoride ion concentration.
  • Statistical Techniques: Since its variance was uneven, the Kruskal-Wallis test was used to check for the spatial and temporal differences in the data. Trends of the sampling data were analyzed by applying the Mann-Kendall (M-K) method. The Mantel test established the association between potential influential factors as well as water quality indicators and tested if water quality indicators could be grouped depending on their characteristics. Pearson correlation was used also to measure the influence that flowed between influential indicators.
  • Information Theory and SPA: To express the uncertainty of the water quality indicators Shannon entropy was used. Transfer entropy (TE) analysis was used to assess how the changes of water quality in the tributaries affected the DJKR. In this paper, the comprehensive set-pair analysis (SPA) method was used to define the connection degree, and to discuss identity, opposition and difference of two correlated sets in uncertain systems. This involved the laying down of criterion based on national and local version, regional development and the availability of indicators.
  • Points of Agreement, Debate, and Gaps in the Research

The literature demonstrates clear areas of agreement and reveals gaps that require further research.

  • Points of Agreement: The understanding of the extent of human involvement seldom comes into doubt with respect to the influences that lead to water pollution, especially as caused by built-up land and agriculture. It is also well accepted due to reliability and simplicity in explaining the WQI as a tool for water quality assessment.
  • Debate: There are no debates in the provided source Aiken, 2006; Aiken & Veitch, 1986; Hargrove & Kopec, 1988; Lindberg & Runnalls, 1984); nevertheless, the source states explicitly that the effect of meteorological factors on water quality can be different if considered on different levels of research. But it has stressed that the water quality in the DJKRB is largely and dominantly impacted by anthropogenic activities.
  • Gaps in Research: Past studies often focused on specific water quality indicators or specific areas, lacking a comprehensive, basin-wide perspective. There is a need for more research into the mechanisms of water quality variations at a finer scale, which can be supported by mechanism models. More long-term series of monitoring data and in-situ experiments are also needed to enhance understanding and improve prediction accuracy.
  • Development in the Field Over Time

The analysis of water quality has been a dynamic field especially with regard to the WQI, the application of information theory and SPA.

  • Evolution of WQI: The WQI has enhanced its method of calculation since the ’60s and has gone through some changes. WQI has been redesigned, modified and fine tuned to various environments and the type of assessment required including the flexibility in weight variations. Through it, they provide a general evaluation of the WQI that gives it more usefulness to those who seek to gauge the state of water quality, with particular importance in its purpose as a tool for popular communication and water-resource management.
  • Integration of Information Theory: Transfer entropy was used to characterize information exchange in river systems within an information theory application. This made it possible to analyse unidirectional information flow for instance the impact of a tributary on the reservoir. This indicates a major advancement in the ability to model the system as a whole and how water quality changes from basin to basin.
  • Application of SPA: Since its inception and development, the use and practicality of set-pair analysis (SPA) has been applied in risk assessment, offering practical as well as informative applications of risk estimations and analysis of the risk factors involved. The complete SPA more precisely used in the DJKRB study supports the validity and coverage of this approach.
  • Critical Insights and Contextual Alignment

The study offers important findings that are commensurate with the general understanding of water quality and risk analyses.

  • Integrated Approach: The present study also offers an integrated framework for WQI, information theory, and SPA to establish an assessment and risk management model for water quality. This integrated approach is vital to manage and solve multifaceted problems related to water quality issues because the method integrates water quality assessment with an investigation into the process that leads to changes and an evaluation of the risk involved.
  • Data-Driven Methodology: The proposed framework offers a new form of risk assessment that is as effective as it is simple for those skilled in the empirical analysis of emission data as well as for policy makers who may not necessarily grasp the technical details of EWY pollutant data. This will enhance extension and users’ participation in capacity enhancement on the overall quality of the water.
  • Contextual Alignment: The integrated risk assessment approach accords with the established need to assess the current state of water quality and also the forces that cause changes in water quality essential for water management. The research complements the rising conception of human endeavors, including built-up land use, agricultural water use, and industrial processes as being principally responsible for the depletion of water resources. Based on this study, sub- basins that need pollution control to protect water sources needs to be prioritized.

VII. Conclusion

This paper aims at reviewing literature in an attempt to achieve two objectives which include the following: First, this paper aims at proving that single risk assessment is inadequate for water quality management hence the call for integrated risk assessment in managing the DJKRB. From the study, it reveals that the DJKRB has a good and stable water quality some of which are affiliated with anthropogenic activities of built-up land and Non-Point Agricultural pollution. The application of the WQI, information theory and SPA clearly shows that there is a novelty, holistic solution for estimating the risks of the water quality. However, there is no previous work available that provides an integrated analysis of the status and risks of water quality in the DJKRB to aid effective and efficient water resources management. Further work must be directed toward more detailed examination of the changes in water quality and to further development of the extended records. The integrated framework presented in this study will thus be useful in scheduling other water resource management decisions in the analysed and similar basins across the globe.

(Zhang et al., 2023)

Reference:

Zhang, C., Nong, X., Shao, D., & Chen, L. (2023). An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China. Science of the Total Environment, 884. https://doi.org/10.1016/j.scitotenv.2023.163731

AQ: Integrated Risk Assessment of Water Quality in the Danjiangkou Reservoir Basin

  • Briefly, the Danjiangkou Reservoir Basin (DJKRB) is and why the quality of water in this basin is consequential?

Danjiangkou Reservoir (DJKR) is the second largest reservoir in China and supplies water for the MRSNWDPC, which transfers water from south to northern China. This project supplies water to more ten-tens of millions of people, making water quality of the DJKRB vital for human health and a plethora of flora and fauna inhabiting this unique region of over 92,500 square kilometers. The need to improve and sustain the quality of water in this basin is apparent in light of safe drinking water supply, and management of agricultural and industrial water use as well as sustaining the delicate balance of ecosystems.

  • What water quality indicators were monitored in the DJKRB study?

The nine selected water quality parameters were measured monthly across 47 sites in the DJKRB between 2020 and 2022. These included: In water quality parameters they are: water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen(NH3-N), total phosphorus (TP), total nitrogen (TN) and fluoride (F-). These indicators give clear and general picture about the physical, chemical and biological quality of the water.

  • What is the Water Quality Index (WQI) and how was it used in this study?

WQI is a single index which is derived from various water quality parameters that describe the quality of a water body. In the present investigation, a WQI was computed for each sampling site by taking into account the obtained parameters. The WQI values were then categorized into five grades: It ranges from excellent (81 - 100), good (61 – 80) moderate (41 – 60), low (21 – 40) and bad (0- 20). This facilitated computation of the water quality status in the DJKRB at different locations as well as over the given period of time comprehensively and a central understanding was easily achievable. The WQI is beneficial in helping both the professional and the non-professional to have a hint of water quality.

  • What were the key findings regarding the water quality in the DJKRB?

Thus, the water in the DJKRB was established to be of satisfactory quality, predominantly “good” and “excellent” in a range of aspects and parameters during the monitoring. But, peculiar longitudinal and lateral trends were observed for few tributaries are having low WQI than the other tributaries. In particular, Shendinghe (SDH) tributary maintained that the lowest value of WQI and occasionally ranged as “moderate.” Daily mean average temperatures indicated a general trend of increased water quality in summer although it was the lowest season for water quality, winter had the next worst quality followed by that of autumn and spring. The chemical quality of the water was determined to be fairly constant all around.

  • What is Transfer Entropy (TE) and how was it used in this study?

Transfer Entropy (TE) is an information theoretic feature that estimates the amount of information flows from a source to a target system. In this study, TE was used to assess the impact that variations in water quality in the tributaries had on the water quality in the DJKR. This enabled the researchers to determine which tributaries contribute most to the reservoir’s overall water quality.

  • What driving factors were identified as most influential in the DJKRB's water quality?

Consequently, the study established several factors that affect water quality in the DJKRB. It was confirmed that higher amounts of built-up land use and agricultural water consumption were the highest contributing factors to the increasing nutrient loadings in the rivers. Other important variables were also tested, including GDP, industrial water intake, forest land area, or population density. Other factors such as precipitation and daylight hours were also found to have an (albeit lesser) effect to the physical and chemical attributes of the water. In this case, the analysis of results indicated that anthropogenic conditions such as land use and the corresponding economical development play a larger role than meteorological conditions.

  • What is Set-Pair Analysis (SPA) and how was it integrated into the risk assessment framework?

Set-Pair Analysis (SPA) is a discussion approach used to evaluate the correlation between two sets in terms of identity, opposition, and difference. This study utilized a comprehensive SPA method in order to examine water quality risks at sub-basin level. Both transfer entropy and SPA were employed, which incorporate local water quality conditions and the effects of sub-basins on the main reservoir into the improved risk assessment model. This approach enabled the assessment of the risk levels of the sub-basins where identified aligned to the DJKR’s potential water quality degradation level classified into level 1 to level five.

  • What were the key findings of the risk assessment and what are the management implications?

The risk assessment indicated that approximately fifty percent of the sub-basins in the DJKRB have comparatively low-risk level; nevertheless, some sub-basins have higher risk levels in terms water quality degradation, especially the sub-basins surrounding close to the water supply point of the MRSNWDPC, such as Laoguanhe, Qihe and Langhe. It is therefore important for pollution management efforts to be focused in these areas to improve on the supply of good quality water to the MRSNWDPC and its consumers. The study further offered an integrated framework that can easily be applied by practitioners as well as the general public in evaluating the water quality on basin-scale and also for pointing out the high-risk areas for management, thereby, showing an effective way of data-driven management approach for water quality risk