Spatial–Temporal Variation and Risk Assessment of Water Quality

1. Introduction

Water is a fundamental resource for ecosystem health and sustainable societal development. However, the aquatic environment faces unprecedented threats from pollution and degradation due to the combined pressures of global climate change, continuous population growth, and intensifying industrial and agricultural activities [1,2]. In 2020, more than two billion people worldwide still lacked access to safely managed drinking water [3]. Studies indicate that approximately four billion people experience severe water scarcity for at least one month each year, while about five billion are affected by chronic water shortages [4]. Water pollution further aggravates this scarcity, endangering the safety of drinking water for humans and livestock, as well as the survival of aquatic organisms. Recent research suggests that, considering both water quantity and quality, the global population affected by water scarcity may increase by an additional three billion by 2050 [5]. Deteriorating water quality poses a pressing global challenge, carrying profound implications for drinking water safety, food production, biodiversity, and economic stability at regional scales. Therefore, a profound understanding of the dynamics of water quality parameters and a scientific assessment of their potential risks are essential for developing effective water resource management and pollution control strategies.

Water quality evolution exhibits significant spatiotemporal heterogeneity. Temporally, it is influenced by periodic processes [6] such as seasonal precipitation, hydrological cycles, and agricultural practices, leading to fluctuations at the daily, monthly, and annual scales. Spatially, variations in pollution source distribution, land use types, topography, and hydrogeological conditions result in distinct regional patterns of water quality [7]. Analyzing these spatiotemporal dynamics helps us to identify the drivers of water quality change, quantify the probability and severity of adverse impacts on specific protection targets—such as human health and aquatic ecosystems—and assess the potential risks posed by changing water quality and its influencing factors. Such analysis enhances our understanding of water pollution characteristics and trends, supports the development of targeted mitigation measures, and provides a scientific basis for water environmental protection and management. This approach is of great significance for controlling water pollution and safeguarding water resources.

This Special Issue presents the latest research in the analysis of spatiotemporal water quality variations and risk assessment. The topics include the spatiotemporal dynamics of water quality, simulation and risk evaluation of sudden water pollution incidents, and source apportionment of aquatic environmental pollution. The Special Issue focuses on themes such as drinking water safety, reservoir water environment modeling, basin-scale water quality assessment and pollution source identification, correlation analysis between extreme rainfall indices and surface water quality, and water quality variations in irrigation districts. Through these contributions, this Special Issue aims to provide a theoretical foundation and methodological support for advancing precise and efficient water environment risk management.

2. An Overview of Published Articles

Contribution 1: This study addresses water quality monitoring and management within the production and transmission systems of the Saudi Water Authority, providing a comprehensive assessment of drinking water quality across the Kingdom of Saudi Arabia. Leveraging an extensive monitoring network, chemical, physical, and biological parameters were analyzed in water samples collected between 2020 and 2022, quantifying spatiotemporal variations in key indicators—such as total dissolved solids, boron, and bromate—during the desalination process. The results show that the proportion of desalinated water in the drinking water supply increased from 58% to 72%; however, the boron concentration exceeded the standard limit in 12% of samples, showing a significant correlation with pre-treatment practices, including chlorine disinfection. For the first time, this study systematically reveals the trade-off between process optimization and water quality safety in large-scale desalination projects in Saudi Arabia. It further proposes innovative strategies for mitigating boron pollution by enhancing both pre-treatment (e.g., activated carbon adsorption) and post-treatment (e.g., calcium ion addition) steps in reverse osmosis membrane processes.

Contribution 2: Water pollution emergencies are characterized by their abrupt onset, complex nature, and long-term impacts on aquatic systems. Once such incidents occur, immediate intervention is required to prevent further deterioration and avoid significant ecological damage. This study introduces a novel early warning method for sudden water pollution incidents, utilizing receptor-specific water quality models for both source and non-source areas. The approach was simulated and applied in the Three Gorges Dam area. Using the Environmental Fluid Dynamics Code (EFDC) model, the transport and transformation processes of phosphorus and its spatial distribution during a leakage event were simulated, enabling risk level assessment across different zones. The application results confirm that this method offers scientific support for emergency decision-making and holds significant value for reservoir water environment management.

Contribution 3: The interplay between intermittent pollution events and long-term cumulative impacts, compounded by human and natural influences, complicates the accurate identification and control of pollution sources. This study seeks to clarify the complex interaction mechanisms among multiple pollution sources. Ten typical sampling sites in the upper Tarim River were selected, and three analytical methods—one-way ANOVA, the comprehensive water quality identification index (WQI), and principal component analysis (PCA)—were applied to 23 water quality parameters measured from 2020 to 2022. The study evaluated the water quality status and pollution sources in the upper Tarim River and quantified the contribution rates of different sources by developing Absolute Principal Component Score–Multiple Linear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) models. The results indicate that the overall water quality in the upper Tarim River is satisfactory, though there is localized pollution—primarily from human activities. The APCS-MLR and PMF models identified six potential pollution sources, among which soil weathering, livestock breeding, and agricultural activities showed higher contribution rates, enabling the quantitative source apportionment of complex pollution.

Contribution 4: This study investigates the influence of extreme rainfall on urban surface water quality. Rainfall data from Hangzhou City collected between June 2021 and May 2024 were analyzed, and a set of Rainfall Extreme Indices (REIs) was calculated. The spatiotemporal variations and correlations between these indices and surface water quality parameters—including water temperature, dissolved oxygen, pH, total phosphorus, total nitrogen, and turbidity—were examined. The results indicate that extreme rainfall events occur mainly in July. Influenced by human activities, natural conditions, and environmental policies, the surface water quality parameters in Hangzhou exhibit significant spatiotemporal variability. This study provides a theoretical foundation for developing models that predict surface water quality changes based on REIs, thereby supporting the protection of urban water bodies.

Contribution 5: This study examines the effects of agricultural and environmental factors on the water environment in the Turkmeli Reservoir irrigation basin in Turkey. The spatiotemporal dynamics of key water quality parameters were analyzed using a wavelet transform model. Seasonal sampling and laboratory analyses yielded data on multiple indicators, including salinity, nitrate, boron, total suspended solids, and chemical oxygen demand (COD). The application of the wavelet transform model effectively captured the fluctuation patterns of these parameters across different temporal scales and spatial locations. The results reveal a significant increasing trend in boron concentration (p < 0.01), which exhibits a lag correlation with industrial wastewater discharge (lag time: 6 months). Meanwhile, COD fluctuations are primarily influenced by agricultural irrigation cycles, such as spring irrigation. This study highlights the non-stationary behavior of water quality driven by climate–human interactions and offers a dynamic regulation model for irrigation water management in arid regions.

3. Concluding Remarks

This Special Issue presents several representative studies on the spatiotemporal variation and risk assessment of water quality in recent years, reflecting a paradigm shift in the field toward a precision risk management research framework that integrates mechanistic understanding with data-driven approaches. The main advances can be summarized in the following two aspects:

(i) The application of diverse analytical methods to reveal the spatiotemporal heterogeneity of water quality under complex driving forces. The studies included in this Special Issue employed a variety of analytical approaches—such as one-way analysis of variance, the water quality index (WQI), principal component analysis (PCA), wavelet analysis, and the Mann–Kendall test—to assess the spatiotemporal characteristics of water quality in the respective study regions. These analyses demonstrate that water quality parameters are spatially influenced by both point sources (e.g., industrial discharges) and non-point sources (e.g., agricultural activities). Temporally, water quality exhibits complex variation patterns shaped by the superposition of natural cycles (e.g., seasonal rainfall and hydrological processes) and human activity cycles (e.g., agricultural irrigation). By applying advanced time-series analysis tools, researchers were able to effectively deconstruct non-stationary water quality monitoring data, clearly distinguishing long-term trends, seasonal fluctuations, and episodic signals of water quality parameters. This has improved the understanding of the dual driving mechanisms of climate and human activities on water quality changes.

(ii) The enhanced forward-looking capacity of risk assessment, enabling a transition from “post-event evaluation” to “pre-incident early warning”. The case studies presented in this Special Issue illustrate that current water quality risk assessment models can effectively simulate the evolution of sudden contamination incidents (e.g., phosphorus leakage) and support dynamic, visualized risk classification, thereby providing valuable time for emergency responses. Furthermore, the application of receptor models such as Positive Matrix Factorization (PMF) and the Absolute Principal Component Score–Multiple Linear Regression (APCS-MLR), along with rainfall extreme indices (REIs), has enabled the quantitative source apportionment of composite pollutants and correlation analysis with emerging risk drivers such as extreme climate. As a result, risk assessment is no longer limited to evaluating historical conditions but has also acquired the capability to identify key risk sources and predict future scenarios. This progress facilitates a strategic shift in water management, moving from post-incident remediation toward proactive source control and early warning.