Water Quality Monitoring Sites Research Articles

An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods

Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water quality parameters and learn more about the impact of complex spatial information based on deep learning models, this study proposes two ensemble models TNX (with temporal attention) and STNX (with spatio-temporal attention) based on seasonal and trend decomposition (STL) method to predict water quality using geo-sensory time series data. Dissolved oxygen, total phosphorus, and ammonia nitrogen were predicted in short-step (1 h, and 2 h) and long-step (12 h, and 24 h) with seven water quality monitoring sites in a river. The ensemble model TNX improved the performance by 2.1%–6.1% and 4.3%–22.0% relative to the best baseline deep learning model for the short-step and long-step water quality prediction, and it can capture the variation pattern of water quality parameters by only predicting the trend component of raw data after STL decomposition. The STNX model, with spatio-temporal attention, obtained 0.5%–2.4% and 2.3%–5.7% higher performance compared to the TNX model for the short-step and long-step water quality prediction, and such improvement was more effective in mitigating the prediction shift patterns of long-step prediction. Moreover, the model interpretation results consistently demonstrated positive relationship patterns across all monitoring sites. However, the significance of seven specific monitoring sites diminished as the distance between the predicted and input monitoring sites increased. This study provides an ensemble modeling approach based on STL decomposition for improving short-step and long-step prediction of river water quality parameter, and understands the impact of complex spatial information on deep learning model.

Predicting macroinvertebrate average score per taxon (ASPT) at water quality monitoring sites in Japanese rivers.

Biomonitoring with bioindicators such as river macroinvertebrates is fundamental for assessing the status of freshwater ecosystems. In Japan, water quality and biomonitoring surveys are conducted separately, leading to a lack of nationwide information on their relationships and the biological status of water quality monitoring (WQM) sites. To understand the biological status of WQM sites across Japan, we developed a multiple linear regression model to estimate the average score per taxon (ASPT) using river macroinvertebrate data surveyed at a total of 237 "aligned" sites based on the co-occurrence of biomonitoring and WQM sites. The resulting regression model with eight predictors, such as biological oxygen demand, the proportion of urban areas in the catchment, could predict ASPT with reasonable accuracy (e.g., an error of ±1 for 96% of the aligned data). Using this model, we estimated ASPT values at 2925 WQM sites in rivers nationwide, categorizing them into four levels of river environment quality: "very good" (29% of WQM sites), "good" (50%), "fairly good" (14%), and "not good" (8%). Furthermore, we observed statistically significant correlations (p < 0.05; 0.4 ≤ r ≤ 0.7) between ASPT and all eight macroinvertebrate metrics examined, such as mayfly and stonefly richness, providing ecological implications of changes in ASPT.

Parallel Intelligent Monitoring System of Port Water Quality Based on the ACP Method

With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation

Accurate predictions of river water quality are vital for sustainable water management. However, even the powerful deep learning model, i.e., long short-term memory (LSTM), has difficulty in accurately predicting water quality dynamics owing to the high non-stationarity and data limitation in a changing environment. To wiggle out of quagmires, wavelet analysis (WA) and transfer learning (TL) techniques were introduced in this study to assist LSTM modeling, termed WA-LSTM-TL. Total phosphorus, total nitrogen, ammonia nitrogen, and permanganate index were predicted in a 4 h step within 49 water quality monitoring sites in a coastal province of China. We selected suitable source domains for each target domain using an innovatively proposed regionalization approach that included 20 attributes to improve the prediction efficiency of WA-LSTM-TL. The coupled WA-LSTM facilitated capturing non-stationary patterns of water quality dynamics and improved the performance by 53 % during testing phase compared to conventional LSTM. The WA-LSTM-TL, aided by the knowledge of source domain, obtained a 17 % higher performance compared to locally trained WA-LSTM, and such improvement was more impressive when local data was limited (+66 %). The benefit of TL-based modeling diminished as data quantity increased; however, it outperformed locally direct modeling regardless of whether target domain data was limited or sufficient. This study demonstrates the reasoning for coupling WA and TL techniques with LSTM models and provides a newly coupled modeling approach for improving short-term prediction of river water quality from the perspectives of non-stationarity and data limitation.

Effect of landscape pattern on river water quality under different regional delineation methods: A case study of Northwest Section of the Yellow River in China

Study regionA typical arid irrigation area of the Yellow River, Ningxia, Northwest China. Study focusAt present, few studies have evaluated the effects of nested watershed and runoff process on the relationships between landscape patterns and water quality. In this study, we aimed to quantify the relationships between landscape pattern and river water quality using two delineation methods. One method involved a water quality monitoring site only corresponding only to its own sub-watershed, while the other method involved a water quality monitoring site corresponding to all of its upstream sub-watersheds. New hydrological insights for the regionWe collected water quality monitoring data, including DO, CODMn, NH3-N, TP, TN, pH for the irrigation period (May, 2021 – August, 2021) and the non-irrigation period (December, 2021 – March, 2022) for seven sub-watersheds. Our results revealed that when a water quality monitoring site corresponded to all the sub-basins involved in the runoff process, the correlations between landscape composition and water quality parameters were stronger. Additionally, the degree to which landscape configuration explained the overall water quality was greater in such cases. We also found that the buffer zone scale accounted for more than 97.3% of the overall water quality variation. These results highlight the importance of aligning water quality monitoring sites with the corresponding sub-watersheds and emphasize the significance of land-use management in the riparian scale near the Yellow River for water quality protection.

Identifying the influence of natural and human factors on seasonal water quality in China: current situation of China's water environment and policy impact.

Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.

A spatiotemporal dissolved oxygen prediction model based on graph attention networks suitable for missing data.

The accurate prediction of dissolved oxygen concentration is crucial for the effective prevention and control of water pollution. A spatiotemporal prediction model for dissolved oxygen content that is suitable for missing data is proposed in this study. The model utilizes a module based on neural controlled differential equations (NCDEs) to handle missing data and graph attention networks (GATs) to capture the spatiotemporal relationship of dissolved oxygen content. To enhance the performance of model, it is optimized from three aspects: an iterative optimization method based on the k-nearest neighbor graph is proposed to enhance the quality of graph; Shapley additive explanations model (SHAP) is used to select the main features into model, enabling it to handle multiple features; and a fusion graph attention mechanism is introduced to improve the robustness of model to noise. The model is evaluated using data from water quality monitoring sites in Hunan Province, China, from January 14, 2021, to June 16, 2022. The proposed model outperforms other models in long-term prediction (step = 18), with MAE of 0.194, NSE of 0.914, RAE of 0.219, and IA of 0.977. The results demonstrate that constructing appropriate spatial dependencies can enhance the accuracy of dissolved oxygen prediction models, and the NCDE module confers robustness to missing data in the model.

Effects of rainfall and land use on nutrient responses in rivers in the Brazilian semiarid region.

This paper investigated whether rainfall promotes dilution or increase in nutrient concentrations and which land use indicators are the main predictors of nutrients in intermittent rivers in a large Brazilian semiarid region. The total phosphorus (TP) and total inorganic nitrogen (TIN) were monitored between 2013 and 2018 at 92 river water quality monitoring sites. The monthly rainfall (Rn) was obtained from 575 rain gauges. Pearson's correlation (R) between Rn and nutrient concentration was performed. The correlation patterns were also analysed based on land use data: urban area (%), agricultural field area (%), demographic density (inhabitants/km2), sewer system coverage (%), and reservoir density (reservoir/km2). Backward stepwise regression was performed to identify predictors of nutrient concentrations. The results revealed a marginal effect of rainfall on nutrients when the effects of urbanisation outweigh all other aspects. However, in regions with greater accumulated rainfall and lower reservoir density, the rainfall was related to a linear increase in nutrient concentrations (R > 0.8). Contrastingly, in the basins with less accumulated rainfall and greater inter-basin hydrological disconnection, there was a linear reduction in nutrient concentration (R < - 0.5). In the backward stepwise regression, sewer system coverage and Rn had the greatest influence for TP, and the urban area was the strongest predictor for TIN. Importantly, our results demonstrated that in semiarid rivers in densely populated regions, there is no single pattern of variability in nutrient concentration, on a wide scale of assessment. Therefore, adaptative and decentralised management can be more effective in improving water quality in these regions.

ASSESSMENT OF ARSENIC CONTAMINATION IN THE INDIAN RIVERS

The present study analyses in order to determine whether or not arsenic contamination exists in rivers and what are cause responsible for it. India has 764 river water quality monitoring sites across the subcontinent. The study analyses 432 monitoring stations due to data availability. The most important rivers in India flow through Himachal Pradesh, Haryana, Punjab Uttarakhand, Uttar Pradesh, Bihar, West Bengal, Assam, Chhattisgarh, Jharkhand, Maharashtra, Karnataka, and others. The current study is shaped by causal research under the applied research approach used by academics and practitioners in river water assessment. Quantitative techniques were implemented during initial secondary data collection &amp; analyses. The study also conducted a content analysis and sweeping review of how institutions in India talk about the origins and effects of arsenic toxicity. The central water commission data has discovered that almost all rivers have arsenic amounts below the permissible limit except eight rivers. Arsenic contamination can be caused by a variety of natural and human activities. However, research indicates that industrial processes are the primary contributor.

Effect of land use change on climate elasticity of water quality at multiple spatial scales

Abstract Investigating the influence of land use change on climate elasticity of water quality (CEWQ) at multiple spatial scales is very critical for sustainable water management policies. The current research work investigated land use change–water–climate nexus through the lens of 32 water quality monitoring sites located at major rivers of Pakistan. The novelty of the current research work is the assessment of the instability consequences of precipitation and temperature CEWQ indicators owing to land use dynamics (2001–2019) at both sub-watershed and buffer scales. Precipitation elasticity values are lower and spatially homogeneous in comparison with temperature elasticity. Majority CEWQ-land use correlation coefficients have not shown any temporal trend with land use change except a few CEWQ indicators, namely pH, CO3, F, Ca, SiO2, silt, and clay. Temperature CEWQ developed many linear models with land use in comparison with precipitation CEWQ. A small number of land use classes cause instability consequences at the buffer scale in comparison with the sub-watershed scale. Savanna, shrublands, and ice and snow decline instability consequences of CEWQ indicators at both spatial scales. The identified land use classes that bring stability in CEWQ indicators are recommended to be incorporated in watershed management policies to bring sustainability in the aquatic environment.

Nitrogen and phosphorus concentration zoning control method of surface water in the Haihe River Basin

Various uncertain influencing factors and incomprehensible mechanisms have posed daunting challenges to the management and treatment of regional surface water environment quality. The unified measures and the “one-size-fits-all” management approach limit the treatment effectiveness. Therefore, considering natural and human activities which are major factors affecting the surface water environment quality, the present study proposed a set of zoning management and control schemes for nitrogen and phosphorus concentrations in surface water based on its natural attributes. Selecting DEM, rainfall, vegetation type, soil type, and land use, and employing “grid transformation”, “data extraction”, “attribute superposition” of GIS software and “correlation analysis”, “cluster analysis”, and “principal component analysis” of SPSS software, the Haihe River Basin was divided into the prevention zone, the control zone, and the non-control zone with different natural attribute sets by the correlation coefficient R2 and the nitrogen and phosphorus pollution therein. Based on the nitrogen and phosphorus data of 276 surface water quality monitoring sites, the multiple nonlinear stepwise regression analysis was conducted to construct the relationship between a single water quality indicator and its natural attributes in the three zones. The results are of essential practical significance to surface water environment quality zoning management and survey in the Haihe River Basin. Meanwhile, it provides innovative insights into environmental zoning management in other regions.

Water Quality Prediction Based on Multi-Task Learning.

Water pollution seriously endangers people’s lives and restricts the sustainable development of the economy. Water quality prediction is essential for early warning and prevention of water pollution. However, the nonlinear characteristics of water quality data make it challenging to accurately predicted by traditional methods. Recently, the methods based on deep learning can better deal with nonlinear characteristics, which improves the prediction performance. Still, they rarely consider the relationship between multiple prediction indicators of water quality. The relationship between multiple indicators is crucial for the prediction because they can provide more associated auxiliary information. To this end, we propose a prediction method based on exploring the correlation of water quality multi-indicator prediction tasks in this paper. We explore four sharing structures for the multi-indicator prediction to train the deep neural network models for constructing the highly complex nonlinear characteristics of water quality data. Experiments on the datasets of more than 120 water quality monitoring sites in China show that the proposed models outperform the state-of-the-art baselines.

Analysis of indicators of surface water pollution in Atlantic Forest preservation areas.

Managing water resources in regions with scarce data, like most developing countries, is still one of the major challenges around the world. Analysis of water quality parameters can provide important information for understanding the current status of water resources and their surroundings, including the changes that have occurred over time. This study aims to evaluate the influence of preservation areas on surface water quality in the Atlantic Forest biome. For this purpose, water quality monitoring sites with a greater number of parameters and longer monitoring time, located in six basins in the Atlantic Forest region of Brazil near preservation areas, were selected. This study employs seven statistical methods, such as cluster and principal component analysis (PCA), and promotes a robust analysis of the pollution of water resources in the Atlantic Forest. The most preserved basins, with more than 87% preservation area, have lower levels of pollution. The second most degraded basin, with 56% preservation area, presents intermediate pollution levels. The most degraded basin has the highest level of pollution. The basin with the lowest area of native vegetation is considered a degraded basin. Finally, non-point sources of pollution from agricultural activities were identified as the main sources of pollution in the region. The cophenetic correlation of 0.97 indicates a good performance of the cluster analysis. In addition, the pre-tests of PCA showed the suitability of the data for performing the test (Bartlett test, < 2.2e-16 and KMO, P= 0.7). The first principal component in the PCA, which accounts for 31.4% of the total variation, is associated with strong ammonia nitrogen and total Kjeldahlnitrogen loads, and moderate biological oxygen demand and nitrite loads. The second component, representing 13.6% of the total variation, indicates periods of self-cleaning of water resources after contamination. The results indicate the importance of maintaining preservation areas in the watershed contribution areas for the improvement of surface water quality in the Atlantic Forest.

The quantification of the influencing factors for spatial and temporal variations in surface water quality in recent ten years of the Huaihe River Basin, China.

Temporal and spatial variations in river water quality and the factors influencing such variations are important basis and prerequisites for identifying pollution sources and improving river water environment. Monthly data for 22 indicators at 485 surface water quality (SWQ) monitoring sites (46,560 groups) in the Huaihe River Basin (HRB) from 2011 to 2018 were analyzed. This paper assessed temporal and spatial changes in SWQ in the HRB and identified the main factors influencing the changes and each factor's contribution to the changes. The five-day biological oxygen demand, permanganate index, fluoride, ammonium nitrogen, and total phosphorus were the main pollutants. Spatial cluster analysis indicated that the HRB could be divided by SWQ into areas I-IV from light to heavy pollution. Areas I and IV were nitrogen and phosphorus nutrients pollution, and areas II and III were heavy metals and organic pollution. Area IV (poor SWQ) locates mainly north of the Huaihe mainstream with annual average rainfall ≤ 640mm. SWQ in the HRB has been improving for two decades, with an inflection point in 2015 between 2011 and 2018, and rainfall change is an important factor for the inflection point. The urbanization rate, industrial water consumption, and rainfall were the key factors influencing SWQ changes in the watershed with significant hydrological zonation, with urbanization rate and rainfall increased, industrial water consumption decreased, the SWQ was gradually improved. The key factors contributing to SWQ changes in the future will be the sewage treatment rate and rainfall changes caused by natural variations.

Molybdenum accumulation in sediments: A quantitative indicator of hypoxic water conditions in Narragansett Bay, RI

Authigenic molybdenum (Mo) accumulation in marine sediments has often been used as a qualitative indicator of hypoxic bottom water. To investigate its use as a quantitative indicator of hypoxic exposure, sediment cores were collected from water quality monitoring sites in Narragansett Bay (RI, USA) that experience varying periods of hypoxia. Total Mo concentrations in surficial (0–1 cm) sediments were determined by total digestion and ICP-MS analysis. Lithogenic contributions to total Mo concentrations were estimated by multiplying measured concentrations of aluminum (Al) by the mean crustal Mo:Al ratio and subtracting them from the total concentrations to yield the authigenic fraction. 210Pb dating was used to determine sediment accumulation rates at each site. Mean annual periods of hypoxia in bottom waters were determined from continuous monitoring data for the years coinciding with the top 1 cm of sediment. Results indicated a linear relationship between authigenic Mo concentrations and frequency of hypoxia, although the relationships differed between different sampling periods. These results demonstrate the potential of sedimentary Mo as a tool for assessing the spatial and temporal extent of hypoxia in coastal waters.

Estimating statistical power for detecting long term trends in surface water Escherichia coli concentrations

Water quality monitoring programs commonly use the Mann-Kendall test or linear regression to identify statistically significant monotonic trends in fecal indicator bacteria concentrations (typically Escherichia coli [E. coli]). The statistical power of these tests to detect trends of different magnitudes (effect size) is rarely communicated to stakeholders, and it is unlikely they are considered when designing monitoring schedules. The statistical power for detecting trends in surface water E. coli bacteria concentrations using Mann-Kendall and linear regression at water quality monitoring sites across Texas was estimated using Monte Carlo simulation. The probability that an individual water quality monitoring site in Texas had adequate statistical power was also estimated using logistic regression.Mann-Kendall and linear regression trend tests show similar statistical power. Both tests are unlikely to achieve adequate statistical power when E. coli concentrations decrease by 20% or less over 7 years under most sampling frequencies. To adequately detect concentration decreases of 30% to 40% over 7 years, monthly sampling is required. Because many sites across Texas are sampled quarterly, monotonic trends tests will not be powerful enough to detect trends of moderate magnitudes. To better facilitate stakeholder decision-making, it is important to communicate the relative power of statistical tests and detectible magnitudes of changes. I suggest data analysts conduct power analyses to improve monitoring program designs and improve communication of trend test limitations. Software and training for water quality analysts could facilitate communication of power and effect sizes. Alternative trend assessment methods may be more reliable for describing changes in fecal indicator bacteria concentrations.

Temporal Changes in Delaware Waters Using Long-Term (1967–2019) Water Temperature Data

The present article provides long-term (1967–2019) water temperature data collected from Delaware water quality monitoring sites. In Delaware, there are approximately 140 water quality monitoring sites in Piedmont, Delaware Bay, Chesapeake Bay, and Inland Bay drainage basins. Long-term quarterly (i.e., four times a year: Q1—January–February–March; Q2—April–May–June; Q3—July–August–September; Q4—October–November–December) water temperature data were calculated from each water quality monitoring sites’ continuous monthly data. This study focuses on water quality monitoring sites with significant (p-value identifying linear regression model) increasing or decreasing trends of water temperature. Quarterly water temperature data, statistical analysis, and maps showing increasing and decreasing trend from water quality monitoring sites with significant trends are presented in this article.

Investigating the 2017 Erratic Fishkill Episode in the Jhelum River, Kashmir Himalaya

Globally, the frequency of fishkill episodes is increasing, owing to natural and human-induced modification of aquatic ecosystems. A massive fishkill took place on 22 October 2017 along an approximately 1.5 km stretch of the Jhelum River in Srinagar City, India. Thousands of fish died during this specific event, not lasting more than three hours, creating chaos and panic among the local population and government circles. In this context, affected fish were assessed for three morphological parameters, which include skin color, eye appearance, and skin texture. To back our findings, three critical water-quality parameters, including pH, water temperature, and dissolved oxygen essential for the survival of fishes were assessed in the affected river stretch. This study assumes importance given that water-quality observation stations for monitoring the health of the Jhelum River are lacking in the highly urbanized Srinagar City. The morphological examination of fish samples revealed discoloration, bulging eyes, and rough skin texture, indicating chemical contamination of waters in the affected river stretch. The water quality analysis revealed neutral pH (7.2), normal temperature (15.6 °C), and mildly depleted dissolved oxygen (6 mg L−1) levels. While the morphological examination of the affected fish indicated chemical contamination, the physicochemical parameters exhibited a typical scenario of river water. For avoiding any such further incident and to precisely ascertain the cause of such fishkill episodes in future, it is suggested that a few continuous water-quality monitoring sites along Jhelum River should be set up, supplemented with robust ecological modeling simulations.

An integrated regional water quality assessment method considering interrelationships among monitoring indicators.

To monitor and manage water environments, China developed a centralized multi-level administrative system where governments and agencies at each level are responsible for water quality within their regions. In this case, regional water quality assessment has become a critical issue. However, as a complex multi-criteria decision making (MCDM) problem, it faces many challenges such as diverse implement indicator framework, complicated indicator interrelations, and lack of reliable assessment methods. Therefore, this paper constructs a novel multistage decision support framework for regional water quality assessment. In phase I, we determine indicator framework strictly according to the national standards, involving PH, dissolved oxygen (DO), chemical oxygen demand (COD), etc., totally 21 water quality indicators where the temperature indicator is excluded due to its lack of assessment standard. In addition, considering the matching between the characteristic of water quality data and the probabilistic linguistic term set (PLTS) technique, we employ PLTS theory to process massive monitoring data. In phase II, relative weight considering indicators' interrelationship is produced by the proposed regression-based decision-making trial and evaluation laboratory (DEMATEL) method, and further forms combined weight by balancing single-factor weight. In phase III, we present a new PLTS measure and extend the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS) method to generate assessment results. Then, we investigate water quality status of 16 administrative districts in Shanghai, China, with the proposed method. The collected data are derived from 26 water quality monitoring sites and covers the period during September 2018 to February 2019. The results confirm a hypothesis that the statistically significant interrelationship does exist among indicators, and point out that Huang Pu District remains the best water quality with highest values of [Formula: see text] in the range of (0.79-0.85) over the 6 months. Moreover, the parameter analysis and comparative analysis are further given that verifies the robustness and reliability of the model in details.

京杭运河杭州段城市景观格局对河网水环境的影响

近年来人类活动显著改变了陆地生态环境，城市化作为人类活动的重要组成部分，对陆地生态环境中的水环境也产生了深刻的影响。以杭州市作为研究区，以京杭运河杭州段为研究对象，在对水质监测点建立不同半径缓冲区的基础上，分析了城市景观格局对河网水环境的影响。结果表明：（1）京杭运河杭州段城市景观和农村景观镶嵌分布，河网水系发达，景观破碎化程度高，斑块形状复杂多变，城乡交错带特征典型，人类活动强度高低分化明显；（2）各种景观类型沿河分布的空间特征明显，景观格局指数随缓冲半径的增大而变化，当缓冲半径达到1000m以后格局指数维持相对稳定状态；（3）京杭运河杭州段水系的氮源污染物含量整体处于较高水平，总磷含量处于低水平。水质受到建设用地和农业用地的影响较高；（4）不同的景观格局指数对不同的水质指标在不同的时期具有一定的解释能力，且同一景观指数在汛期和非汛期对水环境的影响作用差异较大甚至相反，景观格局在河网密集型城市区域对河流水环境影响的整体利弊还需进一步权衡量化。;In recent years, human activities have significantly changed theterrestrial ecological environments. As an essential component of human activities, urbanization has a profound impact on the aquatic environment, which is one of terrestrial ecological environments. In this article, Hangzhou City was used as the research area, and the Hangzhou section of the BeijingHangzhou Grand Canal was taken as the research object. Based on the establishment of buffer zones with different radii for water quality monitoring sites, the impact of urban landscape patterns on the aquatic environment of river networks was analyzed. The results indicate that:(1) the urban and rural landscapes in the Hangzhou section of the Beijing-Hangzhou Grand Canal possess a mosaic structure, developed river network, high degree of landscape fragmentation, complex and diverse patch shape, typical characteristics of urban-rural fringe, and obvious differentiation of human activity intensity. (2) The spatial features of the various landscape types distributed along the river are very distinct. The landscape pattern index changes with the increase of the buffer radius. Once the buffer radius reaches 1000 meters, the landscape pattern index maintains a relatively stable state.(3) The content of nitrogen pollutants in the water system in the Hangzhou section of the Beijing-Hangzhou Grand Canal is generally at a high level, and the total phosphorus (TP) content is at a low level. Lands for construction and agriculture have a relatively high influence on water quality.(4) Different landscape pattern indexes have certain explanatory power for different water quality indexes during different periods, and the impact of the same landscape index on the aquatic environment in flood seasons and non-flood seasons is quite different and even opposite. The overall pros and cons of the impact of landscape pattern in dense river network urban areas on river aquatic environments need to be further weighted.