Long-term Water Quality Monitoring Research Articles

Long-term monitoring of water quality and phytoplankton community structure of Lake Manzala, Mediterranean Coast of Egypt

Coastal wetlands play a crucial role in supporting biodiversity, providing valuable ecosystem services, and contributing to the resilience of coastal ecosystems, making the preservation and restoration of these wetlands essential for sustainable development in coastal regions. This study focuses on Lake Manzala, a coastal wetland located on the Mediterranean Coast of Egypt, highlighting the significance of conserving and managing this unique environment. The objective of this research was to evaluate the water quality and phytoplankton structure of Lake Manzala and establish an updated long-term ecological database for the region, specifically evaluating the effectiveness of development plans that were carried out in 2017. Surface water samples were collected seasonally at eleven sites between 2010 to 2022. The findings revealed that prior to the development plans, the phytoplankton abundance in Lake Manzala exhibited high levels of eutrophication, characterized by increased abundance and species richness. The dominant phytoplankton classes in Lake Manzala were Bacillariophyceae, Chlorophyceae, and Cyanophyceae. Prior to development plans, they accounted for 46.5% and 45.0% respectively. Post-development, Bacillariophyceae increased to 62.8%, while Chlorophyceae decreased to 25.1%. Dinophyceae increased from 1.3% to 9.04%, while Cyanophyceae decreased from 6.1% to 1.6%. Based on the Trophic State Index for chlorophyll a, Lake Manzala underwent a shift from predominantly hypertrophic to eutrophic conditions. The study explored the relationship between biological factors and environmental conditions using principal component analysis, cluster analysis, and the modified water quality index (WQI). The results indicated positive signs of improvement in Lake Manzala during the post-development phase, as it transitioned from a poor to a moderate state. This research emphasizes the need for integrated land and water management approaches. By informing policy direction and development, this research underscores the importance of preserving and restoring ecosystems for the long-term well-being of both local communities and the global environment.

20-year water quality analysis reveals spatial variability and long-term changes at North Carolina's Masonboro Island National Estuarine Research Reserve

Urbanization impacts, especially stormwater runoff and pollutant discharge, pose ecological threats to coastal ecosystems globally. Long-term environmental monitoring datasets and assessments are used to quantify anthropogenic impacts on ecosystem health. This study synthesized twenty years (2002–2021) of water quality data from the National Estuarine Research Reserve System-wide Monitoring Program at the understudied Masonboro Island Reserve (North Carolina, United States) and provided an analytical methodology that can be applied to other Reserves and monitoring programs world-wide. Time series and correlation analyses of biological, chemical, and physical water quality parameters were used to identify spatiotemporal trends of nutrient dynamics and phytoplankton biomass at three stations within a 5-km stretch of the Atlantic Intracoastal Waterway. Heterogenous water quality conditions were observed between stations, especially for chlorophyll a, and were influenced by the relative proximity to marine waters, human developments, and urban tidal creeks. Long-term trends indicated significant water quality changes (i.e., increased chlorophyll a, reduced pH and salinity) related to anthropogenic influences. An unexpected and rapid shift in nutrient dynamics occurred around 2007, marked by significantly decreased ammonium and orthophosphate concentrations, which considerably reduced DIN/DIP ratios. This positive change was potentially influenced by sewage infrastructure improvements and stormwater wetland construction, removing waste-derived nutrients that previously entered the ecosystem. Impacts of major storms were also observed with weeks of hypoxia and low salinity following Hurricane Florence. These findings emphasized the value of long-term trend analyses of monitoring program data at multiple sites and provided insights into the significant water quality impacts of urbanization and benefits from wetland protection. This study further encourages the evaluation of long-term water quality monitoring datasets in estuarine environments globally.

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

Spatial and temporal analysis of physical and chemical properties of Short Creek

Freshwater systems are vitally important. Ecologically, rivers and streams are responsible for immense biodiversity, providing habitat for terrestrial and aquatic flora and fauna. Freshwater systems benefit humans in innumerable ways, providing drinking water to millions of people, means of transportation, food sources, and recreation areas. Nevertheless, the degradation of freshwater ecosystems continues to worsen at alarming rates. Long-term water quality monitoring can help determine the best management practices to improve and restore waterways. This study assessed changes in water chemistry in the Short Creek tributary located in Wheeling, WV, comparing historical data to current, focusing on physical and chemical water quality parameters data recorded from single monthly points obtained from historical data and averaged monthly points from current data. Short Creek represents 4 of 11 sites where data is collected weekly year-round. This data collection has been ongoing since 2018 and is part of a more extensive compilation hosted by Three Rivers Quest and the WVU Water Resource Research Institute. Water temperature, dissolved oxygen, chloride, specific conductivity, pH, and turbidity were recorded and analyzed utilizing Excel and RStudio. The increased frequency of monitoring provides a more accurate representation of Short Creek's overall condition. Both historical and current datasets were incomplete but generally did not show declines. A concerted effort in long-term water quality monitoring is necessary for understanding stream health. Current and historical data combined give us more resolution to estimate trends in water chemistry, better determine stream health progress, and assist in implementing best management practices.

Hydro-meteorological factors and inflowing nutrients drive water quality in an impounded lake of China's South-to-North Water Diversion Project.

Freshwater lakes play a vital role in global hydrological and biogeochemical cycles, serving various functions and maintaining ecological balance. However, freshwater resources are more vulnerable to deterioration due to multiple stressors. Gaoyou Lake is one of the impounded lakes of the Eastern route of South-to-North Water Diversion Project in China, and as an important source of drinking water, the lake has been routinely monitored. Long-term monitoring of water quality in Gaoyou Lake showed that concentrations of nutrients and chlorophyll a as well as trophic state in the water column increased while water transparency decreased, indicating that the water quality has declined during the last 12years. Specifically, there was a notable and statistically significant increase in chlorophyll a concentrations, averaging an annual rate of 9.9%. Despite a slight decline in trophic level index until 2014, subsequent years saw an upward trend, ranging from 50.7 to 56.4 and indicating a light eutrophic state. Spatially, the western area displayed higher nutrient and chlorophyll a concentrations. Changes in hydro-meteorological variables and nutrients from inflowing rivers were the main factors correlated with water quality in Gaoyou Lake. Thus, pollution source apportionment and management within Huaihe River basin should be considered to reduce the external loadings of nutrients in order to improve and sustain long-term water quality.

A High-Frequency and Real-Time Ground Remote Sensing System for Obtaining Water Quality Based on a Micro Hyper-Spectrometer.

The safeguarding of scarce water resources is critically dependent on continuous water quality monitoring. Traditional methods like satellite imagery and automated underwater observation have limitations in cost-efficiency and frequency. Addressing these challenges, a ground-based remote sensing system for the high-frequency, real-time monitoring of water parameters has been developed. This system is encased in a durable stainless-steel shell, suited for outdoor environments, and features a compact hyperspectral instrument with a 4 nm spectral resolution covering a 350-950 nm wavelength range. In addition, it also integrates solar power, Wi-Fi, and microcomputers, enabling the autonomous long-term monitoring of water quality. Positioned on a rotating platform near the shore, this setup allows the spectrometer to quickly capture the reflective spectrum of water within 3 s. To assess its effectiveness, an empirical method correlated the reflective spectrum with the actual chlorophyll a(Chla) concentration. Machine learning algorithms were also used to analyze the spectrum's relationship with key water quality indicators like total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). Results indicate that the band ratio algorithm accurately determines Chla concentration (R-squared = 0.95; RMSD = 0.06 mg/L). For TP, TN, and COD, support vector machine (SVM) and linear models were highly effective, yielding R-squared values of 0.93, 0.92, and 0.88, respectively. This innovative hyperspectral water quality monitoring system is both practical and reliable, offering a new solution for effective water quality assessment.

Long-Term Water Quality Monitoring: Using Satellite Images for Temporal and Spatial Monitoring of Thermal Pollution in Water Resources

Thermal pollution reduces water quality through any process that leads to a change in the water’s ambient temperature. Karun is one of the most relevant sources of water supply in Iran, and its pollution, created by industrial, urban, and agricultural issues, has been one of the most critical challenges throughout the last few years. As the water temperature rises, the amount of dissolved oxygen in it decreases, thereby affecting the entire ecosystem associated with it. Drainage of urban and industrial runoff into surface water sources can increase the water temperature. Dams also constitute a significant part, modifying spatial patterns of temperature along river routes and causing thermal contamination. In this paper, the thermal pollution of the Karun River was assessed, and regions along this river with unusually raised water temperatures were identified and compared over 20 years. By analyzing the results, it can be found that the thermal pollution from dams has a significant impact on the downstream river environment and ecology that is considerably relevant during summer periods, showing average decreases of 3 degrees Celsius immediately beyond the dams’ locations (from 41 degrees Celsius upstream dams to 38 degrees Celsius beyond them) or even bigger (reductions of 13 degrees Celsius in one of the studied dams). Hence, our results showed that water temperature is colder downstream in the hot seasons of the year than upstream of the dams. The results suggest that the usage of remote sensing data effectively could complement collected data from ground-based sensors to estimate water temperature and to identify pollution areas. It provides experts with spatially extensive and highly synchronized data.

Dissolved polycyclic aromatic compounds in Canada’s Athabasca River in relation to Oil Sands from 2013 through 2019

The Canada-Alberta Oil Sands Monitoring (OSM) Program began long-term surface water quality monitoring on the lower Athabasca River in 2012. Sampling of low level, bio-accumulative polycyclic aromatic compounds (PACs) targeted a suite of parent and alkylated compounds in the Athabasca River (AR) mainstem using semi-permeable membrane devices (SPMDs). Samples were collected along a gradient from upstream reference near Athabasca, Alberta, through exposure to the Athabasca oil sands deposit (AOSD), various tributary inflows, and mining activities within the OSMA, to downstream recovery near Wood Buffalo National Park (WBNP) and reference on the Slave River. The program adapted over the years, shifting in response to program review and environmental events. The AOSD chemical fingerprint was present in samples collected within the AOSD, through the oil sands mineable area (OSMA), downstream to recovery from 2013 to 2019. PACs were dominated by alkylated phenanthrenes/anthracenes (PAs) and dibenzothiophenes (Ds), with elevated levels of alkylated fluorenes (Fs), naphthalenes (Ns), fluoranthenes/pyrenes (FlPys) and benzo[a]anthracenes/chrysenes (BaACs), increasing in concentration from C1 < C2 < C3 < C4. Concentrations of these petrogenic PACs were at their highest within the OSMA and downstream of tributaries. The AOSD fingerprint was absent from sites located outside of the influence of the AOSD and downstream of the Peace-Athabasca Delta on the Slave River. PAC concentrations in the AR increased with mainstem discharge and loadings from tributaries, were moderated by the PAD, and diluted by the Peace River. This work bolsters the baseline PAC information previously reported for the Athabasca River and waters downstream, reporting 7 years of data, from all sites within the mainstem monitoring program, and exploring potential regional and hydrological drivers of these between sites and over time.

Machine learning constructs color features to accelerate development of long-term continuous water quality monitoring

Long-term continuous water quality monitoring (LTCM) is crucial to ensure the safety of water resources. However, lab-based pollutant detection via machine learning (ML) usually involves colorimetric materials or sensors, and it cannot be ignored that sensor limitations prevent their use for LTCM. To address this challenge, we propose a novel method that leverages image recognition to establish a relationship between pollutant concentration and color. By extracting efficient color variation features from raw pixel matrices using a combination of Kmeans clustering and RGB average features, the concentrations of pollutants that are difficult to distinguish by the naked eyes can be directly captured without the need for sensors and preprocessing. Four ML models (XGBoost, Linear, support vector regression (SVR), and Ridge) achieved up to a 95.9% increase in coefficient of determination (R2) compared to principal component analysis (PCA). In the prediction of the concentration of simulated pollutants such as Cu2+, Co2+, Rhodamine B, and the concentration of Cr(VI) in actual electroplating wastewater, natural resource water and drinking water, over 95% R2 was achieved. The method reported in our work can effectively capture subtle color changes that cannot be observed by the naked eyes without any preprocessing of water samples, providing a reliable method for LTCM.

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.

Dynamic spatiotemporal change of net anthropogenic phosphorus inputs and its response of water quality in the Liao river basin

The Liao river is one of the seven major rivers in China, and the process of phosphorus (P) cycling and change of water quality in this basin are influenced to a considerable extent human activities. In this work, the traditional net anthropogenic phosphorus inputs (NAPI) model was improved by considering the dynamic change of wastewater treatment capacity and P deposition (PDEP) and reclassifying the sources of phosphorus into human P consumption (PHUM), agriculture P consumption (PAGR), livestock P consumption (PANIM) and PDEP to analyze its dynamic spatio-temporal change in the Liao river basin. The results showed that the annual mean NAPI was 785.53 kg P km−2 yr−1 (2001–2020), the maximum value was 940.49 kg P km−2 yr−1 in 2009, and the minimum value was 586.04 kg P km−2 yr−1 in 2001. The temporal variation of NAPI presented an increasing-fluctuation-increasing trend and was basically in line with that of the water quality throughout the three stages, and the spatial distribution of NAPI gradually increased from upstream to downstream. During the two decades, PANIM was the predominant component of NAPI with a share of 64.32%. PHUM, PAGR, and PDEP accounted for 15.97%, 11.54%, and 8.17%, respectively, and the point source NAPI (NAPIP) contributed to 4.95% of NAPI. Further, the INAPI (Improved NAPI) -MR (Multiple Regression) -SWAT (Soil and Water Assessment Tool) model was developed to predict the spatial distribution of P flux under two scenarios. The results showed that the Liao river basin experienced a reduction in P flux to different degrees due to the improvement of the wastewater treatment system, which was more significant in its downstream area. Long-term water quality monitoring is encouraged to develop refined water quality models in the future.

It's all in the mud - The use of sediment geochemistry to estimate contemporary water quality in lakes

Lake ecosystems around the world are subject to multiple anthropogenic pressures leading to increased eutrophication and degraded ecosystems. The understanding of lake health at national scales is constrained by limited data, however, the increased long-term monitoring required to address such data deficiencies would be impractical, expensive and subject to significant time lags. More efficient methods of assessing contemporary water quality are needed. Lake sediments and the flux of constituents across the sediment-water interface in response to various biogeochemical cycles can result in the surficial sediments providing a time-integrated record of environmental conditions in the lake. As such, sediment geochemistry may offer a valuable and efficient indicator of contemporary lake water quality. To assess the potential of sediment indicators of water quality, geochemistry was analysed in surficial sediment samples (0–2 cm) collected from 101 lakes across New Zealand for which long-term water quality monitoring data was available. The selected lakes spanned various gradients, including lake type, trophic state, depth, latitude, altitude, catchment land use and sediment geochemistry. Linear modelling was undertaken to predict trophic state from sediment geochemistry and lake physiographic data separately and then from the two datasets combined. The combined model proved to be the strongest predictor of contemporary water quality (R2 = 0.80) and is referred to here as the Sediment Geochemistry Trophic Model (SGTM). This model was then used to predict the trophic level in 76 unmonitored lakes. The sediment geochemistry analyses conducted for the development of the SGTM are routinely conducted for lake management projects and, aside from their predictive power for trophic levels, provide useful insights into nutrient cycling and lake restoration planning. The relationship between lake water quality and sediment geochemistry is complex, however, the SGTM offers a highly efficient method for assessing the state and drivers of contemporary water quality in unmonitored lakes.

Spatial and temporal variation of physicochemical parameters of coastal waters in the southwestern region of Sri Lanka

Physicochemical parameters of coastal water in the southwestern area of Sri Lanka were studied from January to November 2016. Surface water samples from 15 selected locations were analysed according to standard methods. The ranges of the values recorded were; water temperature (WT) 26.5-33.9 ºC, pH 7.1-8.4, dissolved oxygen (DO) 4.4-7.4 mg L-1, salinity 14-38 ppt, electrical conductivity (EC) 27.1-53.8 mS cm-1, total suspended solids (TSS) 50.2-403.4 mg L-1, turbidity 1.2-64.9 NTU, nitrate nitrogen (NO3--N) 0.00-0.57mg L-1, nitrite nitrogen (NO2--N) 0.00-0.035mg L-1, ammoniacal nitrogen (NH4+-N) 0.00-1.72mg L-1, dissolved inorganic phosphorous (DIP) 0.00-0.65 mg L-1, biochemical oxygen demand (BOD) 0.1-11.6 mg L-1 and chemical oxygen demand (COD) 40-768 mg L-1. Results of Two-way ANOVA showed that there was a seasonal variability in parameters such as water temperature (p&lt;0.001), dissolved oxygen (p&lt;0.001), BOD (p&lt;0.001), COD (p&lt;0.001), nitrite nitrogen (p&lt;0.001), nitrate nitrogen (p&lt;0.001), dissolved inorganic phosphorous (p&lt;0.001), turbidity (p&lt;0.001), TSS (p&lt;0.001) and Chlorophyll a (p&lt;0.001). Spatial variation was identified in parameters, pH (p&lt;0.001), turbidity (p&lt;0.001), dissolved oxygen (p=0.004) and nitrite nitrogen (p&lt;0.001) whereas the interaction between time and location was significant in pH (p=0.005) and turbidity (p=0.007). However, EC, salinity and ammoniacal nitrogen were not significant by either of the factors of consideration. In conclusion, the physicochemical characteristics of water are highly variable over time and location due to both natural and human factors. Water quality near fish processing areas such as fishery harbours and fish markets relatively deteriorated in terms of pH, DO, turbidity and nitrite nitrogen compared to recreational beaches. It is recommended to regulate any discharges of sewage and industrial wastewater directly into the sea. A long-term coastal water quality monitoring programme is vitally important to comprehensively characterise the coastal segments for a long-term strategic plan for sustainable development.

Long-term monitoring and modelling of pasture regeneration and water quality from a Bothriochloa pertusa site in the Great Barrier Reef catchments

There has been considerable investment in grazing land management in Australia’s Great Barrier Reef catchments targeted at improving off-site runoff and water quality. This study used a systems modelling framework to analyse an 18-year monitoring data set from an Indian couch (Bothriochloa pertusa) dominated hillslope flume site in the Burdekin catchment, Australia. The analyses show important functional differences in B. pertusa pastures, relative to the native tussock pastures they replaced, with implications for how they are monitored and managed: (1) rates of landscape recovery were far slower than rates of degradation. In the practical time frames of most land managers, transition to a B. pertusa state is effectively irreversible, so historical management practices will have to adapt; and (2) transformations in pasture composition and structure can lead to misinterpretation of monitored trends in ground cover (a common proxy for land condition). At 50% pasture cover, B. pertusa pastures have only 33% of the biomass of local tussock pastures. Where B. pertusa invasions are occurring, inferences from positive associations between changes in cover and landscape function can break down: increases in ground cover can instead be accompanied by decreases in biomass, productivity, and runoff retention. The paper contributes an initial calibration of the GRASP pasture grazing systems model for a transformed B. pertusa site, capturing the observed relationships between grazing management, pasture biomass, ground cover and water quality. This serves as a starting point from which these landscapes, of growing extent and concern in Great Barrier Reef catchments, can be better represented in monitoring and modelling assessments, and for revised management options to be explored. The calibrated model was able to accurately represent long-term average runoff and sediment yield (both within 2% of observed) but was only able to adequately represent the year-to-year variation in runoff (not sediment).

Long-term water quality monitoring in agricultural catchments in Sweden: Impact of climatic drivers on diffuse nutrient loads

Water quality related to non-point source pollution continues to pose challenges in agricultural landscapes, despite two completed cycles of Water Framework Directive actions by farmers and landowners. Future climate projections will cause new challenges in landscape hydrology and subsequently, the potential responses in water quality. Investigating the nutrient trends in surface waters and studying the efficiency of mitigation measures revealed that loads and measures are highly variable both spatially and temporally in catchments with different agro-climatic and environmental conditions. In Sweden, nitrogen and phosphorus loads in eight agricultural catchments (470–3300 ha) have been intensively monitored for >20 years. This study investigated the relationship between precipitation, air temperature, and discharge patterns in relation to nitrogen (N) and phosphorus (P) loads at catchment outlets. The time series data analysis was carried out by integrating Mann-Kendall test, Pettitt break-points, and Generalized Additive Model. The results showed that the nutrient loads highly depend on water discharge, which had large variation in annual average (158–441 mm yr−1). The annual average loads were also considerably different among the catchments with total N (TN) loads ranging from 6.76 to 35.73 kg ha−1, and total P (TP) loads ranging from 0.11 to 1.04 kg ha−1. The climatic drivers were highly significant indicators of nutrient loads but with varying degree of significance. Precipitation (28–962 mm yr−1) was a significant indicator of TN loads in five catchments (loamy sand/sandy loam) while annual average temperature (6.5–8.7 °C yr−1) was a significant driver of TN loads in six out of eight catchments. TP loads were associated with precipitation in two catchments and significantly correlated to water discharge in six catchments. Considering the more frequent occurrence of extreme weather events, it is necessary to tailor N and P mitigation measures to future climate-change features of precipitation, temperature, and discharge.

Simulation Study on the Effect of Non-Point Source Pollution on Water Quality in the Upper Reaches of the Lijiang River

Maintaining good water quality in the Lijiang River is a scientific and practical requirement for protecting and restoring the environmental and ecological value of the river. Understanding the influence of non-point source pollution on the water quality of the Lijiang River is important for water quality maintenance. In this study, the pollutant flux in the upper reaches of the Lijiang River was calculated based on water quality monitoring, non-point source pollution, and point source pollution statistics. The Z–Q relation curve method, hydrologic analogy method, and contour map method were used to estimate the flow of the Lijiang River. We then constructed a water quantity–water quality balance model of the upper reaches of the Lijiang River based on an equilibrium equation of water quantity and a modified one-dimensional steady-state model of the river. Water quality changes in the upper reaches were simulated for a wet, normal, and dry season. The simulation errors were all within −30% to 30%, which was in line with the pollution simulation requirements of the Standard for hydrological information and hydrological forecasting (GB/T 22482-2008). The simulated reliability of each water quality indicator is at a high level, based on the calculated Nash–Sutcliffe efficiency coefficient. The overall model simulation results were good. The simulation results show that the impact of non-point source pollution on the water quality of the upper reaches of the Lijiang River was greater than that of point source pollution. The effect of different types of non-point source pollution on the water quality of the Lijiang River was as follows: rural domestic pollution &gt; urban household pollution without centralized treatment &gt; pollution from agricultural cultivation. This study provides technical support for the long-term hydrology and water quality monitoring of the Lijiang River and provides a basis for the reduction in non-point source pollution and the continuous improvement of the water quality in the Lijiang River Basin.

Phosphorus mobilization and delivery estimated from long-term high frequency water quality and discharge data

Efficient and targeted measures to mitigate nutrient losses to water are required to meet the challenges of increased food production and climate change. Diffuse pollution management requires insight into the underlying processes of nutrient transfer and its stability, but there are no standardized ways to evaluate phosphorus (P) mobilization and delivery at the catchments scale. Here we suggest a method that allows to systematically and objectively describe catchment scale processes of P transfer to water. Ten years of sub-hourly P concentration and hydrometric data from six agricultural catchments, with different physical characteristics, were assessed to introduce a method for estimation of a P mobilization index and a P delivery index. The former was defined as P detachment/solubilization expressed as ratios of P concentration percentiles, and the latter as connectivity/retention expressed as ratios of P mass load percentiles. Estimating these indices over several years provided insights on the stability of transfer processes over time and the identification of inter-annual trends. The indices were also used to quantify components of the P transfer continuum. This was used as a screening tool to compare and classify catchment specific and potential P loss risks. While the groundwater driven catchments had a higher P mobilization index for reactive P, the hydrologically flashy catchments had higher delivery indices for total P and particulate P + total organic P. Increasing or decreasing inter-annual trends were mostly found in catchments with a chemo-dynamic response in the discharge-concentration relationship. In three catchments the environmental quality standards were frequently exceeded caused by different risks and the P loss screening tool identified the type of risk. Development of the P mobilization and delivery indices was possible with long-term and high frequency monitoring of water quality and discharge from catchments of different typologies, together with a conceptual understanding of the catchments. More catchments of different typologies, management, scales and from different climatic zones are needed for further development of the indices.

Spatial water quality assessment of a mountain stream in northwestern India using multivariate statistical techniques.

The water quality of Himalayan rivers and streams is deteriorating due to multitude of anthropogenic and natural influences and the changes are more pronounced in smaller hill basins with high human influx. Inadequate data and lack of monitoring further exacerbate the situation. The surface water quality of one such hill stream, Neeru was evaluated for potability and irrigation quality. The water quality parameters were analysed at 25 sampling stations over a stretch of 30km across four seasons during a 2-year study from January 2014 to December 2015. The CCME Water Quality Index (WQI) indicated excellent water quality in upstream stations, while the midstream and a few downstream stations revealed varying degrees of impaired water quality. Although the irrigation quality of the stream was excellent, pollution levels near urban areas were rather high, necessitating considerable treatment for human consumption. The factor analysis yielded seven components with Eigenvalues greater than one that accounted for 83.656% of the variance and enabled the grouping of selected parameters based on comparable characteristics. The hydrogeochemical characteristics of the water samples indicated that they lie within the zone of rock dominance, while the trilinear diagram depicted the Ca-bicarbonaterich stream water. The study concluded that the upstream stations revealed pristine water quality, which significantly degraded towards human settlements. It thus calls for regular and long-term monitoring of the surface water quality to ensure the continuous and sustainable use of this important water body.

Inversion and Driving Force Analysis of Nutrient Concentrations in the Ecosystem of the Shenzhen-Hong Kong Bay Area

Although satellite remote sensing technology is intensively used for the monitoring of water quality, the inversion of coastal water bodies and non-optically active parameters is still a challenging issue. Few ongoing studies use remote sensing technology to analyze the driving forces of changes in water quality from multiple aspects based on inversion results. By the use of Landsat 5/8 imagery and measured in situ data of the total nitrogen (TN) and total phosphorus (TP) in the Shenzhen-Hong Kong Bay area from 1986 to 2020, this study evaluated the modeling effects of four machine learning methods named Tree Embedding (TE), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Back-propagation Neural Network (BPNN). The results show that the BPNN creates the most reliable and robust results. The values of the obtained correlation coefficients (r) are 0.83, 0.92, 0.84, and 0.90, and that of the coefficients of determination (R2) are 0.70, 0.84, 0.70, and 0.81. The calculated mean absolute errors (MAEs) are 0.41, 0.16, 0.06, and 0.02, while the root mean square errors (RMSEs) are 0.78, 0.29, 0.12, and 0.03. The concentrations of TN and TP (CTN, CTP) in the Shenzhen Bay, the Starling Inlet, and the Tolo Harbor were relatively high, fluctuated from 1986 to 2010, and decreased significantly after 2010. The CTN and CTP in the Mirs Bay kept continuously at a low level. We found that urbanization and polluted river discharges were the main drivers of spatial and inter-annual differences of CTN and CTP. Temperature, precipitation, and wind are further factors that influenced the intra-annual changes of CTN and CTP in the Shenzhen Bay, whilethe expansion of oyster rafts and mangroves had little effect. Our research confirms that machine learning algorithms are well suited for the inversion of non-optical activity parameters of coastal water bodies, and also shows the potential of remote sensing for large-scale, long-term monitoring of water quality and the subsequent comprehensive analysis of the driving forces.

Electrospraying Zwitterionic Copolymers as an Effective Biofouling Control for Accurate and Continuous Monitoring of Wastewater Dynamics in a Real-Time and Long-Term Manner.

Long-term continuous monitoring (LTCM) of water quality can provide high-fidelity datasets essential for executing swift control and enhancing system efficiency. One roadblock for LTCM using solid-state ion-selective electrode (S-ISE) sensors is biofouling on the sensor surface, which perturbs analyte mass transfer and deteriorates the sensor reading accuracy. This study advanced the anti-biofouling property of S-ISE sensors through precisely coating a self-assembled channel-type zwitterionic copolymer poly(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA) on the sensor surface using electrospray. The PTFEMA-r-SBMA membrane exhibits exceptional permeability and selectivity to primary ions in water solutions. NH4+ S-ISE sensors with this anti-fouling zwitterionic layer were examined in real wastewater for 55 days consecutively, exhibiting sensitivity close to the theoretical value (59.18 mV/dec) and long-term stability (error <4 mg/L). Furthermore, a denoising data processing algorithm (DDPA) was developed to further improve the sensor accuracy, reducing the S-ISE sensor error to only 1.2 mg/L after 50 days of real wastewater analysis. Based on the dynamic energy cost function and carbon footprint models, LTCM is expected to save 44.9% NH4+ discharge, 12.8% energy consumption, and 26.7% greenhouse emission under normal operational conditions. This study unveils an innovative LTCM methodology by integrating advanced materials (anti-fouling layer coating) with sensor data processing (DDPA).