Water Monitoring Programs Research Articles

Predicting and Evaluating Water Quality using Machine Learning in Maharashtra, India

Water quality is critical for public health and environmental sustainability, necessitating effective monitoring to prevent contamination. In this study, we focus on predicting and evaluating water quality in Maharashtra, India, using machine learning techniques. Groundwater contamination in Maharashtra is a significant issue due to poor waste management, yet research in this area is limited. Traditional water quality monitoring methods involve complex calculations based on fixed parameters, which can lead to errors. This study aims to streamline the monitoring process by identifying the most significant features, thereby saving time, money, and energy. We calculated the Water Quality Index (WQI) using the Weighted Arithmetic Mean method, analyzing data from 2012 to 2022 from the National Water Monitoring Program in India. The analysis identified three key parameters, BOD, pH, and Fecal Coliform, as most correlated with the WQI. Machine learning techniques, including regression and classification, were employed to predict WQI and Water Quality Classification (WQC). The results indicate that Polynomial Regression and Ridge Regression achieved high accuracy in predicting the WQI, while the Decision Tree classifier excelled in WQC classification. This research demonstrates the potential of machine learning to enhance water quality monitoring, offering a cost-effective solution for managing water resources in Maharashtra.

A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort.

Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990's. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.

Novel PFAS-specific monitoring approach for highly impacted surface waters

In regulatory environmental monitoring programs, only a very small fraction of the vast number of per- and polyfluoroalkyl substances (PFAS) are investigated by target analysis. Therefore, non-target analysis (NTA) studies are increasingly conducted to detect unknown or unnoticed PFAS. These studies are often based on a few grab samples. Thus, discontinuously emitted PFAS from industrial batch processes might be easily overlooked. To address this deficiency and obtain in-depth information on the occurrence and temporal trend of PFAS in surface water impacted by treated industrial waste water, a comprehensive target and NTA study was implemented for 29 months.Elevated PFAS concentrations with up to 10.8 μg L−1 were detected in the river water by target analysis. In addition to PFAS target analysis, the water samples were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS). Data processing strategies and various filtering steps were applied to prioritize PFAS. Substances were identified by comparing data to available internal and external PFAS suspect lists, a fragment ion and neutral loss list, and spectral libraries. Several compounds were unequivocally identified based on reference standards.Fifty-five PFAS were (tentatively) identified using NTA. Of those, 43 could be assigned to 13 different homologous series. Partly fluorinated short-chain carboxylic acids (H-PFCA) and sulfonic acids (H-PFSA) were predominantly found in addition to perfluoroalkyl carboxylic acids (PFCA) and the alkyl ether carboxylic acid DONA. To the best of our knowledge, 12 PFAS were reported in surface water for the first time. Signal intensities of individual PFAS and signal ratios varied widely over time, which may indicate batch operations leading to discontinuous emission. Results and insights from this screening approach on PFAS can be used to optimize forthcoming surface water monitoring programs by including newly identified PFAS and selecting appropriate sampling intervals.

Featured collection introduction: Water resources planning and management under changing conditions—Experience of Illinois

Pioneering work by John Stall on hydraulic geometries of streams and rivers was completed in the 1960s Water and Atmospheric Research Monitoring (WARM) program at the ISWS expanded data collection to define the environmental and socio-economic impacts of weather and climate impacts, including: The ISWS, along with the Illinois Natural History Survey (est. 1861) and Illinois State Geological Survey (est. 1905), were transferred out of the University of Illinois in 1917 to be administered by the Illinois Department of Registration and Education, eventually being transferred to the Illinois Department of Energy and Natural Resources and then to a new Illinois Department of Natural Resources. In 2008, the Scientific Surveys came home to where it all started, the University of Illinois, under the umbrella of the Prairie Research Institute with the addition of the Illinois Sustainability and Technology Center and Illinois State Archeological Survey. Together, the Prairie Research Institute has over 800 scientists, technicians, post-docs, research affiliates, visiting scholars, students, and administrative support staff, conducting transformative science that benefits the people, economy, and environment of Illinois, the nation, and the world. From their inception, the Scientific Surveys have always been located on the University of Illinois campus and continues to capitalize on the mutual benefits of collaboration with faculty and staff. The papers presented in this special issue marking the 125th anniversary founding of the ISWS are but a representation of the current contributions and research being conducted by ISWS scientists and colleagues, aiming to demonstrate what Illinois experiences in water resources planning and management under changing conditions, especially changing climate. They build on water supply planning legacy by incorporating future demands and climate uncertainties in Illinois, including the major updating of Illinois precipitation frequency standards. The theme of understanding uncertainties and bias continues in evaluating groundwater static head measurements to understand water-level variability. Of course, projecting climate change impacts is paramount to many water resource issues and authors discuss which climate ensemble approaches produce less uncertain climate model outputs. Nutrient losses in US agricultural lands are an issue not only for loss of crop production but downstream impacts. Winter cover crops have much promise for reducing nitrate, phosphorus, and sediment loads. Authors present possible detrimental impacts on storage volumes for downstream reservoirs in drought years. An Illinois River watershed analysis presents evidence that spatial and temporal shift in phosphorus loading affects recent increases in phosphorus despite point and nonpoint source efforts. The ISWS has long-term datasets which contributed to improved model calibration to capture chloride accumulation in shallow aquifers. Reservoirs serve populations and industry so understanding outflows from them is important for water resource planning and management. Comparing performance of three machine learning models shows promise for simulating reservoir outflow. Finally, colleagues in Illinois and China test a theoretical framework to better understand bivariate return periods of hydrological events, which is important for design criteria of hydraulic structures. The ISWS continues to evolve and adjust research needs, as demonstrated by the first 125 years, and look forward to providing science-based information to societies in the next 125 years. For further reading on ISWS history, the following publications are recommended:

IMPLEMENTATION OF STATE SOCIAL AND HYGIENE MONITORING - THE MAIN TASK OF THE CENTERS FOR DISEASE CONTROL AND PREVENTION

Social and hygienic monitoring systems are being implemented in Ukraine, as the main task of disease control and prevention centers. One of the stages of the formation of this system is the practical implementation of the drinking water monitoring program

High-frequency sensor data capture short-term variability in Fe and Mn concentrations due to hypolimnetic oxygenation and seasonal dynamics in a drinking water reservoir

The biogeochemical cycles of iron (Fe) and manganese (Mn) in lakes and reservoirs have predictable seasonal trends, largely governed by stratification dynamics and redox conditions in the hypolimnion. However, short-term (i.e., sub-weekly) trends in Fe and Mn cycling are less well-understood, as most monitoring efforts focus on longer-term (i.e., monthly to yearly) time scales. The potential for elevated Fe and Mn to degrade water quality and impact ecosystem functioning, coupled with increasing evidence for high spatiotemporal variability in other biogeochemical cycles, necessitates a closer evaluation of the short-term Fe and Mn dynamics in lakes and reservoirs. We adapted a UV-visible spectrophotometer coupled with a multiplexor pumping system and partial least squares regression (PLSR) modeling to generate high spatiotemporal resolution predictions of Fe and Mn concentrations in a drinking water reservoir (Falling Creek Reservoir, Vinton, VA, USA) equipped with a hypolimnetic oxygenation (HOx) system. We quantified hourly Fe and Mn concentrations during two transitional periods: reservoir turnover (Fall 2020) and HOx initiation (Summer 2021). Our sensor system successfully predicted mean Fe and Mn concentrations and trends, ground-truthed by grab sampling and laboratory analysis. During fall turnover, hypolimnetic Fe and Mn concentrations began to decrease more than two weeks before complete mixing of the reservoir, with rapid equalization of epilimnetic and hypolimnetic Fe and Mn concentrations in less than 48 h after full water column mixing. During the initiation of HOx in Summer 2021, Fe and Mn displayed distinctly different responses to oxygenation, as indicated by the rapid oxidation of soluble Fe but not soluble Mn. This study demonstrates that Fe and Mn concentrations are sensitive to changes in redox conditions induced by stratification and oxygenation, although their responses to these changes differ. We also show that high spatio-temporal resolution predictions of Fe and Mn can improve drinking water monitoring programs and reservoir management practices.

Estimating vadose zone water fluxes from soil water monitoring data: a comprehensive field study in Austria

Abstract. Groundwater recharge is a key component of the hydrological cycle, yet its direct measurement is complex and often difficult to achieve. An alternative is its inverse estimation through a combination of numerical models and transient observations from distributed soil water monitoring stations. However, an often neglected aspect of this approach is the effect of model predictive uncertainty on simulated water fluxes. In this study, we made use of long-term soil water content measurements at 14 locations from the Austrian soil water monitoring program to quantify and compare local potential groundwater recharge rates and their temporal variability. Observations were coupled with a Bayesian probabilistic framework to calibrate the HYDRUS-1D model and assess the effect of model predictive uncertainty on long-term simulated recharge fluxes. Estimated annual potential recharge rates ranged from 44 to 1319 mm a−1 with a relative uncertainty (95 % interquantile range/median) in the estimation of between 1 % and 39 %. Recharge rates decreased longitudinally, with high rates and lower seasonality at western sites and low rates with high seasonality and extended periods without recharge at the southeastern and eastern Austrian sites. Higher recharge rates and lower actual evapotranspiration were related to sandy soils; however, climatic factors had a stronger influence on estimated potential groundwater recharge than soil properties, underscoring the vulnerability of groundwater recharge to the effects of climate change.

Citizen Science and Natural Resource Management: A Social Network Analysis of Two Community-Based Water Monitoring Programs

In natural resource management contexts, citizen science programs often involve a diversity of actors collaborating in broad social networks. However, the characteristics and functioning of these networks has received limited attention. In this article, we demonstrate the benefits of applying social network research to citizen science practice. Through a mixed methods approach, we compare data sharing and collaboration networks of two community-based water monitoring programs in Australia. The quantitative component revealed that despite similarities in actor diversity and activities, both programs have created divergent network structures (centralized vs decentralized) corresponding to their scale of influence in environmental decision-making (regional vs local impacts). The qualitative component showed how individuals navigated their relationships and managed key tensions inherent to network functioning. This study provides new insights on the role of social networks in citizen science and concludes by recommending program leaders adopt a “networking mindset” to maximize their sphere of influence.

Global Water Quality of Inland Waters with Harmonized Landsat-8 and Sentinel-2 Using Cloud-Computed Machine Learning

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

Long-term monitoring of cloud water chemistry at Whiteface Mountain: the emergence of a new chemical regime

Abstract. Atmospheric aqueous chemistry can have profound effects on our environment. The importance of chemistry within the atmospheric aqueous phase started gaining widespread attention in the 1970s as there was growing concern over the negative impacts on ecosystem health from acid deposition. Research at mountaintop observatories including Whiteface Mountain (WFM) showed that gas phase sulfur dioxide emissions react in cloud droplets to form sulfuric acid, which also impacted air quality by increasing aerosol mass loadings. The current study updates the long-term trends in cloud water composition at WFM for the period 1994–2021, with special consideration given to samples that have traditionally been excluded from analysis due to inorganic charge imbalance. We emphasize three major findings: (1) a growing abundance of total organic carbon (TOC), with annual median concentrations more than doubling since measurements began in 2009, (2) a growing imbalance between the measured inorganic cations and anions, consistent with independent rain water observations, implying that a substantial fraction of anions are no longer being measured with the historical suite of measurements, and (3) a growing number of samples exhibiting greater ammonium concentrations than sulfate plus nitrate concentrations, which now routinely describes over one-third of samples. Organic acids are identified as the most likely candidates for the missing anions, since the measured inorganic ion imbalance correlates strongly with measured TOC concentrations. An “inferred cloud droplet pH” is introduced to estimate the pH of the vast majority of cloud droplets as they reside in the atmosphere using a simple method to account for the expected mixing state of calcium and magnesium containing particles. While the inferred cloud droplet pH closely matches the measured bulk cloud water pH during the early years of the cloud water monitoring program, a growing discrepancy is found over the latter half of the record. We interpret these observations as indicating a growing fraction of cloud droplet acidity that is no longer accounted for by the measured sulfate, nitrate and ammonium concentrations. Altogether, these observations indicate that the chemical system at WFM has shifted away from a system dominated by sulfate to a system controlled by base cations, reactive nitrogen species and organic compounds. Further research is required to understand the effects on air quality, climate and ecosystem health.

Suspect Screening of Chemicals in Hospital Wastewaters Using Effect-Directed Analysis Approach as Prioritization Strategy.

The increasing number of contaminants in the environment has pushed water monitoring programs to find out the most hazardous known and unknown chemicals in the environment. Sample treatment-simplification methods and non-target screening approaches can help researchers to not overlook potential chemicals present in complex aqueous samples. In this work, an effect-directed analysis (EDA) protocol using the sea urchin embryo test (SET) as a toxicological in vivo bioassay was used as simplified strategy to identify potential unknown chemicals present in a very complex aqueous matrix such as hospital effluent. The SET bioassay was used for the first time here to evaluate potential toxic fractions in hospital effluent, which were obtained after a two-step fractionation using C18 and aminopropyl chromatographic semi-preparative columns. The unknown compounds present in the toxic fractions were identified by means of liquid chromatography coupled to a Q Exactive Orbitrap high-resolution mass spectrometer (LC-HRMS) and using a suspect analysis approach. The results were complemented by gas chromatography-mass spectrometry analysis (GC-MS) in order to identify the widest range of chemical compounds present in the sample and the toxic fractions. Using EDA as sample treatment simplification method, the number of unknown chemicals (>446 features) detected in the raw sample was narrowed down to 94 potential toxic candidates identified in the significantly toxic fractions. Among them, the presence of 25 compounds was confirmed with available chemical standards including 14 pharmaceuticals, a personal care product, six pesticides and four industrial products. The observations found in this work emphasize the difficulties in identifying potential toxicity drivers in complex water samples, as in the case of hospital wastewater.

Statistical process control applied to water quality assessment: a case study of the Cabrero Lagoon, colombian Caribbean

Water in developing countries worldwide faces growing anthropogenic pressures due to population growth, industrialization, among others. These stressors may cause that water bodies lose their biodiversity and quality every day. In the Cabrero lagoon, Cartagena, a water monitoring program has been carried out since 2000 in order to check the quality of water in this natural resource, and in this work statistical process control tools were used to verify the status of compliance with the regulation of water from a quality control view. The variables studied in this research were dissolved oxygen, biochemical oxygen demand (BOD5), chemical oxygen demand (COD), total phosphorus (TP), total coliforms (TC) and faecal coliforms (FC), several are not under statistical control and the lagoon is not capable of maintaining the chemical and microbiological standards established by Colombian law. The results of this investigation may give clarity on the current state of water quality in the Cabrero lagoon, for the municipal agencies in charge of the environment.

Impact of drinking water treatment processes on the residues of plant protection products for consumer and aquatic risk assessment: theoretical and experimental studies.

Pesticides residues can occur in ground and surface waters, and potentially react with chemicals used for water disinfection treatments, such as chlorine. This can lead to the formation of unknown reaction products, which can be more toxic and/or persistent than the active substances themselves, and therefore become a potential risk for human health and environment. Thus, in the framework of the EU Regulation 1107/2009, the identification of these by-products and their potential risk should be assessed. Within the European Food Risk Assessment (EU-FORA) Fellowship Programme, the fellow studied the behaviour of herbicides belonging to the families of imidazolinones and sulfonylureas in waters treated with chlorine disinfectants. Due to their physicochemical properties, these herbicides are susceptible of reaching natural waters. In fact, some of them have been detected in water monitoring programmes. During the experimental part of the present work programme, reactions between the active substances and the most used chlorine disinfecting reactants (hypochlorite and chloramines) were performed. Degradation kinetic parameters such as half-lives and degradation constants were calculated. Results showed that herbicide degradation was both pH and chlorine/chloramines concentration dependent. In order to identify the degradation by-products, high-resolution mass spectrometry experiments were performed, and a possible route of formation of these compounds was proposed. Finally, their risk assessment was carried out by using tox/ecotoxicological properties determined by QSAR methodology and FOCUS modelling for hazard and exposure assessment, respectively. These results will contribute to the definition of a risk assessment scheme for pesticides by-products potentially occurring in drinking water.

Occurrence of Pharmaceutical and Pesticide Transformation Products in Freshwater: Update on Environmental Levels, Toxicological Information and Future Challenges

Pharmaceuticals and pesticides are recognized micropollutants in freshwater systems. Their ever-increasing frequency of detection, levels found and little information available about their effects on non-target organisms, make them emerging contaminants. However, parental compounds are not the only substances of concern. Their metabolites and degradation products, hereby referred to as transformation products, are increasingly detected in freshwater samples and wastewater effluents. In the past years, a wealth of publications provided concentration levels detected in freshwater and some toxicological data, which required critical systematization. This review identified concentrations for 190 transformation products (92 from pesticides and 98 from pharmaceuticals) in water bodies and wastewater effluents. A concentration heatmap was produced to easily spot the substances found at higher levels and plan future research. The very limited available toxicological data link exposure to transformation products to adverse outcomes in humans (genotoxicity and alteration in detoxification processes) and aquatic species (mostly related to apical endpoints). Overall, environmental levels of these transformation products may pose a severe threat to aquatic organisms and need to be further investigated in sound experimental designs, testing for the effects of the single substances as well as of their mixtures. Such toxicological information is highly needed to improve both water treatment technologies and monitoring programmes.

Estimating upper percentiles of surface water monitoring data with sparse samples

AbstractThe estimation of upper percentiles of chemical concentrations in surface water systems within sites and regions may be necessary for the assessment of potential risk to ecosystems and human health. Limited sample sizes at monitoring sites often limit the use of direct methods to estimate upper percentiles. In such cases, upper percentiles within regions within a time frame may be estimated by pooling data across sites and years, and then deriving percentile estimates from the pooled dataset. The method uses the observations resulting from either a known probability‐sampling design or a sampling design treated like one because its observations come close to matching that of a probability‐sample. These observations are then weighted to ensure that estimates are representative of a target population across all the sites within the region and the range of years in the time frame. This method of estimating upper percentiles of annual site concentration profiles is demonstrated using atrazine and validated using the monitoring data from both sparsely sampled and high‐frequency water monitoring programs, where point and interval estimates of the 90th, 95th, and 99th pooled population percentiles are provided. This method shows that the pooled data from multiple sparse datasets can be used to provide estimates of near‐peak concentrations with greater certainty, which are consistent with those generated by high‐frequency sampling monitoring programs.

Spatial water quality analysis of the watercourses in Northwestern New Territories (Hong Kong)

Abstract River water is an important source for drinking water supply in Northwestern New Territories of Hong Kong. Thus, there is no denying the fact that monitor the quality of river water is a must for the locals. In this study, a mixed multivariate analysis method was used to lower monitoring costs by optimizing the layout of water quality monitoring stations. To this purpose, the data from a period of five years and over 36,000 observations was evaluated in this article. The cluster analysis approach was also used to categorize monitoring stations into three groups. What's more, three latent factors that predominantly influence the river water quality were assessed using factor analysis: anthropogenic pollution, seawater intrusion and geological processes, and the nitrification process. A spatial pattern using the three latent factor scores was plotted and six redundant monitoring stations were identified by this pattern. Finally, discriminant analysis was used to extract seven significant parameters. The results showed that the surface water-monitoring program of the watercourses in the Northwestern New Territories (Hong Kong) could be adjusted by reducing the monitoring stations to 18 and the measured chemical parameters to seven to ensure the detection of water quality and reduce the cost.

Early Influence of the COVID-19 Pandemic on Volunteer Water Monitoring Programs in the United States and Canada.

ABSTRACTVolunteer water monitoring programs generate new scientific knowledge, contribute data to decision‐making processes, and increase social networks, technical knowledge, and skills of participants. Declaration of the COVID‐19 pandemic threatened the ability of these programs to continue to engage volunteers to achieve such outcomes. A national water monitoring network hosted a brainstorming webinar to facilitate communication across programs to identify potential solutions to pandemic‐influenced challenges. Following that webinar, a survey of United States and Canadian volunteer monitoring programs that was conducted about 3 months into the pandemic revealed that 72% of 80 responding programs planned to carry on through the 2020 field season despite most having experienced delayed starts. Other common program modifications implemented in the first months of the pandemic included adding COVID‐19 safety information to program guidance, changing field team composition, monitoring timing and logistics, and adopting new communications strategies. Most programs reported loss or anticipated loss in number of data observations (74%) and volunteers (66%), while 44% reported known or anticipated losses in funding. Seventeen percent of responding programs were able to swiftly develop distance learning tools to train participants, which led to increased program capacity to reach broader audiences.

Multivariate analysis of the surface water in tropical river-estuary-sea continuums with changing land use.

The monitoring and assessment of surface water properties has been mainly conducted for freshwater rivers using multivariate analyses. The present study aimed to evaluate the effectiveness of introducing the absorption spectrum of water in order to develop a better monitoring and assessment technique for the surface waters of a river-estuary-sea continuum. Fourteen variables were measured at 17 different sites along three rivers, including estuaries and coastal sea, in Brunei Darussalam, Borneo, from August 2017 to December 2020. Among the 17 sites, freshwater streams surrounded by primary forests and coastal seawaters had the lowest ranges of the absorption coefficient at 350nm, suspended solids, and nutrients, but had the highest range of dissolved oxygen concentration. The other sites were more or less affected by natural leaching of chromophoric dissolved organic matter (CDOM) from forests, soil runoff from agricultural lands, and wastewater discharge from urban and industrial activities. The principal component analysis (PCA) showed that four principal components were significant and explained 91% of the total variance in the dataset. Four most explanatory factors were also extracted using the factor analysis (FA). In addition to the absorption spectrum, only seven basic water variables (salinity, pH, dissolved oxygen, total suspended solid, particulate organic matter, particulate inorganic matter, phosphate) were found to be necessary to assess the spatial variations in water properties at the present river-estuary-sea continuum. We conclude that it is important to include absorption spectrum data in water monitoring programs for more efficient and comprehensive assessment at land-sea interfaces.

Development of a UHPLC-MS/MS method to enhance the detection of Glyphosate, AMPA and Glufosinate at sub-microgram / L levels in water samples

Glyphosate is the best-selling herbicide worldwide. The toxicity on ecosystems and the possible effects on human health have long been at the centre of a complex controversy concerning the authorisation for its use. The peculiar chemical-physical properties of glyphosate, AMPA and glufosinate make their determination at trace levels in the water a real analytical challenge. All three compounds can be derivatised to less polar ones and FMOC-Cl (9-fluorenylmethyl chloroformate) is the most common pre-column derivatisation reagent used for this analysis. It can be successfully combined with Ultra-High-Performance Liquid Chromatography with Mass Spectrometry (UHPLC-MS/MS) to determine all three analytes in one method as part of water monitoring programs. The developed method aims to determine glyphosate with AMPA and glufosinate at sub-micrograms/L levels in groundwater, surface and water by UHPLC-MS/MS after derivatisation with FMOC-Cl. The novelty of this method is its high simplicity, robustness and sensitivity allowing the identification and quantification of the compounds at the detection limits required by the European regulations (0.1 µg /L). No pre-concentration or purification steps (by using Solid Phase Extraction cartridges) are necessary for our method saving time and consumables costs. The method demonstrated an excellent linear relationship (R2 ≥ 0.999) in the concentration range from 0.025 to 10 µg /L for glyphosate and AMPA and 0.025 to 5 µg /L for glufosinate. The method Limit Of Quantification (LOQ) is 0.025 µg/L, the lowest among all previously published studies, and it was demonstrated according to the European SANTE guidelines.

Water Quality Monitoring in Valea Jiului

Abstract The paper presents the monitoring of the water quality in Valea Jiului, related to the Jiu river basin. In the increasingly alert conditions of urbanization and industrialization, contemporary civilization is characterized by a worrying process of deteriorating the ecological balance and pollution of water resources. Monitoring allows the achievement of four primary objectives in knowing the state of water quality, namely: monitoring, forecasting, warning and intervention. The paper presents the objectives of water quality monitoring, aspects related to the implementation of water monitoring programs.