Long-term freshwater quality data from federal and federal-provincial sampling sites throughout Canada's aquatic ecosystems are included in this dataset. Measurements regularly include physical-chemical parameters such as temperature, pH, alkalinity, major ions, nutrients and metals. Collection includes data from active sites, as well as historical sites that have a period of record suitable for trend analysis. Sampling frequencies vary according to monitoring objectives. The number of sites in the network varies slightly from year-to-year, as sites are adjusted according to a risk-based adaptive management framework. The Great Lakes are sampled on a rotation basis and not all sites are sampled every year. Data are collected to meet federal commitments related to transboundary watersheds (rivers and lakes crossing international, inter-provincial and territorial borders) or under authorities such as the Department of the Environment Act, the Canada Water Act, the Canadian Environmental Protection Act, 1999, the Federal Sustainable Development Strategy, or to meet Canada's commitments under the 1969 Master Agreement on Apportionment.

This is an observation data for water quality monitoring.

The Big Sioux River has the following designated beneficial uses established by the State of South Dakota: Domestic Water Supply, Warm water Semi-permanent Fish Life, Warm water Marginal Fish Life, Immersion Recreation, Limited Contact Recreation, Fish and Wildlife Propagation, and Irrigation. The State of South Dakota has established Water Quality Criteria (WQC) to protect this water body for these designated uses. Skunk Creek is a major contributor to the flow and overall character to the water quality of the Big Sioux River in Sioux Falls, however, the Skunk Creek does not have the same designated uses as the Big Sioux River. The Public Works Environmental Division monitors the conditions along the Big Sioux River as well as Skunk Creek in the Sioux Falls area. Monitoring data for Dissolved Oxygen (DO), Escherichia Coli bacteria (E. Coli), and Total Suspended Solids (TSS) is available for public review. The water quality criteria for each of these parameters are outlined on the City's Environmental Division website. To view data on each segment of the river that is monitored, find the parameter of interest and then select either historic data or recent month’s data.

The Environmental Department releases river water quality monitoring data, including River Pollution Index (RPI) and monitored values of major pollutants. Due to the need for monthly on-site sampling, laboratory testing and data quality control procedures, monitoring data is usually provided every other month.

This study utilizes water quality data from Cork Harbour, Moy Killala, and 15 other coastal locations in Ireland. The Environmental Protection Agency (EPA) of Ireland manages and evaluates this dataset as part of their monitoring program. The extracted data comprises 445,125 entries from Cork Harbour, 722,532 entries from Moy Killala, and 15 other randomly selected locations, all stored in .csv format. Upon merging these disparate .csv files, the dataset expanded to 1,276,654 entries. Following a pivot operation on 11 parameters (Alkalinity-total (as CaCO3), Ammonia-Total (as N), BOD - 5 days (Total), Chloride, Conductivity @25°C, Dissolved Oxygen, ortho-phosphate (as P) - unspecified, pH, Temperature, Total Hardness (as CaCO3), True Colour), the dataset was reduced to 29,159 rows. The selection of parameters was based on their availability across the entire dataset and aimed at encompassing fundamental measurements of water quality.This repository has two csv files:Water Quality Monitoring Dataset_ Ireland.csv - with Raw Water Quality DataWQI Results on Dataset - Which has the calculated Water Quallity Indices (CCME, WAWQI, Horton, SRDD, Brown) results of water quality

Historical water quality data measured on a continuous basis at over 23 locations across Canada is included in this dataset. Most locations include hourly temperature, dissolved oxygen, pH, specific conductance and turbidity. Data are collected by Environment and Climate Change Canada (ECCC) and in partnership with other federal departments and provinces and territories to enable the detection of short-term water quality events, and to determine trends in water quality, especially at transboundary sites (or Federal waters) in support of Federal legislation and international agreements, or to report on the status of Government of Canada priority aquatic ecosystems.

Water Quality Monitoring Market size was valued at USD 6.18 Billion in 2024 and is projected to reach USD 9.83 Billion by 2031, growing at a CAGR of 6.60% during the forecast period 2024-2031.

Water Quality Monitoring Market Drivers

Stringent Environmental Regulations: Governments and regulatory bodies enforce strict water quality standards, driving the need for effective monitoring systems.

Rising Awareness of Water Pollution: Increasing awareness of the adverse effects of water pollution on health and the environment boosts the demand for water quality monitoring.

Technological Advancements: Innovations in monitoring technologies, such as real-time data collection, remote sensing, and IoT-enabled devices, enhance the effectiveness and appeal of water quality monitoring systems.

Growing Industrialization: Industrial activities contribute to water pollution, necessitating the implementation of monitoring systems to ensure compliance with environmental standards.

Expanding Urbanization: Rapid urbanization increases the demand for clean water and effective monitoring systems to manage water resources and quality.

Climate Change Impact: Climate change and its effects on water sources drive the need for continuous monitoring to manage water quality and availability.

Government Initiatives and Funding: Government programs and funding for water quality improvement projects support the adoption of monitoring systems.

Increasing Health Concerns: The growing concern over waterborne diseases and health issues related to contaminated water encourages the use of water quality monitoring solutions.

Development of Smart Cities: The integration of smart technologies in urban planning includes advanced water quality monitoring systems to ensure sustainable water management.

Agricultural Activities: Monitoring water quality in agricultural areas is crucial to prevent contamination from pesticides, fertilizers, and other chemicals, driving the demand for monitoring systems.

Data DescriptionWater Quality Parameters: Ammonia, BOD, DO, Orthophosphate, pH, Temperature, Nitrogen, Nitrate.Countries/Regions: United States, Canada, Ireland, England, China.Years Covered: 1940-2023.Data Records: 2.82 million.Definition of ColumnsCountry: Name of the water-body region.Area: Name of the area in the region.Waterbody Type: Type of the water-body source.Date: Date of the sample collection (dd-mm-yyyy).Ammonia (mg/l): Ammonia concentration.Biochemical Oxygen Demand (BOD) (mg/l): Oxygen demand measurement.Dissolved Oxygen (DO) (mg/l): Concentration of dissolved oxygen.Orthophosphate (mg/l): Orthophosphate concentration.pH (pH units): pH level of water.Temperature (°C): Temperature in Celsius.Nitrogen (mg/l): Total nitrogen concentration.Nitrate (mg/l): Nitrate concentration.CCME_Values: Calculated water quality index values using the CCME WQI model.CCME_WQI: Water Quality Index classification based on CCME_Values.Data Directory Description:Category 1: DatasetCombined Data: This folder contains two CSV files: Combined_dataset.csv and Summary.xlsx. The Combined_dataset.csv file includes all eight water quality parameter readings across five countries, with additional data for initial preprocessing steps like missing value handling, outlier detection, and other operations. It also contains the CCME Water Quality Index calculation for empirical analysis and ML-based research. The Summary.xlsx provides a brief description of the datasets, including data distributions (e.g., maximum, minimum, mean, standard deviation).Combined_dataset.csvSummary.xlsxCountry-wise Data: This folder contains separate country-based datasets in CSV files. Each file includes the eight water quality parameters for regional analysis. The Summary_country.xlsx file presents country-wise dataset descriptions with data distributions (e.g., maximum, minimum, mean, standard deviation).England_dataset.csvCanada_dataset.csvUSA_dataset.csvIreland_dataset.csvChina_dataset.csvSummary_country.xlsxCategory 2: CodeData processing and harmonization code (e.g., Language Conversion, Date Conversion, Parameter Naming and Unit Conversion, Missing Value Handling, WQI Measurement and Classification).Data_Processing_Harmonnization.ipynbThe code used for Technical Validation (e.g., assessing the Data Distribution, Outlier Detection, Water Quality Trend Analysis, and Vrifying the Application of the Dataset for the ML Models).Technical_Validation.ipynbCategory 3: Data Collection SourcesThis category includes links to the selected dataset sources, which were used to create the dataset and are provided for further reconstruction or data formation. It contains links to various data collection sources.DataCollectionSources.xlsxOriginal Paper Title: A Comprehensive Dataset of Surface Water Quality Spanning 1940-2023 for Empirical and ML Adopted ResearchAbstractAssessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.Note: Cite this repository and the original paper when using this dataset.

This dataset contain data collected during water quality monitoring for the Pacific Island Network. Water resources in the Pacific Island Network (PACN) support rich and diverse ecosystems and aquatic communities that include corals reefs, anchialine pools communities (endemic to Hawaii), and freshwater stream communities. Aquatic resource protection is required by all the governments of the PACN, and water quality is widely used as an indicator of aquatic resource condition by regulators and ecologists. This data should provide the parks with some of the summary information necessary to determine their compliance with the applicable water quality standards in addition to providing correlative environmental data to ecologists.

This dataset contains a summary of the global data availability for 38 monitored water quality constituents, as described and used in: Jones et al 2024 Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ad6919This includes information on the location (e.g. site_id, latitude, longitude, country_name), the database of origin (database), water quality constituent information (e.g. group, sub-group) and the number of daily measurements in the period 1980-2021.Additionally, the spatial and temporal distribution of water quality data per constituent are provided as Figures.

The global water quality monitoring market is experiencing robust growth, projected to reach $3193.9 million in 2025, expanding at a compound annual growth rate (CAGR) of 4.6% from 2025 to 2033. This growth is fueled by increasing concerns about water pollution, stringent government regulations on water quality, and the rising demand for safe and reliable drinking water across residential, industrial, and commercial sectors. The market is segmented by various types of analyzers, including TOC analyzers, pH meters, dissolved oxygen analyzers, conductivity sensors, and turbidity meters, each catering to specific water quality parameters. Applications span laboratories, industrial processes, government infrastructure monitoring, and commercial spaces, with a growing contribution from agricultural and household applications. Technological advancements, such as the development of portable and user-friendly devices, coupled with the increasing adoption of cloud-based data management and remote monitoring systems, are further driving market expansion. The rising awareness about the importance of real-time water quality monitoring, particularly in developing economies with limited water resources, also presents a substantial growth opportunity. The key players in this market, including Teledyne Technologies, General Electric, Horiba, Xylem, Agilent Technologies, and Danaher, are continuously investing in research and development to improve the accuracy, efficiency, and affordability of water quality monitoring solutions. Competitive strategies involving mergers, acquisitions, and strategic partnerships are shaping the market landscape. While the market is witnessing strong growth, challenges such as high initial investment costs associated with advanced monitoring systems and the lack of standardized testing protocols in certain regions might pose some restraints. However, the overall market outlook remains positive, driven by the growing need for effective water quality management and sustainable water resource utilization.

Water Quality Monitoring Site identifies locations across the state of Vermont where water quality data has been collected, including habitat, chemistry, fish and/or macroinvertebrates. Currently the layer is not maintained as site locations are provided through another means to the ANR Natural Resources Atlas.

Real time water quality data is collected using equipment provided by the National Water Quality Instrumentation Service and is deployed by area teams within the Environment Agency. The link provides access to a map of active water quality monitoring stations and a live graphical display of the data collected. Each site takes regular measurements of a suit of parameters including temperature, conductivity, pH, turbidity, ammonium, dissolved oxygen, chlorophyll and nitrate.

The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Water quality data can be downloaded in Excel, CSV, TSV, and KML formats. Fourteen site types are found in the WQP: aggregate groundwater use, aggregate surface water use, atmosphere, estuary, facility, glacier, lake, land, ocean, spring, stream, subsurface, well, and wetland. Water quality characteristic groups include physical conditions, chemical and bacteriological water analyses, chemical analyses of fish tissue, taxon abundance data, toxicity data, habitat assessment scores, and biological index scores, among others. Within these groups, thousands of water quality variables registered in the EPA Substance Registry Service (https://iaspub.epa.gov/sor_internet/registry/substreg/home/overview/home.do) and the Integrated Taxonomic Information System (https://www.itis.gov/) are represented. Across all site types, physical characteristics (e.g., temperature and water level) are the most common water quality result type in the system. The Water Quality Exchange data model (WQX; http://www.exchangenetwork.net/data-exchange/wqx/), initially developed by the Environmental Information Exchange Network, was adapted by EPA to support submission of water quality records to the EPA STORET Data Warehouse [USEPA, 2016], and has subsequently become the standard data model for the WQP. Contributing organizations: ACWI The Advisory Committee on Water Information (ACWI) represents the interests of water information users and professionals in advising the federal government on federal water information programs and their effectiveness in meeting the nation's water information needs. ARS The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief in-house scientific research agency, whose job is finding solutions to agricultural problems that affect Americans every day, from field to table. ARS conducts research to develop and transfer solutions to agricultural problems of high national priority and provide information access and dissemination to, among other topics, enhance the natural resource base and the environment. Water quality data from STEWARDS, the primary database for the USDA/ARS Conservation Effects Assessment Project (CEAP) are ingested into WQP via a web service. EPA The Environmental Protection Agency (EPA) gathers and distributes water quality monitoring data collected by states, tribes, watershed groups, other federal agencies, volunteer groups, and universities through the Water Quality Exchange framework in the STORET Warehouse. NWQMC The National Water Quality Monitoring Council (NWQMC) provides a national forum for coordination of comparable and scientifically defensible methods and strategies to improve water quality monitoring, assessment, and reporting. It also promotes partnerships to foster collaboration, advance the science, and improve management within all elements of the water quality monitoring community. USGS The United States Geological Survey (USGS) investigates the occurrence, quantity, quality, distribution, and movement of surface waters and ground waters and disseminates the data to the public, state, and local governments, public and private utilities, and other federal agencies involved with managing the United States' water resources. Resources in this dataset:Resource Title: Website Pointer for Water Quality Portal. File Name: Web Page, url: https://www.waterqualitydata.us/ The Water Quality Portal (WQP) is a cooperative service sponsored by the United States Geological Survey (USGS), the Environmental Protection Agency (EPA), and the National Water Quality Monitoring Council (NWQMC). It serves data collected by over 400 state, federal, tribal, and local agencies. Links to Download Data, User Guide, Contributing Organizations, National coverage by state.

This dataset contains water quality monitoring data from Carpenter Creek. Since 2016, SLSS volunteers have been sampling from 4 streams that feed Slocan Lake for basic water quality parameters. The purpose is to identify unusual changes that might indicate disruptive human activities.

Le Groupe de développement durable du Pays de Cocagne (GDDPC) par le biais de son programme de la qualité de l'eau vise à identifier des sites, développer et instaurer des protocoles de surveillance à long-terme avec la communauté afin de maintenir des services écosystémiques du bassin versant de Cocagne et préserver la qualité du milieu et une biodiversité riche. Les données sur la qualité de l'eau sont collectées à l'aide d'une sonde portable et d'échantillons envoyés pour analyse en laboratoire. Ces informations sont analysées avec les données historiques du GDDPC afin de déterminer l'état et les tendances de la qualité de l'eau dans le bassin versant. Entre 2000 et 2013, le GDDPC en collaboration avec la Coalition des bassins versant de Kent géraient des nombreux programmes de surveillance de la qualité d’eau dans les bassins versant de Cocagne, de Bouctouche et Chockpish._EN: The Pays de Cocagne Sustainable Development Group (GDDPC) through its water quality program aims to identify sites, develop and implement long-term monitoring protocols with the community to maintain ecosystem services in the Cocagne watershed and preserve environmental quality and rich biodiversity. Water quality data is collected using a portable probe and samples sent for laboratory analysis. This information is analyzed with historical data from the GDDPC to determine the status and trends of water quality in the watershed. Between 2000 and 2013, the GDDPC in collaboration with the Kent Watershed Coalition managed numerous water quality monitoring programs in the Cocagne, Bouctouche and Chockpish watersheds.

Measurements taken in Lake Michigan and Green Bay in 2012 as part of a water quality monitoring program.

The archive contains the data resulting from the monitoring carried out by Arpat on significant surface water bodies for the purposes of verifying the environmental quality objectives. Some geographical reference files (monitoring points, significant bodies of water, catchment areas) are correlated to the database of analytical results.

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem” and “Qualifier” flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

The global intelligent water quality monitoring system market is experiencing robust growth, driven by increasing concerns about water pollution, stringent government regulations, and the rising demand for real-time water quality data across various sectors. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $9 billion by 2033. Key growth drivers include the expanding industrial and agricultural sectors, the need for efficient irrigation management, and the growing adoption of advanced technologies like IoT sensors and AI-powered analytics for predictive maintenance and improved water resource management. The industrial sector currently holds the largest market share, followed by the agricultural sector, with significant growth potential in government and scientific research applications. Miniature and small-scale systems are currently the most prevalent types, but demand for large-scale systems is expected to increase significantly as larger municipalities and industrial complexes seek comprehensive monitoring solutions. Technological advancements, including the integration of advanced sensors, cloud computing, and data analytics, are reshaping the market landscape. The emergence of wireless sensor networks, enabling remote monitoring and data transmission, is enhancing efficiency and reducing operational costs. Furthermore, the increasing adoption of AI and machine learning algorithms for predictive modeling and anomaly detection is improving the accuracy and effectiveness of water quality monitoring. However, factors such as high initial investment costs associated with advanced technologies and the lack of skilled professionals in certain regions may restrain market growth. Competitive dynamics are shaped by the presence of both established players like Emerson, Hach, and Thermo Fisher Scientific, and emerging innovative companies focused on developing cutting-edge sensor technologies and analytics platforms. Geographic growth is expected to be particularly strong in developing economies in Asia-Pacific and the Middle East & Africa, driven by increasing urbanization and industrialization.