Water pollution is a major concern among Americans. In a 2024 survey, some ** percent of respondents worried a great deal about pollution in drinking water, while ** percent worried a great deal about pollution of rivers, lakes, and reservoirs.

Some 56 percent of American worried a great deal about polluted drinking water in 2024, according to a survey of approximately 1,000 adults. Meanwhile, seven percent of respondents stated they did not worry at all about drinking water pollution. Overall, the share of American adults who worry a great deal about contaminated drinking water has fallen since 1990.

Water Pollution Control Facilities represents data extracted from permit applications submitted to DEEP. Alias is the facility name utilized in DEEPs internal system whereas the Facility Name is the name utilized on the permit itself. Permittee Address is the address utilized for correspondence and Facility Address is the location of the plant. The Class field differentiates 4 levels of plants in CT (I, II, III, and IV) with I being the lowest and IV being the highest based on complexity of treatment and SCADA present at the plant. Permit ID is the NPDES or State Subsurface permit number. Facility ID is a State of Connecticut ID number assigned to each facility in the state for internal use.

This dataset contains Water pollution level statistics in 2000. Data from Water FootPrint Network. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Prior to downloading data, please download the README file. This dataset contains water quality samples collected from Puget Sound, lakes, and streams in the region which can be filtered by "Site Type" and "Area". To see where water quality samples are collected, see the WLRD Water Quality Collection Sites dataset.

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.

The ZTRAX data is a national database of property sales collected by Zillow. The data is available to researchers who submit a research proposal to Zillow. Portions of this dataset are inaccessible because: Not publicly available. They can be accessed through the following means: Requires a data sharing agreement with Zillow. Format: National property sales database https://www.zillow.com/research/ztrax/. This dataset is associated with the following publication: Mamun, S., A. Castillo, K. Swedberg, J. Zhang, K.J. Boyle, D. Cardoso, C.L. King, C. Nolte, M. Papenfus, D. Phaneuf, and S. Polasky. Valuing water quality in the United States using a national dataset on property values. PNAS (PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES). National Academy of Sciences, WASHINGTON, DC, USA, 120(5): e2210417120, (2023).

The California Department of Water Resources (DWR) discrete (vs. continuous) water quality datasets contains DWR-collected, current and historical, chemical and physical parameters found in routine environmental, regulatory compliance monitoring, and special studies throughout the state.

This dataset Contains Saudi Arabia Emissions of Air or Water Pollutants for the period 2010-2018. Data from General Authority for Statistics. Follow datasource.kapsarc.org for timely data to advance energy economics research.Units: Ozone (o3) concentration level in atmosphere, Carbon monoxide (CO) concentration level, Sulphur oxides (SO2) concentration level, Nitrogen oxides (NO2) concentration level.

There are multiple well-recognized and peer-reviewed global datasets that can be used to assess water availability and water pollution. Each of these datasets are based on different inputs, modeling approaches, and assumptions. Therefore, in SBTN Step 1: Assess and Step 2: Interpret & Prioritize, companies are required to consult different global datasets for a robust and comprehensive State of Nature (SoN) assessment for water availability and water pollution.

To streamline this process, WWF, the World Resources Institute (WRI), and SBTN worked together to develop two ready-to-use unified layers of SoN – one for water availability and one for water pollution – in line with the Technical Guidance for Steps 1: Assess and Step 2: Interpret & Prioritize. The result is a single file (shapefile) containing the maximum value both for water availability and for water pollution, as well as the datasets’ raw values (as references). This data is publicly available for download from this repository.

These unified layers will make it easier for companies to implement a robust approach, and they will lead to more aligned and comparable results between companies. A temporary App is available at https://arcg.is/0z9mOD0 to help companies assess the SoN for water availability and water pollution around their operations and supply chain locations. In the future, these layers will become available both in the WRI’s Aqueduct and in the WWF Risk Filter Suite.

For the SoN for water availability, the following datasets were considered:

• Baseline water stress (Hofste et al. 2019), data available here

• Water depletion (Brauman et al. 2016), data available here

• Blue water scarcity (Mekonnen & Hoekstra 2016), data upon request to the authors

For the SoN for water pollution, the following datasets were considered:

• Coastal Eutrophication Potential (Hofste et al. 2019), data available here

• Nitrate-Nitrite Concentration (Damania et al. 2019), data available here

• Periphyton Growth Potential (McDowell et al. 2020), data available here

In general, the same processing steps were performed for all datasets:

1. Compute the area-weighted median of each dataset at a common spatial resolution, i.e. HydroSHEDS HydroBasins Level 6 in this case.

2. Classify datasets to a common range as reclassifying raw values to 1-5 values, where 0 (zero) was used for cells or features with no data. See the documentation for more details.

3. Identify the maximum value between the classified datasets, separately, for Water Availability and for Water Pollution.

For transparency and reproducibility, the code is publicly available at https://github.com/rafaexx/sbtn-SoN-water

Mean level of physico-chemical elements in the region from January 1 to May 27 (Phosphorus/Nitrate/Pesticide). These data are the result of a project led by four students in BTS SNIR (Digital Computer System and Network). The objective of the project is to carry out a feasibility study of the system. So the data is not to be taken seriously, but just to understand how the system works.

The graph shows the level of concern about water pollution in Mexico as of May 2019. During a survey, **** percent of the respondents with internet-connected smartphones considered water pollution to be a severe problem in their city or municipality.

This data package was created 2024-10-23 15:42:39 by NPSTORET and includes selected project, location, and result data. Data contained in Southern Plains Network's NPSTORET back-end file (SOPN_NPSTORET_BE_20241023.ACCDB) were filtered to include: Station: - Include Trip QC And All Station Visit Results Value Status: - Accepted or Certified (exported as Final) or Final The data package is organized into five data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations datatable - Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results Period of record for filtered data is 2010-10-27 to 2022-11-01. This data package is a snapshot in time of one National Park Service project. The most current data for this project, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=SOPN_WQ

Contaminants surveillance data in freshwater at sites in the Pacific Basin are included in this dataset. Measurements may include physical-chemical parameters such as temperature, pH, alkalinity, and major ions; nutrients, metals, pharmaceuticals and personal care products, pesticides and persistent organic pollutants. The number of sites varies from year-to-year, and sampling frequencies vary from one location to another, as surveillance activities are adjusted according to evolving environmental pressures and governmental programs. Data are collected in order to determine baseline water quality status, evaluate the effectiveness of management actions, verify compliance with water quality objectives, and identify emerging issues.

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.

Calculated Water Pollution Levels (WPL) in the world's river basins, WPL_2000N - the water pollution level related to N for year 2000 and WPL_2000P - the water pollution level related to P for year 2000.

The Ancillary Data component of the Indicators of Coastal Water Quality Collection includes a 5 arc-minute (approximately 9 x 9 km at the equator) sequence grid, grid cell centroids that relate to the grid cells in the tabular "Indicators of Coastal Water Quality: Change in Chlorophyll-a Concentration 1998-2007" data set, and a country buffer data set that is divided by exclusive economic zones (EEZ). The data are produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

A major problem related to large-scale water quality modeling has been the lack of available observation data with a good spatiotemporal coverage. This has affected the reproducibility of previous studies and the potential improvement of existing models. In addition to the observation data itself, insufficient or poor quality metadata has also discouraged researchers to integrate the already available datasets. Therefore, improving both the availability and quality of open water quality data woould increase the potential to implement predictive modeling on a global scale. We aim to address the aforementioned issues by presenting the new Global River Water Quality Archive (GRQA) by integrating data from five existing global and regional sources: Canadian Environmental Sustainability Indicators program (CESI), Global Freshwater Quality Database (GEMStat), GLObal RIver Chemistry database (GLORICH), European Environment Agency (Waterbase) and USGS Water Quality Portal (WQP). The resulting dataset covering the timeframe 1898 - 2020 contains a total of over 17 million observations for 42 different forms of some of the most important water quality parameters, focusing on nutrients, carbon, oxygen and sediments. Supplementary metadata and statistics are provided with the observation time series to improve the usability of the dataset.

Last update: 2022-03-11

GRQA_v1.2 contains three updated files compared to GRQA_v1.1:

• NH4N_GRQA.csv
• NO2N_GRQA.csv
• NO3N_GRQA.csv

The files were updated, because the assumed conversion constants used for the corresponding GLORICH observations were found to be incorrect. The corresponding files in GRQA_figures.zip and GRQA_meta.zip are yet to be updated, but will be in GRQA_v1.3.

The explanation for the updated conversion constants is given in this notebook:
https://nbviewer.org/github/LandscapeGeoinformatics/GRQA_src/blob/main/testing/glorich_conversion_test.ipynb

An overview of all the files in the dataset can be found in README_v1.2.txt.

Statistical overview of all 42 parameters is given in the data catalog file GRQA_data_catalog.pdf.

For more information about the development of this dataset look for Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, 2021.

Monthly raw water quality data on alkalinity, hardness, TDS, Turbidity, TOC, pH, and temperature. This dataset is associated with the following publication: Levine, A., J. Yang , and J. Goodrich. Enhancing climate Adaptation Capacity for Drinking Water Treatment Facilities. Journal of Water and Climate Change. IWA Publishing, London, UK, 7(3): 1-13, (2016).

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.