Series Name: Crude death rate attributed to household air pollution (deaths per 100 000 population)Series Code: SH_HAP_MORTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.9.1: Mortality rate attributed to household and ambient air pollutionTarget 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contaminationGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Infant mortality was defined as death before completion of first year of life [1]. We obtained linked birth and infant death data from the U.S. Centers for Disease Control and Prevention for the years 2000–2005, corresponding to the time frame covered by the EQI.

The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files.

This dataset is associated with the following publication: Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018).

The delimitation of areas vulnerable to pollution by nitrates of agricultural origin was made in the context of Decree No 93-1038 of 27 August 1993, which transposes Directive 91/676/EEC into French law. This delineation, prepared in each department and in each region by working groups, involving various departments and agencies concerned as well as representatives of the agricultural profession, is based on the knowledge of nitrate levels in the aquifers and rivers and on the levels observed during various monitoring campaigns (1992-1993, 1997-1998, 2000-2001) on a monitoring network set up for this purpose.

River water quality water is valued for many reasons including ecological function and habitat, recreational value, its role in supporting people and industry, and its cultural significance. Nutrients such as nitrogen and phosphorus are essential for plant growth, however too much can lead to ‘nuisance’ growths of river algae and aquatic plants, degrading habitat. High concentrations in the form of ammoniacal nitrogen and nitrate-nitrogen can be toxic to fish and other aquatic animals. Water clarity is a measure of underwater visibility, and affects habitat of aquatic life such as fish and birds, and can also impact on aesthetic values and recreational use of rivers and streams. Escherichia coli (E.coli) can indicate the presence of pathogens (disease-causing organisms) from animal or human faeces, which can cause illness.

File contains raw data collected at NIWA monitored sites of the National River Water Quality Network (NRWQN) over the period 1989-2013. The NRWQN network is used to calculate national trends in river water quality. Fields are described as follows. Refer to Larned et al. 2015 for further details: * sID ---- Unique site ID * srcid ---- Region site is located in * sflag ---- River (r) or Estuary (e) * river ---- River name * location ---- Name of site * nzmge ---- easting * nzmgn ---- northing * NZReach ---- REC1 segment identifier * sDate ---- sample date * Q ---- Recorded flow when sample was taken, cumecs * npid ---- NIWA parameter ID (as used in Larned et al. 2015) * values ---- Parameter value (units are mg/m3, except CLAR (m) and ECOLI (n/100 mL))

For more information please see: Larned, S, Snelder, T, Unwin, M, McBride, G, Verburg, P, McMillan, H (2015).Analysis of Water Quality in New Zealand lakes and Rivers: data sources, data sets, assumptions, limitations, methods and results. NIWA Client Report no. CHC2015-033. Available at https://data.mfe.govt.nz/x/DDui3u from the Ministry for the Environment dataservice.

This dataset relates to the "River water quality" measures on the Environmental Indicators, Te taiao Aotearoa website.

River water quality is valued for many reasons including ecological function and habitat, recreational value, its role in supporting people and industry, and its cultural significance. Nutrients such as nitrogen and phosphorus are essential for plant growth, however too much in rivers can lead to excessive growth of river algae, which can degrade habitat. High concentrations of nitrogen in the form of ammoniacal nitrogen and nitrate-nitrogen can be toxic to fish and other aquatic animals, and nitrate-nitrogen can be toxic to humans. Water clarity is a measure of underwater visibility, and affects habitat of aquatic life such as fish and birds, and can also impact on aesthetic values and recreational use of rivers and streams. Escherichia coli (E.coli) can indicate the presence of pathogens (disease-causing organisms) from animal or human faeces, which can cause illness. File contains the model outputs for river water quality indicators as medians for each river segment in New Zealand’s digital river network.

This data set contains small-scale base GIS data layers compiled by the National Park Service Servicewide Inventory and Monitoring Program and Water Resources Division for use in a Baseline Water Quality Data Inventory and Analysis Report that was prepared for the park. The report presents the results of surface water quality data retrievals for the park from six of the United States Environmental Protection Agency's (EPA) national databases: (1) Storage and Retrieval (STORET) water quality database management system; (2) River Reach File (RF3) Hydrography; (3) Industrial Facilities Discharges; (4) Drinking Water Supplies; (5) Water Gages; and (6) Water Impoundments. The small-scale GIS data layers were used to prepare the maps included in the report that depict the locations of water quality monitoring stations, industrial discharges, drinking intakes, water gages, and water impoundments. The data layers included in the maps (and this dataset) vary depending on availability, but generally include roads, hydrography, political boundaries, USGS 7.5' minute quadrangle outlines, hydrologic units, trails, and others as appropriate. The scales of each layer vary depending on data source but are generally 1:100,000.

River water quality water is valued for many reasons including ecological function and habitat, recreational value, its role in supporting people and industry, and its cultural significance. Nutrients such as nitrogen and phosphorus are essential for plant growth, however too much in rivers can lead to ‘nuisance’ growths of river algae and aquatic plants, degrading habitat. High concentrations in the form of ammoniacal nitrogen and nitrate-nitrogen can be toxic to fish and other aquatic animals. Water clarity is a measure of underwater visibility, and affects habitat of aquatic life such as fish and birds, and can also impact on aesthetic values and recreational use of rivers and streams.

Trend statistics and calculation results for the periods 1989-2013, 1994-2013, and 2004-2013 are provided by monitored site. Units for parameters are mg/m3, except CLAR (m). Refer to Larned at al. 2015 for further details.

For more information please see: Larned, S, Snelder, T, Unwin, M, McBride, G, Verburg, P, McMillan, H (2015).Analysis of Water Quality in New Zealand lakes and Rivers: data sources, data sets, assumptions, limitations, methods and results. NIWA Client Report no. CHC2015-033. Available at https://data.mfe.govt.nz/x/DDui3u from the Ministry for the Environment dataservice.

This dataset relates to the "River water quality" measures on the Environmental Indicators, Te taiao Aotearoa website.

The City of Sacramento conducts drinking water quality monitoring in accordance with the Safe Drinking Water Act and Title 22 of the California Code of Regulations, and publishes an annual Consumer Confidence Report which summarizes this monitoring. This dataset is intended to supplement the Consumer Confidence Report with more current data for the most recent completed monthly compliance period, and with a more complete dataset including individual results and non-detected constituents which are otherwise summarized by and excluded from the Consumer Confidence Report respectively.People can call the City's water quality lab at (916) 808-5011 if they have questions about their drinking water.Please see the City of Sacramento Consumer Confidence Report for more information on units and abbreviations used in drinking water monitoring.

The third of 20 years of International Social Survey Programme (ISSP) surveys within New Zealand by Professor Philip Gendall, Department of Marketing, Massey University.A verbose rundown on topics covered follows.Attitudes towards environmental protection. Preferred government measures for environmental protection. Obedience or self-determination as most important education goal; private entrepreneurs as best possibility to solve economic problems; responsibility of the government to reduce income differences among the citizens; postmaterialism index; perceived belief in science of the population; attitude to modern science (scale); expected solution of environmental protection problems through science; too many concerns for the future of the environment in comparison to prices and provision of jobs; environmental destruction and modern life; equal rights for animals and people.Respect for nature as creation of God; belief in God; exaggerated environmental sensitivity; judgement on the contrast of environmental protection and economic growth; attitude to animal experiments in pharmacology; nature as struggle for survival; economic growth endangers the environment; readiness for higher prices or higher taxes or to accept reductions in standard of living for the benefit of protection of the environment; personal difficulties in participation in environmental protection; self-classification of participation in environmental protection.Knowledge about manner of functioning of antibiotics as destructive agent for bacteria or virus; belief in astrology; understanding the theory of evolution; artificially produced chemicals as cause for cancer; humans as party responsible for radioactivity; knowledge of the danger of death from radioactivity; knowledge about the dangers of radioactive waste from nuclear power plants; knowledge about the cause for the greenhouse effect; pesticides and chemicals in the production of food as causes for cancer; humans as cause for extinction of animals and plants; cars and air pollution; expected increase of illnesses in large cities as result of air pollution.Classification of nuclear power plants, air pollution from industrial waste gases, pesticides and chemicals in agriculture, water pollution and warming of the atmosphere through the greenhouse effect as dangerous for the environment on the one hand as well as for the respondent and his family on the other; preference for regulation of environmental problems by the government, the population or the economy; personal participation in recycling; purchase of untreated fruits; frequency of doing without meat for moral and environmental reasons; frequency of not using the car for environmental reasons. Membership in an environmental protection organisation; personal environmental political activities through participation in signature lists, donations as well as participation in demonstrations; social origins; employment in the public sector; time worked each week; span of control; company size; personal unemployment in the last few years and length of this unemployment; religiousness; self-assessment of social class; union membership; party preference; party inclination; housing situation and residential status; in some countries: ethnic affiliation of respondent.

Run096 –StepAIC Forward—Countries with nonzero response—no DRCcongo (121) Power response and square root predictor transformations (USDF).

This data set represents the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers, in the conterminous United States.

The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water.

Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation.

One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking.

This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006).

For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in shallow ground water (GWAVA-S) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-s_ffer 2 confined manure (kg/hectare) gwava-s_conf 3 orchards/vineyards (percent) gwava-s_orvi 4 population density (people/km2) gwava-s_popd 5 cropland/pasture/fallow (percent) gwava-s_crpa

Transport to Aquifer Factors 6 water input (km2/cm) gwava-s_wtin 7 carbonate rocks (yes/no) gwava-s_crox 8 basalt and volcanic rocks (yes/no) gwava-s_vrox 9 drainage ditch (km2) gwava-s_ddit 10 slope (percent x 1000) gwava-s_slop 11 glacial till (yes/no) gwava-s_gtil 12 clay sediment (percent x 1000) gwava-s_clay

Attenuation Factors 13 fresh surface water withdrawal gwava-s_swus for irrigation (megaliters/day) 14 irrigation tailwater recovery (km2) gwava-s_twre 15 histosol soil type (percent) gwava-s_hist 16 wetlands (percent) gwava-s_wetl

"Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare.

"Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare.

"Orchards/vineyards" is the percent of orchards/vineyards land cover classification.

"Population density" is 1990 block group population density, in people per square kilometer.

"Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications.

"Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter.

"Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks.

"Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks.

"Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers.

"Slope" is the soil surface slope, in percent times 1000.

"Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains.

"Clay sediment" is the amount of clay sediment in the soil, in percent times 1000.

"Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day.

"Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers.

"Histosol soil type" is the amount of histosols soil taxonomic order, in percent.

"Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications.

Reference cited:

Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network The following selected graphics are included in this data release in .pdf format. Also included in this data release are web pages accessible for people with disabilities provided in compressed .zip format. The web pages present the same information as the .pdf files: Annual and seasonal discharge trends.pdf -- Graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Annual_and_seasonal_discharge_trends_htm.zip -- Compressed web page presenting graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of simulated quarterly sampling frequency trends.pdf -- Graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_simulated_quarterly_sampling_frequency_trends_htm.zip -- Compressed web page presenting graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of median parameter values.pdf -- Graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_median_parameter_values_htm.zip -- Compressed web page presenting graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus time.pdf -- Scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_time_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus discharge.pdf -- Scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_discharge_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by season.pdf -- Seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_season_htm.zip -- Compressed web page presenting seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of sampled discharge compared with mean daily discharge.pdf -- Boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_sampled_discharge_compared_with_mean_daily_discharge_htm.zip -- Compressed web page presenting boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by month.pdf -- Monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_month_htm.zip -- Compressed web page presenting monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report.

This data set represents the percent of woody wetlands and emergent herbaceous wetlands land cover in the conterminous United States.

The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water.

Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation.

One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking.

This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006).

For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in shallow ground water (GWAVA-S) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-s_ffer 2 confined manure (kg/hectare) gwava-s_conf 3 orchards/vineyards (percent) gwava-s_orvi 4 population density (people/km2) gwava-s_popd 5 cropland/pasture/fallow (percent) gwava-s_crpa

Transport to Aquifer Factors 6 water input (km2/cm) gwava-s_wtin 7 carbonate rocks (yes/no) gwava-s_crox 8 basalt and volcanic rocks (yes/no) gwava-s_vrox 9 drainage ditch (km2) gwava-s_ddit 10 slope (percent x 1000) gwava-s_slop 11 glacial till (yes/no) gwava-s_gtil 12 clay sediment (percent x 1000) gwava-s_clay

Attenuation Factors 13 fresh surface water withdrawal gwava-s_swus for irrigation (megaliters/day) 14 irrigation tailwater recovery (km2) gwava-s_twre 15 histosol soil type (percent) gwava-s_hist 16 wetlands (percent) gwava-s_wetl

"Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare.

"Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare.

"Orchards/vineyards" is the percent of orchards/vineyards land cover classification.

"Population density" is 1990 block group population density, in people per square kilometer.

"Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications.

"Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter.

"Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks.

"Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks.

"Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers.

"Slope" is the soil surface slope, in percent times 1000.

"Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains.

"Clay sediment" is the amount of clay sediment in the soil, in percent times 1000.

"Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day.

"Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers.

"Histosol soil type" is the amount of histosols soil taxonomic order, in percent.

"Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications.

Reference cited:

Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers, in the conterminous United States.

The data set was used as an input data layer for a national model to predict nitrate concentration in ground water used for drinking.

Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation.

One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking.

This data set is one of 14 data sets (1 output data set and 13 input data sets) associated with the GWAVA-DW model. Full details of the model development are in Nolan and Hitt (2006).

For inputs to the model, spatial attributes representing 13 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in ground water used for drinking (GWAVA-DW) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-dw_ffer 2 confined manure (kg/hectare) gwava-dw_conf 3 orchards/vineyards (percent) gwava-dw_orvi 4 population density (people/km2) gwava-dw_popd

Transport to Aquifer Factors 5 water input (km2/cm) gwava-dw_wtin 6 glacial till (yes/no) gwava-dw_gtil 7 semiconsolidated sand aquifers gwava-dw_semc (yes/no) 8 sandstone and carbonate rocks gwava-dw_sscb (yes/no) 9 drainage ditch (km2) gwava-dw_ddit 10 Hortonian overland flow gwava-dw_hor (percent of streamflow)

Attenuation Factors 11 fresh surface water withdrawal gwava-dw_swus for irrigation (megaliters/day) 12 irrigation tailwater recovery (km2) gwava-dw_twre 13 Dunne overland flow gwava-dw_dun (percent of streamflow) 14 well depth (meters) -

"Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare.

"Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare.

"Orchards/vineyards" is the percent of orchards/vineyards land cover classification.

"Population density" is 1990 block group population density, in people per square kilometer.

"Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter.

"Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains.

"Semiconsolidated sand aquifers" is the presence or absence of semiconsolidated sand aquifers.

"Sandstone and carbonate rocks" is the presence or absence of sandstone and carbonate rock aquifers.

"Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers.

"Hortonian overland flow" is infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow.

"Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day.

"Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers.

"Dunne overland flow" is saturation overland flow estimated by TOPMODEL, in percent of streamflow.

"Well depth" is the depth of the well, in meters. Well depth was not compiled as a spatial data set. Well depth equals 50 meters for the model simulation being presented.

Reference cited:

Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents "water input," the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter, in the conterminous United States.

The data set was used as an input data layer for a national model to predict nitrate concentration in ground water used for drinking.

Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation.

One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking.

This data set is one of 14 data sets (1 output data set and 13 input data sets) associated with the GWAVA-DW model. Full details of the model development are in Nolan and Hitt (2006).

For inputs to the model, spatial attributes representing 13 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in ground water used for drinking (GWAVA-DW) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-dw_ffer 2 confined manure (kg/hectare) gwava-dw_conf 3 orchards/vineyards (percent) gwava-dw_orvi 4 population density (people/km2) gwava-dw_popd

Transport to Aquifer Factors 5 water input (km2/cm) gwava-dw_wtin 6 glacial till (yes/no) gwava-dw_gtil 7 semiconsolidated sand aquifers gwava-dw_semc (yes/no) 8 sandstone and carbonate rocks gwava-dw_sscb (yes/no) 9 drainage ditch (km2) gwava-dw_ddit 10 Hortonian overland flow gwava-dw_hor (percent of streamflow)

Attenuation Factors 11 fresh surface water withdrawal gwava-dw_swus for irrigation (megaliters/day) 12 irrigation tailwater recovery (km2) gwava-dw_twre 13 Dunne overland flow gwava-dw_dun (percent of streamflow) 14 well depth (meters) -

"Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare.

"Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare.

"Orchards/vineyards" is the percent of orchards/vineyards land cover classification.

"Population density" is 1990 block group population density, in people per square kilometer.

"Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter.

"Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains.

"Semiconsolidated sand aquifers" is the presence or absence of semiconsolidated sand aquifers.

"Sandstone and carbonate rocks" is the presence or absence of sandstone and carbonate rock aquifers.

"Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers.

"Hortonian overland flow" is infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow.

"Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day.

"Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers.

"Dunne overland flow" is saturation overland flow estimated by TOPMODEL, in percent of streamflow.

"Well depth" is the depth of the well, in meters. Well depth was not compiled as a spatial data set. Well depth equals 50 meters for the model simulation being presented.

Reference cited:

Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

In rural Alberta, 90 per cent of people use private well water supplies for domestic use (e.g., drinking, cooking, bathing). Domestic well water systems are not regulated by the provincial or federal governments. The Government of Alberta along with Alberta Health Services (AHS) provides water chemistry testing of private well water and information and advice on safe water for domestic purposes; however, it is the responsibility of well owners to ensure the quality and safety of their water supply. Water quality may be impacted by contamination from natural sources or human activities and cause noticeable aesthetic issues or potential health concerns. Water samples are collected and submitted by well owners through local AHS sites for analysis of routine chemistry and trace element parameters. Routine chemistry testing focuses on the suitability of the water for drinking and household use with two health-related parameters. For trace elements, testing used to be conducted only when there were health concerns or when the water was suspected to contain chemicals of concern (2001 to Sep 2018). Currently, trace element testing is completed for all samples submitted for routine analysis (if the sample volume is sufficient). The Alberta Centre for Toxicology has conducted the analyses of raw domestic well water samples since March 2004. From 2001 to Mar 2004, testing was conducted by Enviro-Test Laboratories. Limited information is available regarding the analytical methods and detection limits for this lab; therefore, users are advised to exercise caution when using the 2001 to Mar 2004 data. These datasets contain the routine chemistry results for raw well water samples collected from 2001 to 2018. Corrections may be made to the dataset over time (e.g., removal of samples deemed to be treated); users should regularly check for updates and download the most current versions. For additional information, refer to the publications on the “Related” tab of this webpage.

The Bushy Park Reservoir is a relatively shallow impoundment in a semi-tropical climate and is the principal water supply for the 400,000 people of the City of Charleston and the surrounding areas including the industries in the Bushy Park Industrial Complex. Although there is an adequate supply of freshwater in the reservoir, there are taste-and-odor water-quality concerns. The U.S. Geological Survey (USGS) worked in cooperation with the Charleston Water System to study the hydrology and water-quality of the Bushy Park Reservoir to identify factors affecting water-quality conditions. This data release is for the water-quality data collected with an autonomous underwater vehicle (AUV) for a water-quality study of Bushy Park Reservoir. Sixteen water-quality surveys were collected over the period of September 2013 to May 2015. Data includes water temperature, specific conductance, pH, dissolved oxygen, turbidity, chlorophyll, and blue-green algae. The typical water-quality survey lasted around four hours, collected data every second (or 3 feet) for the seven parameters for a total 100,000 data points. This Data Release is for USGS Scientific Investigations Report entitled Characterization of the Water Quality of Bushy Park Reservoir, South Carolina 2013-2015 (in press).

This dataset is updated more frequently and can be visualized on NCWQR's data portal.

If you have any questions, please contact Dr. Laura Johnson or Dr. Nathan Manning.

The National Center for Water Quality Research (NCWQR) is a research laboratory at Heidelberg University in Tiffin, Ohio, USA. Our primary research program is the Heidelberg Tributary Loading Program (HTLP), where we currently monitor water quality at 22 river locations throughout Ohio and Michigan, effectively covering ~half of the land area of Ohio. The goal of the program is to accurately measure the total amounts (loads) of pollutants exported from watersheds by rivers and streams. Thus these data are used to assess different sources (nonpoint vs point), forms, and timing of pollutant export from watersheds. The HTLP officially began with high-frequency monitoring for sediment and nutrients from the Sandusky and Maumee rivers in 1974, and has continually expanded since then.

Each station where samples are collected for water quality is paired with a US Geological Survey gage for quantifying discharge (http://waterdata.usgs.gov/usa/nwis/rt). Our stations cover a wide range of watershed areas upstream of the sampling point from 11.0 km2 for the unnamed tributary to Lost Creek to 19,215 km2 for the Muskingum River. These rivers also drain a variety of land uses, though a majority of the stations drain over 50% row-crop agriculture.

At most sampling stations, submersible pumps located on the stream bottom continuously pump water into sampling wells inside heated buildings where automatic samplers collect discrete samples (4 unrefrigerated samples/d at 6-h intervals, 1974–1987; 3 refrigerated samples/d at 8-h intervals, 1988-current). At weekly intervals the samples are returned to the NCWQR laboratories for analysis. When samples either have high turbidity from suspended solids or are collected during high flow conditions, all samples for each day are analyzed. As stream flows and/or turbidity decreases, analysis frequency shifts to one sample per day. At the River Raisin and Muskingum River, a cooperator collects a grab sample from a bridge at or near the USGS station approximately daily and all samples are analyzed. Each sample bottle contains sufficient volume to support analyses of total phosphorus (TP), dissolved reactive phosphorus (DRP), suspended solids (SS), total Kjeldahl nitrogen (TKN), ammonium-N (NH4), nitrate-N and nitrite-N (NO2+3), chloride, fluoride, and sulfate. Nitrate and nitrite are commonly added together when presented; henceforth we refer to the sum as nitrate.

Upon return to the laboratory, all water samples are analyzed within 72h for the nutrients listed below using standard EPA methods. For dissolved nutrients, samples are filtered through a 0.45 um membrane filter prior to analysis. We currently use a Seal AutoAnalyzer 3 for DRP, silica, NH4, TP, and TKN colorimetry, and a DIONEX Ion Chromatograph with AG18 and AS18 columns for anions. Prior to 2014, we used a Seal TRAACs for all colorimetry.

2017 Ohio EPA Project Study Plan and Quality Assurance Plan

Project Study Plan

Quality Assurance Plan

Data quality control and data screening

The data provided in the River Data files have all been screened by NCWQR staff. The purpose of the screening is to remove outliers that staff deem likely to reflect sampling or analytical errors rather than outliers that reflect the real variability in stream chemistry. Often, in the screening process, the causes of the outlier values can be determined and appropriate corrective actions taken. These may involve correction of sample concentrations or deletion of those data points.

This micro-site contains data for approximately 126,000 water samples collected beginning in 1974. We cannot guarantee that each data point is free from sampling bias/error, analytical errors, or transcription errors. However, since its beginnings, the NCWQR has operated a substantial internal quality control program and has participated in numerous external quality control reviews and sample exchange programs. These programs have consistently demonstrated that data produced by the NCWQR is of high quality.

A note on detection limits and zero and negative concentrations

It is routine practice in analytical chemistry to determine method detection limits and/or limits of quantitation, below which analytical results are considered less reliable or unreliable. This is something that we also do as part of our standard procedures. Many laboratories, especially those associated with agencies such as the U.S. EPA, do not report individual values that are less than the detection limit, even if the analytical equipment returns such values. This is in part because as individual measurements they may not be considered valid under litigation.

The measured concentration consists of the true but unknown concentration plus random instrument error, which is usually small compared to the range of expected environmental values. In a sample for which the true concentration is very small, perhaps even essentially zero, it is possible to obtain an analytical result of 0 or even a small negative concentration. Results of this sort are often “censored” and replaced with the statement “<DL” or “<2”, where DL is the detection limit, in this case 2. Some agencies now follow the unfortunate convention of writing “-2” rather than “<2”.

Censoring these low values creates a number of problems for data analysis. How do you take an average? If you leave out these numbers, you get a biased result because you did not toss out any other (higher) values. Even if you replace negative concentrations with 0, a bias ensues, because you’ve chopped off some portion of the lower end of the distribution of random instrument error.

For these reasons, we do not censor our data. Values of -9 and -1 are used as missing value codes, but all other negative and zero concentrations are actual, valid results. Negative concentrations make no physical sense, but they make analytical and statistical sense. Users should be aware of this, and if necessary make their own decisions about how to use these values. Particularly if log transformations are to be used, some decision on the part of the user will be required.

Analyte Detection Limits

https://ncwqr.files.wordpress.com/2021/12/mdl-june-2019-epa-methods.jpg?w=1024

For more information, please visit https://ncwqr.org/

This layer shows only attaining segments of the Integrated List. The Streams Integrated List represents stream assessments in an integrated format for the Clean Water Act Section 305(b) reporting and Section 303(d) listing. Streams are bodies of flowing surface water that collectively form a network that drains a catchment or basin. PA DEP protects 4 stream water uses: aquatic life, fish consumption, potable water supply, and recreation. The 305(b) layers represents stream segments that have been evaluated for attainment of those uses. If a stream segment is not attaining any one of its 4 uses, it is considered impaired. · Aquatic Life use attainment - The integrity reflected in any component of the biological community. (i.e. fish or fish food organisms) · Fish Consumption use attainment - The risk posed to people by the consumption of aquatic organisms (ex. fish, shellfish, frogs, turtles, crayfish, etc.) · Recreational use attainment - The risk associated with human recreation activities in or on a water body. (i.e. exposure to bacteria and other disease causing organisms through water contact recreation like swimming or water skiing) · Potable Water Supply use attainment - The risk posed to people by the ingestion of drinking water. Segments that have appeared on an approved Category 5 Integrated Listing are the entries labeled as approved. Integrated Lists are submitted for approval every other year. Segments entered subsequent to the latest approved Category 5 listing are labeled tentative. After appearing on an approved listing, the tentative entries move to approved. The Stream Integrated List is provided as two separate layers determined if the stream is attaining or not attaining its designated uses. DEP Streams Integrated List layer is maintained by the PADEP Office of Water Management, Bureau of Water Supply & Wastewater Management, Water Quality Assessment and Standards Division. The layer is based on the High Resolution National Hydrography Dataset (NHD). Additional update information is provided by Bureau of Watershed Management, Water Use Planning Division.

The Chicago Park District maintains sensors in the water at beaches along Chicago's Lake Michigan lakefront. These sensors generally capture the indicated measurements hourly while the sensors are in operation during the summer. During other seasons and at some other times, information from the sensors may not be available. See https://data.cityofchicago.org/d/k7hf-8y75 for a dataset with land-based weather measurements at selected beaches. The sensor locations are listed at https://data.cityofchicago.org/d/g3ip-u8rb.

Please note that sensor locations change with the Park District’s operational needs, primarily related to water quality. For more information on beach operations, please see https://www.chicagoparkdistrict.com/parks-facilities/beaches.