This dataset contains information on mortality rates per 100,000 people in Cambodia related to unsafe sanitation and unsafe water and unavailability of handwashing facilities. This data shows the overall mortality rate in Cambodia from 1990 to 2019.

This data set represents saturation overland flow estimated by TOPMODEL, in percent of streamflow, 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.

Health-based drinking water violations impact millions of Americans each year. This study examines the relationship between 13 measures of drinking water quality, related to health-based violations of the Safe Drinking Water Act, and three self-reported health outcomes (general, physical, and mental health). Analyzing cross-sectional data from 3,100 counties in the United States using regression analysis, we found statistically significant relationships between health-based violations and all self-reported health outcomes. Specifically, counties with more health-based violations reported a higher percentage of people with fair or poor health, and more physically and mentally unhealthy days. Our findings indicate that a single health-based violation in an average county incurs yearly medical costs of approximately $3.48 million for physical health and $4.85 million for mental health. These results highlight the need for policymakers and health professionals to prioritize interventions that address these violations, particularly in vulnerable communities, to mitigate their long-term health impacts.

1,4-Dioxane is a persistent and mobile organic chemical that has been found by the United States Environmental Protection Agency (USEPA) to be an unreasonable risk to human health in some occupational contexts. 1,4-Dioxane is released into the environment as industrial waste and occurs in some personal-care products as an unintended byproduct. However, limited exposure assessments have been conducted outside of an occupational context. In this study, the USEPA simulation modeling tool, Stochastic Human Exposure and Dose Simulator-High Throughput (SHEDS-HT), was adapted to estimate the exposure and chemical mass released down the drain (DTD) from drinking water consumption and product use. 1,4-Dioxane concentrations measured in drinking water and consumer products were used by SHEDS-HT to evaluate and compare the contributions of these sources to exposure and mass released DTD. Modeling results showed that compared to people whose daily per capita exposure came from only products (2.29 × 10–7 to 2.92 × 10–7 mg/kg/day), people exposed to both contaminated water and product use had higher per capita median exposures (1.90 × 10–6 to 4.27 × 10–6 mg/kg/day), with exposure mass primarily attributable to water consumption (75–91%). Last, we demonstrate through simulation that while a potential regulatory action could broadly reduce DTD release, the proportional reduction in exposure would be most significant for people with no or low water contamination.

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.

This data set represents 1990 block group population density, in people per square kilometer, 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 (2 ...

This data set represents the area of National Resources Inventory surface drainage, field ditch 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.

This data set represents the percent of orchards/vineyards 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.

Access to clean drinking water is a cornerstone of community resilience and sustainability. Contaminated drinking water can expose people to a variety of pollutants and pathogens increasing the burden of disease within the community. Here, we focus on community sustainability by assessing multiple stressors on groundwater quality in the Alabama Gulf Coast. Geochemistry, land use, and climate change can all act as stressors on groundwater quality. Understanding groundwater quality in domestic wells can help to answer questions about mechanistic stressors on groundwater contamination like, (i) how does the underlying aquifer geology control variations in groundwater chemistry?, (ii) how does proximity to agricultural land affect domestic well water quality?, (iii) what geochemical and geophysical variables predict groundwater contamination?, and (iv) what relationships exist between precipitation, flooding, and other extreme events and contaminant concentrations in well water? Together, these efforts will enhance community sustainability in the AL Gulf Coast. Purpose This study examines private well water quality in southern Alabama. This region is part of the Coastal Lowlands Aquifer system, which supplies private well water to approximately 1.2 million people, with nearly 70,000 people relying on private well water in southern Alabama. The two coastal counties in southern Alabama were selected for this study as part of the Alabama Center of Excellence focused on coastal community sustainability. Sampling campaigns were conducted in September-October 2022, April-May 2023, September 2023, and May 2024 as participants joined the program. A total of 43 wells sampled, and some wells were tested more than once in the study so that a total of 126 well water samples were collected. This study underscores the need to understand both geologic and anthropogenic factors that relate to well water quality at the regional and local scale. DOI: 10.57778/yrre-rm02 Suggested Citation

This data set represents the amount of fresh surface water withdrawal for irrigation, in megaliters per day, 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 the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare, 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 the presence or absence of basalt and volcanic rocks 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.

Goal 3Ensure healthy lives and promote well-being for all at all agesTarget 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live birthsIndicator 3.1.1: Maternal mortality ratioSH_STA_MORT: Maternal mortality ratioIndicator 3.1.2: Proportion of births attended by skilled health personnelSH_STA_BRTC: Proportion of births attended by skilled health personnel (%)Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsIndicator 3.2.1: Under-5 mortality rateSH_DYN_IMRTN: Infant deaths (number)SH_DYN_MORT: Under-five mortality rate, by sex (deaths per 1,000 live births)SH_DYN_IMRT: Infant mortality rate (deaths per 1,000 live births)SH_DYN_MORTN: Under-five deaths (number)Indicator 3.2.2: Neonatal mortality rateSH_DYN_NMRTN: Neonatal deaths (number)SH_DYN_NMRT: Neonatal mortality rate (deaths per 1,000 live births)Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseasesIndicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populationsSH_HIV_INCD: Number of new HIV infections per 1,000 uninfected population, by sex and age (per 1,000 uninfected population)Indicator 3.3.2: Tuberculosis incidence per 100,000 populationSH_TBS_INCD: Tuberculosis incidence (per 100,000 population)Indicator 3.3.3: Malaria incidence per 1,000 populationSH_STA_MALR: Malaria incidence per 1,000 population at risk (per 1,000 population)Indicator 3.3.4: Hepatitis B incidence per 100,000 populationSH_HAP_HBSAG: Prevalence of hepatitis B surface antigen (HBsAg) (%)Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseasesSH_TRP_INTVN: Number of people requiring interventions against neglected tropical diseases (number)Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingIndicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseSH_DTH_NCOM: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)SH_DTH_NCD: Number of deaths attributed to non-communicable diseases, by type of disease and sex (number)Indicator 3.4.2: Suicide mortality rateSH_STA_SCIDE: Suicide mortality rate, by sex (deaths per 100,000 population)SH_STA_SCIDEN: Number of deaths attributed to suicide, by sex (number)Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcoholIndicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disordersSH_SUD_ALCOL: Alcohol use disorders, 12-month prevalence (%)SH_SUD_TREAT: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%)Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcoholSH_ALC_CONSPT: Alcohol consumption per capita (aged 15 years and older) within a calendar year (litres of pure alcohol)Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidentsIndicator 3.6.1: Death rate due to road traffic injuriesSH_STA_TRAF: Death rate due to road traffic injuries, by sex (per 100,000 population)Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmesIndicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methodsSH_FPL_MTMM: Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years)Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age groupSP_DYN_ADKL: Adolescent birth rate (per 1,000 women aged 15-19 years)Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for allIndicator 3.8.1: Coverage of essential health servicesSH_ACS_UNHC: Universal health coverage (UHC) service coverage indexIndicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or incomeSH_XPD_EARN25: Proportion of population with large household expenditures on health (greater than 25%) as a share of total household expenditure or income (%)SH_XPD_EARN10: Proportion of population with large household expenditures on health (greater than 10%) as a share of total household expenditure or income (%)Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contaminationIndicator 3.9.1: Mortality rate attributed to household and ambient air pollutionSH_HAP_ASMORT: Age-standardized mortality rate attributed to household air pollution (deaths per 100,000 population)SH_STA_AIRP: Crude death rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_STA_ASAIRP: Age-standardized mortality rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_AAP_MORT: Crude death rate attributed to ambient air pollution (deaths per 100,000 population)SH_AAP_ASMORT: Age-standardized mortality rate attributed to ambient air pollution (deaths per 100,000 population)SH_HAP_MORT: Crude death rate attributed to household air pollution (deaths per 100,000 population)Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)SH_STA_WASH: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (deaths per 100,000 population)Indicator 3.9.3: Mortality rate attributed to unintentional poisoningSH_STA_POISN: Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population)Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriateIndicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and olderSH_PRV_SMOK: Age-standardized prevalence of current tobacco use among persons aged 15 years and older, by sex (%)Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for allIndicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programmeSH_ACS_DTP3: Proportion of the target population with access to 3 doses of diphtheria-tetanus-pertussis (DTP3) (%)SH_ACS_MCV2: Proportion of the target population with access to measles-containing-vaccine second-dose (MCV2) (%)SH_ACS_PCV3: Proportion of the target population with access to pneumococcal conjugate 3rd dose (PCV3) (%)SH_ACS_HPV: Proportion of the target population with access to affordable medicines and vaccines on a sustainable basis, human papillomavirus (HPV) (%)Indicator 3.b.2: Total net official development assistance to medical research and basic health sectorsDC_TOF_HLTHNT: Total official development assistance to medical research and basic heath sectors, net disbursement, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_HLTHL: Total official development assistance to medical research and basic heath sectors, gross disbursement, by recipient countries (millions of constant 2018 United States dollars)Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basisSH_HLF_EMED: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis (%)Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing StatesIndicator 3.c.1: Health worker density and distributionSH_MED_DEN: Health worker density, by type of occupation (per 10,000 population)SH_MED_HWRKDIS: Health worker distribution, by sex and type of occupation (%)Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risksIndicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparednessSH_IHR_CAPS: International Health Regulations (IHR) capacity, by type of IHR capacity (%)Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organismsiSH_BLD_MRSA: Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose

ObjectiveA focus on bacterial contamination has limited many studies of water service delivery in slums, with diarrheal illness being the presumed outcome of interest. We conducted a mixed methods study in a slum of 12,000 people in Mumbai, India to measure deficiencies in a broader array of water service delivery indicators and their adverse life impacts on the slum’s residents.MethodsSix focus group discussions and 40 individual qualitative interviews were conducted using purposeful sampling. Quantitative data on water indicators—quantity, access, price, reliability, and equity—were collected via a structured survey of 521 households selected using population-based random sampling.ResultsIn addition to negatively affecting health, the qualitative findings reveal that water service delivery failures have a constellation of other adverse life impacts—on household economy, employment, education, quality of life, social cohesion, and people’s sense of political inclusion. In a multivariate logistic regression analysis, price of water is the factor most strongly associated with use of inadequate water quantity (≤20 liters per capita per day). Water service delivery failures and their adverse impacts vary based on whether households fetch water or have informal water vendors deliver it to their homes.ConclusionsDeficiencies in water service delivery are associated with many non-health-related adverse impacts on slum households. Failure to evaluate non-health outcomes may underestimate the deprivation resulting from inadequate water service delivery. Based on these findings, we outline a multidimensional definition of household “water poverty” that encourages policymakers and researchers to look beyond evaluation of water quality and health. Use of multidimensional water metrics by governments, slum communities, and researchers may help to ensure that water supplies are designed to advance a broad array of health, economic, and social outcomes for the urban poor.

description: This data set represents infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow, 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.; abstract: This data set represents infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow, 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.

Intermittently operated distribution systems serve over one billion people and may be impacted by the intrusion of contaminated waters carrying disinfection byproduct (DBP) precursors. The impact of intrusion on the formation of 19 DBPs was evaluated in an intermittent water system supplied by deep aquifers in Dhaka, Bangladesh. Untreated piped water samples were collected from residential taps and chlorinated under controlled conditions. Chloride, dissolved organic carbon, and the artificial sweetener sucralose were measured as indicators of intrusion. Most piped water samples had low concentrations of DBPs and indicators; however, a subset had higher levels of DBPs and indicators, suggesting the intrusion of contaminated water into the distribution system, particularly during the rainy season. Piped water samples with evidence of intrusion typically formed higher concentrations of haloacetaldehydes and haloacetonitriles when chlorinated, which greatly increased the calculated cytotoxicity. DBP formation and calculated cytotoxicity in piped water samples impacted by intrusion were comparable to those in piped water samples supplied by horizontal and vertical recharge-impacted groundwaters, yet lower than piped surface waters from other regions of Dhaka. The results demonstrated that intrusion can increase DBP formation in an unpredictable fashion, highlighting the need to sample from many locations in intermittent water systems to accurately evaluate DBP exposure risk.

This data set represents the percent of orchards/vineyards 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 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.

Urban cities generate considerable potential for ecological disturbance leading to environmental degradation (Programme 2: Anthropogenic Disturbances of Coastal Habitats). In particular, there are problems for organisms on hard and soft substrata because of building jetties, seawalls, pilings, etc. Human disturbances include foraging for bait and food and disposal of wastes. The research to understand ecological changes due to these disturbances is time-consuming and long-term, particularly when experimental analyses of processes during disturbances are planned. Predicting, measuring and interpreting impacts are hamstrung by poor sampling and analysis - often dictated by inadequate statutory requirements for Environmental Impact Statements. One major problem is that natural ecological assemblages of species vary enormously from time to time and place to place. Thus, even when undisturbed by people, ecological patterns are very different from place to place and change rapidly from time to time. Anywhere that people live and work creates wastes, by-products and contamination. We dispose of sewage, industrial, agricultural and domestic chemicals, heat and freshwater in numerous outfalls. These operate continuously or sporadically (such as storm-water drains) to create a mosaic of short and long-term, acute and chronic contamination of coastal waters. The Centre for Research on Ecological Impacts of Coastal Cities (EICC) has generated many scientific papers and theses from research projects on the effects of different forms of contamination. The link to the URL provided in this record provides a link to this research

A survey was carried out in 2016 among households in Amsterdam who suffered from rainfall damage in the past years. This database contains the survey response data. A paper that discusses the background, survey design and first results is published in Natural Hazards and Earth System Sciences, and also available in this dataset.Take note that additional conditions of use apply, which can be found in the file 'Creative Commons Attribution-NonCommercial 4.0 International CC BY-NC 4.pdf'.Abstract.Flooding is assessed as the most important natural hazard in Europe, causing thousands of deaths, affecting millions of people and accounting for large economic losses in the past decade. Little is known about the damage processes associated with extreme rainfall in cities, due to a lack of accurate, comparable and consistent damage data. The objective of this study is to investigate the impacts of extreme rainfall on residential buildings and how affected households coped with these impacts in terms of precautionary and emergency actions. Analyses are based on a unique dataset of damage characteristics and a wide range of potential damage explaining variables at the household level, collected through computer-aided telephone interviews (CATI) and an online survey. Exploratory data analyses based on a total of 859 completed questionnaires in the cities of Münster (Germany) and Amsterdam (the Netherlands) revealed that the uptake of emergency measures is related to characteristics of the hazardous event. In case of high water levels, more efforts are made to reduce damage, while emergency response that aims to prevent damage is less likely to be effective. The difference in magnitude of the events in Münster and Amsterdam in terms of rainfall intensity and water depth, is probably also the most important cause for the differences between the cities in terms of the suffered financial losses. Factors that significantly contributed to damage in at least one of the case studies are water contamination, the presence of a basement in the building and people’s awareness of the upcoming event. Moreover, this study confirms conclusions by previous studies that people’s experience with damaging events positively correlates with precautionary behaviour. For improving future damage data acquisition, we recommend to include cell-phones in a CATI survey to avoid biased sampling towards certain age groups.

This data set represents the presence or absence of sandstone and carbonate rock aquifers 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.