Data set for Region 4 based on block group data from the 2000 census. This data set extends beyond Region 4 boundaries, in case block groups within buffer distance are needed for analysis. The block groups are given the label "Low Income", "Minority", or "Low Income and Minority." The designation is assigned based on the following calculation: if the individual block group has a greater proportion of Low Income Residents/Total Residents than the STATE where the block group is, then the block group received the "Low Income"designation. Therefore, each state has a different proportion for assigning the EJ designation. A block group that has a greater proportion of Low Income and Minority residents than the state recieves only the designation "Low Income and Minority."Link: https://ky.box.com/v/epa-env-justice-data-2000

Data for United States Environmental Protection Agency (EPA) Risk Screening Environmental Indicators (RSEI) model, with US decennial Census crosswalks 1990 - 2020. Original data were downloaded 25 July 2025 from https://gaftp.epa.gov/RSEI/Census_XWalks/ . Data dictionary included was copied and pasted from the data dictionary website (https://www.epa.gov/rsei/rsei-data-dictionary-census-crosswalks) into a Microsoft Word document, and is also included here. From the "readme", also included as a file in this archive: There is one crosswalk for each area and year for decennial Census years 1990, 2000, 2010). Note that the Northern Mariana Islands are in the Guam file and the Virgin Islands are in the Puerto Rico file. There are no crosswalks for Puerto Rico, the Virgin Islands, Mariana Islands, Guam, or American Samoa for 1990. For these areas, RSEI uses 2000 block boundaries and scales each cell's population by the overall ratio of 1990/2000 population for each area. For Census year 2020, there are additional crosswalks from grid to block group, census tract, and ZIP code. Field descriptions can be found in the RSEI Data Dictionary. https://www.epa.gov/rsei/rsei-data-dictionary-census-crosswalks

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). 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: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

This map service displays all air-related layers used in the USEPA Community/Tribal-Focused Exposure and Risk Screening Tool (C/T-FERST) mapping application (http://cfpub.epa.gov/cferst/index.cfm). The following data sources (and layers) are contained in this service: USEPA's 2005 National-Scale Air Toxic Assessment (NATA) data. Data are shown at the census tract level (2000 census tract boundaries, US Census Bureau) for Cumulative Cancer and Non-Cancer risks (Neurological and Respiratory) from 139 air toxics. In addition, individual pollutant estimates of Ambient Concentration, Exposure Concentration, Cancer, and Non-Cancer risks (Neurological and Respiratory) are provided for: Acetaldehyde, Acrolein, Arsenic, Benzene, 1,3-Butadiene, Chromium, Diesel PM, Formaldehyde, Lead, Naphthalene, and Polycyclic Aromatic Hydrocarbon (PAH). The original Access tables were downloaded from USEPA's Office of Air and Radiation (OAR) http://www.epa.gov/ttn/atw/nata2005/tables.html. The data classification (defined interval) for this map service was developed for USEPA's Office of Research and Development's (ORD) Community-Focused Exposure and Risk Screening Tool (C-FERST) per guidance provided by OAR. The 2005 NATA provides information on 177 of the 187 Clean Air Act air toxics (http://www.epa.gov/ttn/atw/nata2005/05pdf/2005polls.pdf) plus diesel particulate matter (diesel PM was assessed for non-cancer only). For additional information about NATA, go to http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf or contact Ted Palma, USEPA (palma.ted@epa.gov). NATA data disclaimer: USEPA strongly cautions that these modeling results are most meaningful when viewed at the state or national level, and should not be used to draw conclusions about local exposures or risks (e.g., to compare local areas, to identify the exact location of "hot spots", or to revise or design emission reduction programs). Substantial uncertainties with the input data for these models may cause the results to misrepresent actual risks, especially at the census tract level. However, we believe the census tract data and maps can provide a useful approximation of geographic patterns of variation in risk within counties. For example, a cluster of census tracts with higher estimated risks may suggest the existence of a "hot spot," although the specific tracts affected will be uncertain. More refined assessments based on additional data and analysis would be needed to better characterize such risks at the tract level. (http://www.epa.gov/ttn/atw/nata2005/countyxls/cancer_risk02_county_042009.xls). Note that these modeled estimates are derived from outdoor sources only; indoor sources are not included in these examples, but may be significant in some cases. The modeled exposure estimates are for a median individual in the geographic area shown. Note that in some cases the estimated relationship between human exposure and health effect may be calculated as a high end estimate, and thus may be more likely to overestimate than underestimate actual health effects for the median individual in the geographic area shown. Other limitations to consider when looking at the results are detailed on the EPA 2005 NATA website. For these reasons, the NATA maps included in C-FERST are provided for screening purposes only. See the 2005 National Air Toxic Assessment website for recommended usage and limitations on the estimated cancer and noncancer data provided above. USEPA's NonAttainment areas data. C-FERST displays Ozone for 8-hour Ozone based on the 1997 standard for reporting and Particulate Matter PM-2.5 based on the 2006 standard for reporting. These are areas of the country where air pollution levels consistently exceed the national ambient air quality standards. Details about the USEPA's NonAttainment data are available at http://www.epa.gov/airquality/greenbook/index.html. Center of Disease Control's (CDC) Environmental Public Health Tracking (EPHT) data. Averaged over three years (2004 - 2006). The USEPA's ORD calculated a three-year average (2004 - 2006) using the values for Ozone (number of days with the maximum 8-hour average above the National Ambient Air Quality Standards (NAAQS)) and PM 2.5 (annual ambient concentration). These data were extracted by the CDC from the USEPA's ambient air monitors and are displayed at the county level. USEPA received the Monitor and Modeled data from the CDC and calculated the three year average displayed in the web service. For more details about the CDC EPHT data, go to http://ephtracking.cdc.gov/showHome.action.

This dataset contains counts of polluting sites in each United States census tract and within a 0.5-mile buffer to capture spillover effects. Polluting sites are taken from the US Environmental Protection Agency’s (EPA) Toxics Release Inventory. These facilities are typically larger and involved in manufacturing, metal mining, electric power generation, chemical manufacturing, and hazardous waste treatment. A curated version of this data is available through ICPSR at http://dx.doi.org/10.3886/ICPSR38597.v1

The US Environmental Protection Agency's (EPA) National Health and Environmental Effects Research Laboratory (NHEERL) in the Environmental Public Health Division (EPHD) is currently engaged in research aimed at developing a measure that estimates overall environmental quality at the county level for the United States. This work is being conducted as an effort to learn more about how various environmental factors simultaneously contribute to health disparities in low-income and minority populations, and to better estimate the total environmental and social context to which humans are exposed. This dataset contains the finalized transformed variables chosen to represent the Air, Water, Land, Built, and Sociodemographic Domains of the total environment.Six criteria air pollutants and 81 hazardous air pollutants are included in this dataset. Data sources are the EPA's Air Quality system (https://www.epa.gov/ttn/airs/airsaqs/) and the National-scale Air Toxics Assessment (https://www.epa.gov/nata/). Variables are average pollutant concentrations or emissions for 2000-2005 at the county level for all counties in the United States. Data on water impairment, waste permits, beach closures, domestic water source, deposition for 9 pollutants, drought status, and 60 chemical contaminants. Data sources are the EPA's WATERS (Watershed Assessment, Tracking and Environmental ResultS) Database (https://www.epa.gov/waters/), the U.S. Geological Survey Estimates of Water Use in the U.S. for 2000 and 2005 (https://water.usgs.gov/watuse/), the National Atmospheric Deposition Program (http://nadp.sws.uiuc.edu/), the U.S. Drought Monitor Data (http://droughtmonitor.unl.edu/), and the EPA's National Contaminant Occurrence Database (https://water.epa.gov/scitech/datait/databases/drink/ncod/databases-index.cfm). Variables are calculated for the time period from 2000-2005 at the county level for all counties in the United States. Data represents traffic safety, public transportation, road type, the business environment and public housing. Data sources are the Dun and Bradstreet North American Industry Classification System (NAICS) codes; Topologically Integrated Geographic Encoding and Referencing (TIGER); Fatality Annual Reporting System (FARS); and Housing and Urban Development (HUD) data. This dataset contains the finalized variables chosen to represent the sociodemographic domain of the total environment. Data represents socioeconomic and crime conditions. Data sources are the United States Census and the Federal Bureau of Investigation Uniform Crime Reports. Variables are calculated for the time period from 2000-2005 at the county level for all counties in the United States.

As included in this EnviroAtlas dataset, the community domestic water use was calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking, hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also considered representative of local self-supplied water use. Specific to Durham, NC, the Division of Water Resources (DWR), part of the North Carolina Department of Natural Resources (NCDENR), has made local water supply plans centrally available online. All local governments are required to provide public water service. Community water systems with 1,000+ service connections or 3,000+ residents are required to prepare a local water supply plan. These plans include residential, also known as domestic, water usage. To account for variations due to weather, a ten-year average was calculated for Durham, Hillsborough, and the Orange Water and Sewer Authority (OWASA), which supplies southeast Orange County, including Chapel Hill and Carrboro. The ten-year average included available data between 2000 and 2010. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

R2GIS Combined county boundary data from TANA, Navteq and Census: TANA county boundaries.(static.R2GIS.TANA_BOUNDARY_COUNTY) for all of Region 2 except the Virgin Islands which were not found in the data set. TANA provided more detailed county coastlines. Navteq.County(static.R2GIS.NAVTEQ_BOUNDARY_2014_COUNTY) for the smaller surrounding islands of the Virgin Islands which had more detail than the CENSUS representations. Counties (CENSUS) VI. The CENSUS county boundaries were used only for the three main islands of the Virgin Islands which had finer detail than that provided by Navteq. The Dynamap(R)/2000 County Boundary file is a non-generalized polygon layer that represents all U.S. government-defined entities named County. A County is a type of governmental unit that is the primary legal subdivision of every U.S. state. In Louisiana, the County-equivalent entity is 'parish.' In Alaska, the statistically equivalent entities are the organized 'boroughs,' 'city and boroughs,' 'municipalities' and 'census areas.' The Dynamap(R)/2000 County Boundary file is a non-generalized polygon layer that represents all U.S. government-defined entities named County. A County is a type of governmental unit that is the primary legal subdivision of every U.S. state. In Louisiana, the County-equivalent entity is 'parish.' In Alaska, the statistically equivalent entities are the organized 'boroughs,' 'city and boroughs,' 'municipalities' and 'census areas.'

Dataset contains information on births in NC during the study period, linked with air pollutant concentrations during pregnancy periods, and index of neighborhood deprivation developed from US census 2000 and 2010 variables. 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: Birth data can be requested through the North Carolina Department of Health and Human Services website. Air pollution data is available through the EPA's RSIG gateway. Census data is available through the US Census website. Code will be provided on request to authors. Format: csv, SAS, and R files. This dataset is associated with the following publication: Cowan, K., A. Krajewski, M. Jimenez, T. Luben, L. Messer, and K. Rappazzo. Examining modification of the associations between air pollution and birth outcomes by neighborhood deprivation in a North Carolina birth cohort, 2011-2015. Frontiers in Reproductive Health. Frontiers, Lausanne, SWITZERLAND, 6(July): 1304749, (2024).

This data set contains the average census tract-scale scores, from 2000-2013, for the composite HWBI, each _domain within the HWBI, each indicator within domains, and each metric within indicators. Domain and composite scores at the beginning and end of the study period (2000, 2013) are also given. This dataset is associated with the following publication: Yee, S., E. Paulukonis, and K. Buck. Downscaling a human well-being index for environmental management and environmental justice applications in Puerto Rico. Applied Geography. ELSEVIER, AMSTERDAM, HOLLAND, 123: 14, (2020).

As included in this EnviroAtlas dataset, community level domestic water demand is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking, hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also applied and considered representative of local self-supplied water use. Within the EnviroAtlas Phoenix boundary, there are 53 service providers with 2000-2009 water use estimates ranging from 108 to 366 GPD. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This data contains treatment and confounder data used in the preprint "Understanding Spatial Regression Models from a Weighting Perspective in an Observational Study of Superfund Remediation" (Woodward, Dominici, Zubizarreta). The final dataset, named buffers, is at the level of the Superfund site (n = 1583). This dataset can be accessed by loading preprocessed_superfunds.RData into R. The binary treatment data, describing whether a Superfund site was remediated and removed from the National Priorities List between 2001 and 2015, is derived from publicly-available data on Superfund site status (source: EPA ArcGIS). Confounder data is derived from the 2000 Decennial Census using tidycensus. The R code used to curate this dataset directly from the publicly available data sources is provided (preprocessing.R).

This data layer provides access to Hazardous Waste Corrective Action sites as part of the CIMC web service. Hazardous waste is waste that is dangerous or potentially harmful to our health or the environment. Hazardous wastes can be liquids, solids, gases, or sludges. They can be discarded commercial products, like cleaning fluids or pesticides, or the by-products of manufacturing processes. The RCRA Corrective Action Program, run by EPA and 43 authorized states and territories, works with facilities that have treated, stored, or disposed of hazardous wastes (TSDs) to protect public health and the environment by investigating and cleaning up hazardous releases to soil, ground water, surface water, and air at their facilities. RCRA Corrective Action sites in all 50 states and four U.S. territories cover 18 million acres of land. EPA estimates that more than 35 million people, roughly 12 percent of the U.S. population, live within one mile of a RCRA Corrective Action site (based on the 2000 U.S. Census). RCRA Corrective Action facilities include many current and former chemical manufacturing plants, oil refineries, lead smelters, wood preservers, steel mills, commercial landfills, and a variety of other types of entities. Due to poor practices prior to environmental regulations, Corrective Action facilities have left large stretches of river sediments laden with PCBs; deposited lead in residential yards and parks beyond site boundaries; polluted drinking water wells in rural areas with chlorinated solvents; tainted municipal water supplies used by millions; and introduced mercury into waterways, necessitating fish advisories. At these sites, the Corrective Action Program ensures that cleanups occur. EPA and state regulators work with facilities and communities to design remedies based on the contamination, geology, and anticipated use unique to each site. Note: RCRA facilities which are not undergoing corrective action are not considered “Cleanups” in Cleanups in My Community. The complete set of RCRA facilities can be accessed via the EPA RCRA database in Envirofacts (https://www.epa.gov/enviro/rcrainfo-overview). The CIMC web service was initially published in 2013, but the data are updated twice a month. The full schedule for data updates in CIMC is located here: https://ofmpub.epa.gov/frs_public2/frs_html_public_pages.frs_refresh_stats.

Private domestic well use estimates for US Census block groups were calculated using a combination of population and housing unit data from the 1990, 2000 and 2010 Decennial Censuses in conjunction with available state level domestic well completion reports for domestic wells constructed between April 1, 1990 and March 31, 2010. A detailed description of how this data was created can be found in Weaver et. al (2017).

Municipal Separate Storm Sewer System (MS4) is a conveyance or system of conveyances (including roads, catch basins, curbs, gutters, ditches, man-made channels, or storm drains) owned or operated by a public body, designed and used for collecting storm water, is not a combined sewer, an is not a Publicly Owned Treatment Works (POTW). The U.S. EPA's storm water program addressed storm water runoff in two phases. Phase I addressed storm water runoff from large and medium MS4s. Large municipalities with a separate storm sewer system serving a population greater than 250,000 and medium municipalities with a service population between 100,000 and 250,000 had to obtain NPDES permits. Initial application deadlines for large and medium municipalities were November 16, 1992 and May 17, 1993, respectively. As part of their individual NPDES permit applications, the large and medium MS4s had to develop a storm water management program (SWMP). The Phase II regulations address storm water runoff of MS4s serving populations less than 100,000, called small MS4s. More particularly, small MS4s located partially or fully within urbanized areas (UAs), as determined by the U.S. Bureau of the Census, and also on a case-by-case basis for those small MS4s located outside of UAs that Ohio EPA designates into the program. Automatically designated Small MS4s, those in UAs, were required to apply for permit coverage and develop and submit a SWMP by March 10, 2003.Ohio considers the Urban Areas defined under the 2000, 2010, and 2020 Census, as well as more than 30 cities located outside of the the urban areas that are also covered by a storm water permit, as a Regulated MS4 Area.

Data shows polygon locations of Potential Environmental Justice Areas (PEJA) and is defined in the PEJA field. PEJA's have been identified based on data from the 2014-2018 5-year American Community Survey (ACS), conducted by the US Census Bureau. Environmental justice efforts focus on improving the environment in communities, specifically minority and low-income communities, and addressing disproportionate adverse environmental impacts that may exist in those communities. The information balloon for each census block group area displays the census block group ID, population, percent minority, percent below poverty level, county, municipality, and a link to more information on the Department of Environmental Conservation's website https://www.dec.ny.gov/public/333.html The data was collected by the US Census Bureau as part of the American Community Survey. Reported income and race/ethnicity data were analyzed by OEJ to determine the presence of Potential Environmental Justice Areas. The designated areas are then considered for additional outreach within the permitting process, for grant eligibility, and for targeted enforcement of Environmental Conservation Law violations. Utilized established methods as originally detailed in the Interim Environmental Justice Policy, US EPA Region 2, December 2000, and recommended by the Environmental Justice Advisory Group, Recommendations for the New York State Department of Environmental Conservation Environmental Justice Program, January 2, 2002. Individual thresholds for low-income populations (statewide), minority populations (rural communities), and minority populations (urban communities) were determined by using ArcGIS 10.3 (used to indicate if census block groups overlapped Census designated urban areas) and IBM SPSS Statistics 26 (to conduct a K-means clustering algorithm on ACS data for the three categories). More detail is provided under processing steps. Service updated annually. For more information or to download layer see https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1273Download the metadata to learn more information about how the data was created and details about the attributes. Use the links within the metadata document to expand the sections of interest see http://gis.ny.gov/gisdata/metadata/nysdec.PEJA.xml

Data shows polygon locations of Potential Environmental Justice Areas (PEJA) and is defined in the PEJA field. PEJA's have been identified based on data from the 2014-2018 5-year American Community Survey (ACS), conducted by the US Census Bureau. Environmental justice efforts focus on improving the environment in communities, specifically minority and low-income communities, and addressing disproportionate adverse environmental impacts that may exist in those communities. The information balloon for each census block group area displays the census block group ID, population, percent minority, percent below poverty level, county, municipality, and a link to more information on the Department of Environmental Conservation's website https://www.dec.ny.gov/public/333.html The data was collected by the US Census Bureau as part of the American Community Survey. Reported income and race/ethnicity data were analyzed by OEJ to determine the presence of Potential Environmental Justice Areas. The designated areas are then considered for additional outreach within the permitting process, for grant eligibility, and for targeted enforcement of Environmental Conservation Law violations. Utilized established methods as originally detailed in the Interim Environmental Justice Policy, US EPA Region 2, December 2000, and recommended by the Environmental Justice Advisory Group, Recommendations for the New York State Department of Environmental Conservation Environmental Justice Program, January 2, 2002. Individual thresholds for low-income populations (statewide), minority populations (rural communities), and minority populations (urban communities) were determined by using ArcGIS 10.3 (used to indicate if census block groups overlapped Census designated urban areas) and IBM SPSS Statistics 26 (to conduct a K-means clustering algorithm on ACS data for the three categories). More detail is provided under processing steps. Service updated annually. For more information or to download layer see https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1273Download the metadata to learn more information about how the data was created and details about the attributes. Use the links within the metadata document to expand the sections of interest see http://gis.ny.gov/gisdata/metadata/nysdec.PEJA.xml

In Omaha, NE, more than 25 GI projects have been completed to date, with several featuring GI practices in public parks. Using a repeat sales model , we examined the effect of GI on the value of nearby single-family homes, based on housing sales and characteristic data from 2000 to 2018. We evaluated the sales price for homes using a buffer zone of 0-0.5km, and three additional models: homes within 0-0.25km, 0.25-0.5km, and greater than 0.5km from parks where GI was installed for 25,472 sale pairs. In addition to the repeat sales model, we performed a hot spot analysis on several demographic characteristics to capture systematic differences at a smaller spatial scale and over a longer time period than the repeat sales model could capture. We used US Census data on race and household income to examine changing patterns over time and space, and a spatial lag Maximum Likelihood Estimation model to determine if the _location of GI correlated with either of these demographics. This dataset is associated with the following publication: Hoover, F., J. Price, and M. Hopton. Examining the Effects of Green Infrastructure on Residential Sales Prices in Omaha, NE. Urban Forestry & Urban Greening. Elsevier B.V., Amsterdam, NETHERLANDS, 54: 126778, (2020).