An inventory of facilities that bottle water or other beverages containing water (including soft drinks, beer, wine, or spirits) or that manufacture ice was compiled by combining available datasets from multiple sources. This water bottling inventory dataset includes facilities within all 50 states of the United States (U.S.), one federal district (Washington, District of Columbia), and three territories (Guam, Puerto Rico, and Virgin Islands). The inventory focuses on active facilities in 2024. Most closed water bottling facilities are not included; however, facilities identified as being a former production site (meaning the facility is still active but the business function has changed) or as closed during data review were kept and had their status marked. This data release includes water bottling facilities that operate their own infrastructure and source water through their own water sources, including wells, springs, and surface waters; are on a public-supply water system; or are diversified in that they obtain water from their own sources and receive public-supply water deliveries. Facility classifications were based on the North American Industry Classification System (NAICS) code. The NAICS is the standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy. The NAICS is organized into a hierarchical structure with 3-digit codes representing the subsector and 6-digit codes representing the national industry. Beverage types in subsector 312 (Beverage and Tobacco Product Manufacturing) were compiled for this effort and included facilities with the following NAICS codes as a primary or secondary classification type: 312111: Soft drink manufacturing 312112: Bottled water manufacturing 312113: Ice manufacturing 312120: Breweries 312130: Wineries 312140: Distilleries First posted: October 16, 2023 Revised: November 27, 2024 This version supersedes the previous version of the data release: Buchwald, C.A., Luukkonen, C.L., Martin, G.R., Kennedy, J.L., Wilson, J.T., Hian, M.E., and Dieter, C.A., 2023, Inventory of water bottling facilities in the United States, 2023, and select water-use data, 1955-2022, U.S. Geological Survey data release, https://doi.org/10.5066/P90Z125H Version 2.0 This data release has been updated as of November 27, 2024. Data and knowledge gaps for version 1 of the data release are described in the report, Luukkonen, C.L., Buchwald, C.A., Martin, G.R., and Johnson Mckee, A.E., 2024, Data and knowledge gaps of a water bottling facility inventory and select water-use dataset, United States, U.S. Geological Survey Scientific Investigations Report 2024-5106, 41 p. During creation of this report several issues were identified in the version 1 data release which have been revised in version 2. These revisions include addition of some facilities, removal of some facilities, updated location, status, and attributes for some facilities, and addition of aquifer information for self-supplied bottled water facilities relying on groundwater sources. This data release includes five tables: WBinventory_FacilityList.txt - a tab-delimited text file (TXT) with the inventory of water bottling facilities and associated facility information WBinventory_WaterUse.txt - a tab-delimited text file (TXT) with water-use (withdrawal) information for selected water bottling facilities WBinventory_DataSources.txt - a tab-delimited text file (TXT) that lists the source name, state, year acquired, data type, and how data were acquired to construct the facility inventory and water-use tables. WBinventory_Aquifers.txt - a tab-delimited text file (TXT) that lists assigned aquifers based on a national-scale mapping method for facilities that rely on groundwater sources. version_history.txt - a tab-delimited text file (TXT) describing changes in version 2.0 This project is part of the Water Availability and Use Science Program, which assists in the determination of water that is available for human and ecological uses, now and in the future.

This dataset was utilized a join from enriched tables from ESRI which was curated from the 2020 Census from the United States Census Bureau and for city taxing districts polygon dataset are from 2024 published by the Idaho State Tax Commission. This layer has information for all cities within Idaho regarding the city population's Age, Sex and Race, Household Income, Disposable Income, Alcoholic Beverage Consumption, Total Population, Poverty Index, Employment, Housing, Total Population (2029), Home Value (2024), Housing Costs. For more information on how the data is curated for the Enrich tool please go the link below.

2024/2029 Esri Updated Demographics

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

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 non-transformed variables chosen to represent the Air, Water, Land, Built, and Sociodemographic Domains of the total environment. This does not represent the final variables for the EQI. The Transformed dataset was used to create the EQI. This dataset is for information purposes only for those who want to see the original non-transformed variables.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.

Alcohol consumption among both men and women is higher in rural India than in urban India, the National Family Health Survey-5 (NFHS-5), 2019-21 has found. Overall, 1% of women aged 15 and over drink alcohol, compared to 19% of men in the same age group. This breaks up into 1.6% (rural) and 0.6% (urban) among women, and 19.9% and 16.5% respectively among men.

Source: NFHS-5 Of all states, Arunachal Pradesh has the highest proportion of both men (53%) and women (24%) who drink alcohol. Among women, Arunachal Pradesh is followed by Sikkim (16%); among men, it is followed by Telangana (43%). Besides Arunachal and Telangana, alcohol consumption among men is higher (40% and above) in upper Brahmaputra region of Assam, districts in Jharkhand and Bastar region of Chhattisgarh, and the Chhota Nagpur region of Jharkhand and Odisha.

Based on the delivery period, the publication date of the delinquent list becomes effective five business days (M-F) subsequent to the due date excluding federal and state holidays, Saturdays and Sundays.

**To avoid unnecessary delays in processing time and to allow retailers adequate time to reconcile delinquencies, please report delinquent accounts by the reporting dates shown.

If the due date (10th or 25th) or publication date falls on a Saturday, Sunday, or a state or federal holiday, the due date or publication date shall be the next regular business day.

To allow adequate processing time, Sellers that have been granted an exception waiver to utilize a paper-based process to submit paper notices of default and/or cash law affidavits to the Commission, should submit those notices to the appropriate regional office within two business days following the 10th or 25th for credit law and with two days of receipt of the NFS check for cash law affidavits.

Alcohol consumption in India amounted to about five billion liters in 2020 and was estimated to reach about 6.21 billion liters by 2024. The increase in consuming these beverages can be attributed to multiple factors including the rising levels of disposable income and a growing urban population among others.

  Alcohol market in India India’s alcohol market consisted of two main kinds of liquor – Indian made Indian liquor or IMIL, and Indian made foreign liquor or IMFL. This was in addition to beer, wine and other imported alcohol. Country liquor accounted for the highest market share, while spirits took up the majority of the consumption market .

Young consumers Although the average per adult intake of alcohol was considerably low in India when compared to other countries such as the United States, heavy drinkers among young Indians were more prevalent. Men were more likely to drink than women by a large margin and were also more prone to episodic drinking.
According to a study, over 88 percent of Indians aged under 25 purchase or consume alcoholic beverages even though it is illegal. This was despite bans on alcohol in some states across the country and limitations on sales in some others.