In the past ten years, the monthly combined water and sewer bills in the United States have increased constantly. The monthly water and sewage utility bills in 2023 amounted to approximately 120.7 U.S. dollars, representing an increase of 3.9 percent compared to the previous year.

An average U.S. family of four pays about ***** U.S. dollars for water every month as of 2019, if each person used about 100 gallons per day. The price index of water and sewage maintenance have increased in recent years as infrastructure continues to age across the United States.

Setting water rates

Cities that have increased prices in water, generally use the increased rate to improve infrastructure. Families generally pay a fixed charge every month which is independent of water consumption, and a variable charge which is related to the amount of water used. Higher fixed charges are more commonly used to ensure revenue stability due to increased pipe repair costs, however, it reduces the incentive to conserve water and may punish households that use less water.

Water prices worldwide

Water prices vary across the countries and cities due to the various processes that are used to assign a price. Utilities generally set a water rate or tariff based on costs of water treatment, water storage, transport, wastewater treatment and collection, and other administrative operations. On the other hand, direct abstraction of water from sources such as lakes, is usually not charged, however, some countries require payment based on volume or abstraction rights.

Descriptions Excel Application Tool for Statewide Agricultural Water Use Data 2016 - 2020 Department of water resources, Water Use Efficiency Branch, Water Use Unit program, has developed an Excel application tool, which calculates annual estimates of irrigated crop area (ICA), crop evapotranspiration (ETc), effective precipitation (Ep), evapotranspiration of applied water (ETaw), consumed fraction (CF), and applied water (AW) for 20 crop categories by combinations of detailed analysis unit and county (DAUCo) over California. The 2016 – 2020 statewide agricultural water use data were developed by all 4 DWR’s Regional Offices (Northern Region Office, North Central Region Office, South Central Region Office, and Southern Region Office) using Cal_simetaw model for updating the information in the California Water Plan Updates-2023. Therefore, this current Excel application tool just covers agricultural water use data from the period of 2016 - 2020 water years. It should also be mentioned that there are 3 other similar Excel applications that cover 1998 - 2005 and 2006 – 2010, & 2011 - 2015 agricultural water use data for the California Water plan Updates 2005/2009, 2013, and 2018 respectively. Outputs data provided from this Excel application include ICA in acres, EP, both in unit values (Acre feet per acre) & volume (acre feet), ETc both in unit values (acre feet per acre), & volume (acre feet), ETaw, both in unit value (acre feet per acre), & volume (acre feet), AW, both in unit value (acre feet per acre) & volume (acre feet), CF (in percentage %) for WYs 2016 – 2020 at Detailed Analysis Unit by County (DAUCO), Detailed Analysis Unit (DAU), County, Planning Area (PA), Hydrological Region (HR), and Statewide spatial scales using the dropdown menu. Furthermore, throughout the whole process numerous computations and aggregation equations in various worksheets are included in this Excel application. And for obvious reasons all worksheets in this Excel application are hidden and password protected. So, accidentally they won’t be tampered with or changed/revised. Following are definitions of terminology and listing of 20 crop categories used in this Excel application. Study Area Maps The California Department of Water Resources (DWR) subdivided California into study areas for planning purposes. The largest study areas are the ten hydrologic regions (HR), The next level of delineation is the planning area (PAS), which are composed of multiple detailed analysis units (DAU). The DAUs are often split by county boundaries, so the smallest study areas used by DWR is DAU/County. Many planning studies begin at the Dau or PA level, and the results are aggregated into hydrologic regions for presentation. Irrigated Crop Area (ICA) in acres The total amount of land irrigated for the purpose of growing a crop (includes multi-cropping acres) 3- Multi-cropping (MC) in acres A section of land that has more than one crop grown on it in a year, this included one crop being planted more than once in a season in the same field. Please note that there are no double cropping acreages for 2017. Because on a normal year when Regional Offices (RO) receive data from Land IQ, they were able to provide double cropping acreages. Since the 2017 land use data was derived from average crop acres between water years 2016 and 2018,2019, & 2020, they lost spatial and temporal data necessary to calculate double cropping. Evapotranspiration (ET) Combination of soil evaporation and transpiration is referred to as evapotranspiration or ET. The rate of evapotranspiration from the plant-soil environment is primarily dependent on the energy available from solar radiation but is also dependent on relative humidity, temperature, cloud cover, and wind speed. It is an indication for how much your crops, lawn, garden, and trees need for healthy growth and productivity. Reference Evapotranspiration (ETo) Reference evapotranspiration (ETo) is an estimate of the evapotranspiration of a 10-15 cm tall cool season grass and not lacking for water. The daily Standardized Reference Evapotranspiration for short canopies is calculated using the Penman-Monteith (PM) equation (Monteith, 1965) as presented in the United Nations FAO Irrigation and Drainage Paper (FAO 56) by Allen et al. (1988). Penman-Monteith Equation (PM) Equation is used to estimate ETo when daily solar radiation, maximum and minimum air temperature, dew point temperature, and wind speed data are available. It is recommended by both the America Society of Civil Engineers and United Nations FAO for estimating ETo. Crop Evapotranspiration (ETc), both in unit value (acre feet per acre), & volume (acre feet) Commonly known as potential evapotranspiration, which is the amount of water used by plants in transpiration and evaporation of water from adjacent plants and soil surfaces during a specific time period. ETc is computed as the product of reference evapotranspiration (ETo) and a crop coefficient (Kc) value, i.e., ETc = ETo x Kc. One Acre foot equals about 325851 gallons, or enough water to cover an acre of land about the size of a football field, one foot deep. Crop Coefficient (Kc) Relates ET of a given crop at a specific time in its growth stage to a reference ET. Incorporates effects of crop growth state, crop density, and other cultural factors affecting ET. The reference condition has been termed "potential" and relates to grass. The main sources of Kc information are the FAO 24 (Doorenbos and Pruitt 1977) and FAO 56 (Allen et al. 1988) papers on evapotranspiration. Effective Precipitation (Ep), both in unit value (acre feet per acre), & volume (acre feet) Fraction of rainfall effectively used by a crop, rather than mobilized as runoff or deep percolation Evapotranspiration of Applied Water (ETaw), both in unit value (acre feet per acre), & volume (acre feet) Net amount of irrigation water needed to produce a crop (not including irrigation application efficiency). Soil characteristic data and crop information with precipitation and ETc data are used to generate hypothetical water balance irrigation schedules to determine ETaw. Applied Water (AW), both in unit value (acre feet per acre), & volume (acre feet) Estimated as the ETaw divided by the mean seasonal irrigation system application efficiency. Consumed Fraction (CF) in percentage (%) An estimate of how irrigation water is efficiently applied on fields to meet crop water, frost protection, and leaching requirements for a whole season or full year. Crop category numbers and descriptions Crop Category Crop category description. 1 Grain (wheat, wheat_winter, wheat_spring, barley, oats, misc._grain & hay) 2 Rice (rice, rice_wild, rice_flooded, rice-upland) 3 Cotton 4 Sugar beet (sugar-beet, sugar_beet_late, sugar_beet_early) 5 Corn 6 Dry beans 7 Safflower 8 Other field crops (flax, hops, grain_sorghum, sudan,castor-beans, misc._field, sunflower, sorghum/sudan_hybrid, millet, sugarcane 9 Alfalfa (alfalfa, alfalfa_mixtures, alfalfa_cut, alfalfa_annual) 10 Pasture (pasture, clover, pasture_mixed, pasture_native, misc._grasses, turf_farm, pasture_bermuda, pasture_rye, klein_grass, pasture_fescue) 11 Tomato processing (tomato_processing, tomato_processing_drip, tomato_processing_sfc) 12 Tomato fresh (tomato_fresh, tomato_fresh_drip, tomato_fresh_sfc) 13 Cucurbits (cucurbits, melons, squash, cucumbers, cucumbers_fresh_market, cucumbers_machine-harvest, watermelon) 14 Onion & garlic (onion & garlic, onions, onions_dry, onions_green, garlic) 15 Potatoes (potatoes, potatoes_sweet) 16 Truck_Crops_misc (artichokes, truck_crops, asparagus, beans_green, carrots, celery, lettuce, peas, spinach, bus h_berries, strawberries, peppers, broccoli, cabbage, cauliflower) 17 Almond & pistachios 18 Other Deciduous (apples, apricots, walnuts, cherries, peaches, nectarines, pears, plums, prunes, figs, kiwis) 19 Citrus & subtropical (grapefruit, lemons, oranges, dates, avocados, olives, jojoba) 20 Vineyards (grape_table, grape_raisin, grape_wine)

Operational, financial, and land use data to estimate drinking water treatment cost functions for 2006 calendar year. Data are organized for surface water and groundwater systems. 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: US EPA's Office of Water, Office of Science and Technology, Engineering and Analysis Division is the holder of the survey data. US EPA's Office of Water maintains a database of point coordinates for surface water intakes and wells used for public water supply. Format: Our dataset is described in detail in Section 3 of the paper. We include a link to the 2006 Community Water System Survey that excludes the identifiers. Other data are confidential business information.

This dataset is associated with the following publication: Price, J., and M. Heberling. The Effects of Agricultural and Urban Land Use on Drinking Water Treatment Costs: An Analysis of United States Community Water Systems. Water Economics and Policy. World Scientific Publishing Co. Pte. Ltd., 5 Toh Tuck Link, SINGAPORE, 6(4): 2050008, (2020).

Monthly consumption and cost data by borough and development. Data set includes utility vendor and meter information.

This submission contains a set of U.S.-specific potable reuse capital and operations and maintenance (O&M) cost data ($2020) found in published presentations and reports from engineering consulting firms, utility and water agency press releases or websites, and literature. For any unbuilt facilities, the reported costs found in technical documents are mostly engineer estimates and may be subject to change as construction proceeds. This data set contains a mix of both facility specific and total capital costs, which include conveyance infrastructure. Note that this dataset does not include detailed cost breakdowns for each of the facilities. This submission also contains the sources used to build this dataset in pdf format.

United States - Depreciation and Amortization Charges for Water Transportation, All Establishments, Employer Firms was 5737.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Depreciation and Amortization Charges for Water Transportation, All Establishments, Employer Firms reached a record high of 5888.00000 in January of 2020 and a record low of 3179.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Depreciation and Amortization Charges for Water Transportation, All Establishments, Employer Firms - last updated from the United States Federal Reserve on October of 2025.

This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

United States - Consumer Price Index: Housing, water, electricity, gas and other fuels (COICOP 04): Electricity, gas and other fuels: Total for the Republic of Korea was 0.00707 Growth rate previous period in November of 2023, according to the United States Federal Reserve. Historically, United States - Consumer Price Index: Housing, water, electricity, gas and other fuels (COICOP 04): Electricity, gas and other fuels: Total for the Republic of Korea reached a record high of 23.86359 in December of 1997 and a record low of -11.72588 in July of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index: Housing, water, electricity, gas and other fuels (COICOP 04): Electricity, gas and other fuels: Total for the Republic of Korea - last updated from the United States Federal Reserve on December of 2025.

Table from the American Community Survey (ACS) 5-year series on housing tenure and cost related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B25003 Tenure of Occupied Housing Units, B25070 Gross Rent as a Percentage of Household Income in the Past 12 Months, B25063 Gross Rent, B25091 Mortgage Status by Selected Monthly Owner Costs as a Percentage of Household Income in the Past 12 Months, B25087 Mortgage Stauts and Selected Monthly Owner Costs, B25064 Median Gross Rent, B25088 Median Selected Monthly Owner Costs by Mortgage Status. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B25003, B25070, B25063, B25091, B25087, B25064, B25088Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The dataset consists of quantitative microbial risk assessment, life cycle assessment and life cycle cost analysis. This dataset is associated with the following publication: Arden, S., B. Morelli, M. Schoen, S. Cashman, M. Jahne, C. Ma, and J. Garland. Human health, economic and environmental assessment of onsite non-potable water reuse systems for a large, mixed-use urban building. Sustainability. MDPI AG, Basel, SWITZERLAND, 12(13): 5459 - 5475, (2020).

Consumer Price Index: Housing, water, electricity, gas and other fuels (COICOP 04): Maintenance and repairs of the dwellings: Total for the United States was 1.75345 Growth rate same period previous Yr. in December of 2024, according to the United States Federal Reserve. Historically, Consumer Price Index: Housing, water, electricity, gas and other fuels (COICOP 04): Maintenance and repairs of the dwellings: Total for the United States reached a record high of 16.33217 in July of 2023 and a record low of -1.53185 in June of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Consumer Price Index: Housing, water, electricity, gas and other fuels (COICOP 04): Maintenance and repairs of the dwellings: Total for the United States - last updated from the United States Federal Reserve on November of 2025.

This data was used by the Department of Agriculture, Water and Environment to produce Figure 25 in the Urban chapter of the 2021 Australian State of the Environment Report.

Key Table Information.Table Title.Annual Water and Sewer Costs.Table ID.ACSDT1Y2024.B25134.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of ...

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2018-2022 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2018-2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Gross Value Added Index: sa: QoQ: State of São Paulo: Industry: Electricity, Gas & Water data was reported at -0.105 % in Dec 2024. This records an increase from the previous number of -1.208 % for Sep 2024. Gross Value Added Index: sa: QoQ: State of São Paulo: Industry: Electricity, Gas & Water data is updated quarterly, averaging 0.390 % from Jun 2002 (Median) to Dec 2024, with 91 observations. The data reached an all-time high of 7.484 % in Mar 2024 and a record low of -5.708 % in Jun 2020. Gross Value Added Index: sa: QoQ: State of São Paulo: Industry: Electricity, Gas & Water data remains active status in CEIC and is reported by State System of Data Analysis Foundation. The data is categorized under Brazil Premium Database’s National Accounts – Table BR.AH023: SNA 2008: Gross Value Added: Southeast: São Paulo: State System of Data Analysis Foundation: Quarterly. [COVID-19-IMPACT]

Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: UF: Kurgan Region data was reported at 509.978 RUB mn in Jan 2025. This records a decrease from the previous number of 6,509.135 RUB mn for Dec 2024. Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: UF: Kurgan Region data is updated monthly, averaging 2,114.372 RUB mn from Jun 2016 (Median) to Jan 2025, with 98 observations. The data reached an all-time high of 7,702.677 RUB mn in Dec 2023 and a record low of 200.904 RUB mn in Jan 2020. Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: UF: Kurgan Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Energy Sector – Table RU.RBA007: Energy Production Value: OKVED2: Water Supply, Sewerage, Waste Management & Remediation Activities: ytd.

Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: NC: Republic of Ingushetia data was reported at 57.064 RUB mn in Jan 2025. This records a decrease from the previous number of 877.571 RUB mn for Dec 2024. Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: NC: Republic of Ingushetia data is updated monthly, averaging 378.082 RUB mn from Jun 2016 (Median) to Jan 2025, with 103 observations. The data reached an all-time high of 972.199 RUB mn in Dec 2023 and a record low of 35.815 RUB mn in Jan 2020. Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: NC: Republic of Ingushetia data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Energy Sector – Table RU.RBA007: Energy Production Value: OKVED2: Water Supply, Sewerage, Waste Management & Remediation Activities: ytd.

Network of measurement stations for the period from 2015 to 2020, established as part of the surface water quality monitoring program in accordance with the Water Framework Directive (2000/60/EC). The measurement network includes two components: the surveillance control network and the operational control network. The results of this monitoring program (2015-2020) were used for assessing the state of surface water bodies within the framework of the 3rd management plan. Physico-chemical parameters as well as chemical parameters are sampled at the same station. The location of biological parameter measurement stations may vary slightly.

Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: VR: Penza Region data was reported at 671.268 RUB mn in Jan 2025. This records a decrease from the previous number of 8,933.943 RUB mn for Dec 2024. Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: VR: Penza Region data is updated monthly, averaging 3,429.627 RUB mn from Jun 2016 (Median) to Jan 2025, with 103 observations. The data reached an all-time high of 9,489.384 RUB mn in Dec 2023 and a record low of 304.284 RUB mn in Jan 2020. Water Supply, Sewerage, Waste Management & Remediation Activities: OKVED2: Year to Date: VR: Penza Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Energy Sector – Table RU.RBA007: Energy Production Value: OKVED2: Water Supply, Sewerage, Waste Management & Remediation Activities: ytd.