Data is collected from a house.There are two tanks. Sump is underground, from which the water is pumped to the overhead tanks Tank1 and Tank2.

An Ultrasonic distance sensor is mounted near the top of each tank that monitors and logs the distance of the water surface from the sensor at fixed intervals.

There are 13 sets of data for Tank 1 and 16 sets of data for Tank 2. The description reg. these datasets are given in Tank 1-Data Description.xlsx and Tank 2-Data Description.xlsx, respectively.

The data logged is the vertical distance of the water surface in the tank from the sensor's location. The minimum reading of sensor is 19 cm which indicates full tank and the maximum reading is 88cm which indicates an empty tank.

So to calculate the percentage of tank that has been filled as of now, we use the formula: percent_full=100-(100*(data-19)/(88-19)))

The data logged is the one adjusted with calibration equation. The equation found is as follows:

Sensor calibration results: f(x) = p1*x + p2 Coefficients (with 95% confidence bounds): p1 = 1.033 (1.024, 1.042) p2 = 1.187 (0.7033, 1.67)

Goodness of fit: SSE: 7.109 R-square: 0.9995 Adjusted R-square: 0.9995 RMSE: 0.4951

Note Reg. consumption:

The experimental setup was installed in a house with a Solar water heater (capacity 300 Liters) that gets filled every midnight at 12.00 onwards from Tank 1. Hence, the level changes logged for tank 1 during the hours 00.00 to 03.00 am are high. During the rest of the day, the consumption is that of a regular household.

The household has four adult occupants, with three occupants leaving the house by 10.00 am every weekday and returning by 4.00 pm. Tank 1 is used for supplying all household needs. Tank 2 is used only for gardening purposes.

There is no description for this dataset.

Potable water use by sector and average daily use for Canada, provinces and territories.

China Water Consumption: City: Daily per Capita: Residential data was reported at 188.799 l in 2023. This records an increase from the previous number of 184.732 l for 2022. China Water Consumption: City: Daily per Capita: Residential data is updated yearly, averaging 178.638 l from Dec 1978 (Median) to 2023, with 46 observations. The data reached an all-time high of 220.240 l in 2000 and a record low of 120.600 l in 1978. China Water Consumption: City: Daily per Capita: Residential data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

A family in Flanders consists of an average of 2.3 people in 2022. This average family has an average tap water consumption of 70 m3 per year or 84 liters per person per day.

This dataset contain per capita water consumption In Saudi regions during the period 2009-2018. Data from General Authority for Statistics. Follow datasource.kapsarc.org for timely data to advance energy economics research.*The Per Capita Average Daily Use Of Water Is Calculated As Follows:Total quantity consumed by municipal sector (liter)/population*365

A family in Flanders consists of an average of 2.3 people in 2022. This average family has an average tap water consumption of 70 m3 per year or 84 liters per person per day.

Kolkata had the highest average water consumption per person across major Indian cities in 2021, at 2.31 liters per day. Bhubaneshwar followed, with an average consumption of 2.3 liters per day. The recommended amount of water intake to stay hydrated is at least two liters every day.

Water Consumption: City: Daily per Capita: Residential: Shanghai data was reported at 210.898 l in 2023. This records an increase from the previous number of 207.038 l for 2022. Water Consumption: City: Daily per Capita: Residential: Shanghai data is updated yearly, averaging 210.898 l from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 361.680 l in 2004 and a record low of 174.830 l in 2010. Water Consumption: City: Daily per Capita: Residential: Shanghai data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

​Monthly residential water consumption grouped by zip code and customer class.

Water Consumption: City: Daily per Capita: Residential: Beijing data was reported at 167.264 l in 2023. This records an increase from the previous number of 163.221 l for 2022. Water Consumption: City: Daily per Capita: Residential: Beijing data is updated yearly, averaging 187.520 l from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 281.840 l in 1998 and a record low of 152.910 l in 2005. Water Consumption: City: Daily per Capita: Residential: Beijing data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

Drinking Water Protected Areas (Surface Water) are defined by the Water Environment (Water Framework Directive) (England & Wales) Regulations 2017 (or WFD Regulations) as locations where raw water is abstracted for human consumption providing, on average, more than 10 cubic metres per day, or serving more than 50 persons, or is intended for such future use. For surface water Drinking Water Protected Areas water may be abstracted from rivers, lakes, canals and reservoirs. Drinking Water Protected Areas are based on the River Basin Management Plan water body area within which the abstraction is located. Water sources used for drinking supplies need to be protected under the WFD Regulations to ensure they are not polluted and avoid / minimise the need for additional purification treatment which can be costly and resource intensive. The water companies must ensure compliance with the Drinking Water Directive and the Priority Substances Directive chemical parameters at the tap, as regulated by the Drinking Water Inspectorate (DWI). Water Companies and the Environment Agency identify Drinking Water Protected Areas that are ‘at risk’ of deterioration from certain substances which could affect treatment and non-statutory Safeguard Zones are established. Within these zones, the Environment Agency works with the Water Companies to plan and implement targeted measures to address the identified risks. This dataset includes all of the current Drinking Water Protected Areas with a qualifying abstraction, indications of whether or not the area is deemed ‘at risk’ and, if a Safeguard Zone has been defined, which substances are affecting them. For more information on Safeguard Zones please refer to the "Drinking Water Safeguard Zone (Surface Water)" dataset also available on the Defra DSP. This data has been updated in 2022 following a national review of surface water protected area records for the Cycle 3 River Basin Management Plans. Please note the status of Drinking Water Protected Areas and any associated Safeguard Zones are continuously under review and details may change and/or become out of date once published. Please send any data or other enquiries in relation to this dataset to the point of contact identified below.

This U.S. Geological Survey (USGS) data release contains estimated daily streamflow and base flow for HUC12 in the nontidal areas of the Chesapeake Bay watershed, monthly average streamflow and base flow, flow statistics, MATLAB scripts, and a document that describes how to create similar datasets in other watersheds. Daily streamflow was estimated for all the nontidal parts of the Chesapeake Bay watershed with the program "Unit Flows in Networks of Channels" (UFINCH; Holtschlag, 2016), together with the observations of measured streamflow at gages at the downstream ends of major rivers. The estimated streamflow was aggregated at the HUC12 level and reformatted as an Optimal Hydrograph Separation (OHS) input file using MATLAB scripts. Base flow was calculated at each HUC12 outlet using the base flow index (BFI) hydrograph separation methods developed by Wahl and Wahl (Wahl and Wahl, 1988; Wahl and Wahl, 1995) and by Eckhardt (Eckhardt, 2005) with the parameter estimation method developed by Collischonn and Fan (Collischonn and Fan, 2013) which are incorporated into the OHS program (Raffensperger and others, 2017). This data release supports the following publication: • Buffington, P.C., and Capel, P.D., 2020, Estimating streamflow and base flow within the nontidal Chesapeake Bay riverine system: U.S. Geological Survey Scientific Investigations Report 2020-5055, 26 p., https://doi.org/10.3133/sir20205055. References cited: • Collischonn, W. and Fan, F.M., 2013, Defining parameters for Eckhardt's digital baseflow filter: Hydrological Processes, v. 27, no. 18, p. 2614-2622, https://doi.org/10.1002/hyp.9391. • Eckhardt, K., 2005, How to construct recursive digital filters for baseflow separation: Hydrological Processes, v. 19, no. 2, p. 507-515, https://doi.org/10.1002/hyp.5675. • Holtschlag, D.J., 2016, UFINCH-A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074. • Raffensperger, J.P., Baker, A.C., Blomquist, J.D., and Hopple, J.A., 2017, Optimal hydrograph separation using a recursive digital filter constrained by chemical mass balance, with application to selected Chesapeake Bay watersheds: U.S. Geological Survey Scientific Investigations Report 2017-5034, 51 p., https://doi.org/10.3133/sir20175034. • Wahl, K.L., and Wahl, T.L., 1988, Effects of regional ground water declines on streamflows in the Oklahoma Panhandle, in Symposium on Water-Use Data for Water Resources Management, Tucson, Arizona, American Water Resources Association, p. 239-249. • Wahl, K.L., and Wahl, T.L., 1995, Determining the flow of Comal Springs at New Braunfels, Texas, Texas Water '95: San Antonio, Texas, American Society of Civil Engineers, p. 77-86, http://www.usbr.gov/tsc/techreferences/hydraulics_lab/pubs/PAP/PAP-0708.pdf.

Abstract copyright UK Data Service and data collection copyright owner.

The Households Below Average Income (HBAI) data presents information on living standards in the UK based on household income measures for the financial year.

HBAI uses equivalised disposable household income as a proxy for living standards in order to allow comparisons of the living standards of different types of households (that is, income is adjusted to take into account variations in the size and composition of the households in a process known as equivalisation). A key assumption made in HBAI is that all individuals in the household benefit equally from the combined income of the household. This enables the total equivalised income of the household to be used as a proxy for the standard of living of each household member.

In line with international best practice, the income measures used in HBAI are subject to several statistical adjustments and, as such, are not always directly relatable to income amounts as they might be understood by people on a day-to-day basis. These adjustments, however, allow consistent comparison over time and across households of different sizes and compositions. HBAI uses variants of CPI inflation when estimating how incomes are changing in real terms over time.

The main data source used in this study is the Family Resources Survey (FRS), a continuous cross-sectional survey. The FRS normally has a sample of 19,000 - 20,000 UK households. The use of survey data means that HBAI estimates are subject to uncertainty, which can affect how changes should be interpreted, especially in the short term. Analysis of geographies below the regional level is not recommended from this data.

Further information and the latest publication can be found on the gov.uk HBAI webpage. The HBAI team want to provide user-friendly datasets and clearer documentation, so please contact team.hbai@dwp.gov.uk if you have any suggestions or feedback on the new harmonised datasets and documentation.

An earlier HBAI study, Institute for Fiscal Studies Households Below Average Income Dataset, 1961-1991, is held under SN 3300.

Latest Edition Information

For the 19th edition (April 2025), resamples data have been added to the study alongside supporting documentation. Main data back to 1994/95 have been updated to latest-year prices, and the documentation has been updated accordingly.

Using the HBAI files

Users should note that either 7-Zip or a recent version of WinZip is needed to unzip the HBAI download zip files, due to their size. The inbuilt Windows compression software will not handle them correctly.

Labelling of variables
Users should note that many variables across the resamples files do not include full variable or value labels. This information can be found easily in the documentation - see the Harmonised Data Variables Guide.

HBAI versions

The HBAI datasets are available in two versions at the UKDS:

1. End User Licence (EUL) (Anonymised) Datasets:

These datasets contain no names, addresses, telephone numbers, bank account details, NINOs or any personal details that can be considered disclosive under the terms of the ONS Disclosure Control guidance. Changes made to the datasets are as follows:

• All ages above 80 are instead top-coded to 80 years of age.
• The variable for the amount of Council Tax liability for the household and pensioner flags for the head and spouse have been removed.
• All amount variables have been rounded to the nearest £1.
• A very small number of large households (with 10 or more individuals) have been removed from the dataset.

2. Secure Access Datasets:

Secure Access datasets for HBAI are held under SN 7196. The Secure Access data are not subject to the same edits as the EUL version and are, therefore, more disclosive and subject to strict access conditions. They are currently only available to UK HE/FE applicants. Prospective users of the Secure Access version of the HBAI must fulfil additional requirements beyond those associated with the EUL datasets.

The HBAI data provide information on potential living standards in the United Kingdom as determined by net (equivalised) disposable income and allows for the analysis of changes in income patterns over time.

A January 2014 industrial accident contaminated the public water supply of approximately 300,000 homes in and near Charleston, West Virginia (USA) with low levels of a strongly-smelling substance consisting principally of 4-methylcyclohexane methanol (MCHM). The ensuing state of emergency closed schools and businesses. Hundreds of people sought medical care for symptoms they related to the incident. We surveyed 498 households by telephone to assess the episode’s health and economic impact as well as public perception of risk communication by responsible officials. Thirty two percent of households (159/498) reported someone with illness believed to be related to the chemical spill, chiefly dermatological or gastrointestinal symptoms. Respondents experienced more frequent symptoms of psychological distress during and within 30 days of the emergency than 90 days later. Sixty-seven respondent households (13%) had someone miss work because of the crisis, missing a median of 3 days of work. Of 443 households reporting extra expenses due to the crisis, 46% spent less than $100, while 10% spent over $500 (estimated average about $206). More than 80% (401/485) households learned of the spill the same day it occurred. More than 2/3 of households complied fully with “do not use” orders that were issued; only 8% reported drinking water against advice. Household assessments of official communications varied by source, with local officials receiving an average “B” rating, whereas some federal and water company communication received a “D” grade. More than 90% of households obtained safe water from distribution centers or stores during the emergency. We conclude that the spill had major economic impact with substantial numbers of individuals reporting incident-related illnesses and psychological distress. Authorities were successful supplying emergency drinking water, but less so with risk communication.

Abstract

A Knowledge, Attitudes and Practices (KAP) survey was conducted in Ajuong Thok and Pamir Refugee Camps in October 2019 to determine the current Water, Sanitation and Hygiene (WASH) conditions as well as hygiene attitudes and practices within the households (HHs) surveyed. The assessment utilized a systematic random sampling method, and a total of 1,474 HHs (735 HHs in Ajuong Thok and 739 HHs in Pamir) were surveyed using mobile data collection (MDC) within a period of 21 days. Data was cleaned and analyzed in Excel. The summary of the results is presented in this report.

The findings show that the overall average number of liters of water per person per day was 23.4, in both Ajuong Thok and Pamir Camps, which was slightly higher than the recommended United Nations High Commissioner for Refugees (UNHCR) minimum standard of at least 20 liters of water available per person per day. This is a slight improvement from the 21 liters reported the previous year. The average HH size was six people. Women comprised 83% of the surveyed respondents and males 17%. Almost all the respondents were refugees, constituting 99.5% (n=1,466). The refugees were aware of the key health and hygiene practices, possibly as a result of routine health and hygiene messages delivered to them by Samaritan´s Purse (SP) and other health partners. Most refugees had knowledge about keeping the water containers clean, washing hands during critical times, safe excreta disposal and disease prevention.

Geographic coverage

Ajuong Thok and Pamir Refugee Camps

Analysis unit

Households

Universe

All households in Ajuong Thok and Pamir Refugee Camps

Kind of data

Sample survey data [ssd]

Sampling procedure

Households were selected using systematic random sampling. Enumerators systematically walked through the camp block by block, row by row, in such a way as to pass each HH. Within blocks, enumerators started at one corner, then systematically used the sampling interval as they walked up and down each of the rows throughout the block, covering every block in Ajuong Thok and Pamir. In each location, the first HH sampled in a block was generated using an Excel tool customized by UNHCR which generated a Random Start and Sampling Interval.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire used to collect the data consists of the following sections: - Demographics - Water collection and storage - Drinking water hygiene - Hygiene - Sanitation - Messaging - Distribution (NFI) - Diarrhea prevalence, knowledge and health seeking behaviour - Menstrual hygiene

Cleaning operations

The data collected was uploaded to a server at the end of each day. IFormBuilder generated a Microsoft (MS) Excel spreadsheet dataset which was then cleaned and analyzed using MS Excel. Given that SP is currently implementing a WASH program in Ajuong Thok and Pamir, the assessment data collected in these camps will not only serve as the endline for UNHCR 2018 programming but also as the baseline for 2019 programming. Data was anonymized through decoding and local suppression.

Context

Urine comprises water, salts, and waste products from the kidneys. The balance of these compounds can affect the urine's acidity levels, which specialists measure in pH. According to the American Association for Clinical Chemistry, the average value for urine pH is 6.0, but it can range from 4.5 to 8.0. Urine under 5.0 is acidic, and urine higher than 8.0 is alkaline, or basic. Different laboratories may have different ranges for "normal" pH levels. The laboratory report will explain the normal and abnormal levels for the specific laboratory.

If a person has a high urine pH, meaning that it is more alkaline, it might signal a medical condition such as: kidney stones, urinary tract infections (UTIs), kidney-related disorders.

If a person has low urine pH, meaning that it is more acidic, it might indicate a medical condition such as: diabetic ketoacidosis, which is a complication of diabetes, diarrhea, starvation. Acidic urine can also create an environment where kidney stones can form.

Content

The dataset is formed by a single 60 years old man, which is diagnosed with initial stage of kidney stones. Doctor recommended to keep pH level on average level (pH: 6.4 - 6.8) to stop forming of new stones and drink more water to get rid of already formed stones. pH level was measured with "PH-02" device (Measuring range: 0.0 - 14.0 pH, Measurement accuracy: ± 0.1 pH). Measurements of pH were made on everyday basis almost without erros or misses on track. With pH measurements all the food (and some other conditions like gym) were accurately logged. Dataset consist of 2 parts.

First part was logged from 06.09.2017 to 05.04.2018 on daily basis. e.g. no exact time of pH measurements or time of eating were recorded. Only day is available. File: ph_v1_days.csv

Second part logged from from 09.04.2018 to 25.09.2018 with exact time of day when measurement was made. Files: prods.csv and phlist.csv

Inspiration

Predict what combination of products is best to eat to keep pH on average level (6.4 - 6.8), which is recommended to prevent kidney stones.

WMS service for water abstraction areas for supply designated in accordance with Article 7 of the WFD, under which all bodies of water used for the abstraction of water intended for human consumption providing on average more than 10 m3 per day or supplying more than 50 persons must be specified. In the demarcation there are 25 abstractions in surface water bodies for supply and 61 abstractions in groundwater. There are no abstractions for supply in transitional water bodies, no desalination plants for the production of water suitable for human consumption, nor is their construction planned. In the case of groundwater abstractions for supply, protection perimeters, safeguard areas and sites of hydrogeological interest have been defined. Author: General Directorate of Planning and Management of the Public Hydraulic Domain. Hydrological Plan 2009-2015. Node of the Andalusian Environmental Information Network. Regional Government of Andalusia. Integrated in the Spatial Data Infrastructure of Andalusia, following the guidelines of the Cartographic System of Andalusia.

In this table you will find information on the extent to which Dutch people participate traffic broken down by region by motive and general travel characteristics (e.g. time of departure and day of the week). A journey is a journey or part of a journey. with one motive. For example, the distance travelled from home to work is one movement, whether or not this involves one or more means of transport used.

The traffic participation of persons is expressed in three quantities:

— Average number of trips per person per day.

— Average number of kilometres travelled per person per day.

— Average travel time per person per day: based on departure and arrival times of movements.

The mobility data for the years 1985-2003 were obtained from the the Central Statistical Office (CBS) survey carried out annually Research Displacement Behavior (OVG). Since 2004 the mobility data have been from the Mobility Research Netherlands (MON) of the Transport Service and Shipping (DVS), a part of the Ministry of Transport and Water condition.

Data available from: 1985

Status of the figures Figures based on OVG/MON are always final.

When will there be new figures? This table was discontinued as of 20-03-2012 and continued as ‘Mobility in the Netherlands; mobility characteristics and motives, regions’. See also paragraph 3.