Japan JP: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 0.200 Ratio in 2016. Japan JP: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 0.200 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Japan JP: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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

Mexico MX: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 1.100 Ratio in 2016. Mexico MX: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 1.100 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Mexico MX: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Sierra Leone SL: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 81.300 Ratio in 2016. Sierra Leone SL: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 81.300 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Sierra Leone SL: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sierra Leone – Table SL.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Saudi Arabia SA: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 0.100 Ratio in 2016. Saudi Arabia SA: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 0.100 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Saudi Arabia SA: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Jamaica JM: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 0.600 Ratio in 2016. Jamaica JM: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 0.600 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Jamaica JM: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jamaica – Table JM.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Background: Billions of the world’s poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.

Objectives: This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.

Methods: We calculated the total change in disability-adjusted life years (DALYs) from household air pollution (HAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies.

Results: Boiling drinking water reduced the diarrhea disease burden by a mean of 1100 DALYs and 367 DALYs per 10,000 people for those under and over 5 years of age in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, though with fewer avoided DALYs in children under 5 due to different demographics. In both countries, for households with high baseline HAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in HAP DALYs due to the log-linear exposure-response curves. Boiling, even at low effectiveness, was associated with net DALY reductions for medium- and high-risk water, even with unclean stoves/fuels. Use of clean stoves coupled with effective boiling significantly reduced total DALYs.

Discussion: Boiling water generally resulted in net decreases in DALYs. Future efforts should empirically measure health outcomes from HAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.

Methods

The goal of this study was to develop a framework to compare health risks. We focus on two countries, Uganda and Vietnam to show how the framework is used. We synthesized established modeling tools to build an analytical framework to compare health impacts from IAP and fecally-contaminated drinking water at the household level, using DALYs as the primary metric to compare multiple risks. Input variables were selected from the best available data in the literature. We used DALYs to quantify health burdens because they account for morbidity with differential disease severity and mortality. Quantitative Microbial Risk Assessment (QMRA) models are commonly used to determine the risk associated with consuming water from a particular water source (Havelaar & Melse, 2003). For IAP, the population attributable fraction based on a dose-response curve for individual diseases is used to calculate the burden of disease (Asikainen et al., 2016; Pillarisetti et al., 2016).

The first module is called the water risk module, which uses a QMRA to calculate the DALYs from drinking water contaminated by fecal matter before and after treatment by boiling. The second module is the air risk module. This uses an indoor box model to quantify the PM2.5 concentrations for different stoves and uses combined with the Household Air Pollution Intervention Tool (HAPIT) to quantity the DALYs associated with IAP under various scenarios. We designed the model to be used for any country. However, we selected Uganda and Vietnam as case study countries as they are in distinct regions, have different population demographics, and high prevalences of boiling among household water treatment users. The R code was designed to run a Monte Carlo simulation for different scenarios and produce outputs of indoor air pollution concentrations, and drinking water and air pollution DALYs in csv files.

Malaysia Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 0.400 Ratio in 2016. Malaysia Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 0.400 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Malaysia Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Malaysia – Table MY.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

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

This statistic presents the countries with the highest number of people living without access to safe water worldwide in 2017. Almost ** million people living in Nigeria had no household access to safe water at this time.

Lacking water access – additional information

The World Health Organization states that ** liters of water per person per day is the recommended “intermediate” quantity for health, hygiene, and domestic uses. Comparatively, the United States consumed about ***** cubic meters of water per capita in total in 2017. For those who have no direct access to water at home, buying water can be a significant burden, and those that cannot afford it often resort to using water from unsafe sources. The main reasons people struggle to access water is due to a lack of money or political priority, ineffective institutions and management regimes, as well as social inequalities.

There are over *** million people living in India without household access to safe water, a figure higher than many countries have people. Poor management of water resources in India is one of the major problems preventing adequate water access. Aquifers are the main source of water in the country and the use of hand pumps is quickly depleting shallow aquifers. Other countries in Asia, such as Indonesia and Pakistan, have some ** million and ** million people, respectively, who also have to go without safe water in their own homes.

Globally, about 63 percent of the population in Papua New Guinea lives without safe water access in their homes. In the capital city, Port Moresby, many people live in areas that are outside of existing water utilities and infrastructure. More extreme weather and rising sea levels will likely lead to more uncertainty and unreliable water supplies.

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

Mozambique MZ: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 27.600 Ratio in 2016. Mozambique MZ: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 27.600 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Mozambique MZ: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mozambique – Table MZ.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

This data collection includes connection pipes from gullies and houses to the main sewer, as well as the geometry of the sewer strands and wells of the main sewer. The data related to connections can also be retrieved using a web feature service: https://data.riox.online/eindhoven/wfs

Do you want to measure distances in the map? This can be done in the ArcGIS viewer: click here to go to the viewer.

https://data.eindhoven.nl/assets/theme_image/riool.PNG" alt="">

(Click to measure on the ruler on the right.)

A number of points for attention:

This data collection contains a large amount of line pieces, to view them all you need to zoom in for performance reasons.

Fittings House Connection, some Connection Lines House Connection, and Wells are originally points that are shown as closed line objects in the map.

The home connections (both connection lines and fittings) are not yet complete for the whole of Eindhoven, these are still being added per area at the time of writing.

The attribute TYPE can be used to deduce whether it is a part of the main sewer, a well, or data related to home connections.

Maindriole

The location of the main sewer is only visible for reference. The available categories are: Mixed water, Rainwater (sky water), Dirty water, Dirty water + Roof surface.

Putting

For reference purposes only, the name of the well and its location are visible from the wells.

Home connections

The connection pipe and the associated attachment have been made clear from the house connections.

For ‘House Connection Tools’, the following attributes are regularly available: ADRES (adres waarop het hulpstuk van toepassing is, dit attribuut is niet altijd gevuld), PLAATS (zou altijd Eindhoven moeten zijn voor deze data, betreft het niet Eindhoven, dan een fout), STELSEL (stelsel waarop het hulpstuk is aangesloten), DIAMETER (diameter van het hulpstuk in millimeters, als 0 dan onbekend), MATERIAAL (materiaal van het hulpstuk), de BEGINPUT en EINDPUT (stemmen overeen met de PUTNAAM van een rioolput uit het hoofdriool, dit attribuut is niet altijd gevuld), PUTAFSTAND (afstand hulpstuk tot de BEGINPUT, dit attribuut is niet altijd gevuld), DIEPTE (maar sporadisch gevuld, “-“ of leeg wanneer onbekend), JAAR (jaar van aanleg, niet altijd gevuld), DATUM (plaatsingsdatum in bestand, niet altijd gevuld), NLCS (laagnaam conform Nederlandse CAD standaard, niet altijd gevuld), REFERENTIE (dit attribuut is niet altijd gevuld), en TYPE (of het gaat om een ontstoppingsstuk of een inlaat hulpstuk).

The following attributes are available for ‘House Connection Lines’: ADDRESS (address to which the attachment applies, this attribute is not always filled), PLACE (should always be Eindhoven for these dates, it is not Eindhoven, then an error), STELSEL (system to which the attachment is connected), DIAMETER (diameter of the attachment in millimetres, if 0 then unknown), MATERIAL (material of the attachment), BEGINPUT and EINDPUT (corresponding to the PUTNAME of a sewer well from the main sewer, this attribute is not always filled), PUTAFSTAND (distance attachment from the BEGINPUT, this attribute is not always filled), DIEPTE (but sporadically filled, “-“ or empty when unknown), YEAR (year of construction, not always filled), DATE (placement date in file, not always filled), NLCS (low name according to Dutch CAD standard, not always filled), REFERENCE (this attribute is not always filled).

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

BackgroundOne of the tropical illnesses that is often overlooked is soil-transmitted helminths, or STHs. In tropical and subtropical nations, where poor sanitation and contaminated water sources are common, they mostly impact the most vulnerable populations.ObjectiveThe aim of this study was to ascertain the prevalence of STHs and related risk factors among the people living in Jigjiga town, Somali region, Eastern Ethiopia.MethodsA community-based cross-sectional study was revealed from June 1 to July 21, 2023. Study participants were selected through a multistage sampling method, where households were randomly chosen from the kebeles. A semi-structured questionnaire and observational checklist were used to collect some of the data. A stool sample was collected from each participant, and a single Kato-Katz was performed to detect STHs. Bivariate and multivariate logistic regression analyses were performed, and statistical significance was declared at a level of p-value

A water pump from the 60s - built between blocks of flats in People's Republic of Poland (when Poland was behind the Iron Curtain). The water is deemed unsafe for humans to drink, but it can be used to water the plants, etc.

Mostly the reason for making this kind of water pumps between block of flats was due to cold war - many blocks had an atomic shelter (with air and water purification equipment, heavy steel doors with lead inserts) and those that didn't have shelters still got decent concrete basements, that could withstand great deal of damage. And if not hit directly or really close - people could probably survive hiding in those basements. In general, any survival guide says you should keep away from the fallout for two weeks and minimize leaving shelter/safehouse during that time. Radioactive decay would make it 'survivable' by that time (not perfect, but hey). Now - everything will be contaminated, but groundwater should be pretty ok, so having water pump might have meant survival.

Source: Objaverse 1.0 / Sketchfab

Botswana BW: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 11.800 Ratio in 2016. Botswana BW: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 11.800 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Botswana BW: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank: Health Statistics. Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

A cleanup site is a place where a toxic substance is harming or threatening humans or the environment.Toxic substances can include:Petroleum (gasoline, diesel, oil, etc.)Heavy metals (lead, arsenic, etc.)Chemicals and pesticidesPersistent organic pollutants (PCBs, dioxins, furans, etc.)Toxic substances can contaminate multiple types of media, including:SoilSediment (in bays, shorelines, estuaries, lakes, rivers, etc.)Water (groundwater, fresh or marine water, and stormwater or surface runoff)Air (indoor and outdoor air, soil gas, and vapor intrusion)Under state and federal laws, people or entities who pollute the air, land, or water are responsible for cleaning up the contamination.DescriptionContaminated site locations and status. Data contains links to any reports or actions that are required for existing sites and is updated regularly. This data layer is created from a script using data from the Washington Department of Ecology. DATA LINKED FROM Washington Department of EcologyFor more information visit the Department of Ecology's Cleanup site website.

Intermittent water supplies (IWS) deliver piped water to one billion people; this water is often microbially contaminated. Contaminants that accumulate while IWS are depressurized are flushed into customers’ homes when these systems become pressurized. In addition, during the steady-state phase of IWS, contaminants from higher-pressure sources (e.g., sewers) may continue to intrude where pipe pressure is low. To guide the operation and improvement of IWS, this paper proposes an analytic model relating supply pressure, supply duration, leakage, and the volume of intruded, potentially-contaminated, fluids present during flushing and steady-state. The proposed model suggests that increasing the supply duration may improve water quality during the flushing phase, but decrease the subsequent steady-state water quality. As such, regulators and academics should take more care in reporting if water quality samples are taken during flushing or steady-state operational conditions. Pipe leakage increases with increased supply pressure and/or duration. We propose using an equivalent orifice area (EOA) to quantify pipe quality. This provides a more stable metric for regulators and utilities tracking pipe repairs. Finally, we show that the volume of intruded fluid decreases in proportion to reductions in EOA. The proposed relationships are applied to self-reported performance indicators for IWS serving 108 million people described in the IBNET database and in the Benchmarking and Data Book of Water Utilities in India. This application shows that current high-pressure, continuous water supply targets will require extensive EOA reductions. For example, in order to achieve national targets, utilities in India will need to reduce their EOA by a median of at least 90%.

Antibiotic resistance is a leading cause of hospitalization and death worldwide. Heavy metals such as arsenic have been shown to drive co-selection of antibiotic resistance, suggesting arsenic-contaminated drinking water is a risk factor for antibiotic resistance carriage. This study aimed to determine the prevalence and abundance of antibiotic-resistant Escherichia coli (AR-Ec) among people and drinking water in high (Hajiganj, >100 μg/L) and low arsenic-contaminated (Matlab,