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Access to clean water is one of our most basic human needs.

But, one in four people in the world do not have access to safe drinking water. This is a major health risk. Unsafe water is responsible for more than a million deaths each year.

In this article, we look at data on access to safe water and its implications for health worldwide.

Unsafe water is a leading risk factor for death Unsafe water sources are responsible for over one million deaths each year Unsafe water is one of the world's largest health and environmental problems – particularly for the poorest in the world.

The Global Burden of Disease is a major global study on the causes and risk factors for death and disease published in the medical journal The Lancet. These estimates of the annual number of deaths attributed to a wide range of risk factors are shown here.

Lack of access to safe water sources is a leading risk factor for infectious diseases, including cholera, diarrhea, dysentery, hepatitis A, typhoid, and polio.1 It also exacerbates malnutrition and, in particular, childhood stunting. In the chart, we see that it ranks as a very important risk factor for death globally.

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;

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;

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.

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;

Zambia ZM: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 34.900 Ratio in 2016. Zambia ZM: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 34.900 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Zambia ZM: 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 Zambia – Table ZM.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;

This dataset appears to contain medical records of individuals diagnosed with various diseases. Each row represents a patient case, detailing information about the disease, its symptoms, causes, transmission mode, and medical indicators such as heart rate, blood pressure, fever, and infection status.

Columns Description id: A unique identifier for each record. disease_name: The name of the diagnosed disease (e.g., Tuberculosis, Lung Cancer, Cholera). type The category of the disease: Communicable (infectious), Non-communicable (chronic), or Water-borne. symptoms: Common symptoms associated with the disease (e.g., cough, fever, weight loss). causes: The primary cause of the disease (e.g., bacterial infection, smoking, genetic factors). transmission_mode: How the disease spreads (e.g., Airborne, Contaminated water). Some non-communicable diseases may not have a transmission mode. treatment :The recommended medical treatments (e.g., antibiotics, surgery, chemotherapy). prevention:Preventive measures (e.g., vaccination, good hygiene, safe drinking water). heart_rate:The patient’s heart rate (in beats per minute). blood_pressure_systolic:The systolic blood pressure (upper value in mmHg). blood_pressure_diastolic: The diastolic blood pressure (lower value in mmHg). fever: Indicates whether the patient has a fever (TRUE or FALSE). body_temperature: The patient's recorded body temperature (°C). duration_of_infection: Number of days the patient has been infected. infection_status: The current infection status (Infected or possibly another status in more data).

Insights from the Data

Disease Classification:

1. Communicable diseases (e.g., Tuberculosis, Cholera) spread through transmission modes like airborne or contaminated water.
2. Non-communicable diseases (e.g., Lung Cancer) have causes such as smoking or genetic factors and don't spread between individuals.
3. Water-borne diseases (e.g., Cholera, Giardiasis) are spread through contaminated water sources.

Symptoms & Severity Indicators:

Symptoms vary by disease but often include cough, chest pain, diarrhea, or weight loss. Some patients have a fever (fever = TRUE), while others do not. The heart rate and blood pressure vary between patients, which may indicate the severity of their condition.

Infection Status:

Patients classified as "Infected" are actively suffering from the disease.

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;

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

Mongolia MN: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 1.300 Ratio in 2016. Mongolia MN: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 1.300 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Mongolia MN: 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 Mongolia – Table MN.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.

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.

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;

According to the monitoring data from the Embassy of the United States, there was on average 39 micrograms of PM2.5 particles per cubic meter to be found in the air in Beijing during 2023. The air quality has improved considerably since 2013.

Reasons for air pollution in Beijing

China’s capital city Beijing is one of the most populous cities in China with over 20 million inhabitants. Over the past 20 years, Beijing’s GDP has increased tenfold. With the significant growth of vehicles and energy consumption in the country, Beijing’s air quality is under great pressure from the economic development. In the past, the city had a high level of coal consumption. Especially in winter, in which coal consumption increased due to heating, the air quality could get extremely bad on the days without wind. In spring, the wind from the north would bring sand from Mongolian deserts, resulting in severe sandstorms in Beijing. The bad air quality also affected the air visibility and threatened people’s health. On days with very bad air quality, people wearing masks for protection can be seen on the streets in the city.

Methods to improve air quality in Beijing

Over the past years, the government has implemented various methods to improve the air quality in Northern China. Sandstorms, which were quite common 15 years ago, are now rarely seen in Beijing’s spring thanks to afforestation projects on China’s northern borders. The license-plate lottery system was introduced in Beijing to restrict the growth of private vehicles. Large trucks were not allowed to enter certain areas in Beijing. Above all, the coal consumption in Beijing has been restricted by shutting down industrial sites and improving heating systems. Beijing’s efforts to improve air quality has also been highly praised by the UN as a successful model for other cities. However, there is also criticism pointing out that the improvement of Beijing’s air quality is based on the sacrifice of surrounding provinces (including Hebei), as many factories were moved from Beijing to other regions. Besides air pollution, there are other environmental problems like water pollution that China is facing. The industrial transformation is the key to China’s environmental improvement.

Luxembourg LU: 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. Luxembourg LU: 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. Luxembourg LU: 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 Luxembourg – Table LU.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;

Zimbabwe ZW: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data was reported at 24.600 Ratio in 2016. Zimbabwe ZW: Mortality Rate Attributed to Unsafe Water, Unsafe Sanitation and Lack of Hygiene: per 100,000 Population data is updated yearly, averaging 24.600 Ratio from Dec 2016 (Median) to 2016, with 1 observations. Zimbabwe ZW: 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 Zimbabwe – Table ZW.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 is part of the Proposed Regional Plan. It has no legal effect.The Marine Pollution Regulation defines areas safe for sewage disposal which are identified in this dataset.In our harbours, untreated sewage can contaminate the shellfish we eat or make water unsafe for swimming for a long time after it is discharged. Boat sewage is a serious risk to human and animal health and the environment.To continue to enjoy Northland's coast and safely gather and eat kaimoana (seafood), our waters must be kept free of sewage. There are strict rules covering sewage discharges from boats in Northland waters – these apply to every type of boat or craft. Untreated boat sewage must be discharged well outside of any harbour or at a marina pumping facility.It is illegal to discharge ‘Grade A' treated sewage within 100 metres of a marine farm. It's also illegal to discharge ‘Grade B' treated sewage within 500 metres of a marine farm or gazetted Maitaitai Reserve.Boat sewage is much more concentrated than sewage from land because it has not been diluted or treated. It is estimated that an untreated discharge from a single weekend boatie can put the same quantity of bacterial pollution into the water as the treated sewage from thousands of people on land.Boat sewage can contaminate the water with long-living viruses and nasties which can cause harmful diseases, like Hepatitis A, or severe stomach upsets.Shellfish can become unsafe to eat for weeks after exposure to sewage as they are filter feeders – they concentrate viruses and other nasties in the water. Shellfish in estuaries and bays are particularly at risk because any contaminated water in them usually takes longer to flush out.There are different ways you can comply with marine pollution regulations, including:Using on-shore toilet facilities Using an ordinary portable toilet and taking it ashore to empty it Installing a sewage holding tank on your boat Installing a sewage treatment system on board.The appropriate scale of use is 1:50,000

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,