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Record W1490510444 · doi:10.5772/32055

Assessing Water Quality in the Developing World: An Index for Mexico City

2012· book-chapter· en· W1490510444 on OpenAlexafffund
Spíndola Flores Fabiola

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Waterloo
FundersSaudi Arabian Cultural MissionUniversity of Waterloo
KeywordsHygieneEnvironmental healthWaterborne diseasesWater qualityDeveloping countryWater supplyBusinessGeographyResource (disambiguation)Public healthFamineEnvironmental protectionSocioeconomicsWater resource managementEconomic growthMedicineEnvironmental engineeringEnvironmental scienceEconomics

Abstract

fetched live from OpenAlex

Safe water supply is one of the most significant worldwide concerns.Since water has become a scarce resource in some regions, over the last several decades efforts have generally focused on supplying more water to people, without considering its quality.Although the quantity of water that people receive influences hygiene and promotes public health, poor quality of water also affects humans and the environment, increasing famine, child mortality, waterborne diseases, environmental deterioration, and social inequalities.The likelihood of being infected by waterborne diseases is not homogeneous among all age groups, indeed the groups most likely to be affected by these diseases are children under five years, the elderly, and people living in poor hygiene conditions (no-running water or proper waste disposal) (World Health Organization [WHO] & United Nations Children's Fund [UNICEF], 2011; WHO, 2008).Today, freshwater resources (surface and groundwater) are threatened by over-exploitation and pollution.Consequently, they do not necessarily meet the quality standards for ensuring safe human consumption, and must undergo a purification process capable of 1) reducing turbidity, odors and unpleasant taste; 2) removing suspended solids, natural organic compounds (i.e., ammonia nitrogen), synthetic organic compounds (i.e., oil, detergents, pesticides and industrial solvents), and inorganic compounds (i.e., lead, cadmium, mercury, copper and zinc); and 3) eliminating pathogenic microorganisms (bacteria, viruses, protozoa and parasitic worms) that can cause outbreaks of waterborne diseases.Some of these compounds and microorganisms may be present in drinking water, but they must not exceed specific ranges if water safety is to be assured (WHO, 2011; Semarnat, 2011; U.S. Environmental Protection Agency [U.S. EPA], 2001).Nevertheless, some water management practices have increased people's exposure to health risks and insufficient water supply.For example, untreated wastewater discharge to freshwater sources, run-off from agricultural fields with high concentrations of pesticides, or wastewater reuse for crop irrigation.Use of water polluted with microorganisms and organic and inorganic compounds affects people's health, increasing their exposure to waterborne and other water-related diseases.Waterborne diseases are infectious diseases caused by bacterial, viral or protozoa infections spread primarily through contaminated water (Ashbolt et al., 2001;Grabow et al., 2001; www.intechopen.comWater Quality Monitoring and Assessment 496 Payment et al., 2003).The majority of these diseases is transmitted through fecal waste; therefore, lack of or low water quality contributes to their dissemination.The most common waterborne diseases include cholera, typhoid, dysentery, amoebic dysentery, and diarrhea (WHO, 2011).Although water is an important source for the generation and transmission of infectious organisms, waterborne diseases can also be disseminated through different routes, including the ingestion of contaminated food (either due to lack of hygienic conditions for its preparation or because crops were irrigated with untreated wastewater), person to person contact, unsanitary living conditions and/or poor hygiene.Water-related diseases can as well be caused by high concentrations of organic and inorganic compounds.Many of these compounds exist naturally, but their concentration has increased as a result of human activities.For instance, high concentrations of pesticides, lead, fluoride, nitrates, arsenic, and other heavy metals can affect the nervous system and kidneys; and also cause reproductive disorders, cancer, skin lesions, endocrinal damage, and vascular diseases (WHO,

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.377
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2012
Admission routes2
Has abstractyes

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