The Impact of Water Quality on Jordanian Public Health Status: A Case Study in Zarqa Basin
Bibliographic record
Abstract
Climate change significantly impacts water quality, posing serious risks to public health. Rising global temperatures and altered precipitation patterns contribute to water pollution, increasing the proliferation of harmful microorganisms and contaminants. This study examines the effects of climate change on water quality in the Zarqa Basin, Jordan, by analyzing key water quality indicators, including microbiological contamination and chemical composition. Water samples were collected and assessed for compliance with Jordanian and WHO water standards. The study followed a descriptive analytical approach, incorporating field sampling and laboratory analysis to determine the concentrations of minerals, organic matter, pesticides, and microbiological indicators. Results indicate that climate change has exacerbated water contamination, particularly through increased bacterial presence, higher turbidity, and deviations in chemical composition beyond permissible limits. The presence of Escherichia coli and total coliforms in water samples suggests heightened risks of waterborne diseases, necessitating urgent mitigation measures. This study provides valuable insights for policymakers, water managers, and public health authorities in addressing climate-induced water quality challenges in Jordan and similar regions. Water samples from key locations—including Zarqa, Jerash, and King Talal Dam—were analyzed to assess their compliance with international water safety standards set by the World Health Organization (WHO), the U.S. Environmental Protection Agency (EPA), Health Canada, the European Union, and Australia’s National Medical and Health Research Council. Results indicate that 55.1% of the 49 assessed water quality components exceeded safe limits, highlighting substantial contamination. High concentrations of sodium, chloride, nitrogen compounds, and microbiological contaminants, particularly Escherichia coli and total coliform bacteria, were detected, posing severe health risks. Elevated pollutant levels were attributed to climate-induced changes in precipitation patterns, industrial and agricultural runoff, and insufficient wastewater treatment. Additionally, significant fluctuations in pH, water temperature, and electrical conductivity suggest instability in water quality, necessitating continuous monitoring. The study underscores the urgent need for adaptive water management strategies, regular surveillance of water quality, and policy interventions to mitigate contamination risks. Key recommendations include enhancing water purification systems, improving wastewater treatment infrastructure, and promoting community awareness about water conservation. Addressing these challenges is crucial for ensuring sustainable access to safe water in the Zarqa Basin and similar vulnerable regions. The findings emphasize the critical need for adaptive water management strategies, improved monitoring systems, and policy interventions to ensure water safety. Reducing contamination, securing alternative water resources, and implementing sustainable land-use practices are essential to protecting public health and the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".