Pollution Index Research Articles

Health and Environmental Risk Assessment of Heavy Metals Contamination from Medical Wastes Ash of Baghdad Hospitals

Abstract One of the complex problems that many countries suffer from is how to deal with medical waste because of its harmful and devastating effects on humans and the environment. Hospitals around the world use and consume many different products. These products are composed of various materials, and many of them contain heavy metals in their production and manufacturing, which are ultimately emitted into the environment, posing a major danger and risk to human health. This work focused on assessing the contamination levels of medical waste ash after the incineration process. Ash samples were collected from major hospitals in the capital, Baghdad. The health risks resulting from heavy metal pollution were calculated. Medical waste ash was collected at different times from three major hospitals within the capital, Baghdad, and tested for contamination with heavy metals. To measure the bottom ash samples, X-ray spectroscopy (XRF) technology was used to determine heavy metal concentrations. The results showed the presence of more than twenty elements in the ash of medical waste, including the heavy metals found in Baghdad hospitals. Work was done on eight of these heavy metals due to their high concentrations. These selected elements are considered highly toxic and dangerous if exposed to them, including zinc (Zn), lead (Pb), arsenic (As), cobalt (Co), and copper. (Cu), nickel (Ni), chromium (Cr), and mercury (Hg). To evaluate the health risks of this pollution. All of these elements are a major problem if exposed to them. The amount of contamination was calculated using the contamination factor (CF). The results showed that the percentage of As, Cd, Hg, Zn, Pb, and Cu was very high. Meanwhile, the concentrations of Co and Ni were moderate values in medical waste ash. Adult health risks were calculated using the non-cancer health risk ratio (HL). The calculated values show values higher than the safe level in Imam Ali Central Hospital, indicating a potential high health risk. Meanwhile, other hospitals (Sheikh Zayed and Dhari Al Fayyad) recorded risk values below the safe level, indicating no health risks. The health risk assessment indicated that there are three main routes of human exposure: “ingestion, inhalation, and skin contact.” The cancer risk index (Risk) was also calculated. The values obtained were within the acceptable range. An analysis of the metal pollution index was also conducted, and it was found that heavy metals pose a non-cancerous risk. But the harmful effects of pollution from ash disposal have been particularly serious because they may contain high concentrations of heavy metals.

Comprehensive Analysis of Modified Heavy Metal Pollution Index and Health Risk Assessment in the Yamuna River of Delhi, India: Crucial Study for Environmental Health Management

Comprehensive Analysis of Modified Heavy Metal Pollution Index and Health Risk Assessment in the Yamuna River of Delhi, India: Crucial Study for Environmental Health Management

Assessment of heavy metal contamination in the groundwater of Gujarat, India using the Heavy Metal Pollution Index

Groundwater serves as a vital water source for a significant population in the Gujarat region of India. However, substantial contamination from heavy metals, pose a serious threat to human health through various pathways, including drinking water. The rapid industrial and agricultural growth in recent years has exacerbated heavy metal pollution in the state. This study focuses on assessing the heavy metal contamination in the groundwater of Gujarat using the Heavy Metal Pollution Index (HPI). The research covers the entire state, considering its diverse physical, climatic, topographical, and geographical conditions. The HPI scores obtained from individual studies highlight the extent of pollution caused by heavy metals. The overall findings underscore the severe problem of heavy metal contamination in Gujarat's groundwater and the associated health risks. Various other pollution indicators, including the Heavy Metal Evaluation Index, Degree of Contamination, Metal Index, and Water Pollution Index are discussed as tools to assess contamination levels. These indices compare concentrations of different heavy metals with established limits to determine the pollution level. The goal is to provide valuable insights for investors and policymakers in formulating strategies to manage and reduce heavy metal contamination across the state. Additionally, the paper explores effective, environmentally friendly, and economically viable treatment techniques to remove heavy metals from aquatic systems, safeguarding the environment. By employing pollution indicators and remedial actions, this study aims to guide efforts in mitigating the impact of heavy metal contamination in the groundwater of Gujarat.

Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database

Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.

Groundwater Quality and Potential Health Risk Assessment for Potable Use

The Ramganga River basin, comprising three rivers, the Dhela, Dhandi, and Ramganga, plays a vital role in groundwater recharge, sustaining numerous industries, urban areas, and rural communities reliant on these rivers for daily activities. The study’s primary purpose was to analyze the groundwater quality in the context of potability, irrigation, and health risks to the local inhabitants of the Ramganga River basin. In 2021–2022, 52 samples (26 × 2) were collected from 13 locations in two different seasons, i.e., pre-monsoon and post-monsoon, and 20 physico-chemical and heavy metal and metalloids were analyzed using the standard protocols. The result shows that heavy metal and metalloids and metalloid concentrations of Zn (0.309–1.787 and 0.613–1.633); Fe (0.290–0.965 and 0.253–1.720), Cd (0.001–0.002 and 0.001–0.002); As (0.001–0.002 and 0.001–0.002), Cr (0.009–0.027 and 0.011–0.029), and Pb (−0.001–0.010 and 0.00–0.010) values in mg/L are present in both seasons. The groundwater quality index (GWQI), heavy metal pollution Index (HPI), and heavy metal evaluation index (HEI) were used to assess the water quality and metal pollution in the basin area. As per GWQI values, water quality lies from excellent water quality (41.639 and 43.091) to good water quality (56.326 and 53.902); as per HPI values, it shows good (29.51 and 30.03) to poor quality (60.26 and 59.75) and HEI values show the low-level contamination (1.03–2.57 and 1.13–3.37) of heavy metal and metalloids in both seasons. According to the potential health risk assessment, infants show low risk in pre-monsoon and low risk to medium post-monsoon, while children and adults show low risk to high risk in both seasons. From the health risk perspective, it shows that children and adults have more concerns about non-carcinogenic effects, so adequate remedial measures and treatment are required to avoid the groundwater quality of the Ramganga River basin.

Classification of Air Pollutant Index on Data with Outliers and Imbalance Class Problem

The problem of air pollution has become a global issue that has received attention from various countries. Jakarta, Indonesia's capital city, is unavoidable from the same problem. This study will use four parameters of substances PM10, SO2, CO, O3, and nitrogen dioxide to categorize Jakarta's air quality (NO2). The data used is daily data taken from the Air Quality Monitoring Station in Jakarta throughout 2020. The methods used include SVM, Random Forest, Logistic Regression, KNN, CART, and Stacking Algorithm. At the data preparation stage, we found missing values, outliers, and class imbalance problems. Before applying machine learning methods and evaluating accuracy, we used data pre-processing techniques such as the MissForest method, median substitution, and ADASYN. The results prove that the original dataset has a higher accuracy score (0.882 – 0.977) than the balanced dataset (0.829 – 0.976). According to the evaluation results, the Random Forest method has the highest accuracy score for original and balanced datasets. The overall result is better than the identical research, which produces 96.61% accuracy using a neural network. It shows that preprocessing steps such as missing values handling an imbalanced class handling is essential in classification performance.

Impact of Landfill Leachate on Groundwater Quality and Health Risk Assessment in Mohammedia Prefecture, Morocco

Several studies have been conducted to identify the potential impact of landfills on groundwater resources. This study evaluates the impact of landfills on groundwater resources in Mohammedia prefecture, Morocco. The groundwater was analysed from 2015 to 2022. The groundwater quality was evaluated based on electrical conductivity, pH, biological oxygen demand, chemical oxygen demand, total Kjeldahl nitrogen, phosphate, suspended solids, dissolved oxygen, ammonia, and total hydrocarbon, aluminium, iron, cadmium, chromium, copper, iron-nickel, zing, and mercury. The assessment was based on the water quality index, leachate pollution index, non-carcinogenic risk assessment, and carcinogenic risk assessment. A leachate pollution index <5 indicates that it poses a severe risk to groundwater resources. The non-carcinogenic risk HQ was determined to be <1, which infers no potential risk. The carcinogenic risk index value of 10-4 indicated that it is within the threshold of acceptable limit. The current study concludes that leachate from the analysed landfills does not infiltrate the groundwater resources of Mohammedia prefecture. However, the leachate pollution, even though it varies, is increasing over time. This is validated by the fact that the landfill is protected with a membrane covering the ground, which inhibits any possible infiltration of soil or water resources. Hence, this study calls for continuous monitoring of groundwater resources in the region. Future studies are required to investigate the groundwater in Mohammedia prefecture in terms of emerging pollutants to identify any potential risk.

Application of Geographical Information System –Water Quality Index Techniques for Evaluating the Groundwater Quality in Kaniqirzhala Landfill, Kurdistan Region, Iraq

The aim of the current study through uses the Water Quality Index for drinking and irrigation purposes and Heavy Metal Pollution Index to determine quality groundwater in 25 wells spread throughout Kaniqirzhala landfill within the Erbil governorate during 2022. Groundwater samples from various wells have been gathered for chemical and physical analysis in order to determine the water quality. Turbidity, pH, electrical conductivity, total dissolved solids, total alkalinity, total hardness, major anions (SO4-2, Cl-1, Hco3-1, NO3-2, PO4-3) and major cations (Ca+2, Mg+2, Na+1, K+1) are all included in the analysis. In addition to heavy metals investigations were conducted on a variety of metals, including manganese (Mn), nickel (Ni), zinc (Zn), arsenic (As), barium (Ba), boron (B), cadmium (Cd), copper (Cu), iron (Fe), and lead (Pb). Results indicated that higher values of nitrate were calculated in sites 10 to 17. But, in all wells higher concentrations of alkalinity and total hardness were recorded .The concentrations of heavy metals in wells water ranged from low to high according to WHO guidelines for drinking purposes. Drinking water quality index ranged from 52.20 (good quality) at site 9 to 137.01 (poor quality) at site 14. The value of the irrigation water quality index, which is low at site 15, which indicates low restriction for irrigation uses, while the value of 50.01 recorded at site 4 indicates high restriction for irrigation purposes. In all groundwater sites under study, a heavy metal pollution index of greater than 100 indicates extremely poor water quality unsuitable for human consumption. This study concluded all examined wells were deemed to be of extremely poor to good quality for to use in for drinking water based on the water quality index, while extremely low quality based on the heavy metals pollution index .

Relationship Between Financial Development and Carbon Emissions: Empirical Evidence from Türkiye with Fractional Frequency Fourier Approaches

Abstract This study empirically analyzes the relationship between carbon emissions, one of the most important indicators of environmental pollution, and financial development. Using data from Türkiye for the period 1995-2019, the fractional frequency Fourier ADL cointegration method -previously unused in similar studies- is employed for the analysis. The results, which also account for economic growth, demonstrate a cointegration relationship between the variables. Additionally, the FMOLS method is utilized for model estimation, concluding that financial development and growth lead to increased carbon emissions. The study suggests that loans provided to the financial sector should be directed towards technological investments that reduce carbon emissions

Assessment of metal-associated health risk in different trophic levels in tropical estuary on the southeast coast of India

Metal contamination in coastal and marine ecosystems has become a significant global concern due to its hazardous characteristics, environmental persistence, and ability to bioaccumulate in aquatic ecosystems. This poses a serious threat to the environment and the health of humans worldwide. To address these concerns, this study estimated the concentrations of metals in various trophic levels, including phytoplankton, zooplankton, bivalve, and fish. Monthly sampling was carried out in Pondicherry Fishing Harbor (PFH) and Pondicherry Open Sea (POS) between January 2017 and December 2018. The value of Cd, Pb, Cr, Co, Ni, Cu, Zn, and Mn in sediment at the PFH was considerably higher compared to the POS, indicating the impact of human activity there. The contamination factor (CF) for other metals was <0.5, suggesting minor contamination in the Pondicherry coastal sediment, the CF value for Cd was higher at PFH. Comparably, the risk index (RI) at the PFH was likewise greater because of Cd, leading to an overall risk grade of “considerable” at the PFH whereas it was “low” at the POS. The marine pollution index (MPI) showed minimal values in fish regardless of the collection sites, which was calculated based on the values of all metals. The estimated daily intake, target hazard quotient, and hazard index assessed for potential human health risk suggest that the values were within acceptable thresholds for adults and children for fish consumption from POS. However, the direct consumption of bivalve for the long term poses significant non-carcinogenic health risks in both age groups, particularly in children, who are 1.31 times more susceptible than adults. These findings highlight the need to evaluate the presence of metals in the food chain to determine their transfer to the different trophic levels, which can help mitigate the associated risks for sustainable coastal ecosystem management.

Salix humboldtiana as an Indicator of Air Pollution by Trace Metals in the Urban Areas of the City of Loja, Southern Ecuador

Air pollution is the most important environmental problem in urban areas related to vehicular traffic and industrial activities. The widespread presence of common urban trees, such as Salix humboldtiana, and their ability to tolerate diverse environmental conditions make this species an especially promising candidate for assessing environmental metal contamination. Therefore, biomonitoring with vascular plants has been widely used to assess air pollution, especially the accumulation of trace metal concentrations. Therefore, for the first time, we analyzed the concentration of trace metals using Salix humboldtiana in twelve areas with different levels of pollution in a city in Southern Ecuador. For this purpose, samples were taken from each site to assess the accumulation of trace metals such as Zn, Mn, Fe, Cd, Cr, Pb, Cu, Al, and Ni. The results obtained showed significant differences in the concentrations of Zn, Mn, Fe, and Cd between the urban areas and the control area, indicating that the central areas were the most polluted by vehicular traffic. However, these findings suggest that Salix humboldtiana may not be a particularly effective tool for quantifying levels of environmental metal contamination such as Cu and Ni, at least in urban areas in the city of Loja. This study has demonstrated that Salix humboldtiana leaves can effectively monitor trace metals associated with road traffic emissions in areas with varying levels of vehicular activity, indicating that vascular plants can be utilized for this purpose in tropical cities.

Association of ambient air pollution and Air Quality Index with risk of sudden sensorineural hearing loss: a cross-sectional study

ObjectivesTo explore the associations of air pollutants and Air Quality Index (AQI) with risk of sudden sensorineural hearing loss (SSNHL)DesignCross-sectional studySettingMedical record data and local population data collected between 2014...

Water quality assessment of Upper Ganga and Yamuna river systems during COVID-19 pandemic-induced lockdown: imprints of river rejuvenation.

Clean river water is an essential and life-sustaining asset for all living organisms. The upper Ganga and Yamuna river system has shown signs of rejuvenation and tremendous improvement in the water quality following the nationwide lockdown due to the coronavirus pandemic. All the industrial and commercial activity was shut down, and there was negligible wastewater discharge from the industries. This article addresses the water quality assessment from the study area, which is based on the original data of physical parameters, major and trace elements, and stable isotopes (hydrogen and oxygen) systematics during the nationwide lockdown. The impact of the lockdown could be seen in terms of an increase in dissolved oxygen (DO). Water samples were collected from the Upper Ganga and Yamuna river basins (Alaknanda, Bhagirathi, and Tons rivers) during an eight-week lockdown in Uttarakhand, India. We discussed the signs of rejuvenation of riverine based on physical parameters, major ions, trace elements, isotopic ratios, and water pollution index (WPI). Results reveal that the water quality of the entire upper basins of the Ganga has significantly improved by 93%, reflecting the signs of self-rejuvenation of the rivers. Multivariate analysis suggests a negative factor loading for an anthropogenic element ( ), implying that they contribute little to the river water during the lockdown. Further, bicarbonate ( ) is a dominant element in both river basins. The geochemical facies are mainly characterized by the ( ) type of water, suggesting that silicate rock weathering dominates with little influence from carbonate weathering in the area. The positive factor loadings of some cations, like , , and reflect their strong association with the source of origin in the lockdown phases. Stable isotopic reveals that the glaciated region contributed the most to the river basin, as evidenced by the low d-excess in riverine water compared to anthropogenic contributions. Rivers can self-rejuvenate if issues of human influence and anthropogenic activities are adequately resolved and underline our responsibility for purifying the ecosystem. We observed that this improvement in the river water quality will take a shorter time, and quality will deteriorate again when commercial and industrial activity resumes.

Groundwater quality assessment for irrigation and drinking water: health risks from heavy metals and PAHs in the Niger Delta, Nigeria

ABSTRACT The mean value for the samples ranged from 0.004 to 0.24 mg/L. Cadmium, cobalt, nickel, chromium, manganese, zinc, and lead were not significant and below the permissible limit set by the World Health Organization. Based on the calculation of the water pollution index, the mean values of water samples from all sampling locations fall within 29.02–134.78, indicating poor water quality. The order of decreasing HQing and HQderm values was as follows: cobalt &amp;gt; lead &amp;gt; chromium &amp;gt; cadmium &amp;gt; manganese &amp;gt; zinc &amp;gt; copper &amp;gt; nickel &amp;gt; iron, for both children and adults, respectively. Thirteen distinct polycyclic aromatic hydrocarbons were detected, with mean concentrations of 0.101 ± 0.09 for sample A, 0.1010 ± 0.09 for sample B, 0.0819 ± 0.09 for sample C, 0.01735 ± 0.02 for sample D, and 0.0118 ± 0.0 for sample E. The result showed total BaPeq (mg/kg) values ranging from 2.74E-1 for sample A, 2.80E-1 for sample B, 2.82E-1 for sample C, 2.64E-3 for sample D, and 1.22E-5 for sample E. Elevated levels of fluorene, naphthalene, and dibenzyl (a–h) anthracene were observed in all the sampled points. Irrigation quality of the investigated water sources are sodium adsorption ratio (A: 6.31, B: 4.42), magnesium adsorption ratio (B: 52.27), %Na (A: 55.61), residual sodium carbonate (B: −53.36), permeability index (A: 50.31), soluble sodium percentage (B: 52.62), and Kelly's ratio (equilibrium).

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

WASTEWATER GLOBAL POLLUTION INDEX AS AN INDICATOR IN ASSESSING THE QUALITY OF THE EFFLUENT FROM THE ARAD MUNICIPAL TREATMENT PLANT

The main aim of the present study was to characterize the quality of the sewage leaving the Arad Municipal Wastewater Treatment Plant and highlight the emergence and importance of water reclamation, recycling, and reuse as vital components in integrated aquatic resources management for further reuse. The sewer quality assessment was performed for the period 2018 - 2021. For this purpose, the Global Pollution Index (𝐼􀯀􀯉 ∗ ) was used to determine the degree of pollution of the wastewater influent and effluent from the Arad treatment plant during the mentioned period. These results on the quality of incoming and outgoing sewerage can contribute to developing a management plan for protecting and properly managing water resources.

Spatial distribution, ecological and ecotoxicity evaluation of heavy metals in agricultural soils along Lala-Manjo Highway, Cameroon Volcanic Line

This study aims at assessing the spatial distribution, ecological and toxicity levels of heavy metals in agricultural soils of Lala-Manjo Highway in Cameroon. The soil samples were collected and analyzed by inductively coupled plasma-mass spectrometry. The result shows that Co, Cr, Cu, Hg, Mn, Ni, Sr, V and Zn concentrations are higher than those of the upper continental crust reference values except those of As, Cd and Pb. Correlation coefficient indicate similar input sources governed by same geochemical characteristics. Spatial distribution maps show high concentration of heavy metals in east, south and western part of the study area. Pollution indicators show that enrichment factor varied from moderate to high enrichment of heavy metals. The contamination degree (27.12–35.59), geo-accumulation index (0.01–4.94), and potential ecological risk index (RI = < 150) revealed a low to moderate levels of metallic pollution. The majority of stations have pollution load index and Nemerow integrated pollution index values greater than one, indicating serious pollution. Modified hazard quotient and toxic risk index suggest significant severity of pollution and low toxicity risk of heavy metals, pointing low toxicity effects of heavy metals to fauna and flora in the soil ecosystem. Considering the factors governing soils accumulation such as geology, climate and anthropogenic activities as well as the correlation with commonly used environmental background, it is proposed that the data from this study can be considered as a baseline of pollution to plan major agricultural developments in the area. The study underlines the importance of continuous monitoring and adaptive management for the preservation of soil ecosystem. The results may assist companies and local population in setting realistic targets for monitoring and remediation programs.

Assessment of persistent and emerging pollutants levels in marine bivalves in the Gulf of Suez, Egypt

Bivalves possess important ecological and economic values. They have been extensively used as bioindicators for both emerging and classical pollutants in the aquatic environment. This study investigates the levels of trace metals, polycyclic aromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs), alongside microplastic (MPs), in Tridacna maxima, Paphia textile, and Paratapes undulatus, collected from the Gulf of Suez. This work represents the first investigation of MPs in marine bivalves from the Gulf of Suez. MPs were detected in 72% of the specimens examined and four types of MPs were identified. The metal pollution index indicated that bivalves may have long-term toxic effects on human consumers. The results showed minimal hydrocarbon pollution. Diagnostic ratios indicated a combination of pyrolytic and petrogenic sources, with a notable influence from pyrolytic origins. The risk assessment reflected that the levels of certain trace metals, PAHs, and OCP contaminants could present a low risk to human health.

Hydrogeochemical evolution and heavy metal characterization of groundwater from southwestern, Nigeria: An integrated assessment using spatial, indexical, irrigation, chemometric, and health risk models

This study examines the hydrogeochemical and heavy metal parameters of groundwater in Ojo District to determine its suitability for use, potential sources, and human health implications. Ten groundwater samples were assessed, and hydrogeochemical modelling was performed via the Aquachem software. The chemical ions were in the following order: EC > (107.78–448.65 μS/cm) > TDS (182.02–320.77 mg/l) > TH (46.22–182.45 mg/l) > pH (5.55–6.35); HCO3− (64.13–125.82 mg/l) > Na+ (36.87–96.49 mg/l) > Ca2+ (47.65–58.88 mg/l) > SO42− (19.94–53.67) > NO3− (15.55–44.25 mg/l) > Cl− (20.43–27.16 mg/l) > Mg2+ (11.09–16.87 mg/l) and K+ (2.55–7.86 mg/l). The concentrations of heavy metals in groundwater were in the range of: Fe (0.11–0.27 mg/l) > Mn (0.003–0.16 mg/l) > Ni (0.05–0.12 mg/l) > Zn (0.003–0.05 mg/l) > Pb (0.001–0.03 mg/l) > As (0.001–0.005 mg/l) > Cr (0.002–0.005 mg/l) > Cd (0.001–0.003 mg/l) and Cu (0.001–0.0002 mg/l), with Pb, Mn, and Ni exceeding their allowable limits. The Schoeller and Gibbs plots revealed that the major mechanisms controlling the aquifer groundwater in Ojo region are geological rock weathering and mineralization, with a minimal influence of saltwater intrusion. The piper trilinear diagram also revealed that none of the cation was dominant while the anions were strongly dominated by HCO3− (weak acids). The hydrogeochemical facies which describes the geochemical characteristics of the groundwater were classified into 3 types; “Ca2+–Mg+–HCO3– (65 %)”, “mixing zones (30 %)”, and “Na+–K+–Cl––HCO3– (5 %)”. The hydrogeochemical modelling revealed that the groundwater is characterized by forward cation exchange, while rock–water interactions (silicate dissolution) were heavily involved in the geochemical processes. The single pollution index showed that Pb, Ni, and Mn contributed significantly to contamination, and the multi–pollution indices showed that the groundwater was slightly-moderately polluted. The integrated groundwater quality index revealed that only 10 % were clean, 50 % were poor or moderately unclean, 30 % were highly unclean, and only 10 % were extremely unclean (unfit for utilization). The water pollution index showed that 70 % of the groundwater was good. The irrigation indices suggest that the groundwater would enhance soil quality and support plant growth. Multivariate analysis revealed that the groundwater is being influenced by geogenic factors and anthropogenic activities. The health risk assessment (Hazard Quotient and Hazard Index) showed that exposure of adults to the investigated groundwaters could result in noncarcinogenic adverse effects. The cancer risk values also exceeded the minimum limit (1.0 x 10−6) and thresholds (1.0 x 10−4) for adults, indicating the carcinogenic potential of the groundwater.

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.