Water Quality Index Research Articles

Investigation of the physicochemical parameters of drinking water in Herat province and its comparison with World Health Organization standards

AbstractThe physical, chemical, and microbial parameters of drinking water are the basis for judging if it is potable. Undesirable changes in these parameters can threaten the users’ health. Therefore, the present study aimed to evaluate the physicochemical qualities of drinking water in Herat Province and compare them with the WHO standards. 45 drinking water samples were collected from Herat Province to undergo physicochemical tests. This study was conducted in 2023. The mean values measured for parameters such as temperature, turbidity, electrical conductivity (EC), pH, total dissolved solids (TDS), fluoride, nitrate, calcium, magnesium, and total hardness (TH) were 26.3℃, 0.93 NTU, 1328 μS/cm /cm, 7.32, 576.8, 0.26, 12.014, 6.8, 11.25, and 58.33 mg/L, respectively. The turbidity, TDS, and EC of some samples were higher than the highest desirable limit (HDL) but lower than the Maximum allowable limit (MAL). Also, there is a shortage of fluoride in samples. The Water Quality Index (WQI) shows that the quality of drinking water in Herat Province is classified as Poor.

Impact of pH, TDS, Chloride, and Nitrate on the Groundwater Quality Using Entropy-Weighted Water Quality Index and Statistical Analysis: A Case Study in the Districts of North India

Impact of pH, TDS, Chloride, and Nitrate on the Groundwater Quality Using Entropy-Weighted Water Quality Index and Statistical Analysis: A Case Study in the Districts of North India

Harnessing Deep Learning for Real-Time Water Quality Assessment: A Sustainable Solution

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications.

An innovative combination of extra trees within adaboost for accurate prediction of agricultural water quality indices

The management of water quality plays a pivotal role in fostering sustainable development, especially in areas where groundwater serves as a vital resource for agricultural purposes. This study investigates methods to predict water quality indicators like potential salinity (PS) and sodium adsorption ratio (SAR) using data-driven techniques. It explores the AdaBoost algorithm and a hybrid model that combines AdaBoost with Extra Trees (ET). The research centers on the Sarayan region in southern Khorasan, Iran, involving the collection and analysis of groundwater quality data, which includes both physical and chemical parameters, spanning a period of four years. Pearson correlation analysis is utilized in this study to determine the critical input variables for predicting Sodium Adsorption Ratio (SAR) and Potential Salinity (PS). Significant performance improvements are demonstrated through the optimization of AdaBoost and the hybrid AdaBoost-Extra Trees (ET) models, achieved via grid search and Bayesian optimization. The results indicate that the hybrid model surpasses AdaBoost in performance. Specifically, the optimized AdaBoost model achieves a Test MSE of 3.57 and a Test R² of 0.87 for SAR prediction, whereas the hybrid model obtains a significantly better Test MSE of 0.87 and Test R² of 0.97. For the prediction of PS, the optimized AdaBoost model yields a Test MSE of 0.223 and a Test R² of 0.976, while the hybrid model delivers a significantly improved Test MSE of 0.018 and Test R² of 0.998. Feature importance analysis identifies critical patterns in the relevance of input parameters. This study underscores the efficacy of hybrid data-driven models in water quality prediction, showcasing their superior accuracy.

Evaluating the water quality of the rice–fish co-culture pattern based on the modified NSF water quality index model

The rice–fish co-culture pattern, a sustainable agricultural production method, plays a significant role in enhancing farmland ecological and economic benefits. However, few studies have evaluated the water quality of this system using the water quality index (WQI). In this study, improvements were made to the United States National Sanitation Foundation WQI (NSFWQI), including indicator selection, weight calculation, and sub-indexing, using principal component analysis (PCA), a minimum data set (MDS), linear function equations, and a comprehensive water quality assessment of the double-cropping rice–fish (DRF) co-culture and (DR) rice patterns. The results showed that using PCA; pH, dissolved oxygen (DO), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), and the Shannon–Wiener index were selected from 11 water quality indicators to construct the MDS for WQI calculation. Based on the WQI results, the DRF had a negative impact on water quality, with the Shannon–Wiener index the greatest contributor to the WQI (22.9 %), consistently ranging between 1 and 3. Although total phosphorus (TP) and chemical oxygen demand (COD) were not included in the MDS for the WQI calculation, their significant increase during the late rice season in the DRF indicated water quality deterioration, further validating the accuracy of the improved NSFWQI. The improved NSFWQI model provides a more objective analysis of water quality under the rice–fish co-culture pattern. Compared with existing water quality evaluation methods, this approach offers advantages such as comprehensiveness, low cost, and standardization, and presents an effective alternative for water quality assessment.

Globally validated non-unique inversion framework to estimate optically active water quality indicators using in situ and space-borne hyperspectral data sets

Globally validated non-unique inversion framework to estimate optically active water quality indicators using in situ and space-borne hyperspectral data sets

Using Dominant Phytoplankton as Assessment of Water Quality: Water Resource Case Studies of University in Chatuchak District

The use of dominant phytoplankton as a water quality indicator was studied. Samples from 6 stations (S1-S6) were collected. From the study, 6 divisions of 34 genera of phytoplankton, i.e. Euglenophyta, Chlorophyta, Bacillariophyta, Chromophyta, and Pyrrhophyta were found, and the significant phytoplankton genera were Euglena, Phacus, and Tetraedron. By analysing water quality assessment in terms of scores by AARL-SPP Score using these three dominant genera, it was found that stations S1–S4 had a moderate level of water quality and stations S5–S6 had a moderate-polluted level of water quality. When the relationship between phytoplankton species and the water quality parameters were analysed by stepwise multiple regression analysis, the results showed that the prominent genus Merismopedia of Cyanophyta (Blue-green algae), correlated with transparency, salinity, chlorophyll a, and soluble phosphate while the genus Oscillatoria correlated with the total suspended solids, Chlorophyll a content, transparency, pH, temperature, and ammonia-nitrogen. The correlation indicates the high rate of phytoplankton growth was due to reduced water quality. According to water quality standards for surface water, the water quality was classified in Category 3.

A hybrid machine learning approach for imbalanced irrigation water quality classification

Global food security is increasingly dependent on irrigation, particularly in regions experiencing freshwater scarcity. Conventional laboratory methods for assessing Irrigation Water Quality Index (IWQI) are often slow and inaccessible to small-scale farmers, especially in developing countries. This study proposes an efficient machine learning (ML) approach to enhance the classification performance of IWQI into five classes: no restriction, low restriction, moderate restriction, high restriction, and severe restriction. A dataset comprising 62,499 samples with six hydrochemical parameters (EC, Cl−, HCO3−, Na+, Ca2+, and Mg2+) was collected, preprocessed, and labeled. Missing values were imputed using a Random Forest model, achieving an R2 of 0.98. To address class imbalance, synthetic resampling, class weighting, and apportioned margins were employed to train three ML models: two stacked ensembles and an Apportioned Margin Support Vector Machine (AMSVM). Class weighting was applied to the first ensemble, adaptive synthetic sampling (ADASYN) resampling was utilized for the second, and AMSVM was adjusted for class imbalance through apportioned margins and class weighting. The class-weighted ensemble achieved 98.5% accuracy, precision, recall, and F1-score, while the ADASYN ensemble attained 97.5% accuracy and recall, with 97.4% precision and F1-score. AMSVM recorded 86.8% accuracy, 74.7% precision, 83.6% recall, and a 79% F1-score. This study improves IWQI classification, explores trade-offs between accuracy and class balance, and provides information on the effectiveness of class weighting, apportioned margins, and resampling techniques for the development of the ML model. The proposed models can facilitate the development of a low-cost IoT-based IWQI assessment system, supporting sustainable irrigation management to enhance agricultural productivity.

Water Quality Assessment, Possible Pollution Source Identification from Anthropogenically Stressed River Yamuna, India using Hydrochemical, Water Quality Indices and Multivariate Statistics Analysis

Water Quality Assessment, Possible Pollution Source Identification from Anthropogenically Stressed River Yamuna, India using Hydrochemical, Water Quality Indices and Multivariate Statistics Analysis

Fluoride and nitrogen contamination and potential health risks in the groundwater of a typical agricultural region

ABSTRACT Fluoride and nitrogen contamination is a global concern and has been a serious problem in agricultural areas. This study aims to identify the source of fluoride and nitrogen in the groundwater and assess groundwater quality and human health risks in the Guanzhong Plain, northwest China. The results showed that the concentrations were 0.15–4.74 mg/L for F−, 0.02–89.89 mg/L for NO3−-N, and BDL-2.40 mg/L for NH4+-N in groundwater. Distinct area-dependent distributions of fluoride and nitrogen were observed in the study region. Higher F− and NO3−-N concentrations in groundwater were detected in the northern part, and higher NH4+-N levels were observed in the southern part. Water–rock interaction and agricultural activities were the controlling factors for fluoride and nitrogen distribution in groundwater. About 80% of samples are considered to have good water quality with a Water Quality Index (WQI) < 100. Exposure to fluoride and nitrogen through drinking should require more attention. The total non-carcinogenic risks through oral ingestion of groundwater were 0.22–3.19 for adults and 0.51–7.44 for children, respectively. The order of pollutants in the groundwater in terms of their hazard to residents was F− > NH4+-N > NO3−-N > NO2−-N. The findings of this study could provide more insights into groundwater management.

Groundwater quality assessment and pollution sources identification using statistical analyses at Missan Governorate, Southeast Iraq

The shortage in surface water quantities led to a shift in dependence on the groundwater as an alternative water source in southern parts of Iraq. The groundwater is decreasing in quantity and water quality is degrading due to different factors. Therefore, it is important to assess the groundwater quality of the Missan Governorate of the country by analyzing the physicochemical parameters and distinguishing the probable sources of contaminants in the area. The present study used water quality diagrams and statistical methods such as factor analysis and agglomerative cluster analysis to determine the sources of chemical ions in the forty-four groundwater samples collected from wells in the study area. In addition, the Water Quality Index (WQI) was calculated to evaluate the groundwater quality by combining the parameters into a single index. The WQI results varied from − 66.27 to 1064.13 indicating that the groundwater is unsuitable for drinking at various places and good in quality at most of the wells of the area. The main factors that contribute to the differences in the groundwater quality include the variation in geological materials, influence of climate such as the high rate of evaporation, and anthropogenic factors such as industrial activities in central areas such as oil production in the Bazergan oil field. The most affected wells by the petroleum industry that were clustering in cluster R1which show high levels in most water chemistries. The results have implications for the management of the groundwater resources, which are expected to be under more risk of contamination in near future.

Spatial-Temporal Characteristics and Driving Factors of Surface Water Quality in the Jing River Basin of the Loess Plateau

Water quality safety in the water source constitutes a crucial guarantee for public health and the ecological environment. This study undertakes a comprehensive assessment of the water quality conditions within the Jing River Basin of the Loess Plateau, emphasizing the spatial and temporal characteristics, as well as the determinants influencing surface water quality in the Shaanxi section. We utilized data from seven monitoring stations collected between 2016 and 2022, employing an enhanced comprehensive Water Quality Index (WQI) method, redundancy analysis (RDA), and Spearman’s correlation analysis. The results show that the average annual WQI value of the water quality of the Shaanxi section of the Jing River increased from 68.01 in 2016 to 76.18 in 2022, and the river’s water quality has gradually improved, with a significant improvement beginning in 2018, and a series of water quality management policies implemented by Shaanxi Province is the primary reason for the improvement. The river’s water quality has deteriorated slightly in recent years, necessitating stricter supervision of the coal mining industry in the upper section. The river has an average WQI value of 73.70 and is rated as ‘good’. The main pollution indicators influencing the river’s water quality are CODMn, COD, BOD5, NH3-N, and TP. From the upstream to the downstream, the water quality of the river shows a pattern of increasing and then decreasing, among which S4 (Linjing Bridge in Taiping Town) and S5 (Jinghe Bridge) have the best water quality. The downstream part (S6, S7) of the Jing River near the Weihe River has poor water quality, which is mostly caused by nonpoint source contamination from livestock and poultry rearing, agricultural activities, and sewage discharge. Redundancy analysis revealed that the spatial scale of the 2500 m buffer zone best explained water quality changes, and the amount of bare land and arable land in land use categories was the key influencing factor of river water quality.

Advancements in DNA Metabarcoding Protocols for Monitoring Zooplankton in Marine and Brackish Environments

Over the past century, numerous studies have proposed various organisms for the biomonitoring of aquatic systems, but only recently has zooplankton emerged as a promising indicator of water quality. The traditional identification methods, however, can be inefficient in the context of monitoring efforts, as they are often time consuming and costly. DNA metabarcoding offers a powerful alternative, providing a more efficient and reliable approach to monitor zooplankton communities. In this review, we assess the current state-of-the-art methodologies used to evaluate marine and brackish zooplankton communities through the DNA metabarcoding workflow. While several emerging approaches have been reported, no standardization has been achieved so far. The DNA extraction step has gained the most consensus, with the widespread use of commercial kits (DNeasy Blood & Tissue kit employed in ca. 25% of the studies), though there is still a significant variation in kit selection. Additionally, 18S and COI were the main molecular markers employed (ca. 61% and 54%, respectively) though the target region varied in the former. Moreover, many methodologies, particularly those used for processing zooplankton samples, lack practical validation. Some studies also fail to provide sufficient detail in their methodology descriptions hindering reproducibility. Overall, DNA metabarcoding shows great potential for the efficient monitoring of zooplankton communities, but further effort is needed to establish standardized practices and optimize the current approaches across the entire methodological pipeline.

Hydrochemical assessment of groundwater utilizing statistical analysis, integrated geochemical methods, and EWQI: a case study of Laiwu region, North China.

The investigation of groundwater quality and hydrochemical assessment holds immense significance in safeguarding and ensuring the rational utilization of groundwater resources. This study utilizes groundwater sampling and testing data from the Laiwu region (LWR), encompassing both dry and wet seasons, to delve into the hydrochemical characteristics, ion sources, and overall groundwater quality. The research findings indicate that the groundwater in LWR exhibits weak alkalinity, with the dominant ions being Ca2+ followed by Mg2+, Na+, and K+, and HCO3-, SO42-, NO3-, Cl-, and F-. The average total dissolved solids (TDS) concentrations during the dry and wet seasons are recorded as 683mg/L and 679mg/L, respectively, classifying LWR's groundwater primarily as hard-fresh water. The spatial pattern of TDS concentration in LWR displays consistency throughout both the dry and wet seasons, with relatively low TDS levels observed in the northern and southeastern regions and higher concentrations in the lower reaches of the Dawen River and nearby Gangcheng. Predominantly, Ca2+, Mg2+, and HCO3- ions in groundwater originate from the dissolution of calcite and dolomite, with the hydrogeochemical process of carbonate rock weathering involving the presence of sulfuric acid. It is noteworthy that human activities significantly impact the chemical composition of groundwater in LWR. Notably, during the dry and wet seasons, the average concentration of NO3- in groundwater is 102.81 and 106.61mg/L respectively, and the analysis shows that agricultural practice is the main source. Furthermore, the calculated average values of the entropy water quality index (EWQI) during those seasons are 58.44 and 57.24, respectively. The EWQI shows good to moderate water quality in most areas, except for a few poor-quality spots in the west. It is worth mentioning that LWR's groundwater is deemed suitable for agricultural irrigation. These research findings provide valuable insights and serve as a significant reference for the rational development and sustainable utilization of groundwater resources in the LWR region.

Assessment of groundwater quality of unconfined aquifers in an urbanized area using the water quality index method.

This study performed an evaluation of the groundwater quality of unconfined aquifers in Yangzhou, located in the Yangtze River Delta, eastern China. The study area is the source area of the Eastern Route (ER) of the South-to-North Water Diversion Project (SNWDP), typical of the lower alluvial plain of the lower Yangtze River. Water samples from monitoring wells and domestic wells were collected to analyze common variables (pH, EC), major ions, trace metals, etc. The water quality index method was employed to evaluate groundwater quality in both eastern and western parts of the study area. The study found that the groundwater hydrochemistry is typical of HCO3-Ca and HCO3-Na, similar between the eastern and western parts of the study area. In addition, the eastern part of the study area is more affected by nitrate pollution than the western part due to agricultural activities. The water quality analysis suggests that the groundwater in the western region was slightly better than that in the eastern region. The comparison between different scenarios indicates that the water quality using only major ions can differ significantly from that using all chemical parameters, but may offer some preliminary insights into the groundwater quality useful for conducting further more detailed analysis. Our study shows that using the combination of major ions and heavy metals could provide relatively robust results of the groundwater quality in our study area. Our study has important implications for the assessment of groundwater quality in regions with similar conditions.

Public complaints of water pollution: A long-term spatiotemporal analysis in Japan

Public complaints about water pollution are important indicators of public environmental risk perception and provide crucial feedback for evaluating the effectiveness of water resource management strategies. This study conducts a comprehensive analysis of water pollution complaints in Japan from 1989 to 2021, utilizing integrated datasets that include river networks, water quality monitoring stations, regional industrial demographics, and socioeconomic indicators. Our findings reveal a significant decline in complaints—over 45% from the peak in 2006—following the implementation of various environmental policies aimed at reducing industrial discharge and restoring natural ecosystem function. The shift in complaint targets from industries to individuals suggests that these policies have been effective in mitigating industrial pollution, while also highlighting the need to address individual behaviors that impact water quality. Despite long-term improvement in water quality indicators such as Suspended Solids (SS) and Dissolved Oxygen (DO), no significant correlation was observed between these indicators and the temporal or spatial patterns of water pollution complaints. Instead, a strong positive correlation was observed between complaints and reported water quality incidents, reflecting heightened public sensitivity to acute pollution events. Spatial analysis further identified a significant association between complaints and the manufacturing workforce, indicating that industrial activity continues to shape public perception of water pollution risks. Our findings underscore the complex interaction between public perception, environmental policies, and industrial activities, and suggest that effective water management requires a multifaceted approach. Policymakers should continue to enforce stringent industrial regulations, while also enhancing public education and communication strategies to improve awareness of individual responsibilities in water protection. This research provides a theoretical basis for developing more responsive and effective environmental policies and public education strategies.

Pollution Level and Assessment of a Tropical River in Pahang Malaysia

Rivers are often contaminated with metals. This study was conducted on the Jempul River in Jengka, Pahang, Malaysia, due to the agricultural activities carried out along this river. The aims are to investigate the levels of certain metals (namely Al, Fe, Mn, and Pb), identify their likely sources, assess their toxicity loads, and estimate the associated health risks. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine metal concentrations. The concentrations were discovered in the following order: Fe > Al > Mn > Pb, for both dry and wet conditions. The highest concentration in water sample was observed for Fe (1.28 mg/l) in the dry condition and the lowest was detected for Pb (0.04 mg/l) in the wet condition. Principal component analysis (PCA) indicated that the metals originate from natural and anthropogenic sources. Based on metal toxicity load (MTL) calculations, it is recommended that approximately 73% of Pb, 60% of Mn, and 58% of Al be eliminated from the river water to ensure its safety. Children are more susceptible to non-cancer and cancer hazards than adults. This study suggests that extensive exposure assessment and detailed monitoring of water quality indicators should be carried out.

Assessing the impact of human activities on the health condition of the Lukosi River catchment using selected physico-chemical parameters as indicators of water quality

The Lukosi River catchment plays a crucial role in the economic and ecological framework of the country, serving as a major contributor to the Great Ruaha River (GRR). This study aimed to assess the impact of human activities on the health of the Lukosi River by analyzing key water parameters. Three sampling sites across the catchment were selected, and water samples were collected and analyzed using the American Public Health Association technique. A one-sample t-test was used to measure the significance of the difference between the measured water quality parameters and the water quality criteria established by the Tanzania Bureau of Standards (TBS) and the World Health Organization (WHO). The results revealed mean values for various parameters: pH (8.67 ± 0.57), temperature (24.803 ± 1.361°C), EC (93.30 ± 3.34 mgL−1), BOD (14.85 ± 1.49 mgL−1), TDS (46.95 ± 1.55 mgL−1), total hardness (27.53 ± 1.28 mgL−1), DO (3.4 ± 0.53 mgL−1), turbidity (109.83 ± 40.99 mgL−1), NO2 (79.04 ± 0.73 mgL−1), Cu (6.07 ± 0.60 mgL−1), Ca (7.88 ± 0.36 mgL−1), Mn (0.16 ± 0.07 mgL−1), Cl (7.58 ± 3.97 mgL−1), F (0.12 ± 0.0008 mgL−1), SO4 (20.42 ± 4.12 mgL−1), Mg (1.92 ± 0.13 mgL−1), Zn (0.14 ± 0.11 mgL−1), Fe (3.56 ± 0.15 mgL−1), and NH3 (4.67 ± 0.51 mgL−1). Parameters such as DO, turbidity, NH3, NO3, and BOD significantly (p = 0.05) exceeded the permissible limits set by the TBS and the WHO. Furthermore, elements including Fe and Cu exceeded allowable limits. Moreover, the WQI was used to indicate that the water in the catchment is unsuitable for sustaining aquatic organisms and is unfit for domestic use. Household questionnaires and direct observations identified key human activities in the catchment: gardening (78.3%), agriculture (68%), livestock keeping (50%), sand mining (21%), and brick-making (21%). Less prevalent were car washing (7%) and fishing (4%). These findings underscore the urgent need for conservation efforts and sustainable management practices to safeguard the Lukosi River catchment (LRC).

Unravelling integrated groundwater management in pollution-prone agricultural cities: A synergistic approach combining probabilistic risk, source apportionment and artificial intelligence

Groundwater is vital for agricultural cities, but intensive farming and fertilizer use have increased contamination risks, particularly for non-carcinogenic health hazards. This study reveals the sources of contaminants in groundwater, their health impacts, and targeted strategies in such cities. The study analyzed 115 groundwater samples, with the main groundwater chemical type being HCO₃-Na·Ca. Significant exceedances were found in Mg²⁺, HCO₃−, F−, total hardness (TH), and Mn, with HCO₃− and Mg²⁺ surpassing standards in nearly all samples. The average Comprehensive Environmental Water Quality Index (CEWQI) was 100.68, indicating that overall groundwater quality in the study area is good. High-quality water is mainly found near reservoirs and rivers, while urban and eastern regions have relatively poorer water quality. The proportion of groundwater unsuitable for drinking is low. Monte Carlo risk assessments revealed that F− and NO₃− pose non-carcinogenic risks to both adults and children, with NO₃− presenting a higher potential health risk. The Positive Matrix Factorization (PMF) model identified that groundwater pollution primarily results from natural geological processes and human activities, with agriculture being the major anthropogenic factor. AI-based zoning strategies highlighted industrial areas and high-fluoride zones as critical areas requiring enhanced prevention and control measures.

Insights into relationship of environmental inequalities and multimorbidity: a population-based study

BackgroundSubstantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators.MethodsThis ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency.ResultsThe overall prevalence of multimorbidity was 33.4% [32.6%—34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02–1.03; OR = 1.01, 95% CI 1.00–1.02 and OR = 1.03, 95% CI 1.02–1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA.ConclusionThere is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships.