Bioindicators: the natural indicator of environmental pollution

Application of water pollution and heavy metal pollution indices to evaluate the water quality in St. Sebastian Canal, Colombo, Sri Lanka

Cimanuk River water is the main raw water source in West Java Province. The quality of the water must be maintained so that it can be useful in the long term. Cimanuk River water is planned to be a source of raw water for clean water services in the West Java Region. Based on this, it is necessary to know the quality of this river water. This study aims to calculate the water and sediment pollution index considering that there is a strong relationship between the water column and sediment in the river. The research method uses the Pollution Index method to determine the water pollution index and the Contamination Factor and Metal Pollution Index to assess the quality of sediment due to heavy metal pollution. . There are 6 (six) parameters that do not meet the quality standards, namely: BOD5, COD, Total Ammonia (NH3-N), Sulfur as H2S, Free Chlorine (Cl2), and Copper (Cu). Sources of pollution are predicted to come from domestic and agricultural activities in the Cimanuk river basin. The results of the analysis of sediment quality using the Contamination Factor method showed that the sediment of the Cimanuk River was polluted with heavy metals Cadmium and Copper with a very high category. Based on the results of calculations using the Metal Pollution Index method, it was concluded that the sediment of the Cimanuk River was polluted with heavy metals.

Garut Regency is one of the famous leather producing areas in Indonesia. The existence of leather tanning industry gives some positive impact in economic but also some negative impact to the environment. This study aims to analyze the condition of Ciwalen River using the Pollution Index (PI) method to monitor the river water quality. Water sampling was carried out using the grab sampling technique during dry and rainy season, on June and December 2024. The parameters measured include BOD, COD, TSS, pH, oil and fat, ammonia, total chrome, and sulfide concentration. These parameters were used to calculate Water Pollution Index (PI). Based on the PI, the condition of the Ciwalen river that passes through the Sukaregang leather tanning industry area is polluted. In dry season, the condition of the water cannot be used for drinking water and is moderately polluted for class II-IV needs such as livestock, fisheries, and plant watering. While in rainy season there is a decrease in polluted conditions to moderate polluted for classes I-III and light polluted for class IV due to higher debit and self purification process.

The Klampok Sub-watershed flows in the Semarang Regency, Central Java, Indonesia, located in the upstream part of the Jragung Watershed, used as a source of irrigation. Klampok Sub-watershed experiences environmental pressures in the form of decreasing water quality because of various human activities. For this reason, this study aims to determine the changes in the water quality and pollution index of the Klampok Sub-watershed in 2016 and 2020. The water quality observations were carried out in 2020 utilizing the Total Suspended Solids (TSS), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Nitrate, Zinc (Zn) and Copper (Cu) parameters. The laboratory analysis results were compared with water quality standards based on Government Regulation Number 22 of 2021 and the status of water quality based on the pollution index method under the Decree of the Minister of the Environment Number 115 of 2003. The study results showed a change in the quality and index of water pollution in 2016 and 2020. The study results revealed a decrease in the value of water quality on the parameters BOD, COD, Zn and Cu, which indicated that the water quality was improving. Moreover, the Klampok Sub-watershed pollution index decreased in 2016 and 2020 so that the Klampok Sub-watershed had quality criteria for lightly polluted water to moderately polluted to lightly polluted at each sampling point. Therefore, several efforts to control pollution and management of the Klampok Sub-watershed, such as community outreach, water quality monitoring and industrial compliance, are expected to improve so that the Klampok Sub-watershed's sustainability can provide various benefits to the community.

Nitrate contamination in drinking water poses significant health risks, particularly in rapidly urbanizing areas of developing countries. This study presents an integrated computational and graphical approach to evaluate the geochemistry and health risks of nitrate-contaminated water for six age groups in Southeast, Nigeria. The research employed a detailed methodology combining water nutrient pollution index (WNPI), nitrate pollution index (NPI), water pollution index (WPI), geochemical plotting techniques, stoichiometry, and health risk computations. Water samples from several locations were analyzed for physicochemical parameters and nitrate concentrations. Results revealed predominantly acidic conditions and varying levels of nitrate contamination. Geochemical analysis indicated that silicate weathering and ion exchange processes were the primary influences on water chemistry. The WPI identified 14.29% of samples as "extremely polluted" (WPI > 1), while the WNPI classified 7.14% of samples as "moderately polluted" (WNPI > 1). However, the NPI categorized the samples as safe, indicating low nitrate inputs from anthropogenic sources. Health risk assessments indicated low-moderate risks, with the highest total hazard index of 0.839 for the 6-12 months age group; thus, higher vulnerability for infants. Oral exposure was found to be the dominant pathway, contributing over 99.90% to the total risk. This research provides crucial insights for achieving the Sustainable Development Goals (SDGs) related to water quality and public health protection. The integrated approach offers a robust framework for water resource management and interventions in risk-prone areas. Future research should focus on expanding the spatial coverage, incorporating sensitivity analyses, and exploring advanced technologies for real-time monitoring and predictive modeling of water quality.

Domestic wastewater discharge remains a major factor contributing to the deterioration of river water quality, as untreated effluents are still commonly released directly into river systems. This concern is particularly evident in the downstream segment of the Kusan River tributary, situated in the villages of Sungai Lembu and Beringin. The intensifying expansion of residential settlements in the area has been identified as a key driver of pollution in the Kusan River, with its scale increasing annually. Environmental degradation arises when the pollutant load exceeds the assimilative capacity of the aquatic ecosystem. Under such conditions, the natural recovery process becomes protracted and increasingly ineffective, resulting in persistent contamination. This research is designed to assess the influence of surrounding anthropogenic activities on the water pollution index and the ecological condition of the Kusan River tributary in the aforementioned villages, within Tanah Bumbu Regency, South Kalimantan Province, employing the Water Pollution Index (WPI) methodology. Based on laboratory analyses, Total Suspended Solids (TSS) concentrations in the lower reaches of the tributary exceed 50 mg/L, thus surpassing the threshold set by Government Regulation No. 22 of 2021 concerning Water Quality Management and Pollution Control. Moreover, findings from the pollution index evaluation indicate that water quality in the study area ranges from good to lightly polluted, with index scores varying between 0.33 and 1.73.

This study presents an innovative approach for predicting water and groundwater quality indices (WQI and GWQI) in the Eastern Province of Saudi Arabia, addressing critical challenges of scarcity and pollution in arid regions. Recent literature highlights the increasing attention towards WQI based on water pollution index (WPI) and GWQI as essential tools for simplifying complex hydrogeological data, thereby facilitating effective groundwater management and protection. Unlike previous works, the present research introduces a novel hybrid method that integrates non-parametric kernel Gaussian learning (GPR), adaptive neuro-fuzzy inference system (ANFIS), and decision tree (DT) algorithms. This approach marks the first application of a non-parametric kernel for groundwater quality pollution index prediction in Saudi Arabia, offering a significant advancement in the field. Through laboratory analysis and the combination of various machine learning (ML) techniques, this study enhances prediction capabilities, particularly for unmonitored sites in arid and semi-arid regions. The study’s objectives include feature engineering based on dependency sensitivity analysis to identify the most influential variables affecting WQI and GWQI, and the development of predictive models using ANFIS, GPR, and DT for both indices. Furthermore, it aims to assess the impact of different data portions on WQI and GWQI predictions, exploring data divisions such as (70% / 30%), (60% / 40%), and (80% / 20%) for training and testing phase, respectively. By filling a critical gap in water resource management, this research offers significant implications for the prediction of water quality in regions facing similar environmental challenges. Through its innovative methodology and comprehensive analysis, this study contributes to the broader effort of managing and protecting water resources in arid and semi-arid areas. The result proved that GPR-M1 exhibited exceptional testing phase accuracy with RMSE = 0.0169 for GWQI. Similarly, for WPI, the ANFIS-M1 achieved high testing predictive skills with RMSE = 0.0401. The results emphasize the critical role of data quality and quantity in training for enhancing model robustness and prediction precision in water quality assessment.

To investigate if mutagenicity could be expressed by known water pollution indicators, we determined the mutagenic activity of blue rayon extracts from sampled river water with the Ames test utilizing new strains of bacteria, and compared the results with those of known indicators of water pollution. Water samples were collected by the blue rayon adsorption method at sixteen sites in six rivers in the North Kyushu district. The Assay of mutagenicity was carried out using the Ames test. The test strains wereSalmonella typhimurium TA100, YG1024, YG1041 and YG1042. B(a)P, Trp-P-1 and Trp-P-2 were quantified by HPLC. Determinations of SS, BOD, COD, T-N, T-P, DOC, and A(260)/DOC were performed. The extracts from five sampling sites showed higher mutagenicity toward strain YG1024 with or without S9mix, and the extracts from two of these five sites showed higher mutagenicity toward strain YG1041 with and without S9mix. However, the water pollution indicators did not show specific trends that were consistent with the mutagenic activity. Since the mutagenic activity of river water could not be predicted using known water pollution indicators, we recommend that biological examinations such as mutagenicity tests be added to the indicators that are currently in use.

Labuan Coastal Fishing Port (PPP) is an area with great potential for development in the economic and tourism sectors. Apart from being the center of the fishing industry in Pandeglang Regency, PPP Labuan also has beach tourist attractions. Some activities could cause the entry of heavy metals into waters and affect water quality. These activities include ships refuelling, ships exhaust emissions, ships washing and paint spills, as well as beach tourism activities. The pollution index is used to determine the level of pollution in waters. This research aimed to analyze water quality and pollution indices based on the heavy metals content iron (Fe), nickel (Ni), and copper (Cu). Samples were taken from three observation stations, namely the beach tourist area (station 1), the fish auction site (station 2), and the pier (station 3). Analysis of the heavy metals Fe, Ni, Cu was carried out using the Atomic Absorption Spectrophotometer method and the results were compared with Indonesian government reulation, PP No. 82/2001. Pollution index analysis wasusing the pollution index method. The heavy metals content of iron (Fe) ranged from 1.761 – 6.907 mg/L, nickel (Ni) ranged from 0.079 – 0.269 mg/L, and copper (Cu) is < 0.001 mg/L. Based on the pollution index (IP), station 1 was lightly polluted with an IP value of 3.75. At station 2 it was moderately polluted with an IP value of 5.91, and at station 3 it was lightly polluted with a value of 4.2.

The paper presents the main results of assessing the water quality of the Shlinsky reservoir for the period 2015-2019. Based on the analysis of hydrochemical data obtained by standard chemical methods, according to 22 indicators. Monitoring of the reservoir water quality until 2021 was carried out by the Dubna Ecoanalytic Laboratory. The Shlinsky reservoir is located on the border of the Tver and Novgorod regions, is part of the Vyshnevolotsk water system. The reservoir provides releases for the needs of energy, water transport and improvement of water supply in Moscow. As a result of the study, priority pollutants were identified, the water quality of the reservoir was assessed based on the results of calculating the water pollution index (WPI) and the specific combinatorial index of water pollution (SCIWP). It is shown that there is no significant anthropogenic impact on the ecosystem of a water body. The water quality of the reservoir is assessed as "moderately polluted" water (according to the value of the water pollution index) and slightly polluted - polluted water (according to the value of the specific combinatorial index of water pollution).

Water quality assessment can be defined as the evaluation of the physical, chemical and biological nature of water in relation to natural quality, human effects and intended uses. Water Quality Indices (WQIs) and Water Pollution Indices (WPIs) reduce a great amount of parameters to a simpler expression, to enable easier interpretation of the monitoring data. Main Difference between WQIs and WPIs included the form how they evaluate the processes of pollution and the number of variables taken in account in each formulation. In this context, this paper displays a comparative study of 36 (thirty-six) WQIs and WPIs, on the basis of mathematical structure, similarity parameters and behavior given the same set of data. The results indicate that appreciable differences exist between classifications given by different indices on the same water sample. Differing parameter numbers, calculations and aggregation formulas can explain this. Thus, it was possible to establish that the WPIs developed in Colombia by Ramirez et. al. (1997) and the AMOEBA strategy, developed by de Zwart (1995) in The Netherlands, displays great advantages over traditional formulations, because the different kinds the pollution are segregated better.

Determination of water quality status based on the pollution index method. Water said to polluted if it cannot use according to its standard designation. The purpose of this research is to identify and find out the level of pollution contained in shallow well water used by communities in urban areas. Water quality index measurements using the Storet Method. This study uses ten samples with techniques Purposive random sampling with well water sources used by people in Makassar, especially in Untia Sub-District, Makassar, South Sulawesi. The number of samples used was 15 water samples taken each week with a total of 10 shallow well water sample points. The parameters measured in the study are Iron (Fe), Manganese (Mn), Total Coliforms, Total Dissolved Solids (TDS). The results of the study showed that the condition of shallow well water quality classified in category D, which heavily polluted which means it not recommended to use before filtering. The increase in parameters that have exceeded the maximum quality standard comes from natural sources and high domestic waste from community activities. Water pollution is a condition in which a water reservoir changes due to human activity — the change caused by the entry of substances that should not be in the water.

With over 3 million people, the city of Nairobi faces several water supply and pollution challenges that are exacerbated by burgeoning slums and informal settlements. This paper analyzed the existing status of urban water-pollutant flows in Nairobi city. A box-flow model was developed to identify the most important pollutants in the urbanized area. Total nitrogen (N) and phosphorus (P) loadings in water were used as indicators for water pollution. The paper found that the commercial and industrial water users are the major emitters of N (45,301 ton/yr) to the environment. This was attributed to Nairobi’s majority agro-based industries which release nitrogen rich organic-compounds into the system boundary. Households were major sources of P (2,663 ton/yr) to the environment. Most of the P in wastewater results from the P-rich detergents used for cleaning and washing at the household level. Investment into improved sanitation and wastewater treatment technologies is recommended to ameliorate the amount of nutrient flows into water bodies. Further, prohibiting use of P-rich detergents is important in reducing P emissions. These actions are needed to guarantee safe drinking water supply to the city in order to keep pace with the fast emergent population. An effective multi-sectoral approach to environmental conservation can provide higher sustainability in the long-term.

The contamination of seawater with anthropogenic factors is a global challenge because of its negative impacts on marine environments and coastal societies. Therefore, assessing water contamination is crucial. The present work uses pollution indices and multivariate statistical analyses to document high-level heavy metal concentrations and identify potential polluters by analyzing 35 seawater samples collected from Al-Uqair coastline, Arabian Gulf, Saudi Arabia. The total content of heavy metals (HMs) (μg/L) ranged from 7109 to 7398 for Sr, 7.00–14.50 for Cr, 3.30–9.90 for Zn, 3.00–8.80 for Fe, 4.40–7.60 for As, 1.20–6.90 for Ni, 2.30–4.70 for V, 1.10–3.80 for Cu, 2.50–3.10 for Se, 0.31–1.43 for Al, 0.18–1.10 for Hg, 0.04–0.08 for Cd, 0.09–0.43 for Pb, and 0.02–0.10 for Sb. The recorded average concentrations of HMs were below the maximal admissible concentration of the World Health Organization (WHO). The heavy-metal pollution index (HPI) indicates that 14.29% of the water samples were below medium pollution, and 85.71% were below high pollution. All seawater samples were categorized under low contamination and good water on the basis of the degree of contamination (Cd) and water pollution index (PIj), respectively. Multivariate analysis indicates mixed anthropogenic and natural sources for the investigated metals, with increasing control of the anthropogenic factors for Cr, Zn, As, V, Ni, Se, Cu, Hg, Pb, Sb, and Cd; and the control of natural factors for Sr, Fe, and Al.

Majority of people living on earth rely on groundwater as their primary supply of water for daily needs. However, human activities continuously threaten this natural resource. In an attempt to unravel the extent of impact of human-related activities on groundwater physicochemical characteristics in Nnewi and Awka urban clusters (Nigeria), several techniques were integrated in this study. Groundwater samples were warm and acidic in nature. Concentrations of SO4 2-, NO3 -, PO4 3-, Cl-, HCO3 -, Ca2+, Mg2+, Na+, and K+ were within set benchmarks. Water nutrient pollution index (ranging from 0.060-0.745), nitrate pollution index (varying between -0.999 and -0.790), and water pollution index (ranging from 0.057-0.630) estimated the extent of anthropogenic contamination and showed low anthropogenic impact on the groundwater physicochemical characteristics. The health risks due to the ingestion and skin absorption of the nitrate-contaminated water computed for six age groups (6-12 months, 5-10 years, 10-15 years, 15-20 years, 20-60 years, and > 60 years) showed health risk values that were < 1, implying low chronic health risks to humans. The cumulative total health hazard index ranged between 0.006 and 0.787 with a mean value of 0.167. Chemometric analyses and geochemical plots revealed the relationships between the variables and contamination sources. Chadha’s plot showed that 55% of the samples were Ca2+-Mg2+-Cl- waters, predominating over Na+-Cl- and Ca2+-Mg2+-HCO3 - waters. Bivariate and multivariate geochemical plots also indicated low anthropogenic impact. Furthermore, principal component analysis and R-type hierarchical clustering confirmed that the groundwater chemistry and quality were mostly influenced by geogenic processes than human-related acts. Conclusively, the extent of anthropogenic influence on the groundwater physicochemical characteristics is low. These findings would be useful in future monitoring of groundwater in both urban clusters.