Water Quality Classification Research Articles

ASSESSMENT OF WATER QUALITY OF URBANIZED TERRITORIES ACCORDING TO THE STRUCTURE OF HYDROBIONT GROUPS

The paper proves the possibility of conducting water quality assessment using such an indicator as the species assemblage of Unionidae habitats for the purpose of conducting local bioindication. According to the species assemblage of Unionidae habitats, it was established that of the five analyzed sites of material collection of the main stream of the Teteriv River, in one case the water quality can be assessed as "Dirty", which corresponds to the IV water quality class, and in three cases as "Contaminated", which corresponds to the III class, and in another one - as "Good" - II class of quality. The rivers Guiva (Ivankiv), Kroshenka and Putyatinka within Zhytomyr belong to the IV class of water quality. Five more points are assigned to the II quality class (Guyva River in the area of Pryazhevo and Zarichany villages; Kam'yanka River (Zhytomyr); Kodenka River (Vertokiyivka village) and Hnylop'yat River (Berdychiv).More detailed information about the ecological state of aquatic ecosystems is provided by the use of algal communities of plankton as biological descriptors, but this approach is time-consuming. In general, the bioindication of water quality in the rivers of the Teteriv basin according to phytoplankton showed the predominance of indicators of saprobity, which correspond to the III class of water quality ("Contaminated"). Class II ("Good") indicators make up a significant share. A moderate level of organic water pollution is indicated by the predominance of eurysaprobes in the rivers. In terms of phytoplankton biomass, the Teteriv and Kroshenka rivers belong to the III class of water quality, other watercourses belong to the II class. According to the saprobity index, the water quality of the Teteriv, Putyatinka, and Kroshenka rivers is rated as satisfactory, and that of other watercourses as good. According to primary production and the ratio of production and destruction processes, watercourses were ranked into three groups - those belonging to II, III and IV water quality classes. The average values of the integral index ∑А/∑R for all watercourses, except for the Putyatinka River, are higher than 1. This indicates a positive balance of organic matter, characteristic of aquatic ecosystems with a high intensity of photosynthesis. The predominance of the heterotrophic phase in Putyatynka is most likely a response of the watercourse ecosystem to anthropogenic pressure.A comprehensive assessment of the water quality of watercourses in urbanized areas using as biological descriptors the ecological state of hydroecosystems of algae, plankton and mollusk settlements is the foundation for further ecological monitoring of waters, forecasting changes in the autotrophic and heterotrophic links of aquatic ecosystems under the influence of environmental factors.

Performance of lagoon and constructed wetland systems for tertiary wastewater treatment and potential of reclaimed water in agricultural irrigation

Climate change poses challenges to agricultural water resources, both in terms of quantity and quality. As an adaptation measure, the new European Regulation (EU) 2020/741 establishes different water quality classes for the use of reclaimed water in agricultural irrigation. Italy is also working on the definition of a new regulation on reclaimed water reuse for agricultural irrigation (in substitution of the current one) that will also include the specific requirements imposed by the European one. Nature-based Solutions (NBS) can be a cost-effective and environmentally friendly way to facilitate water reclamation and reuse. The present study reports the outcomes of a long-term monitoring campaign of two NBS (e.g., a constructed wetland (CW) and a lagoon system (LS)) comparing influent and effluent concentrations of different contaminants (e.g., E. coli, BOD5, TSS, TN and TP) with the threshold values imposed by the new regulations. The results showed that in both the case studies, E. coli (about 100 CFU 100 mL−1) and BOD5 (lower than 25 mg L−1) mean effluent concentration need to be further reduced in reclaimed water to be suitable for unlimited reuse. As a negative aspect, in both the monitored NBS, an increase in TSS mean concentration in the effluent was observed, up to 40 mg L−1 in the case of the LS, making reclaimed water unsuitable for agricultural reuse. The CW has proven to be more effective in nitrogen removal (the effluent mean concentration was 3.4 mg L−1), whereas the LS was better at phosphorus removal (with an effluent mean concentration of 0.4 mg L−1). Based on the results, recommendations were made to further improve the performance of both systems in order to have adequate water quality, even for class A. Furthermore, the capacity of reclaimed water to meet crop water and nutrient needs was analyzed, and total nitrogen removal rate coefficients were calculated for the design of future LSs.

Assessment of Physio Chemical Parameters and Water Quality Index of Different Ghats of River Ganga at Varanasi, Uttar Pradesh, India

Ganga, the longest river in India, provides water to millions of people who live along its bank and depend on it for their livelihood throughout the year. For about 40% of the population, it provides water for drinking and irrigation. This study was conducted to analyse the physico-chemical parameters and WQI of five different Ghats of River Ganga at Varanasi. The data recorded from the present investigation revealed that the maximum value of pH was recorded at Dasaswamedh Ghat with 8.57 in the month of March and minimum was 7.53 recorded in the month of February Harish Chandra Ghat. EC value was recorded maximum at Tulsi Ghat with 564 mmhos/cm in the month of April and the minimum value was 131 mmhos/cm in the month of January recorded at Assi Ghat. The maximum value of TDS was 319.67mg/l in the month of February recorded at Assi ghat and minimum data recorded was 229mg/l in the month of January at Manikarnika Ghat. The DO value was recorded maximum at Manikarnika Ghat with 8.20mg/l in the month of January and the minimum value was5.97mg/l recorded in the month of January at Tulsi Ghat. The BOD value was recorded maximum at Assi Ghat with 4.63mg/l in the month of March and minimum of 2.70 mg/l recorded in the month of January at Harish Chandra Ghat. The Total Hardness value was recorded maximum at Tulsi Ghat with 138.33mg/l in the month of February and minimum value of 78.33mg/l recorded in the month of January at Assi Ghat. The Total Alkalinity value was recorded maximum with 114.33mg/l in the month of April at Ghat and minimum value was 78.67mg/l recorded in the month of January at Harish Chandra Ghat. The chloride value was recorded maximum at Harish Chandra Ghat with 31.67mg/l in the month of February and minimum value was 10mg/l recorded in the month of January at Manikarnika Ghat. The results of the different parameters when compared with BIS standards were found to be within the permissible limit except for pH and BOD. The WQI of the different Ghats indicated poor category of water quality.

Groundwater quality assessment for sustainable human consumption in arid areas based on GIS and water quality index in the watershed of Ain Sefra (SW of Algeria)

Groundwater quality in arid areas is an issue that requires attention, as often the only source of irrigation and drinking. In this manuscript, a research area of 4590.2 Km2, which depicts important problems of water scarcity and water-poor quality in an arid environment, was assessed. Therefore, GIS-based mapping of the region was carried out to examine the adequacy of groundwater quality. Forty-three (43) groundwater samples were gathered in the fieldwork in March 2022, and various physicochemical limits were analysed within Gibbs and Piper diagrams. Likewise, spatial distribution maps of twelve (12) main water quality parameters were performed using the Inverse Distance Weighted interpolation method (IDW) in the ArcGIS tool. The Water Quality Index (WQI) has been calculated considering Algerian drinking water quality standards and to know the suitability of water for human consumption. A Pearson’s test was carried out to explore the correlations among the different variables. Major results of the study showed that, among anions and cations, sulphates (SO_{4}^{ - 2}) and calcium (Ca) were the most common ions. The WQI showed that 30.23% of the samples have excellent water quality, 62.79% belong to the good water category, and 6.97% fall into the poor water quality category. Thus, the correlation study between many physicochemical properties also showed significant positive relationships between WQI, TDS, EC, TH, {text{SO}}_{4}^{ - 2}, {text{Ca}}^{ + + }, and {text{Mg}}^{ + + }. In agreement with the Gibbs diagram, most of the samples fall into the field of rock-water interaction dominance. Hence, this research shows that using GIS and WQI as joint approaches could be useful tools for the decision-making process of local and international authorities and water resources management.

Water quality classification model with small features and class imbalance based on fuzzy rough sets

Water quality classification model with small features and class imbalance based on fuzzy rough sets

Water quality prediction using machine learning models based on grid search method

Water quality is very dominant for humans, animals, plants, industries, and the environment. In the last decades, the quality of water has been impacted by contamination and pollution. In this paper, the challenge is to anticipate Water Quality Index (WQI) and Water Quality Classification (WQC), such that WQI is a vital indicator for water validity. In this study, parameters optimization and tuning are utilized to improve the accuracy of several machine learning models, where the machine learning techniques are utilized for the process of predicting WQI and WQC. Grid search is a vital method used for optimizing and tuning the parameters for four classification models and also, for optimizing and tuning the parameters for four regression models. Random forest (RF) model, Extreme Gradient Boosting (Xgboost) model, Gradient Boosting (GB) model, and Adaptive Boosting (AdaBoost) model are used as classification models for predicting WQC. K-nearest neighbor (KNN) regressor model, decision tree (DT) regressor model, support vector regressor (SVR) model, and multi-layer perceptron (MLP) regressor model are used as regression models for predicting WQI. In addition, preprocessing step including, data imputation (mean imputation) and data normalization were performed to fit the data and make it convenient for any further processing. The dataset used in this study includes 7 features and 1991 instances. To examine the efficacy of the classification approaches, five assessment metrics were computed: accuracy, recall, precision, Matthews's Correlation Coefficient (MCC), and F1 score. To assess the effectiveness of the regression models, four assessment metrics were computed: Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Square Error (MSE), and coefficient of determination (R2). In terms of classification, the testing findings showed that the GB model produced the best results, with an accuracy of 99.50% when predicting WQC values. According to the experimental results, the MLP regressor model outperformed other models in regression and achieved an R2 value of 99.8% while predicting WQI values.

Generating Water Quality Maps of Klang River Based on Geographic Information System (GIS) and Water Quality Index (WQI)

Water quality index (WQI) and Geographic Information Systems (GIS) play a critical role in managing and modelling a variety of water resource issues, including urban drainage, point and non-point source pollution. Historically, the evaluation of water quality has been a domain reserved for experts, necessitating laborious and time-consuming in situ sampling and laboratory analysis. However, the integration of WQI and GIS has democratized this information, making it accessible to non-experts, thereby enhancing the comprehension of Klang River's water quality. The objective is to employ WQI and GIS to create comprehensive water quality maps. While WQI offers a straightforward numerical evaluation, the incorporation of graphical data provides a nuanced understanding of river pollution. Therefore, hourly WQI data observed at every week in 2 months from October to November 2021 over four stations (Kampung Medan, Kampung Lombong, Taman Pengkalan Batu and Jeti Sungai Udang) in Malaysia was acquired from the Selangor Maritime Gateway (SMG) website and the Malaysian National Water Quality Standard (NWQS). Adopting Inverse Distance Weighting (IDW) interpolation method, WQI parameters at unsampled locations were estimated based on values of nearby sampled points. Database was built to depict the water quality of the Klang River, particularly during the two-month monitoring. Mapping provides a clear indication of the river's water quality. The WQI mapping outcome fall between class II and class IV. The findings indicate varying water quality classes along the Klang River, revealing potential pollution sources in industrial and development areas. It was concluded from the study that the water pollution may be due to its proximity to industrial and development regions.

Determination of the ecological status of an Aegean river (Türkiye) using benthic macroinvertebrates as an indicator of water quality.

Bakırçay is one of the important rivers of Western Anatolia, Türkiye. The objectives of this study are (a) to determine the diversity of benthic invertebrates distributed in Bakırçay, (b) to evaluate the obtained data using various biological indices, (c) to determine the interactions of species with environmental variables, and (d) to estimate the water quality level of the river using BMWP and ASPT analyses. Samplings were carried out at 15 stations between October 2017 and July 2018, seasonally. A total of 11,897 specimens belonging to 136 benthic macroinvertebrate taxa were determined. Diptera and Oligochaeta were the dominant groups with 32 (24%) and 25 taxa (18%), respectively. Station 4 has the highest species richness with 49 taxa (36%). The highest individual number (3421) was at the 14th station. Tubifex tubifex and Physella acuta have the highest dominancy (25.76%) and frequency (51.67) values, respectively. Diversity (H') values of the stations fluctuated between 1.367 (st. 13) and 3.154 (st. 12). Similarity analysis showed that the 3rd and 11th stations have the highest similarity (> 90%), while the 14th station was an outgroup and has 28% similarity ratio. BMWP and ASPT scores gave similar results, and there was no 1st class water quality in the river. On the other hand, the results of the TR-BMWP score suggested that the 1st, 4th, and 6th stations had 1st class water quality. Although some stations are classified as "good conditions," the adverse effects of pollution were mainly observed in the main channel and downstream of the river.

Using bacteria and benthic macroinvertebrates as water quality parameters in Mat River, Albania

Abstract This research examines the quality of Mat River water to control and reduce the level of environmental pollution in accordance with national rules and regulations. The focus of this study was the assessment of river water quality by using bacteria and benthic macroinvertebrates, as biological markers of stream water quality. The research was conducted from June 2018 to June 2019 at five sampling sites along the Mat River, with monthly sampling for chemical-physical and microbiological parameters and seasonal sampling for benthic macroinvertebrates. The investigated data for the studied parameters were statistically analysed using MINITAB 19 software. The variation in spatial and temporal trends of the investigated parameters showed differences in water quality among different sampling sites. The bacterial load was found to be higher near urban areas, and the pollution increased with the river course. The same tendency in water quality showed up even in the benthic macroinvertebrates population. Pearson correlation coefficients (p > 0.05) between the water quality data revealed the similarity and the associations between parameters. Cluster analysis of the investigated parameters revealed the classification of Mat River water quality and the possibility of using microbiological parameters and/or benthic macroinvertebrates for the assessment of water quality.

Spatiotemporal response of water quality in fragmented mangroves to anthropogenic activities and recommendations for restoration

Mangroves have received substantial attention for their pivotal role as ecological barriers between land and sea, owing to their capacity to effectively capture considerable quantities of terrestrial pollutants. Mangrove fragmentation has been a widespread global trend. There is limited information on the water quality status of these small scattered mangrove patches in coastal sub-developed areas, coupled with a paucity of efficient and intuitive assessment methodologies. To address this gap, the Water Quality Index (WQI) was introduced to evaluate the spatiotemporal characteristics of mangrove water quality. The major sources of pollution and anthropogenic activities that affect mangrove water quality were identified. The results revealed an average WQI value of 44.1 ± 13.3 for mangrove patches, consistently indicating a "low" water quality classification throughout all seasons. Both the size and natural conditions impact the water quality of mangroves. The large artificial patch (WQI: 56.4 ± 7.61) and the natural patch (WQI: 46.6 ± 13.6) exhibited relatively superior water quality, while the WQI value of a size-equivalent artificial patch compared with the natural patch is 38.6 ± 11.8. Aquaculture was the primary human activity that adversely affected the water quality of mangroves, and the potential sources of pollution were rainfall runoff and river discharge. These findings elucidate the unfavorable water quality characteristics and dominant pollution of fragmented mangroves, and validate the applicability of the WQI method for long-term evaluation of the water quality in mangrove patches. This study provides a basis for decision-making in water quality assessment and management of coastal wetlands and marine ecosystems. Scientific guidance to the management for mangrove protection and restoration was offered, such as regulating aquaculture activities, controlling non-point source pollution, implementing mangrove reforestation by using native species in historical mangrove sites.

Delineation and Mapping of Ground Water Quality in Kanchipuram District of Tamil Nadu Using GIS Techniques

Agriculture relies heavily on irrigation especially with groundwater which is a significant source in many countries. However, excessive use of groundwater can lead to a decrease in groundwater levels and cause scarcity of water. Irrigation requires good-quality water which is governed by dissolved ions. The groundwater quality is greatly influenced by global population growth, climate change and human activities including mining, agriculture, industrial effluents, seawater intrusion, household usage, etc., A study was conducted to evaluate the quality of groundwater for agriculture in various blocks of Kanchipuram district in Tamil Nadu. The sampling was done during March 2023 and about one hundred and fifty groundwater samples were collected from different blocks of the Kanchipuram district viz., Sriperumbudur(32 Nos), Kundrathur (28 Nos), Walajabad (34 Nos), Uthiramerur (29 Nos) and Kanchipuram (27 Nos). The physio-chemical (pH and EC) and chemical characteristics of the groundwater samples, including the cations Ca2+, Mg2+, Na+, and K+, as well as the anions CO32-, HCO3-, Cl-, and SO42-, were analyzed and the resulting properties were computed (SAR and RSC). The pH and EC values ranged from 3.29 to 8.49 and 0.09 to 5.22 dS m-1, respectively. The Residual Sodium Carbonate (RSC) ranged from nil to 32 meq L-1, while the Sodium Adsorption Ratio (SAR) ranged from 0.19 to 34.78 mmol L-1. According to the CSSRI, Karnal Water Quality Classification about 38 percent of the samples falls in the good quality category, alkali water was about 57.33 percent and Saline water was 4.67 percent in Kanchipuram district. The Good quality water was dominant in Uthiramerur block followed by the Sriperumbudur block. The Saline and Alkali water was dominant in Sriperumbudur and Kanchipuram blocks respectively.

Trophic Status Assessment In Some Lotic Ecosystem In Turkey (Sakarya Basin)

Benthic diatoms constitute a very important group of organisms in terms of providing information about the water quality of the lotic ecosystems. This research was carried out in Istanbul creek, Aliaga creek, Bostancı creek and Orta creek in the Sakarya basin. The trophic states of these creeks were evaluated according to their diatomes. In the study, the epilithic diatoms were determined to consist of 42 taxa belonging to İstanbul creek, 28 to Bostancı creek, 25 to Orta creek and 20 to Aliaga creek. A total of 51 taxa were determined in the creeks. In this study, Cocconeis pediculus, Ulnaria ulna and Cymbella affinis were found to have the highest abundant at the stations. In the creeks, water temperature, dissolved oxygen and pH were determined as respectively 9.1-14.1 ℃, 8.2-9.4 mg L-1, 7.1- 7.5 and conductivity as 210.2 - 240.4 μS cm-1. Although different results were obtained according to the diatom indices criteria (IBD, SLA,IDSE,IDAP, TID, SID,CEE, WAT, TDI, SHE, TIT) the creeks were determined to be close to water quality class I -II according to the Surface Water Quality Regulation In general, according to the diatom index values, it has been determined that the creeks have good-medium quality water characteristics.

Comparative assessment of water quality classification techniques in the largest north-western river of Cambodia (Sangker River-Tonle Sap Basin)

The lack of adequate and good water quality is one of the serious issues that most developing countries are facing, yet environmental monitoring programs are not efficiently protecting ecosystem health. Here, we aimed to assess the suitability of different water quality assessment frameworks in order to define the most suitable method, especially for data-scarce environments, to quantify river ecosystem health. The study was conducted in the largest north-western river of Cambodia, the Sangker River in the Tonle Sap Lake (TSL) basin (UNESCO Biosphere Reserve). Five water quality assessment methods were selected for the study: Basic & Single Prati indices, Mekong River Commission water quality index (MRC-WQI), French water quality assessment system (SEQ-EAU) and US Environmental Protection Agency (US-EPA) framework. Overall, a physico-chemical gradient of water quality was observed along the river course. The river head was defined by high levels of dissolved oxygen (DO), low nutrients and chloride, while the middle course was marked by a gradual decrease of DO and increased concentration of nutrients up to the TSL floodplains. This degradation was explained by multiple impacts from human activities, e.g. domestic wastes, livestock, crop farming, causing simultaneous eutrophication and oxygen depletion along the river course. Despite those multiple pressures, the Sangker river health status was scored as fairly good reflecting the current situation of the river. So far, we found a convergence of the five water quality assessment methods; however, SEQ-EAU and Basic Prati seemed to be less suitable for tropical river health assessment. The study defined the US-EPA assessment method as the most suitable technique to quantify the Sangker river health. This method could potentially be used to monitor river ecosystem health in data-scarce environments. This would help overcome environmental monitoring challenges in developing countries faced by rapid global environmental changes.

Synchronous stabilization of Pb, Zn, Cd, and As in lead smelting slag by industrial solid waste

In order to prevent heavy metal (HM) pollution from lead smelting slag (LSS) to the surrounding environment, this work investigated the feasibility, influencing factors, and mechanisms of using industrial solid waste such as fly ash (FA), oil sludge pyrolysis residue (PR), and steel slag (SS) as remediation amendments. The results demonstrated that the stabilization process was influenced by the material dosage, water content, and LSS particle size. Compared to single materials, the combination amendment PR2FA1 (with a mass ratio of PR to FA as 2:1) exhibited the best stabilization effect, simultaneously reducing the leaching concentrations of As, Zn, Cd, and Pb in LSS to 0.032, 0.034, 0.002, and 0.014 mg/L, respectively. The pH value of the leachate remained between 8 and 9, which met the requirements of surface water quality class IV (GB3838–2002). Through morphological analysis, microscopic characterization, and simulated solution adsorption experiments, it was determined that the stabilization process of HMs was controlled by various mechanisms, including electrostatic attraction, physical adsorption, ion exchange, and chemical precipitation. PR2FA1 had more active components, and its fine-porous structure provided more active sites, resulting in good stabilization performance for As, Zn, Cd, and Pb. Furthermore, cost analysis showed that PR2FA1, as an environmentally friendly material, could generate profits of 157.2 ¥/ton. In conclusion, the prepared PR2FA1 not only addressed the HMs pollution from lead smelting slag to the surrounding environment but also achieved the safe and resourceful disposal of hazardous waste-oil sludge. Its excellent performance in stabilizing HMs and cost-effectiveness suggested promising commercial applications.

Water Quality Classification and Machine Learning Model for Predicting Water Quality Status—A Study on Loa River Located in an Extremely Arid Environment: Atacama Desert

Water is the most important resource for human, animal, and vegetal life. Recently, the use of artificial intelligence techniques, such as Random Forest, has been combined with other techniques, such as models of logical–mathematical reasoning, to generate predictive water quality models. In this study, a rule-based inference technique to generate water quality labels is described, using historical physicochemical parameter data on seven water monitoring stations in Loa River, collected by the Chilean Ministry of the Environment. Next, a predictive model of water quality status was created, using Random Forest, physicochemical parameters, and expert knowledge. The validation of Random Forest results is described using three quality indicators from the machine learning model: accuracy (acc), precision (p), and recall (r). This paper describes dataset preparation, the refinement of the threshold values used for the physicochemical parameters most significant in the class, and the predictive model labeling water quality. The models obtained yielded the following mean values: acc = 0.897, p = 89.73, and r = 0.928. The ML model reported here is novel since no previous studies of this kind predict the water quality of Loa River, located in an extremely arid zone. This study also helps to create specific knowledge to predict freshwater quality.

Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies in the North of Ireland using newly developed methodologies. The results reveal significant differences between the new technique and the existing “one-out, all-out” approach in rating water quality. The new approach found the water quality status to be “good,” “fair,” and “marginal,” whereas the existing “one-out, all-out” technique classified water quality as “good,” and “moderate,” respectively. The new technique outperformed existing approaches in rating the water quality of different waterbody types, with high R2 = 1, NSE = 0.99, and MEF = 0 values. Furthermore, the final assessment of water quality using the new methodologies had the lowest uncertainty (<1 %), whereas the efficiency measures (NSE and MEF) indicate that the new approaches are bias-free to assess water quality at any geographic scale. The results of this study reveal that the newly proposed methodologies are effective in assessing the water quality states of transitional and coastal waterbodies in the North of Ireland. The study also highlighted the limitations of existing approaches and the importance of updating water resource management systems for better protection of these waterbodies. The findings have significant implications for water resource management and planning in the North of Ireland and other similar regions.

Determination of Pollutant Index (PI) for Assessing the Quality of Groundwater in the Area of Sidoarjo Mud (LUSI) Sidoarjo, East Java, Indonesia

Sidoarjo mud (well known as LUSI) have been occurred since 2006 in Sidoarjo, East Java, Indonesia and gives many impacts to many aspects, as well as environment and water quality condition. One of the major concerns is water quality problem, especially in this current situation in 2022 since clean water still needed by local people and groundwater is the main water source for many local people in this area. Our previous study has carried out to assess the ecological and geochemical conditions of groundwater in the area around Sidoarjo mud in 2022. In this study, by using Pollutant Index (PI) calculation, the water quality classification will be determined from several samples and parameters in various places, including Tanggulangin, and Porong, Sidoarjo, East Java. From the calculation result, based on Pollution Index (PI) value, water in Tanggulangin is categorized as good water (clean water), while water in Porong is categorized as good water (clean) as well for sample B3, and the rest is categorized as lightly polluted water with sample B5 as the most contaminated. Groundwater in the area near to Sidoarjo mud is contaminated by NO2-, Mn, and Hg.

Monitoring the Water Quality of the Tigris River and Producing Maps to Determine the Locations of Turbidity in the Water for the Year 2022 in Baghdad Governorate

Abstract Interest has recently increased in the sustainable development of the natural resources of the land cover, including the water resources stored in natural catchments such as lakes and marshes and flowing from the sources to the rivers, and in increasing human activities from cultivating and fertilizing the land, to establishing factories and throwing industrial waste into rivers to throwing bore water in addition to Clay silt as a result of weathering and erosion of the soil. The purpose of the study is to monitor and determine the quality of the Tigris River water from turbidity, industrial waste and the rest of the materials that pollute the Tigris River. The section of the Tigris River passing through the city of Baghdad was selected as a sample for the study, and the data were used. Remote sensing techniques in the investigation of the polluted area in the Tigris River in Baghdad. The Landsat 8 (OLI) image of Baghdad was used as the best sensor to measure the spectral reflectivity of land features, including water, with a spatial resolution of 30 m on October 18, 2020. The Arc GIS v10.8 application software package was used to process the image data and measure the normalized turbidity index using the green band (3) and the red band (4). Turbidity was calculated using Normalized Difference Turbidity Index (NDTI), and then classified based on the high and low values of (NDTI). The results indicated that three categories of water quality succeeded in assessing and mapping the turbidity of the water that pollutes the Tigris River. The study is worthwhile. I can be deal with the rest of the pollutants using remote sensing data as the best technology and results at the fastest costs.

A Fuzzy Inference System for enhanced groundwater quality assessment and index determination

Abstract Groundwater is a vital resource for human consumption, particularly in rural areas with limited access to treated water. The conventional Water Quality Index models used for this purpose have limitations related to data volatility and judgment uncertainties. To overcome these limitations, our study introduces a novel approach that employs a Fuzzy Inference System to determine the Water Quality Index. The dataset used in our research includes multiple parameters such as pH, EC, TDS, Ca, Mg, Na, K, HCO3, Cl, SO4, TH, DWQI, and other physio-chemical and chemical parameters. Our approach utilizes linguistic variables, fuzzy rules, and the hyperbolic tangent set function to handle imprecise and uncertain water quality data. By employing Fuzzy C-Means clustering, we group similar water samples based on quality parameters and map membership values to linguistic terms representing water quality categories. Suitable defuzzification methods are then applied to convert fuzzy outputs into precise results. This proposed approach provides a comprehensive framework for accurate water quality assessment, enabling informed decision-making and more reliable and precise evaluations of groundwater quality.

Preparation of flotation refined carbon from gasification slag

Highly efficient and environmentally friendly utilization of coal gasification slag is a hot research subject in the coal chemical industry at present. The preparation of activated carbon with a flotation refined carbon from gasification slag, a long flame coal, a high temperature coal tar containing 70% asphalt and an active agent in proper proportion was carried out. The influence of activation temperature and time on the surface properties and compressive strength of the produced activated carbon was investigated in a tube furnace. The oxygen functional group, pore structure and absorption performance of the produced activated carbon were characterized by FT-IR, N2 adsorption-desorption, SEM and iodine adsorption. The COD removal from biochemical waste water by the produced activated carbon was verified. The results show that the key factors for the effective formation and expansion of pore are the suitable activation temperature and time for the floatation refined carbon from gasification slag as the feedstock. The activated carbon prepared by carbonization at 550 °C for 30 min and steam activation at 950 °C for 2 h exhibits a crisscross morphology of organic carbon components and minerals. The surface area, pore capacity and average pore diameter are 566 m2/g, 0.5611 mL/g and 5.1 nm, respectively, with the characteristics of a concentrated pore distribution and a certain quantity of mesopore. Both iodine value (650 mg/g) and methylene blue value (128 mg/g) meet the requirements of the Chinese standard “Technical Specifications and Test Methods of Activated Carbon for Purification of Industrial Wastewater”. The COD in biochemical waste water treated by the activated carbon for 60 min with a solid-to-liquid ratio of 0.6 g/L can be reduced to lower than 30 mg/L, meeting the B class water quality of the Chinese standard “Integrated Discharge Standard of Water Pollutants” (DB11/307—2013).