In this paper, we present the effect of varying humidity levels on the electrical parameters and the multi frequency response of the electrical parameters of an organic-inorganic composite (PEPC+NiPc+Cu2O)-based humidity sensor. Silver thin films (thickness ~200 nm) were primarily deposited on plasma cleaned glass substrates by the physical vapor deposition (PVD) technique. A pair of rectangular silver electrodes was formed by patterning silver film through standard optical lithography technique. An active layer of organic-inorganic composite for humidity sensing was later spun coated to cover the separation between the silver electrodes. The electrical characterization of the sensor was performed as a function of relative humidity levels and frequency of the AC input signal. The sensor showed reversible changes in its capacitance with variations in humidity level. The maximum sensitivity ~31.6 pF/%RH at 100 Hz in capacitive mode of operation has been attained. The aim of this study was to increase the sensitivity of the previously reported humidity sensors using PEPC and NiPc, which has been successfully achieved.
Seagrass is a valuable marine ecosystem engineer. However, seagrass population is declining worldwide. The lack of seagrass research in Malaysia raises questions about the status of seagrasses in the country. The seagrasses in Lawas, which is part of the coral-mangrove-seagrass complex, have never been studied in detail. In this study, we examine whether monthly changes of seagrass population in Lawas occurred. Data on estimates of seagrass percentage cover and water physicochemical parameters (pH, turbidity, salinity, temperature, and dissolved oxygen) were measured at 84 sampling stations established within the study area from June 2009 to May 2010. Meteorological data such as total rainfall, air temperature, and Southern Oscillation Index were also investigated. Our results showed that (i) the monthly changes of seagrass percentage cover are significant, (ii) the changes correlated significantly with turbidity measurements, and (iii) weather changes affected the seagrass populations. Our study indicates seagrass percentage increased during the El-Nino period. These results suggest that natural disturbances such as weather changes affect seagrass populations. Evaluation of land usage and measurements of other water physicochemical parameters (such as heavy metal, pesticides, and nutrients) should be considered to assess the health of seagrass ecosystem at the study area.
The aim of this study is to examine the impact of air pollutants, including mono-nitrogen oxides (NOx), nitrous oxide (N2O), sulfur dioxide (SO2), carbon dioxide emissions (CO2), and greenhouse gas (GHG) emissions on ecological footprint, habitat area, food supply, and biodiversity in a panel of thirty-four developed and developing countries, over the period of 1995-2014. The results reveal that NOx and SO2 emissions both have a negative relationship with ecological footprints, while N2O emission and real GDP per capita have a direct relationship with ecological footprints. NOx has a positive relationship with forest area, per capita food supply and biological diversity while CO2 emission and GHG emission have a negative impact on food production. N2O has a positive impact on forest area and biodiversity, while SO2 emissions have a negative relationship with them. SO2 emission has a direct relationship with per capita food production, while GDP per capita significantly affected per capita food production and food supply variability across countries. The overall results reveal that SO2, CO2, and GHG emissions affected potential habitat area, while SO2 and GHG emissions affected the biodiversity index. Trade liberalization policies considerably affected the potential habitat area and biological diversity in a panel of countries.
Chemical pollution in the transboundary Langat River in Malaysia is common both from point and non-point sources. Therefore, the water treatment plants (WTPS) at the Langat River Basin have experienced frequent shutdown incidents. However, the Langat River is one of the main sources of drinking water to almost one-third of the population in Selangor state. Meanwhile, several studies have reported a high concentration of Arsenic (As) in the Langat River that is toxic if ingested via drinking water. However, this is a pioneer study that predicts the As concentration in the Langat River based on time-series data from 2005-2014 to estimate the health risk associated with As ingestion via drinking water at the Langat River Basin. Several time-series prediction models were tested and Gradient Boosted Tree (GBT) gained the best result. This GBT model also fits better to predict the As concentration until December 2024. The mean concentration of As in the Langat River for both 2014 and 2024, as well as the carcinogenic and non-carcinogenic health risks of As ingestion via drinking water, were within the drinking water quality standards proposed by the World Health Organization and Ministry of Health Malaysia. However, the ingestion of trace amounts of As over a long period might be detrimental to human health because of its non-biodegradable characteristics. Therefore, it is important to manage the drinking water sources to minimise As exposure risks to human health.
Although toxic Cd (cadmium) and Cr (chromium) in the aquatic environment are mainly from natural sources, human activities have increased their concentrations. Several studies have reported higher concentrations of Cd and Cr in the aquatic environment of Malaysia; however, the association between metal ingestion via drinking water and human health risk has not been established. This study collected water samples from four stages of the drinking water supply chain at Langat River Basin, Malaysia in 2015 to analyze the samples by inductivity coupled plasma mass spectrometry. Mean concentrations of Cd and Cr and the time-series river data (2004-2014) of these metals were significantly within the safe limit of drinking water quality standard proposed by the Ministry of Health Malaysia and the World Health Organization. Hazard quotient (HQ) and lifetime cancer risk (LCR) values of Cd and Cr in 2015 and 2020 also indicate no significant human health risk of its ingestion via drinking water. Additionally, management of pollution sources in the Langat Basin from 2004 to 2015 decreased Cr concentration in 2020 on the basis of autoregression moving average. Although Cd and Cr concentrations were found to be within the safe limits at Langat Basin, high concentrations of these metals have been found in household tap water, especially due to the contamination in the water distribution pipeline. Therefore, a two-layer water filtration system should be introduced in the basin to achieve the United Nations Sustainable Development Goals (SDGs) 2030 agenda of a better and more sustainable future for all, especially via SDG 6 of supplying safe drinking water at the household level.
The presence of toxic polonium-210 (Po-210) in the environment is due to the decay of primordial uranium-238. Meanwhile, several studies have reported elevated Po-210 radioactivity in the rivers around the world due to both natural and anthropogenic factors. However, the primary source of Po-210 in Langat River, Malaysia might be the natural weathering of granite rock along with mining, agriculture and industrial activities. Hence, this is the first study to determine the Po-210 activity in the drinking water supply chain in the Langat River Basin to simultaneously predict the human health risks of Po-210 ingestion. Therefore, water samples were collected in 2015⁻2016 from the four stages of the water supply chain to analyze by Alpha Spectrometry. Determined Po-210 activity, along with the influence of environmental parameters such as time-series rainfall, flood incidents and water flow data (2005⁻2015), was well within the maximum limit for drinking water quality standard proposed by the Ministry of Health Malaysia and World Health Organization. Moreover, the annual effective dose of Po-210 ingestion via drinking water supply chain indicates an acceptable carcinogenic risk for the populations in the Langat Basin at 95% confidence level; however, the estimated annual effective dose at the basin is higher than in many countries. Although several studies assume the carcinogenic risk of Po-210 ingestion to humans for a long time even at low activity, however, there is no significant causal study which links Po-210 ingestion via drinking water and cancer risk of the human. Since the conventional coagulation method is unable to remove Po-210 entirely from the treated water, introducing a two-layer water filtration system at the basin can be useful to achieve SDG target 6.1 of achieving safe drinking water supplies well before 2030, which might also be significant for other countries.
In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline-rise-decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type's RSEI values behave differently at different times. As the UANST is the core area of Xinjiang's urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.
Environmental monitoring is important to determine the extent of eco-system pollution and degradation so that effective remedial strategies can be formulated. In this study, an environmentally friendly and cost-effective sensor made up of novel carbon electrode modified with cellulose and hydroxyapatite was developed for the detection of trace lead ions in aqueous system and palm oil mill effluent. Zinc, cadmium, and copper with lead were simultaneously detected using this method. The electrode exhibited high tolerance towards twelve common metal ions and three model surface active substances - sodium dodecyl sulfate, Triton X-100, and cetyltrimethylammonium bromide. Under optimum conditions, the sensor detected lead ions in palm oil mill effluent in the concentration range of 10-50 μg/L with 0.11 ± 0.37 μg/L limit of detection and 0.37 ± 0.37 μg/L limit of quantification. The validation using tap water, blood serum and palm oil mill effluent samples and compared with Atomic Absorption Spectroscopy, suggested excellent sensitivity of the sensor to detect lead ions in simple and complex matrices. The cellulose produced based on "green" techniques from agro-lignocellulosic wastes, in combination with hydroxyapatite, were proven effective as components in the carbon electrode composite. It has great potential in both clinical and environmental use.
A novel synthesis and characterization of cellulose, hydroxyapatite and chemically-modified carbon electrode (Cellulose-HAp-CME) composite was reported for the analysis of trace Pb(II) ions detection and its validation in blood serum. The Field Emission Scanning Electron Microscopy (FESEM) analyses showed that the composite retained the orderly porous structure but with scattered particle size agglomeration. The Fourier Transform Infrared Spectroscopy (FTIR) spectra suggested the presence of functional groups associated with the bending and stretching of carbon bonds and intermolecular H-bonding. X-ray Diffraction (XRD) analyses further elucidated that the crystallite size could have influenced the properties of the electrode. Based on Thermo-gravimetric Analysis (TGA/DTG), the composites showed thermal stability with more than 60% residual content at 700°C. The sensor was successfully developed for trace Pb(II) ions detection in complex medium such as blood serum, in the physiologically relevant range of 10-60ppb, with resulting Limit of Detection (LOD) of 0.11±0.36ppb and Limit of Quantification (LOQ) of 0.36±0.36ppb. The newly fabricated electrode could be advantageous as a sensing platform with favourable electrochemical characteristics for robust, in situ and rapid environmental and clinical analyses of heavy metal ions.
This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.
Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.
The objective of this study is to delineate groundwater flowing well zone potential in An-Najif Province of Iraq in a data-driven evidential belief function model developed in a geographical information system (GIS) environment. An inventory map of 68 groundwater flowing wells was prepared through field survey. Seventy percent or 43 wells were used for training the evidential belief functions model and the reset 30 % or 19 wells were used for validation of the model. Seven groundwater conditioning factors mostly derived from RS were used, namely elevation, slope angle, curvature, topographic wetness index, stream power index, lithological units, and distance to the Euphrates River in this study. The relationship between training flowing well locations and the conditioning factors were investigated using evidential belief functions technique in a GIS environment. The integrated belief values were classified into five categories using natural break classification scheme to predict spatial zoning of groundwater flowing well, namely very low (0.17-0.34), low (0.34-0.46), moderate (0.46-0.58), high (0.58-0.80), and very high (0.80-0.99). The results show that very low and low zones cover 72 % (19,282 km(2)) of the study area mostly clustered in the central part, the moderate zone concentrated in the west part covers 13 % (3481 km(2)), and the high and very high zones extended over the northern part cover 15 % (3977 km(2)) of the study area. The vast spatial extension of very low and low zones indicates that groundwater flowing wells potential in the study area is low. The performance of the evidential belief functions spatial model was validated using the receiver operating characteristic curve. A success rate of 0.95 and a prediction rate of 0.94 were estimated from the area under relative operating characteristics curves, which indicate that the developed model has excellent capability to predict groundwater flowing well zones. The produced map of groundwater flowing well zones could be used to identify new wells and manage groundwater storage in a sustainable manner.
Fig leaf, an environmentally friendly byproduct of fruit plants, has been used for the first time to treat of methylene blue dye. The fig leaf-activated carbon (FLAC-3) was prepared successfully and used for the adsorption of methylene blue dye (MB). The adsorbent was characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and the Brunauer-Emmett-Teller (BET). In the present study, initial concentrations, contact time, temperatures, pH solution, FLAC-3 dose, volume solution, and activation agent were investigated. However, the initial concentration of MB was investigated at different concentrations of 20, 40, 80, 120, and 200 mg/L. pH solution was examined at these values: pH3, pH7, pH8, and pH11. Moreover, adsorption temperatures of 20, 30, 40, and 50 °C were considered to investigate how the FLAC-3 works on MB dye removal. The adsorption capacity of FLAC-3 was determined to be 24.75 mg/g for 0.08 g and 41 mg/g for 0.02 g. The adsorption process has followed the Langmuir isotherm model (R2 = 0.9841), where the adsorption created a monolayer covering the surface of the adsorbent. Additionally, it was discovered that the maximum adsorption capacity (Qm) was 41.7 mg/g and the Langmuir affinity constant (KL) was 0.37 L/mg. The FLAC-3, as low-cost adsorbents for methylene blue dye, has shown good cationic dye adsorption performance.
The heavy metal (HM) pollution in sediment is of serious concern, particularly in the Red Sea environment. This study aimed to review and compile data on the concentrations of four HMs (Cd, Cu, Pb, and Zn) in the coastal surface sediments from the Red Sea, mainly from Saudi Arabia, Egypt, and Yemen, published in the literature from 1992 to 2021. The coastal sediments included those from mangrove, estuaries, and intertidal ecosystems. It was found that the mean values of Cd, Cu, Pb, and Zn in coastal Red Sea sediments were elevated and localized in high human activity sites in comparison to the earth upper continental crust and to reference values for marine sediments. From the potential ecological risk index (PERI) aspect, 32 reports (47.1%) were categorized as 'considerable ecological risk' and 23 reports (33.8%) as 'very high ecological risk'. From the human health risk assessment (HHRA) aspect, the non-carcinogenic risk (NCR) values (HI values < 1.0) of Cd, Cu, Pb, and Zn represented no NCR for the ingestion and the dermal contact routes for sediments from the Red Sea countries. The reassessment of the HM data cited in the literature allowed integrative and accurate comparisons of the PERI and HHRA data, which would be useful in the management and sustainable development of the Red Sea area, besides being a helpful database for future use. This warrants extensive and continuous monitoring studies to understand the current and the projected HM pollution situation and to propose possible protective and conservative measures in the future for the resource-rich Red Sea ecosystem.
The growing interest in the environmental occurrence of veterinary and human pharmaceuticals is essentially due to their possible health implications to humans and ecosystem. This study assesses the occurrence of human pharmaceuticals in a Malaysian tropical aquatic environment taking a chemometric approach using cluster analysis, discriminant analysis and principal component analysis. Water samples were collected from seven sampling stations along the heavily populated Langat River basin on the west coast of peninsular Malaysia and its main tributaries. Water samples were extracted using solid-phase extraction and analyzed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for 18 pharmaceuticals and one metabolite, which cover a range of six therapeutic classes widely consumed in Malaysia. Cluster analysis was applied to group both pharmaceutical pollutants and sampling stations. Cluster analysis successfully clustered sampling stations and pollutants into three major clusters. Discriminant analysis was applied to identify those pollutants which had a significant impact in the definition of clusters. Finally, principal component analysis using a three-component model determined the constitution and data variance explained by each of the three main principal components.
Morphological deformities in parts of the head capsule of Chironomus spp. larvae inhabiting three polluted rivers (Permatang Rawa [PRR], Pasir [PR], and Kilang Ubi [KUR]) in the Juru River Basin, northeastern peninsular Malaysia, were studied. Samples of the fourth-instar larvae at one location in each river were collected monthly from November 2007 to March 2008 and examined for deformities of the mentum, antenna, mandible, and epipharyngis. At each sample location, in situ measurements of water depth, river width, water pH, dissolved oxygen, and water temperature were made. Samples of river water and benthic sediments were also collected monthly from each larval sample location in each river and taken to the laboratory for appropriate analysis. Total suspended solids (TSSs), ammonium-N, nitrate-N, phosphate-P, chloride, sulfate, and aluminum content in water were analyzed. Total organic matter and nonresidual metals in the sediment samples were also analyzed. Among the three rivers, the highest mean deformity (47.17%) was recorded in larvae collected from KUR that received industrial discharges from surrounding garment and rubber factories, followed by PRR (33.71%) receiving primarily residues of fertilizers and pesticides from adjacent rice fields, and PR (30.34%) contaminated primarily by anthropogenic wastes from the surrounding residential areas. Among the various head capsule structures, deformity of the mentum was strongly reflective of environmental stress and amounted to 27.9, 20.87, and 30.19% in the PRR, PR, and KUR, respectively. Calculated Lenat's toxic score index satisfactorily explained the influence of prevailing environmental variables on the severity of mentum deformities. Redundancy analysis and forward selection selected TSSs, sediment Zn, Mn, Cu, and Ni, and water pH, dissolved oxygen, water temperature, total organic matter, nitrate-N, chloride, phosphate-P, ammonium-N, sulfate, and aluminum as parameters that significantly affected some proportion of deformities. The total deformities correlated closely with deformities of mentum but only weakly with deformities in other parts of head. The total deformity incidence was strongly correlated with high contents of sediment Mn and Ni. The mentum and epipharyngis deformities incidence was highly correlated with an increase of TSSs, total aluminum, and ammonium-N and a decrease in pH and dissolved oxygen.
Abundance and diversity of benthic macroinvertebrates as well as physico-chemical parameters were investigated in five rivers of the Juru River Basin in northern Peninsula Malaysia: Ceruk Tok Kun River (CTKR), Pasir River (PR), Permatang Rawa River (PRR), Kilang Ubi River (KUR), and Juru River (JR). The physico-chemical parameters and calculated water quality index (WQI) were significantly different among the investigated rivers (ANOVA, P<0.05). The WQI classified CTKR, PR, and JR into class III (slightly polluted). However, PRR and KUR fell into class IV (polluted). High diversity and abundance of macroinvertebrates, especially the intolerant taxa, Ephemeroptera, Plecoptera, and Trichoptera, were observed in the least polluted river, CTKR. Decreasing abundance of macroinvertebrates followed the deterioration of river water quality with the least number of the most tolerant taxa collected from PR. On the basis of composition and sensitivity of macroinvertebrates to pollutants in each river, the highest Biological Monitoring Working Party (BMWP) index score of 93 was reported in CTKR (good water quality). BMWP scores in PRR and JR were 38.7 and 20.1, respectively, classifying both of them into "moderate water quality" category. Poor water quality was reported in PR and KUR. The outcome of the multivariate analysis (CCA) was highly satisfactory, explaining 43.32% of the variance for the assemblages of macroinvertebrates as influenced by 19 physical and chemical variables. According to the CCA model, we assert that there were three levels of stresses on macroinvertebrate communities in the investigated rivers: Level 1, characterized of undisturbed or slightly polluted as in the case of CTKR; Level 2, characterized by a lower habitat quality (the JR) compared to the CTKR; and Level 3 showed severe environmental stresses (PRR, PR, and KUR) primarily contributed by agricultural, industrial, and municipal discharges.
Morphological mentum deformities which represent sublethal effect of exposure to different types of pollutants were evaluated in Chironomus spp. larvae inhabiting three polluted rivers of Juru River Basin in northwestern peninsular Malaysia. Using mentum deformity incidences, the modified toxic score index (MTSI) was developed based on Lenat's toxic score index (TSI). The suggested MTSI was compared with TSI in terms of its effectiveness to identify different pollutants including heavy metals. The MTSI showed stronger relationship to total deformity incidence expressed as percentage. Additionally, the multivariate RDA model showed higher capability of MTSI to explain the variations in heavy metal contents of the river sediments. The MTSI was recommended in bioassessment of water and sediment quality using the mentum deformities of Chironomus spp. larvae from aquatic ecosystems receiving anthropogenic, agricultural, or industrial discharges.
The influence of physical and chemical parameters on the abundance and diversity of chironomids was studied in six rivers with moderate to highly polluted water in the Juru River Basin. The rivers: Ceruk Tok Kun (CTKR) as reference site, and polluted rivers of Pasir (PR), Juru (JR), Permatang Rawa (PRR), Ara (AR) and Kilang Ubi (KUR) were sampled over a period of five months (November 2007-March 2008). Nine chirnomid species: Chironimus kiiensis, C. javanus, Polypedilum trigonus, Microchironomus sp., Dicrotendipes sp., Tanytarsus formosanus, Clinotanypus sp., Tanypus punctipennis and Fittkauimyia sp. were identified. Assessment of their relationships with several environmental parameters was performed using the canonical correspondence analysis (CCA). Tanytarsus formosanus was the most dominant in the relatively clean CTKR and moderately polluted JR with mean densities of 19.66 and 25.32 m(-2), respectively while C. kiiensis was abundant in more polluted rivers. Tanytarsus formosanus, Dicrotendipes sp. and Microchironomus sp. were grouped under moderate to high water temperature, total organic matter (TOM), total suspended solids (TSS), velocity, pH, phosphates and sulphates. However, Tanypus punctipennis, Fittkauimyia sp., and Clinotanypus sp. were associated with high contents of river sediment such as TOM, Zn and Mn and water ammonium-N and nitrate-N and they were associated with higher dissolved oxygen (DO) content in the water. Chironomus kiiensis, C. javanus and P. trigonus showed positive relationships with TOM, ammonium-N and nitrate-N as well as trace metals of Zn, Cu and Mn. These three species could be considered as tolerant species since they have the ability to survive in extreme environmental conditions with low DO and high concentrations of pollutants. Based on the water parameter scores in all rivers, the highest diversity of chironomid larvae was reported in CTKR. With higher concentrations of organic and/or inorganic pollutants as reported in PPR, KUR and AR, the chironomid larval diversity decreased, and the abundance of tolerant species, mainly Chironomus spp., increased.
Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making.