Monitoring Of Heavy Metal Pollution Research Articles

Evaluation of zinc, lead, and cadmium bioaccumulation in the liver of house sparrows (Passer domesticus) to assess environmental pollution in Meknes, Morocco

ABSTRACT Environmental pollution from heavy metals such as zinc (Zn), lead (Pb), and cadmium (Cd) poses serious risks to human health and ecosystems. This study examines the bioaccumulation of these metals in the livers of house sparrows (Passer domesticus) in Meknes, Morocco, using them as bioindicators of pollution. Fifty adult sparrows were collected from five areas: The Industrial Zone (IZ), Town Centre (TC), Sidi Said Bus Station (SS), Fes-Meknes Main Road (MR), and a Rural Site (Ref). Liver samples were analysed with Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES). The highest Zn concentrations were found at IZ (111.43 µg/g) and Ref (90.66 µg/g), Pb at TC (4.17 µg/g), and Cd at IZ and TC (0.70 µg/g and 0.71 µg/g). Statistical analyses revealed significant correlations between metal levels. Clustering techniques identified three distinct contamination profiles. These results highlight house sparrows as reliable bioindicators for monitoring heavy metal pollution.

Ecological risk assessment of heavy metals contaminated mining sites of eastern india using soil and moss.

The blooming industrialization and urbanization is leading to increased mining operations. These intensified mining activities emit heavy metals into the environment, posing serious threats to ecosystems. Hence, this study focused on assessing heavy metal pollution in mining soil, utilizing mosses as bioindicators. The ecological risk, geo-accumulation factor, and contamination factor have been calculated to know the harmful effect of heavy metals on ecosystem. The study covered three distinct mining sites of eastern India within Odisha: Jajpur's Sukinda Valley (SP1, Cr), Keonjhar's Joda-Barbil (SP2, Fe and Mn), and Sundargarh's Koira-Joda (SP3, Fe). The collection of 48 soil samples through random sampling revealed significant variations in heavy metal concentrations. SP1 recorded Cr concentration of 6572 ± 445mg/kg and Ni of 8042.47 ± 501.38mg/kg, surpassing eco-toxicological levels. The storage site in SP2 exhibited the highest Fe concentration at 9872 ± 502mg/kg, and Mn levels in SP3 were at 7884 ± 432mg/kg. Storage areas in all three regions held the highest concentrations of heavy metals. Mosses in studied area demonstrated as potential bioindicators for monitoring heavy metal pollution. EF and Igeo assessments showed Cd, Pb, Hg, and other heavy metal contamination compared to earlier investigations. This study indicated higher ecological risks for Pb, As, Cu, Ni, and Zn. The Hyophila involuta accumulates Mn, Cr, Cd, Pb, Fe, and Hg, while Barbula arcuata accumulates Mn, As, and Cu in SP1. Hyophila involuta and Trematodon longicollis accumulate Mn, Cr, Cd, Pb, Fe, Hg, and Zn in SP2. Trematodon ambiguous accumulates Cd, Fe, and Ni, while Fissidens diversifolius accumulates Mn, Cr, Hg, As, Cu, and Zn in SP3. These findings emphasize the necessity of monitoring heavy metal pollution in contaminated zones using moss as a potential bioindicator.

Metabolic engineering-enabled dual-color biosensor for discriminative and sensitive detection of toxic lead and mercury in environmental waters

The ubiquity of toxic heavy metals, particularly lead (Pb) and mercury (Hg), in environmental waters severely threatens ecosystems and human health. We developed a metabolic engineering-based dual-color bacterial biosensor for the sensitive and discriminative detection of Pb(II) and Hg(II) at nanomolar levels. The biosensor leverages the synthetic biology approach to integrate two pigments, prodeoxyviolacein (PDV) and deoxyviolacein (DV), into a single construct that responds distinctly to Pb(II) and Hg(II), respectively. The engineered E. coli biosensor exhibits a detection range of 0.732–3000 nM for Pb(II) and 0.183–750 nM for Hg(II), with low detection limits of 0.732 nM for Pb(II) and 0.183 nM for Hg(II). The biosensor demonstrated high sensitivity, selectivity, and accuracy with a recovery rate close to 1 in freshwater and seawater matrices. Notably, the biosensor's visual readout allows for rapid, equipment-free assessment. It is a practical tool for real-world monitoring of heavy metal pollution, especially in remote or resource-limited settings. This study underscores the potential of whole-cell biosensors as a sustainable and effective alternative to traditional heavy metal detection methods.

An intelligent device with double fluorescent carbon dots based on smartphone for visual and point-of-care testing of Copper(II) in water and food samples

The excessive presence of Cu2+ could be harmful to human health. Therefore, a ratiometric fluorescence sensor based on multicolor fluorescent carbon dots (CDs) was developed for Cu2+ detection. The blue and yellow carbon dots (B-CDs/Y-CDs) were synthesized by one-step hydrothermal method. After adding Cu2+, it is captured by the amino groups of B-CDs to form complexes, resulting in a strong fluorescence quenching via photoinduced electron transfer (PET). Meanwhile, the amino groups from Y-CDs also binds with Cu2+ that inhibit the internal PET thus enhancing the fluorescence of Y-CDs. The sensor has the merits in rapid, visual, and selective with a low limit of detection (LOD) at 2.29 nM. Furthermore, an intelligent device composed of portable optical detector and smartphone is constructed, which realizes the visual point-of-care testing (POCT) of Cu2+ with a LOD of 7.51 nM. The strategy provides an accessible approach for monitoring heavy metal pollution and food safety.

New magnetic proxies to reveal source and bioavailability of heavy metals in contaminated soils

Environmental magnetism plays an important role in monitoring heavy metal pollution, but most studies are confined to indicating only the levels of heavy metals using magnetic parameters. This study established new magnetic proxies for accurately depicting the sources and bioavailability of heavy metals in contaminated soils. We observed different relationships between χ and SIRM in the soils contaminated by non-ferrous metal smelting compared to those polluted by coal combustion and steel smelting. Furthermore, we found that the soft magnetic components (IRMsoft) in the soils were mainly controlled by the non-ferrous metal smelting activities, while the hard magnetic components (HIRM) might be affected by the iron erosion. These new magnetic proxies enriched the source composition spectrum and improved the accuracy of the source apportionment analyses (principal component analysis and positive matrix factorization), yielding a result that was comparable to that by Pb isotope fingerprinting. We also found strong relationships between magnetic parameters (especially IRMsoft) and bioavailable fractions of heavy metals, indicating that magnetic measurement may be a powerful tool for monitoring the bioavailability of heavy metals. This study expands the application fields of magnetism in environmental science research.

Ultrasensitive electrochemical sensor based on dimethylglyoxime/carbon paste electrode modified with a bimetallic nanocomposite for nickel detection in environmental water samples

To date, research in the monitoring of heavy metal pollution focuses on developing an advanced and selective sensor. In this study, an electrochemical sensor-based bimetallic nanocomposite modified dimethylglyoxime carbon paste electrode (DMG/CPE) was created to detect Ni2+ selectively. The bimetallic nanocomposite was prepared by depositing a thin layer of silver nanoparticles (AgNPs) on the surface of the magnetite (Fe3O4)/DMG/CPE electrode. The incorporation of Fe3O4 and AgNPs results in a synergistic effect, increasing electron transfer and expanding the electroactive surface area (from 2.78 mm2 to 3.81 mm2), hence leading to an increase in the peak current response (ΔIp = 26.15 ± 0.33 µA at modified CPE). The prepared AgNPs/Fe3O4/DMG/CPE electrode demonstrated excellent properties, allowing for a broad linear dynamic range lying between 2–10 nM; 10–100 nM; and 100–1000 nM and a limit of detection value of 0.6 nM (S/N = 3). Additionally, the developed sensor showed great sensitivity, selectivity, and reliability in the detection of Ni2+ in river water samples, making it promising for a sensitive and selective detection of Ni-contaminated water.

A hyperspectral metal concentration inversion method using attention mechanism and graph neural network

Soil heavy metal contamination has emerged as a global environmental concern, posing significant risks to human health and ecosystem integrity. Hyperspectral technology, with its non-invasive, non-destructive, large-scale, and high spectral resolution capabilities, shows promising applications in monitoring soil heavy metal pollution. Traditional monitoring methods are often time-consuming, labor-intensive, and expensive, limiting their effectiveness for rapid, large-scale assessments. This study introduces a novel deep learning method, SpecMet, for estimating heavy metal concentrations in naturally contaminated agricultural soils using hyperspectral data. The SpecMet model extracts features from hyperspectral data using convolutional neural networks (CNNs) and achieves end-to-end prediction of soil heavy metal concentrations by integrating attention mechanisms and graph neural networks. Results demonstrate that the OR-SpecMet model, which utilizes raw spectral data, achieves optimal prediction performance, significantly surpassing traditional machine learning methods such as multiple linear regression, partial least squares regression, and support vector machine regression in estimating concentrations of lead (Pb), copper (Cu), cadmium (Cd), and mercury (Hg). Moreover, training specialized OR-SpecMet models for individual heavy metals better accommodates their unique spectral-concentration relationships, enhancing overall estimation accuracy while achieving a 20.3 % improvement in predicting low-concentration mercury. The OR-SpecMet method showcases the superior performance and extensive application potential of deep learning techniques in precise soil heavy metal pollution monitoring, offering new insights and reliable technical support for soil pollution prevention and agricultural ecosystem protection. The code and datasets used in this study are publicly available at: https://github.com/zhang2lei/metal.git.

Honey Bee Products as Bio Indicator of Heavy Metals Pollution and Health Risk Assessment Through the Consumption of Multifloral Honey Collected in Azad Kashmir, Pakistan.

The study assessed the health risks associated with heavy metal ingestion and explored the use of honey bee products as a bio-indicator for heavy metal pollution. All honey bee products tested showed heavy metals, but some honey samples had concentrations exceeding permissible limits for Cd, Pb, Ni, and Cr. The mean concentrations of heavy metals (mg/kg) in the honey, propolis, bee wax, and bee pollen were Fe (1.32) > Zn (1.31) > Pb (0.46) > Ni (0.18) > Cr (0.16) > Cu (0.14) > Co (0.12) > Mn (0.05) > Cd (0.03), Fe (8) > Zn (1.13) > Mn (0.59) > Pb (0.13) > Ni (0.07) > Cu (0.06) > Co (0.05) > Cr (0.03) > Cd (0.02), Fe (1.31) > Pb (0.41) > Ni (0.407) > Zn(0.25) > Mn (0.12) > Co(0.10) > Cu (0.07) > Cr (0.05) > Cd (0.002), and Fe (2.2) > Zn (0.75) > Ni (0.25) > Pb (0.16) > Cu (0.05) > Mn (0.045) > Co (0.04) > Cr (0.01) > Cd(0.002), respectively. Similarly, the mean concentration of heavy metals (mg/kg) in the soil, flowers and pine pollen was Fe (539.08) > Zn (89.53) > Mn (66.91) > Ni (58.5) > Co (19.2) > Cr (11.42) > Pb (6.58) > Cu (5.71) > Cd (0.19), Fe (3.12) > Zn (0.95) > Mn (0.72) > Ni (0.29) > Cu (0.16) > Cr (0.14) > Pb (0.059) > Co (0.057) > Cd (0.003) and Fe (2.59) > Zn (1.75) > Mn (0.43) > Pb (0.34) > Co (0.1) > Cr (0.07) > Cu (0.06) > Cd (0.039) > Ni (0.03), respectively. The atomic absorption spectrophotometry procedure was validated through a recovery study and achieved accuracy through the limit of detection (LOD) and limit of quantification (LOQ). The mean Bio concentration factor (BCF) indicated that the transfer from soil to honey was higher than from soil to flower. The metal pollution index (MPI) of the selected indicators was in descending order: soil > honey > flowers > propolis > pine pollen > beeswax > bee pollen. The hazard quotient (HQ) and hazard index (HI) were below one, showing no chronic health risk. The carcinogenic risk (CR) of Cd, Cr, and Ni in honey for children, male and female adults for the consumers exceeds the acceptable level, making Cd, Cr, and Ni the most concerning heavy metals in honey. The study suggests that regular monitoring of heavy metal pollution is essential.

Plants as effective bioindicators for heavy metal pollution monitoring

This study investigated the bioindicator potential of Amaranthus retroflexus L., Plantago lanceolata L., Rumex acetosa L., and Trifolium pratense L. including the use of Lolium multiflorum L. as a reference species, for heavy metal pollution monitoring, in particular Zinc (Zn), Cadmium (Cd), Nickel (Ni), and Lead (Pb). Controlled heavy metal contamination was applied through irrigation with metal nitrate solutions two levels of contamination (low and high). The study also focused on analyzing heavy metals concentration in plant tissues and related physiological responses. Distinct physiological responses to heavy metal stress were observed among the investigated species, highlighting unique variations in their reactions. Hydrogen peroxide, malondialdehyde content, and enzymatic activities emerged as reliable indicators of plant stress induced by heavy metal solutions. P. lanceolata displayed elevated Zn concentrations in both roots and leaves (3271 ± 337 and 4956 ± 82 mg kg−1). For Pb, L. multiflorum and P. lanceolata showed highest root concentrations (2964 ± 937 and 1605 ± 289 mg kg−1), while R. acetosa had higher leaf concentration (1957 ± 147 mg kg−1). For Ni, L. multiflorum had the highest root concentration (1148 ± 93 mg kg−1), and P. lanceolata exhibited the highest leaf concentration (2492 ± 28 mg kg−1). P. lanceolata consistently demonstrated the highest Cd concentrations in both roots (126 ± 21 mg kg−1) and leaves (163 ± 12 mg kg−1). These results provide valuable insights for selecting effective bioindicator species to establish control strategies for heavy metal pollution.

Seasonal Monitoring of Heavy Metal Pollution in Water and Zebra Mussels Dreissena polymorpha as a Potential Bioindicator Species from Lake Habitat.

In aquatic ecosystem, metal pollution is an important environmental hazard. Mussels as a bioindicator species are often used for assessment the presence of potentially toxic metals. Hence, the present study aimed to assess the effect of seasonal variations on some heavy metals (Cd, Cr, Pb, As, Zn and Cu) accumulation in water and Dreissena polymorpha from lake habitat. Our result indicated that Zn accumulated at a very high level in the zebra mussels while As accumulated at a high level in water samples. Seasonal variations significantly affected Cu concentration in the water samples (P < 0.05) while Cr concentration in the mussel samples was significantly affected by seasonal variations (P < 0.05). According to the water analysis, mean concentrations of metals are below the maximum limits established by the World Health Organization and USEPA, except As. Overall, our data emphasize anthropogenic pollution in the Turkish aquatic environment and confirm the use of D. polymorpha as a prospective biomonitor for metal polluted sites'.

Biomarkers for monitoring heavy metal pollution in the Anzali Wetland

This research aims to investigate biochemical activities of Phragmites australis, as a biomarker of heavy metals including Cr, Ni, V, Zn and Co. In order to determine and analyze biochemical parameters including flavonoids, Non-Protein Thiols (NPTs), chlorophyll a and b and total chlorophyll pigments in the roots, stems and leaves of P. australis, sediment and plant samples were collected from 7 stations in the Anzali wetland. Based on the obtained results, there were positive and significant correlation coefficients among the concentrations of the heavy metals in the sediments with non-protein thiols and flavonoids, also negative and significant correlation coefficients were found between the heavy metal contents and the total chlorophyll in the leaves in all the sampling stations. Therefore, it can be concluded that these parameters are appropriate biomarkers to evaluate the heavy metal pollution in the sediments of the aquatic ecosystem.

Deep Learning Based Spatial Distribution Estimation of Soil Pb Using Multi-Phase Multispectral Remote Sensing Images in a Mining Area

Extensive investigation and monitoring of lead (Pb) content of soil is significant for ensuring hazard-free agricultural production, protecting human health, and ecosystem security, especially in a mining area. One temporal period of a hyperspectral image is usually used to estimate the spatial distribution of Pb and other heavy metals, but hyperspectral images are usually difficult to obtain. Multispectral remote-sensing images are more accessible than hyperspectral images. In this study, a deep learning-based model using 3D convolution is proposed to estimate the Pb content from the constructed multi-phase, multispectral remote-sensing images. Multi-phase multispectral remote-sensing images were stacked to generate a data set with more spectral bands to reduce the atmospheric absorptive aerosol effect. At the same time, a neural network based on 3D convolution (3D-ConvNet) was proposed to estimate Pb content based on the constructed data set. Compared with partial least-squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVMR), and gradient-boosting regression (GBR), experimental results showed the proposed 3D-ConvNet has obvious superiority and generates more accurate estimation results, with the prediction dataset coefficient of determination (R2) and the mean normalized bias (MNB) values being 0.90 and 2.63%, respectively. Therefore, it is possible to effectively estimate heavy metal content from multi-phase, multispectral remote-sensing images, and this study provides a new approach to heavy metal pollution monitoring.

Utilizing mollusk soft tissue and shells as biomarkers for monitoring heavy metal pollution in mangrove forests

The primary objective of the study was to examine the distribution of various elements, namely Cadmium (Cd), Copper (Cu), Iron (Fe), Nickel (Ni), and Lead (Pb), in the soft tissues, shells, and associated surface sediments of Cerithidea obtusa (C. obtusa) mangrove snails collected from Sungai Besar Sepang. To conduct the analysis, the preferred and most convenient methods employed were Instrumental Neutron Activation Analysis (INAA) and Atomic absorption spectrometry (AAS). The results showed that the mean concentration of elements in the sediments and soft tissues followed the order Fe > Cu > Ni > Pb > Cd, while for the shell of C. obtusa, it was Fe > Ni > Cu > Pb > Cd.•Iron (Fe) showed the highest concentration among all elements monitored in sediments, soft tissues, and shells of C. obtusa.•The PF results indicated higher incorporation of Pb and Ni into shells.•BSAF results showed that C. obtusa shells accumulated more Cu and Cd from sediments, making them effective biomonitors.

A ratiometric fluorescence platform for lead ion detection via RNA cleavage-inhibited self-assembly of three-arm branched junction

Heavy metal pollution can pose a threat to food safety and human health, and accurate quantification of heavy metal ions is a vital requirement. Emerging DNA nanostructures-based biosensors offer attractive tools toward ultra-sensitive or rapid analysis of heavy metal ions. However, the problems including complex design, severe reaction conditions and undesirable reliability are inevitable obstacle in advancing their extension and application. Herein, a ratiometric fluorescent platform was established for monitoring lead ion (Pb2+) in food based on dual Förster resonance energy transfer (FRET) and RNA cleavage-inhibited self-assembly of three-arm branched junction (TBJ). GR-5 DNAzyme was employed for Pb2+ recognition, and enzyme-free amplification technique catalytic hairpin assembly (CHA) served to form FRET probes-carried TBJ. The substrate strand (S) of DNAzyme triggered the generation of CHA-TBJ, and Pb2+-responsive cleavage of S hindered the assembly of CHA-TBJ, causing opposite changes in the FRET states of FAM/BHQ1 and ROX/BHQ2 pairs. The fluorescence responses were recorded through synchronous fluorescence spectrometry to indicate Pb2+ concentration, allowing sensitive and reliable identification of Pb2+ in the linear range of 0.05–5 ng mL−1 with the detection limit of 0.03 ng mL−1. The Pb2+ detection can be achieved under conventional reaction conditions, simple mixing procedures and one-step measurement operation. The approach can afford excellent specificity for Pb2+ against competing metal ions, and can be applied to analyze Pb2+ in tea samples with satisfactory results. This facile fluorescence platform shows a capable method for Pb2+ detection, and provides new avenue in the development of ratiometric approaches and DNAzyme strategies for monitoring heavy metal pollution, facilitating the transformation of DNAzyme-based biosensors for food safety control.

Characteristics, Chemical Speciation and Health Risk Assessment of Heavy Metals in Paddy Soil and Rice around an Abandoned High-Arsenic Coal Mine Area, Southwest China

The concentrations of the heavy metals Pb, Cd, Cr, Hg, As, Cu and Zn in soil and locally produced grain (rice) were determined in paddy soil and rice around an abandoned high-arsenic coal mine area of Xingren county, southwest China. The health risk assessment was used to assess the multimedia and multipathway health risks of HM exposure in the study area. The results showed that the concentrations of As, Pb and Cd in soil were all higher than the corresponding limits for HMs in China. In terms of the accumulation and transfer capacity, Cd was more likely to transfer from the roots to rice, and its strong mobility may pose potential risks to local residents. The non-carcinogenic risks and carcinogenic risks of HM exposure in different media and exposure pathways were higher in children than adults. The total non-carcinogenic risks and carcinogenic risks in adults and children were higher than the standard limit values because of the HM exposure through ingesting rice husk. Among the exposure pathways evaluated, the contribution of diet was the largest, and As was the most important heavy metal in terms of the non-carcinogenic risk and carcinogenic risk factors. The total non-carcinogenic risks and carcinogenic risks caused by As in dietary crop (rice) accounted for 52% of the total in both adults and children. In order to maintain the health of residents in the study area, it is necessary to strictly strengthen the monitoring of heavy metal pollution in the study area and find effective soil improvement methods to reduce the health risks caused by heavy metal exposure.

Trifolium pratense and the Heavy Metal Content in Various Urban Areas

Effective biomonitoring strategies are essential for identifying and assessing the sources and levels of contamination of heavy metal pollutants in urban areas, given their negative impacts on human health and the environment. This study aimed to assess the potential of common weed, Trifolium pratense as a bioindicator of heavy metal contamination in various land uses in urban areas, with a focus on Cd, Cu, Cr, Ni, and Pb. The results have shown that Cr and Ni had high bioconcentration factor (BCF) values in most sites, in comparison with Cu, Cd and Pb. Contamination factor (CF) values varied across all sites. The industrial area and old town sites had the highest translocation factor (TF) values for Cr and Ni, indicating greater transport of these metals from roots to aerial parts of plants. Differences between heavy metals (HMs) according to land use were observed; especially, Pb and Cu were more concentrated in soils than other heavy metals in industrial areas. Overall, these findings suggest that Trifolium pratense is a promising bioindicator for heavy metal contamination in various land uses in urban areas, making it a potentially valuable tool for monitoring heavy metal pollution in cities of the northern hemisphere.

Prediction of Cr and Ni contents in soil from hyperspectral data combined with Al-Fe minerals

ABSTRACT Hyperspectral analysis is useful in monitoring and evaluating soil heavy metal pollution. The difficulty in estimation of heavy metals by hyperspectral analysis is to extract the spectral features related to soil heavy metal concentration which may be interfered by signals resulting from other soil components. In this study, we established a comprehensive framework for prediction of Cr and Ni from hyperspectral data combined with soil Al-Fe minerals using advanced machine learning methods. The Al-Fe minerals were measured in laboratory and predicted from the spectral data, respectively. The results show that (1) from the original hyperspectral data, the model performances for Cr (R2 = 0.61, RMSE = 16.96) and Ni (R2 = 0.34, RMSE = 6.92) are not satisfactory. (2) With the incorporation of the measured Al-Fe minerals as the predictors, the optimal machine learning method for prediction of Cr and Ni is XgBoost, and the model performances are improved obviously (Cr: R2 = 0.85, RMSE = 10.67; Ni: R2 = 0.71, RMSE = 4.55). (3) With the incorporation of the Al-Fe minerals predicted from the spectral data, the optimal machine learning method for prediction of Cr and Ni is GdBoost, and the model performances are acceptable (Cr: R2 = 0.74, RMSE = 13.93; Ni: R2 = 0.68, RMSE = 4.79). This study provides a preliminary analysis for mapping soil heavy metal content and monitoring heavy metal pollution over a large spatial extent.

Machine learning: An effective technical method for future use in assessing the effectiveness of phosphorus-dissolving microbial agroremediation.

The issue of agricultural pollution has become one of the most important environmental concerns worldwide because of its relevance to human survival and health. Microbial remediation is an effective method for treating heavy metal pollution in agriculture, but the evaluation of its effectiveness has been a difficult issue. Machine learning (ML), a widely used data processing technique, can improve the accuracy of assessments and predictions by analyzing and processing large amounts of data. In microbial remediation, ML can help identify the types of microbes, mechanisms of action and adapted environments, predict the effectiveness of microbial remediation and potential problems, and assess the ecological benefits and crop growth after remediation. In addition, ML can help optimize monitoring programs, improve the accuracy and effectiveness of heavy metal pollution monitoring, and provide a scientific basis for the development of treatment measures. Therefore, ML has important application prospects in assessing the effectiveness of microbial remediation of heavy metal pollution in agriculture and is expected to be an effective pollution management technology.

Environmental Monitoring of Heavy Metal Pollution of Surface Water of the Tributaries to the River Ural

The spatial variability of the content of heavy metals in the surface waters of the right tributaries of the river is presented Ural. The data of the calculation of the index of water pollution are presented, which make it possible to assess the water quality of the studied rivers by categories of pollution. The results of biotesting of water pollution using the seeds of cress Lepidium sativum L. and radish Raphanus sativus as a test objects are discussed.

Monitoring effects of heavy metal stress on biochemical and spectral parameters of cotton using hyperspectral reflectance.

Monitoring heavy metal pollution in agricultural ecosystems is crucial to ensure environmental safety. Heavy metals interfere with plants' biochemical characteristics, such as chlorophyll content and photosynthesis, andalso influence leaves' spectral properties. Spectral changes caused by heavy metal stress can easily be measured using proximal sensing or in-field spectroscopy. This research utilizes a combined approach of biochemical and spectral characteristics to evaluate cottoncrops' performance under different heavy metal (Pb & Cd)stress after artificial contamination with the metal under study. A detailed study of spectroscopy and lab-based measurements for chlorophyll and heavy metal content during the crop's growth cycle revealed some significant findings. Results indicated that the chlorophyll pigments decreased significantly with increased heavy metal levels. Pb accumulation is high in cotton as compared to Cd. The most sensitive stage for the accumulation of Pb is the initial vegetative stageof cotton. The transfer factor from soil to plant was higher for Pb, indicating the feasibility of growing cotton in Pb-contaminated soil. The spectral measurement showed no characteristic changes in standard reflectance spectra due to heavy metal stress. Wavelet decomposition of reflectance spectra amplified the changes indicating Pb stress in cotton during the initial vegetative stage. The significant correlation of greater than - 0.70 between the reconstructed detail wavelet coefficients at the third level of the decomposition in the wavelength range of 651-742nm suggested that Pb stress caused spectral changes in near-infrared and visible ranges in cotton plants. The effects of Cd stress on the cotton plant were negligible due to less absorption. Thus, detailed wavelet coefficients at the third level of decomposition in the range of 651-742nm are a potential indicator of Pb stress. The results of this study can provide a basis for quantifying heavy metal stress in a particular region.