HEAVY METALS BIOACCUMULATION AND RISK ESTIMATION IN EDIBLE FRESHWATER FISH FROM PEDROSO RESERVOIR (MAYABEQUE, CUBA)

This work is a validation of the second step of a heavy metal monitoring procedure in Cuba fluvial ecosystems. Concentrations of seven heavy metals were measured by ICP-MS in water samples collected from the Pedroso reservoir (Mayabeque province, Cuba) and its main tributaries, as well as in edible muscle of three locally consumed fish species: Oreochromis spp., Tinca tinca and Clarias gariepinus. The results show a high concentration of Pb in areas near a paint factory (85.5 μg/L), an asphalt factory and a high traffic area (345.8 μg/L). Metal content (in mg/kg ww) in fish fillet ranged as follows: Cr (0.01-0.58), Co (0.01-0.58), Cu (0.23-88.16), Zn (4.9-29.9), As (0.01-0.86), Cd (0.02-2.93) and Pb (0.01-1.23). According to Cuban regulations, concentrations of Cd in muscle are not safe in 37.5 to 44.0 % of the studied fishes, while Pb is high in 14.0 to 20.0 %. Non-carcinogenic risk (HI) is present when daily intake is above 81 g/day. Carcinogenic risk (ELCR) is calculated to be 5.8 × 10–4 according to the US-EPA methodology. Fishermen families are the most sensitive population sector. Local authorities were informed and different measures were taken to avoid polluted fish consumption and the reduction of pollutant sources.

Inversion of Soil Heavy Metal Content Based on Spectral Characteristics of Peach Trees

There exists serious heavy metal contamination of agricultural soils in China. It is not only time- and labor-intensive to monitor soil contamination, but it also has limited scope when using conventional chemical methods. However, the method of the heavy metal monitoring of soil based on vegetation hyperspectral technology can break through the vegetation barrier and obtain the heavy metal content quickly over large areas. This paper discusses a highly accurate method for predicting the soil heavy metal content using hyperspectral techniques. We collected leaf hyperspectral data outdoors, and also collected soil samples to obtain heavy metal content data using chemical analysis. The prediction model for heavy metal content was developed using a difference spectral index, which was not highly satisfactory. Subsequently, the five factors that have a strong influence on the content of heavy metals were analyzed to determine multiple regression models for the elements As, Pb, and Cd. The results showed that the multiple regression model could better estimate the heavy metal content with stable fitting that has high prediction accuracy compared with the linear model. The results of this research provide a scientific basis and technical support for the hyperspectral inversion of the soil heavy metal content.

Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review

With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration.

A Review of Microfluidic Detection Strategies for Heavy Metals in Water

Heavy metal pollution of water has become a global issue and is especially problematic in some developing countries. Heavy metals are toxic to living organisms, even at very low concentrations. Therefore, effective and reliable heavy metal detection in environmental water is very important. Current laboratory-based methods used for analysis of heavy metals in water require sophisticated instrumentation and highly trained technicians, making them unsuitable for routine heavy metal monitoring in the environment. Consequently, there is a growing demand for autonomous detection systems that could perform in situ or point-of-use measurements. Microfluidic detection systems, which are defined by their small size, have many characteristics that make them suitable for environmental analysis. Some of these advantages include portability, high sample throughput, reduced reagent consumption and waste generation, and reduced production cost. This review focusses on developments in the application of microfluidic detection systems to heavy metal detection in water. Microfluidic detection strategies based on optical techniques, electrochemical techniques, and quartz crystal microbalance are discussed.

Information Detection and Data Denoising Method Using Nano Electrode Array Sensor

Computer technology and sensor technology can be combined. The technology set can be used to monitor the concentration of heavy metals in soil, which can help to prevent the occurrence of heavy metal pollution in time. First, nanotechnology, electrode polarization and the advantages of gold nanoparticles modified electrode are studied, and the design method of the nano electrode array is further analyzed. Also, the internal parameters of the three-electrode equivalent circuit are studied, and the model of the three-electrode equivalent circuit is derived. On this basis, a heavy metal monitoring circuit based on the nano electrode array sensor is designed. While the information monitoring based on this circuit is performed, wavelet domain denoising technology is studied in data processing. In view of the defects of the general hard threshold in practical application, the threshold is improved to recognize the depth of denoising. In the experiment, gold nanoparticles modified mercury electrode is used as working electrode. According to the principle that the precipitation time is inversely proportional to the detection current, 0.01 mol/L HCl is selected as the solution environment; moreover, it is set that pH=4 and the precipitation time is 4 min. The results show that for the same kind of ions, with the increase of the concentration of ions to be measured, the scanning potential range remains unchanged, while the peak current increases significantly. Metal ions can be effectively identified based on the potential corresponding to peak value. In the data processing of the detection circuit, the improved signal denoising method is compared with the default threshold wavelet domain denoising technology. The results show that the improved wavelet domain denoising method has less signal error, and the denoising effect of heavy metal detection is obvious.

Production of genome-edited Daphnia for heavy metal detection by fluorescence

Aquatic heavy metal pollution is a growing concern. To facilitate heavy metal monitoring in water, we developed transgenic Daphnia that are highly sensitive to heavy metals and respond to them rapidly. Metallothionein A, which was a metal response gene, and its promoter region was obtained from Daphnia magna. A chimeric gene fusing the promoter region with a green fluorescent protein (GFP) gene was integrated into D. magna using the TALEN technique and transgenic Daphnia named D. magna MetalloG were produced. When D. magna MetalloG was exposed to heavy metal solutions for 1 h, GFP expression was induced only in their midgut and hepatopancreas. The lowest concentrations of heavy metals that activated GFP expression were 1.2 µM Zn2+, 130 nM Cu2+, and 70 nM Cd2+. Heavy metal exposure for 24 h could lower the thresholds even further. D. magna MetalloG facilitates aqueous heavy metal detection and might enhance water quality monitoring.

Heavy Metals Accumulation in Surface Waters, Bottom Sediments and Aquatic Organisms in Lake Mainit, Philippines

Lake Mainit is one of the largest lakes recognized as Key Biodiversity Areas (KBA) in the Philippines with rich fishery resources. However, the lake is at risk from heavy metal contamination due to inputs of industrial, agricultural effluents and small-scale mining activities. The present work evaluated levels of heavy metals namely cadmium, lead, and mercury from key aquatic fauna and sediments from seven strategic sections of the lake in 2018. Muscle samples of all seven fish species assessed were below detections limits (BDL) for tHg and Cd. Trace concentrations of Pb in the muscles were detected in Oreochromis niloticus, Glossogobius giuris, Channa striata and Vivipara angularis but values were within safe ranges. Trace concentrations of Pb in the riverine crab (Sundathelpusa sp) exceeded safe limits. Both Cd and tHg were below detection limits in the three invertebrates assessed. Traces of Pb were detected in S4 (Magtiaco) and S5 (Jaliobong) below standard limits (0.05 ppm) only during the southwest (SW) monsoon but Pb were not detected across all stations during the NE monsoon of 2018. For Cd, however, trace concentrations were detected only during the NE monsoon wherein Cd in S2 (Mayag), S3 (Magpayang), S4 (Magtiaco), S5 (Jaliobong), S6 (Dinarawan) and S7 (Kalinawan) exceeded standard limits for Cd in waters (0.01 ppm). Concentrations of tHg in the water were not detected across the two sampling seasons in all seven tributary stations. In sediments, Pb were all detected during the southwest monsoon with highest Pb concentrations in S6 (Dinarawan) and S7 (Kalinawan) which exceeded safe limits. Trace Cd in sediments were mostly below detectable limits. Concentrations of tHg in sediments exceeded safe limits during the SE monsoon in S4 (Magtiaco) and S7 (Kalinawan) areas. These findings recommended that continuous heavy metal monitoring must be conducted. It is also strongly suggested to evaluate the presence of heavy metals in other aquatic organisms and assess the ecological risk posed by these heavy metals though heavy metal speciation analysis.

Eco-Friendly Estimation of Heavy Metal Contents in Grapevine Foliage Using In-Field Hyperspectral Data and Multivariate Analysis

Heavy metal monitoring in food-producing ecosystems can play an important role in human health safety. Since they are able to interfere with plants’ physiochemical characteristics, which influence the optical properties of leaves, they can be measured by in-field spectroscopy. In this study, the predictive power of spectroscopic data is examined. Five treatments of heavy metal stress (Cu, Zn, Pb, Cr, and Cd) were applied to grapevine seedlings and hyperspectral data (350–2500 nm), and heavy metal contents were collected based on in-field and laboratory experiments. The partial least squares (PLS) method was used as a feature selection technique, and multiple linear regressions (MLR) and support vector machine (SVM) regression methods were applied for modelling purposes. Based on the PLS results, the wavelengths in the vicinity of 2431, 809, 489, and 616 nm; 2032, 883, 665, 564, 688, and 437 nm; 1865, 728, 692, 683, and 356 nm; 863, 2044, 415, 652, 713, and 1036 nm; and 1373, 631, 744, and 438 nm were found most sensitive for the estimation of Cu, Zn, Pb, Cr, and Cd contents in the grapevine leaves, respectively. Therefore, visible and red-edge regions were found most suitable for estimating heavy metal contents in the present study. Heavy metals played a significant role in reforming the spectral pattern of stressed grapevine compared to healthy samples, meaning that in the best structures of the SVM regression models, the concentrations of Cu, Zn, Pb, Cr, and Cd were estimated with R2 rates of 0.56, 0.85, 0.71, 0.80, and 0.86 in the testing set, respectively. The results confirm the efficiency of in-field spectroscopy in estimating heavy metals content in grapevine foliage.

FEASIBILITY OF USING HYPERSPECTRAL REMOTE SENSING FOR ENVIRONMENTAL HEAVY METAL MONITORING

<p><strong>Abstract.</strong> The use of optical properties as key parameters has been widely used in water quality monitoring, which accelerates the advances of remote sensing in the field of environmental monitoring. Current analytical methods for determining heavy metals in water include flame atomic absorption spectrometry (FAAS), atomic adsorption spectrophotometry (AAS) and inductively coupled plasma (ICP) spectroscopy, which typically require use of chemicals for sample processing and pretreatment as well as high capital input for analysis. Therefore, this study aims at investigating the potential of using non-destructive approaches for rapid water monitoring of heavy metal from green chemistry perspective. The proposed non-destructive sensing techniques include X-ray fluorescence spectrometer (XRF) and visible-near infrared spectroradiometer (VNIR). The former is an elemental analyser specifically for elements with relatively high atomic number, and the latter measures the reflectance or transmittance from samples. Heavy metals of lead (Pb), zinc (Zn) and copper (Cu) were selected as the target water constitutes in the study. The results from the analysis were then be used for determining a correlation model through chemometric approaches. Our results demonstrated that both of the target metals could be analysed via the proposed analytical methods. Reasonable agreements between the measurements from XRF and ICP were observed, whereas moderate correlations were perceived for simple linear regression model using spectral information from VNIR. Results from this study are expected to provide useful information on rapid identification of metal-polluting sources.</p>