Soil parameters affecting the levels of potentially harmful metals in Thessaly area, Greece: a robust quadratic regression approach of soil pollution prediction

Bismuth Film along with dsDNA-Modified Electrode Surfaces as Promising (bio)Sensors in the Analysis of Heavy Metals in Soils

Heavy metals constitute pollutants that are particularly common in air, water, and soil. They are present in both urban and rural environments, on land, and in marine ecosystems, where they cause serious environmental problems since they do not degrade easily, remain almost unchanged for long periods, and bioaccumulate. The detection and especially the quantification of metals require a systematic process. Regular monitoring is necessary because of seasonal variations in metal levels. Consequently, there is a significant need for rapid and low-cost metal determination methods. In this study, we compare and analytically validate absorption spectrometry with a sensitive voltammetric method, which uses a bismuth film-plated electrode surface and applies stripping voltammetry. Atomic absorption spectroscopy (AAS) represents a well-established analytical technique, while the applicability of anodic stripping voltammetry (ASV) in complicated sample matrices such as soil samples is currently unknown. This sample-handling challenge is investigated in the present study. The results show that the AAS and ASV methods were satisfactorily correlated and showed that the metal concentration in soils was lower than the limit values but with an increasing trend. Therefore, continuous monitoring of metal levels in the urban complex of a city is necessary and a matter of great importance. The limits of detection of cadmium (Cd) were lower when using the stripping voltammetry (SWASV) graphite furnace technique compared with those obtained with AAS when using the graphite furnace. However, when using flame atomic absorption spectroscopy (flame-AAS), the measurements tended to overestimate the concentration of Cd compared with the values found using SWASV. This highlights the differences in sensitivity and accuracy between these analytical methods for detecting Cd. The SWASV method has the advantage of being cheaper and faster, enabling the simultaneous determination of heavy elements across the range of concentrations that these elements can occur in Mediterranean soils. Additionally, a dsDNA biosensor is suggested for the discrimination of Cu(I) along with Cu(II) based on the oxidation peak of guanine, and adenine residues can be applied in the redox speciation analysis of copper in soil, which represents an issue of great importance.

Multivariate analysis applied to X-ray fluorescence to assess soil contamination pathways: case studies of mass magnetic susceptibility in soils near abandoned coal and W/Sn mines

Determining the origin and pathways of contaminants in the natural environment is key to informing any mitigation process. The mass magnetic susceptibility of soils allows a rapid method to measure the concentration of magnetic minerals, derived from anthropogenic activities such as mining or industrial processes, i.e., smelting metals (technogenic origin), or from the local bedrock (of geogenic origin). This is especially effective when combined with rapid geochemical analyses of soils. The use of multivariate analysis (MVA) elucidates complex multiple-component relationships between soil geochemistry and magnetic susceptibility. In the case of soil mining sites, X-ray fluorescence (XRF) spectroscopic data of soils contaminated by mine waste shows statistically significant relationships between magnetic susceptibility and some base metal species (e.g., Fe, Pb, Zn, etc.). Here, we show how qualitative and quantitative MVA methodologies can be used to assess soil contamination pathways using mass magnetic susceptibility and XRF spectra of soils near abandoned coal and W/Sn mines (NW Portugal). Principal component analysis (PCA) showed how the first two primary components (PC-1 + PC-2) explained 94% of the sample variability, grouped them according to their geochemistry and magnetic susceptibility in to geogenic and technogenic groups. Regression analyses showed a strong positive correlation (R2 > 0.95) between soil geochemistry and magnetic properties at the local scale. These parameters provided an insight into the multi-element variables that control magnetic susceptibility and indicated the possibility of efficient assessment of potentially contaminated sites through mass-specific soil magnetism.

Assessment of microelement ecotoxicity in fen for ecological state monitoring

Wetlands, including bogs, fens, and swamps, play a crucial role in maintaining ecological balance by absorbing pollutants. They also conserve biodiversity and serve as breeding and migration sites for living organisms whose treated by pollutants entering to the wetland ecosystems. Pollutants entering wetland ecosystems can have detrimental effects on these important functions. The article introduces the method of toxicity assessment of microelements used in the environmental condition monitoring of the Ob River's floodplain fen (Tomsk Oblast, Russia). The impact of freshwater species (PAF m3day/kgemitted) is evaluated by calculating the Life Cycle Assessment Impact score for Be, V, Cr, Mn, Fe, Cu, Zn, As, Sr, Mo, Pb, Cd, Sb, Ba, and Tl at distances of 40, 100, and 160 m from the wastewater discharge site. The study considers the elemental composition and total volume of water from various areas within the research site for assessing freshwater ecotoxicity. 12 out of 15 investigated trace elements have the greatest impact on the freshwater system in the zone of 160 m from the site of anthropogenic impact on the water body. The sampling areas can be ranked based on their ∑IS value, with IS160 = 1.3E+11, followed by IS100 = 7.5E+10, and IS40 = 1.5E+10 [PAF m3day/kgemitted].

Effect of environmental factors on accumulation of trace metals in a typical shale gas exploitation area: A comprehensive investigation by machine learning and geodetector models

Whether a certain relationship is exist between shale gas exploitation and accumulation of trace metals in soil is a controversial issue in recent years. To date, few study clearly reveal the intrinsic contributions of natural and anthropogenic factors to accumulation of trace metals in soil. In this study, machine learning and geodetector models were integrated to investigate to contribution of environmental factors to variations of trace metals concentration. Before modeling, there are 10.33%-25.87% of soil considered as metal pollution, and the value of Pn further suggest that the Ba contribute the most to the comprehensive pollution index of trace metals in soil. The initial prediction of trace metals concentration by machine learning models is less effectively indicating the need for alternative approaches. To address this problem, post-constraints approach was used, and the post-constraint MSLR model demonstrates superior performance (R2 = 0.81) Additionally, through the utilization of geodetector model, the explanatory power (q) of CEC and SOM were identified as dominant natural factors with value of 0.055 and 0.089. respectively. Moreover, distance from working sites and working status were identified as the dominant anthropogenic factors associating to the spatial heterogeneity of trace metals in soil. The interaction between natural and anthropogenic factors showed a siginifacnt nonlinear enhancement effect on accumulation of Cr, Ba and Sr, and the highest value of q was 0.38 for SOM and distance. This study indicated that the potential metal contamination was related to shale gas exploitation and provide reference for controlling soil pollution in shale gas exploitation area and making management strategy.

Exploring effective network design for monitoring soil pH and potentially toxic elements based on geochemical surveys in economically developed area

Periodically visiting soil monitoring sites, i.e., sampling and analysis, is recognized as one of the most important ways to monitor soil quality. However, reconciling the monitoring costs with monitoring precision of the soil monitoring network (SMN) is a key technical problem to be solved. A statistically sound method, which depends on the spatial variation in monitoring indicators, was adopted to determine the number of monitoring sites and the monitoring interval as well as their ability to detect a particular change under an economically feasible scenario. The spatial variation in soil monitoring indicators was inquired from the "Multi-Purpose Regional Geochemical Survey in Zhejiang Province (MRGSZ)" project. Based on the data for soil pH and concentration of potentially toxic elements, the number of monitoring sites and the monitoring intervals that might be used for soil monitoring were determined with the administrative region as the monitoring unit. The results showed that there was great spatial variation in the MRGSZ region, which resulted in discrepancies in the minimum detectable changes (MDCs), monitoring site numbers, and temporal monitoring intervals for revisiting. Our research proposes a number of monitoring sites (nr) that could reconcile the monitoring costs, practicability and monitoring precision; thus, it was recommended for the design of SMNs. Under nr, the MDC values of each monitoring indicator were acceptable for all administrative regions, and the temporal monitoring intervals were practical with variations of 6.7-14.8 years.

Appraisal of contamination, source identification and health risk assessment of selected metals in the agricultural soil of Chakwal, Pakistan

Contamination of metals in agricultural soil is a serious global threat but there are limited reports related to their risks in major agronomic areas. The current study is aimed to assess the distribution of selected macroelements and essential/toxic trace metals (Ca, Mg, Na, K, Sr, Li, Ag, Fe, Zn, Co, Cu, Mn, Cd, Cr, Pb and Ni) in the agricultural soil of Chakwal, Pakistan, in order to appraise their contamination status, source identification and probable human health risks. Quantification of the metals was performed by AAS employing aqua regia digestion method. Among the selected metals, dominant mean concentrations were observed for Ca (48,285 mg/kg) and Fe (30,120 mg/kg), followed by Mg (9171 mg/kg), K (973.3 mg/kg), Mn (399.0 mg/kg) and Na (368.9 mg/kg). The correlation study indicated strong mutual relationships among the metals as well as physicochemical properties. Multivariate analysis (PCA/CA) of the metal levels revealed their diverse anthropogenic sources in the soil. Various pollution indices indicated extremely high contamination/enrichment of Cd, followed by moderate enrichment/contamination of Ag in the soil. The HQ values for most of the metals manifested insignificant non-cancer risks. The average CR value of Cr was exceeding the safe limit (1.0E-06) for both ingestion and inhalation exposure, indicating a considerable lifelong cancer risk for the population. The results of this study will provide a better understanding related to the contamination of agricultural soil and its effects on human health and to promote effective actions to reduce the soil pollution.

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD > 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 < RPD < 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 < RPD < 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling.