Minimizing arsenic intake from food consumption is a key aspect of the public health response in arsenic (As)‐contaminated regions. In many of these regions, rice is the predominant staple food. Here, we present a validated maximum allowable concentration of total As in paddy soil and provide the first derivation of a maximum allowable soil concentration for bioavailable As. We have previously used meta‐analysis to predict the maximum allowable total As in soil based on decision tree (DT) and logistic regression (LR) models. The models were defined using the maximum tolerable concentration (MTC) of As in rice grains as per the codex recommendation. In the present study, we validated these models using three test data sets derived from purposely collected field data. The DT model performed better than the LR in terms of accuracy and Matthews correlation coefficient (MCC). Therefore, the DT estimated maximum allowable total As in paddy soil of 14 mg kg⁻¹ could confidently be used as an appropriate guideline value. We further used the purposely collected field data to predict the concentration of bioavailable As in the paddy soil with the help of random forest (RF), gradient boosting machine (GBM), and LR models. The category of grain As (MTC) was considered as the dependent variable; bioavailable As (BAs), total As (TAs), pH, organic carbon (OC), available phosphorus (AvP), and available iron (AvFe) were the predictor variables. LR performed better than RF and GBM in terms of accuracy, sensitivity, specificity, kappa, precision, log loss, F1score, and MCC. From the better‐performing LR model, bioavailable As (BAs), TAs, AvFe, and OC were significant variables for grain As. From the partial dependence plots (PDP) and individual conditional expectation (ICE) of the LR model, 5.70 mg kg⁻¹ was estimated to be the limit for BAs in soil.

Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be prioritized for future management using the fuzzy technique in the Arc GIS model-builder. The model computing technique was also deployed to determine the different fertility zones, considering 17 soil parameters. The derived fuzzy technique outperformed the traditional method of dividing the sampling sites into clusters to correlate soil fertility classes with the studied soil samples. The prioritization of the soil factors and a spatial analysis of the fertility areas were carried out using the Analytic Hierarchy Process (AHP) and GIS tools, respectively. The AHP analysis outcome indicated that hydraulic properties had the highest weighted value, followed by physical and chemical properties, regarding their influence on SF. The spatial distribution map of physico-chemical properties also clearly depicts the standard classification. A fuzzy priority map was implemented based on all the classes parameters to identify the five fertility classes of the soil, namely very high (0.05%); high (16.59%); medium (60.94%); low (22.34%); and very low (0.07% of total area). This study will be of significant value to planners and policymakers in the future planning and development of activities and schemes that aim to solve similar problems across the country.

In view of scarcity of information, effectiveness of toxicity characteristics leaching procedure (TCLP) in assessing metal hazards of polluted soils was evaluated in relation to human health. Total metal content in the soils studied were 1657 ± 2335 mg kg⁻¹ for Zn, 351 ± 180 mg kg⁻¹ for Cu, 166 ± 79.9 mg kg⁻¹ for Ni, 249 ± 269 mg kg⁻¹ for Pb and 18.6 ± 23.2 mg kg⁻¹ for Cd. The TCLP requires a buffering of acidic leaching extraction based on the soil reaction. The TCLP extractable (reagent 1) metal content was varied as 177 ± 376 mg kg⁻¹ for Zn, 10.9 ± 28.5 mg kg⁻¹ for Cu, 23.1 ± 67.2 mg kg⁻¹ for Ni, 2.79 ± 10.3 mg kg⁻¹ for Pb and 4.92 ± 16.3 mg kg⁻¹ for Cd across the experimental soils. Reagent 2 extractable Zn, Cu, Ni, Pb and Cd were recorded as 136 ± 364, 3.16 ± 1.79, 1.49 ± 2.73, 0.57 ± 2.20 and 1.95 ± 6.92 mg kg⁻¹, respectively. In case of spinach, the values of hazard quotient (HQ) varied from 0.055 to 0.972, 0.002 to 0.019, 0.008 to 0.181, 0.066 to 5.489 and 0.044 to 11.32 in respect of Zn, Cu, Ni, Pb and Cd, respectively. The ranges of HQ for intake of Zn, Cu, Ni, Pb and Cd through consumption of wheat grain were 0.36–1.88, 0.02–0.12, 0.05–0.80, 0.13–1.21 and 0.10–59.5, respectively. While comparing the effectiveness of assessing metal hazard of TCLP with that assessed in terms of HQ, it is clear that in some soil samples the critical value of HQ (>0.5) exceeded for Pb (spinach), whereas TCLP extractable Pb content in those soils were within the safe limit i.e. <50 mg kg⁻¹. It appears that TCLP method is not only useful for judging the ecological risk of metal polluted soil but also assessing the suitability of soil for crop production, provided permissible limit of TCLP should be revised.

Groundwater arsenic (As) pollution is a naturally occurring phenomenon posing serious threats to human health. To mitigate this issue, we synthesized a novel bentonite-based engineered nano zero-valent iron (nZVI-Bento) material to remove As from contaminated soil and water. Sorption isotherm and kinetics models were employed to understand the mechanisms governing As removal. Experimental and model predicted values of adsorption capacity (q_e or q_t) were compared to evaluate the adequacy of the models, substantiated by error function analysis, and the best-fit model was selected based on corrected Akaike Information Criterion (AICc). The non-linear regression fitting of both adsorption isotherm and kinetic models revealed lower values of error and lower AICc values than the linear regression models. The pseudo-second-order (non-linear) fit was the best fit among kinetic models with the lowest AICc values, at 57.5 (nZVI-Bare) and 71.9 (nZVI-Bento), while the Freundlich equation was the best fit among the isotherm models, showing the lowest AICc values, at 105.5 (nZVI-Bare) and 105.1 (nZVI-Bento). The adsorption maxima (q_max) predicted by the non-linear Langmuir adsorption isotherm were 354.3 and 198.5 mg g⁻¹ for nZVI-Bare and nZVI-Bento, respectively. The nZVI-Bento successfully reduced As in water (initial As concentration = 5 mg L⁻¹; adsorbent dose = 0.5 g L⁻¹) to below permissible limits for drinking water (10 µg L⁻¹). The nZVI-Bento @ 1% (w/w) could stabilize As in soils by increasing the amorphous Fe bound fraction and significantly diminish the non-specific and specifically bound fraction of As in soil. Considering the enhanced stability of the novel nZVI-Bento (upto 60 days) as compared to the unmodified product, it is envisaged that the synthesized product could be effectively used for removing As from water to make it safe for human consumption.