The distribution, fate, and environmental impacts of food additive nanomaterials in soil and aquatic ecosystems.

Nanomaterials in the food industry are used as food additives, and the main function of these food additives is to improve food qualities including texture, flavor, color, consistency, preservation, and nutrient bioavailability. This review aims to provide an overview of the distribution, fate, and environmental and health impacts of food additive nanomaterials in soil and aquatic ecosystems. Some of the major nanomaterials in food additives include titanium dioxide, silver, gold, silicon dioxide, iron oxide, and zinc oxide. Ingestion of food products containing food additive nanomaterials via dietary intake is considered to be one of the major pathways of human exposure to nanomaterials. Food additive nanomaterials reach the terrestrial and aquatic environments directly through the disposal of food wastes in landfills and the application of food waste-derived soil amendments. A significant amount of ingested food additive nanomaterials (> 90 %) is excreted, and these nanomaterials are not efficiently removed in the wastewater system, thereby reaching the environment indirectly through the disposal of recycled water and sewage sludge in agricultural land. Food additive nanomaterials undergo various transformation and reaction processes, such as adsorption, aggregation-sedimentation, desorption, degradation, dissolution, and bio-mediated reactions in the environment. These processes significantly impact the transport and bioavailability of nanomaterials as well as their behaviour and fate in the environment. These nanomaterials are toxic to soil and aquatic organisms, and reach the food chain through plant uptake and animal transfer. The environmental and health risks of food additive nanomaterials can be overcome by eliminating their emission through recycled water and sewage sludge.

From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination.

Fluoride (F-) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies. A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 ± 0.02 ppm) and Unsafe (3.69 ± 0.3 ppm) groups differed significantly (t-value = -10.04, p < 0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3-, HCO3-, SO42-, Cl-, Ca2+, Mg2+, K+, Na+ and F-) were recorded from samples of each group. The samples from 'Unsafe group' showed alkaline pH, the abundance of Na+ and HCO3- ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F- abundance. Aspatial distribution map of F- contamination was created, pinpointing the "contaminated pockets." Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F- contamination in groundwater and the developed models can help predicting fluoride contamination in other areas. The combination of different methodologies (Fuzzy-TOPSIS, machine learning algorithms, and classical methods) results in a synergistic effect where the strengths of each approach compensate for the limitations of the other.

Appraisal of treated drinking water quality from arsenic removal units in West Bengal, India: Approach on safety, efficiency, sustainability, future health risk and socioeconomics.

The present study depicts the true failed scenario of the arsenic (As) removal units (ARU) in West Bengal by evaluating their treated water quality. Annual As removal efficiency of the 12 studied ARUs range between 35.2% and 82.6%. A comprehensive physico-chemical parameters and trace elements analysis find almost 25% and 16.7% of treated drinking water samples with poor water quality index (WQI) and high heavy metal evaluation index (HEI), respectively. The pond-based water treatment plant maintains the production of continuous As-safe water with a range between 60.2% and 66.7% due to its high Fe/As ratio. It's a discontent concluding the treated drinking water of the groundwater based-ARUs were observed with sufficient As mediated cancer risk (3 ×10-3). The non-cancer risk (HQ) of As is safe for the surface water treatment plant (0.38), whereas it is threatening for the groundwater based-ARUs (7.44). However, the drinking water samples are safe in view of HQ from the other trace elements like Hg, Al, Cd, Cr, Pb, F- and NO3-. Small scale ARU could be a feasible mitigation strategy in reducing the As menace in the long run if the plants are maintained correctly. Nevertheless, surface treated water is the most sustainable solution as withdrawal of groundwater for drinking purpose is not a viable practice.

Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk assessment.

Arsenic (As) is a well-known human carcinogen, and the consumption of rice is the main pathway for the South Asian people. The study evaluated the impact of the amendments involving CaSiO3, SiO2 nanoparticles, silica solubilizing bacteria (SSB), and rice straw compost (RSC) on mitigation of As toxicity in rice. The translocation of As from soil to cooked rice was tracked, and the results showed that RSC and its combination with SSB were the most effective in reducing As loading in rice grain by 53.2%. To determine the risk of dietary exposure to As, the average daily intake (ADI), hazard quotient (HQ), and incremental lifetime cancer risk (ILCR) were computed. The study observed that the ADI was reduced to one-third (0.24 µg kg-1bw) under RSC+SSB treatments compared to the control. An effective prediction model was established using random forest model and described the accumulation of As by rice grains depend on bioavailable As, P, and Fe which explained 48.5, 5.07%, and 2.6% of the variation in the grain As, respectively. The model anticipates that to produce As benign rice grain, soil should have P and Fe concentration more than 30 mg kg-1 and 12 mg kg-1, respectively if soil As surpasses 2.5 mg kg-1.

Pollution and health risk assessment of mine tailings contaminated soils in India from toxic elements with statistical approaches.

The rapid mining activities of mica mines in Giridih district, India, have led to toxic metal pollution of agricultural soil. This is a key concern for environmental risk and human health. 63 top soil samples were collected at a distance of 10 m (Zone 1), 50 m (Zone 2), and 100 m (Zone 3) from near 21 mica mines with agriculture fields. The mean concentration of total and bio-available toxic elements (TEs - Cr, Ni, Pb, Cu, Zn, and Cd) was higher in zone 1 across three zones. The Positive matrix factorization model (PMF) and Pearson Correlation analysis were used to identify waste mica soils with TEs. Based on PMF results, Ni, Cr, Cd, and Pb were the most promising pollutants and carried higher environmental risks than the other TEs. Using the self-organizing map (SOM), zone 1 was identified as a high-potential source of TEs. Soil quality indexes for TEs risk zone 1 were found to be higher across three zones. Based on the health risk index (HI), children are more adversely affected than adults. Monte Carlo simulations (MCS) model and sensitivity analysis of total carcinogenic risk (TCR), children were more affected by Cr and Ni than adults through ingestion exposure pathways. Finally, a geostatistical tool was developed to predict the spatial distribution patterns of TEs contributed by mica mines. In a probabilistic assessment of all populations, non-carcinogenic risks appeared to be negligible. The fact that there is a TCR can't be ignored, and children are more likely to develop it than adults. Mica mines with TEs contamination were found to be the most significant anthropogenic contributor to health risks based on source-oriented risk assessment.

Determination of bioavailable arsenic threshold and validation of modeled permissible total arsenic in paddy soil using machine learning.

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.

Recent advances in the bioremediation of arsenic-contaminated soils: a mini review.

The carcinogenic metalloid arsenic (As), owing to its persistent behavior in elevated levels in soils, aggravates environmental and human health concerns. The current strategies used in the As decontamination involve several physical and chemical approaches. However, it involves high cost and even leads to secondary pollution. Therefore, it is quite imperative to explore methods that can eradicate As menace from the environment in an eco-friendly, efficient, and cost-competitive way. Searching for such viable alternatives leads to the option of bioremediation technology by utilizing various microorganisms, green plants, enzymes or even their integrated methods. This review is intended to give scientific and technical details about recent advances in the bioremediation strategies of As in soil. It takes into purview the extent, toxicological manifestations, pathways of As exposure and exemplifies the substantive need of bioremediation technologies such as phytoremediation and biosorption in a descriptive manner. Additionally, the paper looks into the wide potential of some plant growth promoting microorganisms (PGPMs) that improve plant growth on one hand and alleviate As toxicity on the other. Furthermore, it also makes a modest attempt to assimilate the use of nanoparticles, non-living biomass and transgenic crops which are the emerging alternative bioremediation technologies.

Complexation, retention and release pattern of arsenic from humic/fulvic acid extracted from zinc and iron enriched vermicompost.

Arsenic (As) is a highly poisonous heavy metal with major environmental ramifications. Inorganic components such as zinc (Zn) and iron (Fe), as well as organic vermicompost, have been used as management solutions, with limited attempts of using them together. The current study involved preparing non-enriched vermicompost as well as six distinct Zn and Fe enriched vermicomposts and analyzing their chemical composition using the standard procedures. Organic fractions from these seven vermicompost and arsenic polluted soils of West Bengal, India were recovered and separated into humic (HA) and fulvic acid (FA) fractions. Potentiometric titrations, viscometric assays, and visible spectrophotometry were used to characterize the HA and FA samples. In aqueous phase the stability constant (log K) of the complexes formed with As indicates that stability of FA extracted from enriched vermicompost V4 (Zn and Fe sulphate @ 10% w/w dry weight basis of composting substrates before application of vermiworms) was maximum as 10.20 with a mole ratio (x) value of 1.36. Fourier-transform infrared (FT-IR) spectroscopy and Scanning Electron Microscopy (SEM) studies confirmed the complexation of As with HA/FA. The release isotherm of As from the HA/FA complexes in the presence of competitive oxy-anions was found to follow the order of sulphate > nitrate > phosphate.

Varietal differences influence arsenic and lead contamination of rice grown in mining impacted agricultural fields of Zamfara State, Nigeria.

In Zamfara state, Nigeria, rice is cultivated in fields contaminated with Pb (lead) from artisanal and illicit mining activities. Rice grown in such contaminated agricultural areas risks not only Pb contamination but also contamination from other toxic elements, like arsenic (As); co-contamination of Pb and As in rice cultivated in mining impacted areas has been previously reported and rice is a hyperaccumulator of As. A field study was conducted with ten different commonly-cultivated Nigerian rice varieties in the mining-impacted farmlands of Dareta village, Zamfara State. The aim was to determine the optimal rice variety for cultivation on these contaminated farmlands; an optimal variety would have the lowest contaminant concentrations and highest essential elements concentrations in the rice grains. A total of 300 paired soil and rice plants were collected. The mean As and Pb concentrations in paddy soils were 0.91 ± 0.82 mg kg-1 and 288.5 ± 464.2 mg kg-1, respectively. Mean As (30.4 ± 15.1 µg kg-1) content in rice grains was an order of magnitude lower than the Codex recommendation of 200 µg kg-1 (for milled rice) while the Pb content in all the rice varieties (overall mean of 743 ± 327 µg kg-1) was approximately four times higher than the Codex recommendation of 200 µg kg-1. Contrary to previous studies, a negative correlation was observed between As and Pb in rice grains across all the varieties. Rice variety Bisalayi was the variety with the lowest Pb transfer factor (TF = 0.08), but the average Pb concentration in rice grain was still above the Codex recommendation. Bisalayi also had the highest TF for iron. Variety ART_15, which had the lowest As uptake (TF = 0.10), had the highest TF for essential elements (magnesium, potassium, manganese, zinc, and copper). In areas of Pb contamination, Bisalayi rice may therefore be a suitable variety to choose for cultivation.

Determination of safe limit for arsenic contaminated irrigation water using solubility free ion activity model (FIAM) and Tobit Regression Model.

Irrigation water contaminated with arsenic acts as a potent source of contamination to humans through water-soil-crop-food transfer so quantification of safe limit for irrigation water is also critical. A pot experiment was conducted to determine the safe limit for As contaminated irrigation water with two soil types (alluvial and red) using ten levels of contaminated irrigation water (0, 0.25, 0.5, 0.75, 1.0, 1.25, 1.50, 1.75, 2.0, 2.25 mg L-1), applied 5 times in rice (Variety: Sushak Samrat),used as a test crop. The results reveal that the different fractions of arsenic in terms of its profusion followed the order F4 > F2 > F5 > F3 > F1 and F4 > F3 > F2 > F5 > F1 across all the doses of As for alluvial soil and red soil respectively. The safe limit of irrigation water in terms of risk assessment expressed as Hazard Quotient (HQ) was at 0.75 mg L-1 and the solubility FIAM can effectively predict the As content in rice grain in both the soils. The Tobit Regression Model in alluvial soil quantified the safe limit for As in irrigation water from 1.20 to 0.10 mg L-1 for available soil As 0.25-3.0 mg kg-1 and in red soil, the range was from 0.10 to 0.40 mg L-1 for soil As 1.0 to 0.25 mg kg-1 provided that the As content in rice grain is < 0.4 mg kg-1. This proved to be an effective protocol for estimation of safe limits after proper validation and calibration.

Persistence behaviour and safety risk evaluation of pyridalyl in tomato and cabbage.

A field experiment was set up to evaluate persistence behaviour of pyridalyl in tomato, cabbage and cultivated field soil over two consecutive seasons. An analytical method was developed to analyze pyridalyl residues in different matrices and duly validated, based on single laboratory method validation criteria. Pyridalyl residues were detected and quantified using a gas chromatograph equipped with an electron capture detector. The compound exhibited low persistence in tomato, cabbage and soil. A safe waiting period of 17-18 d after final insecticide application needs to be maintained before harvesting the crop. Both dietary and soil ecological risk were assessed and it was found that the harvested vegetables were toxicologically safe for consumption. However, there was concern about insecticidal toxicity against the algal population of soil which needs to be reconfirmed by further studies.

Assessing equilibria of organo-arsenic complexes and predicting uptake of arsenic by wheat grain from organic matter amended soils.

In view of limited information, a laboratory experiment was conducted to study the stability of organo-arsenic complexes as affected by competing anions i.e. phosphate, nitrate and sulphate. For this purpose, humic acid (HA) and fulvic acid (FA) were extracted from farmyard manure (FYM), vermicompost (VC), sugarcane bagasse (SB) and soil. A pot experiment was also conducted with 4 levels each of arsenic (As) (10, 20, 30 and 40  mg  kg-1) and amendments (no amendment, FYM, VC and SB at the rate of 10  t  ha-1 each). Results indicate that stability of FA extracted from sugarcane bagasse have the highest stability constant (log K) as 9.77 and the corresponding mole ratio (x) value of 1.51. The phosphate was the most effective in replacing As from organo-As complexes followed by sulphate and nitrate. Under pot culture study, As content in wheat grain was the lowest in sugarcane bagasse amended soil followed by FYM and VC at all levels of As application. Solubility-free ion activity model was most effective in predicting As uptake by wheat grain based on Olsen extractable As, pH and Walkley & Black organic C. Efficacy of organic amendments in reducing health hazard for intake of As through consumption of wheat grain grown on contaminated soil was also reflected in the values of hazard quotient (HQ).