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Article

Appraisal of Heavy Metal Risk Hazards of Eisenia fetida-Mediated Steel Slag Vermicompost on Oryza sativa L.: Insights from Agro-Scale Inspection and Machine Learning Analytics

by
Sonam Jha
1,2,
Sonali Banerjee
1,
Saibal Ghosh
1,
Anjana Verma
2 and
Pradip Bhattacharyya
1,*
1
Agricultural and Ecological Research Unit, Indian Statistical Institute, Giridih 815301, India
2
Department of Zoology, Vinoba Bhave University, Hazaribagh 825301, India
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(11), 2020; https://doi.org/10.3390/agriculture14112020
Submission received: 10 September 2024 / Revised: 4 November 2024 / Accepted: 6 November 2024 / Published: 9 November 2024
(This article belongs to the Special Issue Heavy Metal Pollution and Remediation in Agricultural Soils)
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Abstract

:
The steel industry drives world economic growth, yet it generates heavy metal-rich steel slag, which jeopardizes the environment. The utilization of vermi-technology is essential for the sustainable transformation of toxic steel waste slag (SW) into organic amendments, although field-scale use of vermiprocessed SW remains unexplored. To bridge the gap, this study evaluated the efficacy of vermiprocessed SW as an organic supplement for rice field cultivation, focusing on heavy metal (HM) bioavailability, human health risk, and yield in comparison to raw slag and NPK fertilizer. The results indicated a considerable decrease in the bioavailable fraction of heavy metals in T4 (1:1 SW vermicompost 50% + 50% fertilizer). In treatments, T9 (100% SW) and T10 (50% SW + 50% fertilizer) (FIAM) free ion activity modeling confirmed grain absorption of HMs, and the FIAM HQ values indicated the health risk for the direct application of steel slag waste on the field. The risk factor evaluation of HMs’ presence in treatments T9 and T10 established the possible cancer risk for living beings. Similarly, machine learning models like SOBOL sensitivity analysis and artificial neural networks revealed potential threats associated with HMs on different treatments, respectively. The correlation coefficient revealed the negative effects of bioavailable HMs on various soil microbial and enzymatic properties. Moreover, the abundant yield of rice was attributed to the combination treatment (1:1 50% + NPK 50%), which paved the way for an alternative agronomic approach based on the utilization of vermicomposted steel waste slag.

1. Introduction

Rice, scientifically known as Oryza sativa L., is an essential food crop for significant inhabitants around the globe [1]. Unlike other cereal crops, the rice ecosystem undergoes a unique transition from a flooded to a wetland environment before merging into the terrestrial ecosystem [2]. This characteristic makes rice cropping fascinating beyond its economic significance [3]. In today’s era, agricultural practices rely more and more on inorganic modifications to increase productivity, satisfy population expansion, and meet the rigorous requirements of the world food supply. In the last five decades, green revolution-led modern agriculture practices have played a significant role in advancing rice harvests to meet agronomic challenges [4]. These practices include the expansion of farming areas, the adoption of double cropping systems, the usage of high-yield seed varieties, and the application of inorganic fertilizers and pesticides.
Unfortunately, modern-day agricultural practices include the unrestrained use of inorganic fertilizers, followed by other chemically composed products like pest control spray, insect repellent, etc. These farming practices have led to various environmental pollution and greenhouse gas emissions [5]. Paddy cultivation is considerably affected by agricultural practices like transplanting, tillage, and using nitrogen-based fertilizers. Without integrated nutrient management, improper and uneven application of mineral fertilizers by farmers results in a decline in biodiversity, acidification of the soil, loss of organic matter, leaching of nitrogen, compaction of the soil, and loss of beneficial microorganisms [2]. Besides agriculture practices, another important concern is industrialization, which generates waste byproducts that are noxious and have adverse effects on ecosystems. The black metallurgical industry is one such industry that, during its steel manufacturing, releases a HMs-rich byproduct known as slag [6]. These slags can contribute significantly to environmental challenges as they contain potential amounts of toxic HMs such as Cr, Cd, Ni, Cu, Pb, and Fe [7]. Therefore, the adoption of a sustainable approach is essential for the management of waste slag. Previous studies have demonstrated the viability of vermicomposting as a solution for managing industrial waste like tannery waste, oil sludge, brick kiln-ash, etc. [2,8,9,10]. The earthworm’s gut and gut microbes play a primary role in the decomposition of waste material, and the vermicompost obtained is rich in organic matter, organic carbon, available NPK micronutrients, and various enzyme activities [11]. The intestinal microorganisms of earthworms secrete lipases, amylases, proteases, and cellulases, which aid in the process of biodegradation [12]. According to Goswami et al. [13], with the use of earthworm-mediated vermicomposting technology, the toxic waste was effectively turned into a high-nutrient, environmentally sustainable fertilizer.
Simultaneously, due to the discharge of untreated byproducts by industries in proximity to land areas or croplands, the HMs present in slag may accumulate in the agricultural soil [14]. The steel industry continuously deposits steel waste in the form of slag into the soil, posing severe threats to the environment and the food chain. Eating fruits, vegetables, or commodities cultivated in polluted soil can lead to the accumulation of these HMs in human tissues, which poses a major risk to one’s health [15]. To gain a deeper comprehension, the utilization of models such as the Frendullich equation [16] can provide a better understanding of the absorption of HMs in various crops cultivated on soil containing industrial waste deposits. The chance of exposure to HMs from the consumption of dietary goods grown in a polluted region may be estimated using a risk thermometer (SAMOE). In addition, the health hazards associated with their bioavailable forms of HMs are predicted using an artificial neural network (ANN) sensitivity and Sobol sensitivity assay, as demonstrated by Cho et al. [17] and Sobol [18], respectively.
Nevertheless, there is substantial research regarding the utilization of vermicompost as an organic input in agriculture. However, the utilization of vermicomposted steel waste slag in agricultural practices, along with the assessment of soil and human health risks in field cultivation, remains unexplored. To bridge the gap and for quality assessment purposes, we have tried to employ vermicomposted steel waste slag as organic amendments in field experiments for two consecutive years. During the study period, the soil was amended by the application of steel waste slag vermicompost with and without the combination of chemical fertilizers. The vermi-amended steel waste slag was analogized with other treatments, which included cow dung, inorganic fertilizers, and crude steel waste slag. For the comparative assessment of organic and inorganic amendments, we primarily focused on three main goals: (1) the effectiveness of vermi-amended steel waste slags and their impact on various soil attributes; (2) the evaluation of the dietary risk of rice crops grown in treatment with crude steel waste slag using models like the FIAM and SAMOE-TCR risk thermometers; and (3) predicting the potential threats of HMs in steel slag waste amended treatments by employing ANN sensitivity models and Sobol sensitivity. Furthermore, the study was also coupled with the evaluation of the productivity and quality of harvested rice.

2. Materials and Methods

2.1. Experimental Site and Steel Waste Slag Vermicompost Preparation

Eisenia fetida, an epigeic earthworm species, reared at the vermicompost unit of the Indian Statistical Institute (ISI), Giridih, Jharkhand, was used to prepare vermicompost (VC) from industrial waste (steel slag). The general composition of raw steel waste slag is shown in Table S4. The overall composition of raw steel waste slag is relatively alkaline, shown by pH values of 7.61 ± 0.64. The presence of hazardous metal ions (Pb, Cr, Ni, and Cd) was notably elevated, while the organic carbon content in the steel slag samples was comparatively low. Cow dung (CD), gathered from nearby cattle quarters, was utilized as a mixing agent. To convert steel slag into vermicompost, two distinct feedstocks other than control (cow dung only) were used. These feedstocks include two different ratios of steel waste slag and CD (i.e., 1:1, 2:1), which included ten earthworms for every kilogram of the substrate. The completion of vermicomposting took 90 days. After the completion, the earthworms were hand-sorted, and the prepared vermicompost was sieved and applied in fields for a crop experiment. Table S4 illustrates the typical characteristics of vermicompost, indicating a neutral pH range from 7.05 ± 0.82 to 7.11 ± 0.74 and an organic carbon value from 1.25 ± 0.02 to 1.38 ± 0.03.

2.2. Preparation of Field Experiment

Rice Field Experimental Layout and Crop Collection

Field experiments were conducted for two consecutive years during the monsoon period (July–October) of 2022–2023 for rice crops (Oryza sativa L.). The agricultural land of the Indian Statistical Institute in Giridih, Jharkhand, was used as the experimental site. The agricultural soil employed in this investigation was slightly acidic, lateritic, lower in NPK, and had negligible metal content (Table S4). The experimental region has a paddy-based cultivation system, and mostly monocropping is practiced. Agriculture practices are mostly rain-fed; hence, monsoons are preferred by most paddy cultivators. In this field experiment, treatments shown in Table 1 include control soil, two different ratios of steel waste slag vermicompost, cow dung (CD), raw steel waste slag, and chemical fertilizers. Basically, urea, SSP, and MOP were used as supplements for N, P, and K. The field was designed in randomized blocks with ten treatments, each present in a different block. All the treatments were replicated three times in 2 m × 2 m plots. The application of organic amendments was performed seven days before transplant, and fertilizers were applied during transplanting. The rice variety “Sita” was used for transplant. The other agricultural activities involved the addition of water to the field, maintenance of proper water drainage in the field, periodic de-weeding of the field, and pesticide and insecticide application. After harvest, various agronomic parameters were studied for 10 randomly selected plants from each plot.

2.3. Soil Sample Collection from Different Blocks of Applied Treatments

Soil samples were collected periodically (0, 30, 60 days, and after harvest) for all the treatments from each block. The stones and detritus (leaves, twigs, and roots) were then removed by hand sorting. To create a composite and representative set, the samples were homogenized, and the moist soil was used to analyze the microbial and enzymatic parameters of the soil. For the analysis of physico-chemicals and the presence of HM characteristics, dried soil was utilized.

2.4. Assessment of Soil Physico-Chemical Parameters

The standard protocol of Page et al. [19] was used to measure the various physico-chemical parameters of dry soil for each treatment before and after harvest. This includes pH, organic carbon (OC), exchangeable potassium (Exc. K), available phosphorus (Avl. P), and available nitrogen (Avl. N).

2.5. Estimation of Soil Microbiological Parameters

The moist soil of each treatment at different time intervals (0 days, 30 days, 60 days, and after harvest) was collected to obtain a brief account of the periodic fluctuation among the microbial parameters. The different estimated microbial and enzymatic parameters of the soil were microbial biomass carbon (MBC), estimated using the ninhydrin-reactive N fumigation extraction method of Joergensen et al. [20]. The Alef and Nannipieri [21] protocol was used for the estimation of BSR (basal soil respiration) and SIR (substrate-induced respiration) of soil. An assay of Fluorescein-di-acetate (FDA) enzyme was performed using the procedure of Schnrer and Rosswall [22]. The dehydrogenase enzyme was measured utilizing the protocol of Casida et al. [23].

2.6. Estimation of Bioavailable HM Fractions and Presence of HMs in Vegetative Parts of Rice

Bio-available HM concentrations (Cd, Cr, Ni, Pb, Fe, Cu) and their uptake in rice parts [root, shoot, grain] were calculated using the methods of Tessier et al. [24] and Li et al. [25], respectively. The concentration of different HMs was further estimated using an atomic absorption spectrophotometer (AAS 816, Systronics, Gujarat, India). Along with nutritional aspects, the presence of hazardous HMs and their possible migration within plant parts were analyzed and expressed as translocation factors (TF), bioconcentration factors (BCF), and bioaccumulation factors (BAF) [26]. The formulas for each factor are represented below:
Bioaccumulation factor (BAF) = Cgrain/Csoil
Bioconcentration factors(BCF) = Croot/Csoil
Translocation factor (TF) = Cshoot/Croot

2.7. Quality Assurance and Control

Experiments were carried out using analytical-grade reagents. Samples were analyzed in triplicate, and blank samples were taken to guarantee quality control. The mean values for each HM (Cr, Ni, Cd, Cu, Fe, and Pb) were analyzed three separate times. After completing ten sample analyses, a blank analysis was performed to ensure the instruments’ precision and reliability. The concentration of each HM was analyzed using an atomic absorption spectrophotometer (AAS-816, Systronics, India). The accuracy and repeatability of the tested samples were assessed using certified reference material SRM 2710 (Table S1). The recovery percentage of heavy metals (HMs) in the tested samples indicated a high degree of data accuracy. The relative standard deviation (RSD) of these measurements was less than 10%.

2.8. HMs Content Prediction in Rice Grain Using Free Ion pH-Dependent Solubility Model

The FIAM solubility-free ion activity model was used for the HMs prediction in rice crops grown in HM-contaminated waste steel slag. The Freundlich equation is pH-dependent, which determines the HMs concentrations. The equation represents the ratio between soil HMs ion activity (Mn−) and HMs transfer from soil to crop (TF) [27].
TF = log [M plant]/(Mn−)
where [M plant] = content of HMs in rice grain, and (Mn−) = represents independent HMs ion activity in the soil. The bioavailable HMs fractions were used for the evaluation of HM uptake in rice crops. Here, the potential risk to human health associated with the consumption of rice grown in crude steel waste slag was evaluated based on FIAM-HQ using USEPA procedures.

2.9. Evaluation of Health Risk Employing SAMOE-TCR

A health risk assessment was conducted to confirm the potential health issues resulting from the consumption of cultivated crops. The frequent consumption of food products from contaminated soil and the health risks of HMs (Cu, Ni, Cr, Fe, and Pb) were calculated by Banerjee et al. [16]. The risk thermometer is an important tool used for the assessment of alimentary intake.

2.10. Health Risk Estimation Associated with Crude Steel Waste Amended Treatments Through Models

An Artificial Neural Network (ANN) Sensitivity Test was employed for pattern-based classification of the data set, and for analyzing health risks via HMs, an ANN sensitivity analysis was used. The output nodes were achieved through continuous processing of the signal. This included linking multiple ANN nodes (input, hidden, and output) with different weights. This study utilized artificial neural networks (ANN) to anticipate the risk-related implications of certain groups of treatment HMs in crude steel slag waste amended treatments [17].
Sobol sensitivity analysis was used to identify and assess the influence of input parameters on the variance of exposure outputs. Sobol Sensitivity Indices (SIs) estimate the ratio of a partial variable to the overall variable and are classified as the First Order Sensitivity Index (FOSI), which explains how a single variable affects the outcomes of a model. The Second Order Sensitivity Index (SOSI) explains the influence of interacting variables, whereas the Overall Order Sensitivity Index (TOSI) evaluates the variable’s impact on the ultimate variance. Sensitivity indices larger than 0.1 (very sensitive), 0.01–0.1 (sensitive), and less than 0.01 (insensitive) indicate substantial relevance, prominence, and unresponsiveness inputs [28].

2.11. Evaluation of Biochemical Parameters and Agronomic Attributes of Rice Crops

The total chlorophyll content of rice leaf was estimated using the protocol in [29]. The protein content of rice grains was calculated using the protocol in [30]. TSS (total soluble sugar) was evaluated using the phenol–sulfuric acid method by [31].
The agronomic attributes after the harvest of rice crops include grain yield and straw yield, crop height, and weight of 1000 grains. The data were collected following the method of [32].

2.12. Statistical Analysis

R Studio (version 4.1.1) was used for various statistical analyses.

3. Results

3.1. Dynamics of Physico-Chemical Parameters Before and After Rice Cultivation

The treatment-wise pH variations for 0-day and post-harvest are shown in Table 2. At the outset of 0 days, all the treatments except treatments T9 and T10 showed a pH in the range of 6.54–6.59, which was close to the neutral range. After harvest, all the treatments showed pH values in the range of 7.22–7.54, except treatments T9 and T10, which were in the range of 6.85 and 6.96, which shows their slight acidic inclination. Notably, the addition of organic materials like vermicomposted steel waste slag and cow dung could be responsible for such an increase in pH, and the observations were in line with Zeb et al. [33], who observed similar results during their investigation of fly ash-based vermicompost on rice crop application. The soil organic carbon (Table 2) showed periodic increments in all the treatments from 0 days up to post-harvest. The availability of soil organic carbon depends on organic matter decomposition and the residual crop remnant [34]. The current investigation revealed that the vermi-treated plots had considerably better soil organic carbon (SOC) storage. The observed trend of organic carbon among the treatments was in line with Kumar et al. [35]. The other important finding was an increase in SOC in the after-harvest soils. This observation closely resembles previous studies that revealed that residue-derived carbon forms clusters inside clay structures where it binds to the soil macro-aggregates, increasing the humification ratio and causing a sharp rise in SOC storage capacity when the crop was harvested [36,37].
During the initial study period, the observed soil’s available nitrogen content was low, but it escalated subsequently after harvest, as depicted in Table 2. After harvest, the maximum Avl. N was observed in treatments T8 > T7 > T4 and T6, followed by T3 and T5. The reason for this observation must be the presence of organic matter concentrations and their biological degradation, which confirms the presence of mineralized N in the soil [38].
There was a noticeable rise in the soil’s availability of macronutrients such as potassium (Exc. K) and phosphorus (Avl. P) from 0 days to post-harvest. The contents of the bioavailable P and K are shown in Table 2. The increased trend of Exc K and Avl. P from the initial days to the post-harvest was similar in treatments, and the observed trend was maximum in treatments T8 and T4. These findings coincide with the observations of Das et al. [39], where the balanced application of vermicompost along with chemical fertilizers significantly increased the diversity and proliferation of P- and K-solubilizing microbial communities, which in turn stimulated the release of a variety of endogenous and exogenous enzymes in the soil.

3.2. Periodic Variations of Microbial Dynamics and Enzymatic Attributes

Microbial biomass carbon (MBC) is an intrinsic component of organic matter; a decrease in MBC may profoundly impact the balance of nutrients and mineral availability for plants [40]. The treatment-wise soil MBC showed periodic reduction up to 60 days considering the growing phase of rice, but an elevated MBC rate was observed after crop harvesting. The significantly highest soil MBC was detected on day 0, irrespective of treatments. The addition of organic matter, like cow dung or vermicomposted steel slag, significantly (p treatment = 0.0002; LSD = 5.124) increased the treatment-wise soil MBC in contrast to control plot treatments and plots with sole chemical fertilizer shown in Figure 1a. According to Chakraborty et al. [8], the addition of different organic substrates showed similar outcomes. Higher MBC content and elevated microbial activity were often found in soil systems that receive more organic matter [41]. The plots applied with sole chemical fertilizers tend to have elevated MBC when compared to control plots. The proliferation of microorganisms due to the presence of heavy roots might have resulted in increased MBC in a chemically amended plot. Combining chemical fertilizers with organic vermicomposted steel waste slag and cow dung resulted in higher MBC compared to a single application. During this study period, treatment-wise, the highest soil MBC was observed in treatment T8, followed by treatment T4. Previous reports by Bhattacharya et al. [42] revealed similar results when a combination of organic and inorganic fertilizers was applied. Chemical fertilizers can meet the nutritional demand of microbes, but they cannot fulfill their carbon requirement. Interestingly, the combined application of organic and inorganic amendments balances the nutritional demand and also fulfills their carbon requirements. This was reflected in terms of increased levels of soil MBC in treatments T4 and T8. The periodic decline in soil MBC from different treatments could be attributed to the adverse impact of the waterlogged condition of rice crops. These observations can be supported by earlier reports [43,44]. In contrast, after harvesting, rice crops are prohibited from waterlogging stress, and the leftover root mass in the soil turns out to be another substrate for microorganism proliferation.
Substrate-induced respiration (SIR) and basal soil respiration (BSR) were major indicators of the microbial response to environmental changes. SIR was meant for R-strategists with metabolically active populations of zymogenous microorganisms, whereas K-strategists with inherent metabolism, including autochthonous microbial populations, were responsible for basal soil respiration [45]. During the study period, a significant treatment-wise periodic decline (p day BSR = 0.002; LSD = 2.31; p day SIR = 0.003; LSD = 1.72) of respirations was observed among treatments from 0 to 60 days, whereas after harvest, there was a slight uptick in both respirations, as depicted in Figure 1b,c, respectively. The trend of treatment-wise observations for BSR and SIR at 0 days was observed maximum for treatments T8 > T7 > T4. The reason behind such an observation might be that the organic augmentation of cow dung and vermicomposted steel slag, together with inorganic fertilizers, facilitates cellular activity. The observed trends coincide with the findings of Roy et al. [46]. The treatments T9 and T10 showed the lowest respiration rates throughout the study period, which must possibly be due to the presence of HMs and the toxicity of steel waste slag. Similar observations are reported by Rusinowski et al. [47] and Charan et al. [48]. Fluorescein diacetate enzymes are indicators of the hydrolytic activity of soil [49]. The treatment-wise periodic fluctuations of the FDA were statistically significant (p day =0.004; LSD = 2.45), as shown in Figure 1d. As per the observations, the lone organic amended treatments and treatments with combined organic and chemical substrates like T7, T8, T3, and T4 showed maximum FDA content. Such an observation must be due to an adequate amount of biosynthesis that accelerated biomass in the soil [50]. Meanwhile, steel slag-contaminated treatments like T9 and T10 showed the least FDA activity. This indicates that the HMs present in steel waste slag might have caused the reduction of biomass and also reduced the active microbes, aiding in reduced microbial biomass carbon and inefficient biosynthesis [51].
Enzyme dehydrogenase is the most common oxidoreductase enzyme [52]. During the field study, dehydrogenase was significantly found to be at a maximum in treatments T7 and T8, followed by T4 and T3 (p Treatment = 0.003, LSD = 4.53), as shown in Figure 1e. The reason behind such observations must be the positive correlation between organic matter content and DHA [53]. The observed findings are in line with Macci et al. [54], who earlier reported that the DHA levels were typically higher in the organic amended treatments. Contrastingly, treatments with sole steel waste slag and steel waste slag in combination with chemical fertilizers (T9 and T10) showed the lowest value of DHA. These observations were in line with the findings of Kizilkaya et al. [55], who found that enzyme syntheses in microbial organisms may be negatively impacted by the presence of HMs, as they can lower enzyme activity by interacting with the enzyme-substrate complex, leading to denaturation of the enzyme protein. Thus, the aforementioned results suggest that the utilization of SW-vermicompost may enhance soil microbial activity during the cultivation of rice.

3.3. Bioavailability of HMs and Its Accumulation in Rice Plants

The various HMs’ bioavailability depends on two metal fractions (water-soluble phases and exchangeable phases). These two metal fractions can readily bioaccumulate in the soil matrix and lead to metal poisoning of the soil. As per the HM concentrations (Cd, Cr, Cu, Ni, Pb, and Fe) represented in Figure 2, the maximum Fe concentration was observed in treatments T9 and T10 due to the lone application of steel waste slag, which was reduced by >50% after harvest. Fe upsurge in soil can promptly cause phytotoxicity and have a detrimental long-term effect on soil health due to salt deposition [56]. Initial and after-harvest Cd concentrations were the lowest among all the bioavailable metals among all the treatments. Treatments that combined inorganic and organic amendments significantly reduced Cu concentrations, potentially indicating a balance between the availability of Cu in the soil and its translocation from the soil to the crop. Similar findings were reported by Mondal et al. [9]. According to He et al. [57], soil pH and organic matter content were some of the main determinants of the bioavailability of metallic Cu in the soil. Whereas rhizospheric chemistry, ion exchange, root exudate composition, microbial selectivity, and absorption regulate metal (HMs) sorption in a plant–soil system [58,59]. During the field study, HMs like Pb, Cr, and Ni, which were initially high in concentration, were reduced in different ratios of vermicomposted treatments. These findings were similar to the earlier observations of Sarkar et al. [2]. Therefore, the study offers significant insights into the advantages of utilizing organic vermicompost and its effectiveness in mitigating the availability of heavy metals in soil [60,61].

3.4. Factors Affecting HMs Transportation in Rice Crops

HMs transfer from soil to plants was impacted by several plant characteristics, such as rhizospheric nature, root selectivity, and plant absorption physiology [9,62]. HM movement in plants was studied using different culminating factors such as bioaccumulation factor (BAF), which determines the HM transportation from soil to grains of the crop; translocation factor (TF), based on the HM content of the root and shoot of the plant; and bioconcentration factor (BCF), which depends on HM concentrations and their upliftment from the soil by the root of the crop. As shown in Figure S1, the lowest BCF value was observed for Cd, whereas the BCF value of Cr was observed as high for all the treatments except for treatments T7, T8, T9, and T10. The reason behind such an observation might be the oxidation state of Cr, which readily solubilizes and is absorbed by the root of the crop. Similarly, the highest BCF value of Fe was observed in treatments T10 and T9; the presence of crude steel waste slag might be a possible reason behind such an observation. The BCF values for other HMs like Cu, Fe, Pb, and Ni were least observed in treatment T7. The reason behind such an observation was the initial low concentration of HMs in cow dung. The bioaccumulation factor and the translocation factor were only observed in treatments T9 and T10. The BAF are crucial factors behind metal mobilization, and the observed BAF values of Ni were lowest in treatments T9 and T10, whereas Pb and Fe showed maximum BAF values in both treatments. The TF for Cd was observed to be the highest among all the HMs in both treatments, whereas Cu showed the lowest TF value. Charan et al. [62] observed similar results that the translocation of toxic elements to plants can be significantly arrested by soil incorporation of vermicompost.

3.5. Correlation-Coefficient Based HMs-Microbe Interactions of Rice Field Soil

A correlation plot depiction was used to understand the relationship between bioavailable HM concentration and microbial parameters at the beginning (0 days) and after the harvesting of the rice crop experiment. The microbiological–enzymatic parameters include [MBC, BSR, SIR, FDA, DHA], whereas the bioavailable HMs include [Cr, Cd, Cu, Ni, Pb, Fe]. At the onset of the field experiment (i.e., 0 d), a significant negative correlation was noted between SIR and bioavailable Cr, Pb, and Cu [r: −0.79 (Cr); r: −0.82 (Pb); r: −0.60(Cu); p < 0.01]. Similarly, a significant negative correlation was observed between MBC and bioavailable metals like Pb, Ni, and Cu [r (90 d) r: −0.63 (Pb); r: −0.40(Ni); r: −0.49 (Cu); p < 0.05;]. Similarly, FDA [r (90 d) Pb: −0.82; p < 0.05; r (90 d) Cu: −0.73; p < 0.05], DHG [r (90 d) Pb: −0.83; p < 0.05;], and BSR [r (90 d) Ni: −0.71; p < 0.05; r (90 d) Pb: −0.59; p < 0.05] results were observed. The correlation statistical data indicate that the presence of bioavailable HMs has an impact on the microbial and enzymatic properties of microbes, as shown in Figure 3. Our findings were supported by Chakraborty et al. [63].

3.6. Potential Threat Evaluation on Rice Crops Using the Frendullich Equation

The HM concentrations and their maximum uptake were observed in grains of treatment T9 and T10, as shown in Table S2. The treatment T9 showed negative β1 values for HMs (Cr, Pb, and Cu), whereas T10 showed negative β1 values for HMs (Pb, Cr, and Fe). Several studies report the movability of HMs by the amalgamated effect of pH and OC [64]. The acceptable hazard quotient for FIAM has been set to 0.5 [16]. The hazard quotients for T9 and T10 are, respectively, (Ni: 6.03 × 10−2,, 7.00 × 10−2,, Cr: 8.60 × 10−2,, 8.60 × 10−2; Fe: 1.4 × 10−1, 1.28 × 10−1; Pb: 1.19 × 10−1, 8.97 × 10−2; Cu: 3.87 × 10−2, 4.59 × 10−2). However, the calculated hazard quotient for the treatments was below the permitted limit, as per the findings of Golui et al. [65]. The frequent disposal of steel waste slag on agricultural land can lead to possible threats of HM accumulation in the soil, which ultimately poses health threats due to the consumption of edible parts of plants grown in contaminated fields.

3.7. Dietary Risk Prediction Using SAMOE and Risk Thermometer

The threat to human life from the ingestion of food material exposed to HMs in day-to-day life was evaluated using risk thermometers [16,64]. The potential threat of rice grain is depicted in Table S1. The risk thermometer, as shown in Figure 4, evaluates the presence of HMs in grains of treatment T9 and T10. The results indicate (class 5) the dietary risk of raw rice grain from treatment T9 and T10 for humans [T9Cr SAMOE: 6.11 × 10−3, T9Ni SAMOE: 9.69 × 10−3, T9Pb SAMOE: 8.14 × 10−3, T9Cu SAMOE 1.87 × 100, T9Fe SAMOE: 6.79 × 10−4; T10Cr SAMOE: 7.99 × 10−3, T10Ni SAMOE: 1.01 × 10−2, T10Pb SAMOE: 8.24 × 10−3, T10Cu SAMOE: 1.21 × 100, T10Fe SAMOE: 7.09 × 10−4]. The SAMOE-TCR of rice grains were T9 SAMOETCR Cr: 7.20 × 10−7, T9 SAMOETCR Ni: 2.6 × 10−6, T9 SAMOETCR Pb: 4.61 × 10−8; T10 SAMOETCR Cr: 5.60 × 10−7, T10 SAMOETCR Ni: 2.43 × 10−6, T10 SAMOETCR Pb: 4.55 × 10−8. The above findings reveal that the consumption of rice from steel slag-amended waste content HMs can lead to severe health conditions.

3.8. Health Risk Assessment Through Different Models

3.8.1. ANN Sensitivity Analysis of Machine Learning Model

The ANN sensitivity analysis model has been utilized from a spatial-data viewpoint to estimate the concentration of constituents, such as HMs, in rice, a method not investigated before in this manner. This research notably illustrates that the ANN-based methodology yields cost-effective outcomes with fewer elemental analyses than conventional device-dependent methods. In this study, ANN was used to predict HMs’ threats to various treatments. Figure S2 shows the percentage contribution of the bioavailable HMs to steel slag waste-amended treatments. According to ANN sensitivity analysis, the HMs that were responsible for toxicity and health risks in steel slag waste-contaminated treatments were ordered as follows: Cr > Ni > Pb > Cu > Fe > Cd. This result was consistent with the findings of prior research by Cho et al. [17], which showed that ANN performed better in predicting health risks caused by toxic HMs from steel waste slag.

3.8.2. Sensitivity Analysis of Soil Microbiological Properties, Interactions, and HMs Absorption

The Sobol indices were utilized to analyze variance and ascertain the relative effect of each important input, as well as to elucidate its interaction with the model output, particularly regarding variance [18]. These indices for all input variables were used to assess the effect of bioavailable potential HM fractions on soil microbial dynamics in rice. The analysis includes both the first-order sensitivity index (FOSI) and the total-order sensitivity index (TOSI). The observed index values of FOSI and TOSI for input parameters MBC and BSR were higher in the presence of HMs like Pb, Cr, Fe, Ni, and Fe. Whereas for Cd, the TOSI effect was observed maximum for input parameters SIR and MBC, as shown in Figure 5. Among all the input parameters, DHA showed the least FOSI and TOSI effects. HMs like Cd and Cu indicated a TOSI effect between 0.2 and 0.4. These findings imply that steel waste slag has a considerable deleterious influence on soil microbial health. The presence of these HMs affects soil robustness by affecting parameters like MBC, BSR, and SIR, particularly if it is disposed of in nearby farming regions without proper depuration [28].

3.9. Biochemical Quantification of Vegetative Parts of Rice Crop

Total chlorophyll content was determined for the harvested rice crops, as depicted in Figure S3. The total chlorophyll content was significant (p treatment = 0.0007). The total chlorophyll content was high in treatments with a combination of organic and inorganic amendments, and these observations are in line with Gupta et al. [66]. Integrating organic matter, particularly cow dung and vermicompost, along with chemical fertilizers enhances beneficial soil microbes and provides a sustainable source of nutrients for plants. This improves soil properties and also provides an optimal substrate for roots. Ievinsh [67] found that increased chlorophyll concentration promotes vegetative development. Treatments T9 and T10 showed the least chlorophyll content, which might be due to HM toxicity and low nutritional availability. These observations are in line with Shahid et al. [68].
The protein content was significant (p treatment = 0.0003) in treatment T4, followed by T6 and T8, as shown in Figure S3. The maximum protein content observed in treatment T4 and the findings were similar to Bejbaruah et al. [69]. Heavy metal toxicity in treatments T9 and T10 may have lower protein content, which aligns with the findings of Farhangi-Abriz et al. [70].
Numerous chemical reactions involving sugar were used to generate energy [71]. The total sugar was determined to be maximal in T2 and T3, followed by T5, and statistically significant (p = 0.0005), as shown in Figure S3. The vermicompost treatments like T3 or T5, which do not include inorganic fertilizer, release nitrogen gradually without affecting the sugar content of the rice crop plant. Nitrogen also directly affects the carbon balance, which is necessary for the conversion of sugar. Increasing nitrogen levels resulted in a decrease in sugar content due to the negative impact of nitrogen fertilizer, as reported earlier by Elhanafia et al. [72].

3.10. Estimation of Different Agronomical Vegetative Attributes of Plants

The different treatments showed significant variations in height, and the maximum crop height was shown by treatments T1 and T8, followed by T4, as shown in Table S3. The combined application of organic sources and inorganic fertilizers has been proven to provide better physical attributes than using chemical fertilizers alone. Combining vermicompost with fertilizer had a substantial impact on rice plant height. Chemical fertilizer provides nutrients that are easily soluble in soil, making them available to plants instantly. Organic nutrient availability comes from microbial activity and better soil conditions. Our results are in line with the findings of Sarkar et al. [73]. Treatment T10 showed the least height as it was deprived of proper nutrition due to metal toxicity, which led to decreased chlorophyll content, which affected photosynthesis, resulting in stunted height. Similar observations of decreased photosynthesis in the presence of heavy metals are reported by Shahid et al. [68].
The maximum grain yield was shown by treatment T8, followed by treatments T7 and T4. The lowest grain yield was observed for treatments T9 and T10, as shown in Table S3. Although chemical fertilizers provide nutrients rapidly, a combination of organic sources and inorganic fertilizers has been proven to provide better yield advantages than using chemical fertilizers alone. This was because the nutrients were available for a shorter period in chemical-oriented fertilizers as a result of the rapid mineralization of nitrogen. As a result, there may be greater losses of inorganic nitrogen due to volatilization, denitrification, leaching, and other factors. This information was provided by Sarwar et al. [74].
The straw yield was significant in all the treatments, but the highest straw yield was observed in Treatment T4, followed by other treatments (T3 and T6). The inorganic fertilizers and organic manure may enhance plant vegetative growth and therefore raise the rice straw yield. Implementation of chemical fertilizers and organic manure enhanced rice straw yields [32]. Our findings were consistent with the findings of Hasanuzzaman et al. [75]. The lowest straw yield was observed in treatments T2, T10, and T9. These two treatments could not meet the nutritional demands of the rice crop.
Treatments T8 and T4 showed the highest 1000-grain weight, followed by treatment T1 and other organic and combined inorganic and organic amendments. A combination of chemical fertilizer and organic manure boosted 1000-grain weight as per the findings of Yang et al. [76]. Also, higher chlorophyll content enhances the photosynthesis rate, which in turn increases 1000-grain weight [77]. Treatment T2, along with treatments T9 and T10, showed the least weight. So, the aforementioned results indicate that the vermicompost-amended treatment, particularly T4, offered improved quality and increased yield, demonstrating a considerable difference from the other treatments.

4. Conclusions

The comparative effect of field application of raw steel waste slag and sole vermicomposted steel waste slag in combination with inorganic fertilizer was examined in this study. During the study, no negative impacts on the parameters of crop production or soil quality indicators were observed when experimental steel waste slag vermicompost was applied at realistic levels for submerged rice crops. In contrast, the crops were grown on plots treated with lone raw steel waste slag and in combination with chemical fertilizers, revealing the detrimental aspects of their HM content. Various models, like FIAM and SAMOE-TCR, were used for estimating the dietary risk of edible parts of rice crops grown in crude steel waste slag. The SOBOL sensitivity analysis and AAN were useful in understanding the effect of heavy metals on soil microbial parameters. The comforting prospect of this study was the various agronomic parameters that efficaciously evidence vermicompost quality. The study establishes the efficient transformation of waste steel slag into vermicompost, which is beneficial to soil and complies with the role of organic amendment.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture14112020/s1, Figure S1: Representation of factors associated with metal uptake in different parts of rice crop under various treatments: Translocation factor (TF- T9, T10), Bio-concentration factors (BCF), and bioaccumulation factor (BAF-T9, T10); Figure S2: AAN-artificial neural network model illustrates the relevance of HMs on health risk; Figure S3: Biochemical properties of various rice crop treatments; Figure S4. Layout of rice field treatments in randomized block design; Table S1: The recovery percentage of HMs concentration obtained in the certified (SRM2710) reference HMs; Table S2: An estimated HMs absorption (FIAM) and ingested health threat (SAMOE) of rice grain in treatments with steel waste slag; Table S3: The agronomic attributes associated with different rice crop treatments (Mean ± SD); Table S4. Represents physico-chemical parameters and metal concentration of vermicomposted steel waste slag, raw steel waste slag, and control soil (Mean ± SD).

Author Contributions

S.J.: Field Work, laboratory analysis, data analysis, modeling, original draft preparation; S.B. and S.G.: Modeling, reviewing, editing; A.V.: Supervision; P.B.: Conceptualization, supervision, reviewing, and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a–e) Variability of soil microbial dynamics among different treatments of rice crop.
Figure 1. (a–e) Variability of soil microbial dynamics among different treatments of rice crop.
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Figure 2. Changes in bioavailable metal fraction of different HMs along different treatments.
Figure 2. Changes in bioavailable metal fraction of different HMs along different treatments.
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Figure 3. Correlation plots based on interactions between bioavailable HM (Cr, Ni, Cd, Fe, Pb, Cu) fractions and microbial parameters (MBC, BSR, SIR, FDA, DHA). Here, 0D: 0 day and AH: after harvest.
Figure 3. Correlation plots based on interactions between bioavailable HM (Cr, Ni, Cd, Fe, Pb, Cu) fractions and microbial parameters (MBC, BSR, SIR, FDA, DHA). Here, 0D: 0 day and AH: after harvest.
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Figure 4. Risk thermometer depicts the anticipated accumulation of HMs in rice grains grown on steel waste slag-contaminated treatments (T9 and T10).
Figure 4. Risk thermometer depicts the anticipated accumulation of HMs in rice grains grown on steel waste slag-contaminated treatments (T9 and T10).
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Figure 5. SOBOL sensitivity analysis based on the HMs for microbial parameters considering the first-order effect (FOSI) and total effect (TOSI).
Figure 5. SOBOL sensitivity analysis based on the HMs for microbial parameters considering the first-order effect (FOSI) and total effect (TOSI).
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Table 1. Description of various rice crop treatments.
Table 1. Description of various rice crop treatments.
T1Full dose fertilizer [100% recommended dose of fertilizer]
T2Control soil or CS (without the addition of any inorganic fertilizer or vermicompost)
T31:1 Full dose vermicompost [100% recommended dose of 1:1 vermicompost (20 t/ha)]
T41:1 Half dose vermicompost + fertilizer [50% 1:1 vermicompost supplemented with 50% fertilizer (w/w)]
T51:2 Full dose vermicompost [100% recommended dose of 1:2 vermicompost (20 t/ha)]
T61:2 Half dose vermicompost + fertilizer [50% 2:1 vermicompost supplemented with 50% fertilizer (w/w)]
T7Full dose of cow dung [100% recommended dose of cow dung (20 t/ha)]
T8Half dose of cow dung + fertilizer [50% cow dung supplemented with 50% fertilizer (w/w)]
T9Full dose steel waste slag [100% recommended dose of steel waste (20 t/ha)]
T10Half dose steel waste slag + fertilizer [50% steel waste supplemented with 50% fertilizer (w/w)]
Table 2. Variability in physico-chemical characteristics of rice crop treatments (Mean ± SD).
Table 2. Variability in physico-chemical characteristics of rice crop treatments (Mean ± SD).
Treatment0 Day
pHOC (%)Avl. NAvl. PExc. K
(mg kg−1)
T16.58 ± 0.370.42 ± 0.0340.47 ± 0.02837.4 ± 2.13130.56 ± 9.81
T26.59 ± 0.420.43 ± 0.0250.44 ± 0.02642.47 ± 3.64125.89 ± 8.33
T36.54 ± 0.450.47 ± 0.0320.48 ± 0.03341.56 ± 4.73132.48 ± 11.44
T46.54 ± 0.340.45 ± 0.0250.45 ± 0.03146.99 ± 5.84137.89 ± 12.65
T56.57 ± 0.460.46 ± 0.0310.49 ± 0.02544.12 ± 3.95130.25 ± 10.24
T66.54 ± 0.540.41 ± 0.0220.44 ± 0.01743.47 ± 2.46134.12 ± 9.76
T76.59 ± 0.580.49 ± 0.0330.48 ± 0.01945.47 ± 2.37130.12 ± 9.65
T86.54 ± 0.610.50 ± 0.0300.47 ± 0.02247.47 ± 3.19132.12 ± 8.84
T96.54 ± 0.540.32 ± 0.0210.46 ± 0.02029.75 ± 2.18130.12 ± 11.13
T106.58 ± 0.410.39 ± 0.0260.43 ± 0.02328.56 ± 2.11137.45 ± 11.21
After Harvest
T17.28 ± 0.410.47 ± 0.0390.68 ± 0.04955.4 ± 4.14142.56 ± 11.83
T27.33 ± 0.340.49 ± 0.0210.57 ± 0.03759.6 ± 3.19158.45 ± 13.91
T37.32 ± 0.560.53 ± 0.0430.62 ± 0.03265.89 ± 4.38157.89 ± 14.74
T47.22 ± 0.630.56 ± 0.0350.68 ± 0.05469.97 ± 5.13168.45 ± 11.85
T57.26 ± 0.530.51 ± 0.0400.61 ± 0.03759.89 ± 4.72149.56 ± 13.66
T67.38 ± 0.440.54 ± 0.0340.65 ± 0.04163.47 ± 5.61152.76 ± 14.77
T77.35 ± 0.850.55 ± 0.0350.72 ± 0.05367.89 ± 5.47150.37 ± 11.89
T87.54 ± 0.660.54 ± 0.0410.79 ± 0.04269.17 ± 4.63169.37 ± 13.62
T96.85 ± 0.590.34 ± 0.0270.51 ± 0.03130.22 ± 2.73139.49 ± 11.73
T106.96 ± 0.430.44 ± 0.0380.59 ± 0.02731.12 ± 2.61146.48 ± 13.84
T1—Full dose fertilizer; T2—Control soil; T3—1:1 full dose vermicompost; T4—1:1 half dose (vermicompost + fertilizer); T5—1:2 full dose vermicompost; T6—1:2 half dose (vermicompost + fertilizer); T7—Full dose cow dung; T8—(Half dose cow dung + fertilizer); T9—Full dose steel waste slag; T10—(Half dose steel waste slag + fertilizer).
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Jha, S.; Banerjee, S.; Ghosh, S.; Verma, A.; Bhattacharyya, P. Appraisal of Heavy Metal Risk Hazards of Eisenia fetida-Mediated Steel Slag Vermicompost on Oryza sativa L.: Insights from Agro-Scale Inspection and Machine Learning Analytics. Agriculture 2024, 14, 2020. https://doi.org/10.3390/agriculture14112020

AMA Style

Jha S, Banerjee S, Ghosh S, Verma A, Bhattacharyya P. Appraisal of Heavy Metal Risk Hazards of Eisenia fetida-Mediated Steel Slag Vermicompost on Oryza sativa L.: Insights from Agro-Scale Inspection and Machine Learning Analytics. Agriculture. 2024; 14(11):2020. https://doi.org/10.3390/agriculture14112020

Chicago/Turabian Style

Jha, Sonam, Sonali Banerjee, Saibal Ghosh, Anjana Verma, and Pradip Bhattacharyya. 2024. "Appraisal of Heavy Metal Risk Hazards of Eisenia fetida-Mediated Steel Slag Vermicompost on Oryza sativa L.: Insights from Agro-Scale Inspection and Machine Learning Analytics" Agriculture 14, no. 11: 2020. https://doi.org/10.3390/agriculture14112020

APA Style

Jha, S., Banerjee, S., Ghosh, S., Verma, A., & Bhattacharyya, P. (2024). Appraisal of Heavy Metal Risk Hazards of Eisenia fetida-Mediated Steel Slag Vermicompost on Oryza sativa L.: Insights from Agro-Scale Inspection and Machine Learning Analytics. Agriculture, 14(11), 2020. https://doi.org/10.3390/agriculture14112020

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