Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation.

Appraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it's quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former's effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p < 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices.

Eisenia fetida-driven vermitechnology for the eco-friendly transformation of steel waste slag into organic amendment: An insight through microbial diversity and multi-model approach.

The processing of steel waste slag from the black metallurgical sector seriously threatened the ecology. To counter these dangers, appropriate detoxification methods were required. Vermitechnology was one such strategy that could successfully convert this industrial waste into nutrient-rich products suitable for use in agriculture. This research primarily focuses on employing vermitechnology for the transformation of waste steel slag into vermicompost and to determine changes in microbial composition, nutrient cycling, and metal detoxification facilitated by earthworms (Eisenia fetida). Earthworm populations in steel waste vermibeds (sw-vermibeds) increased by 2.87-3.07 folds. T1(SW + CD-1:1) comparatively showed increased levels of nutrients such as nitrogen, phosphorus, and potassium. Microbial and enzymatic parameters were more pronounced in treatment T1. The findings of phospholipid fatty acid (PLFA) diversity demonstrate microbial diversity and fatty acid composition. Based on PLFA Sobol Sensitivity Analysis (SSA), PUFA and cyclo were the most sensitive inputs to the presence of heavy metal (HMs) concentrations in SW. In accordance with Taylor-based modelling, R-tree, and Mars were the most trusted regression models for predicting HMs toxicity on microbes. The bioavailable metal fractions of HMs (Fe, Ni, Cd, Cu, Pb, and Cr) decreased by 61-83%. The correlation was performed for 0 and 90 days for metal microbial interactions r (0 days), [BSR vs Fe, Cd, Cu, Ni = -0.99, -0.82, -0.43, -0.99] and r (90 days), [FDA vs Fe, Cu, Ni = -0.97, -0.47, -0.95]. Overall, the results indicated that T1(1:1 SW + CD) provided more favorable conditions for the development of microbes and Eisenia fetida. This research presents a new perspective to the world community on the transformation of harmful steel waste slag into advantageous biological resources by introducing a novel method of employing Eisenia fetida to remediate hazardous steel waste slag.

Elucidating the synergistic effect of acidity and metalloid poisoning on the microbiome through metagenomics and machine learning approaches.

The abundance and diversity of the microflora in a complex environment such as soil is everchanging. Mica mining has led to metalloid poisoning and changes in soil biogeochemistry affecting the overall produce and leading to toxic dietary exposure. The study focuses on two prominent stressors acidity and arsenic, in mining-contaminated agricultural locations. Soil samples were collected from agricultural fields at a distance of 50 m (zone 1) and 500 m (zone 2) from active mines. Mean arsenic concentration was higher in zone 1 and pH was lower. Geostatistical and self-organizing maps were employed to report that the pattern of localization of soil acidity and arsenic content is similar indicating a causal relationship. Cluster and principal component analysis were further used to materialize a negative effect of soil acidity fractions and arsenic labile pool on soil enzymatic activity (fluorescein diacetate, dehydrogenase, ß-1,4-glucosidase, phosphatase, and urease), respiration and Microbial biomass carbon. Soil metagenomic analysis revealed significant differences in the abundance of microbial populations with zone 1 (contaminated zone) having lower alpha and beta diversity. Finally, the efficacy of several machine-learning tools was tested using Taylor diagrams and an effort was made to select a potent algorithm to predict the causal stressors responsible for depreciating soil microbial health. Random Forrest had superior predictive power based on numerical evidence and was therefore chosen as the best-fitted model. The aforementioned insights into soil microbial health and sustenance in stressed conditions can be beneficial for predicting remedial strategies and practicing sustainable agriculture.

Financial dependence, labour regimes and industry growth: Do creditor rights matter?

Using 3-digit industry data at the state-level during 2009-2018, the paper assesses the impact of the Insolvency and Bankruptcy Code (IBC) on growth across industries with varying degrees of financial dependence. The findings show that IBC positively impacted industry growth, although this occurred by altering the capital-labour mix in favour of the latter. Robustness tests across industry type and state labour regimes support these findings.

Appraise potassium chemistry and distribution patterns in tailing soil, India: Through quantity - Intensity relations and multi model statistical methods.

Tailings are waste materials left behind after mineral extraction. Giridih district of Jharkhand, India has the second largest ore of mica mines in the country. This study evaluated the forms of potassium (K+) and quantity-intensity relationships in soils contaminated by tailings around the abundant mica mines. A total of 63 rice rhizosphere soil samples (8-10 cm depth) were collected from agricultural fields near 21 mica mines in the Giridih district at different distances: 10 m (zone 1), 50 m (zone 2), and 100 m (zone 3). The samples were collected to quantify various forms of potassium in the soil and characterize non-exchangeable K (NEK) reserves and Q/I isotherms. The semi-logarithmic release of NEK with continuous extractions suggests a decrease in release over time. Significant values of threshold K+ levels were observed in zone 1 samples. As K+ concentrations increased, the activity ratio (AReK) and its corresponding labile K+ (KL) concentrations decreased. The AReK, KL, and fixed K+ (KX) values were higher in zone 1 [AReK: 3.2 (mol L-1)1/2 × 10-4, KL: 0.058 cmol kg-1, and KX: 0.038 cmol kg-1), except for readily available K+ (K0) for zone 2 (0.028 cmol kg-1). The potential buffering capacity and K+ potential values were higher in zone 2 soils. In zone 1, Vanselow selectivity coefficients (KV) and Krishnamoorthy-Davis-Overstreet selectivity coefficients (KKDO) were higher, while Gapon constants were higher in zone 3. It was found that AReK was significantly correlated with K0, KL, K+ saturation, -ΔG, KV, and KKDO. Different statistical methods such as positive matrix factorization, self-organizing maps, geostatistics, and Monte Carlo simulation approaches were employed to predict soil K+ enrichment, source apportionment, distribution patterns, availability for plants, and contribution to soil K+ maintenance. Thus, this study significantly contributes to understanding K+ dynamics in mica mine soils and operational K+ management.

Assessing pollution and health risks from chromite mine tailings contaminated soils in India by employing synergistic statistical approaches.

Potentially toxic elements (PTEs) contamination in the agricultural soil can generate a detrimental effect on the ecosystem and poses a threat to human health. The present work evaluates the PTEs concentration, source identification, probabilistic assessment of health hazards, and dietary risk analysis due to PTEs pollution in the region of the chromite-asbestos mine, India. To evaluate the health risks associated with PTEs in soil, soil tailings and rice grains were collected and studied. The results revealed that the PTEs concentration (mainly Cr and Ni) of total, DTPA-bioavailable, and rice grain was significantly above the permissible limit in site 1 (tailings) and site 2 (contaminated) as compared with site 3 (uncontaminated). The Free ion activity model (FIAM) was applied to detect the solubility of PTEs in polluted soil and their probable transfer from soil to rice grain. The hazard quotient values were significantly higher than the safe (FIAM-HQ < 0.5) for Cr (1.50E+00), Ni (1.32E+00), and, Pb (5.55E+00) except for Cd (1.43E-03), Cu (5.82E-02). Severity adjustment margin of exposure (SAMOE) results denote that the PTEs contaminated raw rice grain has high health risk [CrSAMOE: 0.001; NiSAMOE: 0.002; CdSAMOE: 0.007; PbSAMOE: 0.008] for humans except for Cu. The Positive matrix factorization (PMF) along with correlation used to apportion the source. Self-organizing map (SOM) and PMF analysis identified the source of pollution mainly from mines in this region. Monte Carlo simulation (MCS) revealed that TCR (total carcinogenic risk) cannot be insignificant and children were the maximum sufferers relative to adults via ingestion-pathway. In the spatial distribution map, the region nearer to mine is highly prone to ecological risk with respect to PTEs pollution. Based on appropriate and reasonable evaluation methods, this work will help environmental scientists and policymakers' control PTEs pollution in agricultural soils near the vicinity of mines.

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.

Medical abortion in late second trimester--a comparative study with misoprostol through vaginal versus oral followed by vaginal route.

A randomised controlled trial was conducted to compare the relative efficacies of two regimens: Misoprostol given only vaginally or orally followed by vaginal administration of the same drug, for second trimester abortion. Multiparous women with a pregnancy of 16 to 20 weeks with no contra-indications to the drug were selected. They were randomly allocated into two groups. Patients in the first group, the 'only vaginal misoprostol' group (n = 30), were given misoprostol 400 microg 6 hourly only through vaginal route up to a maximum of 4 such doses. Women in the other group, the 'oral plus vaginal misoprostol' group (n = 24), received 400 microg of the drug at intervals of 12 hours for 2 doses, followed by 400 microg 6 hourly per vaginum up to a maximum of 4 such. Ten units of oxytocin was started in all cases when os was 4 cm dilated. Complete expulsion was 83.33% with an average time of 13.28 hours in the only vaginal misoprostol group. Complete expulsion occurred in 87.5% of women receiving oral followed by vaginal misoprostol with an average time of 8.93 +/- 0.01 hours from the first vaginal dose (p<0.05). More importantly, 66.67% women in second group delivered within 10 hours of the vaginal dose. Complete expulsion was defined in those cases where no check curettage was needed. Side-effects were not significant. This implies that misoprostol given by the vaginal route following oral priming doses had a higher success rate and a potential for a reduced hospital stay and higher bed turn-over rate.