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.

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.

Vermitechnology transforms hazardous red mud into benign organic input for agriculture: Insights on earthworm-microbe interaction, metal removal, and soil-crop improvement.

Bioremediation of hazardous bauxite residues, red mud (RM), through vermicomposting has yet to be attempted. Therefore, the valorization potential of Eisenia fetida in various RM and cow dung (CD) mixtures was compared to aerobic composting. Earthworm fecundity and biomass growth were hindered in RM + CD (1:1) feedstock but enhanced in RM + CD (1:3). The pH of highly alkaline RM-feedstocks sharply reduced (>17%) due to vermicomposting. N, P, and K availability increased dramatically with Ca and Na reduction under vermicomposting. Additionally, ∼40-60% bioavailable metal fractions were transformed to obstinate (organic matter and residual bound) forms upon vermicomposting. Consequently, the total metal concentrations were significantly reduced with considerably high earthworm bioaccumulation. Microbial growth and enzyme activity were more significant under vermicomposting than composting. Correlation statistics revealed that microbial augmentation significantly facilitated a metal reduction in RM-vermibeds. Eventually, RM-vermicompost stimulated sesame growth and improved soil health with the least heavy metal contamination to soil and crop.

Condensation Product of p-anisaldehyde and L-phenylalanine: Fluorescent "on-off" Sensor for Cu2+ and IMPLICATION Logic Gate.

(Z)-2-(4-methoxybenzylideneamino)-3-phenylpropanoic acid (L) synthesized by condensation of p-anisaldehyde and L-phenylalanine acts as selective fluorescent as well as voltammetric sensor for Cu2+ in 2:1 (v/v) CH3OH:H2O. The fluorescence intensity of L (λmax 425 nm) is quenched ca. 65% by Cu2+. Metal ions - Li+, Na+, K+, Al3+, Cu2+, Zn2+, Cd2+, Hg2+, Mn2+, Ni2+ and Pb2+ do not interfere. The binding constant and the detection limits were calculated to be 0.56 × 102 M-1 and 10-6 M respectively. DFT and TDDFT calculations confirmed 2:1 binding stoichiometry between L and Cu2+ obtained from fluorescence data. The interaction between L and Cu2+ is reversible for many cycles with respect to ethylenediamine tetraacetate anion (EDTA2-) which results in IMPLICATION logic gate.

Formation of Polymeric Housene Molecules of Group 15 Elements (N, P, As, and Sb): A DFT Study.

Recently, the formation of the dimeric stibahousene molecule, bis(stibahousene), has been reported. In line with the report, the formation of dimeric housene molecules with N, P, and As is examined in light of density functional theory. Moreover, the extension of the study from dimeric to tetrameric and hexameric molecules (N, P, As, and Sb) is also performed. The study supports the formation of such polymeric housene analogues.

Exploring Cation-π Interaction in the Complexes with B≡B Triple Bond: A DFT Study.

Density functional theory calculations on metal ion-π interactions in cation-π complexes of diboryne and sandwiches of diboryne and benzene formed via metal ions were performed to understand the strength of interaction in these complexes. Results suggest that apart from the smaller metal ions (Li+, Be2+), larger ions (Na+, Mg2+, Ca2+, and Al3+) can also form cation-π complexes with B≡B triple bond and interaction energies of the complexes with larger metal ions (possessing same charges) are less than those obtained with smaller ions. Cations with higher charge lead to stronger interaction with the B≡B triple bonds. The calculated interaction energy further reveals that the sandwiches are more stable than their corresponding cation-π complexes. Stability of the complexes is measured in terms of global hardness (using Koopmans' theorem and ΔSCF method), and the values obtained using ΔSCF method corroborate with the stability trend predicted by interaction energy values. Negative solvent-phase interaction energy for most of the chosen complexes indicates their stability in polar solvents too.

Condensation Product of Phenylalanine and Salicylaldehyde: Fluorescent Sensor for Zn(2.).

The condensation product of phenylalanine and salicylaldehyde (L) was synthesised and characterised which was found to be selective fluorescent "off-on" sensor for Zn(2+) ion with the detection limit 10(-5) M. The sensor is free of interferences from metal ions - Na(+), K(+), Al(3+), Mn(2+), Co(2+), Ni(2+), Cu(2+), Pb(2+), Cd(2+) and Hg(2+). The Fluorescence and the UV/visible spectral data reveals a 1:1 interaction between the sensor and Zn(2+) ion with binding constant 10(8). The DFT and TDDFT calculations confirm the structures of the sensor and the sensor-Zn(2+) complex.

DFT and TDDFT study on cation-π complexes of diboryne (NHC → B ≡ B←NHC).

In this study, density functional theory calculation on mono-cationic cation-π complexes of diborynes has been made to understand the interaction in cation-π complexes of diboryne. Results suggest that apart from the smaller cations Li+ and Na+, larger cation like K+ ion could also form complexes with diboryne compounds via cation-π interaction. From the calculated structural and spectroscopic analysis 11B, 13C NMR (Nuclear Magnetic Resonance), FTIR (Fourier Transform Infra red) (force constant, value), and UV-vis spectra, it is found that the interaction between the cations and π-electron cloud of the diboryne is purely electrostatic. It is also observed that smaller cation (Li+) with high electronegativity interacts more strongly compared to larger cation (K+). Calculated interaction energy advocates that the π-electron cloud of the B2 unit contributes more to the cation-π interaction than the two aromatic phenyl rings of the NHC (N-heterocyclic carbene) substituted with 2,6-diisopropylphenyl group. The aryl substituent at the NHC-ligands undergoes a change in spatial orientation with respect to the size of cations in order to provide suitable space to the cations for effective cation-π interaction. Quantum theory of atoms in molecules study clarifies further the nature and extent of B-B and B2-cation interactions.11B-NMR, 13C-NMR, and time dependent density functional theory analysis indicate that cation-π interaction annihilates the B → C (NHC) π-back donation and favours the B≡B bond formation.

A New Fluorescent "Off-On" Sensor for Al(3+) Derived from L-alanine and Salicylaldehyde.

The condensation product of L-alanine and salicylaldehyde was synthesised and characterised which was found to be selective fluorescent "on" sensor for Al(3+) ion with the detection limit 10(-6) M. The sensor is free of interferences from metal ions - Na(+), K(+), Ca(2+), Mn(2+), Co(2+), Ni(2+), Cu(2+), Pb(2+), Cd(2+), Hg(2+) and Fe(3+). The Fluorescence and the UV/visible spectral data reveals a 1:1 interaction between the sensor and Al(3+) ion with binding constant 10(4.5). The DFT and TDDFT calculations confirm the structures of the sensor and the sensor-Al(3+) complex.

Spatial variability of theaflavins and thearubigins fractions and their impact on black tea quality.

The spatial distribution of theaflavin and thearubigin fractions and their impact on black tea quality were investigated using multivariate and geostatistics techniques. Black tea samples were collected from tea gardens of six geographical regions of Assam and West Bengal, India. Total theaflavin (TF) and its four fractions of upper Assam, south bank and North Bank teas were higher than the other regions. Simple theaflavin showed highest significant correlation with tasters' quality. Low molecular weight thearubigins of south bank and North Bank were significantly higher than other regions. Total thearubigin (TR) and its fractions revealed significant positive correlation with tasters' organoleptic valuations. Tea tasters' parameters were significantly and positively correlated with each other. The semivariogram for quality parameters were best represented by gaussian models. The nugget/sill ratio indicated a strong/moderate spatial dependence of the studied parameters. Spatial variation of tea quality parameters may be used for quality assessment in the tea growing areas of India.

Substituent and Solvent Effects on the Absorption Spectra of Cation-π Complexes of Benzene and Borazine: A Theoretical Study.

Time-dependent density functional theory (TDDFT) has been used to predict the absorption spectra of cation-π complexes of benzene and borazine. Both polarized continuum model (PCM) and discrete solvation model (DSM) and a combined effect of PCM and DSM on the absorption spectra have been elucidated. With decrease in size of the cation, the π → π* transitions of benzene and borazine are found to undergo blue and red shift, respectively. A number of different substituents (both electron-withdrawing and electron-donating) and a range of solvents (nonpolar to polar) have been considered to understand the effect of substituent and solvents on the absorption spectra of the cation-π complexes of benzene and borazine. Red shift in the absorption spectra of benzene cation-π complexes are observed with both electron-donating groups (EDGs) and electron-withdrawing groups (EWGs). The same trend has not been observed in the case of substituted borazine cation-π complexes. The wavelength of the electronic transitions corresponding to cation-π complexes correlates well with the Hammet constants (σp and σm). This correlation indicates that the shifting of spectral lines of the cation-π complexes on substitution is due to both resonance and inductive effect. On incorporation of solvent phases, significant red or blue shifting in the absorption spectra of the complexes has been observed. Kamlet-Taft multiparametric equation has been used to explain the effect of solvent on the absorption spectra of complexes. Polarity and polarizability are observed to play an important role in the solvatochromism of the cation-π complexes.

Effect of new organic supplement (Panchgavya) on seed germination and soil quality.

We studied the suitability of Panchgavya (five products of cow), new organic amendment, application on seed germination, plant growth, and soil health. After characterization, Panchgavya was mixed with water to form different concentration and was tested for seed germination, germination index, and root and shoot growth of different seedlings. Four percent solution of Panchgavya was applied to different plants to test its efficacy. Panchgavya and other two organic amendments were incorporated in soil to test the change of soil chemical and microbiological parameters. Panchgavya contained higher nutrients as compared to farm yard manure (FYM) and vermicompost. Its application on different seeds has positively influenced germination percentage, germination index, root and shoot length, and fresh and dry weight of the seedling. Water-soluble macronutrients including pH and metal were positively and negatively correlated with the growth parameters, respectively. Four percent solution of Panchgavya application on some plants showed superiority in terms of plant height and chlorophyll content. Panchgavya-applied soil had higher values of macro and micronutrients (zinc, copper, and manganese), microbial activity as compared to FYM, and vermicompost applied soils. Application of Panchgavya can be gainfully used as an alternative organic supplement in agriculture.

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.

Evaluating the efficacy of vermicomposted products in rain-fed wetland rice and predicting potential hazards from metal-contaminated tannery sludge using novel machine learning tactic.

The study assessed the ecotoxicity and bioavailability of potential metals (PMs) from tannery waste sludge, alongside addressing the environmental concerns of overuse of chemical fertilizers, by comparing the impacts of organic vermicomposted tannery waste, chemical fertilizers, and sole application of tannery waste on soil and rice (Oryza sativa L.) plants. The results revealed that T3, which received high-quality vermicomposted tannery waste as an amendment, exhibited superior enzymatic characteristics compared to tannery sludge amended (TWS) treatments (T8, T9). After harvesting, vermicomposted tannery waste treatment (T3) showed a more significant decrease in PMs bioavailability. Accumulation of PMs in rice was minimal across all treatments except T8 and T9, where toxic tannery waste was present, resulting in a high-risk classification (class 5 < 0.01) according to the SAMOE risk assessment. Results from Fuzzy-TOPSIS, ANN, and Sobol sensitivity analyses (SSA) further indicated that elevated concentrations of PMs (Ni, Pb, Cr, Cu) adversely impacted soil-plant health synergy, with T3 showing a minimal risk in comparison to T8 and T9. According to SSA, microbial biomass carbon and acid phosphatase activity were the most sensitive factors affected by PMs concentrations in TWS. The results from the ANN assay revealed that the primary contributing factor of toxicity on the TWS was the exchangeable fraction of Cr. Correlation statistics underscored the significant detrimental effect of PMs' bioavailability on microbial and enzymatic parameters. Overall, the findings suggest that vermicomposting of tannery sludge waste shows potential as a viable organic amendment option in the near future.

Synergistic impact of bioavailable PHEs and alkalinity on microbial diversity and traits in agricultural soil adjacent to chromium-asbestos mines.

Soil microbial communities undergo constant fluctuations, particularly in response to environmental factors. Although the deposition of toxic mine waste is recognized for introducing potentially hazardous elements (PHEs) into the soil, its specific impacts on microbial communities remain unclear. This study aims to explore the combined effects of soil alkalinity and bioavailable PHEs on microbial diversity and traits in agricultural soil adjacent to a chromium-asbestos mining area. By employing a comprehensive analysis, this study indicated that microbiological attributes were reduced in contaminated areas (zone 1), whereas both the levels of bioavailable PHEs (CrWs: 31.08 mg/kg, NiWs: 13.90 mg/kg) and alkalinity indices (CROSS, MCAR, MH) were significantly higher. The spatial distribution of soil alkalinity and bioavailable PHEs, primarily originating from chromium-asbestos mines, has been determined. This study also elucidates the negative relationship between soil stressors (Alkalinity and PHEs) and microbial activities (soil enzymatic activity, microbial respiration, and biomass carbon). The vector's length exhibited a notable difference between zone 1 (0.51) and zone 2 (0.32), indicating a substantial limitation on carbon (C). Also, the investigation of soil bacterial diversity unveiled notable disparities in the prevalence of microbial populations inside zone 1. Proteobacteria constituted 57.18% of the total population indicating a noteworthy prevalence in the contaminated soils. Finally, the random forest (RF) algorithm from machine learning was selected and proven to be a robust choice in Taylor diagrams for predicting the causative stressors responsible for the deterioration of soil microbial health. Therefore, this research offers insights into the health and resilience of soil microbial communities under synergistic stress conditions, which will aid environmentalists in planning future interventions and improving sustainable farming techniques.

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.

Metal accumulation, biochemical response and yield of Indian mustard grown in soil amended with rural roadside pond sediment.

This present study aims to discern the effect of roadside pond sediments on Brassica juncea (L.) Czern. (cv. Rohini) by examining heavy metal uptake by different parts of the plant and its influence on biochemical properties, biomass, yield and oil content in plant. Although the treated soils were clearly contaminated with heavy metals (HMs) after application of pond sediments and chemical fertilizers, but the metal content in mustard leaves and seeds are below the permissible limit of consumable food. HMs accumulation was proportionally lesser in grains than in shoots and roots. All the biochemical characteristics were significantly (p<0.05) responded to the pond sediments application as compared to the control. Increase in photosynthetic pigment was also observed during growing period while pond sediment was used as amendment. This study revealed that Brassica juncea is well adapted to tolerate and accumulate high quantities of HMs due to increased level of antioxidants (cysteine and ascorbic acid) in roots, shoots and leaves. Multivariate techniques like principal component analysis and cluster analysis (CA) were used as classification techniques. On the basis of factor loadings and principal component scores, different parameters were grouped based on their similarity or closeness, separately in leaves, roots and seeds. A very similar grouping was also obtained using CA. However, pond sediment did not thwart the enhancement of mustard yield and oil content. Therefore, pond sediment would be a valuable resource for mustard as an alternative soil amendment for nutrients, but long-term use may require the cessation to restrict HMs contamination in soils.

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.

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.

Exorbitant signatures of pesticides and pharmaceuticals in municipal solid wastes (MSWs): Novel insights through risk analysis, dissolution dynamics, and model-based source identification.

Studies on the occurrence and fates of emerging organic micropollutants (EOMPs) like pharmaceuticals and pesticides in MSWs are scarce in the literature. Therefore, MSWs were sampled from 20 Indian landfills and characterized for five widely consumed EOMPs (chlorpyrifos, cypermethrin, carbofuran, carbamazepine, and sodium diclofenac), physicochemical, and biological properties. The pesticide (median: 0.17-0.44 mg kg-1) and pharmaceutical (median: 0.20-0.26 mg kg-1) concentrations significantly fluctuated based on landfill localities. Eventually, principal component and multi-factor (MFA) models demonstrated close interactions of EOMPs with biological (microbial biomass and humification rates) and chemical (N, P, K, Ca, S, etc.) properties of MSWs. At the same time, the MFA resolved that EOMPs' fates in MSWs significantly differ from bigger cosmopolitan cities to smaller rural townships. Correspondingly, the concentration-driven ecological risks were high in 15 MSWs with EOMP-toxicity ranks of diclofenac > carbofuran = chlorpyrifos > cypermethrin > carbamazepine. The EOMPs' dissolution dynamics and source apportionments were evaluated using the positive matrix factorization (PMF) model for the first time on experimental data, extracting four anthropogenic sources (households, heterogeneous business centers, agricultural, and open drains). The most significant contribution of EOMPs to MSWs was due to heterogeneous business activity. Notably, the aging of soluble chemical fractions seems to influence the source characteristics of EOMPs strongly.