Analyzing the impact of urban activities on the quality and composition of groundwater is significant. Comprehending the interplay between land use practices and water quality is crucial for sound water management strategies. To ascertain the comparative impact of various land uses, categorize the types of land influencing hydrochemical processes, and to delineate how land use alterations affect water quality were the core aims of the study. In order to accomplish this objective, a comparative assessment of groundwater quality, land use categories, and landscape metrics was undertaken during the years 2016 and 2021. From 42 wells, water samples were gathered, and various hydro-chemical aspects were assessed to determine the water quality index (WQI). The WQI index varied across a spectrum from 2649 to 15103 in the year 2016. Based on the research, the water quality in most parts of the study area is found to be moderately suitable for drinking and domestic purposes. Radiometrically corrected and orthorectified Sentinel-2 satellite imagery was utilized within the Google Earth Engine platform to categorize land use classes for selected years. The effects of diverse land use types and landscape metrics on water quality within five buffer zones established along each 2-km watershed of well sites were examined. Analysis of the 2016 and 2021 data indicated that variations in land use significantly impacted water quality primarily within buffers 1 (B1), 4 (B4), and 5 (B5) in 2016, and buffers 1 (B1), 3 (B3), and 5 (B5) in 2021. The 2021 results demonstrate that the effects of landscape-level metrics on water quality are mainly observed in buffers B2 and B3, while the class-level impact manifests in buffers B1 and B4. Analyzing the land use classes, landscape metrics, and hydro-chemical variables within each buffer zone, redundancy analysis unveiled their diverse interactive behaviors.By exploring structural and compositional variations in soil-dissolved organic matter (DOM), this study evaluated the impact of agricultural DOM inputs on methylmercury (MeHg) accumulation in the soil and mercury (Hg) bioaccumulation in the rice grains. Pot experiments, employing diverse DOM sources, including maize straw (MaS), rape straw (RaS), rice straw (RiS), composted rice straw (CRiS), cow dung (CD), and composted cow dung (CCD), were then undertaken. The results revealed that, in comparison to the control, adding DOM from different agricultural sources enhanced MeHg levels in soil by 18% to 227%, while only selected DOM samples elevated both total dissolved Hg (DHg) and MeHg (DMeHg) in pore water. RiS, CRiS, and CCD significantly elevated total Hg (THg) and MeHg concentrations in rice grains by 34-64% and 32-118%, respectively, across all DOM species. Relative to RiS, the THg and MeHg concentrations in rice grains under the CRiS treatment exhibited a modest decrease, coinciding with the patterns of DHg and DMeHg in pore water and the fluctuations in soil dissolved organic matter aromaticity. Conversely, the CCD input substantially boosted the accumulation of THg and MeHg in rice grains compared to CD, owing to its ability to drastically curtail soil DOM humification across all stages of rice cultivation, while simultaneously elevating the proportion of low-molecular-weight fractions within soil DOM. A reduction in THg and MeHg levels within the rice grains was observed after treatment with RaS, in contrast to treatments with MaS and RiS. This difference could be attributed to the higher levels of sulfur-containing compounds such as sulfate and cysteine present in rape straw or its dissolved organic matter (DOM). Through the modulation of soil DOM characteristics, diverse agricultural DOM amendments exerted a considerable and discriminatory influence on methylmercury accumulation in the soil and mercury enrichment in rice within Hg-polluted paddy fields.The importance of the digital economy in achieving carbon peaking and neutrality is widely acknowledged. This research examines the effects of the digital economy on carbon emissions and renewable energy development, applying panel data from 67 countries across the period 2005-2019. According to the results, an inverted U-shaped relationship is found between the digital economy and carbon emissions, in line with the Environmental Kuznets Curve (EKC). The U-shaped relationship between the digital economy and renewable energy consumption is in accordance with the Renewable Energy Kuznets Curve (RKC) hypothesis. In contrast to gross domestic product (GDP), the digital economy is more likely to hasten the energy transition and carbon emission reduction, which is critical for achieving carbon peaking. Moreover, the study reveals that the RKC's transition point comes prior to the EKC's, thus establishing the RKC's turning point as a necessary precursor to the EKC's apex.Dissolved organic matter (BDOM), derived from biochar endogenously, may potentially interact with environmental pollutants. In this research project, tetracycline was selected as a representative pollutant; corn straw biochar, pyrolyzed at 300 degrees Celsius, was utilized as the adsorbent. Microscopic characterization, coupled with batch experiments, allowed for an investigation into the releasing kinetics of BDOM and its impact on TC adsorption on biochar. The results clearly demonstrate a preferential release of BDOM, exhibiting traits of lower aromaticity and higher molecular weight. The release of BDOM resulted in a decline of biochar's specific surface area (from 402 to 183 m²/g) and mesopore count. Concomitantly, aromaticity diminished (H/C ratio rose from 0.80 to 0.91). This ultimately led to a weakened interaction between TC and biochar pores, impacting hydrophobic and electron donor-acceptor (EDA) interactions. Simultaneously, the released BDOM has the potential to form a complex with TC in solution, which can impede TC adsorption on biochar. Compared to the BDOM-TC complexation process, the structural modifications of biochar caused by BDOM release demonstrably played a larger part in curtailing TC adsorption in this study. Analysis using EEM-PRARFAC revealed that BDOM's fluorescent components were predominately (63%) humic acid-like and to a lesser extent (37%) tryptophan-like. The humic acid-like components demonstrated a stronger bonding with TC (logKb values of 731 and 648), compared to the tryptophan-like components' logKb value of 645. The study's findings may provide useful insights into strategies for removing organic pollutants from soil and water, and the effectiveness of biochar application in pollution remediation.Within the evolving industrial landscape of Industry 4.0, hydrogen gas is increasingly vital for the world's energy feedstocks. The current work investigates a novel artificial intelligence model for characterizing hydrogen gas production (HGP) from biomass composition and the pyrolysis process using support vector machines (SVMs) and artificial bee colony (ABC) optimization. Innovative modeling establishes the pivotal role of each physico-chemical parameter in hydrogen gas generation, and further provides predictions of HGP. This novel technique's application to the observed dataset produced a coefficient of determination of 0.9464 and a correlation coefficient of 0.9751 for the HGP estimate. A suitable demonstration of this procedure's effectiveness was exhibited by the correspondence between the observed data and the ABC/SVM-based approximation.Using umbilical cord blood samples from 120 mother-newborn pairs, we studied the association between maternal urinary polycyclic aromatic hydrocarbon (PAH) metabolites and thyroid hormone levels. Using high-performance liquid chromatography coupled with tandem mass spectrometry, maternal urinary PAH metabolites were assessed. A flow fluorescence assay provided the means to assess thyroid hormone concentrations. A generalized linear model, along with a restricted cubic spline model, was employed to investigate the dose-response association between PAH metabolites and thyroid hormones. Research showed that OH PAHs present in the urine of pregnant women had a detrimental effect on the amount of triiodothyronine (T3). Umbilical cord blood plasma thyroid hormone levels were found to be related to maternal urinary polycyclic aromatic hydrocarbon (PAH) metabolites. mdm2 signaling The impact of polycyclic aromatic hydrocarbons (PAHs) on the developing thyroid gland, through prenatal exposure, may consequently disrupt neonatal thyroid hormone function.A type of programmed cell death, mitochondrial permeability transition (MPT)-driven necrosis, has seen a recent surge in prominence across various tumor categories. A minimal body of research has been dedicated to understanding how MPT-driven necrosis-related lncRNAs (MPTDNRlncRNAs) influence laryngeal squamous cell carcinoma (LSCC). This investigation is designed to ascertain the prognostic significance of MPTDNRlncRNAs and to explore their potential influence on the progression of LSCC. In conjunction with each other, the TCGA database offered RNA-sequencing and clinical data for LSCC patients, and the GSEA database yielded MPT-driven necrosis-related genes. MPTDNRlncRNAs displayed differential expression in LSCC, as determined by our research findings. Univariate Cox regression analysis enabled us to identify MPT-driven necrosis-related prognostic lncRNAs. Utilizing LASSO-COX, scientists developed a new MPTDNRlncRNAs signature. Evaluating the accuracy and utility of the MPTDNRlncRNAs signature involved the use of a variety of statistical methodologies. Various bioinformatics tools were employed to investigate the divergent biological functions and signaling pathways present in the disparate risk categories. The levels of MPTDNRlncRNAs in LSCC cell lines were measured using the RT-qPCR technique. We arrived at a definitive finding: a 5-MPTDNRlncRNAs signature in LSCC. Outcomes were accurately anticipated by our prognostic model, demonstrating its effectiveness. The subgroups displayed a statistically significant disparity in the proportions of immune cells, including M0 macrophages and T follicular helper cells.