This study is centered on the measurement of carbon emissions at the provincial level in China and the analysis of their spatial distribution. We address the quantification of regional disparities and the dynamic evolution of emission patterns, which are critical for informing climate governance. Moreover, we aim to systematically measure carbon emissions across Chinese provinces and to investigate the regional disparities and the dynamic evolution in their distribution. Our goal is to provide an empirical basis for formulating differentiated and well-targeted emission reduction policies. The modified carbon emission factor method was employed for accounting. The Theil index and its decomposition were used to quantify regional disparities, while kernel density estimation was applied to characterize the dynamic evolution trends of the emission distribution. The results revealed significant regional imbalances in China's carbon emission distribution, with inter-regional differences identified as the primary source of overall disparity. Kernel density curves further showed distinct heterogeneity in distribution shapes and dynamic evolution across regions, reflecting deep-seated differences in emission structures and development stages. Our findings provide critical data for designing differentiated regional carbon reduction strategies and can directly support policy-making for coordinated emission reduction. They offer practical insights for industrial green transition planning at national and provincial levels, aiding in the alignment of economic development with climate targets.
Per- and polyfluoroalkyl substances (PFAS) are hazardous, persistent, and widely used compounds that pose significant risks to human health and the environment. Urgent and effective techniques for removing PFAS from aquatic environments are needed. Carbon-based materials, including activated hydrochar (AHC), biochar (BC), graphene oxide (GO), carbon nanotubes (CNT), and granular activated carbon (GAC), have become valuable adsorbents due to their high surface area, porosity, and adaptable properties. This review comprehensively discusses these five carbon-based materials, outlining their synthesis and applications for PFAS adsorption. Additionally, the review explains the underlying mechanisms of adsorption and the models used, such as the Langmuir and Freundlich isotherms, as well as Pseudo-first- and second-order kinetic models. Furthermore, the study contributes to the current body of knowledge by suggesting and explaining practical modification options tailored to capture low-molecular-weight, short-chain PFAS (S-C PFAS). By moving the focus from classical hydrophobic partitioning to electrostatic-driven design, revealing cationic functionalisation and metal oxide doping as critical approaches for overcoming short-chain compounds' water mobility. Functional group analysis, linkage of molecular structure to adsorption potential, comparison of carbon-based materials for adsorption, and practical cases and techniques for regeneration and reuse of the five carbon-based materials have all been discussed. The review paper also explores potential research directions to improve material design, cost-effectiveness, and sustainability, considering current limitations of each carbon-based adsorbent. To conclude, it references recent publications to examine the various applications of these materials for removing PFAS from the environment. This review aims to assist researchers and academics working towards reducing PFAS contamination.
Background: Laundry wastewater contains a range of hazardous substances, including phosphates, surfactants, BOD (Biochemical Oxygen Demand), COD (Chemical Oxygen Demand), and TSS (Total Suspended Solids), which can pollute the environment. Thus, effective laundry wastewater management is crucial to reducing negative impacts on water quality and aquatic ecosystems. Objective: We aimed to analyse the effectiveness of small-scale laundry wastewater treatment using electrocoagulation technology combined with a bioball media biofilter. Methods: A quantitative experiment with a one-group pretest-posttest design was used. The tests were conducted on three reactors: Electrocoagulation, a bioball biofilter, and a combination of both, with measurements of physical and chemical wastewater parameters, namely TSS, BOD, COD, and phosphate. Results: The results showed that the combination of electrocoagulation and bioball biofilter technology produced a significant reduction in all parameters: TSS (82.5%), BOD (83.91%), COD (82.27%), and phosphate (97.27%) after 12 hours of treatment. The ANOVA test showed significant differences in TSS ( P = 0.000) and BOD (P = 0.036), but not in COD (P = 0.290) or phosphate (P = 0.619). Conclusion: The combination of electrocoagulation and bioball biofilters is highly effective for treating laundry wastewater, achieving significant reductions in TSS, BOD, COD, and phosphate levels and meeting stricter wastewater quality standards.
To improve the complex dynamic transmission lines monsoon weather environment risk early warning and refinement management capability, this paper proposes a digital twin and multi-source data fusion-driven transmission lines monsoon risk early warning method. First, a multi-source heterogeneous data system is constructed, which integrates meteorological, geographic, line ontology, and real-time monitoring data. Based on high-precision three-dimensional modelling and physical attribute binding technology, the digital twin of transmission lines is established and the bidirectional dynamic mapping between physical entity and virtual model at the geometry, attribute and state levels is realized. Beyond the one-way mapping from physical entity to virtual model, a bidirectional dynamic feedback mechanism is designed: real-time monitoring data continuously update the twin state, while the twin's simulation results (e.g., predicted wind-induced responses) are fed back to guide online sensor calibration and inspection strategies, thereby closing the loop between physical and digital spaces at geometry, attribute, and state levels. Next, the temporal and spatial heterogeneity of multi-source data, which are designed based on the deep learning framework of multimodal data fusion model, realize the weather forecast, the geographical environment, and collaborative analysis and dynamic structural response line deduction. Further, by integrating the dynamic mechanical response of the line with its electrical insulation characteristics, the critical state under monsoon conditions and the corresponding dynamic safety thresholds are defined. A real-time probabilistic risk assessment model is then established, enabling a paradigm shift from static threshold-based early warning to dynamic, evolution-based risk early warning. Finally, selecting typical typhoon influence area on the southeastern coast of China's 220 kv transmission line, the presented method is introduced in detail, from the front-end data integration, twin model driven, fusion algorithm operation to the early warning information to generate the whole process of application, and through comparing analysis of early warning effectiveness, more groups of data form. The results show that the warning accuracy of the proposed method is 92.3% and the average effective warning advance time is 98 minutes. Compared with the traditional warning method based on wind speed at meteorological stations, the spatial accuracy and time resolution of the proposed method are significantly improved, which provides more accurate and reliable decision support for the disaster prevention and mitigation and intelligent operation and maintenance of the power grid under extreme weather.
Aerobic denitrifying bacteria are often inhibited by inorganic ions during wastewater treatment, and elucidating their regulatory mechanisms is crucial for enhancing nitrogen removal efficiency. In this study, we systematically investigated the effects of varying concentrations of inorganic ions (0–200 mg/L) on the growth, denitrification performance, and metabolic activity of Pseudomonas mosselii H6. Based on enzyme kinetics, energy metabolism indicators, and measurements of electron transport chain activity, CO32− was found to significantly enhance denitrification capacity. At 200 mg/L, electron transport system activity (ETSA) increased by 26.42%, ATP synthesis peaked at 1.25 μmol/mg prot, and the NADH/NAD+ ratio rose to 22.69. Nitrate reductase (NR) and nitrite reductase (NiR) activities reached 28.37 and 17.76 U/mg prot, respectively. In contrast, Cl⁻ and NO3− at 200 mg/L markedly suppressed cellular metabolism, reducing NH4+-N removal efficiency to 21.41% and 16.42%, respectively. These findings revealed a concentration-dependent and ion-specific regulatory mechanism. CO32− enhances nitrogen removal by optimizing electron transfer and improving enzyme stability. This study provides a theoretical foundation for optimizing the biological treatment of high-salinity wastewater.
Microplastic (MP) pollution has emerged as a growing environmental concern in freshwater ecosystems, yet data from northeastern Thailand, a key hydrological region within the Mekong River Basin, remain limited. In this study, we investigated the abundance, characteristics, and seasonal variations of microplastics in six tributary rivers and four Mekong mainstream sites in Ubon Ratchathani Province. Surface-water samples were collected during the dry (April 2021) and wet (October 2021) seasons using a surface-trawl microplastic sampler, and particles were isolated through density separation and oxidative digestion before identification by FTIR spectroscopy. Nine polymer types were detected across all sites and seasons, with polypropylene (PP) and polyethylene (PE) as the predominant polymers. Seasonal differences were evident, with fragment-shaped MPs more abundant in the wet season, reflecting intensified hydrodynamic fragmentation, while fibers and sheets were more prevalent during the dry season. Tributary sites exhibited consistently higher MP abundances than Mekong mainstream sites, highlighting the importance of localized land-based inputs such as municipal wastewater, stormwater runoff, and urban activities. Overall, the findings demonstrate that hydrological conditions and land-use characteristics influence microplastic distribution within the region. This study provides baseline data for northeastern Thailand and contributes to understanding microplastic distribution in the Mekong River Basin.
The study involved pyrolyzing biomass from Parthenium hysterophorus at temperatures of 300, 500, and 700°C to generate charcoal adsorbents (PTC 300, PTC 500, and PTC 700) at reduced prices. Subsequently, the PTCs were evaluated for their efficacy in eliminating Pb (II) from industrial effluent. Through the manipulation of operational parameters and the analysis of mathematical models, PTC 500 exhibited the highest adsorption capacity among the synthesized adsorbents, achieving 20.40 mg Pb (II)/g. The major mechanisms for Pb (II) adsorption onto biochar derived from P. hysterophorus are ion exchange with inherent mineral cations and surface complexation with oxygenated functional groups (-COOH, -OH). The enhanced immobilization efficacy is due to electrostatic attraction and lead precipitation within the pores of biochar. The pseudo-first order and Langmuir models provided the most precise characterization of Pb (II) adsorption onto PTCs in the batch adsorption study. An increased Pb (II) concentration, reduced flow velocity, and elevated bed height collectively enhanced Pb (II) sorption in the fixed bed column. The Clark model most accurately represented the adsorption process and aligned with the experimental data in comparison to the other column models (Thomas, Yoon-Nelson, Clark, and Bohart-Adams models). The supplemental experimental results were consistent with the breakthrough curves. The maximum adsorption capacity achieved using the column approach was 8.16 mg/g. In contrast to rival adsorbents, PTCs exhibited significant reusability potential and enhanced adsorption efficiency.
Genomic data on multidrug (MDR) airborne bacteria from nonclinical settings in Tunisia, particularly childcare facilities, remain scarce. We aimed to characterize the genome of an MDR Serratia marcescens (S. marcescens) isolate recovered from indoor air and assess its relevance for public health surveillance. Phenotypic characterization included antibiotic susceptibility testing (disc diffusion and broth microdilution) and quantification of biofilm formation. The underlying genetic basis was investigated via whole-genome sequencing (WGS), followed by bioinformatic analysis to characterize the resistome, virulome, and plasmid content. The S. marcescens strain EA1 exhibited an MDR phenotype,