Delhi, a mega-city, has faced serious pollution issues for decades. This is due to rapid urban growth, industrial development, and poor sewage treatment. The pollution in Delhi is particularly severe. This ongoing environmental neglect poses a major health risk to the city's residents, yet there has been no thorough long-term assessment.

This study aims to gather and review historical water quality data from the Central Pollution Control Board (CPCB) and the Delhi Pollution Control Committee (DPCC) from 2015 to 2025. We want to evaluate the overall environmental risk and its potential health effects. Our research looks into the link between high levels of pollutants and reported health problems in riverside communities. We highlight the gap between policy initiatives and actual results. Our method includes a systematic review of available water quality data, focusing on key factors like Biochemical Oxygen Demand (BOD), Faecal Coliform (FC), and Dissolved Oxygen (DO) at important monitoring sites such as Palla, Wazirabad, and ITO Bridge. We conduct a correlation analysis between pollution levels and public health data. This includes records of waterborne diseases in impacted areas, allowing us to identify clear trends and effects. The compiled historical data shows consistently high pollution levels in the Delhi section of the Yamuna River. For example, DPCC data from July 2025 indicated FC levels at ITO Bridge reached 9,200,000 MPN/100 ml, far above the safe bathing standard of 2,500 MPN/100 ml. CPCB data from 2021 recorded a maximum BOD value of 127 mg/L at Asgarpur, which is much higher than the safe limit of less than 3 mg/L. At the same time, dissolved oxygen levels are critically low or nonexistent in the most polluted areas. Our analysis also points out a strong connection between high pollution periods and seasonal outbreaks of waterborne diseases in communities that depend on the river. The gap between Delhi’s sewage generation, around 3,600 MLD in 2024, and its treatment capacity, which was 2,574 MLD utilized in 2024, is crucial. Only 14 out of 37 sewage treatment plants meet the required standards. The long-term pollution of the Yamuna River in Delhi represents a serious environmental and public health crisis tied to ongoing governance and infrastructure problems. Our findings highlight the ongoing public health risk from untreated pollutants and reveal the failures of past conservation efforts. To tackle this issue, we need a new, multi-faceted approach. This should focus on improving sewage infrastructure, enforcing stricter industrial regulations, and ensuring ecological flow. This study offers important, data-driven insights for reassessing urban environmental policies in Asian mega-cities, with the goal of protecting public health and promoting sustainable development.

Global climate change has intensified droughts, leading to severe water scarcity in various regions. With conventional freshwater supplies proving increasingly inadequate, exploring alternative water resources has become indispensable. Thus, desalination and water reuse have become key strategies for ensuring a reliable and sustainable water supply. In particular, reverse osmosis (RO)-based membrane technology plays a central role in this transition, as it effectively removes bacteria, micropollutants, and dissolved solids, producing high-quality water suitable for urban reuse applications. However, despite its technical advantages, RO faces fundamental barriers to wider implementation, such as high energy consumption and membrane fouling. Energy requirements remain one of the most significant contributors to operating cost and environmental burden, while fouling processes reduce membrane efficiency, shorten lifespan, and complicate system operation. Artificial intelligence (AI) provides new opportunities to address these limitations. AI-based algorithms enable the identification of optimal operating conditions that minimize specific energy consumption while maintaining high recovery and water quality. Also, machine learning enables predictive fouling analysis, allowing early detection of fouling and formulating effective mitigation strategies. Building on such AI-driven insights, identifying fouling mechanisms can further guide system design for improved efficiency. This study highlights the potential of AI integration with RO technology as both a means to lower energy demand and enhance membrane reliability, and as a pivotal pathway toward sustainable urban water management in the face of climate change.

This study aimed to establish an air pollution health monitoring network in Urumqi to characterize pollution and its health impacts, thereby providing a basis for interventions. Monitoring was conducted in Tianshan and Midong Districts (2024 data). We collected environmental and meteorological data (PM₂.₅, PM₁₀, etc.), analyzed PM₂.₅ components (PAHs, heavy metals), and gathered health data (mortality, emergency room/outpatient visits, student health). Generalized Additive Models (GAM) and Spearman correlation were used to analyze pollution-health links and protection effectiveness. Midong District recorded the highest annual PM₂.₅ concentration (51 μg/m³), with the most significant exceedance of standards occurring in February (exceedance rate: 35.07%). Fluoranthene (16.68%) and pyrene (11.75%) were the dominant PAHs. The average concentration of Benzo[a]pyrene was 0.617 ng/m³, below the standard limit. Arsenic and chromium posed excess lifetime cancer risks (1.74×10⁻⁵ and 1.19×10⁻⁵, respectively), exceeding acceptable thresholds. PM₂.₅ and PM₁₀ concentrations were positively correlated with respiratory mortality (r=0.134, 0.141; P<0.05) and outpatient visits (CO excess risk: 59.73%). Throat symptoms among students (Midong: 67 cases, Tianshan: 91 cases) were significantly associated with pollution levels (P<0.05). Public education initiatives reached over 50,000 people, effectively improving awareness. This research reveals that Urumqi's winter PM₂.₅ pollution is severe, with heavy metals like arsenic and chromium and PAHs posing significant health risks, necessitating enhanced multi-sectoral coordination and public health protection to reduce population exposure. Consequently, stricter controls on PM₂.₅ components, particularly PAHs and heavy metals (As, Cr), are urgently needed. We recommend enhancing cross-departmental data sharing, strengthening technical capacity at the grassroots level, and expanding the coverage of health education.

More than 10% of the adult population suffers from diabetes worldwide, making the anti-diabetic drug metformin (MET) one of the most produced and consumed drugs globally. Of particular concern is its potential contribution to the transmission of antibiotic resistance genes (ARGs), yet its impact on engineered ecosystems remains unknown. Herein, we show that the presence of MET intensifies the transmission of ARGs during anaerobic digestion (AD) in antibiotic-rich conditions. Specifically, in contrast to individual exposure to MET or sulfamethoxazole (SMZ), the coexistence of MET and SMZ increased the abundance of three typical ARGs (tetA, sul1, macB) by 9.84–333.36%. Co-occurrence network analysis reveals that combined exposure to MET and SMZ significantly increased network complexity in the AD system, with the average degree rising from 1.556 to 5.836 compared to single exposure. The combined exposure enhanced interactions between microorganisms, ARGs, and mobile genetic elements (MGEs), thereby increasing the diversity of ARG hosts and facilitating the diffusion of ARGs. Metagenomic binning analysis identified 43 out of 58 ARG hosts as being associated with fermentation, acetogenesis, and methanogenesis, indicating their involvement in the core metabolic processes of AD. Additionally, the co-exposure of MET and SMZ promoted their proliferation within the AD system, enhanced the conversion of substrates to methane. Further analysis indicated that this combined exposure may exacerbate oxidative stress in microbial cells, increase cell membrane permeability, enhance transmembrane transport, and promote conjugative plasmid transfer, all of which contribute to the horizontal gene transfer (HGT) of ARGs. Structural equation modeling analysis confirmed that MGEs are the most significant driving factor for the transfer of ARGs during AD under combined exposure to MET and SMZ. Overall, our findings highlight the role of MET in promoting the transmission of ARGs in a typical man-made anaerobic digestion ecosystem. In the context of the “One Health” concept, reassessing the environmental impact of MET could inspire new approaches to understanding the health of both human society and the environment.

Effective management and treatment of food waste is an increasingly prominent issue for countries around the world. Approximately 50 million tonnes of food waste were generated in China. The amount of food waste generated is expected to further increase with growing population and economic activity. Besides the resources needed to collect and dispose it, food waste contaminates recyclables, compromises recycling efforts, and causes odour nuisance and vermin proliferation if not managed properly. Due to the high moisture content and high biodegradability of food waste, the disposal of food waste has caused severe environmental pollution in many countries. In view of rising costs for waste disposal as well as depleting energy resources, the anaerobic digestion (AD) of food waste was found to be a more sustainable treatment method due to the high degree of waste stabilization and methane generation.

During AD, anaerobic microorganisms degrade organic waste through metabolic processes while recovering energy in the form of methane, thus serving as a crucial means for pollution reduction and carbon mitigation. However, the lengthy metabolic cycle and low conversion efficiency of methanogens result in suboptimal methane yields, thereby impeding the progress of organic waste valorization. Recent studies have demonstrated that the addition of iron/carbon materials as electron mediators can significantly enhance microbial metabolic processes and improve methane production. Nevertheless, the mechanism of interfacial electron transfer between iron/carbon materials and microorganisms remains unclear. Interfacial electron transfer is closely related to microbial metabolism and energy utilization. The varying characteristics of different iron/carbon materials result in distinct electron mediator-microorganism interfacial transfer efficiencies, limiting their effectiveness in addressing specific environmental issues. Understanding the electron mediator-microorganism interfacial electron transfer mechanisms mediated by iron/carbon materials during anaerobic methanogenesis is crucial for enhancing interfacial reactions. Additionally, leveraging these interfacial electron transfer relationships to address microbial-microbial electron transfer issues is an important approach to improving the efficacy of iron/carbon materials. Therefore, it is essential to investigate the unique electron transfer properties of iron/carbon materials in the context of real environmental problems to enhance methane production efficiency in AD.

To address these issues, our study focuses on three common iron/carbon materials: zero-valent iron (ZVI), iron oxides, and biochar. Based on their shared conductive properties and distinct electron mediator characteristics, the study investigates the limitations of intracellular and extracellular interfacial electron transfer in AD. The research examines the mechanisms and energy flows of microbial intracellular and extracellular interfacial electron transfer mediated by iron/carbon materials in different environmental contexts. The aim is to regulate intracellular and extracellular electron transfer pathways, enhance interfacial electron transfer efficiency, and promote methane production by anaerobic microorganisms. This study seeks to provide a scientific basis for enhancing the efficiency of methane production in AD through iron/carbon materials, thereby accelerating the efficient and stable valorization of organic waste.

Polyhydroxybutyrate (PHB) is a biodegradable polymer synthesized and degraded by
microorganisms and is widely regarded as an environmentally friendly alternative to conventional
plastics. However, its fate under landfill conditions, especially in alkaline soils, remains poorly
understood. In Japan, incineration has been the predominant municipal waste treatment method since
the 1970s, and the resulting incineration ash accounts for approximately 70% of landfill waste.
Therefore, landfill soils are often highly alkaline, yet studies on PHB degradation under such
conditions are limited. This study aimed to evaluate the biodegradability of PHB in operational
landfill soil and to elucidate the effects of elevated pH and associated microbial communities with its
degradation. Soil samples were collected from the Nishi-Iburi Regional Union Final Disposal Site in
Muroran, Hokkaido, and uncontaminated campus soil (pH 7.0) from the Muroran Institute of
Technology was used as a control. PHB films were buried in both soils, and their weight loss was
measured periodically. As a result, delayed PHB biodegradation was observed in the landfill soil. In
the university soil, fragmentation of the films progressed by day 28, and substantial degradation was
evident by day 49. In contrast, in the landfill soil, although fragmentation was observed in some
films, little degradation occurred overall. Specifically, on day 49, the residual PHB content had
decreased to 32.5% in the control soil, whereas it remained at 63.8% in the landfill soil.
Microbial counts also differed substantially: bacterial colonies were 4.0×10⁸ CFU/g in the control
versus 1.0×10⁷ CFU/g in landfill soil, while fungal counts decreased from 2.0×10⁶ CFU/g to 7.0×10³
CFU/g, corresponding to 40-fold and 300-fold reductions, respectively. Microbial community
analysis and PHB-specific isolation revealed actinomycetes (Actinomadura, Kitasatospora,
Streptomyces), and spore-forming bacteria (Priestia). These taxa are known for their resilience in
harsh environments, particularly through spore formation, suggesting adaptation to alkaline stress.
These results suggest that although landfill soils harbor microorganisms adapted to the highly
alkaline conditions caused by incineration ash, such alkalinity may suppress both the viable cell
numbers and the degradation activity of PHB-degrading bacteria. Furthermore, according to the
literature, incineration ash may contain heavy metals, and their effects, such as inducing oxidative
stress, should also be considered.
Our findings provide new insights into the post-disposal fate of biodegradable plastics and contribute
to improving evaluation and management strategies for landfill environments. Understanding the
interactions between environmental conditions, microbial communities, and PHB degradation is
essential for promoting the effective use of biodegradable plastics in sustainable waste management.

In response to global carbon neutrality initiatives and the promotion of sustainable forest resource utilization, microbial lignin degradation has emerged as a promising strategy for simultaneously expanding renewable energy use and reduce greenhouse gas emissions. Lignin-degrading bacteria offer a biologically efficient and environmentally low-impact alternative to conventional chemical pretreatment methods. However, current strains often exhibit limitations in degradation rate and environmental adaptability, necessitating the discovery of novel strains capable of stable and rapid lignin decomposition under diverse conditions.

In this study, we aimed to isolate lignin-degrading bacteria from decayed wood and plastic-contaminated soil collected from a landfill site in Muroran City and a polluted area in Sapporo City, Hokkaido, respectively. Initially, 20 g of each sample was suspended in 100 mL of phosphate-buffered saline (PBS) to remove soil particles adhering to microbial cells. To increase biomass, 20 mL of the PBS suspension was inoculated into 100 mL of Nutrient Broth (NB) and incubated with shaking for 12 hours. The resulting pre-culture was serially diluted from the original concentration to 10⁻⁶ and screened on glucose–yeast extract–peptone (GYP) agar medium supplemented with Remazol Brilliant Blue R (RBBR). Colonies that decolorized the blue dye to white were further inoculated into alkaline lignin liquid media at concentrations of 1.0, 2.0, 3.0, and 5.0 g/L to evaluate the degree of decolorization.

Samples exhibiting a shift from the lignin-derived dark brown coloration to light brown or colorless were subjected to adsorption assays. Cultures grown under static conditions in lignin medium for five days were centrifuged, and the wet weight of the resulting cell pellets was measured. Equal amounts of biomass were then inoculated into fresh lignin medium, and their adsorption behavior was assessed at 30, 60, and 90 minutes.

Screening on RBBR agar yielded approximately 400 decolorizing strains from both wood and soil samples. Among these, only five strains demonstrated decolorization in alkaline lignin medium. Adsorption tests confirmed that the decolorization was not due to physical adsorption, as no lignin-derived coloration was observed on the cell pellets. These five strains were designated CTU1 through CTU5. After five days of incubation, CTU1 exhibited a decolorization rate of 2.17%, CTU2 of 56.2%, CTU3 of 54.9%, CTU4 of 1.00%, and CTU5 of 79.9%.

These findings enhance our understanding of bacterial lignin degradation and provide a foundation for the development of low-environmental-impact pretreatment technologies. Future work will focus on the taxonomic identification of the CTU strains and detailed characterization of their enzymatic systems. This study suggests that bacterial approaches may complement or even surpass existing fungal-based methods in lignin bioconversion.

Improving healthy life expectancy is fundamental to achieving sustainable urban development and resilient cities globally. Identifying its social determinants is thus critical. Following the WHO Dahlgren-Whitehead model, our series of studies examined the impact of multilevel factors on HLE, from macro societal trends to individual circumstances. Population aging, alongside increasing size and shifting age structures, drives spatiotemporal disease burden evolution, directly impacting HLE. We project that by 2030, globally, age-structure changes may cause HLE losses of 0.76 years (premature mortality burden) and 0.89 years (disability burden). Similarly, population growth may incur losses of 1.21 years (premature mortality burden) and 1.17 years (disability burden). Critically, these demographic shifts will amplify HLE losses from chronic diseases, significantly exceeding the combined adverse impact of communicable, maternal, neonatal, and nutritional diseases.
As demographic trends are largely immutable macro-factors, mitigating their negative effects necessitates modifying alterable determinants. Achieving Sustainable Development Goal target 3.4 (Reducing premature mortality from the major chronic diseases between the ages of 30 and 70 years by a third from 2015 levels by 2030) through health investment could gain 3.13 years in HLE, effectively offsetting demographic-driven HLE loss. Further eliminating disability from the major chronic diseases could yield an additional gain of up to 4.53 years. Attaining this requires multidimensional investments. For instance, comprehensive environmental management targeting climate change, air quality, and heavy metal pollution (measured by Environmental Performance Index increases) significantly reduces HLE loss associated with mental disorders—a major disability cause—by 31.84, 281.23, and 331.59 person-years per 100,000 population, respectively. However, single-dimensional interventions yield limited gains, underscoring the imperative for multisectoral collaboration.
Simultaneously, macro-level policies can enhance HLE by influencing individual behaviors and vulnerabilities. For example, in China, middle-aged and older adults providing care for elderly parents see their risk of chronic multimorbidity substantially increased by the heavy caregiving burden. Compared to non-caregivers, their HLE is reduced by 3.63 years. Strengthening social eldercare services could alleviate this burden, narrowing the HLE gap between these groups and promoting health equity.
In conclusion, advancing HLE requires concerted societal effort, directly contributing to sustainable urban development and resilient cities. Integrating health considerations into all policies emerges as the pivotal strategy for achieving these interconnected goals.

Effective household waste segregation and recycling are crucial to achieving urban sustainable development. The incentive-based segregation guaranteed by digital technologies has been demonstrated as a practical pathway to promote waste segregation behavior in emerging megacities. Due to the imbalance of resource inputs and management policies in different regions in megacities, spatial and temporal heterogeneity of population participation behavior exists broadly. However, beyond questionnaire and interview-based studies, there is limited literature quantifying the heterogeneity and its spatiotemporal dynamics using big urban data and Internet of Things (IoT) technology. With the support of spatiotemporal Bayesian-based machine learning modeling, we analyze incentive-based Shanghai Green Account data that covers more than 7.6 M households and 22 M population, to conduct quantification analysis via spatiotemporal data calibration, data mapping, and disparity analysis of public engagement across regions in the megacity Shanghai. Drawing on calibrated data from 16 administrative regions, we systematically evaluate and visualize spatiotemporal patterns of incentive-based waste segregation behavior, identifying significant disparities in population engagement across time and space. These patterns reveal clear hotspots and temporal peaks, which align closely with variations in socioeconomic development, infrastructure provision, policy visibility, and demographic composition.We further construct a structural equation model to elucidate the underlying mechanisms driving this spatiotemporal heterogeneity, quantifying the influence of economic development levels, public health incidents, demographic composition, infrastructure indices, and publicity intensity. This study employs large-scale natural population experiments that feed into data mining modeling, enriching methodological approaches to population participation analysis with multimodal urban big data in megacities, while providing theoretical and empirical insights to optimize incentive-based waste segregation management and advance sustainable smart city strategies.

Climate change is a global problem driven by rapidly rising greenhouse-gas emissions. In 2024 the planet registered the warmest year in the instrumental record and 2015–2024 was the warmest decade; warming is intensifying heavy precipitation and “fire weather,” while reducing snow and ice and accelerating permafrost thaw. Glaciers are losing mass at a globally significant and faster rate—about 273 ± 16 Gt of ice per year from 2000–2023, with ~36% faster loss in 2012–2023 than in 2000–2011—contributing to sea-level rise and altering seasonal water supplies. These signals are expanding glacial lakes, amplifying short-duration downpours, and raising flood and landslide risk. Although Pakistan emits well under 1% of global greenhouse gases, it is highly exposed. In the north, Gilgit-Baltistan (GB) sits in the Hindu Kush–Karakoram–Himalaya, where a warmer, wetter monsoon is heightening cloudburst and glacial-lake outburst flood (GLOF) hazards. In 2025 alone, a 21 July cloudburst along Babusar Road caused at least five deaths, fifteen missing, and four injuries; a late-July flood cut the Danyore–Sultanabad canal; a predawn 11 August landslide killed seven volunteers repairing the channel; Shishper-fed flows damaged protective works and the Karakoram Highway; and on 22–23 August a major GLOF Event hits Raoshan village and dammed the Ghizer River, forming a ~7 km temporary lake and forcing evacuations.

To match global fairness with local protection, we propose a dual track. Mitigation targets the biggest, fastest levers—coal phase-down, system-wide efficiency, rapid methane and HFC cuts, and black-carbon abatement from diesel, brick kilns, cookstoves, and open burning—to limit further heating that drives cloudbursts and ice loss. Adaptation prioritizes measures communities can run and maintain: finish early-warning coverage with monthly drills and last-mile redundancy (sirens plus mosque/FM alerts); treat canals, bridges, and access roads as critical infrastructure (armoring, debris screens, bypass valves, prepositioned quick-repair kits); enforce no-build flow corridors; and deploy site-specific controls at choke points (controlled lake drawdown via spillways or gravity siphons, debris-flow barriers, bridge-abutment hardening, and culverts sized to 1-hour extremes). GB should maintain an up-to-date glacial-lake inventory, classify potentially dangerous lakes, and proactively lower volumes at critical sites. New operational ideas include valley micro-bund networks optimized with drone/DEM mapping and participatory flow-path walks; a shared Nowcast & Lake-Watch data commons; parametric early-action financing tied to sub-daily rainfall or discharge thresholds; and “bridge-as-spillway” retrofits with safe-to-fail culverts. Provide shelter for impacted people. Together, these actions convert warnings into avoided losses while aligning rapid global emissions cuts with practical, locally maintainable protection for front-line communities.