Rapid urbanization and the algorithmically mediated digital environment have been linked to a "digital pandemic" in youth mental health. As Generation Z transitions from play-based to phone-based childhoods, understanding how digital architecture interacts with urban stressors is critical. This review delineates the socio-neurobiological mechanisms underlying this crisis and proposes a comprehensive multi-tiered public health framework for technology-led intervention. Emerging evidence suggests that attention-optimizing algorithms exploit neurodevelopmental vulnerabilities, intensifying negative affect and maladaptive social comparison. Recent studies link digital immersion to circadian disruption, hypothalamic-pituitary-adrenal axis dysregulation, and systemic low-grade inflammation. Urban stressors - including sensory overload and reduced green space - further sensitize the "social brain," creating an evolutionary mismatch that amplifies algorithmic influence and psychological distress. We define the "digital pandemic" as a population-level phenomenon associated with algorithmic pathogenesis, and propose a "Digital Precision Psychiatry" framework that shifts the clinical paradigm from episodic, subjective observation to continuous, objective management. By utilizing digital phenotyping and Just-in-Time Adaptive Interventions (JITAI), this vertically integrated strategy aims to restore bio-psycho-social resilience in youth, turning the digital environment from a source of pathology into a tool for neuroprotection.

• Behavioral adaptations follow a sequenced shift from retiming to rerouting to cancellation. • System performance degrades in parallel through slower speeds, higher VHT, and detours. • Flood, built-environment, and trip factors jointly structure mobility disruption. • Mobility losses are uneven across groups, modes, and activity types. • Integrated individual-system evidence remains limited across the literature. This systematic review synthesizes 61 peer-reviewed studies to clarify how urban flooding jointly reshapes travel behavior and transport system performance, and how these coupled changes evolve through feedback between traveler decisions and network disruption. The inquiry is motivated by the convergence of extreme rainfall and rapid urbanization, a combination that accelerates runoff, heightens exposure, and places sustained pressure on urban mobility. Three findings emerge. First, flood-induced mobility disruption unfolds as a sequenced and time-dependent process in which behavioral adaptation and system degradation progress together. Travelers adjust departure times, shift routes or modes as options narrow, and cancel trips, while system performance deteriorates in parallel through declining speeds, rising vehicle hours traveled, expanding detours, and suppressed demand. Second, the distribution of flood-related mobility loss is sharply unequal, with the greatest burdens borne by low-income, marginalized, transit-dependent, and physically constrained travelers whose limited flexibility heightens exposure during walking, waiting, and transferring, and with work and school travel absorbing many of the largest delays and cancellations. Third, the evidence base exhibits a methodological imbalance, as most studies examine either behavioral responses or system performance in isolation. This review identifies four future research directions: (i) tracing floods, behavior, and operations together through time, (ii) calibrating models to observed behavioral thresholds under flood conditions, (iii) shifting equity analysis to activity-space exposure, and (iv) expanding comparative and multimodal evidence across under-studied regions and non-motorized and informal modes. These insights establish a unified basis for understanding how urban flooding restructures mobility and for strengthening planning and modeling efforts under intensifying climate conditions.

Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji

Solid waste dumping differentially impacts soil prokaryotic, fungal, and viral communities: Insights from metagenomics.

Drugs in paradise: caffeine, cocaine, and painkillers detected in sharks from The Bahamas.

Assessment of the economic and socio-territorial sustainability of cow milk production systems around coastal cities of West Africa: A case study in Benin

A case study of urban heat island dynamics in metro-integrated urban canyons in Chennai, India

Effects of rapid urbanisation on human–snake conflicts in a tropical mega-city: Challenges to biodiversity conservation and healthcare systems

Road safety is a critical public health and economic concern in India, leading to a significant loss of human lives and a decline in economic productivity. Rapid urbanization, increased vehicle usage, and inadequate adherence to traffic regulations have further intensified the problem. This conceptual paper explores how road safety challenges can be transformed into opportunities for economic growth, with a specific focus on Gandhinagar city. The study examines existing road safety conditions, including behavioural issues such as negligence and non-compliance with traffic rules, infrastructural limitations like inadequate pedestrian facilities, and gaps in policy implementation. The paper emphasizes the crucial role of education and awareness programmes in shaping responsible road user behaviour. It also highlights the importance of effective governance, enforcement of traffic laws, and community participation in improving road safety outcomes. By adopting an integrated approach that combines enforcement, education, engineering, and emergency care, the study suggests that road accidents can be significantly reduced. Furthermore, the paper argues that improved road safety contributes to economic growth by reducing healthcare costs, enhancing workforce productivity, and promoting efficient transportation systems. Safer roads not only save lives but also support sustainable urban development and attract investment. The study concludes with practical recommendations for policymakers, educational institutions, and communities to strengthen road safety measures and create a safer, more economically sustainable urban environment.

This paper presents the design and implementation of a smart waste collection and automatic alert system aimed at improving waste management efficiency. Rapid urbanization has increased waste generation, making traditional collection methods inefficient. The proposed system uses ultrasonic sensors to detect garbage levels and load cells to measure weight. An Arduino UNO processes the data and transmits it using LoRa communication. At the receiver side, an ESP32 controller analyzes the data and triggers alerts through IoT platforms and buzzers. The system ensures timely waste collection, reduces overflow, and promotes a cleaner environment. This solution contributes to smart city development by integrating automation, wireless communication, and real-time monitoring

Rapid urbanization has led to a significant increase in traffic congestion and noise pollution in metropolitan areas. Excessive honking and traffic signal violations are common issues at busy intersections, contributing to unsafe road conditions and environmental noise. This paper presents Hush-Traffic, an IoT-based smart traffic monitoring system designed to detect excessive honking and monitor traffic rule violations in real time. The proposed system integrates a sound sensor, an Arduinobased traffic controller, and an ESP32-CAM module to create an intelligent monitoring platform. The sound sensor continuously measures environmental noise levels near traffic signals, and when the detected noise exceeds a predefined threshold, the system identifies it as a potential violation. The microcontroller then triggers the ESP32-CAM module to capture images and stream live video through a localhost-based web interface. The traffic signal operates with predefined timing sequences while the monitoring system records abnormal events for analysis. A web-based dashboard provides real-time visualization of signal status and camera output. Experimental results demonstrate that the proposed system effectively detects excessive honking and enables efficient monitoring of traffic behavior. The system offers a low-cost, scalable, and practical solution for improving traffic discipline and reducing noise pollution in smart city environments.

Waste management has become a critical environmental and social challenge due to rapid urbanization and population growth. Improper segregation of waste reduces recycling efficiency and increases health risks for workers involved in manual sorting. This paper presents an Automated Waste Segregation System using a Robotic Arm, designed to segregate waste into metal and non-metal categories. The proposed system uses an inductive proximity sensor for metal detection, a microcontrollerbased control unit, and a servo motor driven robotic arm for physical segregation. The system minimizes human intervention, improves safety, and provides a compact and cost-effective solution suitable for small-scale applications. Experimental results show reliable detection and accurate placement of waste materials into respective bins.

Urban areas frequently encounter environmental challenges such as the reduction of green open spaces, water shortages during the dry season, and water ponding during the rainy season. These problems are mainly caused by rapid urbanization and the expansion of impervious surfaces, which reduce natural infiltration capacity and increase surface runoff. As a result, groundwater recharge decreases while the potential for urban flooding becomes higher. Similar conditions are observed in Enggal District, located in the central area of Bandar Lampung City, Indonesia. Therefore, effective stormwater management strategies are required to mitigate surface runoff while improving groundwater conservation. This study aims to estimate the required number of infiltration wells needed to achieve zero run-off conditions in Enggal District. The study area covers approximately 2.80 km² and consists of six urban villages with a population of 24,611 in 2025 based on the Enggal District Report. Land-use analysis indicates that the area has a surface runoff coefficient of 0.55. Hydrological analysis was conducted using 20 years of rainfall data (2005–2024) to determine rainfall characteristics and runoff potential. The rainfall intensity analysis for a 10-year return period produced a peak discharge of 15.88 m³/s with a rainfall intensity of 37.23 mm/h. Several designs with 2 scenarios were developed by considering variations in infiltration well dimensions and land availability within the study area. The results show that achieving full runoff reduction requires 934 infiltration wells with a diameter of 0.8 m and a depth of 2.0 m,

Enhancing geological data interpolation by integrating convolutional neural networks (CNN) with the Ordinary Kriging (OK) method: a case study of Wuhan

The rapid urbanization intensifies the conflict between land use and ecological protection. While existing studies acknowledge the role of ecological networks in mitigating human-land conflicts, most focus on one-way impact analyses and neglect the dynamic feedback mechanisms between land use change and ecological network evolution. This study develops a bidirectional feedback framework integrating ecological networks and land use simulation. By quantifying the ecological network's constraint effect, we adjust the development probabilities in land use simulations, update predicted land use patterns, and synchronously update the ecological network structure. Through iterative feedback, we identify core areas critical to regional ecological security. Results reveal a strong feedback mechanism between land use change and ecological network structure, with spatially differentiated mutual influences. The bidirectional feedback suppresses construction land expansion near ecological networks, redirecting development to distant areas. Applying the framework to Huanggang City, we identify 2257.55 km2 of key ecological source areas and 20.56 km2 of core ecological corridors as future ecological security core areas (FESCA). This study advances dynamic feedback analysis of ecological network-land use co-evolution, offering scientific insights for regional spatial planning and ecological conservation.

Sedimentary record of black carbon in Daya Bay: Temporal evolution and source apportionment over the past 140years.

Groundwater contamination from dumpsite leachate poses a significant environmental and public health challenge, particularly in developing regions like Southwestern Nigeria, where rapid urbanization and inadequate waste management exacerbate the problem. Leachate, a toxic byproduct of decomposing waste, infiltrates aquifers, posing a threat to water quality. This review synthesizes existing research on groundwater contamination risks from dumpsite leachate, employing geophysical and hydrochemical methods. The study aims to elucidate contamination mechanisms, evaluate methodological synergies, and identify gaps for future research. These methods collectively map leachate plumes and quantify pollutant concentrations, providing a comprehensive assessment of the extent of contamination. Geophysical surveys consistently identify low-resistivity zones (<100 Ωm) that correlate with dumpsite locations and hydrochemical indicators associated with leachate contamination. Hydrochemical data reveal elevated levels of heavy metals: chloride, nitrate, often exceeding both local and international limits. The Basement Complex geology, with fractured aquifers, exacerbates contaminant spread, while seasonal rainfall intensifies leachate mobility. The findings underscore thr critical need for improved waste management and groundwater protection in Nigeria. Persistent heavy metal contamination highlights long-term health risks, even where general water quality parameters appear acceptable. Alternative water sources should mitigate health impacts, while advanced remediation technologies and policy enforcement are essential for sustainable solutions.

The double burden of malnutrition (DBM), characterized by the coexistence of undernutrition and overnutrition, remains a major public health challenge in Africa. Sub-Saharan Africa continues to report some of the highest global rates of stunting, wasting, and underweight, with stunting affecting up to 57.7% of children under five, while overweight and obesity are rising sharply, particularly in urban populations. This review synthesizes published evidence on the prevalence, mechanisms, and socio-environmental drivers of malnutrition-related non-communicable diseases (NCDs) across the continent. Literature was identified through a narrative review of peer-reviewed articles, institutional datasets, and global health reports published between 2000 and 2025. Biological pathways, including early-life nutritional deprivation, micronutrient deficiencies, and obesity-induced inflammation, interact with food insecurity, rapid urbanization, and socioeconomic inequalities to increase NCD risk. Effective interventions include community-based nutrition programs, food fortification policies, fiscal measures such as sugar taxes, and technology-enabled approaches like mHealth. Strengthening public-private partnerships and integrating nutrition and NCD prevention within health, agriculture, and social protection systems are essential. In addition, greater emphasis on implementation capacity, equitable policy design, and improved monitoring systems is critical for ensuring sustainable impact. Addressing Africa’s double burden requires coordinated, context-specific, and multisectoral strategies to reduce both undernutrition and overnutrition and improve long-term population health outcomes.

With the rapid urbanization, the problem of municipal solid waste has become increasingly severe, and waste classification has become a key measure to achieve sustainable development. However, traditional manual sorting is inefficient and expensive, and the existing automatic identification methods based on deep learning have different limitations. In order to solve these problems, this paper proposes a waste classification scheme that combines Canny edge detection pretreatment with the lightweight ShuffleNet v2 network. First, denoise the image, and then use Canny edge detection to extract the waste profile to enhance the target characteristics and suppress background interference. Finally, enter the processed images into the ShuffleNet v2 network for classification. Experiments conducted on the kitchen garbage subset of Huawei’s garbage classification data set show that the proposed method achieves superior overall performance than SSD, YOLOv3 and unprocessed ShuffleNet v2. While maintaining the advantages of lightweight architecture, the method significantly improves the identification accuracy, thus expanding the technical path of garbage classification and accelerating its intelligent development.

ABSTRACT Introduction Rapid urbanisation over the past century has led to increased traffic density and higher levels of ambient air pollutants. We aimed to investigate whether the prevalence of respiratory symptoms, asthma, and chronic bronchitis increases with the level of self-reported exposure to traffic and occupational-related pollution. Material and methods The study population comprised 25,889 general population subjects ages 16–75 years participating in the Swedish part of the GA2LEN study. Data were collected in the cities of Umeå, Uppsala, Stockholm, and Gothenburg. Self-reported data on disturbance of fumes from traffic in the residential area or occupational exposure to gas, dust, or smoke, as well as on patient demographics, respiratory symptoms, asthma and chronic bronchitis, were obtained from questionnaires. Results The prevalence of respiratory symptoms, asthma, and chronic bronchitis was higher both among participants reporting traffic-related exposure and those with a history of occupational exposure to fumes, smoke, and dust than among those unexposed; (wheeze 27 vs 13%), nocturnal cough (35 vs 22%), asthma (11 vs 6%) and chronic bronchitis (22 vs 8%); all p < 0.0001. When combining traffic and occupational exposure, the highest prevalence was found amongst those exposed to both. Associations with occupational exposure were generally stronger in women, and those with traffic exposure were stronger in men. Conclusion In this large population-based sample, self-reported residential traffic exposure and occupational exposure to dust, fumes, or smoke were associated with increased prevalence of respiratory symptoms, asthma, and chronic bronchitis. Although causality cannot be inferred, these findings underscore the importance of assessing environmental and occupational exposures in evaluating respiratory health.