Abnormal industrial wastewater discharge into urban sewer networks can disrupt the operation of wastewater treatment plants (WWTPs) and increase pollution risks to receiving water bodies. However, large-scale deployment of total phosphorus (TP) chemical monitoring at numerous sewer nodes is costly and difficult to maintain for high-frequency data acquisition, limiting rapid anomaly identification and source tracing. This study develops a hierarchical online monitoring and spatiotemporal tracing framework using the service area of the Xiani WWTP in Dongguan, China, as a case study. The study area was divided into six monitoring zones and 169 drainage blocks, with 57 online monitoring stations deployed at trunk and branch/secondary pipelines. Class-A stations directly monitored TP, while Class-B stations continuously monitored electrical conductivity (EC), pH, and liquid level. A “plant–network–zone–block–source” association framework was established by integrating pipe network topology, flow direction, and estimated travel time. Random forest-based contribution analysis, Fréchet distance trajectory matching, and Bayesian-Markov Chain Monte Carlo (MCMC) inference were combined to progressively rank candidate nodes, drainage blocks, and discharge sources. Results showed a moderate positive correlation between TP and EC (Spearman’s $\rho$ = 0.50, $p$ $<$ 0.05, $n$ = 116), indicating that EC can serve as an auxiliary indicator for screening phosphorus-containing industrial wastewater inputs but cannot replace direct TP measurement. In two verified abnormal discharge events, EC changes at Station 19B and Station 17B preceded TP anomalies at the WWTP influent by approximately 4 h and 6 h, respectively, consistent with estimated flow travel times. Field investigations further identified abnormal industrial wastewater discharge clues in the corresponding areas. Following targeted inspections, enterprise rectification, and enforcement actions, TP concentration at the A-Dapu Station decreased by 50%. The study demonstrates that the proposed hierarchical monitoring and multi-model tracing framework can progressively narrow anomaly investigation from the WWTP service area to candidate pipelines, drainage blocks, and discharge sources, supporting abnormal discharge screening and digital drainage management in industrially dense urban areas.

Natural disasters frequently disrupt transport infrastructure and restrict access to affected communities, making the timely delivery of humanitarian aid a major challenge for disaster management. Selecting a suitable multimodal transport solution requires decision-makers to balance delivery time, cost, infrastructure availability, reliability, safety, flexibility, and capacity. This study investigates the selection of a resilient multimodal transport solution for humanitarian aid delivery under disaster-induced infrastructure disruptions in Bosnia and Herzegovina. An integrated Full Consistency Method (FUCOM) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) model was applied to a case involving the delivery of humanitarian aid from the Armed Forces logistics base at Rajlovac Barracks to Donja Jablanica following the floods and landslides of October 2024. The FUCOM was used to determine the weights of seven evaluation criteria, and the MARCOS method was employed to rank five transport alternatives. Delivery time received the highest weight (0.236), followed by transport system reliability and resilience (0.184) and safety (0.154). The results showed that the integrated road–rail–air alternative achieved the highest utility value (0.7309), narrowly exceeding direct air transport (0.7151). A sensitivity analysis covering 70 weight-variation scenarios showed that the leading alternatives retained stable positions across nearly all scenarios. The findings indicate that integrated multimodal transport can provide a resilient response when individual transport links are disrupted or unavailable. The proposed model offers a transparent decision-support framework for transport planning, infrastructure contingency assessment, and humanitarian logistics management during natural disasters.

Green space accessibility is an important component of sustainable urban development because of its environmental, health, social, and economic benefits. However, inadequate and uneven access to green spaces remains a challenge in many developing regions, particularly in sub-Saharan Africa. This study assessed green space accessibility in Oguta Local Government Area (LGA), Nigeria, and examined its relationship with residents’ socio-economic wellbeing and green space utilization. Geographic information systems (GIS), remote sensing, and household survey methods were employed. Landsat 8 and Sentinel-2 imagery were used for land use/land cover (LULC) classification and Normalized Difference Vegetation Index (NDVI) analysis, respectively, while buffer and network analyses were conducted to assess green space accessibility. A total of 400 questionnaires were administered, of which 390 were retrieved and 385 were valid for analysis. The results showed that agricultural land (31.8%), forest (26.5%), and wetlands (17.9%) constituted major land-cover classes in the study area. NDVI values ranged from −0.18 to 0.82, indicating substantial spatial variation in vegetation density. Accessibility analysis showed that 58.7% of respondents were located within the adopted 300 m accessibility threshold, whereas 41.3% were located beyond this distance. Green space accessibility was positively and significantly correlated with respondents’ socio-economic wellbeing (r = 0.641, p < 0.001). Regression analysis also indicated a significant association between green space accessibility and utilization frequency (p < 0.001). The findings highlight the importance of improving the spatial accessibility, management, and supporting infrastructure of green spaces to enhance residents’ wellbeing and support sustainable urban development in Oguta and environs.

Bulk cargo—dry commodities such as grain, coal, ores, and construction materials, plus liquid bulk such as petroleum products, liquefied gases, and chemicals—is central to urban material supply, yet remains comparatively under-digitalized relative to containerized logistics and is a persistent source of congestion, dust, noise, and emissions at the port–city interface. Dry and liquid bulk involve substantially different handling technologies, risks, and regulatory regimes and are therefore treated as distinct sub-segments rather than as a single homogeneous category. Based on an integrative review of peer-reviewed studies, industry analyses, and policy reports published or first made available online between January 2015 and April 2026, bulk-specific evidence is distinguished from findings derived from broader port and container logistics. Five interacting technology clusters are identified: Internet of Things (IoT) sensing and connectivity; Artificial Intelligence (AI) and predictive analytics; digital twins and simulation; blockchain-enabled documentation; and automation and autonomous systems. The functions and sustainability implications of these clusters are mapped across the economic, environmental, and social dimensions of port–city development. A four-layer conceptual framework—physical assets, data infrastructure, intelligence services, and governance—is proposed, together with testable propositions linking these layers to urban outcomes. Land-use conflict, freight-corridor planning, environmental zoning, and equity in the distribution of port-related environmental burdens are integrated directly into the framework. The trade-offs associated with digitalization—including energy use, cybersecurity exposure, labor transition, technological dependence and interoperability failures, digital exclusion of smaller operators, and rebound effects—are critically assessed. A stakeholder-differentiated roadmap and research agenda are also proposed to support future bulk-specific empirical validation.

Advancing the digital economy and improving eco-efficiency in a coordinated manner are important for high-quality and sustainable regional development. However, the spatio-temporal patterns of their coupling relationship and the factors associated with coordinated development remain insufficiently understood, particularly within the Yangtze River Economic Belt (YREB). To address this gap, the levels of digital economy development and eco-efficiency in 108 YREB cities were evaluated for the period 2011–2022 by integrating the entropy weight method (EWM) and the super epsilon-based measure (Super-EBM) model. The coupling coordination degree (CCD) model was subsequently employed to quantify the degree of coordinated development between the two systems, while the extreme gradient boosting (XGBoost) machine learning algorithm was applied to identify and interpret the major factors associated with the CCD. The results indicate that the digital economy development level exhibited an overall upward, albeit fluctuating, trajectory and was characterized by a distinct spatial gradient of “downstream > midstream > upstream.” In contrast, eco-efficiency remained relatively stable with moderate fluctuations and displayed the opposite spatial distribution. The CCD between the digital economy and eco-efficiency increased steadily throughout the study period. Pronounced spatial heterogeneity was observed, with consistently higher CCD values in downstream cities than in midstream and upstream areas. Improvements in the upstream and midstream regions occurred at a comparatively slower pace, which may be related to disparities in development conditions and resource endowments. Feature importance analysis based on the XGBoost model further showed that openness to the outside world, technological innovation, and urbanization generally made positive contributions to the predicted CCD, whereas government intervention and industrial structure generally made negative contributions. These findings offer new insights into the coordinated evolution of digitalization and ecological sustainability and suggest that better-targeted policy support, strengthened interregional collaboration, enhanced digital infrastructure, innovation-driven development, and deeper integration of digital technologies with green transformation should be promoted to achieve balanced and sustainable development across the YREB.

Changes in the functional use of urban land have become an important challenge affecting urban structure and sustainable development. An-Najaf City, Iraq, has experienced substantial functional transformation over the past two decades, including residential expansion, commercial growth, industrial development, and the emergence of informal settlements. This study analyzes functional land-use changes in An-Najaf City and predicts future transformation using a Markov chain model. Land-use data for 2005, 2015, and 2025 were obtained from official records, geographic information system (GIS)-based land-use maps, recent satellite imagery, and field verification. Transition probability matrices were constructed for the 2005–2015 and 2015–2025 periods, and the 2015–2025 matrix was used to predict the functional land-use distribution for 2035. The results show that residential and commercial land uses increased substantially from 2005 to 2025, with commercial land recording the largest increase, from 200.30 ha to 2,204.30 ha. Informal settlements were also identified as an important factor affecting urban functional change. Based on the Markov prediction, residential land is projected to increase to approximately 5,204.7 ha by 2035, slum areas to 482.3 ha, and industrial land to 876.4 ha. By contrast, commercial land is projected to decline to approximately 1,816.1 ha, while the “Others” category is expected to decrease to 204.5 ha. Since the prediction is applied within the defined 2025 study boundary, the total land area remains constant, and the results represent internal functional transformation rather than further physical urban expansion. The findings highlight the need for planning policies that control informal settlement growth, manage residential and industrial expansion, protect supporting urban functions, and promote balanced urban development in An-Najaf City.

Many smart city initiatives present technological sophistication as synonymous with urban resilience. This paper challenges that assumption by introducing the concept of the “Digital Mirage”, a condition in which the appearance of smart infrastructure conceals structural dependency on external vendors and foreign-hosted data architectures. The paper proposes a transferable theoretical framework, the Sovereign Oasis model, for Infrastructure Sovereignty in geopolitically isolated exclaves, using the Nakhchivan Autonomous Republic, Azerbaijan, as the primary illustrative application context. A Most Dissimilar Systems Design (MDSD) comparative analysis across three cases, Aghali Smart Village as a greenfield reference case, Tallinn, Estonia, as a digital sovereignty benchmark, and Nakhchivan City as a legacy retrofit context, is combined with a process-tracing illustration of the smart grid dependency chain. Drawing on theories of high-modernist planning, polycentric governance, and the political nature of technological artefacts, the paper proposes a three-pillar resilience architecture: a Sovereign Tech Stack maintained through distributed community governance; an Edge Computing model ensuring Graceful Degradation upon link severance; and a Participatory Infrastructure Auditing mechanism grounded in local knowledge. Mean Time to Recovery (MTTR) is proposed as the primary metric for future empirical testing. The Sovereign Oasis model is offered as a design template for exclaves and comparably isolated urban contexts where digital infrastructure dependency constitutes a strategic vulnerability.

As globalization deepens and the digital economy advances, Shandong Province, one of China’s major economic regions, is undergoing shifts in its population structure alongside changes in its economic growth drivers. Exploring the impacts of education support, technological progress, and population aging on regional economic development is of great significance for formulating scientific regional economic policies. Based on macro-level data for Shandong Province from 2004 to 2022, this study constructs a double-logarithmic regression model. The ordinary least squares (OLS) method is used to estimate the model parameters, and stepwise regression is applied to address the multicollinearity problem. The empirical results show that fiscal education expenditure is significantly and positively associated with regional gross domestic product (GDP), suggesting that education support may play an important role in promoting regional economic development. Population aging also shows a significant positive association with regional economic development in the corrected model, indicating that the silver economy and the continued utilization of older adults’ human capital may provide new development opportunities. However, the direct effect of technological progress, measured by technology market turnover, is not fully verified in the current model. The findings suggest that Shandong Province should continue to prioritize education development, improve elderly-care industries and flexible retirement mechanisms, and transform demographic challenges into potential drivers of regional development.

This study evaluates the spatial exposure of road infrastructure and road users to nomadic herder–farmer conflicts (HFCs) in Nigeria, focusing on four critical nomadic pastoral corridors (NPCs): Kaduna–Plateau (North West–North Central, NW–NC), Taraba–Benue (North East–North Central, NE–NC), Nasarawa–Benue (North Central–North Central, NC–NC), and Benue–Enugu (North Central–South East, NC–SE). Using geospatial data, the study develops a Road Exposure Index (REI) by integrating road density, population density, and conflict density across local government areas within these NPCs. Kernel density estimation (KDE) was applied in ArcGIS to visualize the spatial distribution of REI values and identify areas of heightened exposure during HFC-affected years. The results reveal substantial variations in REI across the corridors, with tertiary roads in the Benue–Enugu (NC–SE) and Nasarawa–Benue (NC–NC) corridors showing particularly high levels of exposure. These areas are important because they include major food-producing zones, suggesting that HFC-related road exposure may have implications for food accessibility and rural–urban market connectivity. The findings highlight the need for targeted intelligent transport system (ITS) infrastructure and security interventions to improve road monitoring, enhance transport resilience, and reduce mobility risks in conflict-affected corridors in Nigeria.