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.

Road-safety management in data-constrained settings is complicated by the uneven temporal and geographic availability of official records. This study conducts a two-scale Geographic Information System (GIS)-based analysis of road traffic safety in Libya by combining a detailed nationally disaggregated historical baseline with recent national statistics and a longitudinal comparison of Tripoli. Official annual reports for 2016 and 2017 were used to examine national change, spatial divergence among reporting jurisdictions, spatial concentration, and broad geographic patterns of crash frequency and severity. More recent Ministry of Interior statistics provide a national benchmark for 2024 and the first half of 2025, while complete 2025 statistics released by the Tripoli Security Directorate permit a descriptive comparison of the capital across widely separated years. Nationally, recorded crashes fell by 7.0% and recorded deaths by 14.7% between 2016 and 2017, although several jurisdictions moved in the opposite direction. The concentration of recorded crashes and deaths across jurisdictions increased, and the pooled severe-crash share displayed significant positive spatial autocorrelation. The recent national benchmark reported 9,916 recorded crashes and 2,441 deaths in 2024; the first two quarters of 2025 together account for 4,952 crashes and 1,279 deaths. In Tripoli, total recorded crashes increased from 657 in 2016 and 631 in 2017 to 1,628 in 2025, whereas fatal crashes and deaths remained close to their historical levels. Consequently, the fatal-crash share fell from 37.3% and 34.5% to 13.8%. The decline in fatal-crash share may partly reflect changes in reporting practices, traffic exposure, classification or jurisdictional boundaries. The findings support a tiered approach to road-safety assessment in which national spatial screening is combined with current urban monitoring and more consistent geocoded crash-data systems.

Urban and regional transport authorities need reliable methods to identify road sections where traffic exposure and crash outcomes indicate comparatively unfavorable safety performance, particularly when planning and infrastructure-management resources are limited. This study investigates how inverse data envelopment analysis (DEA) can support the comparative assessment and prioritization of two-lane road sections for road safety-oriented transport planning and infrastructure management. Twelve main road sections within the jurisdiction of the East Sarajevo Police Administration were analyzed using data from 2019–2023. Annual average daily traffic (AADT) was used to represent traffic exposure, while the average annual numbers of fatal, serious-injury, and slight-injury crashes were used as adverse safety outcomes. A Charnes–Cooper–Rhodes (CCR) model-based inverse DEA framework was applied to determine the relative position of each road section against an empirically defined efficiency frontier. A constrained radial scenario was then used to estimate reference values for the three crash-severity indicators while keeping AADT unchanged. The results showed that two road sections formed the relative efficiency frontier, while substantial differences were observed among the remaining sections. The two least favorable sections deviated from the reference level by more than 91%, whereas another section was only 2.40% below the frontier. These findings indicate that the proposed framework can distinguish between road sections requiring different levels of planning attention rather than relying on a simple efficient–inefficient classification. The approach provides a transparent decision-support tool for urban and regional transport planning, helping authorities screen road sections, direct detailed road-safety assessments, and support the sequencing of maintenance, reconstruction, and infrastructure-improvement activities.

Urban development and management (UDM) plays a central role in addressing the environmental, social, and governance challenges associated with rapid urbanization. Although bibliometric studies have examined specific areas of urban research, the broader intellectual structure and long-term thematic development of UDM remain fragmented across multiple research streams. This study investigates the evolution, knowledge structure, and emerging research directions of UDM through a large-scale bibliometric and science-mapping analysis. A dataset of 40,435 journal articles and reviews published between 1925 and 2025 was retrieved from Scopus and analyzed following the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. Performance analysis, co-citation analysis, bibliographic coupling, keyword co-occurrence analysis, and thematic mapping were conducted using bibliometrix and VOSviewer. The results showed a marked acceleration in research output, with 45.9% of all documents published during 2021–2025. The dataset accumulated 1,285,702 citations, averaging 31.8 citations per document. China recorded the largest publication output (9,309 documents), whereas the United States received the highest total citations (348,371) and h-index (223). Journal co-citation patterns revealed a multidisciplinary knowledge base centered on environmental and water sciences, while country-level bibliographic coupling showed a core–periphery structure organized around China, the United States, and Europe. Sustainable development and urban planning emerged as broad foundational themes, whereas water management occupied a central and well-developed position. The science-mapping results further identified climate adaptation, blue–green infrastructure, nature-based solutions, and digitally enabled urban management as prominent recent research directions. These findings demonstrate how UDM has developed from a predominantly planning- and growth-oriented field into a multidisciplinary research domain increasingly concerned with environmental limits, climate resilience, and data-supported urban governance. The study provides an integrated knowledge map of UDM and identifies research priorities concerning the Global South, sustainability measurement, blue–green infrastructure, climate–health interactions, digital urban management, circular urban systems, and urban governance.