As regions with concentrated emission levels, metropolitan areas play an important role in achieving sustainable development by improving carbon-emission efficiency. However, the relationship between digital technology adoption (DTA) and carbon-emission efficiency in metropolitan contexts remains underexplored. To address this gap, this study investigates the underlying mechanisms of this relationship using panel data from Chinese metropolitan areas spanning 2010–2023. Using a Tobit model and panel threshold regression, our empirical analysis pays particular attention to the moderating effect of transportation infrastructure (TRIN). The results indicate that DTA significantly enhances carbon-emission efficiency and shows a stage-dependent core-periphery pattern. Core cities initially capture stronger effects while diffusion toward peripheral areas depends on collaborative governance and adequate transportation infrastructure. Moreover, DTA exerts a coordination compensation effect, with a stronger positive influence on carbon-emission efficiency in regions with weaker initial market and policy coordination. TRIN exhibits a nonlinear moderating effect, exerting the strongest positive influence when there is a medium level of development, while insufficient and excessive TRIN weaken this effect. Theoretically, this study identifies the key drivers and mechanisms of coordinated regional carbon governance, contributing to the literature on carbon efficiency within collaborative governance frameworks. Practically, it offers actionable insights for promoting sustainable development strategies in the digital era.
Flow and pollutant dispersion in urban street canyons are critically influenced by building geometry and spatial arrangement, yet the coupled effects of building length-to-height ratio (L/H) and layout type (aligned vs. staggered) remain insufficiently understood. This paper uses a CFD model validated against wind-tunnel experiments to investigate the combined effects of L/H, its associated building coverage density, and layout on flow and pollutant dispersion in urban street canyons under perpendicular wind. Several key findings emerge. First, on the central vertical plane, a clockwise vortex drives pollutant accumulation on the leeward side, and the aligned layout at L/H = 5 produces the highest pollutant concentration among all cases. Second, at pedestrian breathing height (PBH), the maze-like flow in the staggered layout markedly reduces the wind speed and induces reverse airflow within the building gaps for L/H = 2, 3, and 5, causing pollutants to spread both upstream and downstream, whereas in the aligned layout pollutants only travel downstream. Third, for the central street canyon, the aligned layout at L/H = 1 exhibits the strongest ventilation among all nine cases, while increasing L/H from 1 to 5 reduces ventilation by 52.1
Due to the significant non-stationarity, multi-scale fluctuations, and complex temporal dependencies of PM2.5 concentration time series, traditional deep learning models often exhibit issues such as feature weight averaging and insufficient response during critical periods when making long-term PM2.5 concentration predictions.In order to solve this problem, this paper proposes a hybrid prediction model (SFLA-VMD-CBiLSTM-AM) that integrates the frog jumping algorithm to optimize variational mode decomposition and attention enhanced deep networks, in order to improve the accuracy and stability of PM2.5 concentration prediction. Adopting a single step prediction strategy, the dataset is divided into the top 70
Road-dust resuspension may materially affect roadside particulate-matter predictions in dust-prone urban corridors where site-specific road-surface data are limited. This study evaluated the sensitivity of American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD)-predicted PM2.5 and PM10 to prescribed paved-road dust resuspension along a 1-km traffic corridor in Imphal, Northeast India. A closed-circuit television (CCTV)-derived, class-resolved traffic profile was used to construct weekday–hour emissions. A Reference scenario comprising exhaust, tyre wear, brake wear and road-surface abrasion was compared with three U.S. Environmental Protection Agency (EPA) AP-42 resuspension scenarios using prescribed silt loadings (sL) of 0.03, 0.06 and 0.20 g/m2. Model–observation evaluation at the Canchipur roadside Continuous Ambient Air Quality Monitoring Station (CAAQMS) receptor used 3,439 common-valid hourly pairs and 343 complete daily daytime means. For PM10, the Reference predicted 63.43 µg/m3 versus 78.02 µg/m3 observed. At sL = 0.06 g/m2, the predicted mean increased to 76.29 µg/m3, fractional bias improved from − 0.206 to − 0.022, mean absolute error (MAE) decreased from 18.33 to 15.11 µg/m3, and index of agreement (IOA) increased from 0.855 to 0.894; daily daytime MAE decreased from 15.99 to 9.83 µg/m3. Predicted concentrations increased monotonically with prescribed sL, whereas model–observation performance was metric-dependent; sL = 0.20 g/m2 produced substantial PM10 overprediction, while PM2.5 showed a weaker response. Paired moving-block bootstrap analysis supported robust reductions in error and absolute bias for the Reference-to-sL = 0.06 contrast despite lower R2. The findings demonstrate conditional sensitivity to prescribed road-surface silt loading at the evaluated receptor under otherwise fixed modelling assumptions.
This study attempted to establish if medical centers fulfill their intended purpose of promoting health and well-being, or inadvertently contribute to long-term health issues of patients and medical workers. Therefore, it examined the availability, concentrations, and health risks associated with endocrine-disrupting compounds (EDCs) in the indoor aerosol of medical centers of seven universities in southwestern Nigeria.Using the indoor aerosols from air-conditioning systems as our samples, the presence of some EDCs (i.e.., methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), butylparaben (BuP), bisphenol A (BPA), and triclosan (TCS)) in the ambient systems of the medical centers were investigated. The highly carbonaceous nature of the aerosols was established, and the possession of a surface area of 8.07 m2/g indicated the tendency for rapid mobility and retentionin the alveolar region. Methylparaben (3609 ng/g), butylparaben (8,017 ng/g), and triclosan (23,571 ng/g) recorded the highest mean concentrations across board. The university in the most densely populatedStateexhibiting the greatest concentration level for all monitored EDCs except for propylparaben and triclosan. Butylparaben predominated in more than half of the institutions (5,289.29–8,017.03 ng/g). Ingestion was the principal exposure route, and hazard quotient estimates indicated elevated risks for vulnerable populations, with bisphenol A (0.000004–20.8) contributing substantially to the hazard index. Significant correlations of > 0.75 among several EDCs suggest shared sources. This study demonstrates that university health centers can be hotspots for EDC exposure, necessitating urgent policy interventions.
