Studies in actual urban settings that integrate chemical reaction modeling, radiation, and particular emissions are mandatory to evaluate the effects of traffic-related air pollution on street canyons. In this paper, airflow patterns and reactive pollutant behavior for over 24 h, in a realistic urban canyon in Osaka City, Japan, was conducted using a computational fluid dynamics (CFD) model coupled with a chemical reaction model (CBM-IV). The boundary conditions for the CFD model were obtained from mesoscale meteorological and air quality models. Inherent street canyon processes, such as ground and wall radiation, were evaluated using a surface energy budget model of the ground and a building envelope model, respectively. The CFD-coupled chemical reaction model surpassed the mesoscale models in describing the NO, NO₂, and O₃ transport process, representing pollutants concentrations more accurately within the street canyon since the latter cannot capture the local phenomena because of coarse grid resolution. This work showed that the concentration of pollutants in the urban canyon is heavily reliant on roadside emissions and airflow patterns, which, in turn, is strongly affected by the heterogeneity of the urban layout. The CFD-coupled chemical reaction model characterized better the complex three-dimensional site and hour-dependent dispersion of contaminants within an urban canyon.

Traffic-related air pollution in street canyons can cause health problems for pedestrians. In order to clarify the behavior of reactive pollutants, such as NOx and O₃, in street canyons, a computational fluid dynamics (CFD) model coupled with a chemistry model and tree canopy model was developed, and then, a set of numerical experiments were performed to investigate the impacts of chemical reactions and aerodynamic effects of trees planted in a canyon. The results were compared with the observation data. Through the results of the numerical experiments designed to simulate a realistic urban street canyon, it was found that chemical reactions have a dominant impact on the NO/NO₂ ratio and O₃ concentration. While the tree canopy had little impact on the NO/NO₂ ratio, it had a moderate impact on the flow field in the canyon and the amount of NOx and O₃ in the canyon. In accordance with the aerodynamic effects of tree canopies, the local NOx concentration in the experiments increased and decreased by up to 51% and 11%, respectively. The current findings of this study demonstrate the utility of the proposed model for conducting air quality investigations in urban areas.

This study presents an integrated assessment of anchorage-related emissions and air quality impacts in the Panama Canal region through Automatic Identification System (AIS) data, bottom-up emission estimation, and atmospheric dispersion modeling. One year of terrestrial AIS observations (July 2024–June 2025) captured 4641 vessels with highly variable waiting times: mean 15.0 h, median 4.9 h, with maximum episodes exceeding 1000 h. Annual emissions totaled 1,390,000 tons of CO₂, 20,500 tons of NO_x, 4250 tons of SO₂, 656 tons of PM₁₀, and 603 tons of PM_2.5, with anchorage activities contributing 497,000 tons of CO₂, 7010 tons of NO_x, 1520 tons of SO₂, 232 tons of PM₁₀, and 214 tons of PM_2.5. Despite the main engines being shut down during anchorage, these activities consistently accounted for 34–36% of the total emissions across all pollutants. High-resolution emission mapping revealed hotspots concentrated in anchorage zones, port berths, and canal approaches. Dispersion simulations revealed strong meteorological control: northwesterly flows transported emissions offshore, sea–land breezes produced afternoon fumigation peaks affecting Panama City, and southerly winds generated widespread onshore impacts. These findings demonstrate that anchorage operations constitute a major source of shipping-related pollution, highlighting the need for operational efficiency improvements and meteorologically informed mitigation strategies.

The poultry industry faces significant challenges in efficiently monitoring large populations, especially under resource constraints and limited connectivity. This paper introduces the Automated Counting and Monitoring System for Poultry Tracking (ACMSPT), an innovative solution that integrates edge computing, Artificial Intelligence (AI), and the Internet of Things (IoT). The study begins by collecting a custom dataset of 1300 high-resolution images from real broiler farm environments, encompassing diverse lighting conditions, occlusions, and growth stages. Each image was manually annotated and used to train the YOLOv10 object detection model with carefully selected hyperparameters. The trained model was then deployed on an Orange Pi 5B single-board computer equipped with a Neural Processing Unit (NPU), enabling on-site inference and real-time poultry tracking. System performance was evaluated in both small- and commercial-scale sheds, achieving a precision of 93.1% and recall of 93.0%, with an average inference time under 200 milliseconds. The results demonstrate that ACMSPT can autonomously detect anomalies in poultry movement, facilitating timely interventions while reducing manual labor. Moreover, its cost-effective, low-connectivity design supports broader adoption in remote or resource-limited environments. Future work will focus on improving adaptability to extreme conditions and extending this approach to other livestock management contexts.

This study addresses the emissions from mobile sources in a busy avenue. Latest mobile emission inventories estimated pollutants based on fuel sales activities in the country. While this is an approved methodology, it does not consider the characteristics of vehicles and their emissions control systems. Applying the International Vehicle Emissions (IVE) model involves low costs, the vehicle fleet’s technology features, and the study region’s environmental parameters. The IVE model allows a better understanding of how vehicle technology impacts air quality. This study aims to generate information about vehicle emissions in Panama using a model that considers factors like vehicle technology, driving patterns, and local conditions, all of which influence air pollution in the region. The focuses are on passenger vehicles, the most common in the country, particularly in one of the densest districts. Carbon monoxide (CO), sulfates (SOx), nitrogen oxide (NO, and particulate matter (PMx) emissions were estimated with the IVE model, the first one stood out, represented 98% (6479.82 g/km) of the air pollutants emitted into the atmosphere by these four gases. Finally, it is established that the combination of emission control technologies and vehicle features is what will determine its emission reduction efficiency. Only in diesel vehicles, an increase in PMx emissions was identified.

In recent years, computational fluid dynamics (CFD) has become a method widely used by the scientific community to study the dispersion of air pollutants in urban areas. This article analyzes the effectiveness of computational fluid dynamics models and their validation methods used to estimate pedestrian exposure to traffic-related air pollutants. This work proposes an exploratory methodology based on a literary review. A total of 28 articles were selected and analyzed from 455 articles published in the Scopus database in 2018–2023. The results show the effects of meteorological variables, such as wind speed and wind direction, on the dispersion of pollutants, especially the effects demonstrating that, at higher wind speeds, they tend to disperse more quickly, which reduces the concentration of these pollutants at the level of the pedestrian respiratory zone. Computational fluid dynamics is an advantageous tool; however, it is necessary to complement it with other models that consider the physical activity of people and thus more precisely evaluate the effect of inhaled pollutants on the entire respiratory system of pedestrians.