Integrated GIS and air dispersion modeling approach for roadside pollutant mapping: a case study in Baguio City, Philippines

Ramos, R. V.; Blanco, A. C.

doi:10.1007/s41324-022-00438-5

Cited by 1 publication

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A research study focusing on roadside pollutant mapping by integrating GIS techniques and air dispersion modeling (Ramos and Blanco, 2022) was conducted to monitor the behaviour of air pollutants in Baguio City. The study used the Line-source Gaussian dispersion model as a screening and regulatory model in predicting PM concentrations from motor vehicles and roadway intersections.…”

Section: Air Quality Monitoringmentioning

confidence: 99%

A CFD-Based Air Quality Dispersion Modeling for Urban Areas Using Openfoam

Lastrollo,

Ang,

Hizon

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Air pollution in urban areas posed a significant threat to public health and environmental sustainability. This research addressed the urgent need for accurate air quality monitoring systems in urban environments, focusing on the University of the Philippines-Diliman as the study area. The study used 3D modeling and Computational Fluid Dynamics (CFD) to simulate and assess air quality dispersion in the urban setting of the Campus. The research objectives included: (a) developing a 3D model of the Campus using LiDAR and geospatial techniques, (b) modeling the three-dimensional PM2.5 dispersion in the area using CFD, and (c) validating the effectiveness of the CFD-based PM2.5 dispersion model using on-ground air quality monitoring data. The LiDAR and GIS datasets, including Digital Terrain Models, Digital Surface Models, orthophotos, building footprints, and road networks, were utilized to create the 3D campus model. Meteorological data (wind speed, direction, cloudiness, solar irradiation), and emission parameters (pollutant sources and concentrations) were integrated into the model for CFD simulations. SimFlow, an OpenFOAM-based software, facilitated the dispersion modeling process. The study's results revealed the impact of urban factors on PM2.5 dispersion, highlighting challenges in areas with restricted air movement due to buildings and narrow streets. Discrepancies between model predictions and on-ground measurements suggested the influence of unaccounted local factors. Nevertheless, the model demonstrated its utility in capturing general PM2.5 trends. In conclusion, the combination of 3D modeling and CFD simulation proved to be a robust approach for urban air quality monitoring. While improvements were needed to address local influences, this research provided a foundation for better air quality comprehension and urban management, essential for achieving sustainable cities and climate action goals.

show abstract

Section: Air Quality Monitoringmentioning

confidence: 99%