Roadside air quality estimation system using vehicular traffic and meteorological conditions

• Main Author: Malana, Mary Grace B.
• Format: text
• Language: English
• Published: Animo Repository 2019
• Subjects: Information storage and retrieval systems—Air quality; Air quality—Philippines—Metro Manila; Air—Pollution—Philippines—Metro Manila—Measurement; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/6560; https://animorepository.dlsu.edu.ph/context/etd_masteral/article/13576/viewcontent/Malana__Mary_Grace_B.2___thesis_document_Redacted.pdf
• Institution: De La Salle University

Abstract In the National Capital Region (NCR) of the Philippines, PM2.5 levels are 70%higher than what is considered safe by the World Health Organization’s (WHO) air quality guidelines. One main cause of this is the pervasive use and the increasing number of vehicles in the region. In other countries, monitoring stations and emissions models have aided air quality planning and policy-making. However, acquiring and maintaining air quality monitoring stations are costly. On the other hand, the existing vehicle emissions model for estimating emission quantities requires in-depth adjustments and calibration of the model. As such, this study explored and developed a low-cost alternative system to estimate ambient air quality in line with the Philippine land transport system conditions. The system accepts vehicle counts and meteorological information as input. It can predict PM2.5 levels by applying statistical and machine learning techniques. Among the six models created in this study, LSTM and SVR with time lags produced lower errors and higher correlation with the actual PM2.5 levels. Additionally, the system also generates a dynamic schematic visualization of the inferred PM2.5 levels and the model’s performance.