Dispersion analysis of criteria pollutants from mobile emission sources over Tacloban City using WRF-chem model

• Main Author: Roque, Floro Junior C.
• Format: text
• Language: English
• Published: Animo Repository 2023
• Subjects: Air—Pollution > Tacloban City > Philippines; Automobiles—Pollution control devices; Physics
• Online Access: https://animorepository.dlsu.edu.ph/etdm_physics/10
• Institution: De La Salle University

Air pollution is one of the most serious environmental risks particularly in highly urbanized areas where it can cause strong negative impacts on human health. In Tacloban City, transportation is a critical enabler of economic activity accounting for 15% of the city’s GDP. However, it is also a major source of air pollution. It is expected that as the area becomes more highly urbanized, the continuous use of vehicles would increase and therefore the possibility of exposing more people to harmful air pollutants. In this study, a local transport emissions inventory was established using traffic and network data in the major thoroughfares and speed-PCU flow function calibration for different road classifications to calculate the emission factors and activity per road length. The results were disaggregated into a gridded 1km 1km resolution and was set as an input for the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) to investigate the spatial and temporal variations of PM10, PM2.5, NO2, SO2, and CO. Four scenarios using (a) Tacloban City Total Emissions Inventory; (b) Tacloban City Mobile Emissions Inventory; (c) Emissions Database for Global Atmospheric Research (EDGAR) total emissions; and (d) EDGAR transport emissions were simulated to evaluate the contribution of transport sources to the air quality in comparison to the total emissions and identify the transport emission hotspots. The results showed that the transport sources can account for up to 60.4% and 71.4% of the total CO and SO2 emissions, respectively. However, PM and NO2 emissions were dominated by the contributions of area sources primarily coming from household emissions. An air quality prediction model using regression and statistical models with the atmospheric and transport conditions as inputs was developed for the determined road network hotspots as an alternative system for areas with high emissions but without continuous monitoring stations. Among the models created to predict values of criteria pollutants, the bagged ensemble (r2=0.51) and gaussian process regression model with rational quadratic kernel (r2=0.58) produced the highest correlations. This study provides a scientific basis for the implementation of rules and regulations to be set by the policy-making bodies responsible such as the regional offices of the Department of Environment and Natural Resources-Environmental Management Bureau. This would contribute to identifying the pollution hotspot locations and attainment or non-attainment areas. The analysis can also be used to deliver control measures which can be implemented to significant factors affecting the air quality.