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

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

Elemental distribution and source analysis of atmospheric aerosols from Meycauayan, Bulacan, Philippines

One of the industrialized cities in the Philippines is Meycauayan, Bulacan. This study reports the elemental distribution and source apportionment in eight varying land cover-land use type sampling points located along the Marilao-Meycauayan- Obando Rivers System. Elemental analysis was conducted using a scanning electron microscope coupled with energy dispersive x-ray. Cu, Pb, Zn, Cr, Mn, As, Cd, Co, Fe, Ni, Ti, and V concentrations were determined using Inductively Coupled Plasma Mass Spectrometry, and Hg concentrations by Mercury analyzer. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and Pearson's r correlation were used to analyze different sources of heavy metals and its corresponding land use-land cover type. The aerosol samples showed the presence of heavy metals Pb and Hg, elements that were also detected in trace amounts in the water measurements. Concentrations of heavy metals such as Cu, Fe, Pb, Zn, V, Ni, and As found in the atmospheric aerosols and urban dusts were attributed to anthropogenic sources such as residential, commercial and industrial wastes. Other source of aerosols in the area were traffic and crustal emissions in Meycauayan. Using HCA, there are 3 clusters observed based on the similar sets of heavy metals: (1) AQS1 (Caingin), AQS2 (Banga), and AQS8 (Malhacan); (2) AQS3(Calvario), AQS4 (Camalig), and AQS5(Langka); (3) AQS1(Sto Nino-Perez), and (AQS7) (Sterling). These groups are related based on different land use setting such as residential/commercial, agricultural, and commercial/industrial areas. Our study recommends the need to address heavy metal pollution in Meycauayan in support to the ongoing implementation of laws and regulations by the local and private sectors