Articles | Volume 10, issue 9
Atmos. Meas. Tech., 10, 3313–3323, 2017
Atmos. Meas. Tech., 10, 3313–3323, 2017

Research article 11 Sep 2017

Research article | 11 Sep 2017

Spatial estimation of air PM2.5 emissions using activity data, local emission factors and land cover derived from satellite imagery

Hezron P. Gibe1 and Mylene G. Cayetano1,2 Hezron P. Gibe and Mylene G. Cayetano
  • 1Institute of Environmental Science and Meteorology, University of the Philippines, Diliman, 1101 Quezon City, Philippines
  • 2International Environmental Research Institute, Gwangju Institute of Science and Technology, Cheomdan-gwagiro, Buk-gu, 500-712 Gwangju, South Korea

Abstract. Exposure to particulate matter (PM) is a serious environmental problem in many urban areas on Earth. In the Philippines, most existing studies and emission inventories have mainly focused on point and mobile sources, while research involving human exposures to particulate pollutants is rare. This paper presents a method for estimating the amount of fine particulate (PM2.5) emissions in a test study site in the city of Cabanatuan, Nueva Ecija, in the Philippines, by utilizing local emission factors, regionally procured data, and land cover/land use (activity data) interpreted from satellite imagery. Geographic information system (GIS) software was used to map the estimated emissions in the study area. The present results suggest that vehicular emissions from motorcycles and tricycles, as well as fuels used by households (charcoal) and burning of agricultural waste, largely contribute to PM2.5 emissions in Cabanatuan. Overall, the method used in this study can be applied in other small urbanizing cities, as long as on-site specific activity, emission factor, and satellite-imaged land cover data are available.

Short summary
This paper presents a low-technical method of estimating particulate air pollution in small cities, using Cabanatuan City, Philippines, as the study site, intended for use by stakeholders requiring minimal training in geospatial methods and GIS, for expediting air pollution emission inventories. This method utilizes local emission data and land cover derived from Google Earth satellite images. Results in the study site suggest high emissions from vehicular sources and rice straw burning.