Applying Positive Matrix Factorization to Identify Pollution Sources of Fine Particles in Forest Environments
Date Issued
2015
Date
2015
Author(s)
Chen, Ching-Chun
Abstract
In recent years, problem of air pollution in Taiwan is worsening. Anthropogenic emissions are increasing, resulting in a lot of pollutants present in the atmosphere, including particulate matters. Expoures to the fine particles (PM2.5) with aerodynamic diameter less than 2.5 micron may cause adverse effect on human health, such as lung function impairment or respiratory-related diseases. In this study, PM2.5 were measured at the nursery in Xitou natural education area from September 2013 to July 2014. PM2.5 samples were collectd for 80 days and each sample covered 22 hours. The analysis results showed that the average PM2.5 concentration during day and night was 28.86 ± 7.02 µg/m3 and 19.12 ± 5.14 µg/m3 in Fall, respectively; the average of PM2.5 concentration is 19.16 ± 10.74 µg/m3 in winter, 26.33 ± 10.81 µg/m3 in spring and 11.52 ± 6.31 µg/m3 in summer. For PM2.5 compositions, sixteen elements (Mg, Al, Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Ba, Pb), organic carbon (OC), elemental carbon (EC) as well as concentrations of twelve inorganic ions (Na+, NH4+, K+, Mg2+, Ca2+, Cl−, NO2-, NO3−, PO43-, SO4=) were determined. The model of the positive matrix factorization (PMF) was applied to estimate pollution sources in this study. We assessed the best-fitted solution by evaluating the Q value, Maximum Individual Column Mean (IM), Maximum Individual Column Standard Deviation (IS), and Error Estimation (EE). Based on the resolved source profiles and source contribution, four sources were identified: Diesel/Secondary, Fuel-oil combustion/ Traffic, Sea salt transported and Biomass burning, while the largest contributors of PM2.5 were Diesel/Secondary pollution source (44%).
Subjects
Forest
Fine Particle Matter
Source apportionment
Receptor model
Positive Matrix Factorization
Error Estimation
SDGs
Type
thesis
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