Abstract
ABSTRACT The capital city of Mongolia, Ulaanbaatar, suffers from high levels of pollution due to excessive airborne particulate matter (APM). A lack of systematic data for the region has inspired investigation into the type, origin and seasonal variations of this pollution, the effects of meteorological conditions and even the time-dependence of anthropogenic sources. This work reports source apportionment results from a large data set of 184 samples each of fine (PM2.5) and coarse (PM2.5-10) fraction atmospheric PM collected over a three-year period (2014–2016) in Ulaanbaatar, Mongolia. Positive Matrix Factorization (PMF) was applied using the concentrations of 16 elements measured by an energy dispersive X-ray fluorescence spectrometer along with the black carbon content measured by a reflectometer as input data. The PMF results revealed that whereas mixed sources dominate the coarse fraction, soil and traffic sources are the principle contributors to the fine fraction. The source profiles and the seasonal variations of their contributions indicate that fly ash emanating from coal combustion mixes with traffic emissions and resuspended soil, resulting in variable chemical source profiles. Four sources were identified for both fractions, namely, soil, coal combustion, traffic and oil combustion, which respectively contributed 35%, 16%, 41% and 8% to the coarse fraction and 31%, 27%, 31% and 11% to the fine fraction. Additionally, the probable source contributions from long-range transport events were assessed via concentration-weighted trajectory analysis.
Highlights
Atmospheric pollution due to particulate matter (PM) is a wide concern for both human health and the climate (Cohen et al, 2004; Ostro et al, 2015)
The presented values indicated that annual concentrations of both the fractions are much higher than Mongolian National Ambient Air Quality Standards (MNS 4585:2007), set to yearly averages of 50 μg m–3 for PM10 and 25 μg m–3 for PM2.5, as well as the World Health Organization (WHO)’s guideline (WHO, 2005) corresponding values of 20 μg m–3 and 10 μg m–3
The minimum detection limits (MDLs), number of elemental constituents which were detected above their respective MDLs, as well as average elemental concentrations during warm and cold season are included in the table
Summary
Atmospheric pollution due to particulate matter (PM) is a wide concern for both human health and the climate (Cohen et al, 2004; Ostro et al, 2015). Aerosol particles can be formed either from precursor gases via chemical reactions in the atmosphere or be directly emitted from various sources of anthropogenic and natural origin (Davy et al, 2011). The process of identification and apportionment of pollutants to their sources is an important step in air quality management, since this can be used as baseline data for the establishment of air pollution mitigation strategies. Multivariate receptor models are very useful tools as they may be applied directly to the observed PM composition data (Santoso et al, 2008). The dataset of the PM elemental composition is an essential input for the characterization of specific emission sources using statistical apportionment tools such as the Positive Matrix Factorization (PMF) model (Paatero et al, 2003), and can be accompanied by complementary data (e.g., black carbon, organic carbon, water-soluble ions).
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