Source Apportionment of Particulate Matter in Urban Snowpack Using End-Member Mixing Analysis and Positive Matrix Factorization Model

Abstract

The aim of this study was to identify particulate matter (PM) sources and to evaluate their contributions to PM in the snowpack of three East Siberian cities. That was the first time when the PM accumulated in the snowpack during the winter was used as the object for source apportionment study in urban environment. The use of long-term integrated PM samples allowed to exclude the influence of short-term weather conditions and anthropogenic activities on PM chemistry. To ascertain the real number of PM sources and their contributions to air pollution the results of source apportionment using positive matrix factorization model (PMF) were for the first time compared to the results obtained using end-member mixing analysis (EMMA). It was found that Si, Fe and Ca were the tracers of aluminosilicates, non-exhaust traffic emissions and concrete deterioration respectively. Aluminum was found to be the tracer of both fossil fuel combustion and aluminum production. The results obtained using EMMA were in good agreement with those obtained using PMF. However, in some cases, the non-point sources identified using PMF were the combinations of two single non-point sources identified using EMMA, whereas the non-point sources identified using EMMA were split by PMF into two single non-point sources. The point sources were clearly identified using both techniques.

Revealing the Chemical Profiles of Airborne Particulate Matter Sources in Lake Baikal Area: A Combination of Three Techniques

“…Factor 2 (Figure 4b) was also characterized by high Si concentration; however, it was attributed to aluminosilicates, because the Si/Al ratio was equal to 3.8, which is typical for aluminosilicates [25]. The obtained profile of aluminosilicates matched that obtained previously for PM derived from urban snowpack in Irkutsk city [25]. The obtained composition of aluminosilicates was also in agreement with the existing knowledge of aluminosilicates chemistry [52] (Figure 5b).…”

“…DRs were used as source tracers for the source apportionment of PM using end-member mixing analysis (EMMA) [24]. The verification of PMF-derived source profiles using EMMA should be able to compensate the uncertainty related to the number of factors that should be retained in PMF analysis [25]. The verified PMF-derived source profiles will form the basis for establishing a regional library of pollution source profiles.…”

Identifying and Characterizing Critical Source Areas of Organic and Inorganic Pollutants in Urban Agglomeration in Lake Baikal Watershed

“…The identical TM composition of soils and sediments probably means that the TMs in soil and sediment particles are embedded within water-insoluble compounds that do not undergo any changes neither in soil nor in sediments. The TM compositions of soils and bottom sediments are also similar to that of fossil fuel combustion residues [9,58]. That probably means that a significant proportion of the TMs in soil and sediments is of anthropogenic origin.…”

Multivariate Analysis for Characterization of Air Pollution Sources: Part 1 Prior Data Screening and Underlying Assumptions

“…For each run, the researcher checks the values of Q (object function), specifically the lowest Q robust and Q true, which are highlighted by default in PMF.5. Changing the FPEAK values provides a better chance for understanding the rotational freedom of the obtained solutions, with further details about this FPEAK parameter provided in previous studies [37,38]. In summary, for PMF to solve the task of factor analysis, the program incorporates and resolves all equations from 1-6.…”