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

2021 ◽  
Vol 13 (24) ◽  
pp. 13584
Author(s):  
Mikhail Y. Semenov ◽  
Natalya A. Onishchuk ◽  
Olga G. Netsvetaeva ◽  
Tamara V. Khodzher

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.

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pallavi Pradeep Khobragade ◽  
Ajay Vikram Ahirwar

Purpose The purpose of this study is to monitor suspended particulate matter (SPM), PM2.5 and source apportionment study for the identification of possible sources during the year 2018–2019 at Raipur, India. Design/methodology/approach Source apportionment study was performed using a multivariate receptor model, positive matrix factorization (PMFv5.0) with a view to identify the various possible sources of particulate matter in the area. Back-trajectory analysis was also performed using NOAA-HYSPLIT model to understand the origin and trans-boundary movement of air mass over the sampling location. Findings Daily average SPM and PM2.5 aerosols mass concentration was found to be 377.19 ± 157.24 µg/m³ and 126.39 ± 37.77 µg/m³ respectively. SPM and PM2.5 mass concentrations showed distinct seasonal cycle; SPM – (Winter ; 377.19 ±157.25 µg/m?) > (Summer; 283.57 ±93.18 µg/m?) > (Monsoon; 33.20 ±16.32 µg/m?) and PM2.5 – (Winter; 126.39±37.77 µg/m³) > (Summer; 75.92±12.28 µg/m³). Source apportionment model (PMF) have been applied and identified five major sources contributing the pollution; steel production and industry (68%), vehicular and re-suspended road dust (10.1%), heavy oil combustion (10.1%), tire wear and brake wear/abrasion (8%) and crustal/Earth crust (3.7%). Industrial activities have been identified as major contributing factor for air quality degradation in the region. Practical implications Chemical characterization of aerosols and identification of possible sources will be helpful in abatement of pollution and framing mitigating strategies. It will also help in standardization of global climate model. Originality/value The findings provide valuable results to be considered for controlling air pollution in the region.


2018 ◽  
Vol 202 ◽  
pp. 253-263 ◽  
Author(s):  
Regina Maura de Miranda ◽  
Maria de Fatima Andrade ◽  
Flavia Noronha Dutra Ribeiro ◽  
Kelliton José Mendonça Francisco ◽  
Pedro José Pérez-Martínez

2007 ◽  
Vol 57 (6) ◽  
pp. 741-752 ◽  
Author(s):  
Steven G. Brown ◽  
Anna Frankel ◽  
Sean M. Raffuse ◽  
Paul T. Roberts ◽  
Hilary R. Hafner ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document