scholarly journals Geochemical Mapping, Risk Assessment, and Source Identification of Heavy Metals in Road Dust Using Positive Matrix Factorization (PMF)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 614
Author(s):  
Muhammad Faisal ◽  
Zening Wu ◽  
Huiliang Wang ◽  
Zafar Hussain ◽  
Chenyang Shen
Keyword(s):  
Heavy Metals ◽  
Risk Assessment ◽  
Matrix Factorization ◽  
Anthropogenic Activities ◽  
Contamination Factor ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
Prevention Measures ◽  
Positive Matrix ◽  
The City

Heavy metals in road dust pose a significant threat to human health. This study investigated the concentrations, patterns, and sources of eight hazardous heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg) in the street dust of Zhengzhou city of PR China. Fifty-eight samples of road dust were analyzed based on three methods of risk assessment, i.e., Geo-Accumulation Index (Igeo), Potential Ecological Risk Assessment (RI), and Nemerow Synthetic Pollution Index (PIN). The results exhibited higher concentrations of Hg and Cd 14 and 7 times higher than their background values, respectively. Igeo showed the risks of contamination in a range of unpolluted (Cr, Ni) to strongly polluted (Hg and Cd) categories. RI came up with the contamination ranges from low (Cr, Ni, Cu, Zn, As, and Pb) to extreme (Cd and Hg) risk of contamination. The risk of contamination based on PIN was from safe (Cu, As, and Pb) to seriously high (Cd and Hg). The results yielded by PIN indicated the extreme risk of Cd and Hg in the city. Positive Matrix Factorization was used to identify the sources of contamination. Factor 1 (vehicular exhaust), Factor 2 (coal combustion), Factor 3 (metal industry), and Factor 4 (anthropogenic activities), respectively, contributed 14.63%, 35.34%, 36.14%, and 13.87% of total heavy metal pollution. Metal’s presence in the dust is a direct health risk for humans and warrants immediate and effective pollution control and prevention measures in the city.

scholarly journals Evaluating simulated primary anthropogenic and biomass burning organic aerosols during MILAGRO: implications for assessing treatments of secondary organic aerosols

2009 ◽  
Vol 9 (16) ◽  
pp. 6191-6215 ◽  
Author(s):  
J. Fast ◽  
A. C. Aiken ◽  
J. Allan ◽  
L. Alexander ◽  
T. Campos ◽  
...  
Keyword(s):  
Organic Matter ◽  
Biomass Burning ◽  
Matrix Factorization ◽  
Emission Inventory ◽  
Organic Aerosols ◽  
Positive Matrix Factorization ◽  
Total Organic Matter ◽  
Positive Matrix ◽  
Large Fires ◽  
The City

Abstract. Simulated primary organic aerosols (POA), as well as other particulates and trace gases, in the vicinity of Mexico City are evaluated using measurements collected during the 2006 Megacity Initiative: Local and Global Research Observations (MILAGRO) field campaigns. Since the emission inventories, transport, and turbulent mixing will directly affect predictions of total organic matter and consequently total particulate matter, our objective is to assess the uncertainties in predicted POA before testing and evaluating the performance of secondary organic aerosol (SOA) treatments. Carbon monoxide (CO) is well simulated on most days both over the city and downwind, indicating that transport and mixing processes were usually consistent with the meteorological conditions observed during MILAGRO. Predicted and observed elemental carbon (EC) in the city was similar, but larger errors occurred at remote locations since the overall CO/EC emission ratios in the national emission inventory were lower than in the metropolitan emission inventory. Components of organic aerosols derived from Positive Matrix Factorization of data from several Aerodyne Aerosol Mass Spectrometer instruments deployed both at ground sites and on research aircraft are used to evaluate the model. Modeled POA was consistently lower than the measured organic matter at the ground sites, which is consistent with the expectation that SOA should be a large fraction of the total organic matter mass. A much better agreement was found when modeled POA was compared with the sum of "primary anthropogenic" and "biomass burning" components derived from Positive Matrix Factorization (PMF) on most days, especially at the surface sites, suggesting that the overall magnitude of primary organic particulates released was reasonable. However, simulated POA from anthropogenic sources was often lower than "primary anthropogenic" components derived from PMF, consistent with two recent reports that these emissions are underestimated. The modeled POA was greater than the total observed organic matter when the aircraft flew directly downwind of large fires, suggesting that biomass burning emission estimates from some large fires may be too high.

scholarly journals Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)

2016 ◽  
Author(s):  
M. H. Sowlat ◽  
S. Hasheminassab ◽  
C. Sioutas
Keyword(s):  
Los Angeles ◽  
Matrix Factorization ◽  
Particle Number ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
The Other ◽  
Auxiliary Variables ◽  
Positive Matrix ◽  
Urban Background ◽  
Secondary Aerosol

Abstract. In this study, the Positive Matrix Factorization (PMF) receptor model (version 5.0) was used to identify and quantify major sources contributing to particulate matter (PM) number concentrations, using PM number size distributions in the range of 13 nm to 10 μm combined with several auxiliary variables, including black carbon (BC), elemental and organic carbon (EC/OC), PM mass concentrations, gaseous pollutants, meteorological, and traffic counts data, collected for about 9 months between August 2014 and 2015 in central Los Angeles, CA. Several parameters, including particle number and volume size distribution profiles, profiles of auxiliary variables, contributions of different factors in different seasons to the total number concentrations, diurnal variations of each of the resolved factors in the cold and warm phases, weekday/weekend analysis for each of the resolved factors, and correlation between auxiliary variables and the relative contribution of each of the resolved factors, were used to identify PM sources. A six-factor solution was identified as the optimum for the aforementioned input data. The resolved factors comprised nucleation, traffic 1, traffic 2 (having a larger mode diameter than traffic 1 factor), urban background aerosol, secondary aerosol, and soil/road dust. Traffic sources (1 and 2) were the major contributor to PM number concentrations, collectively making up to above 60 % (60.8–68.4 %) of the total number concentrations during the study period. Their contribution was also significantly higher in the cold phase compared to the warm phase. Nucleation was another major factor significantly contributing to the total number concentrations (an overall contribution of 17 %, ranging from 11.7 % to 24 %), having a larger contribution during the warm phase than in the cold phase. The other identified factors were urban background aerosol, secondary aerosol, and soil/road dust, with relative contributions of approximately 12 % (7.4–17.1), 2.1 % (1.5–2.5 %), and 1.1 % (0.2–6.3 %), respectively, overall accounting for about 15 % (15.2–19.8 %) of PM number concentrations. As expected, PM number concentrations were dominated by factors with smaller mode diameters, such as traffic and nucleation. On the other hand, PM volume and mass concentrations in the study area were mostly affected by sources having larger mode diameters, including secondary aerosols and soil/road dust. Results from the present study can be used as input parameters in future epidemiological studies to link PM sources to adverse health effects as well as by policy makers to set targeted and more protective emission standards for PM.

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
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Matrix Factorization ◽  
Anthropogenic Activities ◽  
Positive Matrix Factorization ◽  
Point Sources ◽  
Aluminum Production ◽  
Positive Matrix ◽  
Factorization Model ◽  
First Time

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.

Road dust contribution to PM levels – Evaluation of the effectiveness of street washing activities by means of Positive Matrix Factorization

2011 ◽  
Vol 45 (13) ◽  
pp. 2193-2201 ◽  
Author(s):  
Angeliki Karanasiou ◽  
Teresa Moreno ◽  
Fulvio Amato ◽  
Julio Lumbreras ◽  
Adolfo Narros ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Road Dust ◽  
Positive Matrix Factorization ◽  
Positive Matrix

Spatial distribution, radiological risk assessment and positive matrix factorization of gamma-emitting radionuclides in the sediment of the Boka Kotorska Bay

2021 ◽  
Vol 169 ◽  
pp. 112491
Author(s):  
Milena Radomirović ◽  
Slavka Stanković ◽  
Milica Mandić ◽  
Mihajlo Jović ◽  
Ljiljana Janković Mandić ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Spatial Distribution ◽  
Matrix Factorization ◽  
Positive Matrix Factorization ◽  
Positive Matrix ◽  
Radiological Risk

scholarly journals Volatile organic compounds over Eastern Himalaya, India: temporal variation and source characterization using Positive Matrix Factorization

2014 ◽  
Vol 14 (23) ◽  
pp. 32133-32175 ◽  
Author(s):  
C. Sarkar ◽  
A. Chatterjee ◽  
D. Majumdar ◽  
S. K. Ghosh ◽  
A. Srivastava ◽  
...  
Keyword(s):  
Volatile Organic Compounds ◽  
Solar Radiation ◽  
Organic Compounds ◽  
Matrix Factorization ◽  
Anthropogenic Activities ◽  
Positive Matrix Factorization ◽  
Eastern Himalaya ◽  
Source Characterization ◽  
Positive Matrix ◽  
Volatile Organic

Abstract. A first ever study on the characterization of volatile organic compounds (VOCs) has been made over a Himalayan high altitude station in India. A total of 18 VOCs (mono aromatics-BTEX (benzene, toluene, ethylbenzene, xylene), non-BTEX substituted aromatics and halocarbon) have been measured over Darjeeling (27.01° N, 88.15° E, 2200 m a.s.l.) in the eastern Himalaya in India during the period of July 2011–June 2012. The annual average concentration of the sum of 18 target VOCs (TVOC) was 376.3 ± 857.2 μg m−3. Monoaromatics had the highest contribution (72%) followed by other substituted aromatics (22%) and halocarbon (6%) compounds. Toluene was the most abundant VOC in the atmosphere of Darjeeling with the contribution of ~37% to TVOC followed by benzene (~21%), ethylbenzene (~9%) and xylenes (~6%). TVOC concentrations were highest during the postmonsoon season with minimum solar radiation and lowest during the premonsoon season with maximum solar radiation. Anthropogenic activities related mainly to tourists like diesel and gasoline emissions, biomass and coal burning, use of solvent and solid waste emissions were almost equal in both the seasons. Seasonal variation in TVOCs over Darjeeling was mainly governed by the incoming solar radiation rather than the emission sources. Source apportionment study using Positive Matrix Factorization (PMF) model indicated that major fraction of (~60%) TVOC were contributed by diesel and gasoline exhausts followed by solvent evaporation (18%) and other sources. Diesel exhaust was also found to have the maximum potential in tropospheric ozone formation. The atmospheric loading of BTEX over Darjeeling was found to be comparable with several Indian metro cities and much higher than other cities around the world.

Sign in / Sign up

Export Citation Format

Share Document