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Rusanen, Anton, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach. "A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization." Atmospheric Measurement Techniques 17, no. 4 (February 22, 2024): 1251–77. http://dx.doi.org/10.5194/amt-17-1251-2024.
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Abstract. The concentrations of atmospheric particulate matter and many of its constituents are temporally auto-correlated. However, this information has not been utilized in source apportionment methods. Here, we present a Bayesian matrix factorization model (BAMF) that considers the temporal auto-correlation of the components (sources) and provides a direct error estimation. The performance of BAMF is compared with positive matrix factorization (PMF) using synthetic Time-of-Flight Aerosol Chemical Speciation Monitor data, representing different urban environments from typical European towns to megacities. We find that BAMF resolves sources with overall higher factorization performance (temporal behavior and bias) than PMF on all datasets with temporally auto-correlated components. Highly correlated components continue to be challenging and ancillary information is still required to reach good factorizations. However, we demonstrate that adding even partial prior information about the chemical composition of the components to BAMF improves the factorization. Overall, BAMF-type models are promising tools for source apportionment and merit further research.
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Ryoo, Ilhan, Jieun Park, Taeyeon Kim, Jiwon Ryu, Yeonseung Cheong, Joonyoung Ahn, and Seung-Muk Yi. "Study of PM2.5 Using PMF Receptor Model and Advancement of Source Apportionment." Journal of Korean Society for Atmospheric Environment 38, no. 4 (August 31, 2022): 493–507. http://dx.doi.org/10.5572/kosae.2022.38.4.493.
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Molnár, Peter, Sandra Johannesson, and Ulrich Quass. "Source Apportionment of PM2.5 Using Positive Matrix Factorization (PMF) and PMF with Factor Selection." Aerosol and Air Quality Research 14, no. 3 (2014): 725–33. http://dx.doi.org/10.4209/aaqr.2013.11.0335.
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Behl, Rachna, and Indu Kashyap. "Locus recommendation using probabilistic matrix factorization techniques." Ingeniería Solidaria 17, no. 1 (January 11, 2021): 1–25. http://dx.doi.org/10.16925/2357-6014.2021.01.10.
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Introduction: The present paper is the outcome of the research “Locus Recommendation using Probabilistic
Matrix Factorization Techniques” carried out in Manav Rachna International Institute of Research and Studies, India in the year 2019-20.
Methodology: Matrix factorization is a model-based collaborative technique for recommending new items to
the users.
Results: Experimental results on two real-world LBSNs showed that PFM consistently outperforms PMF.
This is because the technique is based on gamma distribution to the model user and item matrix. Using gamma distribution is reasonable for check-in frequencies which are all positive in real datasets. However, PMF is based on Gaussian distribution that can allow negative frequency values as well.
Conclusion: The motive of the work is to identify the best technique for recommending locations with the
highest accuracy and allow users to choose from a plethora of available locations; the best and interesting
location based on the individual’s profile.
Originality: A rigorous analysis of Probabilistic Matrix Factorization techniques has been performed on popular LBSNs and the best technique for location recommendation has been identified by comparing the accuracy viz RMSE, Precision@N, Recall@N, F1@N of different models.
Limitations: User’s contextual information like demographics, social and geographical preferences have
not been considered while evaluating the efficiency of probabilistic matrix factorization techniques for POI Recommendations.
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Drosatou, Anthoula D., Ksakousti Skyllakou, Georgia N. Theodoritsi, and Spyros N. Pandis. "Positive matrix factorization of organic aerosol: insights from a chemical transport model." Atmospheric Chemistry and Physics 19, no. 2 (January 24, 2019): 973–86. http://dx.doi.org/10.5194/acp-19-973-2019.
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Abstract. Factor analysis of aerosol mass spectrometer measurements (organic aerosol mass spectra) is often used to determine the sources of organic aerosol (OA). In this study we aim to gain insights regarding the ability of positive matrix factorization (PMF) to identify and quantify the OA sources accurately. We performed PMF and multilinear engine (ME-2) analysis on the predictions of a state-of-the-art chemical transport model (PMCAMx-SR, Particulate Matter Comprehensive Air Quality Model with extensions – source resolved) during a photochemically active period for specific sites in Europe in an effort to interpret the diverse factors usually identified by PMF analysis of field measurements. Our analysis used the predicted concentrations of 27 OA components, assuming that each of them is “chemically different” from the others. The PMF results based on the chemical transport model predictions are quite consistent (same number of factors and source types) with those of the analysis of AMS measurements. The estimated uncertainty of the contribution of fresh biomass burning is less than 30 % and of the other primary sources less than 40 %, when these sources contribute more than 20 % to the total OA. The PMF uncertainty increases for smaller source contributions, reaching a factor of 2 or even 3 for sources which contribute less than 10 % to the OA. One of the major questions in PMF analysis of AMS measurements concerns the sources of the two or more oxygenated OA (OOA) factors often reported in field studies. Our analysis suggests that these factors include secondary OA compounds from a variety of anthropogenic and biogenic sources and do not correspond to specific sources. Their characterization in the literature as low- and high-volatility factors is probably misleading, because they have overlapping volatility distributions. However, the average volatility of the one often characterized as a low-volatility factor is indeed lower than that of the other (high-volatility factor). Based on the analysis of the PMCAMx-SR predictions, the first oxygenated OA factor includes mainly highly aged OA transported from outside Europe, but also highly aged secondary OA from precursors emitted in Europe. The second oxygenated OA factor contains fresher secondary organic aerosol from volatile, semivolatile, and intermediate volatility anthropogenic and biogenic organic compounds. The exact contribution of these OA components to each OA factor depends on the site and the prevailing meteorology during the analysis period.
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Ulbrich, I. M., M. R. Canagaratna, Q. Zhang, D. R. Worsnop, and J. L. Jimenez. "Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data." Atmospheric Chemistry and Physics 9, no. 9 (May 5, 2009): 2891–918. http://dx.doi.org/10.5194/acp-9-2891-2009.
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Abstract. The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer (Q-AMS) collected at the Pittsburgh Air Quality Study (PAQS) in September 2002 was analyzed with Positive Matrix Factorization (PMF). Three components – hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated OA (OOA-1) that correlates well with sulfate, and a less-oxygenated, semi-volatile OA (OOA-2) that correlates well with nitrate and chloride – are identified and interpreted as primary combustion emissions, aged SOA, and semivolatile, less aged SOA, respectively. The complexity of interpreting the PMF solutions of unit mass resolution (UMR) AMS data is illustrated by a detailed analysis of the solutions as a function of number of components and rotational forcing. A public web-based database of AMS spectra has been created to aid this type of analysis. Realistic synthetic data is also used to characterize the behavior of PMF for choosing the best number of factors, and evaluating the rotations of non-unique solutions. The ambient and synthetic data indicate that the variation of the PMF quality of fit parameter (Q, a normalized chi-squared metric) vs. number of factors in the solution is useful to identify the minimum number of factors, but more detailed analysis and interpretation are needed to choose the best number of factors. The maximum value of the rotational matrix is not useful for determining the best number of factors. In synthetic datasets, factors are "split" into two or more components when solving for more factors than were used in the input. Elements of the "splitting" behavior are observed in solutions of real datasets with several factors. Significant structure remains in the residual of the real dataset after physically-meaningful factors have been assigned and an unrealistic number of factors would be required to explain the remaining variance. This residual structure appears to be due to variability in the spectra of the components (especially OOA-2 in this case), which is likely to be a key limit of the retrievability of components from AMS datasets using PMF and similar methods that need to assume constant component mass spectra. Methods for characterizing and dealing with this variability are needed. Interpretation of PMF factors must be done carefully. Synthetic data indicate that PMF internal diagnostics and similarity to available source component spectra together are not sufficient for identifying factors. It is critical to use correlations between factor and external measurement time series and other criteria to support factor interpretations. True components with <5% of the mass are unlikely to be retrieved accurately. Results from this study may be useful for interpreting the PMF analysis of data from other aerosol mass spectrometers. Researchers are urged to analyze future datasets carefully, including synthetic analyses, and to evaluate whether the conclusions made here apply to their datasets.
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Wu, Jiaying, Zhijun Wu, and Robert Holländer. "The application of Positive Matrix Factorization (PMF) to eco-efficiency analysis." Journal of Environmental Management 98 (May 2012): 11–14. http://dx.doi.org/10.1016/j.jenvman.2011.12.022.
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Han, J. S., K. J. Moon, S. J. Lee, Y. J. Kim, S. Y. Ryu, S. S. Cliff, and S. M. Yi. "Size-resolved source apportionment of ambient particles by positive matrix factorization." Atmospheric Chemistry and Physics Discussions 5, no. 4 (July 22, 2005): 5223–52. http://dx.doi.org/10.5194/acpd-5-5223-2005.
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Abstract. Size- and time-resolved aerosol samples were collected using an eight-stage DRUM sampler from 29 March to 29 May in 2002 at Gosan, Jeju Island, Korea, which is one of the representative background sites in East Asia. These samples were analyzed using synchrotron X-ray fluorescence for 3-h average concentrations of 19 elements consisting of S, Si, Al, Fe, Ca, Cl, Cu, Zn, Ti, K, Mn, Pb, Ni, V, Se, As, Rb, Cr, Br. The size-resolved data sets were then analyzed using the positive matrix factorization (PMF) technique in order to identify possible sources and estimate their contribution to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Fifteen sources were resolved in eight size ranges (0.07–12 µm) and included Chinese aerosol, soil dust, sea salt, biomass burning, coal combustion, oil heating furnace, residual oil-fired boiler, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, copper smelter, and volcano emission. PMF analysis of size-resolved source contributions showed that natural sources represented by soil dust, sea salt and Chinese aerosol contributed about 79% to the predicted primary PM mass in the coarse size range (1.15–12 µm). On the other hand, anthropogenic sources such as coal combustion and biomass burning contributed about 60% in the fine size range (0.56–2.5 µm). The diesel vehicle source contributed the most in the ultra-fine size range (0.07–0.56 µm) and was responsible for about 52% of the primary PM mass.
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Ulbrich, I. M., M. R. Canagaratna, Q. Zhang, D. R. Worsnop, and J. L. Jimenez. "Interpretation of organic components from positive matrix factorization of aerosol mass spectrometric data." Atmospheric Chemistry and Physics Discussions 8, no. 2 (April 9, 2008): 6729–91. http://dx.doi.org/10.5194/acpd-8-6729-2008.
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Abstract. The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer (Q-AMS) collected at the Pittsburgh Air Quality Study in September 2002 was analyzed for components with Positive Matrix Factorization (PMF). Three components – hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated OA (OOA-I) that correlates well with sulfate, and a less-oxygenated, semi-volatile OA (OOA-II) that correlates well with nitrate and chloride – are identified and interpreted as primary combustion emissions, aged SOA, and semivolatile, less aged SOA, respectively. The complexity of interpreting the PMF solutions of unit mass resolution (UMR) AMS data is illustrated by a detailed analysis of the solutions as a function of number of components and rotational state. A public database of AMS spectra has been created to aid this type of analysis. A sensitivity analysis with realistic synthetic data is also used to characterize the behavior of PMF for choosing the best number of factors, rotations of non-unique solutions, and the retrievability of more (or less) correlated factors. The ambient and synthetic data indicate that the variation of the PMF quality of fit parameter (Q, a normalized chi-squared metric) vs. number of factors in the solution is useful to identify the minimum number of factors, but more detailed analysis and interpretation is needed to choose the best number of factors. The maximum value of the rotational matrix is not useful for determining the best number of factors. In synthetic datasets, factors are "split" into two or more components when solving for more factors than were used in the input. Elements of the "splitting" behavior are observed in solutions of real datasets with several factors. Significant structure remains in the residual of the real dataset after physically-meaningful factors have been assigned and an unrealistic number of factors would be required to explain the remaining variance. This residual structure appears to be due to variability in the spectra of the components (especially OOA-II in this case), which is likely to be a key limit of the retrievability of components from AMS datasets using PMF and similar methods that need to assume constant component mass spectra. Methods for characterizing and dealing with this variability are needed. Values of the rotational parameter (FPEAK) near zero appear to be most appropriate for these datasets. Interpretation of PMF factors must be done carefully. Synthetic data indicate that PMF internal diagnostics and similarity to available source component spectra together are not sufficient for identifying factors. It is critical to use correlations between factor time series and external measurement time series to support factor interpretations. Components with <5% of the mass or with high correlation (R>0.9) with other components are suspect and should be interpreted with care. Results from this study may be useful for interpreting the PMF analysis of data from other aerosol mass spectrometers.
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Han, Sangwoo, Chunsang Lee, KyungChan Kim, Subin Lee, and Jinseok Han. "PMF(Positive Matrix Factorization) 모델을 활용한 대전지역의 초미세먼지 배출원별 기여도 추정 연구." Journal of the Korean Society of Urban Environment 21, no. 4 (December 31, 2021): 289–98. http://dx.doi.org/10.33768/ksue.2021.21.4.289.
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Heo, Jongwon, Chanhyuk Kim, Yoonki Min, Hyeonja Kim, Yeongook Sung, Jongsoo Kim, Kyoungbin Lee, and Jongbae Heo. "Source Apportionment of PM10 at Pyeongtaek Area Using Positive Matrix Factorization (PMF) Model." Journal of Korean Society for Atmospheric Environment 34, no. 6 (December 31, 2018): 849–64. http://dx.doi.org/10.5572/kosae.2018.34.6.849.
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Frischmon, Caroline, and Michael Hannigan. "VOC source apportionment: How monitoring characteristics influence positive matrix factorization (PMF) solutions." Atmospheric Environment: X 21 (January 2024): 100230. http://dx.doi.org/10.1016/j.aeaoa.2023.100230.
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Krecl, P., E. Hedberg Larsson, J. Ström, and C. Johansson. "Contribution of residential wood combustion to hourly winter aerosol in Northern Sweden determined by positive matrix factorization." Atmospheric Chemistry and Physics Discussions 8, no. 2 (March 19, 2008): 5725–60. http://dx.doi.org/10.5194/acpd-8-5725-2008.
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Abstract. The combined effect of residential wood combustion (RWC) emissions with stable atmospheric conditions, which is a frequent occurrence in Northern Sweden during wintertime, can deteriorate the air quality even in small towns. To estimate the contribution of RWC to the total atmospheric aerosol loading, the positive matrix factorization (PMF) method was applied to hourly mean particle number size distributions measured in a residential area in Lycksele during winter 2005/2006. The sources were identified based on the particle number size distribution profiles of the PMF factors, the diurnal contributions patterns estimated by PMF for both weekends and weekdays, and correlation of the modeled particle number concentration per factor with measured aerosol mass concentrations (PM10, PM1, and light-absorbing carbon MLAC). Through these analyses, the factors were identified as local traffic (factor 1), local RWC (factor 2), and local RWC plus long-range transport (LRT) of aerosols (factor 3). In some occasions, it was difficult to detach the contributions of local RWC from background concentrations since their particle number size distributions partially overlapped and the model was not able to separate these two sources. As a consequence, we report the contribution of RWC as a range of values, being the minimum determined by factor 2 and the possible maximum as the contributions of both factors 2 and 3. A multiple linear regression (MLR) of observed PM10, PM1, total particle number, and MLAC concentrations is carried out to determine the source contribution to these aerosol variables. The results reveal RWC is an important source of atmospheric particles in the size range 25–606 nm (44–57%), PM10 (36–82%), PM1 (31–83%), and MLAC (40–76%) mass concentrations in the winter season. The contribution from RWC is especially large on weekends between 18:00 LT and midnight whereas local traffic emissions show similar contributions every day.
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Wang, Xiaoliang, L. W. Antony Chen, Minggen Lu, Kin-Fai Ho, Shun-Cheng Lee, Steven Sai Hang Ho, Judith C. Chow, and John G. Watson. "Apportionment of Vehicle Fleet Emissions by Linear Regression, Positive Matrix Factorization, and Emission Modeling." Atmosphere 13, no. 7 (July 6, 2022): 1066. http://dx.doi.org/10.3390/atmos13071066.
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Real-world emission factors for different vehicle types and their contributions to roadside air pollution are needed for air-quality management. Tunnel measurements have been used to estimate emission factors for several vehicle types using linear regression or receptor-based source apportionment. However, the accuracy and uncertainties of these methods have not been sufficiently discussed. This study applies four methods to derive emission factors for different vehicle types from tunnel measurements in Hong Kong, China: (1) simple linear regressions (SLR); (2) multiple linear regressions (MLR); (3) positive matrix factorization (PMF); and (4) EMission FACtors for Hong Kong (EMFAC-HK). Separable vehicle types include those fueled by liquefied petroleum gas (LPG), gasoline, and diesel. PMF was the most useful, as it simultaneously seeks source profiles and source contributions. Diesel-, gasoline-, and LPG-fueled vehicle emissions accounted for 52%, 10%, and 5% of PM2.5 mass, respectively, while ammonium sulfate (~20%), ammonium nitrate (6%), and road dust (7%) were also large contributors. MLR exhibited the highest relative uncertainties, typically over twice those determined by SLR. EMFAC-HK has the lowest relative uncertainties due to its assumption of a single average emission factor for each pollutant and each vehicle category under specific conditions. The relative uncertainties of SLR and PMF are comparable.
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Krecl, P., E. Hedberg Larsson, J. Ström, and C. Johansson. "Contribution of residential wood combustion and other sources to hourly winter aerosol in Northern Sweden determined by positive matrix factorization." Atmospheric Chemistry and Physics 8, no. 13 (July 10, 2008): 3639–53. http://dx.doi.org/10.5194/acp-8-3639-2008.
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Abstract. The combined effect of residential wood combustion (RWC) emissions with stable atmospheric conditions, which frequently occurs in Northern Sweden during wintertime, can deteriorate the air quality even in small towns. To estimate the contribution of RWC to the total atmospheric aerosol loading, positive matrix factorization (PMF) was applied to hourly mean particle number size distributions measured in a residential area in Lycksele during winter 2005/2006. The sources were identified based on the particle number size distribution profiles of the PMF factors, the diurnal contributions patterns estimated by PMF for both weekends and weekdays, and correlation of the modeled particle number concentration per factor with measured aerosol mass concentrations (PM10, PM1, and light-absorbing carbon MLAC) Through these analyses, the factors were identified as local traffic (factor 1), local RWC (factor 2), and local RWC plus long-range transport (LRT) of aerosols (factor 3). In some occasions, the PMF model could not separate the contributions of local RWC from background concentrations since their particle number size distributions partially overlapped. As a consequence, we report the contribution of RWC as a range of values, being the minimum determined by factor 2 and the possible maximum as the contributions of both factors 2 and 3. A multiple linear regression (MLR) of observed PM10, PM1, total particle number, and MLAC concentrations is carried out to determine the source contribution to these aerosol variables. The results reveal RWC is an important source of atmospheric particles in the size range 25–606 nm (44–57%), PM10 (36–82%), PM1 (31–83%), and MLAC (40–76%) mass concentrations in the winter season. The contribution from RWC is especially large on weekends between 18:00 LT and midnight whereas local traffic emissions show similar contributions every day.
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Gupta, Indrani, Abhaysinh Salunkhe, and Rakesh Kumar. "Source Apportionment of PM10by Positive Matrix Factorization in Urban Area of Mumbai, India." Scientific World Journal 2012 (2012): 1–13. http://dx.doi.org/10.1100/2012/585791.
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Particulate Matter (PM10) has been one of the main air pollutants exceeding the ambient standards in most of the major cities in India. During last few years, receptor models such as Chemical Mass Balance, Positive Matrix Factorization (PMF), PCA–APCS and UNMIX have been used to provide solutions to the source identification and contributions which are accepted for developing effective and efficient air quality management plans. Each site poses different complexities while resolving PM10contributions. This paper reports the variability of four sites within Mumbai city using PMF. Industrial area of Mahul showed sources such as residual oil combustion and paved road dust (27%), traffic (20%), coal fired boiler (17%), nitrate (15%). Residential area of Khar showed sources such as residual oil combustion and construction (25%), motor vehicles (23%), marine aerosol and nitrate (19%), paved road dust (18%) compared to construction and natural dust (27%), motor vehicles and smelting work (25%), nitrate (16%) and biomass burning and paved road dust (15%) in Dharavi, a low income slum residential area. The major contributors of PM10at Colaba were marine aerosol, wood burning and ammonium sulphate (24%), motor vehicles and smelting work (22%), Natural soil (19%), nitrate and oil burning (18%).
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Vestenius, M., P. K. Hopke, K. Lehtipalo, T. Petäjä, H. Hakola, and H. Hellén. "Assessing volatile organic compound sources in a boreal forest using positive matrix factorization (PMF)." Atmospheric Environment 259 (August 2021): 118503. http://dx.doi.org/10.1016/j.atmosenv.2021.118503.
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YAMASHITA, Tomoo, Naoto MURAO, Sadamu YAMAGATA, Satio OHTA, and Hiroshi HARA. "Application of PMF (Positive Matrix Factorization) method to daily wet deposition data in Japan." Proceedings of the Symposium on Global Environment 15 (2007): 71–76. http://dx.doi.org/10.2208/proge.15.71.
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Semenov, Mikhail Y., Natalya A. Onishchuk, Olga G. Netsvetaeva, and Tamara V. Khodzher. "Source Apportionment of Particulate Matter in Urban Snowpack Using End-Member Mixing Analysis and Positive Matrix Factorization Model." Sustainability 13, no. 24 (December 8, 2021): 13584. http://dx.doi.org/10.3390/su132413584.
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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.
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Lee, Gahye, Minkyeong KIM, Duckshin Park, and Changkyoo Yoo. "Fine Particulate Matter (PM2.5) Sources and Its Individual Contribution Estimation Using a Positive Matrix Factorization Model." Toxics 11, no. 1 (January 11, 2023): 69. http://dx.doi.org/10.3390/toxics11010069.
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The effective management and regulation of fine particulate matter (PM2.5) is essential in the Republic of Korea, where PM2.5 concentrations are very high. To do this, however, it is necessary to identify sources of PM2.5 pollution and determine the contribution of each source using an acceptance model that includes variability in the chemical composition and physicochemical properties of PM2.5, which change according to its spatiotemporal characteristics. In this study, PM2.5 was measured using PMS-104 instruments at two monitoring stations in Bucheon City, Gyeonggi Province, from 22 April to 3 July 2020; the PM2.5 chemical composition was also analyzed. Sources of PM2.5 pollution were then identified and the quantitative contribution of each source to the pollutant mix was estimated using a positive matrix factorization (PMF) model. From the PMF analysis, secondary aerosols, coal-fired boilers, metal-processing facilities, motor vehicle exhaust, oil combustion residues, and soil-derived pollutants had average contribution rates of 5.73 μg/m3, 3.11 μg/m3, 2.14 μg/m3, 1.94 μg/m3, 1.87 μg/m3, and 1.47 μg/m3, respectively. The coefficient of determination (R2) was 0.87, indicating the reliability of the PMF model. Conditional probability function plots showed that most of the air pollutants came from areas where PM2.5-emitting facilities are concentrated and highways are present. Pollution sources with high contribution rates should be actively regulated and their management prioritized. Additionally, because automobiles are the leading source of artificially-derived PM2.5, their effective control and management is necessary.
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Leuchner, M., S. Gubo, C. Schunk, C. Wastl, M. Kirchner, A. Menzel, and C. Plass-Dülmer. "Can Positive Matrix Factorization identify sources of organic trace gases at the continental GAW site Hohenpeissenberg?" Atmospheric Chemistry and Physics Discussions 14, no. 6 (March 25, 2014): 8143–83. http://dx.doi.org/10.5194/acpd-14-8143-2014.
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Abstract. From the rural Global Atmosphere Watch (GAW) site Hohenpeissenberg in the pre-alpine area of Southern Germany, a dataset of 24 C2–C8 non-methane hydrocarbons over a period of seven years was analyzed. Receptor modeling was performed by Positive Matrix Factorization (PMF) and the resulting factors were compared to literature source profiles. Photochemical aging during transport to the relatively remote site violates the PMF prerequisite of mass conservation from source to receptor. However, previous studies showed plausible results with this method at remote sites; the applicability and restrictions of the PMF model to such a remote dataset and the influence of photochemical processing on the interpretability of the results are discussed. A six factor solution showed a high stability and the most plausible results. In addition to biogenic sources and remote sources of very stable compounds – reflecting the continental background – four additional anthropogenic factors were resolved that could be divided into two short- and two long-lived patterns from evaporative sources and incomplete combustion processes, respectively. A method to increase the uncertainty for each individual compound by including photochemical reactivity did not improve the results, but decreased the stability of the model output. The contribution of the different source categories at the site over the entire period was, in decreasing order: remote sources, long-lived evaporative sources, residential heating and long-lived combustion sources, short-lived evaporative sources, short-lived combustion sources, and biogenic sources. Despite a low overall impact, biogenic sources played an important role during summer, in particular in terms of reactivity.
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Xie, Shuyang, Yuanjun Gong, Yunbo Chen, Kai Li, and Junfeng Liu. "Characterization and Source Analysis of Pollution Caused by Atmospheric Volatile Organic Compounds in the Spring, Kunming, China." Atmosphere 14, no. 12 (November 30, 2023): 1767. http://dx.doi.org/10.3390/atmos14121767.
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The surface atmospheric O3 concentration in Kunming shows a significant upward trend, with high values mainly occurring in March–May. Volatile organic compounds (VOCs) are one of the most important precursors of O3. However, the sources of VOCs are complex and difficult to identify. In order to understand the pollution levels, the spatial distribution characteristics, and possible sources of VOCs, we conducted simultaneous offline sampling at representative sites in six different functional areas of Kunming using SUMMA canisters for one week. The VOC samples were analyzed via GC/MS. The VOC data were analyzed (using the feature ratio method, ozone formation potential (OFP), and Positive Matrix Factorization (PMF) model). Some important conclusions were drawn. Firstly, VOCs during the spring in Kunming were mainly derived from oxygenated VOCs, aromatic hydrocarbons, and halogenated hydrocarbons, with significant spatial differences. Secondly, we found that the potential for atmospheric ozone formation is higher in Kunming for aromatic hydrocarbons and oxygenated VOCs. Finally, the results of the Positive Matrix Factorization model (PMF) showed that Kunming’s ambient atmospheric VOCs mainly originate from anthropogenic source emissions. These conclusions can provide useful reference information for O3 pollution control in Kunming.
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S, Lodoysamba, Shagjjamba D, Hasenkopf A, Gerelmaa G, and Bulgansaikhan B. "Results of Source Apportionment by Receptor Modeling of Ulaanbaatar City." Физик сэтгүүл 18, no. 397 (March 15, 2022): 121–25. http://dx.doi.org/10.22353/physics.v18i397.827.
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Results of an air pollution source apportionment study using the Positive Matrix Factorization method (PMF) based on sampling of air pollution from a site of Nuclear Research Center, National University of Mongolia 2004-2009 and Zuun ail site 2008-2009 presented. From the statistical analysis of the data, it was possible to allocate factors to sources associated with coal combustion, motor vehicles, road dust, and soil.
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Yang, Dejun, Yong Yang, and Yipei Hua. "Source Analysis Based on the Positive Matrix Factorization Models and Risk Assessment of Heavy Metals in Agricultural Soil." Sustainability 15, no. 17 (September 4, 2023): 13225. http://dx.doi.org/10.3390/su151713225.
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Soil heavy metal pollution is a severe and growing problem, and it is crucial to assess the level of soil heavy metal contamination and determine the origins of pollutants. However, there is limited research on soil heavy metal source apportionment and its carcinogenic and non-carcinogenic hazards. Positive Matrix Factorization (PMF) is a powerful technique for source apportionment of pollutants in environmental matrices such as atmospheric particulate matter and soil, as it can handle missing and imprecise data to ensure data reliability, among other benefits. In order to explore the distribution characteristics and main sources of heavy metals in agricultural land, the contents of Cd, Cr, Cu, Pb, and Ni were collected and determined. The positive matrix factorization (PMF) model was used to analyze the source of heavy metals in the soil in the study area, and the human health risk evaluation was carried out. The results showed that (1) the coefficient of variation of Cd in the four areas was much higher than that of the other four heavy metals, which showed strong variability; (2) the content and distribution of heavy metals in different regions were different under the influence of different environments; (3) the PMF model analysis showed that the heavy metal pollution sources in the four areas were divided into two types: the soil parent material, which had industrial pollution, traffic pollution, and agricultural pollution; and the contribution rate of each pollution source; (4) the non-carcinogenic risks of heavy metals in children at all points in the study area were greater than those of adults, and the carcinogenic risks were the opposite of the carcinogenic risk in the study area. And the most serious carcinogenic risk in the study area was the harm caused by oral ingestion of heavy metal Cr into the adults’ bodies.
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Guha, A., D. R. Gentner, R. J. Weber, R. Provencal, and A. H. Goldstein. "Source apportionment of methane and nitrous oxide in California's San Joaquin Valley at CalNex 2010 via positive matrix factorization." Atmospheric Chemistry and Physics Discussions 15, no. 5 (March 4, 2015): 6077–124. http://dx.doi.org/10.5194/acpd-15-6077-2015.
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Abstract. Sources of methane (CH4) and nitrous oxide (N2O) were investigated using measurements from a site in southeast Bakersfield as part of the CalNex (California at the Nexus of Air Quality and Climate Change) experiment from 15 May to 30 June 2010. Typical daily minimum mixing ratios of CH4 and N2O were higher than daily averages that were simultaneously observed at a similar latitude background station (NOAA, Mauna Loa) by approximately 70 and 0.5 ppb, respectively. Substantial enhancements of CH4 and N2O (hourly averages > 500 ppb and > 7 ppb, respectively) were routinely observed suggesting the presence of large regional sources. Collocated measurements of carbon monoxide (CO) and a range of volatile organic compounds (VOCs) (e.g. straight-chain and branched alkanes, cycloalkanes, chlorinated alkanes, aromatics, alcohols, isoprene, terpenes and ketones) were used with a Positive Matrix Factorization (PMF) source apportionment method to estimate the contribution of regional sources to observed enhancements of CH4 and N2O. The PMF technique provided a "top-down" deconstruction of ambient gas-phase observations into broad source categories, yielding a 7-factor solution. We identified these source factors as emissions from evaporative and fugitive; motor vehicles; livestock and dairy; agricultural and soil management; daytime light and temperature driven; non-vehicular urban; and nighttime terpene biogenics and anthropogenics. The dairy and livestock factor accounted for a majority of the CH4 (70–90%) enhancements during the duration of the experiments. Propagation of uncertainties in the PMF-derived factor profiles and time series from bootstrapping analysis resulted in a 29% uncertainty in the CH4 apportionment to this factor. The dairy and livestock factor was also a principal contributor to the daily enhancements of N2O (60–70%) with an uncertainty of 33%. Agriculture and soil management accounted for ~20–25% of N2O enhancements over the course of a day, not surprisingly given that organic and synthetic fertilizers are known to be a major source of N2O. The evaporative/fugitive source profile resembles a mix of petroleum operation and non-tailpipe evaporative gasoline sources, but was not responsible for any observed PMF resolved-CH4 enhancements. The vehicle emission source factor broadly matches VOC profiles of on-road exhaust sources and had no detected contribution to the N2O signals and negligible CH4 in the presence of a dominant dairy and livestock factor. The CalNex PMF study provides a measurement-based assessment of the state CH4 and N2O inventories for the southern San Joaquin valley. The state inventory attributes ~18% of the total N2O emissions to the transportation sector. Our PMF analysis directly contradicts the state inventory and demonstrates there were no discernible N2O emissions from the transportation sector.
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Kim, Sunhye, Tae-Young Kim, Seung-Muk Yi, and Jongbae Heo. "Source apportionment of PM2.5 using positive matrix factorization (PMF) at a rural site in Korea." Journal of Environmental Management 214 (May 2018): 325–34. http://dx.doi.org/10.1016/j.jenvman.2018.03.027.
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Paatero, P., S. Eberly, S. G. Brown, and G. A. Norris. "Methods for estimating uncertainty in factor analytic solutions." Atmospheric Measurement Techniques 7, no. 3 (March 27, 2014): 781–97. http://dx.doi.org/10.5194/amt-7-781-2014.
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Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.
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Yan, Chao, Wei Nie, Mikko Äijälä, Matti P. Rissanen, Manjula R. Canagaratna, Paola Massoli, Heikki Junninen, et al. "Source characterization of highly oxidized multifunctional compounds in a boreal forest environment using positive matrix factorization." Atmospheric Chemistry and Physics 16, no. 19 (October 12, 2016): 12715–31. http://dx.doi.org/10.5194/acp-16-12715-2016.
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Abstract. Highly oxidized multifunctional compounds (HOMs) have been demonstrated to be important for atmospheric secondary organic aerosols (SOA) and new-particle formation (NPF), yet it remains unclear which the main atmospheric HOM formation pathways are. In this study, a nitrate-ion-based chemical ionization atmospheric-pressure-interface time-of-flight mass spectrometer (CI-APi-TOF) was deployed to measure HOMs in the boreal forest in Hyytiälä, southern Finland. Positive matrix factorization (PMF) was applied to separate the detected HOM species into several factors, relating these “factors” to plausible formation pathways. PMF was performed with a revised error estimation derived from laboratory data, which agrees well with an estimate based on ambient data. Three factors explained the majority (> 95 %) of the data variation, but the optimal solution found six factors, including two nighttime factors, three daytime factors, and a transport factor. One nighttime factor is almost identical to laboratory spectra generated from monoterpene ozonolysis, while the second likely represents monoterpene oxidation initiated by NO3. The exact chemical processes forming the different daytime factors remain unclear, but they all have clearly distinct diurnal profiles, very likely related to monoterpene oxidation with a strong influence from NO, presumably through its effect on peroxy radical (RO2) chemistry. Apart from these five “local” factors, the sixth factor is interpreted as a transport related factor. These findings improve our understanding of HOM production by confirming current knowledge and inspiring future research directions and provide new perspectives on using factorization methods to understand short-lived atmospheric species.
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Li, Tingting, Jun Li, Hongxing Jiang, Duohong Chen, Zheng Zong, Chongguo Tian, and Gan Zhang. "Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon." Atmosphere 11, no. 5 (May 16, 2020): 512. http://dx.doi.org/10.3390/atmos11050512.
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To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO3− between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from 14C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than 14C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning.
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Craven, J. S., L. D. Yee, N. L. Ng, M. R. Canagaratna, C. L. Loza, K. A. Schilling, R. L. N. Yatavelli, et al. "Analysis of secondary organic aerosol formation and aging using positive matrix factorization of high-resolution aerosol mass spectra: application to the dodecane low-NO<sub>x</sub> system." Atmospheric Chemistry and Physics Discussions 12, no. 7 (July 6, 2012): 16647–99. http://dx.doi.org/10.5194/acpd-12-16647-2012.
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Abstract. Positive matrix factorization (PMF) of high-resolution laboratory aerosol mass spectra is applied for the first time, the results of which are consistent with molecular level MOVI-HRToF-CIMS aerosol-phase and CIMS gas-phase measurements. Secondary organic aerosol was generated by photooxidation of dodecane under low-NOx conditions in the Caltech environmental chamber. The PMF results exhibit three factors representing a combination of gas-particle partitioning, chemical conversion in the aerosol, and wall deposition. The slope of the measured high-resolution aerosol mass spectrometer (HR-ToF-AMS) composition data on a Van Krevelen diagram is consistent with that of other low-NOx alkane systems in the same O:C range. Elemental analysis of the PMF factor mass spectral profiles elucidates the combinations of functionality that contribute to the slope on the Van Krevelen diagram.
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Wang, Zhen, Jianqiang Zhang, and Izumi Watanabe. "Source Apportionment and Risk Assessment of Soil Heavy Metals due to Railroad Activity Using a Positive Matrix Factorization Approach." Sustainability 15, no. 1 (December 21, 2022): 75. http://dx.doi.org/10.3390/su15010075.
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The effects of railway operation on soil environments are an important topic. In this research, soil samples were collected from two diesel-driven railways and two electric railways in Japan. A positive matrix factorization (PMF) model was applied to investigate the sources of eight heavy metals in the soil near the railways. The results showed that railway operation was the dominant anthropogenic source of heavy metals in the soil in the study areas among five potential sources, with contributions ranging from 11.73% to 42.55%. Compared with that of electricity-driven railways, the effect of diesel-driven railways was larger. The environmental risk-assessment analysis suggested that the soils near the selected railways fall within the weak-to-extremely strong contamination category, and experienced moderate-to-extremely strong ecological risk. A health risk assessment revealed that the soil presented both noncarcinogenic and carcinogenic risks for children, with ingestion as the principal exposure pathway. The PMF-Environment Risk Assessment and PMF-Human Health Risk Assessment models were developed to obtain the ecological and human health risks for every source category. Railway operation was regarded as the major factor influencing ecology and human health at the diesel-driven railway sampling sites. However, at electricity-driven railway sampling sites, natural sources were dominant.
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Sinha, Baerbel, and Vinayak Sinha. "Source apportionment of volatile organic compounds in the northwest Indo-Gangetic Plain using a positive matrix factorization model." Atmospheric Chemistry and Physics 19, no. 24 (December 18, 2019): 15467–82. http://dx.doi.org/10.5194/acp-19-15467-2019.
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Abstract. In this study we undertook quantitative source apportionment for 32 volatile organic compounds (VOCs) measured at a suburban site in the densely populated northwest Indo-Gangetic Plain using the US EPA PMF 5.0 model. Six sources were resolved by the PMF model. In descending order of their contribution to the total VOC burden these are “biofuel use and waste disposal” (23.2 %), “wheat-residue burning”(22.4 %), “cars” (16.2 %), “mixed daytime sources”(15.7 %) “industrial emissions and solvent use”(11.8 %), and “two-wheelers” (8.6 %). Wheat-residue burning is the largest contributor to the total ozone formation potential (32.4 %). For the emerging contaminant isocyanic acid, photochemical formation from precursors (37 %) and wheat-residue burning (25 %) were the largest contributors to human exposure. Wheat-residue burning was also the single largest source of the photochemical precursors of isocyanic acid, namely, formamide, acetamide and propanamide, indicating that this source must be most urgently targeted to reduce human concentration exposure to isocyanic acid in the month of May. Our results highlight that for accurate air quality forecasting and modeling it is essential that emissions are attributed only to the months in which the activity actually occurs. This is important for emissions from crop residue burning, which occur in May and from mid-October to the end of November. The SOA formation potential is dominated by cars (36.9 %) and two-wheelers (21.1 %), which also jointly account for 47% of the human class I carcinogen benzene in the PMF model. This stands in stark contrast to various emission inventories which estimate only a minor contribution of the transport sector to the benzene exposure (∼10 %) and consider residential biofuel use, agricultural residue burning and industry to be more important benzene sources. Overall it appears that none of the emission inventories represent the regional emissions in an ideal manner. Our PMF solution suggests that transport sector emissions may be underestimated by GAINSv5.0 and EDGARv4.3.2 and overestimated by REASv2.1, while the combined effect of residential biofuel use and waste disposal emissions as well as the VOC burden associated with solvent use and industrial sources may be overestimated by all emission inventories. The agricultural waste burning emissions of some of the detected compound groups (ketones, aldehydes and acids) appear to be missing in the EDGARv4.3.2 inventory.
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Bhandari, Sahil, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz. "Contributions of primary sources to submicron organic aerosols in Delhi, India." Atmospheric Chemistry and Physics 22, no. 20 (October 21, 2022): 13631–57. http://dx.doi.org/10.5194/acp-22-13631-2022.
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Abstract. Delhi, India, experiences extremely high concentrations of primary organic aerosol (POA). Few prior source apportionment studies on Delhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol (COA) on POA. In a companion paper, we develop a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements of this approach over traditional PMF. Here, we quantify the contributions of BBOA, COA, and hydrocarbon-like organic aerosol (HOA) by applying positive matrix factorization (PMF) resolved by time of day on two seasons (winter and monsoon seasons of 2017) using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. We also conduct detailed uncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition to HOA, COA is found to be a major constituent of POA in the monsoon, and BBOA is found to be a major constituent of POA in the winter. Neither COA nor the different types of BBOA were resolved in the seasonal (not time-resolved) analysis. The COA mass spectra (MS) profiles are consistent with mass spectral profiles from Delhi and around the world, particularly resembling MS of heated cooking oils with a high m/z 41. The BBOA MS have a very prominent m/z 29 in addition to the characteristic peak at m/z 60, consistent with previous MS observed in Delhi and from wood burning sources. In addition to separating the POA, our technique also captures changes in MS profiles with the time of day, a unique feature among source apportionment approaches available. In addition to the primary factors, we separate two to three oxygenated organic aerosol (OOA) components. When all factors are recombined to total POA and OOA, our results are consistent with seasonal PMF analysis conducted using EPA PMF. Results from this work can be used to better design policies that target relevant primary sources of organic aerosols in Delhi.
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Wernis, Rebecca A., Nathan M. Kreisberg, Robert J. Weber, Greg T. Drozd, and Allen H. Goldstein. "Source apportionment of VOCs, IVOCs and SVOCs by positive matrix factorization in suburban Livermore, California." Atmospheric Chemistry and Physics 22, no. 22 (November 24, 2022): 14987–5019. http://dx.doi.org/10.5194/acp-22-14987-2022.
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Abstract. Gas- and particle-phase molecular markers provide highly specific information about the sources and atmospheric processes that contribute to air pollution. In urban areas, major sources of pollution are changing as regulation selectively mitigates some pollution sources and climate change impacts the surrounding environment. In this study, a comprehensive thermal desorption aerosol gas chromatograph (cTAG) was used to measure volatile, intermediate-volatility and semivolatile molecular markers every other hour over a 10 d period from 11 to 21 April 2018 in suburban Livermore, California. Source apportionment via positive matrix factorization (PMF) was performed to identify major sources of pollution. The PMF analysis identified 13 components, including emissions from gasoline, consumer products, biomass burning, secondary oxidation, aged regional transport and several factors associated with single compounds or specific events with unique compositions. The gasoline factor had a distinct morning peak in concentration but lacked a corresponding evening peak, suggesting commute-related traffic emissions are dominated by cold starts in residential areas. More monoterpene and monoterpenoid mass was assigned to consumer product emissions than biogenic sources, underscoring the increasing importance of volatile chemical products to urban emissions. Daytime isoprene concentrations were controlled by biogenic sunlight- and temperature-dependent processes, mediated by strong midday mixing, but gasoline was found to be the dominant and likely only source of isoprene at night. Biomass burning markers indicated residential wood burning activity remained an important pollution source even in the springtime. This study demonstrates that specific high-time-resolution molecular marker measurements across a wide range of volatility enable more comprehensive pollution source profiles than a narrower volatility range would allow.
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Damayanti, Seny, and Puji Lestari. "Receptor Modelling of particulate matter at residential area near industrial region in Indonesia using Positive Matrix Factorization." E3S Web of Conferences 148 (2020): 03003. http://dx.doi.org/10.1051/e3sconf/202014803003.
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Positive Matrix Factorization (PMF) was used to identify the sources of ambient TSP and to estimate respective contribution to the total ambient TSP concentration in the residential area surrounding iron and steel industry in Cilegon city. Total of 34 daily samples (24 hours) were collected during the sampling period (August-November 2015) using a High Volume Sampler. The samples then were analyzed for black carbon and 18 metal elements (Si, Al, Fe, S, Cu, Pb, V, Cr, Ni, Zn, Mn, Sn, K, Ca, Cl, Ti, Ba, and Co) using Diffusion Systems EEL 43m Smoke Stain Reflectometer (SSR) and Energy Dispersive X-Ray Fluorescence (ED-XRF), respectively. From the PMF results were found that 10 factors as the optimum solution. The five major sources are crustal matter (40.13%), iron and steel production (22.23%), coal combustion (16.54%), biomass burning (11.83%), smelting (8.63%). Meanwhile, the other sources detected are diesel vehicle (0.28%), sea salt (0.17%), fuel-oil combustion (0.07%), road dust (0.07%), and cement industries/construction (0.05%). The patterns of conditional probability function analysis results were adequately appropriate with the potential locations of the known sources around study site.
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Pereira, Jaqueline Natiele, Adalgiza Fornaro, and Marcelo Vieira-Filho. "Source Apportionment of Atmospheric Deposition Species in an Agricultural Brazilian Region Using Positive Matrix Factorization." Environmental Sciences Proceedings 8, no. 1 (July 22, 2021): 9. http://dx.doi.org/10.3390/ecas2021-10698.
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We investigated the influence of natural and anthropogenic sources on bulk atmospheric deposition chemistry, from November 2017 until October 2019, in a Brazilian agricultural area. The pH mean value was 5.99 (5.52–8.46) and most deposition samples (~98%) were alkaline (pH > 5.60). We identified Ca2+ as the predominant species, accounting for 33% of the total ionic species distribution and the main precursor of atmospheric acidity neutralization (Neutralization Factor = 6.63). PMF analysis resulted in four factors, which demonstrated the influence of anthropogenic and natural sources, such as fertilizer application and production, marine intrusion/biomass burning, and biogenic emissions, and revealed the importance of atmospheric neutralization processes.
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Guha, A., D. R. Gentner, R. J. Weber, R. Provencal, and A. H. Goldstein. "Source apportionment of methane and nitrous oxide in California's San Joaquin Valley at CalNex 2010 via positive matrix factorization." Atmospheric Chemistry and Physics 15, no. 20 (October 29, 2015): 12043–63. http://dx.doi.org/10.5194/acp-15-12043-2015.
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Abstract. Sources of methane (CH4) and nitrous oxide (N2O) were investigated using measurements from a site in southeast Bakersfield as part of the CalNex (California at the Nexus of Air Quality and Climate Change) experiment from mid-May to the end of June 2010. Typical daily minimum mixing ratios of CH4 and N2O were higher than daily minima that were simultaneously observed at a mid-oceanic background station (NOAA, Mauna Loa) by approximately 70 ppb and 0.5 ppb, respectively. Substantial enhancements of CH4 and N2O (hourly averages > 500 and > 7 ppb, respectively) were routinely observed, suggesting the presence of large regional sources. Collocated measurements of carbon monoxide (CO) and a range of volatile organic compounds (VOCs) (e.g., straight-chain and branched alkanes, cycloalkanes, chlorinated alkanes, aromatics, alcohols, isoprene, terpenes and ketones) were used with a positive matrix factorization (PMF) source apportionment method to estimate the contribution of regional sources to observed enhancements of CH4 and N2O. The PMF technique provided a "top-down" deconstruction of ambient gas-phase observations into broad source categories, yielding a seven-factor solution. We identified these emission source factors as follows: evaporative and fugitive; motor vehicles; livestock and dairy; agricultural and soil management; daytime light and temperature driven; non-vehicular urban; and nighttime terpene biogenics and anthropogenics. The dairy and livestock factor accounted for the majority of the CH4 (70–90 %) enhancements during the duration of experiments. The dairy and livestock factor was also a principal contributor to the daily enhancements of N2O (60–70 %). Agriculture and soil management accounted for ~ 20–25 % of N2O enhancements over a 24 h cycle, which is not surprising given that organic and synthetic fertilizers are known to be a major source of N2O. The N2O attribution to the agriculture and soil management factor had a high uncertainty in the conducted bootstrapping analysis. This is most likely due to an asynchronous pattern of soil-mediated N2O emissions from fertilizer usage and collocated biogenic emissions from crops from the surrounding agricultural operations that is difficult to apportion statistically when using PMF. The evaporative/fugitive source profile, which resembled a mix of petroleum operation and non-tailpipe evaporative gasoline sources, did not include a PMF resolved-CH4 contribution that was significant (< 2 %) compared to the uncertainty in the livestock-associated CH4 emissions. The uncertainty of the CH4 estimates in this source factor, derived from the bootstrapping analysis, is consistent with the ~ 3 % contribution of fugitive oil and gas emissions to the statewide CH4 inventory. The vehicle emission source factor broadly matched VOC profiles of on-road exhaust sources. This source factor had no statistically significant detected contribution to the N2O signals (confidence interval of 3 % of livestock N2O enhancements) and negligible CH4 (confidence interval of 4 % of livestock CH4 enhancements) in the presence of a dominant dairy and livestock factor. The CalNex PMF study provides a measurement-based assessment of the state CH4 and N2O inventories for the southern San Joaquin Valley (SJV). The state inventory attributes ~ 18 % of total N2O emissions to the transportation sector. Our PMF analysis directly contradicts the state inventory and demonstrates there were no discernible N2O emissions from the transportation sector in the southern SJV region.
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Han, J. S., K. J. Moon, S. J. Lee, Y. J. Kim, S. Y. Ryu, S. S. Cliff, and S. M. Yi. "Size-resolved source apportionment of ambient particles by positive matrix factorization at Gosan background site in East Asia." Atmospheric Chemistry and Physics 6, no. 1 (January 27, 2006): 211–23. http://dx.doi.org/10.5194/acp-6-211-2006.
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Abstract. Size- and time-resolved aerosol samples were collected using an eight-stage Davis rotating unit for monitoring (DRUM) sampler from 29 March to 29 May in 2002 at Gosan, Jeju Island, Korea, which is one of the representative background sites in East Asia. These samples were analyzed using synchrotron X-ray fluorescence for 3-h average concentrations of 19 elements consisting of S, Si, Al, Fe, Ca, Cl, Cu, Zn, Ti, K, Mn, Pb, Ni, V, Se, As, Rb, Cr, Br. The size-resolved data sets were then analyzed using the positive matrix factorization (PMF) technique in order to identify possible sources and estimate their contribution to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Fifteen sources were resolved in eight size ranges (0.07~12 μm) and included continental soil, local soil, sea salt, biomass/biofuel burning, coal combustion, oil heating furnace, residual oil fired boiler, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, copper smelter and volcanic emission. PMF analysis of size-resolved source contributions showed that natural sources represented by local soil, sea salt and continental soil contributed about 79% to the predicted primary particulate matter (PM) mass in the coarse size range (1.15~12 μm). On the other hand, anthropogenic sources such as coal combustion and biomass/biofuel burning contributed about 60% in the fine size range (0.56~2.5 μm). The diesel vehicle source contributed the most in the ultra-fine size range (0.07~0.56 μm) and was responsible for about 52% of the primary PM mass.
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Craven, J. S., L. D. Yee, N. L. Ng, M. R. Canagaratna, C. L. Loza, K. A. Schilling, R. L. N. Yatavelli, et al. "Analysis of secondary organic aerosol formation and aging using positive matrix factorization of high-resolution aerosol mass spectra: application to the dodecane low-NO<sub>x</sub> system." Atmospheric Chemistry and Physics 12, no. 24 (December 17, 2012): 11795–817. http://dx.doi.org/10.5194/acp-12-11795-2012.
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Abstract. Positive matrix factorization (PMF) of high-resolution laboratory chamber aerosol mass spectra is applied for the first time, the results of which are consistent with molecular level MOVI-HRToF-CIMS aerosol-phase and CIMS gas-phase measurements. Secondary organic aerosol was generated by photooxidation of dodecane under low-NOx conditions in the Caltech environmental chamber. The PMF results exhibit three factors representing a combination of gas-particle partitioning, chemical conversion in the aerosol, and wall deposition. The slope of the measured high-resolution aerosol mass spectrometer (HR-ToF-AMS) composition data on a Van Krevelen diagram is consistent with that of other low-NOx alkane systems in the same O : C range. Elemental analysis of the PMF factor mass spectral profiles elucidates the combinations of functionality that contribute to the slope on the Van Krevelen diagram.
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Weber, Samuël, Dalia Salameh, Alexandre Albinet, Laurent Y. Alleman, Antoine Waked, Jean-Luc Besombes, Véronique Jacob, et al. "Comparison of PM10 Sources Profiles at 15 French Sites Using a Harmonized Constrained Positive Matrix Factorization Approach." Atmosphere 10, no. 6 (June 4, 2019): 310. http://dx.doi.org/10.3390/atmos10060310.
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Receptor-oriented models, including positive matrix factorization (PMF) analyses, are now commonly used to elaborate and/or evaluate action plans to improve air quality. In this context, the SOURCES project has been set-up to gather and investigate in a harmonized way 15 datasets of chemical compounds from PM10 collected for PMF studies during a five-year period (2012–2016) in France. The present paper aims at giving an overview of the results obtained within this project, notably illustrating the behavior of key primary sources as well as focusing on their statistical robustness and representativeness. Overall, wood burning for residential heating as well as road transport were confirmed to be the two main primary sources strongly influencing PM10 loadings across the country. While wood burning profiles, as well as those dominated by secondary inorganic aerosols, present a rather good homogeneity among the sites investigated, some significant variabilities were observed for primary traffic factors, illustrating the need to better characterize the diversity of the various vehicle exhaust and non-exhaust emissions. Finally, natural sources, such as sea salts (widely observed in internal mixing with anthropogenic compounds), primary biogenic aerosols and/or terrigenous particles, were also found as non-negligible PM10 components at every investigated site.
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Li, Yan, Liping Mei, Shenglu Zhou, Zhenyi Jia, Junxiao Wang, Baojie Li, Chunhui Wang, and Shaohua Wu. "Analysis of Historical Sources of Heavy Metals in Lake Taihu Based on the Positive Matrix Factorization Model." International Journal of Environmental Research and Public Health 15, no. 7 (July 20, 2018): 1540. http://dx.doi.org/10.3390/ijerph15071540.
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Analysis of sediment grain sizes and heavy metal correlations in the western part of Lake Taihu shows that the grain size of the sediment is stable as a whole. With increasing depth, the grain size tends to decrease. Heavy metals such as Cr, Cd, Pd and Sr are strongly correlated and influence each other. Based on the positive matrix factorization (PMF) model, this study classified the origin of heavy metals in the sediments of western Lake Taihu into three major categories: Agricultural, industrial and geogenic. The contributions of the three heavy metal sources in each sample were analyzed and calculated. Overall, prior to the Chinese economic reform, the study area mainly practiced agriculture. The sources of heavy metals in the sediments were mostly of agricultural and geogenic origin, and remained relatively stable with contribution rates of 44.07 ± 11.84% (n = 30) and 35.67 ± 11.70% (n = 30), respectively. After the reform and opening up of China, as the economy experienced rapid development, industry and agriculture became the main sources of heavy metals in sediments, accounting for 56.99 ± 15.73% (n = 15) and 31.22 ± 14.31% (n = 15), respectively. The PMF model is convenient and efficient, and a good method to determine the origin of heavy metals in sediments.
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Sowlat, Mohammad Hossein, Sina Hasheminassab, and Constantinos Sioutas. "Source apportionment of ambient particle number concentrations in central Los Angeles using positive matrix factorization (PMF)." Atmospheric Chemistry and Physics 16, no. 8 (April 20, 2016): 4849–66. http://dx.doi.org/10.5194/acp-16-4849-2016.
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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 (with 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 %), with 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 with 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 policymakers to set targeted and more protective emission standards for PM.
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Jaeckels, Jeffrey M., Min-Suk Bae, and James J. Schauer. "Positive Matrix Factorization (PMF) Analysis of Molecular Marker Measurements to Quantify the Sources of Organic Aerosols." Environmental Science & Technology 41, no. 16 (August 2007): 5763–69. http://dx.doi.org/10.1021/es062536b.
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Zhang, YuanXun, Rebecca J. Sheesley, James J. Schauer, Michael Lewandowski, Mohammed Jaoui, John H. Offenberg, Tadeusz E. Kleindienst, and Edward O. Edney. "Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers." Atmospheric Environment 43, no. 34 (November 2009): 5567–74. http://dx.doi.org/10.1016/j.atmosenv.2009.02.047.
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Zivkovic, Marija, Milena Jovasevic-Stojanovic, Anka Cvetkovic, Rastko Jovanovic, and Dragan Manojlovic. "Characterisation of fine particulate matter level, content and sources of a kindergarden microenvironment in Belgrade city center." Thermal Science, no. 00 (2022): 220. http://dx.doi.org/10.2298/tsci220831220z.
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In the present work, we investigated polycyclic aromatic hydrocarbons (PAHs), metals and ions of indoor and outdoor PM2.5 from 80 samples collected in the microenvironment of a kindergarten located in Belgrade city center during weekdays, from March to May 2010. The daily concentrations of PM2.5 were much higher than WHO guidance daily value. Results show similar factor profiles identified by principal component analysis (PCA) and positive matrix factorization (PMF). For indoor and outdoor environment, six principal components (PCs) were identified by PCA, and six and seven factors were identified by PMF, respectively. PCs from PCA were attributed to the following sources: combustion processes, traffic emission, coal/oil combustion, mix (stationary sources/resuspension), road salt and secondary aerosol. The resulting factors from PMF were identified as representing combustion processes, traffic emission, coal/oil combustion, soil dust, secondary aerosol and break wear. For outdoor environment, PMF identified one more source, attributed to road dust.
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Bhandari, Sahil, Zainab Arub, Gazala Habib, Joshua S. Apte, and Lea Hildebrandt Ruiz. "Source apportionment resolved by time of day for improved deconvolution of primary source contributions to air pollution." Atmospheric Measurement Techniques 15, no. 20 (October 21, 2022): 6051–74. http://dx.doi.org/10.5194/amt-15-6051-2022.
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Abstract. Present methodologies for source apportionment assume fixed source profiles. Since meteorology and human activity patterns change seasonally and diurnally, application of source apportionment techniques to shorter rather than longer time periods generates more representative mass spectra. Here, we present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements in this approach over traditional PMF. We report on source apportionment conducted on four example time periods in two seasons (winter and monsoon seasons of 2017), using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. Compared to the traditional seasonal PMF approach, we extract a larger number of factors as well as PMF factors that represent the expected sources of primary organic aerosol using time-of-day PMF. By capturing diurnal time series patterns of sources at a low computational cost, time-of-day PMF can utilize large datasets collected using long-term monitoring and improve the characterization of sources of organic aerosol compared to traditional PMF approaches that do not resolve by time of day.
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Hristova, Elena, Blagorodka Veleva, Emilia Georgieva, and Hristomir Branzov. "Application of Positive Matrix Factorization Receptor Model for Source Identification of PM10 in the City of Sofia, Bulgaria." Atmosphere 11, no. 9 (August 23, 2020): 890. http://dx.doi.org/10.3390/atmos11090890.
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The Positive Matrix Factorization (PMF) receptor model is used for identification of source contributions to PM10 sampled during the period January 2019–January 2020 in Sofia. More than 200 filters were analyzed by X-Ray Fluorescence (XRF), Inductively Coupled Plasma Mass Spectrometry (ICP-MS), and Ion chromatography for chemical elements and soluble ions. Seasonal patterns of PM10 mass and elements’ concentration are observed with minimum in the summer months and maximum in the cold period. The results from source apportionment (SAP) study showed that the resuspension factor is the main contributor to the total PM10 mass (25%), followed by Biomass burning (BB) (23%), Mixed SO42− (19%), Sec (16%), Traffic (TR) (9%), Industry (IND) (4%), Nitrate rich (4%), and Fuel oil burning (FUEL) (0.4%) in Sofia. There are some similarities in relative contribution of the main factors compared to the years 2012–2013. The differences are in identification of the new factor described as mixed sulphate as well as the decrease of the FUEL factor. The results of comparing SAP with EPA PMF 5.0 and chemical transport models (CTM), given by Copernicus Atmosphere Monitoring Service, are presented and discussed for the first time for Bulgaria.
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Traore, Alassane, Moustapha Kebe, Malick Sow, Vasiliki Vasilatou, and Ababacar Sadikhe Ndao. "Comparative Receptor Models Using Principal Component Analysis/Absolute Principal Component Scores and Positive Matrix Factorization to Assess Source Apportionment of PM2.5-10 and PM2.5 in Urban Cities." Indian Journal Of Science And Technology 17, no. 9 (February 27, 2024): 780–86. http://dx.doi.org/10.17485/ijst/v17i9.2969.
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b Source apportionment of PM2.5-10 and PM2.5 was conducted using two receptor models: the principal component analysis/absolute principal component scores (PCA/APCS) and the positive matrix factorization (PMF). Methods: The PCA/APCS model resolved four sources, namely, mineral dust, sea salt/secondary sulfur, a mixed source, and traffic and industry emissions. PMF model also resolved four sources of the same origin based on the proportion of each component from the analysis. All models identified the main sources that contribute to PM2.5-10 and PM2.5 emissions and reconfirmed that the potential sources were the dominants in particulate matter in the Capital city. Findings: The first four extracted component accounted for nearly 88.3% of the variability of the data set. The running matrix elements used for data processing within collected air particulate matter was BC, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Rb, Sr, Ba, and Pb. This discrimination of the area of Dakar cites according to the PCA/APCS results confirms the impact that the major activities have on the environment in the most appropriate way, and it verifies the results of previous studies about the influence of Industry emissions, mineral dust, traffic emissions and sea salt/secondary sulfur on the surrounding urban areas. Novelty: Continued collection of speciation data at the urban areas will enhance the understanding of local versus regional source contributions for air quality index in sub-Sahara region. Keywords: Aerosol, Particulate Matter, Source apportionment, receptor Modelling, XRF, heavy metals
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Ahmad, Eka Fithriani, and Muhayatun Santoso. "Analisis Karaterisasi Konsentrasi dan Komposisi Partikulat Udara (Studi Case : Surabaya)." Jurnal Kimia VALENSI 2, no. 2 (December 1, 2016): 97–103. http://dx.doi.org/10.15408/jkv.v2i2.3602.
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Abstrak Pencemaran udara merupakan dampak yang sangat merugikan, tidak hanya bagi manusia tetapi juga akan berdampak buruk bagi ekosistem hewan dan tumbuhan. Pada penelitian ini akan mengkaji pencemaran udara dari Oktober 2012 hingga Februari 2014 melalui penelitian konsentrasi dan komposisi dari partikulat udara dengan ukuran PM 2.5. Penelitian ini bertujuan untuk menentuan sumber asal pencemaran di Surabaya sehingga dapat dijadikan referensi berbasis ilmiah sebagai langkah untuk membuat keputusan dan kebijakan yang tepat dalam menanggulangi dampak pencemaran. Metode pengolahan data dalam penelitian ini adalah dengan menggunakan analisis reseptor modeling yaitu Positif Matrix Factorization (PMF) untuk mengetahui sumber asal pencemaran. Hasil pengukuran yang diperoleh pada konsentrasi PM 2,5 adalah 15.05 μg/m3 sehingga telah melebihi baku mutu tahunan yang telah ditetapkan PP 41 tahun 1999, USEPA, maupun WHO. Dalam partikulat terdapat konsentrasi black carbon (BC) sebesar 3.20 μg/m3 dan unsur Pb dengan konsentrasi 0.28 μg/m3 yang telah melebihi nilai baku mutu USEPA. Sedangkan hasil analisis reseptor modeling di dapatkan sumber asal polutan berasal dari biomass, vehicle, soil, industri Pb, industri Zn dan indutri Fe. Kata kunci: Partikulat mater 2.5, black carbon, Pb, positive matrix factorization, Surabaya Abstract Air pollution is a very adverse impact, not only for humans but also the ecosystem of plants and animals. This research examine air pollution from October 2012 until February 2014 through the research of concentration and composition of airborne particulates with a size of PM 2.5 μm. This study aims to determine the origin and location of pollution sources in Surabaya so that it can be used as scientific reference as a step to make the right decisions and policies in tackling the impact of pollution. Data processing method in this research used analysis of receptor modeling that is Positive Matrix Factorization (PMF) to determine the source of the pollution. Results obtained at a concentration of PM 2.5 was 15.05 μg/m3 so PM 2.5 has exceeded the quality standard yearly, based on PP 41 1999, USEPA and WHO. There are 3.20 μg/m3 concentration of black carbon (BC), element Pb in particulate matter with a concentration of 0.28 μg/m3 which has exceeded the value of the quality standard USEPA. The source of the pollutants come from biomass, vehicle, soil, industrial Pb, Zn and industries Fe industry. Keywords: Particulate matter 2.5, black carbon, Pb, positive matrix factorization, Surabaya DOI: http://dx.doi.org/10.15408/jkv.v0i0.3602
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Semenov, Mikhail Y., Irina I. Marinaite, Liudmila P. Golobokova, Yuri M. Semenov, and Tamara V. Khodzher. "Revealing the Chemical Profiles of Airborne Particulate Matter Sources in Lake Baikal Area: A Combination of Three Techniques." Sustainability 14, no. 10 (May 19, 2022): 6170. http://dx.doi.org/10.3390/su14106170.
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Positive matrix factorization (PMF) is a widely used multivariate source apportionment technique. However, PMF-derived source profiles are never compared to real ones because of the absence of data on the chemical composition of source emissions. The aim of this study was to verify the validity of PMF-derived source profiles using the diagnostic ratios (DR) method and end-member mixing analysis (EMMA). The composition of polycyclic aromatic hydrocarbons (PAHs) in particulate matter (PM) sampled in the air above Lake Baikal in summer and the composition of inorganic elements (IE) in PM accumulated in Lake Baikal snowpack were used as study objects. Five PAH sources and five IE sources were identified using PMF. Eight PAHs and six IEs selected from PMF-derived source profiles were recognized as eligible for calculating the DRs (species 1/(species 1 + species 2)) suitable for testing PMF results using EMMA. EMMA was based on determining whether most samples in mixing diagrams that use DR values as coordinates of source points could be bound by a geometrical shape whose vertices are pollution sources. It was found that the four PAH sources and four IE sources obtained using PMF were also identified using EMMA. Thus, the validity of the most of PMF-derived source profiles was proved.