scholarly journals Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

2021 ◽  
Vol 21 (13) ◽  
pp. 10081-10109
Author(s):  
Liine Heikkinen ◽  
Mikko Äijälä ◽  
Kaspar R. Daellenbach ◽  
Gang Chen ◽  
Olga Garmash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Boreal Forest ◽  
Mass Fraction ◽  
Mass Concentration ◽  
Mass Spectra ◽  
Organic Aerosol ◽  
Organic Mass ◽  
Data Set ◽  
Wind Speeds

Abstract. The Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II, located within the boreal forest of Finland, is a unique station in the world due to the wide range of long-term measurements tracking the Earth–atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date. Similarly to other previously reported efforts in OA source apportionment from multi-seasonal or multi-annual data sets, we approached the OA characterization challenge through positive matrix factorization (PMF) using a rolling window approach. However, the existing methods for extracting minor OA components were found to be insufficient for our rather remote site. To overcome this issue, we tested a new statistical analysis framework. This included unsupervised feature extraction and classification stages to explore a large number of unconstrained PMF runs conducted on the measured OA mass spectra. Anchored by these results, we finally constructed a relaxed chemical mass balance (CMB) run that resolved different OA components from our observations. The presented combination of statistical tools provided a data-driven analysis methodology, which in our case achieved robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, and 2.6 µg m−3) into three sub-categories – low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA) – proving that the tested methodology was able to provide results consistent with literature. LV-OOA was the most dominant OA type (organic mass fraction IQR: 49 %, 62 %, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19 %, 31 %, and 43 %). Due to SV-OOA's clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. Two nearby sawmills also played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2 %, 6 %, and 13 %). Notably, the quantification of POA at SMEAR II using ACSM data was not possible following existing rolling PMF methodologies. Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. Similar wind direction and speed dependence was not observed among other OA types. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In the event of slower wind speeds, POA likely evaporated and/or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements 2 to 4 years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that short-term (POA) pollution plumes were averaged out due to the slow time resolution of the ACSM combined with the further 3 h data averaging needed to ensure good signal-to-noise ratios (SNRs). Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the 8-year measurement period. Due to the unique and realistic seasonal cycles and meteorology dependences of the independent OA subtypes complemented by the reasonably low degree of unexplained OA variability, we believe that the presented data analysis approach performs well. Therefore, we hope that these results encourage also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine-learning approaches. This way the presented method could be further optimized and its usability explored and evaluated also in other environments.

scholarly journals Eight years of sub-micrometre organic aerosol composition data from the boreal forest characterized using a machine-learning approach

2020 ◽  
Author(s):  
Liine Heikkinen ◽  
Mikko Äijälä ◽  
Kaspar R. Daellenbach ◽  
Gang Chen ◽  
Olga Garmash ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Data Analysis ◽  
Mass Fraction ◽  
Mass Concentration ◽  
Organic Aerosol ◽  
Organic Mass ◽  
Data Set ◽  
Wind Speeds

Abstract. The Station for Measuring Ecosystem Atmosphere Relations (SMEAR) II is a unique station in the world due to the wide range of long-term measurements tracking the Earth-atmosphere interface. In this study, we characterize the composition of organic aerosol (OA) at SMEAR II by quantifying its driving constituents. We utilize a multi-year data set of OA mass spectra measured in situ with an Aerosol Chemical Speciation Monitor (ACSM) at the station. To our knowledge, this mass spectral time series is the longest of its kind published to date, and its detailed analysis required development of a new methodology. To this purpose, we developed an efficient and robust data analysis framework utilizing machine learning tools. These included unsupervised feature extraction and classification stages to manage and process the large amounts of data. The extensive chemometric analysis was conducted with a combination of Positive Matrix Factorization (PMF), rolling window analysis, bootstrapping, K-Means clustering, data weighting and diagnostics based algorithmic choice-making, among others. This combination of statistical tools provided a data driven analysis methodology to achieve robust solutions with minimal subjectivity. Following the extensive statistical analyses, we were able to divide the 2012–2019 SMEAR II OA data (mass concentration interquartile range (IQR): 0.7, 1.3, 2.6 µg m−3) to three sub-categories: low-volatility oxygenated OA (LV-OOA), semi-volatile oxygenated OA (SV-OOA), and primary OA (POA). LV-OOA was the most dominant OA type (organic mass fraction IQR: 49, 62, and 73 %). The seasonal cycle of LV-OOA was bimodal, with peaks both in summer and in February. We associated the wintertime LV-OOA with anthropogenic sources and assumed biogenic influence in LV-OOA formation in summer. Through a brief trajectory analysis, we estimated summertime natural LV-OOA formation of tens of ng m−3 h−1 over the boreal forest. SV-OOA was the second highest contributor to OA mass (organic mass fraction IQR: 19, 31, and 43 %). Due to SV-OOA’s clear peak in summer, we estimate biogenic processes as the main drivers in its formation. Unlike for LV-OOA, the highest SV-OOA concentrations were detected in stable summertime nocturnal surface layers. However, also the nearby sawmills likely played a significant role in SV-OOA production as also exemplified by previous studies at SMEAR II. POA, taken as a mix of two different OA types reported previously, hydrocarbon-like OA (HOA) and biomass burning OA (BBOA), made up a minimal OA mass fraction (IQR: 2, 6, and 13 %). Both POA organic mass fraction and mass concentration peaked in winter. Its appearance at SMEAR II was linked to strong southerly winds. The high wind speeds probably enabled the POA transport to SMEAR II from faraway sources in a relatively fresh state. In case of slower wind speeds, POA likely evaporated or aged into oxidized organic aerosol before detection. The POA organic mass fraction was significantly lower than reported by aerosol mass spectrometer (AMS) measurements two to four years prior to the ACSM measurements. While the co-located long-term measurements of black carbon supported the hypothesis of higher POA loadings prior to year 2012, it is also possible that ACSM was less efficiently capturing short term (POA) pollution plumes. Despite the length of the ACSM data set, we did not focus on quantifying long-term trends of POA (nor other components) due to the high sensitivity of OA composition to meteorological anomalies, the occurrence of which is likely not normally distributed over the eight year measurement period. We hope that our successfully applied methodology encourages also other researchers possessing several-year-long time series of similar data to tackle the data analysis via similar semi- or unsupervised machine learning approaches. This way aerosol chemometric analysis procedures would be further developed into yet more streamlined and autonomous directions.

scholarly journals Dependence of SOA oxidation on organic aerosol mass concentration and OH exposure: experimental PAM chamber studies

2011 ◽  
Vol 11 (4) ◽  
pp. 1837-1852 ◽  
Author(s):  
E. Kang ◽  
D. W. Toohey ◽  
W. H. Brune
Keyword(s):  
Mass Concentration ◽  
Mass Spectra ◽  
Organic Aerosol ◽  
Organic Mass ◽  
Spectra Analysis ◽  
Aerosol Mass ◽  
Aerosol Mass Concentration ◽  
Degree Of Oxidation ◽  
Mass Fractions ◽  
Chamber Studies

Abstract. The oxidation of secondary organic aerosol (SOA) is studied with mass spectra analysis of SOA formed in a Potential Aerosol Mass (PAM) chamber, a small flow-through photo-oxidation chamber with extremely high OH and ozone levels. The OH exposure from a few minutes in the PAM chamber is similar to that from days to weeks in the atmosphere. The mass spectra were measured with a Quadrupole Aerosol Mass Spectrometer (Q-AMS) for SOA formed from oxidation of α-pinene, m-xylene, p-xylene, and a mixture of the three. The organic mass fractions of m/z 44 (CO2+) and m/z 43 (mainly C2H3O+), named f44 and f43 respectively, are used as indicators of the degree of organic aerosol (OA) oxidation that occurs as the OA mass concentration or the OH exposure are varied. The degree of oxidation is sensitive to both. For a fixed OH exposure, the degree of oxidation initially decreases rapidly and then more slowly as the OA mass concentration increases. For fixed initial precursor VOC amounts, the degree of oxidation increases linearly with OH exposure, with f44 increasing and f43 decreasing. In this study, the degree of SOA oxidation spans much of the range observed in the atmosphere. These results, while sensitive to the determination of f44 and f43, provide evidence that some characteristics of atmospheric OA oxidation can be generated in a PAM chamber. For all measurements in this study, the sum of f44 and f43 is 0.25 ± 0.03, so that the slope of a linear regression is approximately −1 on an f44 vs. f43 plot. This constancy of the sum suggests that these ions are complete proxies for organic mass in the OA studied.

scholarly journals Dependence of SOA oxidation on organic aerosol mass concentration and OH exposure: experimental PAM chamber studies

2010 ◽  
Vol 10 (10) ◽  
pp. 24053-24089
Author(s):  
E. Kang ◽  
D. W. Toohey ◽  
W. H. Brune
Keyword(s):  
Mass Concentration ◽  
Mass Spectra ◽  
Organic Aerosol ◽  
Organic Mass ◽  
Spectra Analysis ◽  
Aerosol Mass ◽  
Aerosol Mass Concentration ◽  
Degree Of Oxidation ◽  
Small Flow ◽  
Chamber Studies

Abstract. The oxidation of secondary organic aerosol (SOA) is studied with mass spectra analysis of SOA formed in a Potential Aerosol Mass (PAM) chamber, a small flow-through photo-oxidation chamber with extremely high OH and ozone levels. Oxidation for a few minutes in the PAM chamber is equivalent to days to weeks in the atmosphere. The mass spectra were measured with a Quadrupole Aerosol Mass Spectrometer (Q-AMS) for SOA formed from oxidation of α-pinene, m-xylene, p-xylene, and a mixture of the three. The organic mass fraction of m/z 44 (CO2+) and m/z 43 (mainly C2H3O+), named f44 and f43, respectively, are used as indicators of the degree of organic aerosol (OA) oxidation that occurs as the OA mass concentration or the OH exposure are varied. The degree of oxidation is sensitive to both. For a fixed OH exposure, the degree of oxidation initially decreases rapidly and then more slowly as the OA mass concentration increases. For fixed initial precursor VOC amounts, the degree of oxidation increases linearly with OH exposure, with linear f44 increase and f43 decrease. The degree of oxidation seen in this study is similar to that seen in large environmental chambers for the least oxidized OA and similar to the atmosphere for the most oxidized OA. These results, while sensitive to the determination of f44 and f43, provide evidence that characteristics of atmospheric OA oxidation can be generated in a PAM chamber. For all measurements in this study, the sum of f44 and f43 is 0.25± 0.03, so that the slope of a linear regression is approximately −1 on an f44 vs. f43 plot. This constancy of the sum suggests that these ions are complete proxies for organic mass in the OA studied.

Machine learning model for feature recognition of sports competition based on improved TLD algorithm

2020 ◽  
pp. 1-12
Author(s):  
Qinglong Ding ◽  
Zhenfeng Ding
Keyword(s):  
Machine Learning ◽  
Feature Recognition ◽  
Experimental Results ◽  
Pedestrian Tracking ◽  
Data Set ◽  
Recognition Model ◽  
Standard Data ◽  
Machine Learning Model ◽  
Environmental Background

Sports competition characteristics play an important role in judging the fairness of the game and improving the skills of the athletes. At present, the feature recognition of sports competition is affected by the environmental background, which causes problems in feature recognition. In order to improve the effect of feature recognition of sports competition, this study improves the TLD algorithm, and uses machine learning to build a feature recognition model of sports competition based on the improved TLD algorithm. Moreover, this study applies the TLD algorithm to the long-term pedestrian tracking of PTZ cameras. In view of the shortcomings of the TLD algorithm, this study improves the TLD algorithm. In addition, the improved TLD algorithm is experimentally analyzed on a standard data set, and the improved TLD algorithm is experimentally verified. Finally, the experimental results are visually represented by mathematical statistics methods. The research shows that the method proposed by this paper has certain effects.

scholarly journals Combined effects of boundary layer dynamics and atmospheric chemistry on aerosol composition during new particle formation periods

2018 ◽  
Author(s):  
Liqing Hao ◽  
Olga Garmash ◽  
Mikael Ehn ◽  
Pasi Miettinen ◽  
Paola Massoli ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Chemical Composition ◽  
Atmospheric Chemistry ◽  
Mass Fraction ◽  
Organic Aerosols ◽  
Organic Aerosol ◽  
Sulfate Aerosol ◽  
Organic Mass ◽  
Night Time ◽  
Aerosol Mass

Abstract. Characterizing aerosol chemical composition in response to meteorological changes and atmospheric chemistry is important to gain insights into new particle formation mechanisms. A BAECC (Biogenic Aerosols-Effects on Clouds and Climate) campaign was conducted during the spring 2014 at SMEAR II station (Station for Measuring Forest Ecosystem-Aerosol Relations) in Finland. The particles were characterized by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). A PBL (planetary boundary layer) dilution model was developed to assist interpreting the measurement results. Right before nucleation events, the mass concentrations of organic and sulfate aerosol species were both decreased rapidly along with the growth of PBL heights. However, the mass fraction of sulfate aerosol of the total aerosol mass was increased, in contrast to a decrease for the organic mass fraction. Meanwhile, an increase of LVOOA (low-volatility oxygenated organic aerosol) mass fraction of the total organic mass was observed, in distinct comparison to a reduction of SVOOA (semi-volatile OOA) mass fraction. Our results demonstrate that, at the beginning of nucleation events, the observed sulfate aerosol mass was mainly driven by vertical turbulent mixing of sulfate-rich aerosols between the residual layer and the newly formed boundary layer, while the condensation of sulfuric acid played a minor role in interpreting the measured sulfate mass concentration. For the measured organic aerosols, their temporal profiles were mainly driven by dilution from PBL development, organic aerosol mixing in different boundary layers and/or condensation of organic vapors, but accurate measurements of organic vapor concentrations and characterization on the spatial aerosol chemical composition are required. In general, the observed aerosol particles by AMS are subjected to joint effects of PBL dilution, atmospheric chemistry and aerosol mixing in different boundary layers. During aerosol growth periods in the night time, the mass concentrations of organic aerosols and organic nitrate aerosols were both increased. The increase of SVOOA mass correlated well with the calculated increase of condensed HOMs (highly oxygenated organic molecules) mass. To our knowledge, our results are the first atmospheric observations showing a connection between increase in SVOOA and condensed HOMs during the night time.

scholarly journals Enhancing non-refractory aerosol apportionment from an urban industrial site through receptor modeling of complete high time-resolution aerosol mass spectra

2014 ◽  
Vol 14 (15) ◽  
pp. 8017-8042 ◽  
Author(s):  
M. L. McGuire ◽  
R. Y.-W. Chang ◽  
J. G. Slowik ◽  
C.-H. Jeong ◽  
R. M. Healy ◽  
...  
Keyword(s):  
Mass Spectrum ◽  
Mass Spectra ◽  
Organic Aerosol ◽  
Mass Resolution ◽  
Receptor Modeling ◽  
Unit Mass ◽  
Organic Mass ◽  
Aerosol Mass ◽  
Inorganic Species ◽  
Unit Mass Resolution

Abstract. Receptor modeling was performed on quadrupole unit mass resolution aerosol mass spectrometer (Q-AMS) sub-micron particulate matter (PM) chemical speciation measurements from Windsor, Ontario, an industrial city situated across the Detroit River from Detroit, Michigan. Aerosol and trace gas measurements were collected on board Environment Canada's Canadian Regional and Urban Investigation System for Environmental Research (CRUISER) mobile laboratory. Positive matrix factorization (PMF) was performed on the AMS full particle-phase mass spectrum (PMFFull MS) encompassing both organic and inorganic components. This approach compared to the more common method of analyzing only the organic mass spectra (PMFOrg MS). PMF of the full mass spectrum revealed that variability in the non-refractory sub-micron aerosol concentration and composition was best explained by six factors: an amine-containing factor (Amine); an ammonium sulfate- and oxygenated organic aerosol-containing factor (Sulfate-OA); an ammonium nitrate- and oxygenated organic aerosol-containing factor (Nitrate-OA); an ammonium chloride-containing factor (Chloride); a hydrocarbon-like organic aerosol (HOA) factor; and a moderately oxygenated organic aerosol factor (OOA). PMF of the organic mass spectrum revealed three factors of similar composition to some of those revealed through PMFFull MS: Amine, HOA and OOA. Including both the inorganic and organic mass proved to be a beneficial approach to analyzing the unit mass resolution AMS data for several reasons. First, it provided a method for potentially calculating more accurate sub-micron PM mass concentrations, particularly when unusual factors are present, in this case the Amine factor. As this method does not rely on a priori knowledge of chemical species, it circumvents the need for any adjustments to the traditional AMS species fragmentation patterns to account for atypical species, and can thus lead to more complete factor profiles. It is expected that this method would be even more useful for HR–ToF–AMS data, due to the ability to understand better the chemical nature of atypical factors from high-resolution mass spectra. Second, utilizing PMF to extract factors containing inorganic species allowed for the determination of the extent of neutralization, which could have implications for aerosol parameterization. Third, subtler differences in organic aerosol components were resolved through the incorporation of inorganic mass into the PMF matrix. The additional temporal features provided by the inorganic aerosol components allowed for the resolution of more types of oxygenated organic aerosol than could be reliably resolved from PMF of organics alone. Comparison of findings from the PMFFull MS and PMFOrg MS methods showed that for the Windsor airshed, the PMFFull MS method enabled additional conclusions to be drawn in terms of aerosol sources and chemical processes. While performing PMFOrg MS can provide important distinctions between types of organic aerosol, it is shown that including inorganic species in the PMF analysis can permit further apportionment of organics for unit mass resolution AMS mass spectra.

scholarly journals The link between organic aerosol mass loading and degree of oxygenation: an α-pinene photooxidation study

2013 ◽  
Vol 13 (13) ◽  
pp. 6493-6506 ◽  
Author(s):  
L. Pfaffenberger ◽  
P. Barmet ◽  
J. G. Slowik ◽  
A. P. Praplan ◽  
J. Dommen ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Organic Aerosol ◽  
Mass Loading ◽  
Smog Chamber ◽  
Organic Mass ◽  
Aerosol Composition ◽  
Ambient Aerosol ◽  
Aerosol Mass ◽  
Hygroscopic Properties ◽  
Mass Concentrations

Abstract. A series of smog chamber (SC) experiments was conducted to identify factors responsible for the discrepancy between ambient and SC aerosol degree of oxygenation. An Aerodyne high-resolution time-of-flight aerosol mass spectrometer is used to compare mass spectra from α-pinene photooxidation with ambient aerosol. Composition is compared in terms of the fraction of particulate CO2+, a surrogate for carboxylic acids, vs. the fraction of C2H3O+, a surrogate for aldehydes, alcohols and ketones, as well as in the Van Krevelen space, where the evolution of the atomic hydrogen-to-carbon ratio (H : C) vs. the atomic oxygen-to-carbon ratio (O : C) is investigated. Low (near-ambient) organic mass concentrations were found to be necessary to obtain oxygenation levels similar to those of low-volatility oxygenated organic aerosol (LV-OOA) commonly identified in ambient measurements. The effects of organic mass loading and OH (hydroxyl radical) exposure were decoupled by inter-experiment comparisons at the same integrated OH concentration. An OH exposure between 3 and 25 × 107 cm−3 h is needed to increase O : C by 0.05 during aerosol aging. For the first time, LV-OOA-like aerosol from the abundant biogenic precursor α-pinene was produced in a smog chamber by oxidation at typical atmospheric OH concentrations. Significant correlation between measured secondary organic aerosol (SOA) and reference LV-OOA mass spectra is shown by Pearson's R2 values larger than 0.90 for experiments with low organic mass concentrations between 1.2 and 18 μg m−3 at an OH exposure of 4 × 107 cm−3 h, corresponding to about two days of oxidation time in the atmosphere, based on a global mean OH concentration of ~ 1 × 106 cm−3. α-Pinene SOA is more oxygenated at low organic mass loadings. Because the degree of oxygenation influences the chemical, volatility and hygroscopic properties of ambient aerosol, smog chamber studies must be performed at near-ambient concentrations to accurately simulate ambient aerosol properties.

scholarly journals Source Apportionment of Urban Particulate Matter using Hourly Resolved Trace Metals, Organics, and Inorganic Aerosol Components

2016 ◽  
Author(s):  
Cheol-Heon Jeong ◽  
Jon M. Wang ◽  
Greg J. Evans
Keyword(s):  
Trace Metals ◽  
Black Carbon ◽  
Biomass Burning ◽  
Mass Spectra ◽  
Road Dust ◽  
Organic Aerosol ◽  
High Time Resolution ◽  
Organic Mass ◽  
Related Factors ◽  
Inorganic Species

Abstract. Source apportionment analysis of hourly resolved particulate matter (PM) speciation data was performed using positive matrix factorization (PMF). The data were measured at an urban site in downtown Toronto, Canada during two campaign periods (April–July, 2013; November, 2013–February, 2014), and included trace metals, black carbon, and mass spectra for organic and inorganic species (PMFFull). The chemical composition was measured by collocated high time resolution instrumentation, including an Aerosol Chemical Speciation Monitor, an Xact metals monitor, and a seven-wavelength Aethalometer. Separate PMF analyses were conducted using the trace metal only data (PMFmetal) and organic mass spectra only (PMForg), and compared with the PMFFull results. Comparison of these three PMF analyses demonstrated that the full analysis offered many advantages in the apportionment of local and regional sources compared to using the organic or metals data individually. In combining the high time resolution data, this analysis enabled i) the quantification of metal-rich sources of PM2.5 (PM < 2.5 μm), ii) the resolution of more robust factor profiles and contributions, and iii) the identification of additional organic aerosol sources. Nine factors were identified through the PMFFull analysis: five local factors (i.e. Road Dust, Primary Vehicle Emissions, Tire Wear, Cooking, and Industrial Sector) and four regional factors (i.e. Biomass Burning, Oxidised Organics, Sulphate and Oxidised Organics, and Nitrate and Oxidised Organics). The majority of the metal emissions (83 %) and almost half of the black carbon (49 %) were associated with the three traffic-related factors which, on average, contributed a minority (17 %) of the overall PM2.5 mass. Strong seasonal patterns were observed for the traffic-related emissions: higher contributions of resuspended road dust in spring vs. a winter high for tire wear related emissions. Biomass Burning contributed the majority of the PM2.5 mass (52 %) in June and July due to a major forest fire event. Much of this mass was due to photochemical aging of the biomass burning aerosol. On average, industrially related factors contributed almost half (49 %) of the PM2.5; most of this mass was secondary aerosol species. Nitrate coupled with highly oxidised organics was the largest contributor, accounting for 30 % of PM2.5 on average, with higher levels in winter and at night. Including the temporal variabilities of inorganic ions and trace metals in the PMFFull analysis provided additional structure to subdivide the low volatility oxidised organic aerosol into three sources. Resuspended road dust was identified as a potential source of aged organic aerosol. The novelty of this study is the application of PMF receptor modeling to hourly resolved trace metals in conjunction with organic mass spectra, inorganic species, and black carbon for different seasons, and the comparison of separate PMF analyses applied to metals or organics alone. The inclusion of these different types of hourly data allowed more robust apportionment of PM sources, as compared to analysing organic or metals data individually.

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