Application Of Positive Matrix Factorization Research Articles

Source Identification of Potentially Toxic Metals in Plants of Alpine Ecosystems of Mt. Madra by Positive Matrix Factorization.

In this study, the concentrations of cadmium (Cd), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in plants sampled from Mt. Madra were investigated. Furthermore, the distribution characteristics and source identification of potentially toxic metals were investigated with the application of Positive Matrix Factorization (PMF) modelling. Samples of 26 different plant species were taken from Mt. Madra at elevations ranging from 177 to 1347m using the multi-point sampling approach. The metal quantities measured by ICP-OES are the following sequences (mean ± SD) (mg/kg): Fe (974.96 ± 29.6) > Mn (111.81 ± 2.6) > Zn (27.28 ± 0.2) > Ni (2.17 ± 0.03) > Pb (0.77 ± 0.01) > Cd (0.12 ± 0.01). According to the plant samples in which the highest values were determined, the metals are as follows: Cd (Lathyrus laxiflorus, 0.401mg/kg), Fe (Ajuga orientalis, 7621.207mg/kg), Mn (Castanea sativa, 724.927mg/kg), Ni (Prunella laciniata, 6.947mg/kg), Pb (Crataegus stevenii, 3.955mg/kg) and Zn (Prunella laciniata, 50.802mg/kg). The results of the PMF model showed that Cd had an atmospheric transport factor originated and transported from industrial activites, Ni had a substrate factor, Fe, Mn, Pb and Zn were influenced by different anthropogenic factors.

Sources apportionments of heavy metal(loid)s in the farmland soils close to industrial parks: Integrated application of positive matrix factorization (PMF) and cadmium isotopic fractionation

Understanding the source identification and distribution of heavy metal(loid)s in soil is essential for risk management. The sources of heavy metal(loid)s in farmland soil, especially in areas with rapid economic development, were complicated and need to be explored urgently. This study combined geographic information system (GIS) mapping, positive matrix factorization (PMF) model and cadmium (Cd) isotope fingerprinting methods to identify heavy metal(loid) sources in a typical town in the economically developed Yangtze River Delta region of China. Cd, As, Cu, Zn, Pb, Ni and Co in different samples were detected. The results showed that Cd was the most severely contaminated element, with an exceedance rate of 78.0 %. GIS mapping results indicated that the hotspot area was located in the northeastern area with prolonged operational histories of electroplating and non-ferrous metal smelting industries. The PMF model analysis also identified emissions from smelting and electroplating enterprises as the main sources of Cd in the soil, counted for 49.28 %, followed by traffic (25.66 %) and agricultural (25.06 %) sources. Through further isotopic analysis, it was found that in soil samples near the industrial park, the contribution of electroplating and non-ferrous metal smelting enterprises to cadmium pollution was significantly higher than other regions. The integrated use of various methodologies allows for precise analysis of sources and input pathways, offering valuable insights for future pollution control and soil remediation endeavors.

The application of positive matrix factorization with diagnostics to BIG DATA

Over the past decade, Positive Matrix Factorization (PMF) has become the most commonly used tool for source apportionment of air pollutants with a large fraction of those studies using the implementation developed by the U.S. Environmental Protection Agency (EPA-PMF) and its very useful diagnostic tools. During the same period, more studies are utilizing monitoring tools that provide hourly or even higher time resolution data leading to large data sets (millions of data points). Because of the nature of the graphic user interface in EPA-PMF, it cannot handle data sets more than approximately half a million data points. Thus, alternative approaches are needed to permit these larger data sets to be analyzed and still be able to provide the diagnostic outputs that permit better interpretations of the results. We have developed a protocol to use the multilinear engine (ME-2) that is the solver used in EPA-PMF to make such analyses. Here we report how to use this approach and present results for a representative data set of particle size distributions obtained in Rochester, New York between 2008 and 2019.

Application of Positive Matrix Factorization for Source Apportionment of Polycyclic Aromatic Hydrocarbons (PAH) in the Adriatic Sea, and the Evaluation of PAH-Related Carcinogenic Risks

Concentrations of polycyclic aromatic hydrocarbons (PAH) were studied in 36 sediment samples collected from 29 sites in the central Adriatic Sea. The total concentration of PAH showed high variability, ranging from 42.85 µg kg−1 in open sea sediments to 28,662 µg kg−1 in the sediments of Šibenik Bay. Unsubstituted, parent PAH were dominant PAH compounds in the entire area of Šibenik Bay and most of the stations in the coastal area. In Kaštela Bay, methyl PAH concentrations were higher than the concentrations of unsubstituted PAH, whereas in the sediments along the mid-Adriatic transect, similar concentrations of both PAH fractions were determined. PAH sources were investigated by applying the Positive Matrix Factorization software developed by the U.S. Environmental Protection Agency (USEPA). The results indicate the presence of four sources, two of which can be considered petrogenic and two pyrogenic. The contribution of the sources to the total concentration PAH at each station indicates the dominance of pyrogenic sources in Šibenik Bay and at most stations in the coastal area. In the sediments along the mid-Adriatic transversal, as well as in Kaštela Bay, PAHs originate mainly from petrogenic sources. The obtained results show that PMF can be a suitable tool for control of PAH pollution and thus, for the management of various activities in the Adriatic region. Seasonal differences between the contributions of sources in the Kaštela and Šibenik bays indicate that the contribution of traffic to the total concentration of PAH was higher during the tourist season. The carcinogenic risk of PAH determined by toxicity equivalent (TEQ) calculations showed an increased carcinogenic risk in the whole area of Šibenik Bay, regardless of the number of PAH compounds considered for the calculations. A low PAH-related carcinogenic risk was found for the rest of the study area.

An approach to quantify heavy metals and their source apportionment in coal mine soil: a study through PMF model.

Mining activities in the opencast coal mines contaminate the surrounding soil by releasing coal dust containing heavy metals (HMs). The objective of the present study was to quantify the concentration of HMs like Fe, Cu, Mn, Ni, Cr, Zn, and Co in soil on profile and distance basis in the vicinity of the coal mines. This research also proposed the synthesis application of positive matrix factorization (PMF) model for the quantitative assessment of pollution sources. The results showed that the soil was more affected due to the presence of Cr in mining areas., and the contamination factor (Cf)of Cr was high at the edge of coal mine. It was observed from the study thatCfof the HMs was decreased with the increase indistance from the mine edge. The application ofPMF model demonstratedthat the contributions of Zn (4.2%), Ni (16.8%), and Mn (100%) were maximum in the pollution. The study concluded that soil contamination is inexorable due to opencast coal mining activities, and it can be mitigated by developing a green belt or through the process of ecological restoration and phytoremediation.

Application of positive matrix factorization and pollutants tracing for identification of dust sources: A case study in Khuzestan, Iran

In this study for identification of internal and external origins of dust events in the southwest of Iran, for the first time, a comprehensive dust sampling was performed in nine regions of Khuzestan over the four seasons. The dust samples were analyzed using INAA nuclear technique. Factors obtained from applying the PMF Modeling indicated five kinds of pollutant sources which include 1) Sedimentary surface soil/dried bed of wetlands, 2) steel and metalworking industries, 3) refineries, 4) waste, and 5) solid fuel as well as oil fuel power plants. These identified sources were used as the tracers to identify the internal dust sources. Investigation of NASA AOT images and the synoptic data at the event dates showed that in the period of mid-autumn up to the early winter, dust events had external origins, that are mainly situated in Iraq and Saudi Arabia, while in the period of mid-summer to early autumn and mid-winter up to the early spring, the internal sources such as mud-salt zones or areas with fine sediments with evaporitic deposits and puffy grounds in the regions between Omidieh - Mahshahr, south, and southeast of Ahvaz, “Dasht-E-Azadegan,” and dried bed of Hoor-Al-Azim are more dominant.

Insights into Elemental Composition and Sources of Fine and Coarse Particulate Matter in Dense Traffic Areas in Toronto and Vancouver, Canada.

Traffic is a significant pollution source in cities and has caused various health and environmental concerns worldwide. Therefore, an improved understanding of traffic impacts on particle concentrations and their components could help mitigate air pollution. In this study, the characteristics and sources of trace elements in PM2.5 (fine), and PM10-2.5 (coarse), were investigated in dense traffic areas in Toronto and Vancouver, Canada, from 2015–2017. At nearby urban background sites, 24-h integrated PM samples were also concurrently collected. The PM2.5 and PM10-2.5 masses, and a number of elements (i.e., Fe, Ba, Cu, Sb, Zn, Cr), showed clear increases at each near-road site, related to the traffic emissions resulting from resuspension and/or abrasion sources. The trace elements showed a clear partitioning trend between PM2.5 and PM10-2.5, thus reflecting the origin of some of these elements. The application of positive matrix factorization (PMF) to the combined fine and coarse metal data (86 total), with 24 observations at each site, was used to determine the contribution of different sources to the total metal concentrations in fine and coarse PM. Four major sources were identified by the PMF model, including two traffic non-exhaust (crustal/road dust, brake/tire wear) sources, along with regional and local industrial sources. Source apportionment indicated that the resuspended crustal/road dust factor was the dominant contributor to the total coarse-bound trace element (i.e., Fe, Ti, Ba, Cu, Zn, Sb, Cr) concentrations produced by vehicular exhaust and non-exhaust traffic-related processes that have been deposited onto the surface. The second non-exhaust factor related to brake/tire wear abrasion accounted for a considerable portion of the fine and coarse elemental (i.e., Ba, Fe, Cu, Zn, Sb) mass at both near-road sites. Regional and local industry contributed mostly to the fine elemental (i.e., S, As, Se, Cd, Pb) concentrations. Overall, the results show that non-exhaust traffic-related processes were major contributors to the various redox-active metal species (i.e., Fe, Cu) in both PM fractions. In addition, a substantial proportion of these metals in PM2.5 was water-soluble, which is an important contributor to the formation of reactive oxygen species and, thus, may lead to oxidative damage to cells in the human body. It appears that controlling traffic non-exhaust-related metals emissions, in the absence of significant point sources in the area, could have a pronounced effect on the redox activity of PM, with broad implications for the protection of public health.

Reapportioning the sources of secondary components of PM2.5: A combined application of positive matrix factorization and isotopic evidence

Secondary particles account for a considerable proportion of fine particles (PM2.5) and reasonable reapportioning them to primary sources is critical for designing effective strategies for air quality improvement. This study developed a method which can reapportion secondary sources of PM2.5 solved by positive matrix factorization (PMF) to primary sources based on the isotopic signals of nitrate, ammonium and sulfate. Actual PM2.5 data in Beijing were used as a case study to assess the feasibility and capacity of this method. In the case, 20 chemical components were used to apportion PM2.5 sources and source contributions of nitrate were applied to reapportion secondary source to primary sources. The model performance was also estimated by radiocarbon measurement (14C) of organic (OC) and elemental (EC) carbons of eight samples. The PMF apportioned seven sources: the secondary source (36.1%), vehicle exhausts (18.7%), industrial sources (13.6%), biomass burning (11.4%), coal combustion (8.10%), construction dust (7.93%) and fuel oil combustion (4.24%). After the reapportionment of the secondary source, vehicle exhausts (28.7%) contributed the most to PM2.5, followed by biomass burning (25.1%) and industrial sources (18.9%). Fossil oil combustion and coal combustion increased to 8.00% and 11.4%, respectively, and construction dust contributed the least. The average gap between contributions of identified sources to OC and EC and the 14C measurements decreased 2.5 ± 1.2% after the reapportionment than 13.2 ± 10.8%, indicating the good performance of the developed method.

Assessing contributions of natural surface and anthropogenic emissions to atmospheric mercury in a fast-developing region of eastern China from 2015 to 2018

Abstract. Mercury (Hg) is a global toxic pollutant that can be released into the atmosphere through anthropogenic and natural sources. The uncertainties in the estimated emission amounts are much larger from natural than anthropogenic sources. A method was developed in the present study to quantify the contributions of natural surface mercury emissions to ambient gaseous elemental mercury (GEM) concentrations through application of positive matrix factorization (PMF) analysis with temperature and NH3 as indicators of GEM emissions from natural surfaces. GEM concentrations were continuously monitored at a 2-hourly resolution at a regional background site in the Yangtze River Delta in eastern China during 2015–2018. Annual average GEM concentrations were in the range of 2.03–3.01 ng m−3, with a strong decreasing trend at a rate of -0.32±0.07 ng m−3 yr−1 from 2015 to 2018, which was mostly caused by reduced anthropogenic emissions since 2013. The estimated contributions from natural surface emissions of mercury to the ambient GEM concentrations were in the range of 1.00–1.13 ng m−3 on annual average, with insignificant interannual changes, but the relative contribution increased significantly from 41 % in 2015 to 57 % in 2018, gradually surpassing those from anthropogenic sources.

Contamination assessment and source apportionment of heavy metals in agricultural soil through the synthesis of PMF and GeogDetector models

Heavy metal pollution in soils has attracted great attention worldwide in recent decades. Selecting Hangzhou as a case study location, this research proposed the synthesis application of positive matrix factorization (PMF) and GeogDetector models for quantitative analysis of pollution sources, which is the basis for subsequent soil pollution prevention and remediation. In total, 2150 surface soil samples were collected across the study area. Although the mean concentrations of As, Cd, Cr, Hg, and Pb in the soils were lower than the National Environmental Quality Standards for Soils in China, the mean contents of As and Cd were higher than their corresponding local background values by approximately 1.31 and 1.59 times, respectively, indicating that heavy metals have been enriched in topsoil. Agricultural activities, industrial activities, and soil parent materials were the main sources of heavy metal pollution in the soils, accounting for 63.4%, 19.8%, and 16.8% of the total heavy metal accumulation, respectively. Cr was derived mainly from soil parent materials (80.72%). Cd was closely associated with agricultural activities (73.68%), such as sewage irrigation and application of fertilizer. Mercury was mainly attributed to industrial activities (92.38%), such as coal mining and smelting. As was related to agricultural (57.83%) and natural (35.56%) sources, and Pb was associated with industrial (42.42%) and natural (41.83%) sources. The new synthesis models are useful for estimating the source apportionment of heavy metals in soils.

Quantitative source apportionment of heavy metal(loid)s in the agricultural soils of an industrializing region and associated model uncertainty

Heavy metal(loid)s are natural constituents of the Earth’s crust, and apportionment of their sources in surface soils is a challenging task. This study evaluated the application of positive matrix factorization (PMF) model, assisted with regression modeling and geospatial mapping, in the quantitative source apportionment of heavy metal(loid)s in the agricultural soils of Handan, a region covering >12,000 km2. Obvious enrichment of As, Cd, Cu, Pb, and Zn was found in the surface soils, with Cd alone accounted for 73 % of the overall potential ecological risk. PMF model revealed that Cd (56.9 %) and Pb (47.8 %) in the region’s agricultural soils were predominantly contributed by industrial sources, Fe (71.8 %), Cr (60.0 %), V (52.9 %), Cu (50.7 %), Ni (42.2 %), and Mn (41.4 %) were primarily of lithogenic origin, while Co (54.1 %), As (42.9 %), and Zn (40.0 %) mainly came from the mixed sources of natural background, agricultural sources, and vehicle emissions. Uncertainty analysis showed that the contributions of pollution sources to the soil heavy metal(loid)s estimated by PMF model had considerable variations. While quantitative source apportionment of heavy metal(loid)s in soils could be achieved with PMF based on their spatial distributions, combination with emission inventory and reactive transport are probably necessary to obtain more accurate results.

Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

Abstract. Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.

Application of positive matrix factorization to identify potential sources of water quality deterioration of Huaihe River, China

Identification of nonpoint source (NPS) pollution is essential for effective water management. In this study we used a combined approach of hierarchal cluster analysis (HCA) and positive matrix factorization (PMF) to identify NPS pollution for Huaihe River basin in China. NH3-N, COD, DO and pH were regularly monitored weekly over 2 years (2011–2012) from 27 monitoring stations subjected to high anthropogenic and natural changes. As identified by multiple correspondence analyses, the monitoring stations #3, #9 and #21 are located away from the rest of sites. HCA classified all the stations into 4 groups. PMF identified four factors on each group and each season. They were associated with the major causes of Huaihe River water quality deterioration resulted by discharges inputs from urban, agricultural and industrial land uses. Seasonal NPS pollution variation was found, and it is possibly linked with natural processes, for instance hydrological regime. This research work demonstrates the usefulness of PMF model for the identification of NPS pollution in surface waters. Furthermore, our study also shows that urban, agricultural and industrial land uses were the main factors impairing surface water quality, and limiting NPS pollution would be critical for enhancing surface water quality in the study area.

Positive matrix factorization: A data preprocessing strategy for direct mass spectrometry-based breath analysis

Interest in exhaled breath has grown considerably in recent years, as breath biosampling has shown promise for non-invasive disease diagnosis, therapeutic drug monitoring, and environmental exposure. Real time breath analysis can be accomplished via direct online mass spectrometry (MS)-based methods, which can provide more accurate and detailed data and an enhanced understanding of the temporal evolution of exhaled VOCs in the breath; however, the complicated chemical composition and large raw datasets involved in breath analysis have hindered the discovery of sources contributing to the exhaled VOCs. The positive matrix factorization (PMF) receptor model has been widely used for source apportionment in atmospheric studies. Since the exhaled VOCs contain compounds from various sources, such as alveolar air, mouth air and respiratory dead-space air, PMF may be also helpful for source apportionment of exhaled VOCs in the breath. Thus, this study explores the application of PMF in the pretreatment of direct breath measurement data. The results indicate that (i) endogenous compounds and background contaminants sources can be readily distinguished by PMF in data obtained from replicate measurements of human exhaled breath at single time points (~30 s/measurement), which may benefit both exhalome investigations and the identification of exposure biomarkers; (ii) sources resolved from online measurement data collected over longer periods (1.5 h) can be used to isolate the evolution of exhaled VOCs and investigate processes such as the pharmacokinetics of ketamine and its major metabolites. Therefore, PMF has shown promise for both data processing and subsequent data mining for the ambient MS-based breath analysis.

Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City.

Fine particulate matter (PM2.5) has a small particle size, which allows it to directly enter the respiratory mucosa and reach the alveoli and even the blood. Many countries are already aware of the adverse effects of PM2.5, and determination of the sources of PM2.5 is a critical step in reducing its concentration to protect public health. This study monitored PM2.5 in the summer (during the southwest monsoon season) of 2017. Three online monitoring systems were used to continuously collect hourly concentrations of key chemical components of PM2.5, including anions, cations, carbon, heavy metals, and precursor gases, for 24 h per day. The sum of the concentrations of each compound obtained from the online monitoring systems is similar to the actual PM2.5 concentration (98.75%). This result suggests that the on-line monitoring system of this study covers relatively complete chemical compounds. Positive matrix factorization (PMF) was adopted to explore and examine the proportion of each source that contributed to the total PM2.5 concentration. According to the source contribution analysis, 55% of PM2.5 can be attributed to local pollutant sources, and the remaining 45% can be attributed to pollutants emitted outside Taipei City. During the high-PM2.5-concentration (episode) period, the pollutant conversion rates were higher than usual due to the occurrence of vigorous photochemical reactions. Moreover, once pollutants are emitted by external stationary pollutant sources, they move with pollution air masses and undergo photochemical reactions, resulting in increases in the secondary pollutant concentrations of PM2.5. The vertical monitoring data indicate that there is a significant increase in PM2.5 concentration at high altitudes. High-altitude PM2.5 will descend to the ground and thereby affect the ground-level PM2.5 concentration.

Seasonal variations in high time-resolved chemical compositions, sources, and evolution of atmospheric submicron aerosols in the megacity Beijing

Abstract. A severe regional haze problem in the megacity Beijing and surrounding areas, caused by fast formation and growth of fine particles, has attracted much attention in recent years. In order to investigate the secondary formation and aging process of urban aerosols, four intensive campaigns were conducted in four seasons between March 2012 and March 2013 at an urban site in Beijing (116.31° E, 37.99° N). An Aerodyne high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS) was deployed to measure non-refractory chemical components of submicron particulate matter (NR-PM1). The average mass concentrations of PM1 (NR-PM1+black carbon) were 45.1 ± 45.8, 37.5 ± 31.0, 41.3 ± 42.7, and 81.7 ± 72.4 µg m−3 in spring, summer, autumn, and winter, respectively. Organic aerosol (OA) was the most abundant component in PM1, accounting for 31, 33, 44, and 36 % seasonally, and secondary inorganic aerosol (SNA, sum of sulfate, nitrate, and ammonium) accounted for 59, 57, 43, and 55 % of PM1 correspondingly. Based on the application of positive matrix factorization (PMF), the sources of OA were obtained, including the primary ones of hydrocarbon-like (HOA), cooking (COA), biomass burning OA (BBOA) and coal combustion OA (CCOA), and secondary component oxygenated OA (OOA). OOA, which can be split into more-oxidized (MO-OOA) and less-oxidized OOA (LO-OOA), accounted for 49, 69, 47, and 50 % in four seasons, respectively. Totally, the fraction of secondary components (OOA+SNA) contributed about 60–80 % to PM1, suggesting that secondary formation played an important role in the PM pollution in Beijing, and primary sources were also non-negligible. The evolution process of OA in different seasons was investigated with multiple metrics and tools. The average carbon oxidation states and other metrics show that the oxidation state of OA was the highest in summer, probably due to both strong photochemical and aqueous-phase oxidations. It was indicated by the good correlations (r = 0.53–0.75, p < 0.01) between LO-OOA and odd oxygen (Ox = O3 + NO2), and between MO-OOA and liquid water content in aerosols. BBOA was resolved in spring and autumn, influenced by agricultural biomass burning (e.g., field preparation burnings, straw burning after the harvest). CCOA was only identified in winter due to domestic heating. These results signified that the comprehensive management for biomass burning and coal combustion emissions is needed. High concentrations of chemical components in PM1 in Beijing, especially in winter or in adverse meteorological conditions, suggest that further strengthening the regional emission control of primary particulate and precursors of secondary species is expected.

Organic aerosol source apportionment by offline-AMS over a full year in Marseille

Abstract. We investigated the seasonal trends of OA sources affecting the air quality of Marseille (France), which is the largest harbor of the Mediterranean Sea. This was achieved by measurements of nebulized filter extracts using an aerosol mass spectrometer (offline-AMS). In total 216 PM2. 5 (particulate matter with an aerodynamic diameter < 2.5 µm) filter samples were collected over 1 year from August 2011 to July 2012. These filters were used to create 54 composite samples which were analyzed by offline-AMS. The same samples were also analyzed for major water-soluble ions, metals, elemental and organic carbon (EC ∕ OC), and organic markers, including n-alkanes, hopanes, polycyclic aromatic hydrocarbons (PAHs), lignin and cellulose pyrolysis products, and nitrocatechols. The application of positive matrix factorization (PMF) to the water-soluble AMS spectra enabled the extraction of five factors, related to hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), oxygenated OA (OOA), and an industry-related OA (INDOA). Seasonal trends and relative contributions of OA sources were compared with the source apportionment of OA spectra collected from the AMS field deployment at the same station but in different years and for shorter monitoring periods (February 2011 and July 2008). Online- and offline-AMS source apportionment revealed comparable seasonal contribution of the different OA sources. Results revealed that BBOA was the dominant source during winter, representing on average 48 % of the OA, while during summer the main OA component was OOA (63 % of OA mass on average). HOA related to traffic emissions contributed on a yearly average 17 % to the OA mass, while COA was a minor source contributing 4 %. The contribution of INDOA was enhanced during winter (17 % during winter and 11 % during summer), consistent with an increased contribution from light alkanes, light PAHs (fluoranthene, pyrene, phenanthrene), and selenium, which is commonly considered as a unique coal combustion and coke production marker. Online- and offline-AMS source apportionments revealed evolving levoglucosan : BBOA ratios, which were higher during late autumn and March. A similar seasonality was observed in the ratios of cellulose combustion markers to lignin combustion markers, highlighting the contribution from cellulose-rich biomass combustion, possibly related to agricultural activities.

Source identification and apportionment of PM2.5 and PM2.5-10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models.

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM2.5 and PM2.5-10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m(3) for PM2.5 and 331.36, 190.01, and 184.60 μg/m(3) for PM2.5-10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM2.5-10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM2.5-10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.

Seasonality in size-segregated ionic composition of ambient particulate pollutants over the Indo-Gangetic Plain: Source apportionment using PMF

Size-segregated particulate pollutants (PM<0.95, PM0.95–1.5, PM1.5–3.0, PM3.0–7.2 and PM>7.2) were collected over Patiala (30.33°N, 76.40°E; 250 m amsl), a semi-urban city located in northwestern Indo-Gangetic Plain (IGP), during October, 2012 to September, 2013. Mass concentration of total suspended particulates (TSP), derived by summation of particulate (aerosol) mass in different size range, varied from 88 to 387 μg m−3 with highest mass concentration (∼55% of total mass) in submicron size (PM<0.95) during the entire study period, which broadly reflects relative higher contribution of various anthropogenic sources (emissions from biomass and bio-fuel burning, vehicles, thermal power plants, etc) to ambient particles. Concentration of SO42−, NO3−, NH4+, K+ and Ca2+ exhibited large variability ranging from 0.52 to 40, 0.20 to 19, 0.14 to 12, 0.06 to 5.3 and 0.08 to 5.6 μg m−3, respectively, in different size ranges with varying size distribution for most of the species, except NH4+. A strong linear correlation (r = 0.97) between (SO42− + NO3−) and (K+ + NH4+) concentrations has been observed in submicron particles collected in different seasons, suggesting the formation of secondary inorganic salts. However, relatively poor correlation is observed in higher size ranges where significant correlation between (SO42− + NO3−) and (Ca2+ + Mg2+) has been observed. These observations indicate the acid neutralization by dust in coarser modes of particles. Chemical composition of submicron particulates (PM<0.95) in different seasons as well as for whole year was used to identify PM sources through the application of Positive Matrix Factorization (PMF, version 5.0) model. Based on annual data, PMF analyses suggests that six source factors namely biomass burning emission (24%), vehicular emission (22%), secondary organic aerosols (20%), power plant emission (13%), secondary inorganic aerosols (12%) and mineral dust (9%) contribute to PM<0.95 loading over the study region. Such studies are important in dispersion modeling, health impact assessment, and planning of pollution mitigation strategies.

Application of PMF and CMB receptor models for the evaluation of the contribution of a large coal-fired power plant to PM10 concentrations

The evaluation of the contribution of coal-fired thermo-electrical power plants to particulate matter (PM) is important for environmental management, for evaluation of health risks, and for its potential influence on climate. The application of receptor models, based on chemical composition of PM, is not straightforward because the chemical profile of this source is loaded with Si and Al and it is collinear with the profile of crustal particles. In this work, a new methodology, based on Positive Matrix Factorization (PMF) receptor model and Si/Al diagnostic ratio, specifically developed to discriminate the coal-fired power plant contribution from the crustal contribution is discussed. The methodology was applied to daily PM10 samples collected in central Italy in proximity of a large coal-fired power plant. Samples were simultaneously collected at three sites between 2.8 and 5.8km from the power plant: an urban site, an urban background site, and a rural site. Chemical characterization included OC/EC concentrations, by thermo-optical method, ions concentrations (NH4+, Ca2+, Mg2+, Na+, K+, Mg2+, SO42−, NO3−, Cl−), by high performances ion chromatography, and metals concentrations (Si, Al, Ti, V, Mn, Fe, Ni, Cu, Zn, Br), by Energy dispersive X-ray Fluorescence (ED-XRF). Results showed an average primary contribution of the power plant of 2% (±1%) in the area studied, with limited differences between the sites. Robustness of the methodology was tested inter-comparing the results with two independent evaluations: the first obtained using the Chemical Mass Balance (CMB) receptor model and the second correlating the Si-Al factor/source contribution of PMF with wind directions and Calpuff/Calmet dispersion model results. The contribution of the power plant to secondary ammonium sulphate was investigated using an approach that integrates dispersion model results and the receptor models (PMF and CMB), a sulphate contribution of 1.5% of PM10 (±0.3%) as average of the three sites was observed.