Trace Metal Chemical Fractionation in Airborne Particulate Matter from a Tropical Urban Area in Brazil

Trace Metal Chemical Fractionation in Airborne Particulate Matter from a Tropical Urban Area in Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trace Metal Chemical Fractionation in Airborne Particulate Matter from a Tropical Urban Area in Brazil Silvanio Silvério Lopes Costa, Jeferson Cavalcante Alves, Erivaldo Vieira da Silva, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6147994/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jul, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract This study aims to employ a modified BCR method for metal chemical fractionation in total suspended particulate (TSP) and inhalable particulate matter (PM 10 ) samples. The chemical fractionation of Cd, Cu, Fe, Mn, Ni, Ti and V in airborne particulate matter (APM) samples from a tropical urban area in the city of Aracaju, Sergipe, Northeastern Brazil was performed, adapting a sequential extraction method encompassing six fractions characterized by a gradual increase in the solvent strength, as follows: (1) water-soluble fraction; (2) ion exchange fraction; (3) carbonate-bound fraction; (4) metal oxide-bound fraction; (5) organic metal complex or sulfide-bound fraction; and (6) residual fraction. Iron and Ti concentrations were the most abundant among organic metal complex and sulfides and silicate fractions for both TSP and PM 10 . Cadmium concentrations, in general, were higher in the organic metal complex and sulfides, silicate, and oxide fractions, while Cu was most detected in the organic metal complex and sulfides, oxide, and silicate fractions. Nickel, Mn and V were not determined in either of the samples. A multivariate data analyses indicated three groups, associating Cu and Cd to similar sources, i.e ., vehicular traffic and fossil fuel burning. This is the first time this sequential method was applied to investigate metal mobility and metal distribution in APM, assessing beyond the bioavailability, solubility, and geochemical transport trace metals. Chemical fractionation Sequential extraction Airborne particulate matter PCA HCA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The United Nations, through its “The Sustainable Development Agenda 2023” aims to “substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination” through its Sustainable Development Goal 3 (3.9) (United Nations 2015 ). In this sense, anthropogenic activities comprise one of the main global pollution sources, contributing to increased air contamination in major cities due the growth of the world’s economy, which has, in turn, led to important demographic increases in urban centers (Schleicher et al. 2011 ; Karagulian et al. 2015 ; Rossini-Oliva and Nuñez 2024 ). This has significantly elevated atmospheric particle emission rates in major cities, implying in the continuous and increasing human exposure to airborne particulate matter (APM) from both natural and anthropogenic origins (Schleicher et al. 2011 ; Stabile et al. 2015 ). Increased APM emissions expose both the environment and humans to higher levels of trace metals, which are key air pollution components, due to their small size and ability to transport toxic substances. This is also likely to promote metal biogeochemical cycle changes and impose human health risks, justifying interest in determining APM composition (Schleicher et al. 2011 ; Stabile et al. 2015 ). In addition to determining mass concentrations, particle sizes and total metal concentrations present in atmospheric particles (Schleicher et al. 2011 ), metal chemical forms should also be quantified. In this sense, understanding metal chemical forms could potentially determine which chemical processes these compounds have undergone, as well as their (bio)availabilities and probable environmental mobility (Schleicher et al. 2011 ; Mukhtar and Limbeck 2013 ; Limbeck et al. 2012 ). Metal mobility is the ability of a metal to move among environmental compartments, depending on how it is bound to a certain matrix and its environmental availability. In Bureau Community of Reference (BCR) procedures, acid-soluble fractions can be easily released into aquatic environments and absorbed by aquatic organisms, while reducible and oxidizable fractions can be released due to changes in sediment physicochemical properties or microbiological activities (Döndü et al. 2024 ). Chemical fractionation, in this sense, may also enable the development of emission prevention or mitigation policies (Schleicher et al. 2011 ; Limbeck et al. 2012 ). Sequential extraction procedures are intended to be followed in order, so that the reagent used in each stage releases metals associated with particular sample phases, as metal bioavailability depends on oxidation state and metal form (Schleicher et al. 2011 ; Limbeck et al. 2012 ). Fractions 1–6 of the extraction procedure applied herein were previously described by Tessier et al. ( 1979 ) (fractions ion exchange; bound to carbonates; bound to metal oxides; bound to organic metal complex or sulfides), followed residual fraction described by Wang et al. ( 1999 ), and Chang et al. ( 2009 ) investigated a water-soluble fraction was first time. In another study, Wu et al. ( 2008 ) applied an extraction procedure consisting of three fractions for atmospheric deposition characterization, reporting Cd and Zn, as predominantly present in the labile fraction. Chromium and Cu, in turn, were mainly bound to organic matter and silicates. Depending on the amount of organic matter, the labile fraction also includes metals in the form of soluble organic complexes. In this sense, Schleicher et al. ( 2011 ) studied total suspended particulate (TSP) and respirable particulate matter (PM 2.5 ) metal mobility employing a sequential extraction procedure developed by Tessier et al. ( 1979 ), and modified by Espinosa et al. ( 2002 ). The authors suggested that the As, Cd, Cu, Mn, Pb and Zn concentrations detected in highly mobile fractions probably cause adverse environmental and human health effects (Wu et al. 2008 ). Cadmium and Pb, for example, can cause proximal tubular renal damage and glomeruli declines (Jang et al. 2024 ). Another extraction procedure, established by BCR in Belgium, was applied by Sipos et al. ( 2016 ) to characterize and evaluate metal mobility in TSP. A hierarchical cluster analysis (HCA) was applied to assess elemental behavior at each extraction fraction, highlighting two groups, one composed of highly water-soluble elements (Ca, Cd, K, Mg, Mn, Na, S and Zn) and another composed of elements present in the highest amounts in the residual fraction (Al, Cr, Fe, K, Ni and Ti). The authors suggested that Cd and Zn, present in the most labile fractions, probably originate from waste incineration ash. Lead, present in the reducible fraction, can be associated to combustion or traffic sources, as well as Cu, associated to carbonates (Sipos et al. 2016 ). In another assessment, Chang et al. ( 2009 ) applied a sequential extraction method based on six fractions, carrying out total analyte extraction using a fly ash certified reference material (BCR 176) at the same time, validating the analytical method. The authors indicated that the carbonate-bound fraction contained higher Cd, Cu, Pb and Zn concentrations, while Fe and Mn were higher in the organic matter fraction bound. Chemical fractionation allows for the quantification of specific metallic forms and assessments on bioavailability, solubility, geochemical transport and metal cycles through physico-chemical speciation assessments. Knowledge on chemical particle speciation, thus, is vital to understand human health and environmental effects (Espinosa et al. 2002 ). The modified BCR method for particulate matter has been employed in some studies to date. Dabek-Zlotorzynska et al. ( 2003 ), for example, developed a capillary electrophoresis (CE) method to determine the distribution of Fe, Zn, Cu, Mn and Cd in APM following a sequential extraction employing a three-stage BCR extraction method, as follows: (1) exchangeable water and acid-extractable fraction; (2) reducible fraction and (3) oxidizable fraction. The authors also modified the BCR procedure, adding an ultrasonic bath fraction to accelerate metal extraction, reducing the analysis time from 48 to 2 h. In another study, Dabek-Zlotorzynska et al. ( 2005 ) applied the same conditions to evaluate the same elements in PM 2.5 , offering a fast and reliable way to provide chemical metal fractionation information. This study aims to employ a modified BCR method for metal chemical fractionation in TSP and inhalable particulate matter (PM 10 ) for samples from a tropical urban area (Chang et al. 2009 ). The studied urban area is located in Northeastern Brazil, and is mainly influenced by traffic, resuspended particles, biomass burning, industrial emissions and marine aerosols (Almeida et al. 2013 ; Gois et al. 2016 ). To the best of our knowledge, this is the first time that chemical element fractionation is employed to study metal TSP and PM 10 mobility in this urban area. This is expected to comprise an important tool for predicting the potential effects of environmental changes and airborne metals on the redistribution of chemical metal forms in tropical APM, due to certain particularities noted between tropical and temperate climate environments, as observed by Silveira et al. ( 2006 ). Material and methods Sampling From July to November 2013, four total suspended particulate (TSP) and four inhalable particulate matter (PM 10 ) samples were obtained simultaneously from a large volume sampler (Hi-vol, AGV PTS/CVV, Energética, Rio de Janeiro, Brazil) employing glass fiber filters (E55, 8×10 inch, Energética, Rio de Janeiro, RJ, Brazil) and collecting an average volume of 1440 m 3 during 24 (± 1) hours. Atmospheric conditions were collected for later use in calculating concentrations in µg m − 3 . The glass fiber filters were heated in a vacuum drying oven at 110–120°C for 2 h prior to use, and conditioned at a controlled temperature (25°C ) with temperature variations lower than ± 3°C after sampling. After the final weighing, the exposed and unused filters (blank) were dried in a desiccator for 24 h and ground for 15 min to particle sizes lower than 63 µm (Almeida et al. 2013 ) using an agate ball mill (Retsch, Düsseldorf, Germany). At the time of collections, the city of Aracaju, Sergipe, had a population of 614,577 inhabitants, and a fleet of 248,834 vehicles. The sampling station is located in the Industrial District of Aracaju ( Distrito Industrial de Aracaju - DIA), in the urban Aracaju city area, Sergipe, Brazil (10°56’53.7”S 37°04’27.5”W) under the responsibility of the Department of Environmental and Water Resources ( Departamento de Meio Ambiente e Recursos Hídricos ) (Fig. 1). In addition to traffic and marine aerosols, the main potential emission sources near the urban area where the sampling took place encompass construction, metal processing, wood and fabrics, electrical distribution and control equipment (Brazil 2023). Instrumentation and analysis Trace metals in TSP deposited on glass fiber filter were extracted using a closed digester block (TECNAL, TE007A, São Paulo, Brazil) with a temperature control and a polytetrafluoroethylene (PTFE) reactor. Cadmium, Cu, Fe, Mn, Ni, Ti and V concentrations were determined employing an inductively coupled plasma optical emission spectrometer (ICP OES) (E-720, Mulgrave, Varian, Australia) equipped with a Sturman-Masters nebulizer chamber and V-Groove nebulizer (VAR04123, Mulgrave, Varian, Australia). The instrumental parameters were as follows: radio frequency (RF) of 40 MHz, applied RF power of 1.20 kW, outer plasma gas rate of 15.0 L min − 1 , auxiliary gas flow rate of 1.5 L min − 1 , nebulizer gas flow rate of 1.0 L min − 1 , sample uptake rate of 0.8 mL min − 1 and signal integration time of 1 s. The limits of detection (LoD) and quantification (LoQ) were calculated using the background equivalent concentration (BEC) and signal-to-background ratio (SBR), as BEC = C RS /SBR, where SBR = (I RS - I blank )/I blank , C RS is the reference element concentration in the solution, and I RS and I blank are the emission intensities for the reference element and blank solutions, respectively (Montaser and Golightly 1992 ). Precision was expressed as the relative standard deviation (RSD), calculated using 10 consecutive measurements of the blank solution. The LoD was then calculated as (3 x RSD x BEC)/100, and the LoQ as (10 x RSD x BEC)/100. The LoD and LoQ values (in mg kg − 1 ) were 0.002 and 0.007 for Ti (334.941 nm), 0.002 and 0.008 for V (292.401 nm), 0.006 and 0.02 for Cd (214.439 nm), 0.006 and 0.022 for Ni (231.604 nm), 0.012 and 0.04 for Cu (327.395 nm), 0.02 and 0.07 for Mn (257.610 nm) and, 0.04 and 0.12 for Fe (259.940 nm), respectively, all considered acceptable for the quantitative determination of these elements (Colombo et al. 2008 ). A road dust (BCR 723) certified reference material (CRM) was acquired from the Bureau of Reference, Brussels, Belgium (Meeravali et al. 2014 ; Palacio et al. 2016 ) and an urban particulate matter (NIST 1648a) CRM, from the National Institute of Standards and Technology (NIST, United States) (Limbeck et al. 2012 ; Montaser and Golightly 1992 ), both analyzed for quality control. All materials used in the experiments were previously decontaminated overnight with a 10% v v − 1 nitric acid solution, rinsed with deionized water, and dried at room temperature (Almeida et al. 2013 ). Sequential Extraction Procedure All employed reagents were of analytical grade (Merck, Darmstadt, Germany) and solutions were prepared using deionized water ( 18.2 MΩ cm − 1 resistivity) obtained from a MS2000 GEHAKA purification system (São Paulo, Brazil). External calibration curves were prepared using multielement standard solutions at 1000 mg L − 1 (Specsol®, São Paulo, Brazil) for Cd, Cu, Fe, Mn, Ni, Ti and V. The sequential extraction method was adapted from Chang et al. ( 2009 ), comprising six fractions (F1 – F6) characterized by a gradual increase in solvent strength: 1. Water-soluble fraction : 10 mL of deionized water were mixed with about 50 mg of each sample in polyethylene centrifuge tubes. The slurries were then stirred for 3 h at room temperature at 150 rpm. The supernatants were then separated by centrifugation for 5 min for subsequent ICP OES analysis. Chang et al. ( 2009 ) performed other fractions such as removing the supernatant, followed by further shaking, centrifuging and filtering, which were not carried out herein. 2. Ion exchange fraction : 10 mL of a 0.1 mol L − 1 MgCl 2 solution were mixed with the residues recovered from F1 and stirred for 3 h at room temperature at 150 rpm. The supernatants were then separated by centrifugation for 5 min prior to further analysis. Chang et al. ( 2009 ) performed other fractions, as described above, which were also not carried out herein. 3. Carbonate-bound : 10 mL of a 0.10 mol L − 1 NaOAc/HOAc solution at pH 5.0 were mixed with the residues obtained in F2 and stirred for 3 h at room temperature at 150 rpm. The supernatants were then separated by centrifugation for 5 min prior to further analysis. The same procedure was employed by Chang et al. ( 2009 ) in this fraction. 4. Metal oxide-bound fraction : 10 mL of a 0.10 mol L − 1 NH 2 OH.HCl solution were mixed with the residues obtained in F3 and shaken at 60°C for 3 h at 150 rpm. The supernatants were separated by centrifugation for 5 min to determine analyte concentrations. Chang et al. ( 2009 ) performed other fractions, as described above, which were also not carried out herein. 5. Organic metal complex- or sulfide-bound fraction : 10 mL of a mixed 2% HNO 3 and 30% H 2 O 2 (3:7 v v − 1 ) solution were added to the residues obtained in F4 and heated at 85°C in a water bath under shaking for 3 h at 150 rpm. The procedures reported for the previous fractions were followed. Chang et al. ( 2009 ) also performed extraction and solid-liquid separation, as well as extract storage, as described in F4, which were not carried out herein. 6. Residual fraction : 3 mL of a 65% m m − 1 HNO 3 , 2 mL of 30% m m − 1 H 2 O 2 and 5 mL of a 38% m m − 1 HF solution were mixed with the residues obtained in F5, which were then heated in a closed system with PTFE vessels at 190°C for 5 h in a closed digester block (TECNAL, TE007A, São Paulo, Brazil). After cooling, the system was opened and allowed to dry at 85°C, followed by the addition of 0.5 mol L − 1 HNO 3 for residual salt dissolution, completed to 10 mL. Chang et al. ( 2009 ) employed 10 mL of a mixed acid (HNO 3 + H 2 O 2 + HF = 3:5:2 mL) at different ratios than the ones applied herein. The determination of Cd, Cu, Fe, Mn, Ni, Ti and V in all extraction process fractions were performed by ICP OES. The procedure described in F6 (HNO 3 : H 2 O 2 :HF, 3:2:5 mL) was also applied for total sample and CRM digestion. The blank analytical solution was used for quality control. All samples were prepared in triplicate. Application of chemometric tools The ICP OES results are arranged as Cd, Cu, Fe, Mn, Ni, Ti and V in the six chemical fractions obtained following the sequential extraction method applied to PM 10 and TSP samples from an urban area in Aracaju, Northeastern Brazil. Metal correlations can be determined, suggesting the main contributions for each metal fraction, such as traffic, soil resuspension particles and industrial activity. A principal component analysis (PCA) and hierarchical cluster analysis (HCA) were, thus, employed to examine potential trends and similarities between the concentrations of the investigated metals in TSP and PM 10 , using the Statistica software (version 6.0, SoftStat, Tulsa, USA). Both statistical methods can be effectively applied to distinguish among phase associations and metal sources in environmental media. The data were normalized by dividing the element concentration value by the highest element concentration measured in the samples, in order to assign a scale with a maximum value of 1. The objective was transform the data in order to eliminate the differences between the orders of magnitude of the values and the range of variables, while maintaining statistical information (Souza et al. 2024 ). Results and discussion Sequential extraction Certified Reference Material (CRM) evaluation Sequential extractions seek to identify in which characteristic range of the natural environment each element becomes more readily available (Mukhtar and Limbeck 2013). The procedure adopted herein consists in obtaining six sample fractions that may suggest which possible physicochemical process took place and how much the determined elements are bioavailable in the TSP and PM 10 samples. Moreover, it may also indicate how metals are bound or interact with which part of the particulate matter matrix, according to the characteristics of each reagent employed through each fraction (Chang et al. 2009). The fractioned trace metal content in TSP and PM 10 , as well as the sum of the concentrations of the six fractions (total concentrations), indicate how much each element is extracted in each fraction and how much it represents of its total concentration. However, according to the World Health Organization (WHO 2000) and European standards (European Comission 2001), sequential extraction processes are subject to uncertainties during the sample preparation and analysis fractions. Strategies are, thus, required to assess and mitigate these types of errors. To validate the sequential extraction procedure employed herein, two CRM, comprising 50 mg of urban particulate matter (NIST SRM1648a) and 50 mg of road dust (BCR 723), were also extracted. As a comparative method, total acid digestion was also applied to the samples using a HNO 3 :HF:H 2 O 2 , 3:5:2 v v − 1 mixture in a closed digester block (Chang et al. 2009). The results are depicted in Table 1. Table 1 Concentrations of Cd, Cu, Fe, Mn, Ni, Ti and V in road dust (BCR 723) and urban particulate matter (NIST SRM1648a) Certified Reference Materials following sequential extraction and total digestion methods and ICP OES analyses. Sequential extraction Total digestion BCR 723 Certified values F1 F2 F3 F4 F5 F6 Sum Relative error % Agreement % Found value Relative error % Agreement % t calc Fe / % 3.29 ± 0.20 < 0.12 * < 0.12 * < 0.12 * 0.29 ± 0.02 2.23 ± 0.03 0.98 ± 0.02 3.50 6.4 106.4 2.71 ± 0.08 -17.6 82.4 ± 2.6 1.674 Mn / g kg − 1 1.28 ± 0.04 0.11 ± 0.01 0.056 ± 0.003 0.14 ± 0.006 0.18 ± 0.01 0.83 ± 0.09 0.11 ± 0.02 1.43 11.7 111.4 1.10 ± 0.03 -14.1 85.9 ± 2.5 2.598 Ni / mg kg − 1 171.0 ± 3.0 5.32 ± 0.66 < 0.024 9.56 ± 0.72 13.1 ± 1.4 128.6 ± 14.2 35.4 ± 2.9 192.0 12.3 112.3 145.7 ± 4.9 -14.8 85.2 ± 2.8 4.869 Ti / g kg − 1 2.58 ± 0.13 < 0.007 * < 0.007 * < 0.007 * < 0.007 * 0.45 ± 0.01 1.66 ± 0.24 2.11 -18.2 81.8 2.09 ± 0.02 -19.0 81.0 ± 0.6 2.176 V / mg kg − 1 74.9 ± 1.9 1.77 ± 0.11 < 0.008 1.13 ± 0.47 6.47 ± 0.32 30.6 ± 3.7 28.4 ± 1.9 68.4 -8.7 91.3 60.5 ± 11.1 -19.2 80.8 ± 14.8 4.376 NIST 1648a Certified values F1 F2 F3 F4 F5 F6 Sum Relative error % Agreement % Found value Relative error % Agreement % Cd / mg kg − 1 73.7 ± 2.3 21.3 ± 3.7 17.7 ± 3.0 10.6 ± 1.1 6.44 ± 0.54 6.00 ± 0.79 4.72 ± 0.35 66.8 -9.4 90.6 60.5 ± 1.0 -17.9 82.1 ± 1.3 3.314 Cu / mg kg − 1 610.0 ± 70.0 63.2 ± 10.3 13.0 ± 4.46 23.4 ± 1.79 24.1 ± 0.79 502.9 ± 44.2 31.4 ± 2.74 658.0 7.9 107.9 497.8 ± 9.7 -18.4 81.6 ± 1.6 0.925 Fe / % 3.92 ± 0.21 < 0.12 * < 0.12 * < 0.12 * 0.21 ± 0.01 1.58 ± 0.14 1.46 ± 0.12 3.25 -17.1 82.9 3.30 ± 0.08 -15.8 84.2 ± 2.1 1.705 Mn / mg kg − 1 790.0 ± 44.0 231 ± 34 61.9 ± 21.3 29.3 ± 1.8 45.1 ± 3.9 196 ± 19 104 ± 6 667.2 -15.5 84.4 687.9 ± 16.7 -12.9 87.1 ± 2.1 1.340 Ni / mg kg − 1 81.1 ± 6.8 16.4 ± 3.1 2.55 ± 1.10 1.18 ± 0.19 2.77 ± 0.22 23.3 ± 1.5 21.3 ± 0.2 67.5 -16.8 83.2 67.1 ± 0.9 -17.3 82.7 ± 1.1 1.189 V / mg kg − 1 127.0 ± 11.0 11.6 ± 1.9 6.23 ± 0.64 6.18 ± 0.31 15.8 ± 0.7 51.0 ± 4.2 29.0 ± 3.7 119.8 -5.7 94.3 106.2 ± 13.9 -16.4 83.6 ± 11.0 1.092 * mg kg -1 . Agreement (%) = [(measured value - certified value)/certified value] x 100; Relative error (%) = [(measured value – certified value)/certified value] x 100; results expressed as means ± standard deviations (n=3). F1: Water-soluble; F2: Ion Exchange; F3: Bound to carbonates; F4: Bound to metal oxides; F5: Bound to organic complex and sulfides; F6: Residual fraction. (Critical value of t is 9.925 at the 99% confidence interval for freedom level of 2. If the calculated t value is lower than the critical value of t, there is no significant difference between the experimental results and certified value of the reference material at a 95% confidence level). The agreement values between certified and measured values ranged from 81.8% (Ti) to 112.3% (Ni) for the sum of the six sequential extraction fractions and from 80.8% (V) to 87.1% (Mn) for the total digestion of the two CRM. The percentage relative errors are also presented in Table 1, indicating that the Cd, Cu, Fe, Mn, Ni, Ti and V determinations can be considered quantitative, as absolute relative errors did not exceed ± 20% (Kira and Maihara 2007). The calculated t-values are lower than the critical f value of 9.925 at a 99% confidence interval for 2 levels of freedom for all analytes. This indicates no evidence of systematic errors in the applied analytical method, confirming good agreement with certified values. The following section summarizes the results obtained for each element: Cadmium (Cd): Present in all sequential extraction procedure stages, at 29% (F1: water-soluble fraction), 23% (F2: ion exchange fraction), 14% (F3: carbonate-bound fraction), 9% (F4: metal oxide-bound fraction), 8% (F5: organic metal complex- or sulfide-bound fraction) and 6% (F6: residual fraction). Water-soluble (F1) and ion-exchangeable (F2) species fractions comprised mainly Cd species for the urban particulate matter (NIST 1648a) CRM, representing this metal’s availability, which is susceptible to ionic composition or pH variations. Similar results have been reported in the literature. For example, Dabek-Zlotorzynska et al. (2003) reported about 55% of total Cd partitioned in water- and acid-phases in the same urban particulate matter (NIST 1648) CRM. Copper (Cu): This element presented concentrations of up to 10% in the urban particulate matter (NIST 1648a) CRM in fractions (F1), (F2), (F3), (F4) and (F6), while 82% was bound to organic complexes and sulfides (F5). This indicates that Cu is mainly bound to the analyzed matrices (Espinosa et al. 2002). Dabek-Zlotorzynska et al. (2003) reported Cu in the exchangeable or acid-soluble and residual fractions (~ 30% each), as well as in the reducible (~ 15%), and oxidizable (~ 24%) phases in the same CRM (NIST 1648). Iron (Fe): Fractions (1), (2), (3) contained relatively low Fe levels, below the LoQ (< 0.12 mg kg − 1 ), while fraction 4 contained 5.4% Fe in the urban particulate matter (NIST 1648a) CRM and 8.8% in the road dust (BCR 723) CRM bound to metal oxides. The highest concentration of this metal was verified in the two last fractions, totaling 78% in the urban particulate matter (NIST SRM1648a) CRM and 98% in the road dust (BCR 723) CRM. In the latter, Fe concentrations were mostly bound to organic metal complexes (68%) and silicates (30%), while in the urban particulate matter (NIST SRM1648a) CRM this element was mostly bound to organic complexes (40%) and silicates (37%). This indicates that Fe is not easily released under natural conditions (Smeda and Zyrnicki 2002). Similar results have been reported, i. e., Dabek-Zlotorzynska et al. (2003) verified 1% Fe in the water/acid phase, 13% in the oxide phase, 6% under oxidized conditions, and ~ 80% refractory species also in the urban particulate matter (NIST 1648) CRM. Manganese (Mn): About 29% of Mn concentrations were released from the NIST SRM1648a CRM in the water-soluble fraction (F1). This element, however, was not easily dissolved in the ion-exchangeable (8%), carbonate-bound (4%), and metal oxide-bound (6%) fractions. This element was mostly bound to organic complexes (25%) and silicates (13%). Concerning the road dust (BCR 723) CRM, 65% of Mn was released in the organic complex and sulfide fraction (F5) and 14% in the metal oxide fraction (F4). Concentrations in the other fractions, namely (F1), (F2), (F3) and (F6), were less than 11%, indicating that Mn is tightly bound to the analyzed matrix. Dabek-Zlotorzynska et al. (2003) detected Mn mainly in the residual fraction (~ 60%), while 8% was detected in the reducible phase, and 33%, in the exchangeable or acid-soluble fraction. Nickel (Ni): A very small amount of Ni was released in the first four fractions, less than 8%. The highest concentration was observed in the last two fractions, i.e. , 75% in the organic metal complex fraction (F5) and 21% in the silicate fraction (F6) for the road dust (BCR 723) CRM. The same was observed for the urban particulate matter (NIST 1648a) CRM, with 29% of Ni present in the organic metal complex fraction (F5), 26% in the silicate fraction (F6), and 20% in the water-soluble fraction (F1). Fractions (2), (3) and (4) presented small amounts of Ni, less than 3%. Similar results were noted by Richter et al. (2007), who indicated that Ni is mainly bound to silicates and organic matter in APM from Santiago, Chile. Titanium (Ti): This metal was the major element present in the road dust (BCR 723) CRM, binding to organic metal complexes (F5) (17%) and silicates (F6) (64%), indicating that the Ti compounds are not easily released in natural environments. A very small amount of Ti was released in the first four fractions, below than LoQ (< 0.007 mg kg − 1 ), demonstrating that Ti is intrinsically bound into the investigated matrix (Espinosa et al. 2002). In one study, Richter et al. (2007) reported Ti in airborne particulate mainly bound to carbonates and oxides. Vanadium (V): Over 60% of V was bound to organic complexes and sulfides and silicates (residual fraction), in both CRM, at 41% and 40%, in F5 and 38% and 23% in F6, for road dust (BCR 723) and urban particulate matter (NIST 1648a), respectively. Both CRM presented similar behaviors, where V concentration did not exceed 12% in the other fractions, (F1, F2, F3 and F4). Richter et al. (2007) reported V in airborne matter particulate as mainly bound to carbonates and oxides. Based on the applied sequential extraction procedures, a large fraction of all chemical elements could be extracted at pH 5.0, as Fe (68%), Mn (65%), Ni (75%), V (41%) for road dust (BCR 723), and Cu (82%), V (40%), Ni, Mn and Cd (29%) for urban particulate matter (NIST 1648a). The good agreement between the obtained values from the sum of the metals extracted in the six fractions with the total metals obtained in the total digestion procedure indicate that this method is highly accurate. The sample preparation procedure was then applied to the real TSP and PM 10 samples. Evaluation of TSP and PM samples from an urban area applying the sequential extraction method The agreement between the sum of fractions and total digestion for the metal concentrations in the TSP samples are displayed in Table 2, ranging from 86.6% (Cu) to 117.5% (Ti) while PM 10 concentrations ranged from 82.1% (Ti) to 112.4% (Cd) for PM 10 (Table 3). Concentrations were below the analytical method LoQ for all samples concerning Mn (< 0.07 mg kg − 1 ), Ni (< 0.020 mg kg − 1 ) and V (< 0.008 mg kg − 1 ). Table 2 Concentration of Cd, Cu, Fe and Ti distributed in the six fractions of the sequential extraction method applied for TSP samples collected in the urban area of Aracaju. TSP F1 F2 F3 F4 F5 F6 Sum Total digestion Agreement % TSP 1 Cd mg kg − 1 < 0.022 < 0.022 0.54 ± 0.11 0.64 ± 0.060 1.78 ± 0.090 0.81 ± 0.32 3.77 3.94 ± 0.82 95.7 Cu mg kg − 1 1.14 ± 0.91 < 0.043 < 0.043 6.06 ± 0.090 13.08 ± 0.74 3.13 ± 0.12 27.1 25.7 ± 2.98 91.1 Fe mg kg − 1 < 0.12 2.4 ± 0.84 16.6 ± 3.21 401.1 ± 61.2 423.4 ± 18.0 457.6 ± 39.0 1301 1129 ± 497.8 115.2 Ti mg kg − 1 < 0.007 < 0.007 0.066 ± 0.002 0.25 ± 0.023 107.6 ± 1.75 224.3 ± 34.8 332.2 317.9 ± 64.8 104.5 TSP 2 Cd mg kg − 1 < 0.022 < 0.022 < 0.022 < 0.022 1.26 ± 0.24 0.21 ± 0.17 1.47 1.51 ± 0.81 97.3 Cu mg kg − 1 < 0.043 < 0.043 11.8 ± 1.70 3.77 ± 0.56 22.5 ± 1.65 2.05 ± 0.31 40.1 46.3 ± 13.0 86.6 Fe mg kg − 1 < 0.12 1.37 ± 1.13 3.72 ± 1.05 231.2 ± 13.2 886.5 ± 73.7 510.8 ± 39.7 1633 1573 ± 86.0 103.8 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 128.4 ± 7.50 139.4 ± 11.9 267.8 228.0 ± 3.4 117.5 TSP 3 Cd mg kg − 1 < 0.022 0.22 ± 0.14 < 0.022 0.24 ± 0.13 2.49 ± 0.20 1.60 ± 0.41 4.55 4.93 ± 0.55 92.3 Cu mg kg − 1 < 0.043 < 0.043 15.4 ± 0.17 6.31 ± 0.40 25.0 ± 2.13 1.80 ± 1.05 48.5 47.5 ± 8.55 102.1 Fe mg kg − 1 < 0.12 < 0.12 8.22 ± 3.78 341.2 ± 9.40 771.7 ± 55.9 629.9 ± 162.6 1756 1868 ± 384.3 94.0 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 92.4 ± 1.77 219.5 ± 5.85 311.9 353.7 ± 36.7 88.2 TSP 4 Cd mg kg − 1 < 0.022 < 0.022 < 0.022 1.80 ± 1.43 2.14 ± 0.56 0.58 ± 0.32 4.52 4.19 ± 0.44 107.9 Cu mg kg − 1 < 0.043 < 0.043 6.21 ± 0.93 6.15 ± 1.58 34.7 ± 4.48 < 0.043 47.1 49.4 ± 10.7 95.3 Fe mg kg − 1 < 0.12 < 0.12 < 0.12 334.7 ± 25.0 917.3 ± 17.0 885.4 ± 63.3 2137 1973.2 ± 198.4 108.3 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 107.1 ± 6.29 248.1 ± 6.11 355.2 321.6 ± 12.6 110.4 Cd mg kg − 1 n.d. 0.055 0.135 0.67 1.92 0.80 3.58 3.64 98.3 Mean Cu mg kg − 1 0.285 n.d. 8.35 5.57 23.8 1.74 40.7 42.2 93.8 Fe mg kg − 1 n.d. 0.942 7.13 327.0 749.7 620.9 1707 1636 105.3 Ti mg kg − 1 n.d. n.d. 0.016 0.062 108.9 207.8 167.1 305.3 105.1 Results expressed as average concentration ± standard deviation (n = 3). Sum is the sum of the values, higher the quantification limits, obtained from the extraction steps. TSP = total suspended particles. Total digestion is the total decomposition of the sample using a mixture of nitric and hydrofluoric acid, and hydrogen peroxide. Sum is the sum of fractions 1 to 6. Agreement is the proximity between the values obtained from total digestion and the sum of the step extractions. n.d. is not determined. For TSP (Table 2), the lowest concentrations were observed in the water-soluble (F1) and ion exchange (F2) extraction phases [Fe (< 0.04–2.4 mg kg − 1 ), Ti (< 0.007 mg kg − 1 ), Cu (< 0.04–1.14 mg kg − 1 ) and Cd (< 0.022–0.22 mg kg − 1 )]. The highest carbonate-bound (F3) and metal oxide-bound (F4) metals were Cu (< 0.04–15.4 mg kg − 1 ), Fe (16.6–401.1 mg kg − 1 ), Ti (0.07–0.25 mg kg − 1 ) and Cd (< 0.006–1.80 mg kg − 1 ). The two subsequent fractions, related to elements bound to organic complexes and sulfides (F5) and the residual fraction (F6), contained the highest concentrations of Fe (423.4–917.3 mg kg − 1 ) and Ti (92.4–248.1 mg kg − 1 ) in the total particulate matter. In the same fractions, Cd and Cu ranged from 0.21 to 2.49 mg kg − 1 and < 0.01 to 34.7 mg kg − 1 , respectively. Table 3 Concentration of Cd, Cu, Fe and Ti distributed in the six fractions of the sequential extraction method applied for PM 10 samples from the urban area of Aracaju, Sergipe, Northeastern Brazil. PM 10 F1 F2 F3 F4 F5 F6 Sum Total digestion Agreement % PM 10 1 Cd mg kg − 1 1.09 ± 0.81 0.16 ± 0.050 0.13 ± 0.053 0.27 ± 0.13 < 0.022 3.45 ± 2.06 5.10 4.93 ± 0.54 103.4 Cu mg kg − 1 2.28 ± 0.84 < 0.043 19.2 ± 1.61 6.85 ± 0.60 < 0.043 12.7 ± 2.73 41.0 45.4 ± 3.35 90.3 Fe mg kg − 1 < 0.12 0.92 ± 0.10 9.84 ± 8.24 188.0 ± 4.14 506.5 ± 79.5 380.9 ± 18.1 1087 1233 ± 110.0 88.0 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 99.1 ± 15.0 112.6 ± 3.72 211.7 257.7 ± 22.3 82.1 PM 10 2 Cd mg kg − 1 < 0.022 < 0.022 < 0.022 < 0.022 1.05 ± 0.076 0.62 ± 0.052 1.67 1.77 ± 0.28 94.3 Cu mg kg − 1 < 0.043 < 0.043 30.7 ± 1.22 10.4 ± 1.75 33.2 ± 4.23 1.41 ± 0.16 75.7 78.6 ± 1.36 96.3 Fe mg kg − 1 < 0.12 < 0.12 7.50 ± 0.33 226.2 ± 41.6 656.3 ± 86.6 429.1 ± 19.9 1319 1314 ± 40.5 100.4 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 101.2 ± 10.8 129.6 ± 3.75 230.8 243.5 ± 4.32 94.8 PM 10 3 Cd mg kg − 1 < 0.022 0.14 ± 0.038 < 0.022 0.24 ± 0.095 2.54 ± 0.017 1.65 ± 0.17 4.59 5.46 ± 0.55 83.9 Cu mg kg − 1 < 0.043 < 0.043 1.31 ± 0.44 < 0.043 5.01 ± 0.63 1.27 ± 0.75 7.59 9.12 ± 0.45 83.2 Fe mg kg − 1 < 0.12 1.34 ± 0.28 12.69 ± 1.42 67.8 ± 1.82 234.1 ± 18.5 351.6 ± 25.3 667.5 802.9 ± 16.9 83.1 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 73.0 ± 6.53 132.9 ± 11.2 205.9 221.8 ± 16.6 92.8 PM 10 4 Cd mg kg − 1 < 0.022 < 0.022 < 0.022 0.27 ± 0.040 2.65 ± 0.32 2.06 ± 0.10 4.98 4.43 ± 0.29 112.4 Cu mg kg − 1 < 0.043 < 0.043 6.93 ± 1.03 2.41 ± 0.20 14.9 ± 1.65 1.25 ± 1.33 25.5 25.2 ± 1.87 101.2 Fe mg kg − 1 < 0.12 < 0.12 7.29 ± 1.34 174.1 ± 12.4 490.4 ± 72.6 405.8 ± 3.76 1077 1121 ± 91.4 96.1 Ti mg kg − 1 < 0.007 < 0.007 < 0.007 < 0.007 76.6 ± 5.87 126.3 ± 11.2 202.9 220.7 ± 16.0 91.9 Cd mg kg − 1 0.272 0.075 0.032 0.26 1.56 1.94 4.18 4.15 98.5 Mean Cu mg kg − 1 0.57 n.d. 6.87 4.91 13.3 4.16 37.4 39.6 92.7 Fe mg kg − 1 n.d. 0.565 9.33 164.0 471.8 391.8 1037.6 1117.7 91.9 Ti mg kg − 1 n.d. n.d. n.d. n.d. 87.5 125.3 212.8 235.9 90.4 Results expressed as mean concentration ± standard deviation (n = 3). Sum is the sum of the values, higher the quantification limits, obtained from the extraction steps. PM10: particulate matter smaller than 10 micrometers. Total digestion is the total decomposition of the sample using a mixture of nitric and hydrofluoric acid, and hydrogen peroxide. Sum is the sum of fractions 1 to 6. Agreement is the proximity between the values obtained from total digestion and the sum of the step extractions. n.d. is not determined. Variations between the fractions were similar for Fe and Cd (F1 < F2 < F3 < F4 < F6 < F5), while Ti (F1 = F2 = F3 = F4 < F5 < F6) and Cu (F2 < F1 < F6 < F4 < F3 < F5), were different (Fig. 2). The concentrations presented in Table 3 indicate that water soluble fraction (F1) and ion exchange fraction (F2) contained the lowest Cd (< 0.022–1.09 mg kg − 1 ), Cu (< 0.04–2.28 mg kg − 1 ), Fe (< 0.12–1.34 mg kg − 1 ) and Ti (< 0.007 mg kg − 1 ) concentrations in the PM 10 sample. The two subsequent fractions, concerning metals bound to carbonates and metal oxides, indicated increasing Fe (7.29–226.2 mg kg − 1 ) and Cu (< 0.04–19.2 mg kg − 1 ) concentrations, while only small changes in concentrations were observed for Ti (< 0.007 mg kg − 1 ) and Cd (< 0.022–0.27 mg kg − 1 ) when compared to F1 and F2. Regarding the extraction fractions related to these elements bound to organic complexes and sulfides (F5) and the residual fraction (F6), high Fe (234.1–656.3 mg kg − 1 ) and Ti (73.0–132.9 mg kg − 1 ) concentrations were noted, became more present. Cadmium and Cu ranged from < 0.022– 33.2 mg kg − 1 and < 0.04–3.45 mg kg − 1 for the PM 10 , respectively. The distribution profiles for Fe (F1 < F2 < F3 < F4 < F6 < F5) and Ti (F1 = F2 = F3 = F4 < F5 < F6) in PM 10 were similar to those observed in TSP for Cd (F3 < F2 < F4 < F1 < F5 < F6) and Cu (F2 < F1 < F4 < F6 < F3 < F5), although the relationships between extraction fractions was different (Fig. 2). The average percentage variation of each fraction in relation to total extraction values was also evaluated. Differences between TSP and PM 10 were of 8% (Cu), 12% (Cd), 33% (Ti) and 39% (Fe), highlighting Fe and Ti, both present in high amounts in the TSP samples, and Cd and Cu were higher in PM 10 . Similar results were reported by Schleicher et al. (2011). We suggest that Cd and Cu in the investigated APM present a strong anthropogenic contribution, as they are present in the reducible (F4) and oxidizable (F5) fractions. On other hand, Fe and Ti are probably derived from soil resuspension, due to the presence of less labile fractions. In another study in the same area in 2017, Almeida et al. (2017) analyzed total APM (TSP) and reported main Fe, Mn, Ni and Ti sources as resuspension of urban dust, while Cu origins were mainly vehicular traffic, and for V the source was fuel burning. A small percentage of Cd was present in the most labile fractions (Fig. 2), possibly from fly ash originated from waste incineration or from fossil fuel burning (Petit and Rucandio 1999; OSHA a 2016). Cadmium sulfates, nitrates and chlorides are highly water soluble and, therefore, potentially harmful to the environment and to human health. The International Agency Research of Cancer (IARC) considers Cd and its compounds as carcinogenic (IARC 2012). The United States Occupational Safety and Health Administration (OSHA) establishes a human health risk limit of 0.002–0.010 mg m − 3 for exposure to dust containing Cd and its compounds (OSHA b 2016). In this sense, the Cd levels determined herein ranged from 0.16 to 0.053 µg m − 3 , indicating no health risks up to now. Copper concentrations were considerable in the reducible and oxidizable fractions (Fig. 2), indicating affinity for organic matter, thus making this element potentially mobile in the environment, potentially causing negative environmental and human health impacts. The profile verified herein suggests a relationship with traffic and oil combustion emissions. The mobilization of Fe may be associated to the presence of hydroxides in the analyzed TSP and PM 10 (Sipos et al. 2016). The OSHA sets the limit of 1.0 mg m − 3 for Fe (soluble salts) and Cu (dust and mist) in air (Almeida et al. 2013; OSHA a 2016; OSHA c 2016). Additionally, Ti concentrations in the residual fraction were over 50% of their total concentrations, indicating low environmental mobility. The IARC considers a 1.5 mg m − 3 limit for exposure to respirable dust, excluding ultrafine particles, containing titanium dioxide (IARC 2010). Considering the above, Cd, Cu and Fe present low mobility, due to significant percentages of these elements detected in the organic complexes and sulfides (F5) and residual (F6) fractions, above 50%. Bioavailability index (BI) and contamination factor (CF) of the metals The potential mobility and bioavailability of a metal is referred as a bioavailability index (BI), being assessed through the contributions of soluble, exchangeable, carbonates, oxides and reducible metals. BI values metals are evaluated as: low bioavailability (BI < 30%), medium bioavailability (30% < BI 50%). The calculated BI value in this study was performed as: BI (%) = [(F1 + F2 + F3) / Total metal concentration] x 100, where F1 is water-soluble fraction; F2 is ion exchange fraction; and F3 is carbonate-bound fraction (Sah, Verma, Kumari and Lakhani, 2017; Rajouriya, Pipal and Taneja, 2024). The calculated BI values are presented in Fig. 3(a). From the results, it is clear that only Cu present in PM 10 samples had medium bioavailability, Cd, Fe and Ti obtained values that classified them as low bioavailability. For TSP samples, all elements were classified as low bioavailability. Contamination factor (CF) is used to be assessed the contamination of metals in the environment with respect to its retention time. CF values for metals are categorized as: low (CF < 1), moderate (1 ≤ CF ≤ 3), high (3 ≤ CF ≤ 6) and very high (CF ≥ 6), and is calculated as: CF = (F1 + F2 + F3 + F4 + F5) / F6, where F1 is water-soluble fraction; F2 is ion exchange fraction; F3 is carbonate-bound fraction; F4 is metal-oxide bound fraction; F5 is organic metal complex- or sulfide-bound fraction; and F6 is the residual fraction. The calculated CF values are presented in Fig. 3(b) (Sah, Verma, Kumari and Lakhani, 2017; Rajouriya, Pipal and Taneja, 2024). From the results, it is clear that for PM 10 samples, Ti was characterized as low contaminated, Cd and Fe as moderate contaminated, and Cu as very high contaminated. And for TSP, Ti was characterized as low contaminated, Fe as moderate contaminated, Cd as high contaminated, and Cu as very high contaminated. Multivariate data analysis The metal fractionation of the TSP and PM 10 samples was evaluated by multivariate data analyses employing unsupervised methods to examine trends and similarities among the data set variables. In this sense, a principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to evaluate the results (Correia and Ferreira, 2007; Rodinova, Kucheryavskiy and Pomerantsev, 2021), aiming at identifying correlations between chemical elements and their probable sources. The autoscaling technique was applied to pre-process the data (Souza et al. 2024). A data matrix composed of 32 samples and six concentrations obtained by extraction fractions (32 × 6) was constructed. Thus, the samples were grouped according to the Fe, Ti, Cu and Cd distributions observed in the six sequential extraction fractions considering the investigated TSP and PM 10 samples. Figure 4 indicates the three clusters at a distance of 1200, indicating elemental grouping in relation to TSP and PM 10 (particle size) and the applied extraction fractions. The first group is characterized by Fe, probably from soil resuspension, similar to that described by Fernandéz et al. (2000). Likewise, we suggest that Ti distribution may be derived from the continental crust, as observed by Sipos et al. (2016), possibly due to soil resuspension. Additionally, Ti appears to bind to Fe in particles at a distance of 6800 (Fig. 4). The third group was composed of Cu and Cd, suggesting similar sources, but differing from the other determined chemical elements, indicating anthropogenic influences. As the study area presents intense traffic, Cu probably originates from the wear of automotive parts and fossil fuel burning, associated to an urban bus terminal and vehicular traffic near the sampling point in the urban area of Aracaju. Cadmium can be also linked to fossil fuel burning. Similar findings have been reported by Sipos et al. (2016). When applying the PCA to the data sets the first two principal components (PC) accounted for 74.9% of the total data variance, which is sufficient to explain the studied system (Fig. 5(a)). The PC1 represented 53.4% of the total variance. The sequential extraction F2 (ion exchange), F4 (metal oxides), F5 (organic complexes and sulfides) and F6 (residual fraction) were predominant in this PC, with negative loadings. The PC2 explained 21.5% of the total data variance. Fractions F1 (water soluble) and F3 (carbonate bound) were dominant in this PC, with positive loadings. A similar pattern was observed by Fernandéz et al. (2000). Figure 5(b) displays the scores graph between PC2 and PC1. Distinct groups were noted for Fe, Ti, Cu and Cd in the TSP and PM 10 , indicating different sources. Iron is probably derived from soil resuspension in the form of oxides and silicates (Fernandéz et al. 2000), while Cu and Cd are probably not associated with Fe-Mn oxides, and may, instead, be derived from fuel oil and fertilized soils when associated with organic matter (Jayaprakash et al. 2010). These metals can also originate from industries and foundries, and when mixed in the air and with TSP and PM 10 from the continental crust can become associated with organic compounds derived from fuel combustion (Fernandéz et al. 2000). However, Cu was extracted mainly in F1 and F3 fractions, associated to carbonates and the water-soluble fraction, respectively. Titanium was the least soluble element, suggesting stronger bonds with oxides and silicates from the continental crust, indicating associations with soil resuspension. Ti is still associated with the burning of fossil fuels, wear and tear of automotive parts, and industrial activity, sources present at the sampling site. The multivariate data analysis identified that the sampled TSP and PM 10 (airborne particulate matter) probably originate from soil, fertilizers, road traffic, industries and foundries. However, no differentiation concerning the composition of the particles forming TSP and PM 10 relative to the determined elements was observed, suggesting a high possibility that the contribution sources for the TSP and PM 10 formation are analogous. Conclusions The modified BCR method applied herein consisted of a direct study of metal distribution following a sequential extraction method (six fractions), and pollution source characterization. The challenge of the applied sequential extraction procedure comprises its dependence on previous fractions, and the fact that certain factors can influence the extracted amount of metals. The adaptations in the BCR procedure simplified the extraction process fractions, allowing for satisfactory elemental extractions, with good precision and accuracy. In the PM 10 samples the mobility sequence of the metals was: Cu > Fe > Cd > Ti. And in the TSP samples it was Cu > Cd > Fe > Ti. Iron and Ti were the most abundant metals bound to organic metal complexes and sulfides, as well as silicates in both TSP and PM 10 . Cadmium, in general, was present at higher concentrations bound to organic metal complexes and sulfides, silicates, and oxides, while Cu was most present bound to organic metal complexes and sulfides, oxides, and silicates. Nickel and V were not determined in the samples but presented the highest concentrations in the organic metal complex and sulfides fractions, as well as the silicate fractions in the analyzed CRM. Manganese was also not present in the investigated samples, but was most abundant in water-soluble, organic metal complex and sulfides, and silicates fractions in the road dust (BCR 723) and urban matter particulate (NIST 1648a) CRM. The high concentrations of Fe are associated to the strong influence of soil resuspension. Titanium is related to continental crust, resuspension, or city dust. Copper and Cd concentrations suggest automotive part wear and fossil fuel burning and fertilized soils when associated with organic matter. These metals can also originate from industries and foundries. The PCA and HCA analyses indicated a different origin for Fe and Ti, while Cu and Cd concentrations were shown to be from similar sources. The Cu and Fe results compared to particulate material reported by study in the same sampling area in 2013 decreased by half, and to Fe, increased 2 times. However, speciation studies are required to better evaluate the local air quality to determine the degree of toxicity assigned to the particles and the pollution sources. The applied chemical form fractionation method in comparison with total digestion was efficient and comprise the first study on metal mobility in TSP and PM 10 from the urban area of Aracaju, Sergipe, Northeastern, Brazil. In PM 10 , Ti was characterized as low contaminated, Cd and Fe as moderate contaminated, and Cu as very high contaminated. And in TSP, Ti, Fe and Cu had the same characterization as the PM 10 , and Cd was characterized as high contaminated. Declarations Acknowledgements The authors are grateful to Instituto Tecnológico e de Pesquisas do Estado de Sergipe (ITPS, Aracaju, Brazil), Department of Environmental and Water Resources (Sergipe State, Brazil), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brasília, Brazil), Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB, Salvador, Brazil), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES). Funding The authors are grateful for the support granted by Instituto Tecnológico e de Pesquisas do Estado de Sergipe (ITPS, Aracaju, Brazil) and the Department of Environmental and Water Resources (Sergipe State, Brazil). Tuse research was conducted with financial support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brasília, Brazil), Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB, Salvador, Brazil), through grants and fellowships. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. Authors contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Silvânio Silvério Lopes Costa, Jeferson Cavalcante Alves, Erivaldo Vieira da Silva, Joel Marques da Silva and Larissa Santos Xavier. The first draft of the manuscript was written by Silvânio Silvério Lopes Costa, Dayara Virgínia Lino Ávila, Vaniele Souza Ribeiro and Sidnei de Oliveira Souza. The conceptualization and funding were performed by Carlos Alexandre Borges Garcia, Gisele Olímpio da Rocha and Rennan Geovanny Oliveira Araujo. All authors commented on previous versions of the manuscript, and read and approved the final manuscript. Ethics approval Not applicable. Consent to participate Not applicable. Consent to publish Not applicable. Competing interests The authors declare no competing interests. Data Available Statements The authors declare that the data supporting the findings of this study are available within the paper. References Almeida TS, Sant’Ana MO, Cruz JM, Tormen L, Bascuñan VLAF, Azevedo PA, Garcia CAB, Alves JPH, Araujo RGO (2017) Characterization and source identification of the total airborne particulate matter collected in an urban area of Aracaju, Northeast, Brazil. Environ Pollut 226:444-451. https://doi.org/10.1016/j.envpol.2017.04.018 Almeida TS, Sant’Ana MO, Cruz JM, Tormen L, Curtius AJ, Alves JPH, Garcia CAB, Santos PA, Araujo RGO (2013) Optimization method for sequential determination of Cu and Fe in airborne particulate matter collected on glass fiber filters by slurry Sampling FAAS. J Brazil Chem Soc 24(4):700–706. https://doi.org/10.5935/0103-5053.20130088 Brazil, FIES - Federação das Indústrias do Estado de Sergipe. Cadastro industrial de Sergipe. http://www.fies.org.br/cadastro-industrial, accessed in December 2023. Chang CY, Wang CF, Mui DT, Chiang HL (2009) Application of methods (sequential extraction procedures and high-pressure digestion method) to fly as particles to determine the element constituents: A case study for BCR 176. J Hazard Mater 163(2-3):578–587. https://doi.org/10.1016/j.jhazmat.2008.07.039 Colombo C, Monhemius AJ, Plant JA (2008) Platinum, palladium and rhodium release from vehicle exhaust catalysts and road dust exposed to simulated lung fluids. Ecototoxicol Environ Saf 71(3):722–730. https://doi.org/10.1016/j.ecoenv.2007.11.011 Correia PRM, Ferreira MMC (2007) Non-supervised pattern recognition methods: exploring chemometrical procedures for evaluating analytical data. Quim Nova 30(2):481-487. https://doi.org/10.1590/S0100-40422007000200042 Dabek-Zlotorzynska E, Kelly M, Chen H, Chakrabarti CL (2003) Evaluation of capillary electrophoresis combined with a BCR sequential extraction for determining distribution of Fe, Zn, Cu, Mn, and Cd in airborne particulate matter. Anal Chim Acta 498(1-2):175-187. htts://doi.org/10.1016/j.aca.2003.06.002 Dabek-Zlotorzynska E, Kelly M, Chen H, Chakrabarti CL (2005) Application of capillary electrophoresis combined with a modified BCR sequential extraction for estimating of distribution of selected trace metals in PM2.5 fractions of urban airborne particulate matter. Chemosphere 58(10):1365-1376. https://doi.org/10.1016/j.chemosphere.2004.09.082 Döndü M, Özdemir N, Keskin F, Demirak A, Zeynalova N (2024) The effect of heavy metals mobility on their bioavailability in Güllük Lagoo, Aegean Sea. Reg Stud Mar Sci 71:103414. https://doi.org/10.1016/j.rsma.2024.103414 European Commission DG Environment - EC (2001). Working Group on Arsenic, Cadmium and Nickel Compounds, Ambient Air Pollution by As, Cd and Ni Compounds. Position Paper (Final Version). https://ec.europa.eu/environment/enveco/air/pdf/report_heavy_metals.pdf, accessed in May 2024. Espinosa AJF, Rodríguez MY, De la Rosa JB, Sánchez JCJ (2002) A chemical speciation of trace metals for fine urban particles. Atmos Environ 36(5):773–780. https://doi.org/10.1016/S1352-2310(01)00534-9 Fernandéz AJ, Ternero M, Barragán FJ, Jiménez JC (2000) An approach to characterization of sources of urban airborne particles through heavy metal speciation. Chemosphere 2(2):123–136. https://doi.org/10.1016/S1465-9972(00)00002-7 Gois JS, Almeida TS, Alves JC, Araujo RGO, Borges DLG (2016) Assessment of the halogen content of Brazilian inhalable particulate matter (PM 10 ) using high resolution molecular absorption spectrometry and electrothermal vaporization inductively coupled plasma mass spectrometry, with direct solid sample analysis. Environ Sci Technol 50(6):3031–3038. https://doi.org/10.1021/acs.est.5b01934 IARC - International Agency for Research on Cancer (2010). Monographs on the Evaluation of Carcinogenic Risks to Humans. Carbon Black, Titanium Dioxide, and Talc. 93–276. https://monographs.iarc.who.int/wp-content/uploads/2018/06/mono93.pdf, accessed in April 2024. IARC - International Agency for Research on Cancer (2012). Monographs on the Evaluation of Carcinogenic Risks to Humans. Cadmium and cadmium compounds 100c. https://monographs.iarc.who.int/wp-content/uploads/2018/06/mono100C-8.pdf, accessed in April 2024. Jang H, Choi K-H, Cho YM, Han D, Hong YS (2024) Environmental risk score of multiple pollutants for kidney damage among residents in vulnerable areas by occupational chemical exposure in Korea. Environ Sci Pollut Res 31:35938-35951. https://doi.org/10.1007/s11356-024-33567-5 Jayaprakash M, Urban B, Velmurugan PM, Srinivasalu S (2010) Accumulation of total trace metals due to rapid urbanization in microtidal zone of Pallikaranai marsh, South of Chennai, India. Environ Monit Assess 170:609–629. https://doi.org/10.1007/s10661-009-1261-6 Karagulian F, Belis CA, Dora CFC, Prüss-Ustün AM, Bonjour S, Adair-Rohani H, Amann M (2015) Contributions to cities’ ambient particulate matter (PM): A systematic review of local source contributions al global level. Atmos Environ 120:475–483. https://doi.org/10.1016/j.atmosenv.2015.08.087 Kira CS, Maihara VA (2007) Determination of major and minor elements in dairy products through inductively coupled plasma optical emission spectrometry after wet partial digestion and neutron activation analysis. Food Chem 100(1):390–395. https://doi.org/10.1016/j.foodchem.2005.09.014 Limbeck A, Wagner C, Lendl B, Mukhtar A (2012) Determination of water soluble trace metals in airborne particulate matter using a dynamic extraction procedure with on-line inductively coupled plasma optical emission spectrometric detection. Anal Chim Acta 750:111–119. https://doi.org/10.1016/j.aca.2012.05.005 Meeravali NN, Madhavi K, Manjusha R, Kumar AJ (2014) Sequential extraction of platinum, cisplatin and carboplatin from environmental samples and pre-concentration/separation using vesicular coacervative extraction and determination by continuum source ETAAS. Talanta 118:37–44. https://doi.org/10.1016/j.talanta.2013.09.045 Montaser A, Golightly DW (1992) Inductively coupled plasma in analytical atomic spectroscopy , 2nd Ed., VHC Publisher, New York, United States of America. Mukhtar A, Limbeck A (2013) Recent developments in assessment of bio-accessible trace metal fractions in airborne particulate matter: A review. Anal Chim Acta 774:11–25. https://doi.org/10.1016/j.aca.2013.02.008 OSHA a - Occupational Chemical Database (2016). Chemical Sampling information – Copper dusts & mists (as Cu). https://osha.gov/chemicaldata/1030, accessed in May 2024. OSHA b - Occupational Chemical Database (2016). Chemical Sampling information – Cadmium dust (as Cd). https://osha.gov/chemicaldata/1032, accessed in April 2024. OSHA c - Occupational Chemical Database (2016). Chemical Sampling information – Iron salts, soluble (as Fe). https://osha.gov/chemicaldata/499, accessed in April 2024. Palacio IC, Oliveira IF, Franklin RL, Barros SBM, Roubicek DA (2016) Evaluating the mutagenicity of the water-soluble fraction of air particulate matter: A comparison of two extraction strategies. Chemosphere 158:124–130. https://doi.org/10.1016/j.chemosphere.2016.05.058 Petit MD, Rucandio MI (1999) Sequential extractions for determination of cadmium distribution in coal fly ash, soil and sediment samples. Anal Chim Acta 401(1-2):283–291. https://doi.org/10.1016/S0003-2670(99)00487-0 Rajouriya K, Pipal AS, Taneja A (2024) A review on sequential extraction of metals bound particulate matter and their health risk assessment. J Atmos Chem 81:7. https://doi.org/10.1007/s10874-024-09460-3 Richter P, Griño P, Ahumada I, Giordano A (2007) Total element concentration and chemical fractionation in airborne particulate matter from Santiago, Chile. Atmos Environ 41(32):6729-6738. https://doi.org/10.1016/j.atmosenv.2007.04.053 Rodinova O, Kucheryavskiy S, Pomerantsev A (2021) Efficient tools for principal component analysis of complex data – a tutorial. Chemometr Intell Lab Syst 213:104304. https://doi.org/10.1016/j.chemolab.2021.104304 Rossini-Oliva S, Nuñez RL (2024) Is it healthy urban agriculture? Human exposure to potentially toxic elements in urban gardens from Andalusia, Spain. Environ Sci Pollut Res May15. https://doi.org/10.1007/s11356-024-33500-w Sah D, Verma PK, Kumari KM, Lakhani A (2017) Chemical partitioning of fine particle-bound As, Cd, Cr, Ni, Co, Pb and assessment of associated cancer risk due to inhalation, ingestion and dermal exposure. Inhal Toxicol 29:11, 483-493. https://doi.org/10.1080/08958378.2017.1406563 Schleicher NJ, Norra S, Chai F, Chen Y, Wang S, Cen K, Yu Y, Stüben D (2011) Temporal variability of trace metal mobility of urban particulate matter from Beijing – A contribution to health impact assessments of aerosols. Atmos Environ 45(39):7248–7265. https://doi.org/10.1016/j.atmosenv.2011.08.067 Silveira ML, Alleoni LRF, O’Connor GA, Chang AC (2006) Heavy metal sequential extraction methods – A modification for tropical soils. Chemosphere 64:1929-1938. https:// doi.org/10.1016/j.chemosphere.2006.01.018 Sipos P, Choi C, May Z (2016) Combination of single and sequential chemical extractions to study the mobility and host phases of potentially toxic elements in airborne particulate matter. Geochemistry 76(4):481–489. https://doi.org/10.1016/j.chemer.2016.08.005 Smeda A, Zyrnicki W (2002) Application of sequential extraction and ICP-AES method for study of the partitioning of metals in fly ashes. Microchem J 72(1):9–16. https://doi.org/10.1016/S0026-265X(01)00143-6 Souza AS, Bezerra MA, Cerqueira UMFM, Rodrigues CJO, Santos BC, Novaes CG, Almeida ERV (2024) An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples. Food Sci Biotechnol 33:1323-1336. Stabile L, Arpino F, Buonanno G, Russi A, Frattolillo A (2015) A simplified benchmark of ultrafine particle dispersion in idealized urban street canyons: A wind tunnel study. Build Environ 93(Part 2):186–198. https://doi.org/10.1016/j.buildenv.2015.05.045 Tessier A, Campbell PGC, Bisson M (1979) Sequential extraction procedure for the speciation of particulate trace metals. Anal Chem 51(7):844–851. https://doi.org/10.1021/ac50043a017 United Nations (2015). Transforming our world: the 2030 Agenda for Sustainable Development. https://www.sdgs.un.org/2030agenda, accessed in June 2024. Wang CF, Chang CY, Chin CJ, Men LC (1999) Determination of arsenic and vanadium in airborne related reference materials by inductively coupled plasma-mass spectrometry. https://doi.org/10.1016/S0003-2670(99)00242-1 WHO - World Health Organization. Occupational and Environmental Health Team (2000). Guidelines for Air Quality, Geneva. http://apps.who.int/iris/handle/10665/66537, accessed in May 2024. Wu FY, Liu CQ, Tu CL (2008) Atmospheric deposition of metals in TSP of Guiyang, PR China. Bull Environ Contam Toxicol 80:465–468. https://doi.org/10.1007/s00128-008-9397-6 Supplementary Files floatimage1.png Graphical Abstract Text: Airborne particulate matter sampled from a tropical urban area in Brazil evaluated employing a modified BCR sequential extraction method consisting of six fractions. Cite Share Download PDF Status: Published Journal Publication published 31 Jul, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 10 May, 2025 Reviewers agreed at journal 21 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor invited by journal 18 Mar, 2025 Editor assigned by journal 04 Mar, 2025 First submitted to journal 04 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6147994","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430491089,"identity":"09177239-e094-4b88-b33c-01214aa9ea27","order_by":0,"name":"Silvanio Silvério Lopes Costa","email":"","orcid":"","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Silvanio","middleName":"Silvério Lopes","lastName":"Costa","suffix":""},{"id":430491090,"identity":"bec97e69-f3f4-466a-b9d2-3f729cc74305","order_by":1,"name":"Jeferson Cavalcante Alves","email":"","orcid":"","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Jeferson","middleName":"Cavalcante","lastName":"Alves","suffix":""},{"id":430491091,"identity":"81da9b1e-67cf-4996-aaa4-e60b52e1aa20","order_by":2,"name":"Erivaldo Vieira da Silva","email":"","orcid":"","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Erivaldo","middleName":"Vieira da","lastName":"Silva","suffix":""},{"id":430491092,"identity":"88b3a3e8-f40e-4b8a-964e-52b1f7b2e724","order_by":3,"name":"Joel Marques da Silva","email":"","orcid":"","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"Marques da","lastName":"Silva","suffix":""},{"id":430491093,"identity":"eff7a583-5051-459c-9869-d34bab8be0aa","order_by":4,"name":"Vaniele Souza Ribeiro","email":"","orcid":"","institution":"Instituto Federal Baiano: Instituto Federal de Educacao Ciencia e Tecnologia Baiano","correspondingAuthor":false,"prefix":"","firstName":"Vaniele","middleName":"Souza","lastName":"Ribeiro","suffix":""},{"id":430491094,"identity":"a69c3154-7aeb-40cb-a8ab-b0943da74b1a","order_by":5,"name":"Dayara Virgínia Lino Ávila","email":"","orcid":"","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Dayara","middleName":"Virgínia Lino","lastName":"Ávila","suffix":""},{"id":430491095,"identity":"87eef979-a32a-4164-a2cb-add806e5b041","order_by":6,"name":"Sidnei Oliveira Souza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie3PMQrCMBSA4VcexCXqmlKkV0gROilepSJ0quCkq1CoS8G1Tt4ijioFJ2/QpVPndhEHB9O6OCWODvkJgYR8JAEwmf4xBA7t6G3x1m2Q3q+EXkgIEEiC+nt4N9ML9TsCOuLuUJT1Cubp8P4om6dwBwhW3USKK3Ky8TIuCVuevCwovAQB7YNQEKS+QyU5s75waFBYkhDsK4gbS/Li3cOqlsy0BHJJoCUQkZbMtUT+ZW2nnI1TFvp2FhaLBK1Y+Rd3nwv2fE1G6TCvWD0ppsddfK0b1cM+se+FtdWeN5lMJpO6N8vvRO9KfDGJAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8630-8599","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":true,"prefix":"","firstName":"Sidnei","middleName":"Oliveira","lastName":"Souza","suffix":""},{"id":430491096,"identity":"0da41a19-c725-478e-97b2-0a9ec168f7aa","order_by":7,"name":"Carlos Alexandre Borges Garcia","email":"","orcid":"","institution":"Federal University of Sergipe: Universidade Federal de Sergipe","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Alexandre Borges","lastName":"Garcia","suffix":""},{"id":430491097,"identity":"d01d4c5f-4248-4b22-8b46-5fec3002cea7","order_by":8,"name":"Gisele Olímpio da Rocha","email":"","orcid":"","institution":"Federal University of Bahia: Universidade Federal da Bahia","correspondingAuthor":false,"prefix":"","firstName":"Gisele","middleName":"Olímpio da","lastName":"Rocha","suffix":""},{"id":430491098,"identity":"b2317423-4c81-40c0-8c7e-4328631bdfba","order_by":9,"name":"Rennan Geovanny Oliveira Araujo","email":"","orcid":"","institution":"Federal University of Bahia: Universidade Federal da Bahia","correspondingAuthor":false,"prefix":"","firstName":"Rennan","middleName":"Geovanny Oliveira","lastName":"Araujo","suffix":""}],"badges":[],"createdAt":"2025-03-03 16:37:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6147994/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6147994/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-36779-5","type":"published","date":"2025-07-31T16:12:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79365181,"identity":"bb15abcb-bbac-4a49-acba-e9865801468a","added_by":"auto","created_at":"2025-03-27 13:09:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3161280,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the sampling site location indicating potential emission sources.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/043c5e53816e7a67321d56b6.jpg"},{"id":79365186,"identity":"3995a871-7967-45d5-ab9c-564c06ee1c02","added_by":"auto","created_at":"2025-03-27 13:09:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148813,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage distributions of mean metal concentrations in the TSP and PM\u003csub\u003e10 \u003c/sub\u003esamples.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/4d2d5f4e9e0f9fe3137493c8.jpg"},{"id":79365674,"identity":"73c44695-6f69-425c-b342-98ce87bcd23b","added_by":"auto","created_at":"2025-03-27 13:17:25","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69420,"visible":true,"origin":"","legend":"\u003cp\u003eBioavailability index (a), and contamination factor (b) of Cd, Cu, Fe and Ti metals in PM\u003csub\u003e10\u003c/sub\u003e and TSP samples.\u003c/p\u003e","description":"","filename":"Figure3aandb.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/2937aa6a198980efe0eb6ad8.jpg"},{"id":79365182,"identity":"6213372e-a6af-4ad0-b6d2-56c094a272f0","added_by":"auto","created_at":"2025-03-27 13:09:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48599,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram: Euclidean distance calculation for interpoints and Ward's linkage method for the extraction fractions applied to the TSP and PM\u003csub\u003e10\u003c/sub\u003e samples.\u003c/p\u003e","description":"","filename":"Figure4Dendogram.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/93db94824baa67685d32624b.jpg"},{"id":79365184,"identity":"86318291-f6ac-4535-aa81-bb01161cc58d","added_by":"auto","created_at":"2025-03-27 13:09:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70056,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis results considering the six fractions of the sequential extraction \u003cstrong\u003e(a)\u003c/strong\u003e. Graph of PC1 versus PC2 scores concerning the sequential extraction fractions for Cd, Cu, Fe and Ti in the TSP and PM\u003csub\u003e10\u003c/sub\u003e samples \u003cstrong\u003e(b)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure5aandb.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/f54a18573e970be1d0a3d148.jpg"},{"id":88268178,"identity":"d83c474b-0185-4572-892c-b160eed1c342","added_by":"auto","created_at":"2025-08-04 16:49:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4888893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/1e14b5ad-5832-4e5c-a362-cafc773b912d.pdf"},{"id":79365678,"identity":"e2f556a6-d8b8-41cd-b31f-47d655671a77","added_by":"auto","created_at":"2025-03-27 13:17:25","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1488308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract Text: \u003c/strong\u003eAirborne particulate matter sampled from a tropical urban area in Brazil evaluated employing a modified BCR sequential extraction method consisting of six fractions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6147994/v1/3fdd7fab0039605240c81e9b.png"}],"financialInterests":"","formattedTitle":"Trace Metal Chemical Fractionation in Airborne Particulate Matter from a Tropical Urban Area in Brazil","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe United Nations, through its \u0026ldquo;The Sustainable Development Agenda 2023\u0026rdquo; aims to \u0026ldquo;substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination\u0026rdquo; through its Sustainable Development Goal 3 (3.9) (United Nations \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this sense, anthropogenic activities comprise one of the main global pollution sources, contributing to increased air contamination in major cities due the growth of the world\u0026rsquo;s economy, which has, in turn, led to important demographic increases in urban centers (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Karagulian et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Rossini-Oliva and Nu\u0026ntilde;ez \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This has significantly elevated atmospheric particle emission rates in major cities, implying in the continuous and increasing human exposure to airborne particulate matter (APM) from both natural and anthropogenic origins (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Stabile et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Increased APM emissions expose both the environment and humans to higher levels of trace metals, which are key air pollution components, due to their small size and ability to transport toxic substances. This is also likely to promote metal biogeochemical cycle changes and impose human health risks, justifying interest in determining APM composition (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Stabile et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to determining mass concentrations, particle sizes and total metal concentrations present in atmospheric particles (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), metal chemical forms should also be quantified. In this sense, understanding metal chemical forms could potentially determine which chemical processes these compounds have undergone, as well as their (bio)availabilities and probable environmental mobility (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mukhtar and Limbeck \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Limbeck et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Metal mobility is the ability of a metal to move among environmental compartments, depending on how it is bound to a certain matrix and its environmental availability. In Bureau Community of Reference (BCR) procedures, acid-soluble fractions can be easily released into aquatic environments and absorbed by aquatic organisms, while reducible and oxidizable fractions can be released due to changes in sediment physicochemical properties or microbiological activities (D\u0026ouml;nd\u0026uuml; et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Chemical fractionation, in this sense, may also enable the development of emission prevention or mitigation policies (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Limbeck et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSequential extraction procedures are intended to be followed in order, so that the reagent used in each stage releases metals associated with particular sample phases, as metal bioavailability depends on oxidation state and metal form (Schleicher et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Limbeck et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Fractions 1\u0026ndash;6 of the extraction procedure applied herein were previously described by Tessier et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) (fractions ion exchange; bound to carbonates; bound to metal oxides; bound to organic metal complex or sulfides), followed residual fraction described by Wang et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and Chang et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) investigated a water-soluble fraction was first time. In another study, Wu et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) applied an extraction procedure consisting of three fractions for atmospheric deposition characterization, reporting Cd and Zn, as predominantly present in the labile fraction. Chromium and Cu, in turn, were mainly bound to organic matter and silicates. Depending on the amount of organic matter, the labile fraction also includes metals in the form of soluble organic complexes. In this sense, Schleicher et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) studied total suspended particulate (TSP) and respirable particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) metal mobility employing a sequential extraction procedure developed by Tessier et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), and modified by Espinosa et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The authors suggested that the As, Cd, Cu, Mn, Pb and Zn concentrations detected in highly mobile fractions probably cause adverse environmental and human health effects (Wu et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Cadmium and Pb, for example, can cause proximal tubular renal damage and glomeruli declines (Jang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother extraction procedure, established by BCR in Belgium, was applied by Sipos et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to characterize and evaluate metal mobility in TSP. A hierarchical cluster analysis (HCA) was applied to assess elemental behavior at each extraction fraction, highlighting two groups, one composed of highly water-soluble elements (Ca, Cd, K, Mg, Mn, Na, S and Zn) and another composed of elements present in the highest amounts in the residual fraction (Al, Cr, Fe, K, Ni and Ti). The authors suggested that Cd and Zn, present in the most labile fractions, probably originate from waste incineration ash. Lead, present in the reducible fraction, can be associated to combustion or traffic sources, as well as Cu, associated to carbonates (Sipos et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn another assessment, Chang et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) applied a sequential extraction method based on six fractions, carrying out total analyte extraction using a fly ash certified reference material (BCR 176) at the same time, validating the analytical method. The authors indicated that the carbonate-bound fraction contained higher Cd, Cu, Pb and Zn concentrations, while Fe and Mn were higher in the organic matter fraction bound. Chemical fractionation allows for the quantification of specific metallic forms and assessments on bioavailability, solubility, geochemical transport and metal cycles through physico-chemical speciation assessments. Knowledge on chemical particle speciation, thus, is vital to understand human health and environmental effects (Espinosa et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe modified BCR method for particulate matter has been employed in some studies to date. Dabek-Zlotorzynska et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), for example, developed a capillary electrophoresis (CE) method to determine the distribution of Fe, Zn, Cu, Mn and Cd in APM following a sequential extraction employing a three-stage BCR extraction method, as follows: (1) exchangeable water and acid-extractable fraction; (2) reducible fraction and (3) oxidizable fraction. The authors also modified the BCR procedure, adding an ultrasonic bath fraction to accelerate metal extraction, reducing the analysis time from 48 to 2 h. In another study, Dabek-Zlotorzynska et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) applied the same conditions to evaluate the same elements in PM\u003csub\u003e2.5\u003c/sub\u003e, offering a fast and reliable way to provide chemical metal fractionation information.\u003c/p\u003e \u003cp\u003eThis study aims to employ a modified BCR method for metal chemical fractionation in TSP and inhalable particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) for samples from a tropical urban area (Chang et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The studied urban area is located in Northeastern Brazil, and is mainly influenced by traffic, resuspended particles, biomass burning, industrial emissions and marine aerosols (Almeida et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gois et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To the best of our knowledge, this is the first time that chemical element fractionation is employed to study metal TSP and PM\u003csub\u003e10\u003c/sub\u003e mobility in this urban area. This is expected to comprise an important tool for predicting the potential effects of environmental changes and airborne metals on the redistribution of chemical metal forms in tropical APM, due to certain particularities noted between tropical and temperate climate environments, as observed by Silveira et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eSampling\u003c/h2\u003e\n \u003cp\u003eFrom July to November 2013, four total suspended particulate (TSP) and four inhalable particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) samples were obtained simultaneously from a large volume sampler (Hi-vol, AGV PTS/CVV, Energ\u0026eacute;tica, Rio de Janeiro, Brazil) employing glass fiber filters (E55, 8\u0026times;10 inch, Energ\u0026eacute;tica, Rio de Janeiro, RJ, Brazil) and collecting an average volume of 1440 m\u003csup\u003e3\u003c/sup\u003e during 24 (\u0026plusmn;\u0026thinsp;1) hours. Atmospheric conditions were collected for later use in calculating concentrations in \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. The glass fiber filters were heated in a vacuum drying oven at 110\u0026ndash;120\u0026deg;C for 2 h prior to use, and conditioned at a controlled temperature (25\u0026deg;C ) with temperature variations lower than \u0026plusmn;\u0026thinsp;3\u0026deg;C after sampling. After the final weighing, the exposed and unused filters (blank) were dried in a desiccator for 24 h and ground for 15 min to particle sizes lower than 63 \u0026micro;m (Almeida et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) using an agate ball mill (Retsch, D\u0026uuml;sseldorf, Germany). At the time of collections, the city of Aracaju, Sergipe, had a population of 614,577 inhabitants, and a fleet of 248,834 vehicles. The sampling station is located in the Industrial District of Aracaju (\u003cem\u003eDistrito Industrial de Aracaju\u003c/em\u003e - DIA), in the urban Aracaju city area, Sergipe, Brazil (10\u0026deg;56\u0026rsquo;53.7\u0026rdquo;S 37\u0026deg;04\u0026rsquo;27.5\u0026rdquo;W) under the responsibility of the Department of Environmental and Water Resources (\u003cem\u003eDepartamento de Meio Ambiente e Recursos H\u0026iacute;dricos\u003c/em\u003e) (Fig. 1).\u003c/p\u003e\n \u003cp\u003eIn addition to traffic and marine aerosols, the main potential emission sources near the urban area where the sampling took place encompass construction, metal processing, wood and fabrics, electrical distribution and control equipment (Brazil 2023).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eInstrumentation and analysis\u003c/h3\u003e\n\u003cp\u003eTrace metals in TSP deposited on glass fiber filter were extracted using a closed digester block (TECNAL, TE007A, S\u0026atilde;o Paulo, Brazil) with a temperature control and a polytetrafluoroethylene (PTFE) reactor. Cadmium, Cu, Fe, Mn, Ni, Ti and V concentrations were determined employing an inductively coupled plasma optical emission spectrometer (ICP OES) (E-720, Mulgrave, Varian, Australia) equipped with a Sturman-Masters nebulizer chamber and V-Groove nebulizer (VAR04123, Mulgrave, Varian, Australia). The instrumental parameters were as follows: radio frequency (RF) of 40 MHz, applied RF power of 1.20 kW, outer plasma gas rate of 15.0 L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, auxiliary gas flow rate of 1.5 L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, nebulizer gas flow rate of 1.0 L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, sample uptake rate of 0.8 mL min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and signal integration time of 1 s.\u003c/p\u003e\n\u003cp\u003eThe limits of detection (LoD) and quantification (LoQ) were calculated using the background equivalent concentration (BEC) and signal-to-background ratio (SBR), as BEC\u0026thinsp;=\u0026thinsp;C\u003csub\u003eRS\u003c/sub\u003e/SBR, where SBR = (I\u003csub\u003eRS\u003c/sub\u003e - I\u003csub\u003eblank\u003c/sub\u003e)/I\u003csub\u003eblank\u003c/sub\u003e, C\u003csub\u003eRS\u003c/sub\u003e is the reference element concentration in the solution, and I\u003csub\u003eRS\u003c/sub\u003e and I\u003csub\u003eblank\u003c/sub\u003e are the emission intensities for the reference element and blank solutions, respectively (Montaser and Golightly \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). Precision was expressed as the relative standard deviation (RSD), calculated using 10 consecutive measurements of the blank solution. The LoD was then calculated as (3 x RSD x BEC)/100, and the LoQ as (10 x RSD x BEC)/100. The LoD and LoQ values (in mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were 0.002 and 0.007 for Ti (334.941 nm), 0.002 and 0.008 for V (292.401 nm), 0.006 and 0.02 for Cd (214.439 nm), 0.006 and 0.022 for Ni (231.604 nm), 0.012 and 0.04 for Cu (327.395 nm), 0.02 and 0.07 for Mn (257.610 nm) and, 0.04 and 0.12 for Fe (259.940 nm), respectively, all considered acceptable for the quantitative determination of these elements (Colombo et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA road dust (BCR 723) certified reference material (CRM) was acquired from the Bureau of Reference, Brussels, Belgium (Meeravali et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Palacio et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) and an urban particulate matter (NIST 1648a) CRM, from the National Institute of Standards and Technology (NIST, United States) (Limbeck et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Montaser and Golightly \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e), both analyzed for quality control.\u003c/p\u003e\n\u003cp\u003eAll materials used in the experiments were previously decontaminated overnight with a 10% v v\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e nitric acid solution, rinsed with deionized water, and dried at room temperature (Almeida et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSequential Extraction Procedure\u003c/h3\u003e\n\u003cp\u003eAll employed reagents were of analytical grade (Merck, Darmstadt, Germany) and solutions were prepared using deionized water ( 18.2 MΩ cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e resistivity) obtained from a MS2000 GEHAKA purification system (S\u0026atilde;o Paulo, Brazil). External calibration curves were prepared using multielement standard solutions at 1000 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Specsol\u0026reg;, S\u0026atilde;o Paulo, Brazil) for Cd, Cu, Fe, Mn, Ni, Ti and V.\u003c/p\u003e\n\u003cp\u003eThe sequential extraction method was adapted from Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e), comprising six fractions (F1 \u0026ndash; F6) characterized by a gradual increase in solvent strength:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cem\u003e1. Water-soluble fraction\u003c/em\u003e: 10 mL of deionized water were mixed with about 50 mg of each sample in polyethylene centrifuge tubes. The slurries were then stirred for 3 h at room temperature at 150 rpm. The supernatants were then separated by centrifugation for 5 min for subsequent ICP OES analysis. Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) performed other fractions such as removing the supernatant, followed by further shaking, centrifuging and filtering, which were not carried out herein.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e2. Ion exchange fraction\u003c/em\u003e: 10 mL of a 0.1 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e MgCl\u003csub\u003e2\u003c/sub\u003e solution were mixed with the residues recovered from F1 and stirred for 3 h at room temperature at 150 rpm. The supernatants were then separated by centrifugation for 5 min prior to further analysis. Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) performed other fractions, as described above, which were also not carried out herein.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e3. Carbonate-bound\u003c/em\u003e: 10 mL of a 0.10 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NaOAc/HOAc solution at pH 5.0 were mixed with the residues obtained in F2 and stirred for 3 h at room temperature at 150 rpm. The supernatants were then separated by centrifugation for 5 min prior to further analysis. The same procedure was employed by Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) in this fraction.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e4. Metal oxide-bound fraction\u003c/em\u003e: 10 mL of a 0.10 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e NH\u003csub\u003e2\u003c/sub\u003eOH.HCl solution were mixed with the residues obtained in F3 and shaken at 60\u0026deg;C for 3 h at 150 rpm. The supernatants were separated by centrifugation for 5 min to determine analyte concentrations. Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) performed other fractions, as described above, which were also not carried out herein.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e5. Organic metal complex- or sulfide-bound fraction\u003c/em\u003e: 10 mL of a mixed 2% HNO\u003csub\u003e3\u003c/sub\u003e and 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (3:7 v v\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) solution were added to the residues obtained in F4 and heated at 85\u0026deg;C in a water bath under shaking for 3 h at 150 rpm. The procedures reported for the previous fractions were followed. Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) also performed extraction and solid-liquid separation, as well as extract storage, as described in F4, which were not carried out herein.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e\u003cem\u003e6. Residual fraction\u003c/em\u003e: 3 mL of a 65% m m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e HNO\u003csub\u003e3\u003c/sub\u003e, 2 mL of 30% m m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and 5 mL of a 38% m m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e HF solution were mixed with the residues obtained in F5, which were then heated in a closed system with PTFE vessels at 190\u0026deg;C for 5 h in a closed digester block (TECNAL, TE007A, S\u0026atilde;o Paulo, Brazil). After cooling, the system was opened and allowed to dry at 85\u0026deg;C, followed by the addition of 0.5 mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e HNO\u003csub\u003e3\u003c/sub\u003e for residual salt dissolution, completed to 10 mL. Chang et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) employed 10 mL of a mixed acid (HNO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;HF\u0026thinsp;=\u0026thinsp;3:5:2 mL) at different ratios than the ones applied herein.\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe determination of Cd, Cu, Fe, Mn, Ni, Ti and V in all extraction process fractions were performed by ICP OES. The procedure described in F6 (HNO\u003csub\u003e3\u003c/sub\u003e: H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e:HF, 3:2:5 mL) was also applied for total sample and CRM digestion. The blank analytical solution was used for quality control. All samples were prepared in triplicate.\u003c/p\u003e\n\u003ch3\u003eApplication of chemometric tools\u003c/h3\u003e\n\u003cp\u003eThe ICP OES results are arranged as Cd, Cu, Fe, Mn, Ni, Ti and V in the six chemical fractions obtained following the sequential extraction method applied to PM\u003csub\u003e10\u003c/sub\u003e and TSP samples from an urban area in Aracaju, Northeastern Brazil.\u003c/p\u003e\n\u003cp\u003eMetal correlations can be determined, suggesting the main contributions for each metal fraction, such as traffic, soil resuspension particles and industrial activity. A principal component analysis (PCA) and hierarchical cluster analysis (HCA) were, thus, employed to examine potential trends and similarities between the concentrations of the investigated metals in TSP and PM\u003csub\u003e10\u003c/sub\u003e, using the Statistica software (version 6.0, SoftStat, Tulsa, USA). Both statistical methods can be effectively applied to distinguish among phase associations and metal sources in environmental media.\u003c/p\u003e\n\u003cp\u003eThe data were normalized by dividing the element concentration value by the highest element concentration measured in the samples, in order to assign a scale with a maximum value of 1. The objective was transform the data in order to eliminate the differences between the orders of magnitude of the values and the range of variables, while maintaining statistical information (Souza et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eSequential extraction Certified Reference Material (CRM) evaluation\u003c/h2\u003e\n \u003cp\u003eSequential extractions seek to identify in which characteristic range of the natural environment each element becomes more readily available (Mukhtar and Limbeck 2013). The procedure adopted herein consists in obtaining six sample fractions that may suggest which possible physicochemical process took place and how much the determined elements are bioavailable in the TSP and PM\u003csub\u003e10\u003c/sub\u003e samples. Moreover, it may also indicate how metals are bound or interact with which part of the particulate matter matrix, according to the characteristics of each reagent employed through each fraction (Chang et al. 2009).\u003c/p\u003e\n \u003cp\u003eThe fractioned trace metal content in TSP and PM\u003csub\u003e10\u003c/sub\u003e, as well as the sum of the concentrations of the six fractions (total concentrations), indicate how much each element is extracted in each fraction and how much it represents of its total concentration. However, according to the World Health Organization (WHO 2000) and European standards (European Comission 2001), sequential extraction processes are subject to uncertainties during the sample preparation and analysis fractions. Strategies are, thus, required to assess and mitigate these types of errors.\u003c/p\u003e\n \u003cp\u003eTo validate the sequential extraction procedure employed herein, two CRM, comprising 50 mg of urban particulate matter (NIST SRM1648a) and 50 mg of road dust (BCR 723), were also extracted. As a comparative method, total acid digestion was also applied to the samples using a HNO\u003csub\u003e3\u003c/sub\u003e:HF:H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 3:5:2 v v\u003csup\u003e− 1\u003c/sup\u003e mixture in a closed digester block (Chang et al. 2009). The results are depicted in Table 1.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eConcentrations of Cd, Cu, Fe, Mn, Ni, Ti and V in road dust (BCR 723) and urban particulate matter (NIST SRM1648a) Certified Reference Materials following sequential extraction and total digestion methods and ICP OES analyses.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eSequential extraction\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTotal digestion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBCR 723\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCertified values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative error\u003c/strong\u003e %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFound value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative error\u003c/strong\u003e %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003ecalc\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe /\u003c/strong\u003e %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29 ± 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 ± 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23 ± 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 ± 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.71 ± 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.4 ± 2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn /\u003c/strong\u003e g kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28 ± 0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 ± 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056 ± 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 ± 0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18 ± 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 ± 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 ± 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 ± 0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.9 ± 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNi /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171.0 ± 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.32 ± 0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.56 ± 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.1 ± 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.6 ± 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4 ± 2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.7 ± 4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.2 ± 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi /\u003c/strong\u003e g kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.58 ± 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45 ± 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 ± 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09 ± 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.0 ± 0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eV /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.9 ± 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77 ± 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13 ± 0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.47 ± 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.6 ± 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4 ± 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5 ± 11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.8 ± 14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNIST 1648a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCertified values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eF6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative error\u003c/strong\u003e %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFound value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative error\u003c/strong\u003e %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAgreement\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.7 ± 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.3 ± 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.7 ± 3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6 ± 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.44 ± 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00 ± 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.72 ± 0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5 ± 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.1 ± 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e610.0 ± 70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.2 ± 10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.0 ± 4.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4 ± 1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.1 ± 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e502.9 ± 44.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.4 ± 2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e658.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e497.8 ± 9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.6 ± 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe /\u003c/strong\u003e %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.92 ± 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 ± 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.58 ± 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46 ± 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30 ± 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.2 ± 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMn /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e790.0 ± 44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231 ± 34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.9 ± 21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.3 ± 1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.1 ± 3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196 ± 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104 ± 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e667.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e687.9 ± 16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.1 ± 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNi /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.1 ± 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.4 ± 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55 ± 1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 ± 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.77 ± 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.3 ± 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.3 ± 0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.1 ± 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82.7 ± 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eV /\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127.0 ± 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.6 ± 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.23 ± 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.18 ± 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8 ± 0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.0 ± 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.0 ± 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.2 ± 13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.6 ± 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003csup\u003e*\u003c/sup\u003emg kg\u003csup\u003e-1\u003c/sup\u003e. Agreement (%) = [(measured value - certified value)/certified value] x 100; Relative error (%) = [(measured value – certified value)/certified value] x 100; results expressed as means ± standard deviations (n=3). F1: Water-soluble; F2: Ion Exchange; F3: Bound to carbonates; F4: Bound to metal oxides; F5: Bound to organic complex and sulfides; F6:\u0026nbsp;Residual fraction.\u003c/p\u003e\n \u003cp\u003e(Critical value of t is 9.925 at the 99% confidence interval for freedom level of 2. If the calculated t value is lower than the critical value of t, there is no significant difference between the experimental results and certified value of the reference material at a 95% confidence level).\u003c/p\u003e\n \u003cp\u003eThe agreement values between certified and measured values ranged from 81.8% (Ti) to 112.3% (Ni) for the sum of the six sequential extraction fractions and from 80.8% (V) to 87.1% (Mn) for the total digestion of the two CRM. The percentage relative errors are also presented in Table 1, indicating that the Cd, Cu, Fe, Mn, Ni, Ti and V determinations can be considered quantitative, as absolute relative errors did not exceed ± 20% (Kira and Maihara 2007). The calculated t-values are lower than the critical f value of 9.925 at a 99% confidence interval for 2 levels of freedom for all analytes. This indicates no evidence of systematic errors in the applied analytical method, confirming good agreement with certified values. The following section summarizes the results obtained for each element:\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCadmium\u003c/em\u003e (Cd): Present in all sequential extraction procedure stages, at 29% (F1: water-soluble fraction), 23% (F2: ion exchange fraction), 14% (F3: carbonate-bound fraction), 9% (F4: metal oxide-bound fraction), 8% (F5: organic metal complex- or sulfide-bound fraction) and 6% (F6: residual fraction). Water-soluble (F1) and ion-exchangeable (F2) species fractions comprised mainly Cd species for the urban particulate matter (NIST 1648a) CRM, representing this metal’s availability, which is susceptible to ionic composition or pH variations. Similar results have been reported in the literature. For example, Dabek-Zlotorzynska et al. (2003) reported about 55% of total Cd partitioned in water- and acid-phases in the same urban particulate matter (NIST 1648) CRM.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCopper\u003c/em\u003e (Cu): This element presented concentrations of up to 10% in the urban particulate matter (NIST 1648a) CRM in fractions (F1), (F2), (F3), (F4) and (F6), while 82% was bound to organic complexes and sulfides (F5). This indicates that Cu is mainly bound to the analyzed matrices (Espinosa et al. 2002). Dabek-Zlotorzynska et al. (2003) reported Cu in the exchangeable or acid-soluble and residual fractions (~ 30% each), as well as in the reducible (~ 15%), and oxidizable (~ 24%) phases in the same CRM (NIST 1648).\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eIron\u003c/em\u003e (Fe): Fractions (1), (2), (3) contained relatively low Fe levels, below the LoQ (\u0026lt; 0.12 mg kg\u003csup\u003e− 1\u003c/sup\u003e), while fraction 4 contained 5.4% Fe in the urban particulate matter (NIST 1648a) CRM and 8.8% in the road dust (BCR 723) CRM bound to metal oxides. The highest concentration of this metal was verified in the two last fractions, totaling 78% in the urban particulate matter (NIST SRM1648a) CRM and 98% in the road dust (BCR 723) CRM. In the latter, Fe concentrations were mostly bound to organic metal complexes (68%) and silicates (30%), while in the urban particulate matter (NIST SRM1648a) CRM this element was mostly bound to organic complexes (40%) and silicates (37%). This indicates that Fe is not easily released under natural conditions (Smeda and Zyrnicki 2002). Similar results have been reported, \u003cem\u003ei.\u003c/em\u003ee., Dabek-Zlotorzynska et al. (2003) verified 1% Fe in the water/acid phase, 13% in the oxide phase, 6% under oxidized conditions, and ~ 80% refractory species also in the urban particulate matter (NIST 1648) CRM.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eManganese\u003c/em\u003e (Mn): About 29% of Mn concentrations were released from the NIST SRM1648a CRM in the water-soluble fraction (F1). This element, however, was not easily dissolved in the ion-exchangeable (8%), carbonate-bound (4%), and metal oxide-bound (6%) fractions. This element was mostly bound to organic complexes (25%) and silicates (13%). Concerning the road dust (BCR 723) CRM, 65% of Mn was released in the organic complex and sulfide fraction (F5) and 14% in the metal oxide fraction (F4). Concentrations in the other fractions, namely (F1), (F2), (F3) and (F6), were less than 11%, indicating that Mn is tightly bound to the analyzed matrix. Dabek-Zlotorzynska et al. (2003) detected Mn mainly in the residual fraction (~ 60%), while 8% was detected in the reducible phase, and 33%, in the exchangeable or acid-soluble fraction.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNickel\u003c/em\u003e (Ni): A very small amount of Ni was released in the first four fractions, less than 8%. The highest concentration was observed in the last two fractions, \u003cem\u003ei.e.\u003c/em\u003e, 75% in the organic metal complex fraction (F5) and 21% in the silicate fraction (F6) for the road dust (BCR 723) CRM. The same was observed for the urban particulate matter (NIST 1648a) CRM, with 29% of Ni present in the organic metal complex fraction (F5), 26% in the silicate fraction (F6), and 20% in the water-soluble fraction (F1). Fractions (2), (3) and (4) presented small amounts of Ni, less than 3%. Similar results were noted by Richter et al. (2007), who indicated that Ni is mainly bound to silicates and organic matter in APM from Santiago, Chile.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTitanium\u003c/em\u003e (Ti): This metal was the major element present in the road dust (BCR 723) CRM, binding to organic metal complexes (F5) (17%) and silicates (F6) (64%), indicating that the Ti compounds are not easily released in natural environments. A very small amount of Ti was released in the first four fractions, below than LoQ (\u0026lt; 0.007 mg kg\u003csup\u003e− 1\u003c/sup\u003e), demonstrating that Ti is intrinsically bound into the investigated matrix (Espinosa et al. 2002). In one study, Richter et al. (2007) reported Ti in airborne particulate mainly bound to carbonates and oxides.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eVanadium\u003c/em\u003e (V): Over 60% of V was bound to organic complexes and sulfides and silicates (residual fraction), in both CRM, at 41% and 40%, in F5 and 38% and 23% in F6, for road dust (BCR 723) and urban particulate matter (NIST 1648a), respectively. Both CRM presented similar behaviors, where V concentration did not exceed 12% in the other fractions, (F1, F2, F3 and F4). Richter et al. (2007) reported V in airborne matter particulate as mainly bound to carbonates and oxides.\u003c/p\u003e\n \u003cp\u003eBased on the applied sequential extraction procedures, a large fraction of all chemical elements could be extracted at pH 5.0, as Fe (68%), Mn (65%), Ni (75%), V (41%) for road dust (BCR 723), and Cu (82%), V (40%), Ni, Mn and Cd (29%) for urban particulate matter (NIST 1648a). The good agreement between the obtained values from the sum of the metals extracted in the six fractions with the total metals obtained in the total digestion procedure indicate that this method is highly accurate. The sample preparation procedure was then applied to the real TSP and PM\u003csub\u003e10\u003c/sub\u003e samples.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluation of TSP and PM samples from an urban area applying the sequential extraction method\u003c/h3\u003e\n\u003cp\u003eThe agreement between the sum of fractions and total digestion for the metal concentrations in the TSP samples are displayed in Table 2, ranging from 86.6% (Cu) to 117.5% (Ti) while PM\u003csub\u003e10\u003c/sub\u003e concentrations ranged from 82.1% (Ti) to 112.4% (Cd) for PM\u003csub\u003e10\u003c/sub\u003e (Table 3). Concentrations were below the analytical method LoQ for all samples concerning Mn (\u0026lt; 0.07 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Ni (\u0026lt; 0.020 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and V (\u0026lt; 0.008 mg kg\u003csup\u003e− 1\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eConcentration of Cd, Cu, Fe and Ti distributed in the six fractions of the sequential extraction method applied for TSP samples collected in the urban area of Aracaju.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003eTSP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal digestion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgreement\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSP 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54 ± 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64 ± 0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78 ± 0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 ± 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94 ± 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14 ± 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.06 ± 0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.08 ± 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13 ± 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.7 ± 2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4 ± 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.6 ± 3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e401.1 ± 61.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e423.4 ± 18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457.6 ± 39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1129 ± 497.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066 ± 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 ± 0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.6 ± 1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224.3 ± 34.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317.9 ± 64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSP 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26 ± 0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 ± 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 ± 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8 ± 1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.77 ± 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.5 ± 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05 ± 0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.3 ± 13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37 ± 1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.72 ± 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231.2 ± 13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e886.5 ± 73.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e510.8 ± 39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1573 ± 86.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128.4 ± 7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.4 ± 11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e267.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e228.0 ± 3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSP 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22 ± 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 ± 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49 ± 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60 ± 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93 ± 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4 ± 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.31 ± 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.0 ± 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 ± 1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.5 ± 8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.22 ± 3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e341.2 ± 9.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e771.7 ± 55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e629.9 ± 162.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1868 ± 384.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.4 ± 1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219.5 ± 5.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e353.7 ± 36.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTSP 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80 ± 1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.14 ± 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 ± 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.19 ± 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.21 ± 0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.15 ± 1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.7 ± 4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.4 ± 10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e334.7 ± 25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e917.3 ± 17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e885.4 ± 63.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1973.2 ± 198.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.1 ± 6.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e248.1 ± 6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e355.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.6 ± 12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e110.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e327.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e749.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e620.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eResults expressed as average concentration ± standard deviation (n = 3). Sum is the sum of the values, higher the quantification limits, obtained from the extraction steps. TSP = total suspended particles. Total digestion is the total decomposition of the sample using a mixture of nitric and hydrofluoric acid, and hydrogen peroxide. Sum is the sum of fractions 1 to 6. Agreement is the proximity between the values obtained from total digestion and the sum of the step extractions. n.d. is not determined.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003eFor TSP (Table 2), the lowest concentrations were observed in the water-soluble (F1) and ion exchange (F2) extraction phases [Fe (\u0026lt; 0.04–2.4 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Ti (\u0026lt; 0.007 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Cu (\u0026lt; 0.04–1.14 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Cd (\u0026lt; 0.022–0.22 mg kg\u003csup\u003e− 1\u003c/sup\u003e)]. The highest carbonate-bound (F3) and metal oxide-bound (F4) metals were Cu (\u0026lt; 0.04–15.4 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Fe (16.6–401.1 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Ti (0.07–0.25 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Cd (\u0026lt; 0.006–1.80 mg kg\u003csup\u003e− 1\u003c/sup\u003e). The two subsequent fractions, related to elements bound to organic complexes and sulfides (F5) and the residual fraction (F6), contained the highest concentrations of Fe (423.4–917.3 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Ti (92.4–248.1 mg kg\u003csup\u003e− 1\u003c/sup\u003e) in the total particulate matter. In the same fractions, Cd and Cu ranged from 0.21 to 2.49 mg kg\u003csup\u003e− 1\u003c/sup\u003e and \u0026lt; 0.01 to 34.7 mg kg\u003csup\u003e− 1\u003c/sup\u003e, respectively. \u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eConcentration of Cd, Cu, Fe and Ti distributed in the six fractions of the sequential extraction method applied for PM\u003csub\u003e10\u003c/sub\u003e samples from the urban area of Aracaju, Sergipe, Northeastern Brazil.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF6\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal digestion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgreement\u003c/p\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 ± 0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16 ± 0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 ± 0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 ± 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45 ± 2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93 ± 0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.28 ± 0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.2 ± 1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.85 ± 0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.7 ± 2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.4 ± 3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 ± 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.84 ± 8.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e188.0 ± 4.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e506.5 ± 79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e380.9 ± 18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1233 ± 110.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.1 ± 15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.6 ± 3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e257.7 ± 22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05 ± 0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 ± 0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77 ± 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7 ± 1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4 ± 1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.2 ± 4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41 ± 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.6 ± 1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.50 ± 0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e226.2 ± 41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e656.3 ± 86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429.1 ± 19.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1314 ± 40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.2 ± 10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.6 ± 3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e230.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e243.5 ± 4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 ± 0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 ± 0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.54 ± 0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65 ± 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.46 ± 0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31 ± 0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.01 ± 0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 ± 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.12 ± 0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 ± 0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.69 ± 1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.8 ± 1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234.1 ± 18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.6 ± 25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e667.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e802.9 ± 16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.0 ± 6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.9 ± 11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.8 ± 16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePM\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 ± 0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.65 ± 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06 ± 0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43 ± 0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.93 ± 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41 ± 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.9 ± 1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 ± 1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.2 ± 1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29 ± 1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.1 ± 12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e490.4 ± 72.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405.8 ± 3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1121 ± 91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76.6 ± 5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.3 ± 11.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e220.7 ± 16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCd\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCu\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFe\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e471.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e391.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1037.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1117.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTi\u003c/strong\u003e mg kg\u003csup\u003e− 1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en.d.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults expressed as mean concentration ± standard deviation (n = 3). Sum is the sum of the values, higher the quantification limits, obtained from the extraction steps. PM10: particulate matter smaller than 10 micrometers. Total digestion is the total decomposition of the sample using a mixture of nitric and hydrofluoric acid, and hydrogen peroxide. Sum is the sum of fractions 1 to 6. Agreement is the proximity between the values obtained from total digestion and the sum of the step extractions. n.d. is not determined.\u003c/p\u003e\n\u003cp\u003eVariations between the fractions were similar for Fe and Cd (F1 \u0026lt; F2 \u0026lt; F3 \u0026lt; F4 \u0026lt; F6 \u0026lt; F5), while Ti (F1 = F2 = F3 = F4 \u0026lt; F5 \u0026lt; F6) and Cu (F2 \u0026lt; F1 \u0026lt; F6 \u0026lt; F4 \u0026lt; F3 \u0026lt; F5), were different (Fig. 2).\u003c/p\u003e\n\u003cp\u003eThe concentrations presented in Table 3 indicate that water soluble fraction (F1) and ion exchange fraction (F2) contained the lowest Cd (\u0026lt; 0.022–1.09 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Cu (\u0026lt; 0.04–2.28 mg kg\u003csup\u003e− 1\u003c/sup\u003e), Fe (\u0026lt; 0.12–1.34 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Ti (\u0026lt; 0.007 mg kg\u003csup\u003e− 1\u003c/sup\u003e) concentrations in the PM\u003csub\u003e10\u003c/sub\u003e sample. The two subsequent fractions, concerning metals bound to carbonates and metal oxides, indicated increasing Fe (7.29–226.2 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Cu (\u0026lt; 0.04–19.2 mg kg\u003csup\u003e− 1\u003c/sup\u003e) concentrations, while only small changes in concentrations were observed for Ti (\u0026lt; 0.007 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Cd (\u0026lt; 0.022–0.27 mg kg\u003csup\u003e− 1\u003c/sup\u003e) when compared to F1 and F2. Regarding the extraction fractions related to these elements bound to organic complexes and sulfides (F5) and the residual fraction (F6), high Fe (234.1–656.3 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and Ti (73.0–132.9 mg kg\u003csup\u003e− 1\u003c/sup\u003e) concentrations were noted, became more present. Cadmium and Cu ranged from \u0026lt; 0.022– 33.2 mg kg\u003csup\u003e− 1\u003c/sup\u003e and \u0026lt; 0.04–3.45 mg kg\u003csup\u003e− 1\u003c/sup\u003e for the PM\u003csub\u003e10\u003c/sub\u003e, respectively.\u003c/p\u003e\n\u003cp\u003eThe distribution profiles for Fe (F1 \u0026lt; F2 \u0026lt; F3 \u0026lt; F4 \u0026lt; F6 \u0026lt; F5) and Ti (F1 = F2 = F3 = F4 \u0026lt; F5 \u0026lt; F6) in PM\u003csub\u003e10\u003c/sub\u003e were similar to those observed in TSP for Cd (F3 \u0026lt; F2 \u0026lt; F4 \u0026lt; F1 \u0026lt; F5 \u0026lt; F6) and Cu (F2 \u0026lt; F1 \u0026lt; F4 \u0026lt; F6 \u0026lt; F3 \u0026lt; F5), although the relationships between extraction fractions was different (Fig. 2).\u003c/p\u003e\n\u003cp\u003eThe average percentage variation of each fraction in relation to total extraction values was also evaluated. Differences between TSP and PM\u003csub\u003e10\u003c/sub\u003e were of 8% (Cu), 12% (Cd), 33% (Ti) and 39% (Fe), highlighting Fe and Ti, both present in high amounts in the TSP samples, and Cd and Cu were higher in PM\u003csub\u003e10\u003c/sub\u003e. Similar results were reported by Schleicher et al. (2011). We suggest that Cd and Cu in the investigated APM present a strong anthropogenic contribution, as they are present in the reducible (F4) and oxidizable (F5) fractions. On other hand, Fe and Ti are probably derived from soil resuspension, due to the presence of less labile fractions. In another study in the same area in 2017, Almeida et al. (2017) analyzed total APM (TSP) and reported main Fe, Mn, Ni and Ti sources as resuspension of urban dust, while Cu origins were mainly vehicular traffic, and for V the source was fuel burning.\u003c/p\u003e\n\u003cp\u003eA small percentage of Cd was present in the most labile fractions (Fig. 2), possibly from fly ash originated from waste incineration or from fossil fuel burning (Petit and Rucandio 1999; OSHA\u003csup\u003ea\u003c/sup\u003e 2016). Cadmium sulfates, nitrates and chlorides are highly water soluble and, therefore, potentially harmful to the environment and to human health. The International Agency Research of Cancer (IARC) considers Cd and its compounds as carcinogenic (IARC 2012). The United States Occupational Safety and Health Administration (OSHA) establishes a human health risk limit of 0.002–0.010 mg m\u003csup\u003e− 3\u003c/sup\u003e for exposure to dust containing Cd and its compounds (OSHA\u003csup\u003eb\u003c/sup\u003e 2016). In this sense, the Cd levels determined herein ranged from 0.16 to 0.053 µg m\u003csup\u003e− 3\u003c/sup\u003e, indicating no health risks up to now.\u003c/p\u003e\n\u003cp\u003eCopper concentrations were considerable in the reducible and oxidizable fractions (Fig.\u0026nbsp;2), indicating affinity for organic matter, thus making this element potentially mobile in the environment, potentially causing negative environmental and human health impacts. The profile verified herein suggests a relationship with traffic and oil combustion emissions. The mobilization of Fe may be associated to the presence of hydroxides in the analyzed TSP and PM\u003csub\u003e10\u003c/sub\u003e (Sipos et al. 2016). The OSHA sets the limit of 1.0 mg m\u003csup\u003e− 3\u003c/sup\u003e for Fe (soluble salts) and Cu (dust and mist) in air (Almeida et al. 2013; OSHA\u003csup\u003ea\u003c/sup\u003e 2016; OSHA\u003csup\u003ec\u003c/sup\u003e 2016).\u003c/p\u003e\n\u003cp\u003eAdditionally, Ti concentrations in the residual fraction were over 50% of their total concentrations, indicating low environmental mobility. The IARC considers a 1.5 mg m\u003csup\u003e− 3\u003c/sup\u003e limit for exposure to respirable dust, excluding ultrafine particles, containing titanium dioxide (IARC 2010).\u003c/p\u003e\n\u003cp\u003eConsidering the above, Cd, Cu and Fe present low mobility, due to significant percentages of these elements detected in the organic complexes and sulfides (F5) and residual (F6) fractions, above 50%.\u003c/p\u003e\n\u003ch3\u003eBioavailability index (BI) and contamination factor (CF) of the metals\u003c/h3\u003e\n\u003cp\u003eThe potential mobility and bioavailability of a metal is referred as a bioavailability index (BI), being assessed through the contributions of soluble, exchangeable, carbonates, oxides and reducible metals. BI values metals are evaluated as: low bioavailability (BI \u0026lt; 30%), medium bioavailability (30% \u0026lt; BI \u0026lt; 50%), and high bioavailability (BI \u0026gt; 50%). The calculated BI value in this study was performed as:\u003c/p\u003e\n\u003cp\u003eBI (%) = [(F1 + F2 + F3) / Total metal concentration] x 100, where F1 is water-soluble fraction; F2 is ion exchange fraction; and F3 is carbonate-bound fraction (Sah, Verma, Kumari and Lakhani, 2017; Rajouriya, Pipal and Taneja, 2024). The calculated BI values are presented in Fig.\u0026nbsp;3(a). From the results, it is clear that only Cu present in PM\u003csub\u003e10\u003c/sub\u003e samples had medium bioavailability, Cd, Fe and Ti obtained values that classified them as low bioavailability. For TSP samples, all elements were classified as low bioavailability.\u003c/p\u003e\n\u003cp\u003eContamination factor (CF) is used to be assessed the contamination of metals in the environment with respect to its retention time. CF values for metals are categorized as: low (CF \u0026lt; 1), moderate (1 ≤ CF ≤ 3), high (3 ≤ CF ≤ 6) and very high (CF ≥ 6), and is calculated as: CF = (F1 + F2 + F3 + F4 + F5) / F6, where F1 is water-soluble fraction; F2 is ion exchange fraction; F3 is carbonate-bound fraction; F4 is metal-oxide bound fraction; F5 is organic metal complex- or sulfide-bound fraction; and F6 is the residual fraction. The calculated CF values are presented in Fig. 3(b) (Sah, Verma, Kumari and Lakhani, 2017; Rajouriya, Pipal and Taneja, 2024). From the results, it is clear that for PM\u003csub\u003e10\u003c/sub\u003e samples, Ti was characterized as low contaminated, Cd and Fe as moderate contaminated, and Cu as very high contaminated. And for TSP, Ti was characterized as low contaminated, Fe as moderate contaminated, Cd as high contaminated, and Cu as very high contaminated.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eMultivariate data analysis\u003c/h2\u003e\n \u003cp\u003eThe metal fractionation of the TSP and PM\u003csub\u003e10\u003c/sub\u003e samples was evaluated by multivariate data analyses employing unsupervised methods to examine trends and similarities among the data set variables. In this sense, a principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to evaluate the results (Correia and Ferreira, 2007; Rodinova, Kucheryavskiy and Pomerantsev, 2021), aiming at identifying correlations between chemical elements and their probable sources.\u003c/p\u003e\n \u003cp\u003eThe autoscaling technique was applied to pre-process the data (Souza et al. 2024). A data matrix composed of 32 samples and six concentrations obtained by extraction fractions (32 × 6) was constructed. Thus, the samples were grouped according to the Fe, Ti, Cu and Cd distributions observed in the six sequential extraction fractions considering the investigated TSP and PM\u003csub\u003e10\u003c/sub\u003e samples.\u003c/p\u003e\n \u003cp\u003eFigure 4 indicates the three clusters at a distance of 1200, indicating elemental grouping in relation to TSP and PM\u003csub\u003e10\u003c/sub\u003e (particle size) and the applied extraction fractions. The first group is characterized by Fe, probably from soil resuspension, similar to that described by Fernandéz et al. (2000). Likewise, we suggest that Ti distribution may be derived from the continental crust, as observed by Sipos et al. (2016), possibly due to soil resuspension. Additionally, Ti appears to bind to Fe in particles at a distance of 6800 (Fig.\u0026nbsp;4).\u003c/p\u003e\n \u003cp\u003eThe third group was composed of Cu and Cd, suggesting similar sources, but differing from the other determined chemical elements, indicating anthropogenic influences. As the study area presents intense traffic, Cu probably originates from the wear of automotive parts and fossil fuel burning, associated to an urban bus terminal and vehicular traffic near the sampling point in the urban area of Aracaju. Cadmium can be also linked to fossil fuel burning. Similar findings have been reported by Sipos et al. (2016).\u003c/p\u003e\n \u003cp\u003eWhen applying the PCA to the data sets the first two principal components (PC) accounted for 74.9% of the total data variance, which is sufficient to explain the studied system (Fig.\u0026nbsp;5(a)).\u003c/p\u003e\n \u003cp\u003eThe PC1 represented 53.4% of the total variance. The sequential extraction F2 (ion exchange), F4 (metal oxides), F5 (organic complexes and sulfides) and F6 (residual fraction) were predominant in this PC, with negative loadings. The PC2 explained 21.5% of the total data variance. Fractions F1 (water soluble) and F3 (carbonate bound) were dominant in this PC, with positive loadings. A similar pattern was observed by Fernandéz et al. (2000).\u003c/p\u003e\n \u003cp\u003eFigure 5(b) displays the scores graph between PC2 and PC1. Distinct groups were noted for Fe, Ti, Cu and Cd in the TSP and PM\u003csub\u003e10\u003c/sub\u003e, indicating different sources. Iron is probably derived from soil resuspension in the form of oxides and silicates (Fernandéz et al. 2000), while Cu and Cd are probably not associated with Fe-Mn oxides, and may, instead, be derived from fuel oil and fertilized soils when associated with organic matter (Jayaprakash et al. 2010). These metals can also originate from industries and foundries, and when mixed in the air and with TSP and PM\u003csub\u003e10\u003c/sub\u003e from the continental crust can become associated with organic compounds derived from fuel combustion (Fernandéz et al. 2000). However, Cu was extracted mainly in F1 and F3 fractions, associated to carbonates and the water-soluble fraction, respectively.\u003c/p\u003e\n \u003cp\u003eTitanium was the least soluble element, suggesting stronger bonds with oxides and silicates from the continental crust, indicating associations with soil resuspension. Ti is still associated with the burning of fossil fuels, wear and tear of automotive parts, and industrial activity, sources present at the sampling site.\u003c/p\u003e\n \u003cp\u003eThe multivariate data analysis identified that the sampled TSP and PM\u003csub\u003e10\u003c/sub\u003e (airborne particulate matter) probably originate from soil, fertilizers, road traffic, industries and foundries. However, no differentiation concerning the composition of the particles forming TSP and PM\u003csub\u003e10\u003c/sub\u003e relative to the determined elements was observed, suggesting a high possibility that the contribution sources for the TSP and PM\u003csub\u003e10\u003c/sub\u003e formation are analogous.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe modified BCR method applied herein consisted of a direct study of metal distribution following a sequential extraction method (six fractions), and pollution source characterization. The challenge of the applied sequential extraction procedure comprises its dependence on previous fractions, and the fact that certain factors can influence the extracted amount of metals. The adaptations in the BCR procedure simplified the extraction process fractions, allowing for satisfactory elemental extractions, with good precision and accuracy.\u003c/p\u003e \u003cp\u003eIn the PM\u003csub\u003e10\u003c/sub\u003e samples the mobility sequence of the metals was: Cu\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Ti. And in the TSP samples it was Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Fe\u0026thinsp;\u0026gt;\u0026thinsp;Ti.\u003c/p\u003e \u003cp\u003eIron and Ti were the most abundant metals bound to organic metal complexes and sulfides, as well as silicates in both TSP and PM\u003csub\u003e10\u003c/sub\u003e. Cadmium, in general, was present at higher concentrations bound to organic metal complexes and sulfides, silicates, and oxides, while Cu was most present bound to organic metal complexes and sulfides, oxides, and silicates.\u003c/p\u003e \u003cp\u003eNickel and V were not determined in the samples but presented the highest concentrations in the organic metal complex and sulfides fractions, as well as the silicate fractions in the analyzed CRM. Manganese was also not present in the investigated samples, but was most abundant in water-soluble, organic metal complex and sulfides, and silicates fractions in the road dust (BCR 723) and urban matter particulate (NIST 1648a) CRM.\u003c/p\u003e \u003cp\u003eThe high concentrations of Fe are associated to the strong influence of soil resuspension. Titanium is related to continental crust, resuspension, or city dust. Copper and Cd concentrations suggest automotive part wear and fossil fuel burning and fertilized soils when associated with organic matter. These metals can also originate from industries and foundries. The PCA and HCA analyses indicated a different origin for Fe and Ti, while Cu and Cd concentrations were shown to be from similar sources.\u003c/p\u003e \u003cp\u003eThe Cu and Fe results compared to particulate material reported by study in the same sampling area in 2013 decreased by half, and to Fe, increased 2 times. However, speciation studies are required to better evaluate the local air quality to determine the degree of toxicity assigned to the particles and the pollution sources.\u003c/p\u003e \u003cp\u003eThe applied chemical form fractionation method in comparison with total digestion was efficient and comprise the first study on metal mobility in TSP and PM\u003csub\u003e10\u003c/sub\u003e from the urban area of Aracaju, Sergipe, Northeastern, Brazil.\u003c/p\u003e \u003cp\u003eIn PM\u003csub\u003e10\u003c/sub\u003e, Ti was characterized as low contaminated, Cd and Fe as moderate contaminated, and Cu as very high contaminated. And in TSP, Ti, Fe and Cu had the same characterization as the PM\u003csub\u003e10\u003c/sub\u003e, and Cd was characterized as high contaminated.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eThe authors are grateful to Instituto Tecnol\u0026oacute;gico e de Pesquisas do Estado de Sergipe (ITPS, Aracaju, Brazil), Department of Environmental and Water Resources (Sergipe State, Brazil), Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq, Bras\u0026iacute;lia, Brazil), Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado da Bahia (FAPESB, Salvador, Brazil), and Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior \u0026ndash; Brasil (CAPES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authors are grateful for the support granted by Instituto Tecnol\u0026oacute;gico e de Pesquisas do Estado de Sergipe (ITPS, Aracaju, Brazil) and the Department of Environmental and Water Resources (Sergipe State, Brazil). Tuse research was conducted with financial support from the Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico (CNPq, Bras\u0026iacute;lia, Brazil), Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado da Bahia (FAPESB, Salvador, Brazil), through grants and fellowships. This study was financed in part by the Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior \u0026ndash; Brasil (CAPES) \u0026ndash; Finance Code 001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors contributions\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Silv\u0026acirc;nio Silv\u0026eacute;rio Lopes Costa, Jeferson Cavalcante Alves, Erivaldo Vieira da Silva, Joel Marques da Silva and Larissa Santos Xavier. The first draft of the manuscript was written by Silv\u0026acirc;nio Silv\u0026eacute;rio Lopes Costa, Dayara Virg\u0026iacute;nia Lino \u0026Aacute;vila, Vaniele Souza Ribeiro and Sidnei de Oliveira Souza. The conceptualization and funding were performed by Carlos Alexandre Borges Garcia, Gisele Ol\u0026iacute;mpio da Rocha and Rennan Geovanny Oliveira Araujo. All authors commented on previous versions of the manuscript, and read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Available Statements\u0026nbsp;\u003c/strong\u003eThe authors declare that the data supporting the findings of this study are available within the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida TS, Sant\u0026rsquo;Ana MO, Cruz JM, Tormen L, Bascu\u0026ntilde;an VLAF, Azevedo PA, Garcia CAB, Alves JPH, Araujo RGO (2017) Characterization and source identification of the total airborne particulate matter collected in an urban area of Aracaju, Northeast, Brazil. Environ Pollut 226:444-451. https://doi.org/10.1016/j.envpol.2017.04.018\u003c/li\u003e\n\u003cli\u003eAlmeida TS, Sant\u0026rsquo;Ana MO, Cruz JM, Tormen L, Curtius AJ, Alves JPH, Garcia CAB, Santos PA, Araujo RGO (2013) Optimization method for sequential determination of Cu and Fe in airborne particulate matter collected on glass fiber filters by slurry Sampling FAAS. J Brazil Chem Soc 24(4):700\u0026ndash;706. https://doi.org/10.5935/0103-5053.20130088\u003c/li\u003e\n\u003cli\u003eBrazil, FIES - Federa\u0026ccedil;\u0026atilde;o das Ind\u0026uacute;strias do Estado de Sergipe. Cadastro industrial de Sergipe. http://www.fies.org.br/cadastro-industrial, accessed in December 2023. \u003c/li\u003e\n\u003cli\u003eChang CY, Wang CF, Mui DT, Chiang HL (2009) Application of methods (sequential extraction procedures and high-pressure digestion method) to fly as particles to determine the element constituents: A case study for BCR 176. J Hazard Mater 163(2-3):578\u0026ndash;587. https://doi.org/10.1016/j.jhazmat.2008.07.039\u003c/li\u003e\n\u003cli\u003eColombo C, Monhemius AJ, Plant JA (2008) Platinum, palladium and rhodium release from vehicle exhaust catalysts and road dust exposed to simulated lung fluids. Ecototoxicol Environ Saf 71(3):722\u0026ndash;730. https://doi.org/10.1016/j.ecoenv.2007.11.011\u003c/li\u003e\n\u003cli\u003eCorreia PRM, Ferreira MMC (2007) Non-supervised pattern recognition methods: exploring chemometrical procedures for evaluating analytical data. Quim Nova 30(2):481-487. https://doi.org/10.1590/S0100-40422007000200042\u003c/li\u003e\n\u003cli\u003eDabek-Zlotorzynska E, Kelly M, Chen H, Chakrabarti CL (2003) Evaluation of capillary electrophoresis combined with a BCR sequential extraction for determining distribution of Fe, Zn, Cu, Mn, and Cd in airborne particulate matter. Anal Chim Acta 498(1-2):175-187. htts://doi.org/10.1016/j.aca.2003.06.002\u003c/li\u003e\n\u003cli\u003eDabek-Zlotorzynska E, Kelly M, Chen H, Chakrabarti CL (2005) Application of capillary electrophoresis combined with a modified BCR sequential extraction for estimating of distribution of selected trace metals in PM2.5 fractions of urban airborne particulate matter. Chemosphere 58(10):1365-1376. https://doi.org/10.1016/j.chemosphere.2004.09.082\u003c/li\u003e\n\u003cli\u003eD\u0026ouml;nd\u0026uuml; M, \u0026Ouml;zdemir N, Keskin F, Demirak A, Zeynalova N (2024) The effect of heavy metals mobility on their bioavailability in G\u0026uuml;ll\u0026uuml;k Lagoo, Aegean Sea. Reg Stud Mar Sci 71:103414. https://doi.org/10.1016/j.rsma.2024.103414\u003c/li\u003e\n\u003cli\u003eEuropean Commission DG Environment - EC (2001). Working Group on Arsenic, Cadmium and Nickel Compounds, Ambient Air Pollution by As, Cd and Ni Compounds. Position Paper (Final Version). https://ec.europa.eu/environment/enveco/air/pdf/report_heavy_metals.pdf, accessed in May 2024.\u003c/li\u003e\n\u003cli\u003eEspinosa AJF, Rodr\u0026iacute;guez MY, De la Rosa JB, S\u0026aacute;nchez JCJ (2002) A chemical speciation of trace metals for fine urban particles. Atmos Environ 36(5):773\u0026ndash;780. https://doi.org/10.1016/S1352-2310(01)00534-9\u003c/li\u003e\n\u003cli\u003eFernand\u0026eacute;z AJ, Ternero M, Barrag\u0026aacute;n FJ, Jim\u0026eacute;nez JC (2000) An approach to characterization of sources of urban airborne particles through heavy metal speciation. Chemosphere 2(2):123\u0026ndash;136. https://doi.org/10.1016/S1465-9972(00)00002-7\u003c/li\u003e\n\u003cli\u003eGois JS, Almeida TS, Alves JC, Araujo RGO, Borges DLG (2016) Assessment of the halogen content of Brazilian inhalable particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) using high resolution molecular absorption spectrometry and electrothermal vaporization inductively coupled plasma mass spectrometry, with direct solid sample analysis. Environ Sci Technol 50(6):3031\u0026ndash;3038. https://doi.org/10.1021/acs.est.5b01934\u003c/li\u003e\n\u003cli\u003eIARC - International Agency for Research on Cancer (2010). Monographs on the Evaluation of Carcinogenic Risks to Humans. Carbon Black, Titanium Dioxide, and Talc. 93\u0026ndash;276. https://monographs.iarc.who.int/wp-content/uploads/2018/06/mono93.pdf, accessed in April 2024.\u003c/li\u003e\n\u003cli\u003eIARC - International Agency for Research on Cancer (2012). Monographs on the Evaluation of Carcinogenic Risks to Humans. Cadmium and cadmium compounds 100c. https://monographs.iarc.who.int/wp-content/uploads/2018/06/mono100C-8.pdf, accessed in April 2024. \u003c/li\u003e\n\u003cli\u003eJang H, Choi K-H, Cho YM, Han D, Hong YS (2024) Environmental risk score of multiple pollutants for kidney damage among residents in vulnerable areas by occupational chemical exposure in Korea. Environ Sci Pollut Res 31:35938-35951. https://doi.org/10.1007/s11356-024-33567-5\u003c/li\u003e\n\u003cli\u003eJayaprakash M, Urban B, Velmurugan PM, Srinivasalu S (2010) Accumulation of total trace metals due to rapid urbanization in microtidal zone of Pallikaranai marsh, South of Chennai, India. Environ Monit Assess 170:609\u0026ndash;629. https://doi.org/10.1007/s10661-009-1261-6\u003c/li\u003e\n\u003cli\u003eKaragulian F, Belis CA, Dora CFC, Pr\u0026uuml;ss-Ust\u0026uuml;n AM, Bonjour S, Adair-Rohani H, Amann M (2015) Contributions to cities\u0026rsquo; ambient particulate matter (PM): A systematic review of local source contributions al global level. Atmos Environ 120:475\u0026ndash;483. https://doi.org/10.1016/j.atmosenv.2015.08.087\u003c/li\u003e\n\u003cli\u003eKira CS, Maihara VA (2007) Determination of major and minor elements in dairy products through inductively coupled plasma optical emission spectrometry after wet partial digestion and neutron activation analysis. Food Chem 100(1):390\u0026ndash;395. https://doi.org/10.1016/j.foodchem.2005.09.014\u003c/li\u003e\n\u003cli\u003eLimbeck A, Wagner C, Lendl B, Mukhtar A (2012) Determination of water soluble trace metals in airborne particulate matter using a dynamic extraction procedure with on-line inductively coupled plasma optical emission spectrometric detection. Anal Chim Acta 750:111\u0026ndash;119. https://doi.org/10.1016/j.aca.2012.05.005\u003c/li\u003e\n\u003cli\u003eMeeravali NN, Madhavi K, Manjusha R, Kumar AJ (2014) Sequential extraction of platinum, cisplatin and carboplatin from environmental samples and pre-concentration/separation using vesicular coacervative extraction and determination by continuum source ETAAS. Talanta 118:37\u0026ndash;44. https://doi.org/10.1016/j.talanta.2013.09.045\u003c/li\u003e\n\u003cli\u003eMontaser A, Golightly DW (1992) \u003cem\u003eInductively coupled plasma in analytical atomic spectroscopy\u003c/em\u003e, 2nd Ed., VHC Publisher, New York, United States of America. \u003c/li\u003e\n\u003cli\u003eMukhtar A, Limbeck A (2013) Recent developments in assessment of bio-accessible trace metal fractions in airborne particulate matter: A review. Anal Chim Acta 774:11\u0026ndash;25. https://doi.org/10.1016/j.aca.2013.02.008\u003c/li\u003e\n\u003cli\u003eOSHA\u003csup\u003ea\u003c/sup\u003e - Occupational Chemical Database (2016). Chemical Sampling information \u0026ndash; Copper dusts \u0026amp; mists (as Cu). https://osha.gov/chemicaldata/1030, accessed in May 2024.\u003c/li\u003e\n\u003cli\u003eOSHA\u003csup\u003eb\u003c/sup\u003e - Occupational Chemical Database (2016). Chemical Sampling information \u0026ndash; Cadmium dust (as Cd). https://osha.gov/chemicaldata/1032, accessed in April 2024.\u003c/li\u003e\n\u003cli\u003eOSHA\u003csup\u003ec\u003c/sup\u003e - Occupational Chemical Database (2016). Chemical Sampling information \u0026ndash; Iron salts, soluble (as Fe). https://osha.gov/chemicaldata/499, accessed in April 2024. \u003c/li\u003e\n\u003cli\u003ePalacio IC, Oliveira IF, Franklin RL, Barros SBM, Roubicek DA (2016) Evaluating the mutagenicity of the water-soluble fraction of air particulate matter: A comparison of two extraction strategies. Chemosphere 158:124\u0026ndash;130. https://doi.org/10.1016/j.chemosphere.2016.05.058\u003c/li\u003e\n\u003cli\u003ePetit MD, Rucandio MI (1999) Sequential extractions for determination of cadmium distribution in coal fly ash, soil and sediment samples. Anal Chim Acta 401(1-2):283\u0026ndash;291. https://doi.org/10.1016/S0003-2670(99)00487-0\u003c/li\u003e\n\u003cli\u003eRajouriya K, Pipal AS, Taneja A (2024) A review on sequential extraction of metals bound particulate matter and their health risk assessment. J Atmos Chem 81:7. https://doi.org/10.1007/s10874-024-09460-3\u003c/li\u003e\n\u003cli\u003eRichter P, Gri\u0026ntilde;o P, Ahumada I, Giordano A (2007) Total element concentration and chemical fractionation in airborne particulate matter from Santiago, Chile. Atmos Environ 41(32):6729-6738. https://doi.org/10.1016/j.atmosenv.2007.04.053\u003c/li\u003e\n\u003cli\u003eRodinova O, Kucheryavskiy S, Pomerantsev A (2021) Efficient tools for principal component analysis of complex data \u0026ndash; a tutorial. Chemometr Intell Lab Syst 213:104304. https://doi.org/10.1016/j.chemolab.2021.104304\u003c/li\u003e\n\u003cli\u003eRossini-Oliva S, Nu\u0026ntilde;ez RL (2024) Is it healthy urban agriculture? Human exposure to potentially toxic elements in urban gardens from Andalusia, Spain. Environ Sci Pollut Res May15. https://doi.org/10.1007/s11356-024-33500-w\u003c/li\u003e\n\u003cli\u003eSah D, Verma PK, Kumari KM, Lakhani A (2017) Chemical partitioning of fine particle-bound As, Cd, Cr, Ni, Co, Pb and assessment of associated cancer risk due to inhalation, ingestion and dermal exposure. Inhal Toxicol 29:11, 483-493. https://doi.org/10.1080/08958378.2017.1406563\u003c/li\u003e\n\u003cli\u003eSchleicher NJ, Norra S, Chai F, Chen Y, Wang S, Cen K, Yu Y, St\u0026uuml;ben D (2011) Temporal variability of trace metal mobility of urban particulate matter from Beijing \u0026ndash; A contribution to health impact assessments of aerosols.\u003cem\u003e \u003c/em\u003eAtmos Environ 45(39):7248\u0026ndash;7265. https://doi.org/10.1016/j.atmosenv.2011.08.067\u003c/li\u003e\n\u003cli\u003eSilveira ML, Alleoni LRF, O\u0026rsquo;Connor GA, Chang AC (2006) Heavy metal sequential extraction methods \u0026ndash; A modification for tropical soils. Chemosphere 64:1929-1938. https:// doi.org/10.1016/j.chemosphere.2006.01.018\u003c/li\u003e\n\u003cli\u003eSipos P, Choi C, May Z (2016) Combination of single and sequential chemical extractions to study the mobility and host phases of potentially toxic elements in airborne particulate matter. Geochemistry 76(4):481\u0026ndash;489. https://doi.org/10.1016/j.chemer.2016.08.005\u003c/li\u003e\n\u003cli\u003eSmeda A, Zyrnicki W (2002) Application of sequential extraction and ICP-AES method for study of the partitioning of metals in fly ashes. Microchem J 72(1):9\u0026ndash;16. https://doi.org/10.1016/S0026-265X(01)00143-6\u003c/li\u003e\n\u003cli\u003eSouza AS, Bezerra MA, Cerqueira UMFM, Rodrigues CJO, Santos BC, Novaes CG, Almeida ERV (2024) An introductory review on the application of principal component analysis in the data exploration of the chemical analysis of food samples. Food Sci Biotechnol 33:1323-1336.\u003c/li\u003e\n\u003cli\u003eStabile L, Arpino F, Buonanno G, Russi A, Frattolillo A (2015) A simplified benchmark of ultrafine particle dispersion in idealized urban street canyons: A wind tunnel study. Build Environ 93(Part 2):186\u0026ndash;198. https://doi.org/10.1016/j.buildenv.2015.05.045\u003c/li\u003e\n\u003cli\u003eTessier A, Campbell PGC, Bisson M (1979) Sequential extraction procedure for the speciation of particulate trace metals. Anal Chem 51(7):844\u0026ndash;851. https://doi.org/10.1021/ac50043a017\u003c/li\u003e\n\u003cli\u003eUnited Nations (2015). Transforming our world: the 2030 Agenda for Sustainable Development. https://www.sdgs.un.org/2030agenda, accessed in June 2024.\u003c/li\u003e\n\u003cli\u003eWang CF, Chang CY, Chin CJ, Men LC (1999) Determination of arsenic and vanadium in airborne related reference materials by inductively coupled plasma-mass spectrometry. https://doi.org/10.1016/S0003-2670(99)00242-1\u003c/li\u003e\n\u003cli\u003eWHO - World Health Organization. Occupational and Environmental Health Team (2000). Guidelines for Air Quality, Geneva. http://apps.who.int/iris/handle/10665/66537, accessed in May 2024.\u003c/li\u003e\n\u003cli\u003eWu FY, Liu CQ, Tu CL (2008) Atmospheric deposition of metals in TSP of Guiyang, PR China. Bull Environ Contam Toxicol 80:465\u0026ndash;468. https://doi.org/10.1007/s00128-008-9397-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Chemical fractionation, Sequential extraction, Airborne particulate matter, PCA, HCA","lastPublishedDoi":"10.21203/rs.3.rs-6147994/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6147994/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to employ a modified BCR method for metal chemical fractionation in total suspended particulate (TSP) and inhalable particulate matter (PM\u003csub\u003e10\u003c/sub\u003e) samples. The chemical fractionation of Cd, Cu, Fe, Mn, Ni, Ti and V in airborne particulate matter (APM) samples from a tropical urban area in the city of Aracaju, Sergipe, Northeastern Brazil was performed, adapting a sequential extraction method encompassing six fractions characterized by a gradual increase in the solvent strength, as follows: (1) water-soluble fraction; (2) ion exchange fraction; (3) carbonate-bound fraction; (4) metal oxide-bound fraction; (5) organic metal complex or sulfide-bound fraction; and (6) residual fraction. Iron and Ti concentrations were the most abundant among organic metal complex and sulfides and silicate fractions for both TSP and PM\u003csub\u003e10\u003c/sub\u003e. Cadmium concentrations, in general, were higher in the organic metal complex and sulfides, silicate, and oxide fractions, while Cu was most detected in the organic metal complex and sulfides, oxide, and silicate fractions. Nickel, Mn and V were not determined in either of the samples. A multivariate data analyses indicated three groups, associating Cu and Cd to similar sources, \u003cem\u003ei.e\u003c/em\u003e., vehicular traffic and fossil fuel burning. This is the first time this sequential method was applied to investigate metal mobility and metal distribution in APM, assessing beyond the bioavailability, solubility, and geochemical transport trace metals.\u003c/p\u003e","manuscriptTitle":"Trace Metal Chemical Fractionation in Airborne Particulate Matter from a Tropical Urban Area in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 13:09:20","doi":"10.21203/rs.3.rs-6147994/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-05-10T05:50:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-03-21T19:52:20+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-18T12:31:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2025-03-18T09:06:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-05T04:29:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-03-04T08:43:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f93896bc-eebd-4087-b493-72dfbb26c05f","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:40:14+00:00","versionOfRecord":{"articleIdentity":"rs-6147994","link":"https://doi.org/10.1007/s11356-025-36779-5","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-07-31 16:12:59","publishedOnDateReadable":"July 31st, 2025"},"versionCreatedAt":"2025-03-27 13:09:20","video":"","vorDoi":"10.1007/s11356-025-36779-5","vorDoiUrl":"https://doi.org/10.1007/s11356-025-36779-5","workflowStages":[]},"version":"v1","identity":"rs-6147994","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6147994","identity":"rs-6147994","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}