Bruna Segalin1, Adalgiza Fornaro1, Prashant Kumar2, Otto Klemm3, Maria F. Andrade1, Beatriz Maria Trezza4, Alexandre Busse4, Wilson J. Filho4, Fabio L.T. Gonçalves 1

Departamento de Ciências Atmosféricas, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo, 05508-090 São Paulo, SP, Brasil
Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford GU2 7XH, UK
Climatology Research Group, University of Münster, 48149 Münster, Germany
Divisão de Geriatria, Departamento de Medicina Interna, Faculdade de Medicina, Universidade de São Paulo, 01246-903, São Paulo, SP, Brasil

Received: September 17, 2019
Revised: February 6, 2020
Accepted: March 31, 2020
Download Citation: ||  

  • Download: PDF

Cite this article:

Segalin, B., Fornaro, A., Kumar, P., Klemm, O., Andrade, M.F., Trezza, B.M., Busse, A., Filho, W.J. and Gonçalves, F.L. (2020). Chemical Composition of Quasi-ultrafine Particles and their Sources in Elderly Residences of São Paulo Megacity. Aerosol Air Qual. Res. 20: 1100-1015.


  • Sulphate and nitrate are dominant ions in qUFP found in residences of elderly people.
  • Silicon (Si) and iron (Fe) were the major trace elements in qUFP.
  • The qUFP composition indicated wall painting and cooking as sources of indoor origin.
  • Vehicles and secondary inorganic aerosol are major outdoor sources of indoor qUFP.
  • The maximum respiratory deposition of qUFP and rBC was in the tracheobronchial part.


Atmospheric quasi-ultrafine particles (qUFP; PM<0.25) can cause harmful effects to human health, mainly to elderly people. Although not always considered, these effects can be mostly due to its chemical composition. The scope of this work is (i) to quantify the abundance of ions and trace elements in qUFP in elderly residences, (ii) to identify the sources of these qUFP and (iii) to estimate the respiratory deposition doses (RDD) of qUFP and black carbon (rBC), which is an important component of qUFP, to various parts of the respiratory tract. In order to evaluate the qUFP chemical composition in elderly residences in the Metropolitan Area of São Paulo (MASP), we collected qUFP by using a Personal Cascade Impactor Sampler (PCIS). We analysed ions by chromatography and trace elements by Energy Dispersive X-Ray Fluorescence. We identified the sources of qUFP by applying Positive Matrix Factorization. We calculated the RDD through an equation, which use the tidal volume of lung, the typical breath frequency, the deposition fraction and the mass concentration of different size fractions of a PM. We collected 60 samples from 59 residences between May 2014 and July 2015. The major of ions concentrations in qUFP were found to be SO42- and NH4+, and the major trace elements were Si and Fe. Some residences have a high concentration of the toxic heavy metals Cu, Ni, Pb and Cr. We found six dominant sources of the indoor qUFP: vehicular emission (57%), secondary inorganic aerosol (21%), soil and construction (7%), wall painting (7%), cooking (5%) and industry (3%). The maximum RDD of qUFP and rBC are in the tracheobronchial part. Our results show that vehicular emissions dominate the indoor qUFP concentrations and uptake in elderly residences in the MASP.

Keywords: Indoor air quality; Chemical composition; quasi-ultrafine particulates; Elderly residence; Indoor sources; Respiratory deposition doses


Atmospheric aerosol particles in the urban environment are characterized by different sizes, shapes, and chemical composition (Heal et al., 2012; Kumar et al., 2014). Particles smaller than 0.1 µm in diameter, also referred to as ultrafine particles (UFP), typically occur in large number concentrations while their contribution to the mass concentration is marginal (Kumar et al., 2010; Rivas et al., 2017). On the other hand, coarse particles are low in number but contribute largely to the mass concentration (PM). The UFP contribute to about 80% of the total number concentrations of the atmospheric particles and the ones with diameter up to 0.3 µm contribute to over 99% (Kumar et al., 2009, 2010). It is reported that the smaller the particles, the larger their toxic potential. For example, particles with diameters less than 0.25 µm have higher redox activity (induce oxidative stress in human cells) than coarse and accumulation modes of particles (Hu et al., 2008). The UFP can also translocate for the bloodstream and achieve other organs such as brain and heart (Elder et al., 2006; Heal et al., 2012). From this perspective, it is very important to study particles of that size range. In this work, we aim to focus on particles with diameters up to 0.25 µm, which we refer to as quasi-ultrafine particles (qUFP), hereafter. This concept has also been utilized in earlier studies (Arhami et al., 2009; Hu et al., 2008; Saffari et al., 2013; Viana et al., 2014). The qUFP is an operational definition. In contrast to a significant number of studies on the chemical composition of ambient larger particles, there is hardly any study on the chemical composition of qUFP, specifically if it comes to indoor air pollution (Viana et al., 2014). This is comprehensible in light that the high mass concentrations of particles are expected to be in the coarse and accumulation ranges.

In this work, we sampled qUFP in elderly residences in order to analyze trace elements and ions concentrations. We focused on the elderly population because it is fastest growing in worldwide (United Nations, 2015) and they are a risk group for particles air pollution (Peled, 2011). The expected trend is the increase from 841 million of elderly in 2013 to more than 2 billion in 2050 when they will surpass the number of children (Chatterji et al., 2015). As the elderly population increases, we will have an epidemic of chronic diseases, mainly dementias, cerebrovascular accident, chronic obstructive pulmonary disease, diabetes, heart failure and coronary insufficiency (Mathers et al., 2015). In fact, the elderly are more likely to have multi-morbidities, either by the progressive decrease of the functional reserve related to the senescence process or by the greater exposure to the risk factors. Therefore, they are a risk group for particles of air pollution (Peled, 2011). Aerosol particles can cause many problems in elderly people such as a change in heartbeat frequency (Holguín et al., 2003), aggravate chronic obstructive pulmonary disease (Osman et al., 2007) and cognitive deficit (Weuve et al., 2012). We also focus on the indoor environment because elderly people spend about 80% of their time indoors (Segalin et al., 2017), which can be most relevant for their health. In the best of our knowledge, this is the first study in elderly residences in a Latin American megacity, such as São Paulo, to study the chemical characteristics of the qUFP.

While the sheer number concentration of qUFP is a good indicator of the potential health effects, the chemical composition of these particles is equally or even more important to be considered in health impact assessment (Saffari et al. 2013). For example, airborne urban particles compositions, including those of qUFP, are characterized by several metals such as aluminium (Al), arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg), which affect the neurological system (Pohl et al., 2011). Likewise, metals such as Al, Pb, Hg, zinc (Zn), copper (Cu) and iron (Fe) have been clearly associated with Alzheimer’s disease, which affects elderly people (Lovell et al., 1998; Polizzi et al., 2002; Zatta, 2003; Zatta and Frank, 2007). Black carbon (BC) can affect negatively the respiratory system and cognition of elderly people (Jansen et al., 2005; Power et al., 2013). Exposure to silica (Si) has been associated with kidney disease, lung cancer, rheumatoid arthritis, tuberculosis and pneumoconiosis (Steenland et al., 2001; Attfield and Costello, 2004). Moreover, nickel (Ni) and vanadium (V) together have negative effects on cardiac function and influence in the short-term mortality (Campen et al., 2001). Similarly, BC combined with iron and nickel has been found to affect the cardio regulatory system (Chang et al., 2007). All these metals are components of qUFP, which are the focus of this study. For example, Hu et al. (2008) reported highest concentrations of Na and S in qUFP collected in Los Angeles - Long Beach Harbor. Further, Viana et al. (2014) studied qUFP inside schools in urban environments and found that most chromium (Cr) and Ni (metals with high redox properties) are present in qUFP rather than in larger particles.

The main indoor source of airborne particles is resuspension from vacuum cleaning, soap/cleaning sprays, smoking, incense burning and cooking, mainly frying activities (Vu et al., 2017). Vacuum sweeping, biological and mineral sources produce predominantly coarse particles (Kamnes et al., 1991). Indoor combustion sources (cooking, wood burning, candle burning, fireplace or kerosene heating) produce mainly particles with diameters below 1 µm (Hussein et al., 2006; Kumar et al., 2013). For UFP, cooking was the most important indoor source (Bhangar et al., 2011). Certainly, outdoor air is also a source for indoor PM, which holds especially for qUFP and UFP because they can best penetrate the natural and artificial ventilation systems (Abt et al., 2000). In schools, for example, the indoor qUFP had major trace elements and ions sources from outdoor (Viana et al., 2014; Table 1). In the Metropolitan Area of São Paulo (MASP), many trace elements with health effects such as BC, Cu, Zn, Pb, Fe and V were associated with vehicular sources (Andrade et al., 2012; Miranda et al., 2012). The PM fraction from the vehicular traffic combustion processes aggravates ischemic heart disease in the elderly (Lanki et al., 2006).

We intend to quantify how large the contribution of vehicular emissions is to indoor air pollution in elderly residences. We also want to identify the other possible sources for qUFP. Some studies about indoor air pollution identify various sources of UFP doing measurements specifically for each possible source, human activities and with a focus on the number of particles generated (Table 1). Only one study analysed the source of indoor qUFP based on their chemical composition (Viana et al., 2014; summarized in Table 1). In the best of our knowledge, there are no studies of source attribution for outdoor qUFP and UFP in Brazil, and also no information available about the source of airborne particles in residences in Brazil. There are hardly any studies focusing on elderly residents. Therefore, this is the first study in Brazil with the objectives (i) to quantify ions and trace elements present in qUFP mass concentration inside elderly residences, (ii) to identify the sources of the qUFP indoor elderly residences and (iii) to estimate the respiratory deposition doses (RDD) of the qUFP and its proportion of black carbon (rBC) in different parts of respiratory tract. 


Site Description and Study Design

There are more than 21 million inhabitants in MASP out of which 12 million reside in São Paulo City (IBGE, 2016; Kumar et al., 2016; Andrade et al., 2017). The study of the elderly in this region is important because over 12% of its population is 60 or older. It is worth highlighting that the elderly population is growing fast, not only in MASP but worldwide (Alessandri and Maeda, 2011; United Nations, 2015; IBGE, 2016). The residences sampled in this study are those with at least one elderly volunteer (aged 60 years or older) that were previously approved for this study. Within the framework of this project, we selected volunteers from the Faculty of Medicine Clinics Hospital at the University of São Paulo. The selection criteria were pre-established by the team of geriatricians who selected healthy elderly people without any previous diseases such as diabetes, cardio-respiratory or cancer diseases. All the elderly participants had accomplished at least 4 years of formal education with no neurological or psychiatric illness (Trezza et al., 2015). All of them were submitted to three different tests which are important for evaluating independent living capacity, i.e., (i) the Mini-Mental State Examination (MMSE; Folstein et al., 1975), (ii) the Geriatric Depression Scale (GDS; Sheikh et al., 1991), and (iii) the Short Physical Performance Battery (SPPB; Guralnik et al., 1994). The MMSE is for evaluating any potential presence of a cognitive deficit of the individual. The GDS assesses possible symptoms of depression, while the SPPB checks the balance deficit. Only the elderly that passed all three tests were considered to be in good conditions and thus qualified to be volunteers for this study. In this way, the location of the residences were randomly arranged. All residences were located in the regions with the highest concentration of elderly people within the MASP. We have 59 residences sampled with 60 valid individual samples and referring to 60 approved volunteers. As described by Segalin et al. (2017), only one residence had two samples, collected on different days. Most of these residences sampled in the MASP are located in São Paulo, just two in the city of Osasco and one in Embu das Artes (Fig. 1). The measurements started in the morning in accordance with the availability of the elderly volunteers. The instrument was operated in the living room of the elderly residences for a period of 24 hours in each residence. The residences, as described for Segalin et al. (2017), are natural ventilated and liquefied petroleum gas from individual bottled and piped natural gas was used as cooking fuel. The elderly spend almost 80% of the 24 hours sampling period inside their residences (Segalin et al., 2017). 

Fig. 1. Localization of (a) São Paulo, Brazil, (b) Metropolitan Area of São Paulo (MASP) and (c) the elderly residences. The different colors in (c) represent the group 1 (red) and 2 (blue) found by clusters analysis in section 3.2. Adapted from Segalin et al. (2017), map by Google Maps.Fig. 1. Localization of (a) São Paulo, Brazil, (b) Metropolitan Area of São Paulo (MASP) and (c) the elderly residences. The different colors in (c) represent the group 1 (red) and 2 (blue) found by clusters analysis in section 3.2. Adapted from Segalin et al. (2017), map by Google Maps.


The PM data came from Segalin et al. (2017), who operated a Personal Cascade Impactor Sampler (PCIS), which separates the PM into five size ranges, i.e., 10–2.5, 2.5–1.0, 1.0–0.5, 0.5–0.25, and < 0.25 µm aerodynamic diameters. The quasi-ultrafine particle fraction (qUFP, aerodynamic diameter < 0.25 µm) is thus represented by the last stage of the impactor. The PCIS was described in detail by Misra et al. (2002) and Sioutas, (2004). The PCIS was combined with a Leland Legacy Sample Pump (SKC Inc., Cat. No. 100–3000) with 9 L min1 rate flow, in order to provide reliable results during the entire 24 h sampling periods (Sioutas, 2004). The PCIS was used with a 37 mm Teflon filter for qUFP to avoid any chemical interference with the PM and in chemical analysis (Misra et al., 2002). Although the PCIS is designed as a personal monitor, Segalin et al. (2017) deployed them in the living rooms of the elderly residences. The acceptance and willingness of most elderly people to carry the PCIS as a personal monitor was not good enough, mainly because the pump made a continuous noise (Segalin et al., 2017).

Data Collection And analysis

We have 60 samples from 59 residences collected between May 2014 and July 2015 (Segalin et al., 2017). The days of measurements were randomly selected and coordinated with the elderly availability. Thirty-one measurements were made in 2014 and 29 in 2015, most of them during the respective southern winter periods (May, June and July). All elderly that had been approved to the tests (Section 2.1) participated in the campaign throughout. There were no drop-outs. We did not conduct outdoor sampling during the same sampling period due difficulty to get permission to install the instruments outside the building.

The rBC mass concentration was quantified by optical reflectance measurements with a refractometer (smoke stain reflectometer model 43D; Diffusion Systems Ltd., London, UK) (Segalin et al., 2016). We analysed trace elements present in qUFP using Energy Dispersive X-Ray Fluorescence technique (EDXRF - EDX 700HS; Shimadzu Corporation, Analytical Instruments Division, Tokyo, Japan). The ions analyzes were carried out by using ionic chromatography and conductivity detection (Metrohm 850, Herisau, Switzerland). For the aqueous extraction, we placed each Teflon filters in pre-cleaned low-density polyethylene tubes with 10 mL ultrapure water. These samples were shaken for at least 2.5 hours. Thereafter, the solutions were filtered by Millex polyvinylidene difluoride filters (0.22 µm pore size; Millipore, Bedford, MA, USA), followed by ions analyses with ionic chromatography.

All sample analyses were blank-corrected and the trace elements and ions with 2/3 or more samples below detection limits (DL, see Supplementary information, SI, Tables S1–S2; Leal et al., 2004; Arana, 2014) were excluded from this work (Mg, Mn, Se, Sr, Cr, Cd, Rb, V, Sb, As and C2O42). For the remaining samples, the compounds with values below DL were replaced by half of the average of their DL (Polissar et al., 1998; Khan et al., 2016). During further data analysis of mass concentrations, we considered some elements to be present in their oxidized form, for example, aluminum as aluminum oxide (Al2O3) and iron as iron oxide (Fe2O3) (SI, Table S3; Kotz and Purcell, 1987; Hueglin et al., 2005; Beuck et al., 2011).

Cluster Analysis

In order to group the elderly residence by mass concentration, trace elements and ions, we choose the pvclust package (Suzuki and Shimodaira, 2009) in R programming language for cluster analysis. This algorithm calculates the p-value for each cluster in a cluster hierarchy as generated by the hclust function. The pvclust provides approximately unbiased p-value (AU; Shimodaira, 2002) to reduce test bias and the bootstrap probability p-value (BP) calculated by multiscale bootstrap resampling (Shimodaira, 2004). We chose AU ≥ 95% as a cut point for the clusters because the clusters are robustly supported by the database with this cut point (Shimodaira, 2004; Suzuki and Shimodaira, 2009). For this analysis, the sample #27 was removed because it is an outlier with respect to the mass concentration (see also Segalin et al., 2017). We also analysed the meteorological variables for the groups generated by the cluster analyses. We obtain data on temperature, relative humidity, wind speed and precipitation from the meteorological station of Institute of Astronomy, Geophysics and Atmospheric Science (IAG). This station has an average distance of 13.9 km of the residences (Segalin et al., 2017).

Positive Matrix Factorization (PMF)

We used the Positive Matrix Factorization (PMF; Paatero and Tapper, 1994) free software by the U.S. Environmental Protection Agency (EPA), version 5.0 (Norris et al., 2014). The PMF is a robust tool to find sources in a particulate matter database (Ogulei et al., 2006a, b; Wallace, 2006; Gietl and Klemm, 2009; Beuck et al., 2011; Amato et al., 2014; Al-Dabbous and Kumar, 2015; Brown et al., 2015; Goel and Kumar, 2015; Khan et al., 2016; Vu et al., 2017). The PMF 

allows us to decompose the data matrix, D, into two matrices W and H (Cichocki et al., 2009):

D = WH + ɛ                                                                                (1)

where the matrix W is the matrix with the proportion of each source in each elderly residence, while the matrix H contains the proportion of each ion and trace element in each source, and ɛ is the error matrix. In this way, the inputs of PMF are the mass concentration of the variables for each residence and the uncertainties of their mass concentrations (one advantage of the PMF). The uncertainty (Sij) was calculated with Eq. (2) in accordance with Chueinta et al. (2000), using the estimated measurement error (Eq. (4)) from Ogulei et al. (2006b) and the constant C = 0.04, that produce the best value proximity between the residual sum of squares (Q) and the theoretical value (Ogulei et al., 2006a):

Sij = σij + C Nij                                                                           (2)

σij = 0.01 (Nij +j)                                                                        (3)

where σij is the estimated measurement error, C is a constant, Nij is the mass concentration a j is the arithmetic mean of the Nj values, in the i sample in j variable. An extra modelling uncertainty of 5% was added in PMF model to cover any methodological error (Khan et al., 2016). We categorize the contributions of elements through value of signal to noise (S/N) as “bad” (S/N < 0.5), “weak” (1.0 < S/N ≤ 0.5) and “strong” (S/N ≥ 1.0) as suggested by Norris et al. (2014). We further considered as “weak” all those elements with more than 1/3 of data below DL (Br, Cr, Ti, Ni and NO3; Table 2). For this analysis, sample #27 was also removed.

We chose the number of the factors following the Brown et al. (2015) methodology. They analyse the Qrobust/Qexpected value, and when the changes in this fraction became small with the increasing of the factors, the optimal solution is the number of the factors before the change became small. The PMF gave us the Qrobust and Qexpected as follow:

Qexpected = (S × SS) – ((F × S) + (F × SS))                                   (4)

where S are the number of the samples, SS are the strong species and F is the number of the factors. We run the PMF for 3–8 factors and we found the best solution with 6 factors (SI Table S4). 

Table 2. Information about mass concentration, trace elements, ions and rBC of qUFP in elderly residences in MASP.

Respiratory Deposition Doses (RDD)

We calculated the total respiratory deposition doses (RDD) in the respiratory tract of elderly using the Eq. (6) adapted from Hinds (1999) by Azarmi and Kumar (2016) with the same parameter and values used by other studies (Kumar et al., 2017; Segalin et al., 2017; Rivas et al., 2017). The RDD is calculated as:

RDD = (VT × f) × DF × PMi                                                   (5)

where VT is a tidal volume (m3 per breath), f is the typical breath frequency (breath per minute), DF is deposition fraction of qUFP (Hinds, 1999), and PMi is the mass concentration of qUPF. The DF for the head airway, tracheobronchial and alveolar regions were estimated using parameterizations according to Hind, 1999 (SI, Table S6 and Eqs. (S1)–(S5)). Since VT and f depend on the physical activity and person gender (Hinds, 1999), we calculated RDD for male and female elderly and we chose light exercise and seated positions as their physical activities, as more appropriate for elderly inside their residences (Segalin et al., 2017).


Ions and Trace Elements in qUFP

Fig. 2 shows the concentration of ions and trace elements in qUFP and the correlations among them in elderly residences in the MASP. Both ions and trace elements show high variability and non-normal distributions (Fig. 2(a)), as confirmed by the Shapiro-Wilk normality test. The ions and trace elements are positively asymmetrical, except for Na+ (negatively asymmetrical) and they present high values, except for sulphate (SO42–). The ions SO42– and NH4+ correlate highly with each other (r = 0.9, Fig. 2(b)). They do not correlate significantly with most other ions and trace elements (Fig. 2(b)). The total mass concentration has no significant correlations with trace elements, just a weak negative correlation with Cu (r = –0.2) and a positive correlation with P (r = 0.2), NO3 (r = 0.2) and rBC (r = 0.2, Fig. 2(b)). There are more high positive correlations between Cl, K+, Zn, Pb, Na+, Br, Ni, P and Fe (Fig. 2(b)), which indicates that these compounds have similar sources. 

Fig. 2. (a) Ions and trace elements present in qUFP and (b) the Pearson correlation between them in elderly residences in MASP.Fig. 2. (a) Ions and trace elements present in qUFP and (b) the Pearson correlation between them in elderly residences in MASP.

The analysed mass concentration of qUFP is dominated by rBC (2.78 µg m–3) and ions (0.97 µg m–3), mainly SO42– (0.61 µg m–3, Table 2). This result is similar to results found in Los Angeles beach harbor, where it was SO42– that dominated qUFP mass concentration (rBC was not analysed; Hu et al., 2008). The main source of SO42– in Los Angeles was considered bunker fuel combustion from ships (Arhami et al., 2009). In outdoor environments in MASP, SO42– is the ion with highest concentration in PM2.5 (Ynoue and Andrade, 2004; Miranda et al., 2012; Vieira-filho et al., 2016b) and it shows a modal peak of the mass concentration in particles around 0.3 µm diameter (Ynoue and Andrade, 2004; Vieira-filho et al., 2016a). The residence with the highest concentration of SO42– is the residence #14. It is located very close to the Rebouças Avenue, a large avenue with intense vehicular traffic. The trace elements contributing mostly to qUFP are Si, followed by Fe and Zn (Table 2). The high concentration of Si in the residence #60 is probably due to nearby construction works happening during the sample collection. This residence also has very large concentrations of K+, Cl and Cr. The ions K+ and Cl have a strong positive correlation (Fig. 2(b)) which may indicate biomass burning (Ynoue and Andrade, 2004). In this residence, they are most likely associated with the cooking of fried eggs for lunch. The Cr likely originates from paint (Tan et al., 2013) from both the construction site and the residence itself. The residence #5, which shows the maximum Fe, also has a maximum in Ni and Br, which can be associated with vehicular emissions: the residence is very close to Consolação Avenue and Júlio de Mesquita Viaduct, both very busy road impacted by buses and passenger cars. The Ni and Cu and were the compounds of indoor qUFP that have a mass concentration (Table 2) similar to found in outdoor PM2.5 in MASP: 1.0 ± 1.0 and 10 ± 8 ng m–3, respectively (Andrade et al., 2012). For Cu, this occurs basically due to the maximum in residence #56. This residence is far from urbanized area, and the fungicides can be a source for qUFP with high Cu concentration. Other residences, as number #5, #13 and #60, have a high concentration of heavy metals as Ni, Pb, and Cr, respectively (Table 2).

The acidity of aerosol particles is a parameter obtained not by direct pH measurements, but by proxies based on ion balances and molar ratios of ions, involving the equilibrium relationships of the major ions present in the water-soluble fraction (Vieira-Filho et al., 2016; Weber et al., 2016; Shi et al., 2017; Ding et al., 2019). The importance of aerosol acidity is directly related to its effects on human health (Wyzga and Folinsbee, 1995; Raizenne et al., 1996; Rohr and Wyzga, 2012), as well as being a parameter affecting fundamental atmospheric physical-chemical processes in the formation of secondary aerosol and also in the formation and growth of cloud condensation nuclei. Aerosol acidity affects both the gas-aerosol partition of semi-volatile and volatile species as well as thermodynamic and kinetic parameters of acid-catalysed reactions (Vieira-Filho et al., 2016; Shi et al., 2017; Ding et al., 2019). In order to evaluate the acidity of the qUFP, we analysed the stoichiometry between ammonium (NH4+) and on the one hand sulphate (SO4–2) and sulphate plus nitrate (SO4–2 + NO3) on the other. Ammonium and sulphate in qUFP have a slope of 2.2 (Fig. 3(a)), indicating a good neutralization from sulfuric acid by ammonia. However, the ammonia is not quite enough to neutralize both sulfuric and nitric acid (Fig. 3(b)) and to produce the respective ammonium salts. This pattern indicates that less acidity was present in indoor qUFP than found in outdoor PM1 (< 1 µm; Vieira-filho et al., 2016a). The ammonium linear fit versus sulphate plus nitrate (SO4–2 + NO3) has a more consistent determination coefficient in indoor qUFP (R2 = 0.76) than outdoor PM1 (R2 = 0.45; Vieira-filho et al., 2016a). This good relation between ammonium and sulphate and nitrate also indicates that secondary inorganic aerosol mass is produced effectively by the reaction between ammonia and sulphuric acid and nitrate acid from the gas phase. 

Fig. 3. Stoichiometry ratios (µmol L–1) between (a) ammonium and sulfate and (b) sulfate plus nitrate for qUFP in elderly residences in MASP.Fig. 3. Stoichiometry ratios (µmol L1) between (a) ammonium and sulfate and (b) sulfate plus nitrate for qUFP in elderly residences in MASP.

Chemical Mass Balance and Cluster Analysis of qUFP

We use cluster analyses to classify the elderly residences by trace elements, ions and mass concentrations. The best fit of the cluster analyses grouped the residences into 2 groups framed in red and blue in Fig. 4, respectively. The location of the residences is presented in Fig. 1, where the red and blue colours represent the residences of the Groups 1 and 2, respectively. We also calculated the mass balances for all the residences and for both groups in order to understand the differences between these groups (Fig. 5). The mass balance shows a predominance of rBC in the Group 2, which indicates a stronger relationship between indoor qUFP and outdoor sources than in Group 1 because rBc is usually associated with vehicular emissions (Sánchez-Ccoyllo et al., 2009, Andrade et al., 2012). All the residences with a maximum in trace elements (Table 2) are in the Group 1 (Fig. 4). The mass concentration of the ions NH4+ and SO4–2 and of the K+ is higher in the Group 1 than Group 2 (Fig. 5).

The mass concentration of the groups is an average of the mass concentration of all residences in that group. The difference of mass concentration between the groups is 2.3 µg m–3, Group 2 has 18.9% higher mass concentration (12.2 µg m–3) than Group 1 (9.9 µg m–3), however is a non-significant difference (p-value over 0.36). The Group 2 also present higher average of outdoor temperature, relative humidity, wind speed and, in particular, the precipitation than in Group 1 (SI Table S5). More precipitation during the measurements should cause a drop in the mass concentration in Group 2. However, it is the group with more mass concentration and with a predominance of rBC (Fig. 5). In this context and due to the outdoor sources, we can assume that the localization of the residences can have a stronger influence in the mass concentration and composition of indoor qUFP than the weather conditions during the measurements. This is more evident for the residences in Group 2. 

Fig. 4. Clusters dendrogram of the residences in accordance with their mass concentration, ions and trace elements composition. The two clusters were determined with an approximately unbiased p-value of 0.95. 

Fig. 5. Mass balance of qUFP with the proportions of ions (separated in 3 parts: K+, NH4+ + SO42– and others ions (Ca2+, Na+, Cl–, NO3–)), trace elements, black carbon (rBC - from Segalin et al., 2016) and compound not analyzed in this work, for all the residences (Total) and for the two groups generated for cluster analysis in Fig. 4.Fig. 5. Mass balance of qUFP with the proportions of ions (separated in 3 parts: K+, NH4+ + SO42– and others ions (Ca2+, Na+, Cl, NO3)), trace elements, black carbon (rBC - from Segalin et al., 2016) and compound not analyzed in this work, for all the residences (Total) and for the two groups generated for cluster analysis in Fig. 4.

Sources of qUFP

Through the PMF we identified 6 factors that represent 7, 21, 7, 57, 5 and 3% of the analysed mass concentration of qUFP, respectively (Fig. 6). The first factor has high contributions of Na, Zn, Cu, and Pb, elements which are positively correlated to each other (Fig. 2(b)). Although Zn and Cu are typical vehicular source markers (Andrade et al., 2012), they also have indoor sources for qUFP, usually wall paints (Viena et al., 2014), and Pb is also a component of wall paint, mostly in old residences (Beauchemin et al., 2011), then for this study Factor 1 is wall paint. Factor 2 is considered as secondary inorganic aerosol because of the high concentrations of ions NH4+ and SO42–, which indicate a large contribution of secondary inorganic aerosol production to qUFP, as described in Section 3.1. Measurements in tunnels in MASP showed higher concentrations of NH4+ and SO42– in fine particles within the tunnels than outside, indicating a vehicular contribution to the production of sulphate and ammonium particles in urbanized areas (Vieira-filho et al., 2016b). In the outdoor air in MASP, SO42– presented a modal peak of the mass concentration distribution around 0.3 µm diameter and its source was SO2 from fuel burning by vehicles (Ynoue and Andrade, 2004; Vieira-filho et al., 2016a). Also, in outdoor Los Angeles atmosphere, the SO42– in qUFP was considered originating from fuel combustion (Arhami et al., 2009). Factor 3 is predominantly Al, Ca2+, Si, Fe and Ti, indicating a soil- and construction- related factor (Andrade et al., 2012). Factor 4 is from vehicular emissions since it is dominated by rBC and nitrate (Sánchez-Ccoyllo et al., 2009; Andrade et al., 2012). Factor 5 is predominantly K+ and Cl (Fig. 6). These ions indicate biomass burning (Ynoue and Andrade, 2004) and Cl is also considered to be an indoor source for qUFP (Viana et al., 2014), this factor likely indicates indoor biomass burning. Based on this, Factor 5 is considered cooking process source of qUFP. In Factor 6, there are high percentages of the P, Ni, Fe and Pb. In the MASP air, Ni, Fe, and Pb in fine particles are associated with industrial emissions (Castanho and Artaxo, 2001). Factor 6 is probably representing the qUFP produced in industrial process that penetrates into the residences. The particles inside the residences can be produced there or may came from outside through the windows or doors. In Brazil, the windows are generally always open due to the heat in the summer and the rather mild winters. Factor 1 is a wall paint source, therefore a source of indoor origin. Factor 2 is a secondary inorganic aerosol, Factor 3 is a soil and construction source, Factor 4 is vehicular emissions, all of which are sources of outdoor origin. Factor 5 is cooking (source of indoor origin) and Factor 6 is an industrial source (source of outdoor origin). According to this analysis, the qUFP in elderly residence originated from 2 sources of indoor origin (Factors 1 and 5) and 4 sources of outdoor origin (Factors 2, 3, 4 and 6). Since the factors represent 7, 21, 7, 57, 5 and 3% of the analysed mass concentration of qUFP, the major mass concentration (88%) of qUFP is associated with outdoor sources, mainly vehicular emission (57%, Factor 4). In the MASP, the major source of outdoor fine particles is attributable to vehicular emission due to more than 8.39 million vehicles (CETESB, 2016; DETRAN, 2017). The vehicular emission in the MASP contributes directly to 37% of the PM2.5 concentration. Further, there is an indirect contribution to the production of secondary aerosol mass (51% of PM2.5, CETESB, 2016). Outdoor rBC is produced by vehicles predominantly as UFP (Ynoue and Andrade, 2004) and these particles are small enough to penetrate easily into indoor environments (Abt et al., 2000) and so contribute to the mass concentration of qUFP particles in elderly residences (26%, Segalin et al., 2016). 

Fig. 6. The result of PMF analysis (6 factors) of mass concentrations of trace elements and ions of qUFP sampled in elderly residences in MASP. Blue bars indicate the mass concentrations and red dots the relative contributions to the total mass.Fig. 6. The result of PMF analysis (6 factors) of mass concentrations of trace elements and ions of qUFP sampled in elderly residences in MASP. Blue bars indicate the mass concentrations and red dots the relative contributions to the total mass.

Respiratory Deposition Doses (RDD) of qUFP and rBC Mass Concentrations

Fig. 7 shows the RDD of qUFP and rBC for 24 hours in head airways, tracheobronchial and alveolar regions, for men and women during light exercises and seated position. The RDD is higher for men than for women and higher during light exercise than in a seated position of the persons. The same pattern occurs for RDD in the total respiratory tract because the tidal volume is bigger for men than for women, and it is bigger during light exercises (Hinds, 1999; Azarmi et al., 2016; Segalin et al., 2017). The RDD of qUFP and rBC in tracheobronchial and alveolar regions are similar for both male and female and light exercise and seated positions, with a little more RDD in the tracheobronchial region than in the alveolar region. The maximum RDD occurs in men during light exercise and in the tracheobronchial region. The RDD total is higher for coarse particles than for qUFP (Segalin, et al., 2017), however, the RDD of qUFP is higher in tracheobronchial and alveolar parts than in the head. The latter poses a larger health risk because these particles can translocate from the alveoli to the bloodstream and eventually cause damage to other parts of the body such brain and heart (Elder et al., 2006; Heal et al., 2012). 

Fig. 7. Rates deposition doses (RDD) of qUFP and its proportion of black carbon (rBC - present in qUFP from Segalin et al., 2016) in different regions of the respiratory tract for men and women during light exercises and seated position during 24 hours.Fig. 7. Rates deposition doses (RDD) of qUFP and its proportion of black carbon (rBC - present in qUFP from Segalin et al., 2016) in different regions of the respiratory tract for men and women during light exercises and seated position during 24 hours.


The goal of this project was to characterize the ion and trace elements concentrations in qUFP, to identify the sources of the qUFP inside elderly residences and to estimate of RDD of the qUFP and the rBC in different parts of the respiratory tract. In the best of our knowledge, this is the first research to study the chemical characteristics of the qUFP in elderly residences in a Latin American megacity. We measured qUFP in 59 residences of elderly with the PCIS in the MASP. The measurements were made during 24 hours, once in each residence, during different days, except in one residence where 2 samples were taken because 2 elderly live there. Overall, a total of 60 samples were analysed.

The predominant ions in indoor qUFP were SO42– and NH4+ and the major trace elements were Si and Fe. Some residences had a high concentration of heavy metals such as Cu, Pb, Ni and Cr that pose a significant health risk. There was a good neutralization of sulfuric acid by ammonia-producing secondary inorganic aerosols that form qUFP. However, the ammonia was not sufficient to neutralize both sulfuric and nitric acids and to produce respective amounts of ammonium salts.

Since our measurements were made in different locations and days, we classified the residences in 2 groups by cluster analyses. The mass balance of these groups shows a predominance of rBC in Group 2 and of NH4+, SO42– and Kin Group 1. The precipitation and the number of days with precipitation were higher in the residences in the Group 2 than Group 1. However, the mass concentration was 18.8% higher in Group 2 than in Group 1. We conclude that the location of the residences within the MASP plays a larger role in the mass concentration and chemical balance of indoor qUFP in elderly residence than the weather conditions.

We found 2 indoor sources of qUFP inside the elderly residences, wall painting (7%) and cooking (5%), but the main contribution to the mass concentration of qUFP was associated with the outdoor sources, oil and construction (7%), industry (3%), secondary inorganic aerosol (21%) and mainly vehicular emission (57%).

The RDD of qUFP and rBC were higher in men than in women, higher during light exercise than in the seated position, and higher in the tracheobronchial region than in alveolar and head regions. Higher RDD on the deeper part of respiratory tract causes more damage to the health than deposition in the head.

The above finding confirms that elderly is breathing the air with abundance of qUFPs and a very high mass concentration of toxic metals in some residences, which is mainly due to the high number of outdoor vehicles on roads around their residences. The necessity of a better traffic-related emission control in MASP has been already discussed by many authors. Since the elderly spend most of their daily time inside their residences and considering that demographic projections suggest an increase in their numbers in future, further efforts are needed to improve indoor air quality.


We thank the FAPESP (Foundation for Research Support of the State of São Paulo) for funded the project 2010/10189–5: Human biometeorology: Analysis of the environmental variable effects (weather, thermal comfort and air pollution) and climate change in geriatric population of São Paulo, and UGPN (University Global Partnership Network) for funding the projects BIOBURN (Towards the Treatment of Aerosol Emissions from Biomass Burning in Chemical Transport Models through a case study in the Metropolitan Area of São Paulo) and NEST-SEAS (Next-Generation Environmental Sensing for Local To Global Scale Health Impact Assessment), both supporting this work. Bruna Segalin thanks the DAAD (German Academic Exchange Service) and the CNPq (National Council for Scientific and Technological Development) for the grants provided. We also thank the University of São Paulo, University of Surrey and University of Münster for supporting this research and Kaonan Micadei, Rosana Astolfo and Hanna Leona Lokys for useful discussions. Last not least we appreciate the time and effort of the anonymous reviewers to help improve this manuscript.


  1. Abt, E., Suh, H.H., Catalano, P. and Koutrakis, P. (2000). Relative contribution of outdoor and indoor particle sources to indoor concentrations. Environ. Sci. Technol. 34: 3579–3587. [Publisher Site]

  2. Afshari, A., Matson, U. and Ekberg, L.E. (2005). Characterization of indoor sources of fine and ultrafine particles: A study conducted in a full-scale chamber. Indoor Air 15: 141–150. [Publisher Site]

  3. Al-Dabbous, A.N. and Kumar, P. (2015). Source apportionment of airborne nanoparticles in a Middle Eastern city using positive matrix factorization. Environ. Sci. Processes Impacts 17: 802–812. [Publisher Site]

  4. Alessandri, M.R. and Maeda, M.D. (2011) Informes Urbanos: Cresce o número de idosos. Prefeitura de São Paulo, Secretaria Municipal de Desenvolvimento Urbano de São Paulo, pp. 3. Last Access: 10 January 2016. [PDF Link]

  5. Amato, F., Rivas, I., Viana, M., Moreno, T., Bouso, L., Reche, C., Àlvarez-Pedrerol M., Alastuey A., Sunyer, J. and Querol, X. (2014). Sources of indoor and outdoor PM2.5 concentrations in primary schools. Sci. Total Environ. 490: 757–765. [Publisher Site]

  6. Andrade, M.F., Miranda, R.M., Fornaro, A., Kerr, A., Oyama, B., Andre, P.A. and Saldiva, P. (2012). Vehicle emissions and PM2.5 mass concentrations in six Brazilian cities. Air Qual. Atmos. Health 5: 79–88. [Publisher Site]

  7. Andrade, M.d.F., Kumar, P., de Freitas, E.D., Ynoue, R.Y., Martins, J., Martins, L.D., Nogueira, T., Perez-Martinez, P., de Miranda, R.M., Albuquerque, T., Gonçalves, F.L.T., Oyama, B. and Zhang, Y. (2017). Air quality in the megacity of São Paulo: Evolution over the last 30 years and future perspectives. Atmos. Environ. 159: 66–82. [Publisher Site]

  8. Arana, A.A. (2014). Aerossóis Atmosféricos na Amazônia: Composição elementar orgânica e inorgânica em regiões com diferentes usos do solo. PhD thesis, Instituto Nacional de Pesquisas da Amazônia, Universidade do Estado do Amazonas, Manaus.

  9. Arhami, M., Sillanpää, M., Hu, S., Olson, M.R., Schauer, J.J. and Sioutas, C. (2009). Size-segregated inorganic and organic components of PM in the communities of the Los Angeles Harbor. Aerosol Sci. Technol. 43: 145–160. [Publisher Site]

  10. Attfield, M.D. and Costello, J. (2004). Quantitative exposure-response for silica dust and lung cancer in Vermont granite workers. Am. J. Ind. Med. 45: 129–138. [Publisher Site]

  11. Azarmi, F. and Kumar, P. (2016). Ambient exposure to coarse and fine particle emissions from building demolition. Atmos. Environ. 137: 62–79. [Publisher Site]

  12. Beauchemin, S., MacLean, L.C. and Rasmussen, P.E. (2011). Lead speciation in indoor dust: A case study to assess old paint contribution in a Canadian urban house. Environ. Geochem. Health 33: 343–352. [Publisher Site]

  13. Beuck, H., Quass, U., Klemm, O. and Kuhlbusch, T.A.J. (2011). Assessment of sea salt and mineral dust contributions to PM10 in NW Germany using tracer models and positive matrix factorization. Atmos. Environ. 45: 5813–5821. [Publisher Site]

  14. Bhangar, S., Mullen, N., Hering, S., Kreisberg N. and Nazarof, W. (2011) Ultrafine particle concentrations and exposures in seven residences in northern California. Indoor Air 21: 132–144. [Publisher Site]

  15. Brown, S.G., Eberly, S., Paatero, P. and Norris, G.A. (2015). Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results. Sci. Total Environ. 518: 626–635. [Publisher Site]

  16. Campen, M.J., Nolan, J.P., Schladweiler, M.C., Kodavanti, U.P., Evansky, P.A., Costa, D.L. and Watkinson, W.P. (2001). Cardiovascular and thermoregulatory effects of inhaled PM-associated transition metals: A potential interaction between nickel and vanadium sulphate. Toxicol. Sci. 64: 243–252. [Publisher Site]

  17. Castanho, A.D. and Artaxo, P. (2001). Wintertime and summertime São Paulo aerosol source apportionment study. Atmos. Environ. 35: 4889–4902. [Publisher Site]

  18. CETESB (2016). Companhia Ambiental do Estado de São Paulo. Emissões veiculares no Estado de São Paulo 2015. 1–145. Last Access: 10 January 2017. [Website Link]

  19. Chang, C.C., Hwang, J.S., Chan, C.C. and Cheng, T.J. (2007). Interaction effects of ultrafine carbon black with iron and nickel on heart rate variability in spontaneously hypertensive rats. Environ. Health Perspect. 115: 1012–1017. [Publisher Site]

  20. Chatterji, S., Byles, J., Cutler, D., Seeman, T. and Verdes, E. (2015) Health, functioning, and disability in older adults: Present status and future implications. Lancet 385: 563–575. [Publisher Site]

  21. Chueinta, W., Hopke, P.K. and Paatero, P. (2000). Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailand by positive matrix factorization. Atmos. Environ. 34: 3319–3329. [Publisher Site]

  22. Cichocki, A., Zdunek, R., Phan, A.H. and Amari, S.I. (2009). Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. John Wiley & Sons, New York, USA, p. 473. ISBN 9780470746660. [Publisher Site]

  23. DETRAN (2017). Departamento Estadual de Trânsito de São Paulo. Gabinete de planejamento e gestão. Last Access: 09 March 2017. [Website Link]

  24. Ding. J., Zhao, P., Su, J., Dong, Q., Du, X. and Zhang, Y. (2019). Aerosol pH and its driving factors in Beijing, Atmos. Chem. Phys. 19: 7939–7954. [Publisher Site]

  25. Elder, A., Gelein, R., Silva, V., Feikert, T., Opanashuk, L., Carter, J., Potter, R., Maynard, A., Ito, Y., Finkelstein, J. and Oberdörster, G. (2006). Translocation of inhaled ultrafine manganese oxide particles to the central nervous system. Environ. Health Perspect. 114: 1172–1178. [Publisher Site]

  26. Folstein, M.F., Folstein, S.E. and McHugh, P.R. (1975). “Mini–mental state”: A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12: 189–198. [Publisher Site]

  27. Gietl, J.K. and Klemm, O. (2009). Source identification of size-segregated aerosol in Münster, Germany, by factor analysis. Aerosol Sci. Technol. 43: 828–837. [Publisher Site]

  28. Goel, A. and Kumar, P. (2015). Zone of influence for particle number concentrations at signalised traffic intersections. Atmos. Environ. 123: 25–38. [Publisher Site]

  29. Guralnik, J.M., Simonsick, E.M., Ferrucci, L., Glynn, R.J., Berkman, L.F., Blazer, D.G., Scherr, P.A. and Wallace, R.B. (1994). A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. J. Gerontol. 49: M85–M94. [Publisher Site]

  30. Heal, M.R., Kumar, P. and Harrison, R.M. (2012). Particles, air quality, policy and health. Chem. Soc. Rev. 41: 6606–6630. [Publisher Site]

  31. Hinds, W.C. (1999). Aerosol technology: Properties, behaviour and measurement of airborne particles. Second ed. John Wiley & Sons, USA, p. 483.

  32. Holguín, F., Téllez-Rojo, M.M., Hernández, M., Cortez, M., Chow, J.C., Watson, J.G., Mannino, D. and Romieu, I. (2003). Air pollution and heart rate variability among the elderly in Mexico City. Epidemiology 14: 521–527. [Publisher Site]

  33. Hu, S., Polidori, A., Arhami, M., Shafer, M.M., Schauer, J.J., Cho, A. and Sioutas, C. (2008). Redox activity and chemical speciation of size fractioned PM in the communities of the Los Angeles-Long Beach harbor. Atmos. Chem. Phys. 8: 6439–6451. [Publisher Site]

  34. Hueglin, C., Gehrig, R., Baltensperger, U., Gysel, M., Monn, C. and Vonmont, H. (2005). Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland. Atmos. Environ. 39: 637–651. [Publisher Site]

  35. Hussein, T., Glytsos, T., Ondráček, J., Dohányosová, P., Ždímal, V., Hämeri, K., Lazaridis, M., Smolík, J. and Kulmala, M. (2006) Particle size characterization and emission rates during indoor activities in a house. Atmos. Environ. 40: 4285–4307. [Publisher Site]

  36. IBGE (2016). Instituto Brasileiro de Geografia e Estatística. (Brazilian Institute of Geography and Statistics). Last Access: 28 June 2016. [Website Link]

  37. Jansen, K.L., Larson, T.V., Koenig, J.Q., Mar, T.F., Fields, C., Stewart, J. and Lippmann, M. (2005). Associations between health effects and particulate matter and black carbon in subjects with respiratory disease. Environ. Health Perspect. 113: 1741–1746. [Publisher Site]

  38. Kamens, R., Lee, C.T., Wiener, R. and Leith, D. (1991). A study of characterize indoor particles in three non-smoking homes. Atmos. Environ. 25: 939–948. [Publisher Site]

  39. Khan, M.F., Latif, M.T., Saw, W.H., Amil, N., Nadzir, M.S.M., Sahani, M., Tahir, N.M. and Chung, J.X. (2016). Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment. Atmos. Chem. Phys. 16: 597–617. [Publisher Site]

  40. Kotz, J.C. and Purcell, K.F. (1987). Naming Coordination Compounds. In Chemistry & Chemical Reactivity, Saunders College Publishing, USA, p. 962.

  41. Kumar, P., de Fatima Andrade, M., Ynoue, R.Y., Fornaro, A., de Freitas, E.D., Martins, J., Martins, L.D., Albuquerque, T., Zhang, Y. and Morawska, L. (2016). New directions: From biofuels to wood stoves: The modern and ancient air quality challenges in the megacity of São Paulo. Atmos. Environ. 140: 364–369. [Publisher Site]

  42. Kumar, P., Fennell, P.S., Hayhurst, A.N. and Britter, R.E. (2009). Street versus rooftop level concentrations of fine particles in a Cambridge street canyon. Boundary Layer Meteorol. 131: 3–18. [Publisher Site]

  43. Kumar, P., Robins, A., Vardoulakis, S. and Britter, R. (2010). A review of the characteristics of nanoparticles in the urban atmosphere and the prospects for developing regulatory control. Atmos. Environ. 44: 5035–5052. [Publisher Site]

  44. Kumar, P., Pirjola, L., Ketzel, M. and Harrison, R.M. (2013). Nanoparticle emissions from 11 non-vehicle exhaust sources - A review. Atmos. Environ. 67: 252–277. [Publisher Site]

  45. Kumar, P., Morawska, L., Birmili, W., Paasonen, P., Hu, M., Kulmala, M., Harrison, R.M., Norford, L. and Britter, R. (2014). Ultrafine particles in cities. Environ. Int. 66: 1–10. [Publisher Site]

  46. Kumar, P., Rivas, I. and Sachdeva, L. (2017). Exposure of in-pram babies to airborne particles during morning drop-in and afternoon pick-up of school children. Environ. Pollut. 159: 66–82. [Publisher Site]

  47. Lanki, T., de Hartog, J.J., Heinrich, J., Hoek, G., Janssen, N.A., Peters, A., Stölzel, M., Timonen, K.L., Vallius, M., Vanninen, E. and Pekkanen, J. (2006). Can we identify sources of fine particles responsible for exercise-induced ischemia on days with elevated air pollution? The ULTRA study. Environ. Health Perspect. 114: 655–660. [Publisher Site]

  48. Leal, T.F., Fontenele, A.P., Pedrotti, J.J. and Fornaro, A. (2004). Composição iônica majoritária de águas de chuva no centro da cidade de São Paulo. Quim. Nova 27: 855–861. [Publisher Site]

  49. Lovell, M.A., Robertson, J.D., Teesdale, W.J., Campbell, J.L. and Markesbery, W.R. (1998). Copper, iron and zinc in Alzheimer's disease senile plaques. J. Neurol. Sci. 158: 47–52. [Publisher Site]

  50. Mathers, C.D., Stevens, G.A., Boerma, T., White, R.A. and Tobias, M.I. (2015). Causes of international increases in older age life expectancy. Lancet 385: 540–548. [Publisher Site]

  51. Miranda, R.M., Andrade, M.F., Fornaro, A., Astolfo, R., de Andre, P.A. and Saldiva, P. (2012). Urban air pollution: are presentative survey of PM2.5 mass concentrations in six Brazilian cities. Air Qual. Atmos. Health 5: 63–77. [Publisher Site]

  52. Misra, C., Singh, M., Shen, S., Sioutas, C. and Hall, P.M. (2002). Development and evaluation of a personal cascade impactor sampler (PCIS). J. Aerosol Sci. 33: 1027–1047. [Publisher Site]

  53. Norris, G., Duvall, R., Brown, S. and Bai, S. (2014). EPA positive matrix factorization (PMF) 5.0 fundamentals & user guide, Prepared for the US Environmental Protection Agency, Washington, DC, by the National Exposure Research Laboratory, Research Triangle Park, USA.

  54. Ogulei, D., Hopke, P.K. and Wallace, L.A. (2006a). Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization. Indoor Air 16: 204–215. [Publisher Site]

  55. Ogulei, D., Hopke, P.K., Zhou, L., Pancras, J.P., Nair, N. and Ondov, J.M. (2006b). Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data. Atmos. Environ. 40: 396–410. [Publisher Site]

  56. Osman L.M., Douglas J.G., Garden C., Reglitz K., Lyon J., Gordon S., and Ayres J.G. (2007) Indoor air quality in homes of patients with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 176: 465–472. [Publisher Site]
  57. Ozgen, S., Becagli, S., Bernardoni, V., Caserini, S., Caruso, D., Corbella, L., Dell'Acqua, M. Fermo, P., Gonzalez, R., Lonati, G., Signorini, S., Tardivo, R., Tosi, E., Valli, G., Vecchi, R. and Marinovich, M. (2017). Analysis of the chemical composition of ultrafine particles from two domestic solid biomass fired room heaters under simulated real-world use. Atmos. Environ. 150: 87–97. [Publisher Site]

  58. Paatero, P. and Tapper, U. (1994). Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5: 111–126. [Publisher Site]

  59. Peled, R. (2011). Air pollution exposure: Who is at high risk? Atmos. Environ. 45: 1781–1785. [Publisher Site]

  60. Pohl, H.R., Roney, N. and Abadin, H.G. (2011). Metal ions affecting the neurological system. Met. Ions Life Sci. 8: 247–262. [Publisher Site]

  61. Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C. and Sisler, J.F. (1998). Atmospheric aerosol over Alaska: 2. Elemental composition and sources. J. Geophys. Res. 103: 19045–19057. [Publisher Site]

  62. Polizzi, S., Pira, E., Ferrara, M., Bugiani, M., Papaleo, A., Albera, R. and Palmi, S. (2002). Neurotoxic effects of aluminium among foundry workers and Alzheimer’s disease. Neurotoxicology 23: 761–774. [Publisher Site]

  63. Power, M.C., Weisskopf, M.G., Alexeeff, S.E., Wright, R.O., Coull, B.A., Spiro, A. and Schwartz, J. (2013). Modification by hemochromatosis gene polymorphisms of the association between traffic-related air pollution and cognition in older men: A cohort study. Environ. Health 12: 16. [Publisher Site]

  64. Raizenne, M., Neas, L.M., Damokosh, A.I., Dockery, D.W., Spengler, J.D., Koutrakis, P., Ware, J.H. and Speizer, F.E. (1996). Health effects of acid aerosols on North American children: pulmonary function. Environ. Health Perspect. 104: 506–514. [Publisher Site]

  65. Rivas, I., Viana, M., Moreno, T., Pandolfi, M., Amato, F., Reche, C., Bouso, L., Àlvarez-Pedrerol, M., Alastuey, A., Sunyer, J. and Querol, X. (2014). Child exposure to indoor and outdoor air pollutants in schools in Barcelona, Spain. Environ. Int. 69: 200–212. [Publisher Site]

  66. Rohr, A.C. and Wyzga, R.E. (2012). Attributing health effects to individual particulate matter constituents. Atmos. Environ. 62: 130–152. [Publisher Site]

  67. Saffari, A., Daher, N., Shafer, M.M., Schauer, J.J. and Sioutas, C. (2013). Seasonal and spatial variation of trace elements and metals in quasi-ultrafine (PM0.25) particles in the Los Angeles metropolitan area and characterization of their sources. Environ. Pollut. 181: 14–23. [Publisher Site]

  68. Sánchez-Ccoyllo, O.R., Ynoue, R.Y., Martins, L.D., Astolfo, R., Miranda, R.M., Freitas, E.D., Borges, A.S, Fornaro, A, Freitas H. and Andrade, M.F. (2009). Vehicular particulate matter emissions in road tunnels in São Paulo, Brazil. Environ. Monit. Assess. 149: 241–249. [Publisher Site]

  69. Segalin, B., Gonçalves, F.L.T. and Fornaro, A. (2016). Black Carbon em material particulado nas residências de idosos na Região Metropolitana de São Paulo, Brasil. Rev. Bras. Meteorol. 31: 311–318. [Publisher Site]

  70. Segalin, B., Kumar, P., Micadei, K., Fornaro, A. and Gonçalves, F.L.T. (2017). Size–segregated particulate matter in indoor residences of elderly in the Metropolitan Area of São Paulo, Brazil. Atmos. Environ. 148: 139–151. [Publisher Site]

  71. Sheikh, J.I., Yesavage, J.A., Brooks, J.O., Friedman, L., Gratzinger, P., Hill, R.D., Zadeik, A. and Crook, T. (1991). Proposed factor structure of the Geriatric Depression Scale. Int. Psychogeriatr. 3: 23–28. [Publisher Site]

  72. Shi, G., Xu, J., Peng, X., Xiao, Z., Chen, K., Tian, Y., Guan, X., Feng, Y., Yu, H., Nenes, A. and Russell, A.G. (2017). pH of aerosols in a polluted atmosphere: Source contributions to highly acidic aerosol. Environ. Sci. Technol. 51: 4289–4296. [Publisher Site]

  73. Shimodaira, H. (2002). An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51: 492–508. [Publisher Site]

  74. Shimodaira, H. (2004). Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling. Ann. Stat. 32: 2616–2641. [Publisher Site]

  75. Sioutas, C. (2004). Development of New Generation Personal Monitors for Fine Particulate Matter (PM) and its Metal Content. Mickey Leland National Urban Air Toxics Research Center. p. 29.
  76. Steenland, K., Sanderson, W. and Calvert, G.M. (2001). Kidney disease and arthritis in a cohort study of workers exposed to silica. Epidemiology 12: 405–412. [Publisher Site]

  77. Suzuki, R., and Shimodaira, H. (2009). pvclust: Hierarchical clustering with p-values via multiscale bootstrap resampling. R package version 1.2.1.

  78. Tan, H., Tian, H., Verbeeck, J., Monico, L., Janssens, K. and Van Tendeloo, G. (2013). Nanoscale investigation of the degradation mechanism of a historical chrome yellow paint by quantitative electron energy loss spectroscopy mapping of chromium species. Angew. Chem. Int. Ed. 52: 11360–11363. [Publisher Site]

  79. Torkmahalleh, M.A., Goldasteh, I., Zhao, Y., Udochu, N.M., Rossner, A., Hopke, P.K. and Ferro, A.R. (2012). PM2.5 and ultrafine particles emitted during heating of commercial cooking oils. Indoor Air 22: 483–491. [Publisher Site]

  80. Trezza, B.M., Apolinario, D., de Oliveira, R.S., Busse, A.L., Gonçalves, F.L.T., Saldiva, P.H.N. and Jacob-Filho, W. (2015). Environmental heat exposure and cognitive performance in older adults: A controlled trial. Age 37: 43. [Publisher Site]

  81. United Nations (2015) World Population Prospects: The 2015 Revision, Key Findings and Advance Tables. United Nations, Department of Economic and Social Affairs, Population Division. ESA/P/WP.241. p. 66. Last Access: 10 May 2016. [Publisher Site]

  82. Viana, M., Rivas, I., Querol, X., Alastuey, A., Sunyer, J., Álvarez-Pedrerol, M., Buoso, L. and Sioutas, C. (2014). Indoor/outdoor relationships and mass closure of quasi-ultrafine, accumulation and coarse particles in Barcelona schools. Atmos. Chem. Phys. 14: 4459–4472. [Publisher Site]

  83. Vieira-Filho, M., Pedrotti, J.J. and Fornaro, A. (2016a). Water-soluble ions species of size-resolved aerosols: Implications for the atmospheric acidity in São Paulo megacity, Brazil. Atmos. Res. 181: 281–287. [Publisher Site]

  84. Vieira-Filho, M.S., Ito, D.T., Pedrotti, J.J., Coelho, L.H. and Fornaro, A. (2016b). Gas-phase ammonia and water-soluble ions in particulate matter analysis in an urban vehicular tunnel. Environ. Sci. Pollut. Res. 23: 19876–19886. [Publisher Site]

  85. Vu, T.V., Ondracek, J., Zdímal, V., Schwarz, J., Delgado-Saborit, J.M. and Harrison, R.M. (2017). Physical properties and lung deposition of particles emitted from five major indoor sources. Air Qual. Atmos. Health 101: 1–14. [Publisher Site]

  86. Wallace, L. (2006). Indoor sources of ultrafine and accumulation mode particles: Size distributions, size-resolved concentrations, and source strengths. Aerosol Sci. Technol. 40: 348–360. [Publisher Site]

  87. Wallace, L.A., Ott, W.R. and Weschler, C.J. (2015). Ultrafine particles from electric appliances and cooking pans: Experiments suggesting desorption/nucleation of sorbed organics as the primary source. Indoor Air 25: 536–546. [Publisher Site]

  88. Weber, R.J., Guo, H., Russel, A.G. and Nenes, A. (2016). High aerosol acidity despite declining atmospheric sulfate concentrations over the past 15 years. Nat. Geosci. 9: 282–286. [Publisher Site]

  89. Weuve, J., Puett, R.C., Schwartz, J., Yanosky, J.D., Laden, F. and Grodstein, F. (2012). Exposure to particulate air pollution and cognitive decline in older women. Arch. Int. Med. 172: 219–227. [Publisher Site]

  90. Wyzga, R.E. and Folinsbee, L.J. (1995). Health effects of acid aerosol. Water Air Soil Pollut.85: 177–188. [Publisher Site]

  91. Ynoue, R.Y. and Andrade, M.F. (2004). Size-resolved mass balance of aerosol particles over the São Paulo metropolitan area of Brazil. Aerosol Sci. Technol. 38: 52–62. [Publisher Site]

  92. Zatta, P., Lucchini, R., van Rensburg, S.J. and Taylor, A. (2003). The role of metals in neurodegenerative processes: Aluminum, manganese, and zinc. Brain Res. Bull. 62: 15–28. [Publisher Site]

  93. Zatta, P. and Frank, A. (2007). Copper deficiency and neurological disorders in man and animals. Brain Res. Rev. 54: 19–33. [Publisher Site]

Impact Factor: 2.735

5-Year Impact Factor: 2.827

SCImago Journal & Country Rank

Enter your email below to receive latest published articles in your field.