Leandro Gómez-Plata This email address is being protected from spambots. You need JavaScript enabled to view it.1, Dayana Agudelo-Castañeda2, Margarita Castillo1, Elba C. Teixeira3 

1 Department of Environmental Engineering, Corporation Universitario Reformada, Barranquilla, Colombia
2 Department of Civil and Environmental Engineering, Universidad del Norte, Barranquilla, Colombia
3 Postgraduate Program in Remote Sensing, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil

Received: October 23, 2021
Revised: June 6, 2022
Accepted: July 14, 2022

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 

Download Citation: ||https://doi.org/10.4209/aaqr.210293  

Cite this article:

Gómez-Plata, L., Agudelo-Castañeda, D., Castillo, M., Teixeira, E.C. (2022). PM10 Source Identification: A Case of a Coastal City in Colombia. Aerosol Air Qual. Res. 22, 210293. https://doi.org/10.4209/aaqr.210293


  • Mineral matter species were the largest PM10 component representing 30–41% of PM10 mass.
  • Higher levels of PM10 pollution were observed in southeast of study area.
  • Good correlations between OC and EC show a high contribution of SOA.
  • Major sources of PM10 were road dust resuspension, biomass burning, traffic, and heavy-oil combustion.
  • High concentrations of Cu and Pb revealed the presence of irregular smelter processes.


This paper assesses the spatial variation of EC, OC, major, and trace elements in an industrialized coastal city, allowing identification and tracers of PM10 emission sources. 83 samples (24 h average) were collected on quartz filters during the dry season using high-volume samplers. Major and trace elements were analyzed using ICP-AES and ICP-MS, whereas a thermal/optical carbon analyzer was used to determine OC and EC. Chemical characterization of major elements, SiO2, SO42, MgO, and CaO, showed high spatial variation between sites. The abundance of these major elements and OC confirmed the effect of exposed land resuspension and road dust; mutually with the production of secondary organic aerosol (SOA). Trace elements showed high values of Cu, Pb, Mn, and V, indicating the influence of road traffic and some industries (Cu) and oil burning (V and Mn). Enrichment Factor analysis revealed that Mg, P, S, Cu, and Pb were highly/moderately enriched indicating the substantial contribution of anthropogenic sources. Results of diagnostic ratios and PMF receptor model of the spatially obtained data suggested major sources of PM10 as traffic-related emissions, heavy fuel oil combustion, biomass burning, and industrial processes. Back trajectory analysis (HYSPLIT) indicated air masses were coming from the North-East region of the Atlantic Ocean as the principal origin.

Keywords: PM10, SPATIAL variation, PMF model, Enrichment factor, TRACE elements


Understanding atmospheric particles and chemical characterization is a continuing concern within public health, due to their possible adverse effects (Englert, 2004; Pey et al., 2010a). Studies of particulate matter (PM) with aerodynamic diameter < 10 µm (PM10) have become a major concern in developing countries related due to their association with morbidity and mortality, reduction in lung function, and exacerbation of airway illnesses (Agudelo-Castañeda et al., 2019; Hsu et al., 2016; Rodríguez-Villamizar et al., 2019). For this purpose, positive matrix factorization (PMF) has become the most extensively used pollution source resolution method in research around the world (Gadi et al., 2019; Jain et al., 2020; Pant and Harrison, 2012; Sharma et al., 2016, 2014; Shivani et al., 2019). While EPA PMF (V5.0) incorporates considerably enhanced error estimation methods, which try to estimate the errors happening to the inherent uncertainties in the data and the rotational ambiguity in the result (Hopke, 2016; Paatero et al., 2014). Moreover, the chemical composition of PM10, especially trace metal content, has been widely used to infer PM10 sources (Li et al., 2021; Sharma et al., 2014).

Although the importance of analyzing the global, regional, and local influences in this region, studies in Caribbean urban coastal zones are scarce (Agudelo-Castañeda et al., 2020; Aldabe et al., 2011; Arruti et al., 2011; Kalaiarasan et al., 2016; Li et al., 2021; Moreno et al., 2010a; Police et al., 2016) Because of their location and prevailing wind direction, the study area may receive a high influence from regional air masses, industrial sources, and traffic, causing high loading of trace elements. Even if these elements frequently account for a minor proportion of PM, they have substantial environmental and human health impacts (González-Castanedo et al., 2014; Hao et al., 2018; Minguillón et al., 2014; Ripoll et al., 2015). For instance, cadmium, cobalt, arsenic, chromium, nickel, and lead are considered human carcinogens (Liu et al., 2018). Regardless of the importance of public health and risk, there is no baseline information concerning atmospheric pollutant emissions, dynamics, and principal sources of trace elements in the study area. Therefore, this study makes a major contribution to research on chemical element sources in PM10 in a highly industrialized area.

The first section (i) of this paper assesses the spatial variation of OC, EC, EC, major, and trace elements of PM10 in an industrialized Caribbean city. The next section (ii) focuses on the evaluation of PM10 emission sources focusing on the Enrichment Factor and chemical characterization. The last section (iii) discusses PMF profiles and air masses trajectories to deduce the causes of PM peak pollution. Therefore, this work will provide one of the first investigations through spatially resolved data of trace elements associated with PM10 that may be used for public health studies and fill the nonexistence of information in the region.


2.1 Study Area

The study area is located on the western side of the river delta of the Magdalena River, and nearby the Caribbean Sea. Barranquilla is the central economic and industrial hub of the Colombian Caribbean Region with 1,193,952 inhabitants (Barranquilla, 2018). The average annual rainfall is 750.1 mm in 55 days with dry and wet seasons, where the principal dry season is December–April. Bimodal precipitation consists of two periods in May–June and August–November. In July, a reduction of precipitations is registered, although less pronounced than in January. The annual average relative humidity is 83% with 1652 mm of evapotranspiration. The dry season (December–March) is characterized by its lower relative humidity of 78%. In these dry months, the sun on average shines for 8–9 hours. The month with the lowest solar brightness is October with 6 hours day–1. During the sampling period (March 17–April 4, 2016), weather conditions were like climatological normals, with small deviations. Ambient temperature, relative humidity, wind speed, and direction data were taken from two Mark Davis Vantage PRO 2 stations located in two sampling points: Pies Descalzos and Cotediba. The prevailing wind was NE and NW for Pies Descalzos and Cotediba, respectively. Following the Köppen climate classification, the climate in the study area is tropical wet and dry savanna Aw, characterized by an average annual temperature of 28°C, with an average annual minimum of 25.1°C and a maximum of 31.4°C (IDEAM, 2016). The prevailing wind was from the northeast (31.4%) all year, with the second predominance of winds from the north (29.9%). The annual occurrence of calm winds (< 0.5 m s–1) was 9.7% with an annual average wind speed of 10–11 m s–1 (IDEAM, 2016). Wind speed and relative humidity ranged between 5.7–8.8 m s–1 and 77-87%, respectively, during the sampling period. Moreover, relative humidity decreased in the last week of March. The average ambient temperature was 28.6°C and 27°C for Pies Descalzos and Cotediba, respectively. Some industries use diesel fuel, while most use natural gas, supplementary information is included in SI1. The location of the sampling sites in the study may be observed in Table 1 and Fig. 1.

Table 1. Location of sampling sites.

 Fig. 1. Study area and sampling sites in Barranquilla.Fig. 1. Study area and sampling sites in Barranquilla.

2.2 Sampling and Chemical Analysis

Simultaneous measurements of PM10 (airborne particle matter < 10 µm) concentrations at seven sampling sites representing regional, urban, and industrial environments were carried out. Sampling was conducted by the Environmental Authority of Barranquilla and filters were kindly provided as part of a joint project.

Airborne particle matter < 10 µm (PM10) was measured using high volume samplers (1.1–1.7 m3 min–1) Model TE 6070-MFC PM10, TISCH environmental brand (EPA approved Manual Reference Method: RFPS-1287-063) (U.S. EPA, 1999). QA/QC was included in SI4 and SI5. The sampling period was 17 days (March 17–April 4, 2016), during the dry season. A total of 83 samples were obtained. Airborne particles were collected on ALBET quartz filters (203 × 254 mm) over a period of 24 hours ± hour. Particle mass concentrations were determined by standard gravimetric procedures (SI1). Each filter was weighed before and after sampling to determine the net weight of the collected PM10 sample. More details may be consulted in SI1.

Filters were subjected to different treatments in acidic digestion (HNO3:HF: HClO4) of ½ of each filter; and an analysis of organic carbon (OC) and elemental carbon (EC) in 1.5 cm2 sections. The extracted solution from the acidic digestion was analyzed by Inductively Coupled Atomic Emission Spectrometry, ICP-AES, (IRIS Advantage TJA Solutions, THERMO) for the determination of the major elements, and Inductively Coupled Plasma Mass Spectrometry, ICP-MS, (X Series II, THERMO) for the trace elements. OC and EC were determined by a thermal/optical carbon analyzer (SUNSET), using protocol EUSAAR_2. Details of the procedure and chemical analysis are provided in the supplementary materials (SI1, SI2, SI4, SI5). Other analytical details may be found in Querol et al. (2009).

2.3 Positive Matrix Factorization (PMF)

Positive matrix factorization (PMF) is a multivariate factor analysis tool that partition the data into factor contributions, profiles, and a residual matrix (Brown et al., 2015; Paatero, 1997; Paatero et al., 2014). The fundamental principle of the model is mass conservation may be assumed and thus, a mass balance analysis can be used to detect and apportion sources of atmospheric particles (Hopke, 2016). We used the EPA PMF v5.0 (U.S. EPA, 2018). PMF methodology is explained, briefly in supplementary materials (SI1) and another published study (Agudelo-Castañeda and Teixeira, 2014; Teixeira et al., 2015). The determination of factor profiles in PMF is an important process and depends on the goodness of fit to the original data (Agudelo-Castañeda and Teixeira, 2014). Consequently, six factors were used because they can be explained by known source patterns and physically realistic results. Major and trace elements were chosen based on the signal-to-noise ratio, the percentage of values above the MDL, and the database size requirements. Just 16 species were selected as strong, 29 were classified as bad and 11 were classified as weak. Weak species had their uncertainty magnified by a factor of 3, and bad species were eliminated. The model was run numerous times with a differing factors number (3–8) using random seed mode. Q theoretical value was very similar to the obtained Q in the model, thus representing an applicable uncertainty in the input. Scaled residuals values were in the recommended range (–3 to +3) (Comero et al., 2009). Details of PMF settings, regression diagnostics, and Q values results are provided as supplementary materials (SI1, Table S1, S2, and S3).

2.4 Enrichment Factor (EF) Analysis

The enrichment factor (EF) is widely applied to identify the anthropogenic source of chemical elements (Hans Wedepohl, 1995). Calculation of the enrichment factor of each element detected in the study area was done using Ti as the reference element and composition of the upper continental (Fomba et al., 2013; McLennan, 2001). Eq. (1) was applied for the calculation of the enrichment factor of each sampling site in the study area:


where EF is the enrichment factor of element x that represents the chemical element of interest. Cx is the concentration of each element in the PM10 sample, where CR is the concentration of the reference element in the upper continental crustal. (Cx/CR)PM is the concentration ratio of element x to R element in the PM10 sample and (Cx/CR)Crust is the concentration ratio of X to R element in the crustal material.


3.1 PM10 and Major Chemical Species

Table 2 summarizes the average concentration of major elements, PM10, OC, and EC (µg m–3). Results evidenced high PM10 levels (mean 51.7 ± 10.5 µg m–3), especially in P3 up to P7, the latter nearly 3-fold the WHO annual PM10 guideline. Spatial variation (see Table 1 and Fig. 1 for the location of sampling sites) results indicated that P1 (Portales de Sevilla, a residential area) had the lowest levels for almost all major elements, except for P2O5. Probably, because of their location, upstream of primary emission sources (the predominant wind direction was NE for the meteorological station in P2). Additionally, the highest PM10 concentrations were observed at the southernmost sampling sites: P6 (residential area) and P7 (residential area). Sampling site P3 is characterized by near quarries and exposed land, thus, the slightly higher PM10 concentrations compared with the nearby sites. This pattern was observed for crustal elements, too that presented contributions of Al and Mg elements (Table 2). The sampling period corresponded to the dry season, where resuspension increases due to their dry climate. The soil of the study area is characterized by layers of sandy, marly, and loam limestones (limestone - clay - caliche), and coralline rocks, composed of Ca, Al, and Si (IGAC, 2017). Results showed, too, a high concentration for SiO2, MgO, and CaO, though with a slight spatial variation between sites.

Table 2. The average concentration of major elements, PM10, OC, and EC (µg m–3).

MgO was constant between sites, indicating the presence of limestone (dolomite). Moreover, SiO2 and CaO levels were lower in P1 and P2. Probably, as explained above, because P1 and P2 location is upstream of main emission sources and may be considered “background” sites. MgO, Al2O3, and SiO2 showed the highest concentrations in P3 due to the near quarries.

Cement industries and construction originated CaO high levels, i.e., P6 presented the highest concentrations for CaO, probably of some road construction works done nearby for a stream canalization.

The abundance of all these major elements confirms the effect of the resuspension of exposed land and road dust. The city, too, uses concrete pavement in mostly all the streets. This issue and the problem of inadequate rainwater sewerage system cause the deterioration of roads and possible production of dust that may be resuspended.

Furthermore, P4 (common industries and cemeteries) presented the highest concentrations for SO4, OC, and EC. This site is downstream of diverse industries (the prevailing wind direction for P5 was NW), especially several brickkilns and hazardous waste incinerators. Brickkilns may use combustible sulfur which is a precursor of sulfated aerosols, thus increasing SO4 concentrations and organic compounds.

A comparison of the findings with those of other studies confirms that the relatively high levels of K might indicate that biomass burning impact on PM10 levels is high (Rolong et al., 2021). Though, the high correlation K/Al (R2 = 0.72) suggests that K is present in mineral matter. Also, results showed significant correlations between OC and K (R2 = 0.64). The sum of mineral matter-related oxides accounts for the largest proportion of the PM10 mass (30–32% P1–P2 and 34–41% P3–P7).

Fig. 2 presents the correlation between OC and EC in all sampling sites (a) and by site (b). Results showed significant correlations between OC and EC (R2 = 0.77), although mostly all correlations were > 0.98 (Fig. 2(b)), except for the sampling site P3. These correlations and proportional relative rates of OC and EC emission to each other may indicate the presence of common dominant sources (Kong et al., 2010). Even if the sampling sites were highly impacted by vehicles and industrial sources, levels of EC were low (1 order of magnitude lower than most European and Indian cities, Mahapatra et al., 2021; Reche et al., 2011), and as expected much lower than OC, with OC/EC ratios close to 20 for P1 and P2 (Fig. 2(b)) and 11–15 for P3–P7, possibly pointing to high production of secondary organic aerosol (SOA). P1 and P2 located the nearest to Isla Salamanca Natural Park on the northeastern side of the Magdalena River (Fig. 1) showed similar OC/EC ratios with high correlations values (Fig. 2(b)). This natural Park suffers along in the dry season from several fires caused by the high temperatures or by burning the mangrove wood for coal production, cattle raising, or farming. The Caribbean region is particularly vulnerable to fire occurrence due to large seasonal water deficits (Hoyos et al., 2017). Thus, a high impact of biomass combustion and/or the influence of the dry climate, with high temperatures and relative humidity, the presence of hydric resources, and mangroves that favor SOA production, may account for these high OC/EC ratios. In the study area, roads are constructed with concrete, which requires higher energy use compared with an asphalt surface, which marks in higher fuel consumption hydrocarbon concentrations, resulting probably in high OC/EC values (Adamiec et al., 2016). On the other hand, P3 showed the lowest correlation value (R2 = 0.44). This site is an industrial site impacted by hazardous waste incinerators and brick kilns.

Fig. 2. Correlation between OC and EC in (a) all sampling sites and (b) by the site.Fig. 2. Correlation between OC and EC in (a) all sampling sites and (b) by the site.

3.2 Enrichment Factor

Fig. 3 shows the mean Enrichment Factors (EF) for the seven sampling sites. Some trace elements, classified as toxic compounds by the International Agency for Research on Cancer (IARC, 2017) such as Ni (group 1: “Carcinogenic to humans”) and Pb (group 2A: “Probably carcinogenic to humans”), showed moderately and high EF. EF crust close to one indicates the crust (resuspension of soil) as the likely source of the element. Al, Si, K, Ca, Mn, Cr and Fe showed this pattern, meaning probably crustal influence. Mg and Ni (10 < EF < 50) appeared to be moderately enriched, while P, S, and Cu (EF > 100) were highly enriched for some sites. High EF values for S were obtained for all sampling sites, indicating possible vehicular traffic emissions. In Colombian cities, before May 2021 vehicles use diesel with a sulfur content of 50 ppm (about 5.98% of all), while mostly all private and public cars use gasoline with 300 ppm, including motorcycles (ANDEMOS, 2018; ECOPETROL, 2018). Moreover, the economic growth of the city implied a rising demand for transportation, thus vehicle numbers increased by approximately 10.000 year–1; whereas approximately 20% of additional cars circulate in Barranquilla with a license plate from another city. Closer inspection of both Ni and Cu EF values in Fig. 3 (moderately enriched) shows similar values for all sites (except for P2 and P5 for Cu EF values), thus, confirming the vehicular traffic influence. Probably, in P2 and P5 the presence of irregular smelter processes emitted these high concentrations. On the other hand, Fig. 3 reveals highly enriched EF values for Pb in P1, P2, and P7 sites. The two latter sites are characterized by irregular Pb smelters for artisanal recovery of this metal in old batteries, which were in operation at the time of sampling. Moreover, P1 and P7 high EF values are probably due to their location near the harbors, thus receiving a high influence on shipping emissions. Consequently, high EF values in the city probably are due to Pb in emissions of irregular smelters and harbors. Moderately enriched EF values of P, principally in P1, are probably due to a fertilizer industry located near the sampling site. Moreover, fossil fuel combustion is estimated to be a major source of atmospheric-P in industrialized regions (Weinberger et al., 2016) besides the particle phase phosphorus emitted from biogenic processes (Rizzo et al., 2010)

Fig. 3. Mean Enrichment Factors (EF) for the seven sampling sites.Fig. 3. Mean Enrichment Factors (EF) for the seven sampling sites.

3.3 Tracer Species for Source Identification

Table 3 shows the average concentration of trace elements for all sampling sites. As explained above, high EF values of Cu, Pb, and Ni were obtained. Moreover, high concentrations of Cu, Pb, and V were measured. Vanadium is a tracer of the influence of oil-burning (all sites). The maximum Cu value obtained for all samples was 496.2 ng m–3. Cu mean values reached near 117.5 and 323.1 ng m–3 at P2 and P5, respectively, pointing to the influence of industrial emissions at these sites, such as smelter processes, besides road traffic (González-Castanedo et al., 2014). These results may confirm the high EF values for these two sites (P2 and P5).

Table 3. The average concentration of trace elements for all sampling sites (ng m–3).

Pb reached sporadically near 340 ng m–3 values, without major correlation with other trace elements out of As and Sn. Probably pointing to infrequent emissions of a high-temperature industrial process, principally in P1–P2 sites due to their proximity to the industries area near the harbors. As explained above, these two sites presented high EFs values, whereas P1 showed high values for P, S, and Pb (Table 2 and Table 3), and P2 for the same elements plus Cu. These sporadic Pb and Cu concentrations associated with PM10 may indicate the influence of industrial processes and shipping emissions (Nunes et al., 2017; Russo et al., 2018; Viana et al., 2014b). Ni had similar concentrations in all sites, confirming the influence of vehicular traffic, except for P3. These results can be attributed to industry emissions such as cement and brick production (Taghvaee et al., 2018).

Table 4 shows our results compared to previous studies. Fe concentration is less than that reported for the other cities, as well as the average concentration of Cr, which is lower than that reported for megacities in Latin America such as Mexico City and Bogotá. While Cr, Co, and Cd are close to those reported for other coastal cities in the Caribbean such as Belén, Costa Rica, and Cienfuegos, Cuba. Ni concentration is higher than that reported in other coastal cities, such as Navi, India, and Patras, Greece, and close to the reported concentration for Barcelona, Spain. Pb concentration is higher than that reported for all the cities in Table 4, except for Mexico and Barcelona.

Table 4. Comparison of the concentrations of PM and trace elements obtained in this study with other investigations.

The direct identification of anthropogenic emissions employing just these tracers is difficult because they are markers for many types of combustion processes occurring at the same time, such as industrial processes, traffic, petroleum refinery, and energy generation. Consequently, to better understand these values, some tracer species ratios were calculated for each sampling site (These results are like related studies in other cities -Tables 4 and 5-). Values confirmed the presence of different anthropogenic sources (Table 5). Cu/Pb ratio indicated traffic as a local source in P6 and P1 (Pey et al., 2010a). Moreover, Cr/Pb ratio confirmed traffic as a source in P6 and P7 (Font et al., 2015), while the Sb/Cu ratio fell in the range of traffic for P2, P5, and P6 (Pey et al., 2010a). Sn/Cu ratio, confirmed too, the traffic influence in P2 and P5 sites, where values fall in the range for wear-abrasive sources (Lin et al., 2015). The influence of shipping emissions was confirmed in all sampling sites as combustion processes that use crude oil as the main fuel, where vanadium (V) and nickel (Ni) are commonly identified as markers of shipping emissions (Viana et al., 2014a). The study area is downwind of the prevailing wind direction, thus receiving shipping emissions, confirming the V/Ni and La/Ce ratios values for this type of local source originated in the North and Northeastern side of the city. These two latter ratios correlated very well with shipping emissions, as much of the combustion from ships derives V and Ni associated principally with fine atmospheric particles, and therefore capable of traveling long distances and staying resuspended (Viana et al., 2014a). Moreover, the trace element ratio V/Co can be assigned to oil combustion according to literature (Ledoux et al., 2017) for P1, P2, and P3 sites. Furthermore, the Mn/V ratio could be useful for distinguishing between particles originating from oil burning or coal-burning; while from oil burning this ratio is << 1, from coal-burning, is >> 1 (Kong et al., 2011). Ratios ranged from 1.36 to 5.36 which meant that PM10 was more relative to heavy fuel oil combustion than coal burning in all sampling sites, probably originating from shipping emissions as stated above. Also, these sites may be impacted by possible recoveries of industrial oil. Ratio Cu/Pb and Cr/Pb confirmed the presence of municipal waste incinerators in P3 (Font et al., 2015), where these ratios may be used as tracers. Slightly high Cu/Cd ratios in the study area confirmed the presence of additional emission sources of Cu from some industries, although, at some sampling points, these ratios were lower indicating the presence of close Cd sources, whereas Cu had also been recognized as markers for non-ferrous metal smelters (Kong et al., 2011).

Table 5. Average metal value ratios.

3.4 PMF Profiles

Profiles obtained from the PMF model identified six sources for PM10. Results may be observed in Fig. 4. The identified sources were enriched road dust resuspension, biomass burning, traffic-related emissions, heavy fuel oil combustion, industrial/smelter processes, and brick.

Fig. 4. Source profiles of PMF receptor model for PM10 during the sampling period.Fig. 4. Source profiles of PMF receptor model for PM10 during the sampling period.

Factor 1 was defined as enriched road dust resuspension with soil mineral dust because of their high loadings of crustal elements enriched in OC, EC, V, and Cd, among others. Possible sources may include emissions from near quarries located within the study area, besides resuspension of road dust (Amato et al., 2009).

Factor 2 presented P2O5, MgO, and K2O with dominant concentrations. K is used as a key element for biomass burning or wood combustion (Sharma et al., 2014). As explained before, a significant correlation between OC and K might give evidence of biomass burning as a source of PM10 during the sampling period, which probably originated in the Isla Salamanca Natural Park on the northeastern side of the Magdalena River (Rolong et al., 2021).

Factor 3 accounts for crustal elements and Zr with dominant concentrations. Zr was identified as a tracer of the ceramic industry, clay or clinker loading/transport (Minguillón et al., 2013; Pey et al., 2009; Sánchez de la Campa et al., 2010). Several brickfields/ceramic industries that use coal or wood as main fuels have grown on the southwest side of the city near the P3 site. Consequently, this profile was defined as bricks/ceramics (Begum et al., 2011).

Factor 4 had high loadings of V, Cd, OC, EC, and SO42. An increasing number of studies are showing that these elements are typical of heavy fuel oil combustion, probably originating from shipping emissions (Moreno et al., 2010b; Pey et al., 2010b; Viana et al., 2014b), as explained above.

Factor 5 was identified as industrial and smelter processes because presented high loadings of Cu. As explained above, Cu concentrations were high in some industrial sites, thus confirming this result. Several studies state Cu is a tracer of non-ferrous smelters (Pan et al., 2015) or even Cu-smelter processes (González-Castanedo et al., 2014).

Factor 6 presented high values for Ni, Cr, Cd, EC, and SO42. These metals have been observed in other studies in the coarse fraction of atmospheric particle matter in high-traffic urban areas (Fomba et al., 2018). Road dust resuspension generated by abrasion of vehicle parts and pavements may originate from these elements (Salvador et al., 2012). Consequently, this profile was designed as traffic-related. As explained previously, the economic growth of the study area increased vehicle traffic and number.

3.5 Back Trajectory Analysis

HYSPLIT trajectories may be used to determine possible source regions contributing to air pollution in the study area or to determine air masses that may be affecting (Draxler et al., 2020; Rolph et al., 2017). All masses trajectories are in the supplementary information (SI3). PM10 may travel long distances and stay suspended in air (Sharma et al., 2014), thus 7 days were selected to calculate backward trajectories using HYSPLIT to understand the flow of pollutants from distant source regions. AGL heights of 500 m, 1000 m, and 1500 m were used to reduce the effects of surface friction (500 m) and to understand the behavior of the boundary layer. May be observed three different air masses trajectories during the sampling period (Fig. 5): one coming from the Northeast Atlantic Ocean and entering the northern region of Colombia, another one coming from east South America and crossing Venezuela, and the last one coming from the Caribbean Ocean and going through Venezuela. Recent studies demonstrate the possible influence of biomass burning in the Orinoquia region, thus increasing PM10 (Ballesteros-González et al., 2020).

This study has been of great importance for the environmental authority, since it has allowed us to recognize which are the main emitting sources of these trace metals, strengthening the management of air quality through the development of surveillance and control strategies, such as preparing the air quality management plan that contains programs and projects to reduce atmospheric pollution and trace metal level in Barranquilla, as well as the control of industries that impact air quality in the city. Our study suggests that to further reduce trace metal levels, we must encourage sustainable transport.


This paper assesses the spatial variation of chemical characterization of PM10 in an industrialized Caribbean city and evaluation of emission sources using PMF, EF, and diagnostic ratios. As it is the first research in the study area, obtained results explained the origin of diverse trace elements associated with PM10.

The results of this research support the idea of the contribution of resuspension of exposed land and road dust contribution to the study area.

Sites near brickkilns and hazardous waste incinerators contributed as precursors of sulfated aerosols.

Good correlations were found between OC and EC. Even if the sampling sites are highly impacted by vehicles and industrial sources, levels of EC were low, and as expected much lower than OC, possibly pointing to high production of secondary organic aerosol (SOA).

The impact of biomass burning was demonstrated, too.

High EF values for S were obtained for all sampling sites, indicating possible vehicular traffic emissions. Closer inspection of both Ni and Cu EF, and high EF values for S show similar values for all sites (except for P2 and P5 for Cu EF values), thus, confirming the vehicular traffic influence. Diagnostic ratios confirmed these results, which is the influence of traffic in the study area.

The influence of shipping emissions was confirmed in all sampling sites as combustion processes that use crude oil as the main fuel, where vanadium (V) and nickel (Ni) are commonly identified as markers; and high EF Pb values in P1 and P7 probably due to their location near the harbors. Also, the markers V and Ni pointed out the influence of heavy fuel oil combustion by the shipping emissions, too.

Cu mean values at P2 and P5 showed the influence of industrial emissions, such as smelter processes.

PMF analysis confirmed some identified sources by ratios and EF values: enriched road dust resuspension, biomass burning, traffic-related emissions, heavy fuel oil combustion, industrial/ smelter processes, and brick.

These results could improve emission inventories and air quality management, overcoming the existing lack of information. The presented results show important implications regarding the scientific and environmental panorama.


The Environmental Agency of the District of Barranquilla - DAMAB (Actual EPA- Barranquilla Verde) and the Regional Environmental Agency (CRA – Atlántico) donated the PM10 filters. To the Universidad de la Costa for the economic support for this research. The opinions, findings, ideas, and conclusions expressed in this publication are personal responsibility and do not commit to the Universidad del Norte or those of the supporting agencies.


Reference to any companies or specific commercial products does not constitute an endorsement, recommendation, or favoring of any university that participated in this research.


  1. Adamiec, E., Jarosz-Krzemińska, E., Wieszała, R. (2016). Heavy metals from non-exhaust vehicle emissions in urban and motorway road dusts. Environ. Monit. Assess. 188, 1–11. https://doi.org/10.1007/s10661-016-5377-1

  2. Agudelo-Castañeda, D.M., Teixeira, E.C. (2014). Seasonal changes, identification and source apportionment of PAH in PM1.0. Atmos. Environ. 96, 186–200. https://doi.org/10.1016/j.​atmosenv.2014.07.030

  3. Agudelo-Castañeda, D., Calesso Teixeira, E., Alves, L., Fernández-Niño, J.A., Rodríguez-Villamizar, L.A. (2019). Monthly-Term Associations Between Air Pollutants and Respiratory Morbidity in South Brazil 2013-2016: A Multi-City, Time-Series Analysis. Int. J. Environ. Res. Public Health 16, 1–13. https://doi.org/10.3390/ijerph16203787

  4. Agudelo-Castañeda, D., De Paoli, F., Morgado-Gamero, W.B., Mendoza, M., Parody, A., Maturana, A.Y., Teixeira, E.C. (2020). Assessment of the NO2 distribution and relationship with traffic load in the Caribbean coastal city. Sci. Total Environ. 720, 137675. https://doi.org/10.1016/j.​scitotenv.2020.137675

  5. Aldabe, J., Elustondo, D., Santamaría, C., Lasheras, E., Pandolfi, M., Alastuey, A., Querol, X., Santamaría, J.M. (2011). Chemical characterisation and source apportionment of PM2.5 and PM10 at rural, urban and traffic sites in Navarra (North of Spain). Atmos. Res. 102, 191–205. https://doi.org/10.1016/j.atmosres.2011.07.003

  6. Amato, F., Pandolfi, M., Escrig, A., Querol, X., Alastuey, A., Pey, J., Perez, N., Hopke, P.K. (2009). Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2. Atmos. Environ. 43, 2770–2780. https://doi.org/10.1016/j.atmosenv.2009.02.039

  7. Asociacion Nacional de Movilidad Sostenible (ANDEMOS) (2018). Asociación colombiana de vehículos automotores. (accessed 31 July 2018).

  8. Arruti, A., Fernández-Olmo, I., Irabien, A. (2011). Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): Spatial variations in different urban and rural environments. Atmos. Res. 101, 280–293. https://doi.org/10.1016/j.​atmosres.2011.03.001

  9. Ballesteros-González, K., Sullivan, A.P., Morales-Betancourt, R. (2020). Estimating the air quality and health impacts of biomass burning in northern South America using a chemical transport model. Sci. Total Environ. 739, 139755. https://doi.org/10.1016/j.scitotenv.2020.139755

  10. Barranquilla (2018). Alcaldía de Barranquilla. (accessed 31 July 2018).

  11. Begum, B.A., Biswas, S.K., Hopke, P.K. (2011). Key issues in controlling air pollutants in Dhaka, Bangladesh. Atmos. Environ. 45, 7705–7713. https://doi.org/10.1016/j.atmosenv.2010.10.022

  12. Brown, S.G., Eberly, S., Paatero, P., 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. 518–519, 626–635. https://doi.org/10.1016/j.scitotenv.2015.​01.022

  13. Comero, S., Capitani, L., Gawlik, B.M. (2009). Positive Matrix Factorisation (PMF) - An introductioin to the chemometric evaluation of environmental monitoring data using PMF. Publications Office of the European Union, LU. https://doi.org/10.2788/2497

  14. Draxler, R., Stunder, B., Rolph, G., Stein, A., Taylor, A. (2020). HYSPLIT User’s Guide Version 5. Air Resources Laboratory, National Oceanic and Atmospheric Administration,USA.

  15. ECOPETROL (2018). (accessed 31 July 2018).

  16. Englert, N. (2004). Fine particles and human health - A review of epidemiological studies. Toxicol. Lett. 149, 235–242. https://doi.org/10.1016/j.toxlet.2003.12.035

  17. Fomba, K.W., Müller, K., Van Pinxteren, D., Herrmann, H. (2013). Aerosol size-resolved trace metal composition in remote northern tropical atlantic marine environment: Case study cape verde islands. Atmos. Chem. Phys. 13, 4801–4814. https://doi.org/10.5194/acp-13-4801-2013

  18. Fomba, K.W., van Pinxteren, D., Müller, K., Spindler, G., Herrmann, H. (2018). Assessment of trace metal levels in size-resolved particulate matter in the area of Leipzig. Atmos. Environ. 176, 60–70. https://doi.org/10.1016/j.atmosenv.2017.12.024

  19. Font, A., de Hoogh, K., Leal-Sanchez, M., Ashworth, D.C., Brown, R.J.C., Hansell, A.L., Fuller, G.W. (2015). Using metal ratios to detect emissions from municipal waste incinerators in ambient air pollution data. Atmos. Environ. 113, 177–186. https://doi.org/10.1016/j.atmosenv.2015.​05.002

  20. Gadi, R., Shivani, Sharma, S.K., Mandal, T.K. (2019). Source apportionment and health risk assessment of organic constituents in fine ambient aerosols (PM2.5): A complete year study over National Capital Region of India. Chemosphere 221, 583–596. https://doi.org/10.1016/j.​chemosphere.2019.01.067

  21. González-Castanedo, Y., Moreno, T., Fernández-Camacho, R., Sánchez de la Campa, A.M., Alastuey, A., Querol, X., de la Rosa, J. (2014). Size distribution and chemical composition of particulate matter stack emissions in and around a copper smelter. Atmos. Environ. 98, 271–282. https://doi.org/10.1016/j.atmosenv.2014.08.057

  22. Hans Wedepohl, K. (1995). The composition of the continental crust. Geochim. Cosmochim. Acta 59, 1217–1232. https://doi.org/10.1016/0016-7037(95)00038-2

  23. Hao, Y., Meng, X., Yu, X., Lei, M., Li, W., Shi, F., Yang, W., Zhang, S., Xie, S. (2018). Characteristics of trace elements in PM2.5 and PM10 of Chifeng, northeast China: Insights into spatiotemporal variations and sources. Atmos. Res. 213, 550–561. https://doi.org/10.1016/j.atmosres.2018.​07.006

  24. Hopke, P.K. (2016). Review of receptor modeling methods for source apportionment. J. Air Waste Manag. Assoc. 66, 237–259. https://doi.org/10.1080/10962247.2016.1140693

  25. Hoyos, N., Correa-Metrio, A., Sisa, A., Ramos-Fabiel, M.A., Espinosa, J.M., Restrepo, J.C., Escobar, J. (2017). The environmental envelope of fires in the Colombian Caribbean. Appl. Geogr. 84, 42–54. https://doi.org/10.1016/j.apgeog.2017.05.001

  26. Hsu, C.Y., Chiang, H.C., Lin, S.L., Chen, M.J., Lin, T.Y., Chen, Y.C. (2016). Elemental characterization and source apportionment of PM10 and PM2.5 in the western coastal area of central Taiwan. Sci. Total Environ. 541, 1139–1150. https://doi.org/10.1016/j.scitotenv.2015.09.122

  27. International Agency for Research on Cancer (IARC) (2017). International Agency for Research on Cancer (IARC) Monographs on the Evaluation of Carcinogenic Risks to Humans - Agents Classified by the IARC Monographs, Volumes 1–117. 

  28. Instituto Geográfico Agustín Codazzi (IGAC) (2017). (accessed 31 July 2018).

  29. Jain, S., Sharma, S.K., Vijayan, N., Mandal, T.K. (2020). Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India. Environ. Pollut. 262, 114337. https://doi.org/10.1016/j.envpol.2020.114337

  30. Kalaiarasan, G., Balakrishnan, R.M., Khaparde, V.V (2016). Receptor model based source apportionment of PM10 in the metropolitan and industrialized areas of Mangalore. Environ. Technol. Innov. 6, 195–203. https://doi.org/10.1016/j.eti.2016.10.002

  31. Kong, S., Han, B., Bai, Z., Chen, L., Shi, J., Xu, Z. (2010). Receptor modeling of PM2.5, PM10 and TSP in different seasons and long-range transport analysis at a coastal site of Tianjin, China. Sci. Total Environ. 408, 4681–94. https://doi.org/10.1016/j.scitotenv.2010.06.005

  32. Kong, S., Ji, Y., Lu, B., Chen, L., Han, B., Li, Z., Bai, Z. (2011). Characterization of PM10 source profiles for fugitive dust in Fushun-a city famous for coal. Atmos. Environ. 45, 5351–5365. https://doi.org/10.1016/j.atmosenv.2011.06.050

  33. Ledoux, F., Kfoury, A., Delmaire, G., Roussel, G., El Zein, A., Courcot, D. (2017). Contributions of local and regional anthropogenic sources of metals in PM2.5 at an urban site in northern France. Chemosphere 181, 713–724. https://doi.org/10.1016/j.chemosphere.2017.04.128

  34. Li, J., Michalski, G., Olson, E.J., Welp, L.R., Larrea Valdivia, A.E., Larico, J.R., Zapata, F.A., Paredes, L.M. (2021). Geochemical characterization and heavy metal sources in PM10 in arequipa, peru. Atmosphere 12, 1–14. https://doi.org/10.3390/atmos12050640

  35. Lin, Y.C., Tsai, C.J., Wu, Y.C., Zhang, R., Chi, K.H., Huang, Y.T., Lin, S.H., Hsu, S.C. (2015). Characteristics of trace metals in traffic-derived particles in Hsuehshan Tunnel, Taiwan: Size distribution, potential source, and fingerprinting metal ratio. Atmos. Chem. Phys. 15, 4117–4130. https://doi.org/10.5194/acp-15-4117-2015

  36. Liu, J., Chen, Y., Chao, S., Cao, H., Zhang, A., Yang, Y. (2018). Emission control priority of PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective. Sci. Total Environ. 644, 20–30. https://doi.org/10.1016/j.scitotenv.2018.06.226

  37. Mahapatra, P.S., Panda, U., Mallik, C., Boopathy, R., Jain, S., Sharma, S.K., Mandal, T.K., Senapati, S., Satpathy, P., Panda, S., Das, T. (2021). Chemical, microstructural, and biological characterization of wintertime PM2.5 during a land campaign study in a coastal city of eastern India. Atmos. Pollut. Res. 12, 101164. https://doi.org/10.1016/j.apr.2021.101164

  38. Manousakas, M., Diapouli, E., Papaefthymiou, H., Kantarelou, V., Zarkadas, C., Kalogridis, A., Karydas, A., Eleftheriadis, K. (2018). XRF characterization and source apportionment of PM10 samples collected in a coastal city. X‐Ray Spectrom. 47, 190–200. https://doi.org/10.1002/​xrs.2817

  39. McLennan, S.M. (2001). Relationships between the trace element composition of sedimentary rocks and upper continental crust. Geochem. Geophys. Geosyst. 2, 1021. https://doi.org/​10.1029/2000GC000109

  40. Minguillón, M.C., Monfort, E., Escrig, A., Celades, I., Guerra, L., Busani, G., Sterni, A., Querol, X. (2013). Air quality comparison between two European ceramic tile clusters. Atmos. Environ. 74, 311–319. https://doi.org/10.1016/j.atmosenv.2013.04.010

  41. Minguillón, M.C., Cirach, M., Hoek, G., Brunekreef, B., Tsai, M., de Hoogh, K., Jedynska, A., Kooter, I.M., Nieuwenhuijsen, M., Querol, X. (2014). Spatial variability of trace elements and sources for improved exposure assessment in Barcelona. Atmos. Environ. 89, 268–281. https://doi.org/​10.1016/j.atmosenv.2014.02.047

  42. Moreno, T., Pérez, N., Querol, X., Amato, F., Alastuey, A., Bhatia, R., Spiro, B., Hanvey, M., Gibbons, W. (2010a). Physicochemical variations in atmospheric aerosols recorded at sea onboard the Atlantic-Mediterranean 2008 Scholar Ship cruise (Part II): Natural versus anthropogenic influences revealed by PM10 trace element geochemistry. Atmos. Environ. 44, 2563–2576. https://doi.org/10.1016/j.atmosenv.2010.04.027

  43. Moreno, T., Pérez, N., Querol, X., Amato, F., Alastuey, A., Bhatia, R., Spiro, B., Hanvey, M., Gibbons, W. (2010b). Physicochemical variations in atmospheric aerosols recorded at sea onboard the Atlantic–Mediterranean 2008 Scholar Ship cruise (Part II): Natural versus anthropogenic influences revealed by PM10 trace element geochemistry. Atmos. Environ. 44, 2563–2576. https://doi.org/10.1016/j.atmosenv.2010.04.027

  44. Morera-Gómez, Y., Elustondo, D., Lasheras, E., Alonso-Hernández, C.M., Santamaría, J.M. (2018). Chemical characterization of PM10 samples collected simultaneously at a rural and an urban site in the Caribbean coast: Local and long-range source apportionment. Atmos. Environ. 192, 182–192. https://doi.org/10.1016/j.atmosenv.2018.08.058

  45. Mugica, V., Ortiz, E., Molina, L., De Vizcaya-Ruiz, A., Nebot, A., Quintana, R., Aguilar, J., Alcántara, E. (2009). PM composition and source reconciliation in Mexico City. Atmos. Environ. 43, 5068–5074. https://doi.org/10.1016/j.atmosenv.2009.06.051Murillo

  46. Murillo, J.H., Roman, S.R., Marin, J.F.R., Ramos, A.C., Jimenez, S.B., Gonzalez, B.C., Baumgardner, D.G. (2013). Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America. Atmos. Pollut. Res. 4, 181–190. https://doi.org/10.5094/APR.2013.018

  47. Nunes, R.A.O., Alvim-Ferraz, M.C.M., Martins, F.G., Sousa, S.I.V. (2017). Assessment of shipping emissions on four ports of Portugal. Environ. Pollut. 231, 1370–1379. https://doi.org/10.1016/​j.envpol.2017.08.112

  48. Paatero, P. (1997). Least squares formulation of robust non-negative factor analysis. Chemom. Intell. Lab. Syst. 37, 23–35. https://doi.org/10.1016/S0169-7439(96)00044-5

  49. Paatero, P., Eberly, S., Brown, S.G., Norris, G.A. (2014). Methods for estimating uncertainty in factor analytic solutions. Atmos. Meas. Tech. 7, 781–797. https://doi.org/10.5194/amt-7-781-2014

  50. Pan, Y., Tian, S., Li, X., Sun, Y., Li, Y., Wentworth, G.R., Wang, Y. (2015). Trace elements in particulate matter from metropolitan regions of Northern China: Sources, concentrations and size distributions. Sci. Total Environ. 537, 9–22. https://doi.org/10.1016/j.scitotenv.2015.07.060

  51. Pant, P., Harrison, R.M. (2012). Critical review of receptor modelling for particulate matter: A case study of India. Atmos. Environ. 49, 1–12. https://doi.org/10.1016/j.atmosenv.2011.11.060

  52. Pey, J., Querol, X., Alastuey, A. (2009). Variations of levels and composition of PM10 and PM2.5 at an insular site in the Western Mediterranean. Atmos. Res. 94, 285–299. https://doi.org/​10.1016/j.atmosres.2009.06.006

  53. Pey, J., Alastuey, A., Querol, X., Pérez, N., Cusack, M. (2010a). A simplified approach to the indirect evaluation of the chemical composition of atmospheric aerosols from PM mass concentrations. Atmos. Environ. 44, 5112–5121. https://doi.org/10.1016/j.atmosenv.2010.09.00

  54. Pey, J., Querol, X., Alastuey, A. (2010b). Discriminating the regional and urban contributions in the North-Western Mediterranean: PM levels and composition. Atmos. Environ. 44, 1587–1596. https://doi.org/10.1016/j.atmosenv.2010.02.005

  55. Police, S., Sahu, S.K., Pandit, G.G. (2016). Chemical characterization of atmospheric particulate matter and their source apportionment at an emerging industrial coastal city, Visakhapatnam, India. Atmos. Pollut. Res. 7, 725–733. https://doi.org/10.1016/j.apr.2016.03.007

  56. Police, S., Sahu, S.K., Tiwari, M., Pandit, G.G. (2018). Chemical composition and source apportionment of PM2.5 and PM2.5–10 in Trombay (Mumbai, India), a coastal industrial area. Particuology 37, 143–153. https://doi.org/https://doi.org/10.1016/j.partic.2017.09.006

  57. Querol, X., Alastuey, A., Rodriguez, S., Plana, F., Ruiz, C.R., Cots, N., Massagué, G., Puig, O. (2001). PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain. Atmos. Environ. 35, 6407–6419. https://doi.org/https://doi.org/10.1016/S1352-2310(01)00361-2

  58. Querol, X., Alastuey, A., Pey, J., Cusack, M., Perez, N., Mihalopoulos, N., Theodosi, C., Gerasopoulos, E., Kubilay, N., Kocak, M. (2009). Variability in regional background aerosols within the Mediterranean. Atmos. Chem. Phys. 9, 4575–4591. https://doi.org/10.5194/acp-9-4575-2009

  59. Ramírez, O., Sánchez de la Campa, A.M., Amato, F., Catacolí, R.A., Rojas, N.Y., de la Rosa, J. (2018). Chemical composition and source apportionment of PM10 at an urban background site in a high–altitude Latin American megacity (Bogota, Colombia). Environ. Pollut. 233, 142–155. https://doi.org/https://doi.org/10.1016/j.envpol.2017.10.045

  60. Reche, C., Querol, X., Alastuey, A., Viana, M., Pey, J., Moreno, T., Rodríguez, S., González, Y., Fernández-Camacho, R., De La Campa, A.M.S., De La Rosa, J., Dall’Osto, M., Prév̂t, A.S.H., Hueglin, C., Harrison, R.M., Quincey, P. (2011). New considerations for PM, Black Carbon and particle number concentration for air quality monitoring across different European cities. Atmos. Chem. Phys. 11, 6207–6227. https://doi.org/10.5194/acp-11-6207-2011

  61. Ripoll, A., Minguillón, M.C., Pey, J., Pérez, N., Querol, X., Alastuey, A. (2015). Joint analysis of continental and regional background environments in the western Mediterranean: PM1 and PM10 concentrations and composition. Environ. Pollut. 15, 17–23. https://doi.org/10.1016/j.​envpol.2015.07.004

  62. Rizzo, L.V., Artaxo, P., Karl, T., Guenther, A.B., Greenberg, J. (2010). Aerosol properties, in-canopy gradients, turbulent fluxes and VOC concentrations at a pristine forest site in Amazonia. Atmos. Environ. 44, 503–511. https://doi.org/10.1016/j.atmosenv.2009.11.002

  63. Rodríguez-Villamizar, L.A., Rojas-Roa, N.Y., Fernández-Niño, J.A. (2019). Short-term joint effects of ambient air pollutants on emergency department visits for respiratory and circulatory diseases in Colombia, 2011–2014. Environ. Pollut. 248, 380–387. https://doi.org/10.1016/j.​envpol.2019.02.028

  64. Rolong, G.B., Romo Padilla, A., Agudelo-Castañeda, D. (2021). Aporte de PM10 y PM2.5 en la calidad del aire de Barranquilla por quemas en el Vía Parque Isla de Salamanca, in: Congreso Colombiano y Conferencia Internacional de Calidad de Aire y Salud Pública (CASAP), IEEE, Bogotá, pp. 0–5. https://doi.org/10.1109/CASAP54985.2021

  65. Rolph, G., Stein, A., Stunder, B. (2017). Real-time Environmental Applications and Display sYstem: READY. Environ. Model. Softw. 95, 210–228. https://doi.org/10.1016/j.envsoft.2017.06.025

  66. Russo, M.A., Leitão, J., Gama, C., Ferreira, J., Monteiro, A. (2018). Shipping emissions over Europe: A state-of-the-art and comparative analysis. Atmos. Environ. 177, 187–194. https://doi.org/​10.1016/j.atmosenv.2018.01.025

  67. Salvador, P., Artíñano, B., Viana, M., Alastuey, A., Querol, X. (2012). Evaluation of the changes in the Madrid metropolitan area influencing air quality: Analysis of 1999-2008 temporal trend of particulate matter. Atmos. Environ. 57, 175–185. https://doi.org/10.1016/j.atmosenv.2012.​04.026

  68. Sánchez de la Campa, A.M., de la Rosa, J.D., González-Castanedo, Y., Fernández-Camacho, R., Alastuey, A., Querol, X., Pio, C. (2010). High concentrations of heavy metals in PM from ceramic factories of Southern Spain. Atmos. Res. 96, 633–644. https://doi.org/10.1016/j.atmosres.​2010.02.011

  69. Sharma, S.K., Mandal, T.K., Saxena, M., Rashmi, Sharma, A., Datta, A., Saud, T. (2014). Variation of OC, EC, WSIC and trace metals of PM10 in Delhi, India. J. Atmos. Solar-Terrestrial Phys. 113, 10–22. https://doi.org/10.1016/j.jastp.2014.02.008

  70. Sharma, S.K., Mandal, T.K., Jain, S., Saraswati, Sharma, A., Saxena, M. (2016). Source Apportionment of PM2.5 in Delhi, India Using PMF Model. Bull. Environ. Contam. Toxicol. 97, 286–293. https://doi.org/10.1007/s00128-016-1836-1

  71. Shivani, Gadi, R., Sharma, S.K., Mandal, T.K. (2019). Seasonal variation, source apportionment and source attributed health risk of fine carbonaceous aerosols over National Capital Region, India. Chemosphere 237, 124500. https://doi.org/10.1016/j.chemosphere.2019.124500

  72. Taghvaee, S., Sowlat, M.H., Mousavi, A., Hassanvand, M.S., Yunesian, M., Naddafi, K., Sioutas, C. (2018). Source apportionment of ambient PM2.5 in two locations in central Tehran using the Positive Matrix Factorization (PMF) model. Sci. Total Environ. 628–629, 672–686. https://doi.org/10.1016/j.scitotenv.2018.02.096

  73. Teixeira, E.C., Agudelo-Castañeda, D.M., Mattiuzi, C.D.P. (2015). Contribution of polycyclic aromatic hydrocarbon (PAH) sources to the urban environment: A comparison of receptor models. Sci. Total Environ. 538, 212–219. https://doi.org/10.1016/j.scitotenv.2015.07.072

  74. U.S. Environmental Protection Agency (U.S. EPA) (1999). Sampling of Ambient Air for Suspended Particle Matter (SPM) and PM10 Using High Volume (HV) Sampler. EPA e-CFR Title 40, Part 50, Appendix J: PM10.

  75. U.S. Environmental Protection Agency (U.S. EPA) (2018). Positive Matrix Factorization Model for environmental data analyses. (accessed 31 July 2018).

  76. Viana, M, Pey, J., Querol, X., Alastuey, A., de Leeuw, F., Lükewille, A. (2014a). Natural sources of atmospheric aerosols influencing air quality across Europe. Sci. Total Environ. 472, 825–833. https://doi.org/10.1016/j.scitotenv.2013.11.140

  77. Viana, M., Hammingh, P., Colette, A., Querol, X., Degraeuwe, B., Vlieger, I. de, van Aardenne, J. (2014b). Impact of maritime transport emissions on coastal air quality in Europe. Atmos. Environ. 90, 96–105. https://doi.org/10.1016/j.atmosenv.2014.03.046

  78. Weinberger, R., Weiner, T., Angert, A. (2016). Isotopic signature of atmospheric phosphate emitted from coal combustion. Atmos. Environ. 136, 22–30. https://doi.org/10.1016/j.atmosenv.2016.​04.006

Share this article with your colleagues 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

77st percentile
Powered by
   SCImago Journal & Country Rank

2021 Impact Factor: 4.53
5-Year Impact Factor: 3.668

Aerosol and Air Quality Research partners with Publons

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.