Seasonal and spatial variability of PM2.5 concentration, and associated metal(loid) content in the Toluca Valley, Mexico.

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Ana Barbosa-Sánchez, Ciro Márquez-Herrera, Rodolfo Sosa-Echeverria, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1517308/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Toluca Valley Metropolitan Area (TVMA) is the fifth largest urban center in Mexico, located at high altitude, 2660 meters above sea level (m.a.s.l.) surrounded by mountain ranges. It is composed of heterogenous residential and industrial areas with dense vehicular traffic. This combination of geography and urbanization create a metropolitan area with high levels of air pollutants. The objective of this study was to provide evidence of the seasonal and spatial variation of metal(lloid)s in particulate matter minor to 2.5 microns (PM 2.5) in Toluca Valley. Four sites were sampled between 2013-2014, that include urban and industrial areas, in the dry-cold (November-February) and hot-dry (March-May) season; PM 2.5 were collected using high and medium volume samplers. Metal and metalloids concentrations in PM 2.5 were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Our results show the highest 24-hour PM 2.5 concentration in the northern area, followed by the southern industrial area, and the lowest concentrations was observed in the southwest area independent of the season. Metals and metalloids with a recovery percentage above 80% were Cobalt (Co), Chrome (Cr), Copper (Cu), Manganese (Mn), and Antimony (Sb). The maximum concentrations of them were observed during the dry-cold season, in the urban-industrial sites found in the north and southern areas. Co, Cr, Cu, Mn, and Sb concentrations were up to one hundred or thousand folded in the dry-cold season compared to dry-hot season due to weather and geographical features of TVMA. The 24-hour PM 2.5 and metal(lloid)s concentrations exceed national and international guidelines to protect population health. PM2.5 Toluca valley metals and metalloids seasonal and site variation Figures Figure 1 Figure 2 Introduction Acute and chronic exposure to atmospheric particles less than 2.5 micrometers (PM 2.5 ) is considered an environmental risk to human health. There is a correlation between daily concentration of PM 2.5 in urban areas with an increase in morbidity and mortality associated to cardiovascular and lung diseases, pulmonary cancer, and low birth weight (Pope et al., 2002; Pope et al., 2004; Miller et al., 2007; Pope et al., 2009; WHO, 2013a; Pedersen et al., 2013; Straif et al., 2013). To address this public health problem, various international environmental agencies proposed setting limits to PM 2.5 exposure. In 2013, the United States Environmental Protection Agency (EPA) updated the standard reference values to 35 µg/m 3 for 24-hour exposure periods (US-EPA, 2013a). The World Health Organization (WHO) has also published guidelines for air quality, setting a 25 µg/m 3 limit for the 24-hour average. However, the WHO has stated that "there are not yet safe values regarding PM 2.5 pollution" (US-EPA 2013 a, b). In Mexico the 24-hour average concentration limit is 45 µg/m 3 (Official Mexican Standard, OMS-NOM-025-SSA1-2014). Atmospheric particles are dynamic and polydisperse materials with a complex composition mainly of organic and inorganic compounds; one of the most common PM toxicity mechanisms is the promotion of oxidative stress which is related to their chemical composition, including the presence of transition metals and the metabolism of organic compounds. Metals and metalloids can catalyze or mediate reactive oxygen species (ROS) generation through mechanisms that involve both their soluble and insoluble forms and has been linked with biological effects of particles (Sørensen, M., Schins, R. P., Hertel, O., & Loft, S 2005; Hennigan, C. J., Mucci, A., & Reed, B. E., 2019). The measurement of metal and metalloid PM 2.5 content provides an understanding of the sources of air pollution and PM 2.5 toxicity (Kendall et al., 2011). The metal and metalloid content in PM includes a large list of elements, which depends of the human socioeconomic activities and geography of the studied region. Additionally, the detection system and the extraction method used in the analytical method can influence their quantification. Metals and metalloid exposure can affect health through induction of molecular reactions including the production of ROS, promoting the decline function of organs and tissues. For example, chronic exposure to total Cr increases the carcinogenesis risk (Hu et al., 2012); Co exposure is a contributing factor to developing interstitial lung disease (Wiseman and Zereini, 2014); Mn is neurotoxic, particularly in the immature central nervous system (ATSDR, 2012). In addition, Sb with high solubility and extreme reactivity can contribute to pulmonary toxicity (Hu et al., 2012; ATSDR, 2012; Wiseman and Zereini, 2014). Previous air pollution studies in Mexico have been carried out in Mexico City, with focus on the metallic and non-metallic content of PM 10 and PM 2.5 samples (Aldape et al., 2005 ; Vega et al., 2010 and 2011; Martínez et al., 2012 and Herrera et al., 2012). The Toluca Valley Metropolitan Area (TVMA) is part of Estado de Mexico, one of 32 federal entities of the United Mexican States; it comprises twelve municipalities, it is the fifth metropolitan zone by size in Mexico and it produces 2.3% of the total gross country production suggesting high industrial and economic activities. TVMA is strongly influenced by great mountain ranges, which is a determinant factor of winds dynamics; among them Sierra Nevado de Toluca on the southeast, to the east Sierra de las Cruces and Sierra de Ocoyotepec; Sierra del Monte Alto to the northeast; and to the south Sierra Matlazinca (Del Campo et al., 2014 ; Huster, and Pierce, 2020). In addition to being surrounded by large mountains and volcanoes, the TVMA’s high altitude, between 2,560 and 2,740 m.a.s.l., and the surrounded large mountains and volcanoes favors three climate types; temperate-humid, semi-cold humid and cold. The minimum annual temperature is 0 ºC with a maximum annual temperature of 18 ºC. Such geographic and climatological characteristics contribute to physicochemical changes in particles and their chemical speciation, which can contribute to a detrimental effect on public health, as it has been suggested by environmental protection agencies and studies (US EPA, 1978; Bravo and Urone 1981 ; Bravo et al., 2013). The measurement of environmental respirable particle concentration, such as fine gravimetric mass or PM 2.5 , is important to decrease the health risk of population exposed to such particles. Additionally, the quantification of inorganic and organic chemical compounds can inform sources, bioavailability, solubility, geochemical process, and chemical speciation. This knowledge is crucial to understand the adverse effects on human health and give evidence to develop public policies to regulate environmental emissions. The aim of this study is to evaluate the seasonal and spatial variation of environmental PM 2.5 and the trace metal and metalloid content in this fraction in TVMA, that includes urban and industrial zones. Materials And Methods Sampling location PM 2.5 samples were collected at four sites of TVMA (Fig. 1 ), located in Nueva Oxtotitlán (OX) as urban area nearly to Toluca de Lerdo downtown (19° 17' 0.40" − 99° 41' 0.56"), San Cristóbal Huichotitlán (SC) as urban-rural area with agriculture activity (19° 19' 38.0" − 99° 38' 3.44 "), Airport (AP), that includes runways and hangars, surrounded by industrial area (19° 20' 4.41" − 99° 34' 26. 3"), and San Mateo Atenco (SM) as urban area near the Lerma-Toluca industrial corridor with high activity of commercial and shoes manufacturing (19° 16' 49.5 '' − 99° 32' 30"). In OX site, PM 2.5 was collected on glass fiber filter (G653, 8 × 10 cm, Whatman, UK) using the high-volume air sampler (model TE-2.5I, Tisch Environmental equipment, US), at a flow rate of 28 L min − 1 . In the other three sites, 47 mm PTFE filters with PMP supporting rings (R2PJ047, Pall Corp, US) were used for sampling PM 2.5 using medium volume samplers (Echo TCR Tecora, Italy) with a flow rate 16.7 L min − 1 . Sampling was carried out simultaneously at the four sites, corresponding to four stations of Red Automática de Monitoreo Atmosférico of the Toluca Valley (RAMAT). Samples were collected in 24-hour periods with a six-day frequency spanning from November 28, 2013 to May 17, 2014. This included dry-cold season from November to February and dry-hot season, from March to May. The sampling equipment was calibrated at the sampling sites according to the Official Mexican Standard (NOM-035-SEMARNAT-1993). Extraction And Elemental Chemical Analysis Ambient air filters were weighed before and after sampling to determine the mass of particles collected by gravimetric analysis, which was performed at the Red Automática de Monitoreo Atmosférico of Mexico City. During this procedure quality controls were implemented in the gravimetric laboratory that include the laboratory blank filter, field sampling blank filter and collected filters. After sampling, the filters were weighted for gravimetric PM 2.5 determination according to the NOM-035-SEMARNAT-1993 and US-EPA, 2008. The filters digestion was carried out using an acid method in a hot plate, adding an acid solution of HNO 3 :HCL (5.5%:16.75%), in a 1:5 ratio area: volume, and heated at 44 ± 1°C, for 30 min, always preventing the drying of samples and allowing cooling to room temperature. The extracts were transferred to volumetric flask (25 mL) and the volume adjusted with acid solution for the analysis of elements by ICP-MS (Model ELAN 6100, Perkin Elmer, USA). Calibration curves were prepared using Perkin Elmer multi-element calibration standards in concentrations range between 0.01–100 ng/mL (US-EPA, 2008). All the filters used for quality control were extracted and analyzed together with the sampling filters to determine background concentration and provide accuracy and precision in the detection of metal and metalloids concentrations. The quality control parameters calculated were limit of detection (LOD), limit of quantitation (LOQ), and percent recovery (%R). The LOD was calculated as the element concentration average in the blank filters extracts, per three times the standard deviation of each element (CENAM & EURACHEM, 2005). The LOQ was calculated as the average of each element concentration in blank filters extract per ten times the standard of each element. The efficiency of the method was assessed as the % R of the NIST 2783, a standard reference material, which was subjected to the same extraction process as the environmental samples. Quality controls by Perkin Elmer ELAN® ICP-MS equipment was with argon plasma gas, the daily performance check for analyzing masses: low beryllium 9.0122, medium magnesium 23.985, and high indium 114.904, as well as with the evaluation of the detection system. Statistical Analysis Data processing was performed using Microsoft Excel (Microsoft 365, ver. 2112, US) and summary statistics of sampling and chemical composition are presented by sampling site and season. Temperature and relative humidity are described from the mean, minimum and maximum values; the wind roses were stablished to each station by season using the WRPLOT View; wind rose Plots for Meteorological Data version 8.0.2 (C) 1998–2018 Lakers Environmental Software; gravimetric and chemical PM 2.5 data are presented with the median and range values. Results The weather parameters can dictate the presence of contaminants in the air and their concentration. In the TVMA, the relative humidity (RH) in the four sampling sites was similar among them in both seasons. In the dry-cold season RH was 52.3% (20.36–79.07) in the SC-urban, and 56.12% (24.71–84.64) in the AP-industrial area; 54.0% (20.0–83.0) and 52.6% (23.61–74.89) for SM and OX, respectively. During the dry-hot season, the relative humidity shown statistical differences in all sampling sites. 42.9% (19.0—66.20) in the SC-urban, 47.9% (22.58—71.42) in the AP-industrial; 46.1% (22.15–69.54) and 42.9% (21.31–62.15) for SM and OX sites, respectively (Table 1 ). Table 1 Temperature and relative humidity in Dry-cold and Dry-hot season during the PM2.5 sampling in Toluca Valley Metropolitan Area, Estado de México. Sampling site Dry-cold season Dry-hot season Temperature (°C) RH (%) Temperature (°C) RH (%) Nueva Oxtotitlán (OX) 10.41 (3.42–18.85) 52.59 (23.61–74.89) 14.11 (7.15–21.97) 42.97 (21.31–62.15) San Mateo Atenco (SM) 11.49 (3.46–20.88) 54.04 (20.0–83.0) 14.90 (7.88–22.82) 46.15 (22.15–69.54) Airport (AP) 10.79 (3.46–18.88) 56.12 (24.71–84.64) 13.89 (7.27–21.09) 47.90 (22.58–71.42) San Cristóbal (SC) 11.07 (3.16–20.43) 52.31 (20.36–79.07) 14.81 (6.34–22.76) 42.97 (19.00–66.20) Data are present as mean and the range values. Mean temperature was similar among sampling sites and seasons, with a difference of approximately 4°C among the sites by seasons. Average temperatures during the dry-cold season varied from 10.41 to 11.49. During the dry-hot season, the mean temperatures varied from 13.89°C to 14.9°C (Table 1 ). It was observed that the predominant wind direction, vector direction, in both seasons were similar in SM (Southeast), AP (East), and OX (Southwest); with the exception of SC with vector direction in southeast and northeast during cold and hot season, respectively (Fig. 2 ). Average wind speed (AWS) was similar in both seasons. The AWS for cold season follow the gradient AP (1.48 m/s) > OX (1.15 m/s) > SC (0.93 m/s) > SM (0.89 m/s). In the hot season the highest AWS was observed in SC (1.69 m/s) followed by OX (1.42 m/s) > AP (1.12 m/s) and the lowest AWS was observed in SM (1.06 m/s). Calm winds were more frequent in the cold-dry season compared to the hot-dry season. In the cold season the calm winds followed the gradient SM (34.26%); SC (30.79%); OX (11.11%) and AP (8.1%), however, in the hot season the higher calm winds frequency is observed in the SM site (26.96%), AP being the second site with calm winds (21.47%) and the lowest frequency of calm winds were 5.45% and 0.32% corresponding to OX and SC, respectively. The 24-hour PM 2.5 median (range) concentrations are summarized in Table 2 . For both seasons, the highest PM 2.5 concentration was observed in SC station; followed by AP, SM and the lowest concentration was observed in OX. In the dry-cold season 54.58 µg/m 3 (26.1–79.88) was observed in SC, followed by 41.3 µg/m 3 (1.33–57.97) in AP, 41.16 µg/m 3 (12.6–57.93) in SM; and 15.46 µg/m 3 (9.46–51.25) in OX. In the dry-hot season the PM 2.5 concentration in SC station was 72.85 µg/m 3 (23.48–102.84); followed by AP with 43.08 µg/m 3 (24.1–86.36); SM with 37.28 µg/m 3 (20.61–59.44); and the lowest PM 2.5 concentration was observed in OX with 10.88 µg/m 3 (7.56–27.72). The SC site was the only one that exceeded the median 24-hour PM 2.5 concentration limit of 45 µg/m 3 stablished by the Mexican government (NOM-025-SSA1-2014). However, according with the maximum gravimetric data all the stations showed at least one day over the 24-hour PM 2.5 concentration limit. The OX site did not exceed the concentration limit during the dry-hot season. Table 2 24-hour PM2.5 air gravimetric concentrations in the Toluca Valley Metropolitan Area (TVMA) PM 2.5 (µg/m 3 ) Sites Dry-Cold N Dry-Hot N Nueva Oxtotitlán (OX) 15.64 (9.46–51.25) 18 (1) 10.88 (7.56–27.72) 13 (0) San Mateo Atenco (SM) 41.16 (12.60–57.93) 5 (1) 37.28 (20.61–59.44) 13 (4) Airport (AP) 41.30 (1.33–57.97) 14 (6) 43.08 (24.10–86.36) 12 (5) San Cristobal (SC) 54.58 (26.10–79.88) 14 (10) 72.85 (23.48–102.84) 11 (8) Data are shown as median following range of values in parenthesis. N indicates the number of samples, following by the number of samples, followed by the number of samples exceeding Mexican government guidelines, 45 µg/m 3 , NOM-025-SSA-2014 in parenthesis. The metal and metalloid analysis by ICP-MS showed a recovery range of 95 − 80% for Co, Cr, Cu, Mn and Sb, whereas recovery for Zn and Pb were between 60%; and for Mg, Ba, Al, and Ti the recovery range were below 60% (Table 3 ). Table 3 Quality Control Parameters in the determination of elements present in PM2.5, at the MATV, Mexico, 2013–2014 Limits NIST 2783 air particulate standard filter Element LOD (µg/m 3 ) LOQ (µg/m 3 ) Certified (ng/filter) Measured (ng/filter) Recovery (%) Cr 1.1X10 − 5 2.0X10 − 5 135 ± 25 127 94 Cu 1.1X10 − 5 2.6X10 − 5 404 368 91 Co 7.9X10 − 8 1.1X10 − 7 7.7 6.4 83 Mn 4.9X10 − 6 6.7X10 − 6 320 ± 12 267 83 Sb 1.7X10 − 7 3.2X10 − 7 71.8 59.6 83 Zn 1.6X10 − 2 2.2X10 − 2 1790 ± 130 1376 77 Pb 1.29X10 − 6 2.39X10 − 6 317 ± 54 213 67 Mg 7.3X10 − 4 9.8X10 − 4 8620 5018 58 Ba 2.1X10 − 2 2.7X10 − 2 335 190 57 Al 1.3X10 − 2 2.3X10 − 2 23210 ± 530 7548 33 Ti 1.10X10 − 5 2.5X10 − 5 1490 217 15 (a) The %R could not be calculated for K,Ca,Ni.V, and Fe. Limit of detection, LOD; Limit of quantification, LOQ. The highest concentrations of metal and metalloids were observed in the dry-cold season in all stations. Additionally, concentrations during the dry-cold compared to dry-hot season were above one hundred times in SM, AP, and SC, with the exception of OX where the concentrations were below four times (Table 4 ). Although, the highest PM 2.5 concentration was observed in SC in both seasons, the highest metal and metalloid concentration in dry-cold season was observed in SM station. On the other hand, in the dry-hot season the major metal and metalloid concentration was observed in the SC station, with the exception of copper concentration which was higher in OX station (Table 4 ). In the SM station, the Co average concentration was 140 ng/m 3 (14–231), following by SC site with 39.2 ng/m 3 (0.38–68.6) and AP site with 35.8 ng/m 3 (0.29–180). The lowest concentration was measurement in OX site, with 0.0297 ng/m 3 (0.00652–11.7). During the dry-hot season the highest concentration was observed in SC station, 0.301 ng/m 3 (0.051–0.354); this concentration was one hundred and thirty times below the concentration found in SC on the dry cold season. The Co concentration in SM station during the dry cold season was approximately nine hundred times below the SM dry hot season concentration (0.154 ng/m 3 vs 140 ng/m 3 ). The AP station showed a Co concentration in dry cold season three hundred and sixty times above the concentration found in dry hot season samples (35.8 vs 0.0986 ng/m 3 ). The lowest Co concentration was observed in the OX station, with 0.00821 ng/m 3 (0.00136–0.0571), which was 3.6 times below the concentration found during the dry-cold season at the same station (Table 4 ). Cr concentrations were observed in the dry-cold season in all the stations. The highest concentration was measured in SM (17.33 µg/m 3 ), however, it was detected in only one sample of five. AP was the second site with high Cr concentration, 6.32 µg/m 3 (6.26–9.53), detected in three samples of fourteen. Cr in the SC station, 5.7 µg/m 3 (5.55–5.85), was detected in eleven of fourteen samples. The lowest Cr concentration was observed in the OX site, 0.0046 µg/m 3 (0.0024–0.0096), with twelve samples out of fourteen. Cr concentrations in the dry-hot season were only observed in the OX site, with a concentration of 0.00311 µg/m 3 (0.0028–0.0032), in three samples of thirteen, this value was 1.5 times below the concentration found in the dry-cold season at the same site. Finally, the ambient Cr concentrations for the rest of the sampling stations were below the LOD (Table 4 ). On the dry-cold season the highest copper Cu concentration was observed in SM station, 7.684 µg/m 3 (7.4 -20.48), where Cu was quantified in four of five samples, following AP and SC, with 2.37 µg/m 3 (0.23–27.0) and 2.07 µg/m 3 (0.02–5.89), respectively. Cu was determined in twelve of fourteen and eight of fourteen, in AP and SC stations, respectively. The lowest Cu concentrations were observed in OX station, in four of eighteen samples, 0.0194 µg/m 3 (0.0102–0.318); (Table 4 ). The highest Mn concentrations were observed during the dry-cold season in SM in three of five filters, 7.68 µg/m 3 (7.4 -20.48); followed by SC and AP, with 1.8 µg/m 3 (0.021–14.22) and 1.43 µg/m 3 (0.13–12.63), respectively. In all SC samples Mn was quantified, and in AP site, Mn was observed in thirteen of fourteen samples. The OX site had the lowest Mn concentration, 0.0031 µg/m 3 (0.0009–0.0065); Mn was quantified in seventeen of eighteen samples for dry-cold season. On the other hand, in the dry-hot season the highest concentration was observed in SC site, 0.017 µg/m 3 (0.00195–0.0262), and it was quantified in all the samples. Moreover, Mn was seventy-eight times lower in contrast to the dry-cold season. The Mn concentrations found in SM, and AP stations during the dry hot season where five hundred and ninety and one hundred times below the concentrations measured during the dry-cold season at the same stations (0.013 µg/m 3 , and 0.012 µg/m 3 vs, 7.68 µg/m 3 , and 1.43 µg/m 3 respectively). The lowest Mn concentration was measured in OX site, 0.0016 µg/m 3 (0.00031–0.0051); Mn concentrations in OX site were detected in twelve of thirteen samples, and the Mn concentration in the dry-hot season was 2.7 times below the concentration found in the dry-cold season (Table 4 ). The metalloid Sb concentration in the dry-cold season was highest in SM 6.39 µg/m 3 (0.07–13.83); followed by SC 2.24 µg/m 3 (0.02–3.79); then AP with 1.44 µg/m 3 (0.39–3.74); and OX with 0.0022 µg/m 3 (0.001–0.016). Our data correspond to the detection of Sb in seventeen, five, fourteen, and fourteen filters, respectively. The gradient of concentration for dry-hot season changes, and it showed the following gradient: SC 0.017 µg/m 3 (0.002–0.026); AP 0.0076 µg/m 3 (0.0023–0.018); SM 0.0064 µg/m 3 (0.0017–0.017); and OX 0.00081 µg/m 3 (0.0002–0.0041). Although, in dry-hot season the Sb concentrations were lower than dry-cold season, the presence of Sb was detected in all the filters of each sampling sites. In addition, the Sb concentration of dry-cold season fell in the hot season nearly to one thousand times at SM site, followed by SC and AP sites with around two hundred times, and OX with only 2.7 times (Table 4 ). Table 4 Metal and metalloid concentrations of 24-hour PM2.5 collected in Toluca Valley Metropolitan Area (TVMA) Cobalt (Co, ng/m 3 ) Dry-Cold DF Dry-Hot DF Enrichment OX 0.0297 (0.00652–11.7) 17/18 0.00821 (0.00136–0.0571) 9/13 3.6 SM 140 (14–231) 3/5 0.154 (0.0103–0.384) 7/13 912.9 AP 35.8 (0.29–180) 6/14 0.0986 (0.00903–0.266) 10/12 363.3 SC 39.2 (0.38–68.6) 7/14 0.301 (0.051–0.354) 8/11 130.2 Chromium (Cr, µg/m 3 ) Dry-Cold DF Dry-Hot DF Enrichment OX 0.0046 (0.0024–0.0096) 12/18 0.00311 (0.0028–0.0032) 3/13 1.5 SM 17.33 1/5 ND 0/13 - AP 6.32 (6.26–9.53) 3/14 ND 0/12 - SC 5.70 (5.55–5.85) 11/14 ND 0/11 - Copper (Cu, µg/m 3 ) Dry-Cold DF Dry-Hot DF Enrichment OX 0.0194 (0.0102–0.318) 4/18 0.0085 1/13 2.3 SM 7.24 (5.43–11.58) 4/5 0.0044 (0.000343–0.0135) 12/13 1645.1 AP 2.37 (0.23–27.0) 12/14 0.00615 (0.00445–0.0226) 10/12 384.3 SC 2.07 (0.02–5.89) 8/14 0.00659 (0.00261–0.0346) 9/11 314.0 Manganese (Mn, µg/m 3 ) Dry-Cold DF Dry-Hot DF Enrichment OX 0.0031 (0.0009–0.0065) 17/18 0.0016 (0.00031–0.0051) 12/13 1.9 SM 7.68 (7.4–20.48) 3/5 0.013 (0.0031–0.058) 12/13 593.7 AP 1.43 (0.13–12.63) 13/14 0.012 (0.00017–0.064) 12/12 121.4 SC 1.8 (0.021–14.22) 14/14 0.0229 (0.00209–0.0282) 11/11 78.4 Antimony (Sb, µg/m 3 ) Dry-Cold DF Dry-Hot DF Enrichment OX 0.00222 (0.001–0.0167) 17/18 0.00081 (0.0002–0.0041) 12/13 2.7 SM 6.39 (0.07–13.83) 5/5 0.0064 (0.0017–0.017) 13/13 996.4 AP 1.44 (0.39–3.74) 14/14 0.0076 (0.0023–0.018) 12/12 189.5 SC 2.24 (0.02–3.79) 14/14 0.017 (0.002–0.026) 11/11 209.3 Abbreviations: OX, Nueva Oxtotitlán; SM, San Mateo Atenco; AP, Airport; SC, San Cristobal; DF indicate detection frequency, number of filters in which metal was detected over total filters collected. ND indicate Not-Detected. Discussion Determination of PM 2.5 concentrations is critical for urbanized areas to evaluate the air quality and to prevent adverse effects on human health. Additionally, the chemical composition of particles has been suggested as finger print of the emission sources, and atmospheric chemical processes. The PM 2.5 concentration and composition has been studied in many countries. There are factors that influence PM2.5 concentration, such as weather and topography. In our study area, as in others, topography plays a key role in atmospheric dynamics, impacting air quality of urbanized and industrialized areas; additionally, the predominant weather determines the atmospheric dynamics (Querol et al., 2007a). The season and climate parameters such as temperature, wind direction, relative humidity, rainfall, cloudiness (Kulshrestha et al., 2009), and some atmospheric phenomena such as thermal inversion, can modify the half-life, and concentration of pollutants in the air. Because of its diameter, the PM 2.5 , remains longer in the atmosphere, and is efficiently transported; mountain systems can influence their transport and local deposit (Cheng Miao-Ching et al., 2012). Additionally, wind movement, its force, and direction must be considered in the displacement, distribution and final fate of air pollutants. Wind speed is not constant along each day, week, months, seasons, and also between years, because it undergoes variations due to the topographic and thermal features of a given area. In our study area, we observed discrete differences between seasons, with approximately an increment of 4ºC in the average daily temperature between cold and hot seasons; average daily RH was approximately 10% higher in the dry-cold season compared to the dry-hot season. For AWS, differences were around 0.5 m/s; these discrete changes could be associated with the high altitude of TVMA (2660 m.a.s.l.). The influence of air flow, direction, and calm winds was observed in our study. The AWS was quite different among sites between seasons. However, it seems that direction of air flow in OX displace the atmospheric particles and move the air pollutants to other site. In the rest of the sites, the influence of east winds flows from AP and SM sites to the SC site, where AP and SM can be considered industrial zones and SC the receptor site, a suburban zone with the highest PM 2.5 concentration. Additionally, we observed higher PM 2.5 concentration in the dry-cold season compared to dry-hot season in almost all sites. This can be explained by the frequency of calm winds during the cold-season, concomitant with the major frequency of thermal inversion according to the high altitude and recurrent in winter or fall seasons. We observed different 24h-PM 2.5 concentrations among the four sites between the dry-cold and the dry-hot seasons. The SC site exceeded the 24-hour PM 2.5 concentration limit of 45 µg/m 3 , established by the Mexican government (NOM-025-SSA1-2014). However, at least one day in the sampling period was out of the concentration limit for the rest of the sampling sites. Nevertheless, according to international 24-hour PM 2.5 limit of 25 and 20 µg/m 3 , established by World Health Organization (WHO) and European Community (WHO 2013a, OJEU, 2008) respectively, only OX is under the international concentration limit, indicating that the rest of the population is breathing unhealthy air. Differences among element concentrations between the two seasons were observed, likely due to the seasonal weather parameters mentioned above that determine the element distribution and its presence, such as temperature, WD and WS, as well as the topographical conditions of the region. The highest metalloid (Sb) and transition metals (Co, Cu, Mn) concentrations were found at the SM site, which did not have the highest PM 2.5 concentrations. SM is located in the southeast of MATV, bordered by Lerma-Tenango del Valle and Toluca México highways, neighboring to the Lerma-Toluca industrial park, with the main economic activity being shoes and clothing manufacturing. The AP and SC sites share a similar trend in metalloid (Sb) and transition metals (Co, Cu, Mn) concentrations. Both sites are located north of the Toluca valley, with different economic activities; in addition to airplane transit on AP site, there is an industrial settlement, land dedicated to agriculture, and San Antonio roadway. However, SC is considered an urban settlement without natural barriers, the predominant wind coming from the east, and with a vector wind in the dry-cold season from the southeast that is influenced by the emissions generated in Toluca downtown. It is possible that additional emissions, mainly produced by industrial plants located in the south and south-east AP and SM sites, as well as by the resuspension of road dust and biomass combustion (Quiterio et al., 2005; Mansha et al., 2012) contribute to local pollution. The station with the lowest PM 2.5 concentration was OX site, located to the west of Toluca downtown, is characterized by high population density; it is considered a residential area with low levels of vehicular traffic, without industrial plants. The main difference from the rest of the sites was the air flow direction that comes from the southwest, opposite to the downtown and industrial areas. Near the area, at the north of OX site, there are mountain systems with elevations between 2800–3000 m.a.s.l. that run from west towards east to the limit of Toluca downtown that probably acting as a barrier to the pollutants that flow from Toluca downtown to OX. Additionally, the OX site is a place where calm winds are less frequent, suggesting an efficient removal of pollutants. In the MATV we observed that in the dry-hot season the metal and metalloid concentrations had more important decrease compared to the dry cold-season. When calculating the enrichment of the element concentration of the dry-cold season in contrast to the dry-hot season, we detected that in some sites the increment was a hundred or thousand-fold. However, the PM 2.5 mass did not change in the same magnitude, suggesting that in the dry-cold season the frequency of calm winds and probably the thermal inversion at high altitude induce a major permanence of metals and metalloids, associated with the increment in the economic activity of the area. Comparisons between PM 2.5 and element concentrations detected in our study, during the dry-hot season in the MATV, and those found in other countries showed that the concentrations are similar in range to those reported in other cities in the world (Table 5 ). On the other hand, the content of metalloid and transition metals in PM 2.5 in the dry-cold season at the SM, SC, and AP sites were above those reported in all other cities around the world (Table 5 ) the concentrations of metalloid and transition metals observed in the MATV were in the order of micrograms with respect to other cities with concentrations expressed in nanograms. The concentrations observed in the dry-cold season were higher than the limits recommended by regulatory agencies (e. g. EPA) to prevent human health. Our study has important methodological and study design differences respect local and international studies, including the sampling time, which are relatively short. Moreover, the metal(loid)s extraction method varies among the studies which define the chemical form of the elements and the biological availability (Espinosa et al., 2002 ). The use of efficient and alternative tools, such the ICP-MS, for the analysis of PM 2.5 can help to detect, quantify and design options to address pollution problems in large cities (Saldarriaga et al., 2009; Murillo et al., 2015), although the sensitivity of different detection systems of analytical methods can influence the data observed. Cobalt (Co), which is a recurring pollutant of wastewater, is a metal that is released into the atmosphere as a particle, its main use is in the petrochemical and plastic industry as a catalyst, it can be released in scrap metal recycling, foundry and metal refining, additionally, Co can be released after burning fossil fuel. Co content in PM 2.5 samples from MATV in AP, SC and OX sites were observed in the occupational setting concentration reported (Kim et al., 2006), but Co speciation its needed to be determine to explain the human health adverse effect. Chromium (Cr) is released in the commercial and residential fossil fuel, natural gas, oil and coal combustion in addition to emissions in the metallurgical industries (e.g. ferrochromium or chromium), chromium platers, and paper industry (Kimbrough et al., 1999; Xu, et al., 2013). The Cr content in MATV PM 2.5 is higher respect the annual standard stablished by WHO, and those reported to other countries (Table 5 ). Copper (Cu) atmospheric emission sources include copper smelters, copper and iron ore processing, iron and steel production, combustion sources, municipal incinerators, copper sulfate production, brass and bronze production, carbon black production, cooling systems, brake wear particles, both by direct emissions and by suspended road dust (Georgopoulos et al., 2001 ; Keuken et al., 2013). According with the Cu concentration found in the PM 2.5 from MATV, this metal was under the maximum annual concentration and copper concentration for 24-hour period stablished by EPA (1987a), 30 and 100 µg/m 3 , respectively (Table 5 ). Manganese (Mn) is part of particle component, the crustal rock is the major natural source, other sources include forest fires, vegetation (e.g. leaching from plant tissues and dead plants), volcanic activity, and animal excrement. Anthropogenic source includes mining and mineral processing (e.g. nickel), emission from alloy (e.g. steel), the combustion of fossil fuel and in minor degree from combustion of fuel additives. Mn compounds have many applications such as the production of dry-cell batteries, matches, fireworks, porcelain and glass-bonding materials, as a catalyst in the chlorination of organic compounds, in animal feed to supply essential trace minerals, among others (Howe et al., 2004). PM 2.5 collected in MATV has a higher contend in Mn respect other reported countries. However, concentrations are below annual standard limit stablished by WHO, but Mn content in PM 2.5 from MATV is upper respect the minimal risk level for neurological effects by chronic inhalation (ATSDR, 2012) (Table 5 ). Antimony (Sb) is incorporated in textiles, paper and plastics as coadjutant of fire retardants; the primary emissions sources are related with plastic manufacturing, petroleum industry, and structural metal products (Belzile et al., 2011 ; Tian et al., 2012). Sb contend in PM 2.5 in SM, AP, and SC was > 1µg/m 3 , value referred as industrial area (Table 5 ), however, Sb has not been classified as carcinogenic in humans by U.S. EPA but ATSDR has placed antimony trioxide as possible human carcinogen. According with the high levels found of Sb in MATV further studies are need to describe the chemical speciation and the potential risk for human health. Table 5 Comparison of element concentrations in PM2.5 air samples reported in different countries Location Site type Altitude (m.a.s.l.) PM 2.5 (µg/m 3 ) Metal(loid)s elements (ng/m 3 ) Method / sampling Reference Co Cr Cu Mn Sb Toluca Valley, Mexico ꬷ OX-Urban 2660 15.64 0.0297 4.6 19.4 3.1 2.22 ICP-MS / 24 h; every 6 days, > 8 weeks Aztatzi et al., present study SM-subindustrial 41.16 140 17,330 7,240 7,680 6,390 AP-industrial 41.3 35.8 6,320 2,370 1,430 1,440 SC-Urban 54.58 39.2 5,700 2,070 1,800 2,240 Athens, Greece Urban 129 4-100 - - 7.2 3.4 - ICP-MS / 24 h Remoundaki et al., 2013 Dunkerque, France Industrial 4 24.9 − 33.2 - 0.7 1.7 2.4 0.7 ICP-MS / 12 h Kfoury et al., 2016 Industrial - 0.7 1.6 2.9 0.7 Athens, Greece Urban 10 8.12–34.6 0.48 6.19 7.28 4.73 - ICP-MS / 24 h; every 3 days Manousakas et al., 2014 Urban 430 6.44–45.5 0.23 5.64 4.02 3.30 - USA, 187 countries - - 14.0 ± 0.22 0.7 2.0 3.9 3.0 11.1 ICP-MS / five years/monitor frequency 3–12 days Bell et al., 2007 Guadalajara, Mexico Urban 1576 37–72 4.0 15.6 - 17.6 1.5 ICP-MS / 24 h; every 3 days Murillo-Tovar et al., 2015 Urban 16-49.2 - 10.2 108.8 10.2 - Mexico City, Mexico Urban 2250 39.4 0.2 16.0 65 16.7 4.9 ICP-MS / 24 h; every 6 days Garza-Galindo et al., 2019 Urban 16.2 0.3 20.0 25.0 18.0 2.8 Urban 28.4 0.3 23.2 21.0 20.6 3.9 Manizales, Colombia Urban 2200 - - 38.0 5 - - ICP-OES dos Santos Souza et al., 2021 Guangzhou, China Urban 21 83.3–190 0.9 7.6 57.3 62.4 - ICP-MS /24 h in 10 consecutive days Feng et al., 2009 Shanghai, China Industrial 4 103.0 - 22.0 22.0 92.0 - ICP-AES / 48 h Wang et al., 2013 Urban 62.2 - 31.0 29.0 132.0 - Daejeon, China - 80 5.4–63.3 - 2.4 6.5 3.1 1.8 ICP-MS / 24 h Lee Jin-Hong et al., 2013 Hangzhou, China Industrial 15 - 0.8 8.4 69.9 54.2 - ICP-MS / 18–22 h Dai et al., 2015 Urban 5 - 0.5 4.4 69.8 16.8 - Concentration Criteria or limits † <1 to2 ng/m 3 ; ⸸ 1×10 4 to 1.7 × 10 6 ng/m 3 * 100 and ‡ 12 ng/m 3 § 30 and 100 µg/m 3 * 52 and ⁑ 0.3 µg/m 3 ⸙ 1 µg/m 3 for industry. ꬷ Results of the dry-cold season of Metropolitan area from Toluca Valley, Mexico state, Mexico. † Unpulled sites ⸸ Air concentration range of Cobalt in occupational settings, Kim et al. , 2006. ‡ EPA calculated inhalation unit risk estimate. § Copper maximum annual concentration and copper concentration for 24-hour period at a location within one-half mile of a major source, EPA 1987a. * Annual standard, WHO, 2016. ⁑ Minimal Risk Level, as an estimate of a chronic inhalation exposure that is likely to be without appreciable risk of adverse non-cancer effects during a lifetime; for Mn was based on impairment of neurobehavioral function in people, ATSDR (2012b). ⸙ antimony ambient air range, and in industry area observed data, ATSDR 1992. Conclusion The present study reports the content of metalloid and transition metals in the PM 2.5 fraction by ICP-MS. The airborne particles in each geographic area will depend on local anthropogenic source of emissions, season and weather variables such as temperature, altitude, humidity, wind velocity and air flow direction. This means that the study of atmospheric conditions for dispersion of pollutants must consider temporality and geography of the area to be studied. The levels of PM 2.5 and trace metal(loid)s found in the TVMA provide data for understanding the behavior of elements present locally and in other regions with similar features. This evidence can help to control and regulate emissions of constituents of airborne particles that constitute a potential risk for human health. The control, prevention, and minimization of the levels of these contaminants in the geographic area needs to be studied as well as their impact on human health. Declarations Acknowledgements The authors gratefully acknowledge the technical support of the Red Automática de Monitoreo Atmosférico (RAMA) from Mexico City, and to the Red Automática de Monitoreo Atmosférico from Toluca valley (RAMAT) for providing meteorological data, area localization, equipment calibration, and maintenance. Statements and Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose. Data availability The datasets generated during and/or analyzed during the current study are available in ww.figshare.com repository, https://figshare.com/s/cafed077bcfdf913e9a5, https://doi.org/10.6084/m9.figshare.19500569.v1 Authors’ contribution A. L. Barbosa-Sánchez contributes in the present study to carried out sampling, equipment management, logistic, analytical data analysis and written the first manuscript. Sample analysis, and analytical method development were performed by C. Márquez-Herrera . R. Sosa-Echeverria participates in monitoring and direction of sampling. R.V. Diaz-Godoy participates in sampling, provide resources to monitoring, and sample management. M.E. Gutiérrez-Castillo contribute in conception and design of the study, and monitoring planning, analytical method development. C. Escamilla-Núñez participates in data base curation, and statistical data management. A. M. Rule and M. P. Sierra-Vargas contribute in review and editing results and final manuscript. O. G. Aztatzi-Aguilar participates in data curation, and validation, visualization and result edition, and written final draft manuscript preparation. All authors read and approved the final manuscript. References Aldape F, Flores J, Flores YJA, Retama- Hernández A, Rivera-Hernández O (2005) Elemental composition and source identification of PM 2 5 particles collected in downtown Mexico City. Int J PIXE 15(3):263–270. https://doi.org/10.1142/S012908350500060X ATSDR (Agency for Toxic Substances and Disease Registry) (2012b) Toxicological profile for manganese. U.S. Department of Health and Human Services, Atlanta, GA ATSDR, Agency for Toxic Substances and Disease Registry (2012a) Toxicological profile for Chromium [ATSDR Tox Profile]. Atlanta, GA:U.S.Department of Health and Human Service, Public Health Service ATSDR, Agency for Toxic Substances and Disease Registry (2019) Toxicological profile for antimony and compounds. Department of Health and Human Service, Public Health Service, Atlanta, GA:U.S. Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM (2007) Spatial and Temporal Variation in PM 2.5 Chemical Composition in the United States for Health Effects Studies. Environ Health Perspect 115(7):989–995. https://doi.org/10.1289/ehp.9621 Belzile N, Chen YW, Filella M (2011) Human exposure to antimony: I. Sources and intake. Crit Rev Environ Sci Technol 41(14):1309–1373. https://doi.org/10.1080/10643381003608227 Bravo Alvarez H, Sosa Echeverria R, Sanchez Alvarez P, Krupa S (2013) Air Quality Standards for Particulate Matter (PM) at high altitude cities. Environ Pollut. https://doi.org/10.1016/j.envpol.2012.09.025 Bravo AH, Urone P (1981) The altitude: a fundamental parameter in the use of air quality standards. J Air Pollut Contr Assoc 31(3):264–265 CENAM, & EURACHEM (2005) Métodos Analíticos Adecuados a su Propósito. Guía de Laboratorio para la Validación de Métodos Temas Relacionados. Métodos Analíticos Adecuados a su Propósito, 2nd edn. Centro Nacional de Metrología, Querétaro, México Cheng M-C, You CF, Cao J, Jin Z (2012) Spatial and seasonal variability of water-soluble ions in PM 2.5 aerosols in 14 major cities in China. Atmos Environ 60:182–192. https://doi.org/10.1016/j.atmosenv.2012.06.037 Del Campo MM, Esteller MV, Expósito JL, Hirata R (2014) Impacts of urbanization on groundwater hydrodynamics and hydrochemistry of the Toluca Valley aquifer (Mexico). Environ Monit Assess 186(5):2979–2999. https://doi.org/10.1007/s10661-013-3595-3 Espinosa AJF, Rodríguez MT, de la Rosa FJB, Sánchez JCJ (2002) A chemical speciation of trace metals for fine urban particles. Atmos Environ 36(5):773–780. https://doi.org/10.1016/S1352-2310(01)00534-9 Feng XD, Dang Z, Huang WL, Yang C (2009) Chemical speciation of fine particle bound trace metals. Int J Environ Sci Technol 6(3):337–346. https://doi.org/10.1007/BF03326071 Georgopoulos G, Roy A, Yonone-Lioy MJ, Opiekun RE, Lioy PJ, P (2001) Environmental copper: its dynamics and human exposure issues. J Toxicol Environ Health Part B: Crit Reviews 4(4):341–394 Heal MR, Hibbs LR, Agius RM, Beverland IJ (2005) Total and water-soluble trace metal content of urban background PM 10 , PM 2.5 and blank smoke in Edinburgh. UK Atmos Environ 39:1417–1430. https://doi.org/10.1016/j.atmosenv.2004.11.026 \ \\;\\\Hennigan\\,\ \C\.\ J\.\\\,\ \\Mucci\\,\ \A\.\\\,\ \&\ \\Reed\\,\ \B\.\ E\.\\\\ \(\2019\\)\.\ \Trends\ in\ PM\ 2\.5\\ transition\ metals\ in\ urban\ areas\ across\ the\ United\ States\\.\ \Environmental\ Research\ Letters\\,\ \14\\(\10\\)\,\ \104006\\.\ \https\:\/\/doi\.org\/10\.1088\/1748\-9326\/ab4032\\\;\ Howe P, Malcolm H, Dobson S (2004) Manganese and its compounds: environmental aspects. World Health Organization Hu X, Zhang Y, Ding Z, Wang T, Lian H, Sun Y, Wu J (2012) Bioaccessibility and health risk of arsenic and heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn and Mn) in TSP and PM 2.5 in Nanjing, China. Atmos Environ 57:146–152. https://doi.org/10.1016/j.atmosenv.2012.04.056 Huster AC, Pierce DE (2020) A geochemical baseline for clays of the Toluca Valley, Mexico. J Archaeol Science: Rep 29:102094. https://doi.org/10.1016/j.jasrep.2019.102094 INEGI, Instituto Nacional de Estadística y Geografía. Censo de Población y Vivienda (2010) Kendall M, Pala K, Ucakli S, Gucer S (2011) Airborne particulate matter (PM 2.5 and PM 10 ) and associated metals in urban Turkey. Air Qual Atmos Health 4:235–242. https://doi.org/10.1007/s11869-010-0129-9 Keuken MP, Moerman M, Voogt M, Blom M, Weijers EP, Röckmann T, Dusek U (2013) Source contributions to PM2. 5 and PM10 at an urban background and a street location. Atmos Environ 71:26–35. https://doi.org/10.1016/j.atmosenv.2013.01.032 Kim JH, Gibb HJ, Howe P (2006) Cobalt and inorganic cobalt compounds, vol 69. World health organization Kimbrough DE, Cohen Y, Winer AM, Creelman L, Mabuni C (1999) A critical assessment of chromium in the environment. Crit Rev Environ Sci Technol 29(1):1–46. https://doi.org/10.1080/10643389991259164 Kulshrestha A, Satsangi PG, Masih J, Taneja A (2009) Metal concentration of PM 2.5 and PM 10 particles and seasonal variations in urban and rural environment of Agra, India. Sci Total Environ 407:6196–6204. https://doi.org/10.1016/j.scitotenv.2009.08.050 Lee J-H, Jeong JH, Lim JM (2013) Toxic trace and earth crustal elements of ambient PM 2.5 using CCT-ICP-MS in an urban area of Korea. Environ Eng Res 18(1):3–8. https://doi.org/10.4491/eer.2013.18.1.003 Mansha M, Ghauri B, Rahman S, Amman A (2012) Characterization and source apportionment of ambient air particulate matter (PM 2.5 ) in Karachi. Sci Total Environ 425:176–183. https://doi.org/10.1016/j.scitotenv.2011.10.056 Martínez MA, Caballero P, Carrillo O, Mendoza A, Mejia GM (2012) Chemical characterization and factor analysis of PM 2.5 in two sites of Monterrey. Mexico J Air Waste Manag Assoc 62(7):817–827. https://doi.org/10.1080/10962247.2012.681421 Miller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD (2007) Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 356:447–458. DOI: 10.1056/NEJMoa054409 Murillo-Tovar MA, Saldarriaga-Noreña H, Hernández-Mena L, Campos-Ramos A, Cárdenas-González B, Ospina-Noreña JE, Cosío-Ramírez R, Díaz-Torres J, de Smith J, W. Potential (2015) Sources of Trace Metals and Ionic Species in PM 2.5 in Guadalajara, Mexico: A Case Study during Dry Season. Atmos (Basel) 6(12):1858–1870. https://doi.org/10.3390/atmos6121834 NOM-025-SSA1-2014. Norma Oficial Mexicana Salud ambiental. Valores límite para la concentración de partículas suspendidas. PM 10 yPM 2.5 en el aire ambiente y criterios para su evaluación NOM-035-SEMARNAT-1993 (1993) Norma Oficial Mexicana. Métodos de medición para determinar la concentración de partículas suspendidas totales en el aire ambiente y los procedimientos para la calibración de los equipos de medición. Diario Oficial de la Federación del 18 de octubre de 1993. SEMARNAT, México, Diario Oficial de la Federación OJEU, Official Journal of the European Union (2008) DIRECTIVE 2008/50/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL [Internet]. http://eur-lex.europa.eu/legal-content/ES/TXT/PDF/?uri=CELEX:32008 L0050&from=EN. Accessed December 16 2019 OSHA, Occupational Safety and Health Administration (2006) TABLE Z-1 Limits for air contaminants. Retrieved from https://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS&p_id=9992 . Accessed November 9 2019 OSHA, Occupational Safety and Health Administration: Retrieved from https://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS&p_id=10023 . Accessed November 2 2014 Pedersen M, Giorgis-Allemand L, Bernard C, Aguilera I, Andersen AMN, Ballester F, Brunekreef B (2013) Ambient air pollution and low birth weight: A European cohort study (ESCAPE). Lancet Respir Med 1(9):695–704. https://doi.org/10.1016/S2213-2600(13)70192-9 Pérez N, Pey J, Querol X, Alastuey A, López JM, Viana M (2008) Partitioning of major and trace components in PM 10 –PM 2.5 –PM 1 at an urban site in Southern Europe. Atmos Environ 42:1677–1691. https://doi.org/10.1016/j.atmosenv.2007.11.034 Pope III, Ezzati CA, Dockery DW (2009) Fine Particulate Air Pollution and US County Life Expectancies. N Engl J Med 360(4). DOI: 10.1056/NEJMsa0805646 Pope III, Burnett CA, Thun RT, Calle MJ, Krewski EE, Ito D, Thurston K, G.D (2002) Lung Cancer, Cardiopulmonary Mortality, and Long-Term Exposure to Fine Particulate Air Pollution. JAMA 287:1132–1141. DOI: 10.1001/jama.287.9.1132 Pope III, Burnett CA, Thurston RT, Thun GD, Calle MJ, Krewski EE, Godleski D, J.J (2004) Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution: Epidemiological Evidence of General Pathophysiological Pathways of Disease. Circulation 109:71–77. https://doi.org/10.1161/01 Querol X, Minguillóna MC, Alastueya A, Monfortb E, Mantilla E, Sanz MJ, Sanz F, Roig A, Renau A, Felis C, Miró JV, Aríñano B (2007b) Impact of the implementation of PM abatement technology on the ambient air levels of metals in a highly industrialized area. Atmos Environ 41:1026–1040. https://doi.org/10.1016/j.atmosenv.2006.09.013 Querol X, Viana M, Alastuey A, Amato F, Moreno T, Castillo S, Pey J, de la Campa J, Salvador AArtíñanoB, García Dos Santos P, Fernández-Patierd S, Moreno-Grau R, Negral S, Minguillón L, Monfort MC, Gil E, Inza JI, Ortega A, Santamaría LA, Zabalza JM J., 2007a. Source origin of trace elements in PM from regional background, urban and industrial sites of Spain. Atmos. Environ. 41, 7219–7231. https://doi.org/10.1016/j.atmosenv.2007.05.022 Quiterio SL, Escaleira V, Silva CRS, Maia LFPG, Arbilla G (2005) Assessment of the Concentrations and Emission Sources of Airborne Metals in Particulate Matter in Seven Districts of Baixada Fluminense, Rio de Janeiro, Brazil. Bull Environ Contam Toxicol 75:997–1003. DOI: 10.1007/s00128-005-0848-z Saldarriaga-Noreña H, Hernández-Mena L, Ramírez-Muñiz M, Carbajal-Romero P, Cosío-Ramírez R, Esquivel-Hernández B (2009) Characterization of Trace Metals of Risk to Human Health in Airborne Particulate Matter (PM 2.5 ) at Two Sites in Guadalajara, Mexico. J Environ Monit 11(4):887–894. https://doi.org/10.1007/s00128-011-0240-0 Senlin L, Zhenkun Y, Xiaohui C, Minghong W, Guoying S, Jiamo F, Paul D (2008) The relationship between physicochemical characterization and the potential toxicity of fine particulates (PM 2.5 ) in Shanghai atmosphere. Atmos Environ 42:7205–7214. https://doi.org/10.1016/j.atmosenv.2008.07.030 Sørensen M, Schins RP, Hertel O, Loft S (2005) Transition metals in personal samples of PM 2.5 and oxidative stress in human volunteers. Cancer Epidemiol Prev Biomarkers 14(5):1340–1343. https://doi.org/10.1158/1055-9965.EPI-04-0899 Straif K, Cohen A, Samet MJ (2019) Air pollution and cancer [Internet] 2013. 1st ed. International agency for research on Cancer; 229. Accessed October 29 Tian H, Zhao D, Cheng K, Lu L, He M, Hao J (2012) Anthropogenic atmospheric emissions of antimony and its spatial distribution characteristics in China. Environ Sci Technol 46(7):3973–3980. DOI: 10.1021/es2041465 US-EPA, United States Environmental Protection Agency (2013a) Table of historical particulate matter (PM) national Ambient Air Quality Standards (NAAQS) US-EPA, United States Environmental Protection Agency (2013b)National Ambient Air Quality Standards for Particulate Matter, Final Rule US-EPA, United States Environmental Protection Agency (2008) Compendium of Methods for the Determination of Inorganic Compounds in Ambient Air. Compendium Method IO-3.1. Selection, preparation and extraction of Filter Material. Cincinnati Vega E, Eidels S, Ruiz H, López-Veneroni D, Sosa G, Gonzalez E, Edgerton SA (2010) Particulate air pollution in Mexico City: a detailed view. Aerosol Air Qual Res 10(3):193–211. https://doi.org/10.4209/aaqr.2009.06.0042 Vega E, Ruiz H, Escalona S, Cervantes A, López-Veneroni D, González-Avalos E, Sánchez-Reyna G (2011) Chemical composition of fine particles in Mexico City during 2003–2004. Atmos Pollut Res 2(4). https://doi.org/10.5094/APR.2011.054 Wang J, Hu Z, Chen Y, Chen Z, Xu S (2013) Contamination characteristics and possible sources of PM 10 and PM 2.5 in different functional areas of Shanghai, China. Atmos Environ 68:221–229. https://doi.org/10.1016/j.atmosenv.2012.10.070 WHO, World Health Organization (2013b) Health Effects of Particulate Matter, Policy implications for countries in Eastern Europe, Caucasus and central Asia WHO, World Health Organization (2013a) Review of evidence on health aspects of air pollution – REVIHAAP Project: Final technical report. Copenhagen: World Health Organization Regional Office for Europe. [Internet]. Denmark: Available from: http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report.pdf . Accessed October 3 2019 Wiseman CLS, Zereini F (2014) Characterizing metal(loid) solubility in airborne PM 10 , PM 2.5 and PM 1 in Frankfurt, Germany using simulated lung fluids. Atmos Environ 14. https://doi.org/10.1016/j.atmosenv.2014.02.055 Xu L, Yu Y, Yu J, Chen J, Niu Z, Yin L, Chen Y (2013) Spatial distribution and sources identification of elements in PM 2.5 among the coastal city group in the Western Taiwan Strait region, China. Sci Total Environ 442:77–85. https://doi.org/10.1016/j.scitotenv.2012.10.045 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1517308","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":95925624,"identity":"b2c520b1-e3b5-4b1a-91ea-3a36ecc9b749","order_by":0,"name":"Ana Barbosa-Sánchez","email":"","orcid":"","institution":"Instituto Nacional de Salud Pública: Instituto Nacional de Salud Publica","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Barbosa-Sánchez","suffix":""},{"id":95925625,"identity":"c42770ca-698d-4d86-bb29-3eacdc7c0697","order_by":1,"name":"Ciro Márquez-Herrera","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México Facultad de Química: Universidad Nacional Autonoma de Mexico Facultad de Quimica","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ciro","middleName":"","lastName":"Márquez-Herrera","suffix":""},{"id":95925626,"identity":"0340d2c5-b63c-4581-be64-370c2294a8ac","order_by":2,"name":"Rodolfo Sosa-Echeverria","email":"","orcid":"","institution":"Universidad Nacional Autonoma de Mexico Centro de Ciencias de la Atmósfera: Universidad Nacional Autonoma de Mexico Instituto de Ciencias de la Atmosfera y Cambio Climatico","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rodolfo","middleName":"","lastName":"Sosa-Echeverria","suffix":""},{"id":95925627,"identity":"a9a321d4-490d-4fa9-b104-add7f7736fe5","order_by":3,"name":"Raul Díaz-Godoy","email":"","orcid":"","institution":"Instituto Nacional de Investigaciones Nucleares","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Raul","middleName":"","lastName":"Díaz-Godoy","suffix":""},{"id":95925628,"identity":"c3900916-a624-4c47-81b5-b04578abca25","order_by":4,"name":"Maria E. Gutiérrez-Castillo","email":"","orcid":"","institution":"Instituto Politecnico Nacional Centro Interdisciplinario de Investigaciones y Estudios sobre Medio Ambiente y Desarrollo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maria","middleName":"E.","lastName":"Gutiérrez-Castillo","suffix":""},{"id":95925629,"identity":"1db69003-33eb-432d-9a1b-7964170fd7ea","order_by":5,"name":"Ana Rule","email":"","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Rule","suffix":""},{"id":95925630,"identity":"1ed2fd16-a6eb-4c78-8031-52ff4f4c2369","order_by":6,"name":"Martha P. Sierra-Vargas","email":"","orcid":"","institution":"INER: Instituto Nacional de Enfermedades Respiratorias","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martha","middleName":"P.","lastName":"Sierra-Vargas","suffix":""},{"id":95925631,"identity":"00f1dbfd-aed7-4c28-a1cb-a5eb089327d3","order_by":7,"name":"Octavio Gamaliel Aztatzi-Aguilar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYHADxsYHQJKHj0jlBgw8DIzNBiAtbCRoYWCTADEJapF3P5264cefP/L2/IfbKr/m2MmwMTA/fHQDjxbDM7nbbvbwGBj2MBxsuy27LRnoMDZj4xx8Whpyt93gkTBg7GFsbLstuY0ZqIWHTRqvlv63227+MTCw72FmbCuW3FZPWIu8RO622zwJBok9bIxtjB+3HSasxUDi7bbbMgeMk3vOMDZLM247zsPGTMAv8v1A77/5I2fb3n/84cef26rt+dmbHz7Ga8sBJA4zD5jEoxxsSwMSh/EHAdWjYBSMglEwMgEA/WxHDJcp+wQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1761-9443","institution":"Instituto Nacional de Enfermedades Respiratorias","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Octavio","middleName":"Gamaliel","lastName":"Aztatzi-Aguilar","suffix":""}],"badges":[],"createdAt":"2022-04-02 18:13:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1517308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1517308/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":20030360,"identity":"0ec074d3-1064-4200-8c05-23ebb1a79e72","added_by":"auto","created_at":"2022-04-06 16:18:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":720016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the state of Mexico, showing geophysical structure and urbanized area of Toluca Valley Metropolitan Area (TVMA).\u003c/strong\u003e The four sampling sites are indicated on the map; Nueva Oxtotitlán (OX), San Cristóbal Huichotitlán (SC), Airport (AP), and San Mateo Atenco (SM). Red arrows indicate the specific monitoring sites.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1517308/v1/917721df9a939da544a93ab9.png"},{"id":20030361,"identity":"c972a223-7438-416f-b7df-adf06642ce0e","added_by":"auto","created_at":"2022-04-06 16:18:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":762354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWind direction during environment season sampling. \u003c/strong\u003eWind roses during cold- and hot- dry seasons at four sampling sites in the Toluca Valley Metropolitan Area TVMA, Estado de México. San Mateo A. rose wind have a southeast direction in both season, \u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003eb\u003c/strong\u003e; Airport site shows a northeast direction during dry cold season, \u003cstrong\u003ec\u003c/strong\u003e, but in dry hot season wind direction comes from southeast, \u003cstrong\u003ed\u003c/strong\u003e; San Cristobal H. site have a southeast wind direction during dry-cold season, however, in the dry -hot season wind direction comes from northeast.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1517308/v1/460874e1c83e8d1f5d72dd2e.png"},{"id":20396940,"identity":"39cfb6d1-2a0c-44e2-90c2-55de8269308c","added_by":"auto","created_at":"2022-04-15 19:31:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1681810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1517308/v1/3d54b908-cfd0-4ac1-99a7-89b2903f5dfe.pdf"}],"financialInterests":"","formattedTitle":"Seasonal and spatial variability of PM2.5 concentration, and associated metal(loid) content in the Toluca Valley, Mexico.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute and chronic exposure to atmospheric particles less than 2.5 micrometers (PM\u003csub\u003e2.5\u003c/sub\u003e) is considered an environmental risk to human health. There is a correlation between daily concentration of PM\u003csub\u003e2.5\u003c/sub\u003e in urban areas with an increase in morbidity and mortality associated to cardiovascular and lung diseases, pulmonary cancer, and low birth weight (Pope et al., 2002; Pope et al., 2004; Miller et al., 2007; Pope et al., 2009; WHO, 2013a; Pedersen et al., 2013; Straif et al., 2013).\u003c/p\u003e \u003cp\u003eTo address this public health problem, various international environmental agencies proposed setting limits to PM\u003csub\u003e2.5\u003c/sub\u003e exposure. In 2013, the United States Environmental Protection Agency (EPA) updated the standard reference values to 35 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for 24-hour exposure periods (US-EPA, 2013a). The World Health Organization (WHO) has also published guidelines for air quality, setting a 25 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e limit for the 24-hour average. However, the WHO has stated that \"there are not yet safe values regarding PM\u003csub\u003e2.5\u003c/sub\u003e pollution\" (US-EPA 2013 a, b). In Mexico the 24-hour average concentration limit is 45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (Official Mexican Standard, OMS-NOM-025-SSA1-2014).\u003c/p\u003e \u003cp\u003eAtmospheric particles are dynamic and polydisperse materials with a complex composition mainly of organic and inorganic compounds; one of the most common PM toxicity mechanisms is the promotion of oxidative stress which is related to their chemical composition, including the presence of transition metals and the metabolism of organic compounds. Metals and metalloids can catalyze or mediate reactive oxygen species (ROS) generation through mechanisms that involve both their soluble and insoluble forms and has been linked with biological effects of particles (S\u0026oslash;rensen, M., Schins, R. P., Hertel, O., \u0026amp; Loft, S 2005; Hennigan, C. J., Mucci, A., \u0026amp; Reed, B. E., 2019).\u003c/p\u003e \u003cp\u003eThe measurement of metal and metalloid PM\u003csub\u003e2.5\u003c/sub\u003e content provides an understanding of the sources of air pollution and PM\u003csub\u003e2.5\u003c/sub\u003e toxicity (Kendall et al., 2011). The metal and metalloid content in PM includes a large list of elements, which depends of the human socioeconomic activities and geography of the studied region. Additionally, the detection system and the extraction method used in the analytical method can influence their quantification.\u003c/p\u003e \u003cp\u003eMetals and metalloid exposure can affect health through induction of molecular reactions including the production of ROS, promoting the decline function of organs and tissues. For example, chronic exposure to total Cr increases the carcinogenesis risk (Hu et al., 2012); Co exposure is a contributing factor to developing interstitial lung disease (Wiseman and Zereini, 2014); Mn is neurotoxic, particularly in the immature central nervous system (ATSDR, 2012). In addition, Sb with high solubility and extreme reactivity can contribute to pulmonary toxicity (Hu et al., 2012; ATSDR, 2012; Wiseman and Zereini, 2014).\u003c/p\u003e \u003cp\u003ePrevious air pollution studies in Mexico have been carried out in Mexico City, with focus on the metallic and non-metallic content of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e samples (Aldape et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Vega et al., 2010 and 2011; Mart\u0026iacute;nez et al., 2012 and Herrera et al., 2012). The Toluca Valley Metropolitan Area (TVMA) is part of Estado de Mexico, one of 32 federal entities of the United Mexican States; it comprises twelve municipalities, it is the fifth metropolitan zone by size in Mexico and it produces 2.3% of the total gross country production suggesting high industrial and economic activities. TVMA is strongly influenced by great mountain ranges, which is a determinant factor of winds dynamics; among them Sierra Nevado de Toluca on the southeast, to the east Sierra de las Cruces and Sierra de Ocoyotepec; Sierra del Monte Alto to the northeast; and to the south Sierra Matlazinca (Del Campo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Huster, and Pierce, 2020). In addition to being surrounded by large mountains and volcanoes, the TVMA\u0026rsquo;s high altitude, between 2,560 and 2,740 m.a.s.l., and the surrounded large mountains and volcanoes favors three climate types; temperate-humid, semi-cold humid and cold. The minimum annual temperature is 0 \u0026ordm;C with a maximum annual temperature of 18 \u0026ordm;C. Such geographic and climatological characteristics contribute to physicochemical changes in particles and their chemical speciation, which can contribute to a detrimental effect on public health, as it has been suggested by environmental protection agencies and studies (US EPA, 1978; Bravo and Urone \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Bravo et al., 2013).\u003c/p\u003e \u003cp\u003eThe measurement of environmental respirable particle concentration, such as fine gravimetric mass or PM\u003csub\u003e2.5\u003c/sub\u003e, is important to decrease the health risk of population exposed to such particles. Additionally, the quantification of inorganic and organic chemical compounds can inform sources, bioavailability, solubility, geochemical process, and chemical speciation. This knowledge is crucial to understand the adverse effects on human health and give evidence to develop public policies to regulate environmental emissions. The aim of this study is to evaluate the seasonal and spatial variation of environmental PM\u003csub\u003e2.5\u003c/sub\u003e and the trace metal and metalloid content in this fraction in TVMA, that includes urban and industrial zones.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling location\u003c/h2\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e samples were collected at four sites of TVMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), located in Nueva Oxtotitl\u0026aacute;n (OX) as urban area nearly to Toluca de Lerdo downtown (19\u0026deg; 17' 0.40\" \u0026minus;\u0026thinsp;99\u0026deg; 41' 0.56\"), San Crist\u0026oacute;bal Huichotitl\u0026aacute;n (SC) as urban-rural area with agriculture activity (19\u0026deg; 19' 38.0\" \u0026minus;\u0026thinsp;99\u0026deg; 38' 3.44 \"), Airport (AP), that includes runways and hangars, surrounded by industrial area (19\u0026deg; 20' 4.41\" \u0026minus;\u0026thinsp;99\u0026deg; 34' 26. 3\"), and San Mateo Atenco (SM) as urban area near the Lerma-Toluca industrial corridor with high activity of commercial and shoes manufacturing (19\u0026deg; 16' 49.5 '' \u0026minus;\u0026thinsp;99\u0026deg; 32' 30\").\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn OX site, PM\u003csub\u003e2.5\u003c/sub\u003e was collected on glass fiber filter (G653, 8 \u0026times; 10 cm, Whatman, UK) using the high-volume air sampler (model TE-2.5I, Tisch Environmental equipment, US), at a flow rate of 28 L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In the other three sites, 47 mm PTFE filters with PMP supporting rings (R2PJ047, Pall Corp, US) were used for sampling PM\u003csub\u003e2.5\u003c/sub\u003e using medium volume samplers (Echo TCR Tecora, Italy) with a flow rate 16.7 L min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Sampling was carried out simultaneously at the four sites, corresponding to four stations of Red Autom\u0026aacute;tica de Monitoreo Atmosf\u0026eacute;rico of the Toluca Valley (RAMAT). Samples were collected in 24-hour periods with a six-day frequency spanning from November 28, 2013 to May 17, 2014. This included dry-cold season from November to February and dry-hot season, from March to May. The sampling equipment was calibrated at the sampling sites according to the Official Mexican Standard (NOM-035-SEMARNAT-1993).\u003c/p\u003e \u003c/div\u003e\n\u003ch2\u003eExtraction And Elemental Chemical Analysis\u003c/h2\u003e\n\u003cp\u003eAmbient air filters were weighed before and after sampling to determine the mass of particles collected by gravimetric analysis, which was performed at the Red Autom\u0026aacute;tica de Monitoreo Atmosf\u0026eacute;rico of Mexico City. During this procedure quality controls were implemented in the gravimetric laboratory that include the laboratory blank filter, field sampling blank filter and collected filters. After sampling, the filters were weighted for gravimetric PM\u003csub\u003e2.5\u003c/sub\u003e determination according to the NOM-035-SEMARNAT-1993 and US-EPA, 2008.\u003c/p\u003e \u003cp\u003eThe filters digestion was carried out using an acid method in a hot plate, adding an acid solution of HNO\u003csub\u003e3\u003c/sub\u003e:HCL (5.5%:16.75%), in a 1:5 ratio area: volume, and heated at 44\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, for 30 min, always preventing the drying of samples and allowing cooling to room temperature. The extracts were transferred to volumetric flask (25 mL) and the volume adjusted with acid solution for the analysis of elements by ICP-MS (Model ELAN 6100, Perkin Elmer, USA). Calibration curves were prepared using Perkin Elmer multi-element calibration standards in concentrations range between 0.01\u0026ndash;100 ng/mL (US-EPA, 2008).\u003c/p\u003e \u003cp\u003eAll the filters used for quality control were extracted and analyzed together with the sampling filters to determine background concentration and provide accuracy and precision in the detection of metal and metalloids concentrations. The quality control parameters calculated were limit of detection (LOD), limit of quantitation (LOQ), and percent recovery (%R). The LOD was calculated as the element concentration average in the blank filters extracts, per three times the standard deviation of each element (CENAM \u0026amp; EURACHEM, 2005). The LOQ was calculated as the average of each element concentration in blank filters extract per ten times the standard of each element. The efficiency of the method was assessed as the % R of the NIST 2783, a standard reference material, which was subjected to the same extraction process as the environmental samples. Quality controls by Perkin Elmer ELAN\u0026reg; ICP-MS equipment was with argon plasma gas, the daily performance check for analyzing masses: low beryllium 9.0122, medium magnesium 23.985, and high indium 114.904, as well as with the evaluation of the detection system.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData processing was performed using Microsoft Excel (Microsoft 365, ver. 2112, US) and summary statistics of sampling and chemical composition are presented by sampling site and season. Temperature and relative humidity are described from the mean, minimum and maximum values; the wind roses were stablished to each station by season using the WRPLOT View; wind rose Plots for Meteorological Data version 8.0.2\u003csup\u003e(C)\u003c/sup\u003e 1998\u0026ndash;2018 Lakers Environmental Software; gravimetric and chemical PM\u003csub\u003e2.5\u003c/sub\u003e data are presented with the median and range values.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe weather parameters can dictate the presence of contaminants in the air and their concentration. In the TVMA, the relative humidity (RH) in the four sampling sites was similar among them in both seasons. In the dry-cold season RH was 52.3% (20.36\u0026ndash;79.07) in the SC-urban, and 56.12% (24.71\u0026ndash;84.64) in the AP-industrial area; 54.0% (20.0\u0026ndash;83.0) and 52.6% (23.61\u0026ndash;74.89) for SM and OX, respectively. During the dry-hot season, the relative humidity shown statistical differences in all sampling sites. 42.9% (19.0\u0026mdash;66.20) in the SC-urban, 47.9% (22.58\u0026mdash;71.42) in the AP-industrial; 46.1% (22.15\u0026ndash;69.54) and 42.9% (21.31\u0026ndash;62.15) for SM and OX sites, respectively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eTemperature and relative humidity in Dry-cold and Dry-hot season during the PM2.5 sampling in Toluca Valley Metropolitan Area, Estado de M\u0026eacute;xico.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSampling site\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDry-cold season\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDry-hot season\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRH (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eRH (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003eNueva Oxtotitl\u0026aacute;n (OX)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e10.41\u003c/p\u003e\n\u003cp\u003e(3.42\u0026ndash;18.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e52.59\u003c/p\u003e\n\u003cp\u003e(23.61\u0026ndash;74.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e14.11\u003c/p\u003e\n\u003cp\u003e(7.15\u0026ndash;21.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e42.97\u003c/p\u003e\n\u003cp\u003e(21.31\u0026ndash;62.15)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003eSan Mateo Atenco (SM)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e11.49\u003c/p\u003e\n\u003cp\u003e(3.46\u0026ndash;20.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e54.04\u003c/p\u003e\n\u003cp\u003e(20.0\u0026ndash;83.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e14.90\u003c/p\u003e\n\u003cp\u003e(7.88\u0026ndash;22.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e46.15\u003c/p\u003e\n\u003cp\u003e(22.15\u0026ndash;69.54)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003eAirport (AP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e10.79\u003c/p\u003e\n\u003cp\u003e(3.46\u0026ndash;18.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e56.12\u003c/p\u003e\n\u003cp\u003e(24.71\u0026ndash;84.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e13.89\u003c/p\u003e\n\u003cp\u003e(7.27\u0026ndash;21.09)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e47.90\u003c/p\u003e\n\u003cp\u003e(22.58\u0026ndash;71.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 59px;\"\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003eSan Crist\u0026oacute;bal (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e11.07\u003c/p\u003e\n\u003cp\u003e(3.16\u0026ndash;20.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e52.31\u003c/p\u003e\n\u003cp\u003e(20.36\u0026ndash;79.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e14.81\u003c/p\u003e\n\u003cp\u003e(6.34\u0026ndash;22.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 59px;\" align=\"left\"\u003e\n\u003cp\u003e42.97\u003c/p\u003e\n\u003cp\u003e(19.00\u0026ndash;66.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eData are present as mean and the range values.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eMean temperature was similar among sampling sites and seasons, with a difference of approximately 4\u0026deg;C among the sites by seasons. Average temperatures during the dry-cold season varied from 10.41 to 11.49. During the dry-hot season, the mean temperatures varied from 13.89\u0026deg;C to 14.9\u0026deg;C (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eIt was observed that the predominant wind direction, vector direction, in both seasons were similar in SM (Southeast), AP (East), and OX (Southwest); with the exception of SC with vector direction in southeast and northeast during cold and hot season, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Average wind speed (AWS) was similar in both seasons. The AWS for cold season follow the gradient AP (1.48 m/s)\u0026thinsp;\u0026gt;\u0026thinsp;OX (1.15 m/s)\u0026thinsp;\u0026gt;\u0026thinsp;SC (0.93 m/s)\u0026thinsp;\u0026gt;\u0026thinsp;SM (0.89 m/s). In the hot season the highest AWS was observed in SC (1.69 m/s) followed by OX (1.42 m/s)\u0026thinsp;\u0026gt;\u0026thinsp;AP (1.12 m/s) and the lowest AWS was observed in SM (1.06 m/s).\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eCalm winds were more frequent in the cold-dry season compared to the hot-dry season. In the cold season the calm winds followed the gradient SM (34.26%); SC (30.79%); OX (11.11%) and AP (8.1%), however, in the hot season the higher calm winds frequency is observed in the SM site (26.96%), AP being the second site with calm winds (21.47%) and the lowest frequency of calm winds were 5.45% and 0.32% corresponding to OX and SC, respectively.\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003eThe 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e median (range) concentrations are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. For both seasons, the highest PM\u003csub\u003e2.5\u003c/sub\u003e concentration was observed in SC station; followed by AP, SM and the lowest concentration was observed in OX. In the dry-cold season 54.58 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (26.1\u0026ndash;79.88) was observed in SC, followed by 41.3 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (1.33\u0026ndash;57.97) in AP, 41.16 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (12.6\u0026ndash;57.93) in SM; and 15.46 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (9.46\u0026ndash;51.25) in OX. In the dry-hot season the PM\u003csub\u003e2.5\u003c/sub\u003e concentration in SC station was 72.85 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (23.48\u0026ndash;102.84); followed by AP with 43.08 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (24.1\u0026ndash;86.36); SM with 37.28 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (20.61\u0026ndash;59.44); and the lowest PM\u003csub\u003e2.5\u003c/sub\u003e concentration was observed in OX with 10.88 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (7.56\u0026ndash;27.72). The SC site was the only one that exceeded the median 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e concentration limit of 45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e stablished by the Mexican government (NOM-025-SSA1-2014). However, according with the maximum gravimetric data all the stations showed at least one day over the 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e concentration limit. The OX site did not exceed the concentration limit during the dry-hot season.\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003e24-hour PM2.5 air gravimetric concentrations in the Toluca Valley Metropolitan Area (TVMA)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSites\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Cold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Hot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNueva Oxtotitl\u0026aacute;n (OX)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.64 (9.46\u0026ndash;51.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.88 (7.56\u0026ndash;27.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSan Mateo Atenco (SM)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.16 (12.60\u0026ndash;57.93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.28 (20.61\u0026ndash;59.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAirport (AP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.30 (1.33\u0026ndash;57.97)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.08 (24.10\u0026ndash;86.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSan Cristobal (SC)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.58 (26.10\u0026ndash;79.88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.85 (23.48\u0026ndash;102.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eData are shown as median following range of values in parenthesis. N indicates the number of samples, following by the number of samples, followed by the number of samples exceeding Mexican government guidelines, 45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, NOM-025-SSA-2014 in parenthesis.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe metal and metalloid analysis by ICP-MS showed a recovery range of 95\u0026thinsp;\u0026minus;\u0026thinsp;80% for Co, Cr, Cu, Mn and Sb, whereas recovery for Zn and Pb were between \u0026lt;\u0026thinsp;80 to \u0026gt;\u0026thinsp;60%; and for Mg, Ba, Al, and Ti the recovery range were below 60% (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eQuality Control Parameters in the determination of elements present in PM2.5, at the MATV, Mexico, 2013\u0026ndash;2014\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eLimits\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eNIST 2783 air particulate\u003c/p\u003e\n\u003cp\u003estandard filter\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElement\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLOD\u003c/p\u003e\n\u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLOQ\u003c/p\u003e\n\u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCertified\u003c/p\u003e\n\u003cp\u003e(ng/filter)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMeasured\u003c/p\u003e\n\u003cp\u003e(ng/filter)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRecovery\u003c/p\u003e\n\u003cp\u003e(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCr\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1X10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0X10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e135\u0026thinsp;\u0026plusmn;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCu\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1X10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.6X10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e404\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9X10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.1X10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.9X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.7X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e320\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e267\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7X10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2X10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eZn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6X10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.2X10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1790\u0026thinsp;\u0026plusmn;\u0026thinsp;130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePb\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.29X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.39X10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e317\u0026thinsp;\u0026plusmn;\u0026thinsp;54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e213\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMg\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.3X10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.8X10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8620\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1X10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7X10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e335\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAl\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.3X10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3X10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23210\u0026thinsp;\u0026plusmn;\u0026thinsp;530\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7548\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10X10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5X10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e217\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003e(a)\u003c/sup\u003e The %R could not be calculated for K,Ca,Ni.V, and Fe. Limit of detection, LOD; Limit of quantification, LOQ.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe highest concentrations of metal and metalloids were observed in the dry-cold season in all stations. Additionally, concentrations during the dry-cold compared to dry-hot season were above one hundred times in SM, AP, and SC, with the exception of OX where the concentrations were below four times (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Although, the highest PM\u003csub\u003e2.5\u003c/sub\u003e concentration was observed in SC in both seasons, the highest metal and metalloid concentration in dry-cold season was observed in SM station. On the other hand, in the dry-hot season the major metal and metalloid concentration was observed in the SC station, with the exception of copper concentration which was higher in OX station (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn the SM station, the Co average concentration was 140 ng/m\u003csup\u003e3\u003c/sup\u003e (14\u0026ndash;231), following by SC site with 39.2 ng/m\u003csup\u003e3\u003c/sup\u003e (0.38\u0026ndash;68.6) and AP site with 35.8 ng/m\u003csup\u003e3\u003c/sup\u003e (0.29\u0026ndash;180). The lowest concentration was measurement in OX site, with 0.0297 ng/m\u003csup\u003e3\u003c/sup\u003e (0.00652\u0026ndash;11.7). During the dry-hot season the highest concentration was observed in SC station, 0.301 ng/m\u003csup\u003e3\u003c/sup\u003e (0.051\u0026ndash;0.354); this concentration was one hundred and thirty times below the concentration found in SC on the dry cold season. The Co concentration in SM station during the dry cold season was approximately nine hundred times below the SM dry hot season concentration (0.154 ng/m\u003csup\u003e3\u003c/sup\u003e vs 140 ng/m\u003csup\u003e3\u003c/sup\u003e). The AP station showed a Co concentration in dry cold season three hundred and sixty times above the concentration found in dry hot season samples (35.8 vs 0.0986 ng/m\u003csup\u003e3\u003c/sup\u003e). The lowest Co concentration was observed in the OX station, with 0.00821 ng/m\u003csup\u003e3\u003c/sup\u003e (0.00136\u0026ndash;0.0571), which was 3.6 times below the concentration found during the dry-cold season at the same station (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eCr concentrations were observed in the dry-cold season in all the stations. The highest concentration was measured in SM (17.33 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), however, it was detected in only one sample of five. AP was the second site with high Cr concentration, 6.32 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (6.26\u0026ndash;9.53), detected in three samples of fourteen. Cr in the SC station, 5.7 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (5.55\u0026ndash;5.85), was detected in eleven of fourteen samples. The lowest Cr concentration was observed in the OX site, 0.0046 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0024\u0026ndash;0.0096), with twelve samples out of fourteen. Cr concentrations in the dry-hot season were only observed in the OX site, with a concentration of 0.00311 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0028\u0026ndash;0.0032), in three samples of thirteen, this value was 1.5 times below the concentration found in the dry-cold season at the same site. Finally, the ambient Cr concentrations for the rest of the sampling stations were below the LOD (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOn the dry-cold season the highest copper Cu concentration was observed in SM station, 7.684 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (7.4 -20.48), where Cu was quantified in four of five samples, following AP and SC, with 2.37 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.23\u0026ndash;27.0) and 2.07 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.02\u0026ndash;5.89), respectively. Cu was determined in twelve of fourteen and eight of fourteen, in AP and SC stations, respectively. The lowest Cu concentrations were observed in OX station, in four of eighteen samples, 0.0194 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0102\u0026ndash;0.318); (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe highest Mn concentrations were observed during the dry-cold season in SM in three of five filters, 7.68 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (7.4 -20.48); followed by SC and AP, with 1.8 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.021\u0026ndash;14.22) and 1.43 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.13\u0026ndash;12.63), respectively. In all SC samples Mn was quantified, and in AP site, Mn was observed in thirteen of fourteen samples. The OX site had the lowest Mn concentration, 0.0031 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0009\u0026ndash;0.0065); Mn was quantified in seventeen of eighteen samples for dry-cold season. On the other hand, in the dry-hot season the highest concentration was observed in SC site, 0.017 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.00195\u0026ndash;0.0262), and it was quantified in all the samples. Moreover, Mn was seventy-eight times lower in contrast to the dry-cold season. The Mn concentrations found in SM, and AP stations during the dry hot season where five hundred and ninety and one hundred times below the concentrations measured during the dry-cold season at the same stations (0.013 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, and 0.012 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e vs, 7.68 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, and 1.43 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e respectively). The lowest Mn concentration was measured in OX site, 0.0016 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.00031\u0026ndash;0.0051); Mn concentrations in OX site were detected in twelve of thirteen samples, and the Mn concentration in the dry-hot season was 2.7 times below the concentration found in the dry-cold season (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe metalloid Sb concentration in the dry-cold season was highest in SM 6.39 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.07\u0026ndash;13.83); followed by SC 2.24 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.02\u0026ndash;3.79); then AP with 1.44 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.39\u0026ndash;3.74); and OX with 0.0022 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.001\u0026ndash;0.016). Our data correspond to the detection of Sb in seventeen, five, fourteen, and fourteen filters, respectively. The gradient of concentration for dry-hot season changes, and it showed the following gradient: SC 0.017 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.002\u0026ndash;0.026); AP 0.0076 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0023\u0026ndash;0.018); SM 0.0064 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0017\u0026ndash;0.017); and OX 0.00081 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (0.0002\u0026ndash;0.0041). Although, in dry-hot season the Sb concentrations were lower than dry-cold season, the presence of Sb was detected in all the filters of each sampling sites. In addition, the Sb concentration of dry-cold season fell in the hot season nearly to one thousand times at SM site, followed by SC and AP sites with around two hundred times, and OX with only 2.7 times (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMetal and metalloid concentrations of 24-hour PM2.5 collected in Toluca Valley Metropolitan Area (TVMA)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eCobalt (Co, ng/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Cold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Hot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnrichment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0297 (0.00652\u0026ndash;11.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00821 (0.00136\u0026ndash;0.0571)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140 (14\u0026ndash;231)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3/5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.154 (0.0103\u0026ndash;0.384)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e912.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.8 (0.29\u0026ndash;180)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0986 (0.00903\u0026ndash;0.266)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e363.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.2 (0.38\u0026ndash;68.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.301 (0.051\u0026ndash;0.354)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8/11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eChromium (Cr, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Cold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Hot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnrichment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0046 (0.0024\u0026ndash;0.0096)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00311 (0.0028\u0026ndash;0.0032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1/5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.32 (6.26\u0026ndash;9.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.70 (5.55\u0026ndash;5.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eND\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0/11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eCopper (Cu, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Cold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Hot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnrichment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0194 (0.0102\u0026ndash;0.318)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.24 (5.43\u0026ndash;11.58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4/5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0044 (0.000343\u0026ndash;0.0135)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1645.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.37 (0.23\u0026ndash;27.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00615 (0.00445\u0026ndash;0.0226)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e384.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.07 (0.02\u0026ndash;5.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00659 (0.00261\u0026ndash;0.0346)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9/11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e314.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eManganese (Mn, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Cold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Hot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnrichment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0031 (0.0009\u0026ndash;0.0065)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0016 (0.00031\u0026ndash;0.0051)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.68 (7.4\u0026ndash;20.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3/5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013 (0.0031\u0026ndash;0.058)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e593.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.43 (0.13\u0026ndash;12.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012 (0.00017\u0026ndash;0.064)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8 (0.021\u0026ndash;14.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0229 (0.00209\u0026ndash;0.0282)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eAntimony (Sb, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Cold\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry-Hot\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnrichment\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOX\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00222 (0.001\u0026ndash;0.0167)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17/18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00081 (0.0002\u0026ndash;0.0041)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSM\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.39 (0.07\u0026ndash;13.83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5/5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0064 (0.0017\u0026ndash;0.017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13/13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e996.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.44 (0.39\u0026ndash;3.74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0076 (0.0023\u0026ndash;0.018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12/12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.24 (0.02\u0026ndash;3.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14/14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017 (0.002\u0026ndash;0.026)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11/11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e209.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eAbbreviations: OX, Nueva Oxtotitl\u0026aacute;n; SM, San Mateo Atenco; AP, Airport; SC, San Cristobal; DF indicate detection frequency, number of filters in which metal was detected over total filters collected. ND indicate Not-Detected.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDetermination of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations is critical for urbanized areas to evaluate the air quality and to prevent adverse effects on human health. Additionally, the chemical composition of particles has been suggested as finger print of the emission sources, and atmospheric chemical processes.\u003c/p\u003e \u003cp\u003eThe PM\u003csub\u003e2.5\u003c/sub\u003e concentration and composition has been studied in many countries. There are factors that influence PM2.5 concentration, such as weather and topography. In our study area, as in others, topography plays a key role in atmospheric dynamics, impacting air quality of urbanized and industrialized areas; additionally, the predominant weather determines the atmospheric dynamics (Querol et al., 2007a). The season and climate parameters such as temperature, wind direction, relative humidity, rainfall, cloudiness (Kulshrestha et al., 2009), and some atmospheric phenomena such as thermal inversion, can modify the half-life, and concentration of pollutants in the air. Because of its diameter, the PM\u003csub\u003e2.5\u003c/sub\u003e, remains longer in the atmosphere, and is efficiently transported; mountain systems can influence their transport and local deposit (Cheng Miao-Ching et al., 2012). Additionally, wind movement, its force, and direction must be considered in the displacement, distribution and final fate of air pollutants. Wind speed is not constant along each day, week, months, seasons, and also between years, because it undergoes variations due to the topographic and thermal features of a given area.\u003c/p\u003e \u003cp\u003eIn our study area, we observed discrete differences between seasons, with approximately an increment of 4\u0026ordm;C in the average daily temperature between cold and hot seasons; average daily RH was approximately 10% higher in the dry-cold season compared to the dry-hot season. For AWS, differences were around 0.5 m/s; these discrete changes could be associated with the high altitude of TVMA (2660 m.a.s.l.).\u003c/p\u003e \u003cp\u003eThe influence of air flow, direction, and calm winds was observed in our study. The AWS was quite different among sites between seasons. However, it seems that direction of air flow in OX displace the atmospheric particles and move the air pollutants to other site. In the rest of the sites, the influence of east winds flows from AP and SM sites to the SC site, where AP and SM can be considered industrial zones and SC the receptor site, a suburban zone with the highest PM\u003csub\u003e2.5\u003c/sub\u003e concentration. Additionally, we observed higher PM\u003csub\u003e2.5\u003c/sub\u003e concentration in the dry-cold season compared to dry-hot season in almost all sites. This can be explained by the frequency of calm winds during the cold-season, concomitant with the major frequency of thermal inversion according to the high altitude and recurrent in winter or fall seasons.\u003c/p\u003e \u003cp\u003eWe observed different 24h-PM\u003csub\u003e2.5\u003c/sub\u003e concentrations among the four sites between the dry-cold and the dry-hot seasons. The SC site exceeded the 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e concentration limit of 45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, established by the Mexican government (NOM-025-SSA1-2014). However, at least one day in the sampling period was out of the concentration limit for the rest of the sampling sites. Nevertheless, according to international 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e limit of 25 and 20 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, established by World Health Organization (WHO) and European Community (WHO 2013a, OJEU, 2008) respectively, only OX is under the international concentration limit, indicating that the rest of the population is breathing unhealthy air.\u003c/p\u003e \u003cp\u003eDifferences among element concentrations between the two seasons were observed, likely due to the seasonal weather parameters mentioned above that determine the element distribution and its presence, such as temperature, WD and WS, as well as the topographical conditions of the region.\u003c/p\u003e \u003cp\u003eThe highest metalloid (Sb) and transition metals (Co, Cu, Mn) concentrations were found at the SM site, which did not have the highest PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. SM is located in the southeast of MATV, bordered by Lerma-Tenango del Valle and Toluca M\u0026eacute;xico highways, neighboring to the Lerma-Toluca industrial park, with the main economic activity being shoes and clothing manufacturing.\u003c/p\u003e \u003cp\u003eThe AP and SC sites share a similar trend in metalloid (Sb) and transition metals (Co, Cu, Mn) concentrations. Both sites are located north of the Toluca valley, with different economic activities; in addition to airplane transit on AP site, there is an industrial settlement, land dedicated to agriculture, and San Antonio roadway. However, SC is considered an urban settlement without natural barriers, the predominant wind coming from the east, and with a vector wind in the dry-cold season from the southeast that is influenced by the emissions generated in Toluca downtown. It is possible that additional emissions, mainly produced by industrial plants located in the south and south-east AP and SM sites, as well as by the resuspension of road dust and biomass combustion (Quiterio et al., 2005; Mansha et al., 2012) contribute to local pollution.\u003c/p\u003e \u003cp\u003eThe station with the lowest PM\u003csub\u003e2.5\u003c/sub\u003e concentration was OX site, located to the west of Toluca downtown, is characterized by high population density; it is considered a residential area with low levels of vehicular traffic, without industrial plants. The main difference from the rest of the sites was the air flow direction that comes from the southwest, opposite to the downtown and industrial areas. Near the area, at the north of OX site, there are mountain systems with elevations between 2800\u0026ndash;3000 m.a.s.l. that run from west towards east to the limit of Toluca downtown that probably acting as a barrier to the pollutants that flow from Toluca downtown to OX. Additionally, the OX site is a place where calm winds are less frequent, suggesting an efficient removal of pollutants.\u003c/p\u003e \u003cp\u003eIn the MATV we observed that in the dry-hot season the metal and metalloid concentrations had more important decrease compared to the dry cold-season. When calculating the enrichment of the element concentration of the dry-cold season in contrast to the dry-hot season, we detected that in some sites the increment was a hundred or thousand-fold. However, the PM\u003csub\u003e2.5\u003c/sub\u003e mass did not change in the same magnitude, suggesting that in the dry-cold season the frequency of calm winds and probably the thermal inversion at high altitude induce a major permanence of metals and metalloids, associated with the increment in the economic activity of the area.\u003c/p\u003e \u003cp\u003eComparisons between PM\u003csub\u003e2.5\u003c/sub\u003e and element concentrations detected in our study, during the dry-hot season in the MATV, and those found in other countries showed that the concentrations are similar in range to those reported in other cities in the world (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the content of metalloid and transition metals in PM\u003csub\u003e2.5\u003c/sub\u003e in the dry-cold season at the SM, SC, and AP sites were above those reported in all other cities around the world (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) the concentrations of metalloid and transition metals observed in the MATV were in the order of micrograms with respect to other cities with concentrations expressed in nanograms. The concentrations observed in the dry-cold season were higher than the limits recommended by regulatory agencies (e. g. EPA) to prevent human health.\u003c/p\u003e \u003cp\u003eOur study has important methodological and study design differences respect local and international studies, including the sampling time, which are relatively short. Moreover, the metal(loid)s extraction method varies among the studies which define the chemical form of the elements and the biological availability (Espinosa et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The use of efficient and alternative tools, such the ICP-MS, for the analysis of PM\u003csub\u003e2.5\u003c/sub\u003e can help to detect, quantify and design options to address pollution problems in large cities (Saldarriaga et al., 2009; Murillo et al., 2015), although the sensitivity of different detection systems of analytical methods can influence the data observed.\u003c/p\u003e \u003cp\u003eCobalt (Co), which is a recurring pollutant of wastewater, is a metal that is released into the atmosphere as a particle, its main use is in the petrochemical and plastic industry as a catalyst, it can be released in scrap metal recycling, foundry and metal refining, additionally, Co can be released after burning fossil fuel. Co content in PM\u003csub\u003e2.5\u003c/sub\u003e samples from MATV in AP, SC and OX sites were observed in the occupational setting concentration reported (Kim et al., 2006), but Co speciation its needed to be determine to explain the human health adverse effect.\u003c/p\u003e \u003cp\u003eChromium (Cr) is released in the commercial and residential fossil fuel, natural gas, oil and coal combustion in addition to emissions in the metallurgical industries (e.g. ferrochromium or chromium), chromium platers, and paper industry (Kimbrough et al., 1999; Xu, et al., 2013). The Cr content in MATV PM\u003csub\u003e2.5\u003c/sub\u003e is higher respect the annual standard stablished by WHO, and those reported to other countries (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCopper (Cu) atmospheric emission sources include copper smelters, copper and iron ore processing, iron and steel production, combustion sources, municipal incinerators, copper sulfate production, brass and bronze production, carbon black production, cooling systems, brake wear particles, both by direct emissions and by suspended road dust (Georgopoulos et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Keuken et al., 2013). According with the Cu concentration found in the PM\u003csub\u003e2.5\u003c/sub\u003e from MATV, this metal was under the maximum annual concentration and copper concentration for 24-hour period stablished by EPA (1987a), 30 and 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eManganese (Mn) is part of particle component, the crustal rock is the major natural source, other sources include forest fires, vegetation (e.g. leaching from plant tissues and dead plants), volcanic activity, and animal excrement. Anthropogenic source includes mining and mineral processing (e.g. nickel), emission from alloy (e.g. steel), the combustion of fossil fuel and in minor degree from combustion of fuel additives. Mn compounds have many applications such as the production of dry-cell batteries, matches, fireworks, porcelain and glass-bonding materials, as a catalyst in the chlorination of organic compounds, in animal feed to supply essential trace minerals, among others (Howe et al., 2004). PM\u003csub\u003e2.5\u003c/sub\u003e collected in MATV has a higher contend in Mn respect other reported countries. However, concentrations are below annual standard limit stablished by WHO, but Mn content in PM\u003csub\u003e2.5\u003c/sub\u003e from MATV is upper respect the minimal risk level for neurological effects by chronic inhalation (ATSDR, 2012) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAntimony (Sb) is incorporated in textiles, paper and plastics as coadjutant of fire retardants; the primary emissions sources are related with plastic manufacturing, petroleum industry, and structural metal products (Belzile et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Tian et al., 2012). Sb contend in PM\u003csub\u003e2.5\u003c/sub\u003e in SM, AP, and SC was \u0026gt;\u0026thinsp;1\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, value referred as industrial area (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), however, Sb has not been classified as carcinogenic in humans by U.S. EPA but ATSDR has placed antimony trioxide as possible human carcinogen. According with the high levels found of Sb in MATV further studies are need to describe the chemical speciation and the potential risk for human health.\u003c/p\u003e \u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of element concentrations in PM2.5 air samples reported in different countries\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003cp\u003etype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAltitude\u003c/p\u003e\u003cp\u003e(m.a.s.l.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003cp\u003e(\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u003cp\u003eMetal(loid)s elements (ng/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMethod / sampling\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCo\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eCr\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eSb\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eToluca Valley, Mexico ꬷ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOX-Urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e2660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eICP-MS / 24 h; every 6 days, \u0026gt; 8 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAztatzi et al.,\u003c/p\u003e\u003cp\u003epresent study\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSM-subindustrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17,330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7,240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7,680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6,390\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAP-industrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1,440\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSC-Urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2,240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAthens, Greece\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4-100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eICP-MS / 24 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eRemoundaki et al., 2013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDunkerque, France\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e24.9 \u0026minus;\u0026thinsp;33.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICP-MS / 12 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eKfoury et al., 2016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eAthens, Greece\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.12\u0026ndash;34.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICP-MS / 24 h; every 3 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eManousakas et al., 2014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.44\u0026ndash;45.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUSA, 187 countries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eICP-MS / five years/monitor frequency 3\u0026ndash;12 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eBell et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGuadalajara, Mexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u0026ndash;72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICP-MS / 24 h; every 3 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMurillo-Tovar et al., 2015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16-49.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eMexico City, Mexico\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eICP-MS / 24 h; every 6 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eGarza-Galindo et al., 2019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManizales, Colombia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eICP-OES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003edos Santos Souza et al., 2021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuangzhou, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.3\u0026ndash;190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eICP-MS /24 h in 10 consecutive days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eFeng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eShanghai, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e92.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICP-AES / 48 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWang et al., 2013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e132.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaejeon, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.4\u0026ndash;63.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eICP-MS / 24 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eLee Jin-Hong et al., 2013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eHangzhou, China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustrial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eICP-MS / 18\u0026ndash;22 h\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDai et al., 2015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eConcentration Criteria or limits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e \u0026lt;1 to2 ng/m\u003csup\u003e3\u003c/sup\u003e; ⸸ 1\u0026times;10\u003csup\u003e4\u003c/sup\u003e to 1.7 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e ng/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e* 100 and\u003c/p\u003e\u003cp\u003e\u0026Dagger; 12 ng/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026sect;\u0026nbsp;30 and 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e* 52 and\u003c/p\u003e\u003cp\u003e⁑ 0.3 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e⸙ \u0026lt;20 ng/m\u003csup\u003e3\u003c/sup\u003e. \u0026gt; 1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for industry.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003eꬷ Results of the dry-cold season of Metropolitan area from Toluca Valley, Mexico state, Mexico. \u0026dagger; Unpulled sites ⸸ Air concentration range of Cobalt in occupational settings, Kim \u003cem\u003eet al.\u003c/em\u003e, 2006. \u0026Dagger; EPA calculated inhalation unit risk estimate. \u0026sect; Copper maximum annual concentration and copper concentration for 24-hour period at a location within one-half mile of a major source, EPA 1987a. * Annual standard, WHO, 2016. ⁑ Minimal Risk Level, as an estimate of a chronic inhalation exposure that is likely to be without appreciable risk of adverse non-cancer effects during a lifetime; for Mn was based on impairment of neurobehavioral function in people, ATSDR (2012b). ⸙ antimony ambient air range, and in industry area observed data, ATSDR 1992.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study reports the content of metalloid and transition metals in the PM\u003csub\u003e2.5\u003c/sub\u003e fraction by ICP-MS. The airborne particles in each geographic area will depend on local anthropogenic source of emissions, season and weather variables such as temperature, altitude, humidity, wind velocity and air flow direction. This means that the study of atmospheric conditions for dispersion of pollutants must consider temporality and geography of the area to be studied. The levels of PM\u003csub\u003e2.5\u003c/sub\u003e and trace metal(loid)s found in the TVMA provide data for understanding the behavior of elements present locally and in other regions with similar features. This evidence can help to control and regulate emissions of constituents of airborne particles that constitute a potential risk for human health. The control, prevention, and minimization of the levels of these contaminants in the geographic area needs to be studied as well as their impact on human health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the technical support of the\u0026nbsp;Red Autom\u0026aacute;tica de Monitoreo Atmosf\u0026eacute;rico\u0026nbsp;(RAMA) from Mexico City, and to the\u0026nbsp;Red Autom\u0026aacute;tica de Monitoreo Atmosf\u0026eacute;rico\u0026nbsp;from Toluca valley (RAMAT) for providing meteorological data, area localization, equipment calibration, and maintenance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatements and Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript. The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available in ww.figshare.com repository, https://figshare.com/s/cafed077bcfdf913e9a5, https://doi.org/10.6084/m9.figshare.19500569.v1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. L. Barbosa-S\u0026aacute;nchez\u003c/strong\u003e contributes in the present study to carried out sampling, equipment management, logistic, analytical data analysis and written the first manuscript. Sample analysis, and analytical method development were performed by \u003cstrong\u003eC. M\u0026aacute;rquez-Herrera\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR. Sosa-Echeverria\u003c/strong\u003e participates in monitoring and direction of sampling. \u003cstrong\u003eR.V. Diaz-Godoy\u003c/strong\u003e participates in sampling, provide resources to monitoring, and sample management. \u003cstrong\u003eM.E. Guti\u0026eacute;rrez-Castillo\u003c/strong\u003e contribute in conception and design of the study, and monitoring planning, analytical method development. \u003cstrong\u003eC. Escamilla-N\u0026uacute;\u0026ntilde;ez\u003c/strong\u003e participates in data base curation, and statistical data management. \u003cstrong\u003eA. M. Rule\u003c/strong\u003e and \u003cstrong\u003eM. P. Sierra-Vargas\u003c/strong\u003e contribute in review and editing results and final manuscript. \u003cstrong\u003eO. G. Aztatzi-Aguilar\u003c/strong\u003e participates in data curation, and validation, visualization and result edition, and written final draft manuscript preparation. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAldape F, Flores J, Flores YJA, Retama- Hern\u0026aacute;ndez A, Rivera-Hern\u0026aacute;ndez O (2005) Elemental composition and source identification of PM\u003csub\u003e2 5\u003c/sub\u003e particles collected in downtown Mexico City. Int J PIXE 15(3):263\u0026ndash;270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1142/S012908350500060X\u003c/span\u003e\u003cspan address=\"10.1142/S012908350500060X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eATSDR (Agency for Toxic Substances and Disease Registry) (2012b) Toxicological profile for manganese. U.S. Department of Health and Human Services, Atlanta, GA\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eATSDR, Agency for Toxic Substances and Disease Registry (2012a) Toxicological profile for Chromium [ATSDR Tox Profile]. Atlanta, GA:U.S.Department of Health and Human Service, Public Health Service\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eATSDR, Agency for Toxic Substances and Disease Registry (2019) Toxicological profile for antimony and compounds. Department of Health and Human Service, Public Health Service, Atlanta, GA:U.S.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBell ML, Dominici F, Ebisu K, Zeger SL, Samet JM (2007) Spatial and Temporal Variation in PM\u003csub\u003e2.5\u003c/sub\u003e Chemical Composition in the United States for Health Effects Studies. Environ Health Perspect 115(7):989\u0026ndash;995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1289/ehp.9621\u003c/span\u003e\u003cspan address=\"10.1289/ehp.9621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelzile N, Chen YW, Filella M (2011) Human exposure to antimony: I. Sources and intake. Crit Rev Environ Sci Technol 41(14):1309\u0026ndash;1373. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10643381003608227\u003c/span\u003e\u003cspan address=\"10.1080/10643381003608227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBravo Alvarez H, Sosa Echeverria R, Sanchez Alvarez P, Krupa S (2013) Air Quality Standards for Particulate Matter (PM) at high altitude cities. Environ Pollut. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envpol.2012.09.025\u003c/span\u003e\u003cspan address=\"10.1016/j.envpol.2012.09.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBravo AH, Urone P (1981) The altitude: a fundamental parameter in the use of air quality standards. J Air Pollut Contr Assoc 31(3):264\u0026ndash;265\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCENAM, \u0026amp; EURACHEM (2005) M\u0026eacute;todos Anal\u0026iacute;ticos Adecuados a su Prop\u0026oacute;sito. Gu\u0026iacute;a de Laboratorio para la Validaci\u0026oacute;n de M\u0026eacute;todos Temas Relacionados. M\u0026eacute;todos Anal\u0026iacute;ticos Adecuados a su Prop\u0026oacute;sito, 2nd edn. Centro Nacional de Metrolog\u0026iacute;a, Quer\u0026eacute;taro, M\u0026eacute;xico\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng M-C, You CF, Cao J, Jin Z (2012) Spatial and seasonal variability of water-soluble ions in PM\u003csub\u003e2.5\u003c/sub\u003e aerosols in 14 major cities in China. Atmos Environ 60:182\u0026ndash;192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2012.06.037\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2012.06.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Campo MM, Esteller MV, Exp\u0026oacute;sito JL, Hirata R (2014) Impacts of urbanization on groundwater hydrodynamics and hydrochemistry of the Toluca Valley aquifer (Mexico). Environ Monit Assess 186(5):2979\u0026ndash;2999. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-013-3595-3\u003c/span\u003e\u003cspan address=\"10.1007/s10661-013-3595-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinosa AJF, Rodr\u0026iacute;guez MT, de la Rosa FJB, S\u0026aacute;nchez JCJ (2002) A chemical speciation of trace metals for fine urban particles. Atmos Environ 36(5):773\u0026ndash;780. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S1352-2310(01)00534-9\u003c/span\u003e\u003cspan address=\"10.1016/S1352-2310(01)00534-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng XD, Dang Z, Huang WL, Yang C (2009) Chemical speciation of fine particle bound trace metals. Int J Environ Sci Technol 6(3):337\u0026ndash;346. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF03326071\u003c/span\u003e\u003cspan address=\"10.1007/BF03326071\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgopoulos G, Roy A, Yonone-Lioy MJ, Opiekun RE, Lioy PJ, P (2001) Environmental copper: its dynamics and human exposure issues. J Toxicol Environ Health Part B: Crit Reviews 4(4):341\u0026ndash;394\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeal MR, Hibbs LR, Agius RM, Beverland IJ (2005) Total and water-soluble trace metal content of urban background PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e and blank smoke in Edinburgh. UK Atmos Environ 39:1417\u0026ndash;1430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2004.11.026\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2004.11.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\\\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\\\u0026amp;lt\\;bib\\ id\\=\\\"bib16\\\"\\\u0026amp;gt\\;\\\\\\Hennigan\\\u0026lt;\\/span\\\u0026gt;\\,\\ \\C\\.\\ J\\.\\\u0026lt;\\/span\\\u0026gt;\\\u0026lt;\\/span\\\u0026gt;\\,\\ \\\\Mucci\\\u0026lt;\\/span\\\u0026gt;\\,\\ \\A\\.\\\u0026lt;\\/span\\\u0026gt;\\\u0026lt;\\/span\\\u0026gt;\\,\\ \\\u0026amp;\\ \\\\Reed\\\u0026lt;\\/span\\\u0026gt;\\,\\ \\B\\.\\ E\\.\\\u0026lt;\\/span\\\u0026gt;\\\u0026lt;\\/span\\\u0026gt;\\\u0026lt;\\/aug\\\u0026gt;\\ \\(\\2019\\\u0026lt;\\/span\\\u0026gt;\\)\\.\\ \\Trends\\ in\\ PM\\\u003csub\u003e2\\.5\\\u0026lt;\\/sub\\\u0026gt;\\ transition\\ metals\\ in\\ urban\\ areas\\ across\\ the\\ United\\ States\\\u0026lt;\\/span\\\u0026gt;\\.\\ \\Environmental\\ Research\\ Letters\\\u0026lt;\\/span\\\u0026gt;\\,\\ \\14\\\u0026lt;\\/span\\\u0026gt;\\(\\10\\\u0026lt;\\/span\\\u0026gt;\\)\\,\\ \\104006\\\u0026lt;\\/span\\\u0026gt;\\.\\ \\https\\:\\/\\/doi\\.org\\/10\\.1088\\/1748\\-9326\\/ab4032\\\u0026lt;\\/span\\\u0026gt;\\\u0026amp;lt\\;\\/bib\\\u0026amp;gt\\;\\\u0026lt;\\/p\\\u0026gt;\u003c/sub\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe P, Malcolm H, Dobson S (2004) Manganese and its compounds: environmental aspects. World Health Organization\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu X, Zhang Y, Ding Z, Wang T, Lian H, Sun Y, Wu J (2012) Bioaccessibility and health risk of arsenic and heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn and Mn) in TSP and PM\u003csub\u003e2.5\u003c/sub\u003e in Nanjing, China. Atmos Environ 57:146\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2012.04.056\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2012.04.056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuster AC, Pierce DE (2020) A geochemical baseline for clays of the Toluca Valley, Mexico. J Archaeol Science: Rep 29:102094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jasrep.2019.102094\u003c/span\u003e\u003cspan address=\"10.1016/j.jasrep.2019.102094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eINEGI, Instituto Nacional de Estad\u0026iacute;stica y Geograf\u0026iacute;a. Censo de Poblaci\u0026oacute;n y Vivienda (2010)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKendall M, Pala K, Ucakli S, Gucer S (2011) Airborne particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e) and associated metals in urban Turkey. Air Qual Atmos Health 4:235\u0026ndash;242. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11869-010-0129-9\u003c/span\u003e\u003cspan address=\"10.1007/s11869-010-0129-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeuken MP, Moerman M, Voogt M, Blom M, Weijers EP, R\u0026ouml;ckmann T, Dusek U (2013) Source contributions to PM2. 5 and PM10 at an urban background and a street location. Atmos Environ 71:26\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2013.01.032\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2013.01.032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JH, Gibb HJ, Howe P (2006) Cobalt and inorganic cobalt compounds, vol 69. World health organization\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKimbrough DE, Cohen Y, Winer AM, Creelman L, Mabuni C (1999) A critical assessment of chromium in the environment. Crit Rev Environ Sci Technol 29(1):1\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10643389991259164\u003c/span\u003e\u003cspan address=\"10.1080/10643389991259164\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulshrestha A, Satsangi PG, Masih J, Taneja A (2009) Metal concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e particles and seasonal variations in urban and rural environment of Agra, India. Sci Total Environ 407:6196\u0026ndash;6204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2009.08.050\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2009.08.050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee J-H, Jeong JH, Lim JM (2013) Toxic trace and earth crustal elements of ambient PM\u003csub\u003e2.5\u003c/sub\u003e using CCT-ICP-MS in an urban area of Korea. Environ Eng Res 18(1):3\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4491/eer.2013.18.1.003\u003c/span\u003e\u003cspan address=\"10.4491/eer.2013.18.1.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMansha M, Ghauri B, Rahman S, Amman A (2012) Characterization and source apportionment of ambient air particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) in Karachi. Sci Total Environ 425:176\u0026ndash;183. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2011.10.056\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2011.10.056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;nez MA, Caballero P, Carrillo O, Mendoza A, Mejia GM (2012) Chemical characterization and factor analysis of PM\u003csub\u003e2.5\u003c/sub\u003e in two sites of Monterrey. Mexico J Air Waste Manag Assoc 62(7):817\u0026ndash;827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10962247.2012.681421\u003c/span\u003e\u003cspan address=\"10.1080/10962247.2012.681421\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller KA, Siscovick DS, Sheppard L, Shepherd K, Sullivan JH, Anderson GL, Kaufman JD (2007) Long-term exposure to air pollution and incidence of cardiovascular events in women. N Engl J Med 356:447\u0026ndash;458. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa054409\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa054409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurillo-Tovar MA, Saldarriaga-Nore\u0026ntilde;a H, Hern\u0026aacute;ndez-Mena L, Campos-Ramos A, C\u0026aacute;rdenas-Gonz\u0026aacute;lez B, Ospina-Nore\u0026ntilde;a JE, Cos\u0026iacute;o-Ram\u0026iacute;rez R, D\u0026iacute;az-Torres J, de Smith J, W. Potential (2015) Sources of Trace Metals and Ionic Species in PM\u003csub\u003e2.5\u003c/sub\u003e in Guadalajara, Mexico: A Case Study during Dry Season. Atmos (Basel) 6(12):1858\u0026ndash;1870. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos6121834\u003c/span\u003e\u003cspan address=\"10.3390/atmos6121834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOM-025-SSA1-2014. Norma Oficial Mexicana Salud ambiental. Valores l\u0026iacute;mite para la concentraci\u0026oacute;n de part\u0026iacute;culas suspendidas. PM\u003csub\u003e10\u003c/sub\u003e yPM\u003csub\u003e2.5\u003c/sub\u003e en el aire ambiente y criterios para su evaluaci\u0026oacute;n\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOM-035-SEMARNAT-1993 (1993) Norma Oficial Mexicana. M\u0026eacute;todos de medici\u0026oacute;n para determinar la concentraci\u0026oacute;n de part\u0026iacute;culas suspendidas totales en el aire ambiente y los procedimientos para la calibraci\u0026oacute;n de los equipos de medici\u0026oacute;n. Diario Oficial de la Federaci\u0026oacute;n del 18 de octubre de 1993. SEMARNAT, M\u0026eacute;xico, Diario Oficial de la Federaci\u0026oacute;n\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOJEU, Official Journal of the European Union (2008) DIRECTIVE 2008/50/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL [Internet]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eur-lex.europa.eu/legal-content/ES/TXT/PDF/?uri=CELEX:32008\u003c/span\u003e\u003cspan address=\"http://eur-lex.europa.eu/legal-content/ES/TXT/PDF/?uri=CELEX:32008\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eL0050\u0026amp;from=EN. Accessed December 16 2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOSHA, Occupational Safety and Health Administration (2006) TABLE Z-1 Limits for air contaminants. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS\u0026amp;p_id=9992\u003c/span\u003e\u003cspan address=\"https://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS\u0026amp;p_id=9992\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed November 9 2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOSHA, Occupational Safety and Health Administration: Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS\u0026amp;p_id=10023\u003c/span\u003e\u003cspan address=\"https://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS\u0026amp;p_id=10023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed November 2 2014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedersen M, Giorgis-Allemand L, Bernard C, Aguilera I, Andersen AMN, Ballester F, Brunekreef B (2013) Ambient air pollution and low birth weight: A European cohort study (ESCAPE). Lancet Respir Med 1(9):695\u0026ndash;704. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2213-2600(13)70192-9\u003c/span\u003e\u003cspan address=\"10.1016/S2213-2600(13)70192-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez N, Pey J, Querol X, Alastuey A, L\u0026oacute;pez JM, Viana M (2008) Partitioning of major and trace components in PM\u003csub\u003e10\u003c/sub\u003e\u0026ndash;PM\u003csub\u003e2.5\u003c/sub\u003e\u0026ndash;PM\u003csub\u003e1\u003c/sub\u003e at an urban site in Southern Europe. Atmos Environ 42:1677\u0026ndash;1691. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2007.11.034\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2007.11.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePope III, Ezzati CA, Dockery DW (2009) Fine Particulate Air Pollution and US County Life Expectancies. N Engl J Med 360(4). DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMsa0805646\u003c/span\u003e\u003cspan address=\"10.1056/NEJMsa0805646\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePope III, Burnett CA, Thun RT, Calle MJ, Krewski EE, Ito D, Thurston K, G.D (2002) Lung Cancer, Cardiopulmonary Mortality, and Long-Term Exposure to Fine Particulate Air Pollution. JAMA 287:1132\u0026ndash;1141. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.287.9.1132\u003c/span\u003e\u003cspan address=\"10.1001/jama.287.9.1132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePope III, Burnett CA, Thurston RT, Thun GD, Calle MJ, Krewski EE, Godleski D, J.J (2004) Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution: Epidemiological Evidence of General Pathophysiological Pathways of Disease. Circulation 109:71\u0026ndash;77. \u003cdiv class=\"ExternalRefDOI\"\u003ehttps://doi.org/10.1161/01\u003c/div\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuerol X, Minguill\u0026oacute;na MC, Alastueya A, Monfortb E, Mantilla E, Sanz MJ, Sanz F, Roig A, Renau A, Felis C, Mir\u0026oacute; JV, Ar\u0026iacute;\u0026ntilde;ano B (2007b) Impact of the implementation of PM abatement technology on the ambient air levels of metals in a highly industrialized area. Atmos Environ 41:1026\u0026ndash;1040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2006.09.013\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2006.09.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuerol X, Viana M, Alastuey A, Amato F, Moreno T, Castillo S, Pey J, de la Campa J, Salvador AArt\u0026iacute;\u0026ntilde;anoB, Garc\u0026iacute;a Dos Santos P, Fern\u0026aacute;ndez-Patierd S, Moreno-Grau R, Negral S, Minguill\u0026oacute;n L, Monfort MC, Gil E, Inza JI, Ortega A, Santamar\u0026iacute;a LA, Zabalza JM J., 2007a. Source origin of trace elements in PM from regional background, urban and industrial sites of Spain. Atmos. Environ. 41, 7219\u0026ndash;7231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2007.05.022\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2007.05.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuiterio SL, Escaleira V, Silva CRS, Maia LFPG, Arbilla G (2005) Assessment of the Concentrations and Emission Sources of Airborne Metals in Particulate Matter in Seven Districts of Baixada Fluminense, Rio de Janeiro, Brazil. Bull Environ Contam Toxicol 75:997\u0026ndash;1003. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00128-005-0848-z\u003c/span\u003e\u003cspan address=\"10.1007/s00128-005-0848-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaldarriaga-Nore\u0026ntilde;a H, Hern\u0026aacute;ndez-Mena L, Ram\u0026iacute;rez-Mu\u0026ntilde;iz M, Carbajal-Romero P, Cos\u0026iacute;o-Ram\u0026iacute;rez R, Esquivel-Hern\u0026aacute;ndez B (2009) Characterization of Trace Metals of Risk to Human Health in Airborne Particulate Matter (PM\u003csub\u003e2.5\u003c/sub\u003e) at Two Sites in Guadalajara, Mexico. J Environ Monit 11(4):887\u0026ndash;894. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00128-011-0240-0\u003c/span\u003e\u003cspan address=\"10.1007/s00128-011-0240-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSenlin L, Zhenkun Y, Xiaohui C, Minghong W, Guoying S, Jiamo F, Paul D (2008) The relationship between physicochemical characterization and the potential toxicity of fine particulates (PM\u003csub\u003e2.5\u003c/sub\u003e) in Shanghai atmosphere. Atmos Environ 42:7205\u0026ndash;7214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2008.07.030\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2008.07.030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rensen M, Schins RP, Hertel O, Loft S (2005) Transition metals in personal samples of PM\u003csub\u003e2.5\u003c/sub\u003e and oxidative stress in human volunteers. Cancer Epidemiol Prev Biomarkers 14(5):1340\u0026ndash;1343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/1055-9965.EPI-04-0899\u003c/span\u003e\u003cspan address=\"10.1158/1055-9965.EPI-04-0899\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStraif K, Cohen A, Samet MJ (2019) Air pollution and cancer [Internet] 2013. 1st ed. International agency for research on Cancer; 229. Accessed October 29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian H, Zhao D, Cheng K, Lu L, He M, Hao J (2012) Anthropogenic atmospheric emissions of antimony and its spatial distribution characteristics in China. Environ Sci Technol 46(7):3973\u0026ndash;3980. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/es2041465\u003c/span\u003e\u003cspan address=\"10.1021/es2041465\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS-EPA, United States Environmental Protection Agency (2013a) Table of historical particulate matter (PM) national Ambient Air Quality Standards (NAAQS)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS-EPA, United States Environmental Protection Agency (2013b)National Ambient Air Quality Standards for Particulate Matter, Final Rule\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS-EPA, United States Environmental Protection Agency (2008) Compendium of Methods for the Determination of Inorganic Compounds in Ambient Air. Compendium Method IO-3.1. Selection, preparation and extraction of Filter Material. Cincinnati\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVega E, Eidels S, Ruiz H, L\u0026oacute;pez-Veneroni D, Sosa G, Gonzalez E, Edgerton SA (2010) Particulate air pollution in Mexico City: a detailed view. Aerosol Air Qual Res 10(3):193\u0026ndash;211. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4209/aaqr.2009.06.0042\u003c/span\u003e\u003cspan address=\"10.4209/aaqr.2009.06.0042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVega E, Ruiz H, Escalona S, Cervantes A, L\u0026oacute;pez-Veneroni D, Gonz\u0026aacute;lez-Avalos E, S\u0026aacute;nchez-Reyna G (2011) Chemical composition of fine particles in Mexico City during 2003\u0026ndash;2004. Atmos Pollut Res 2(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5094/APR.2011.054\u003c/span\u003e\u003cspan address=\"10.5094/APR.2011.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Hu Z, Chen Y, Chen Z, Xu S (2013) Contamination characteristics and possible sources of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e in different functional areas of Shanghai, China. Atmos Environ 68:221\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2012.10.070\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2012.10.070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, World Health Organization (2013b) Health Effects of Particulate Matter, Policy implications for countries in Eastern Europe, Caucasus and central Asia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, World Health Organization (2013a) Review of evidence on health aspects of air pollution \u0026ndash; REVIHAAP Project: Final technical report. Copenhagen: World Health Organization Regional Office for Europe. [Internet]. Denmark: Available from:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report.pdf\u003c/span\u003e\u003cspan address=\"http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed October 3 2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiseman CLS, Zereini F (2014) Characterizing metal(loid) solubility in airborne PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e1\u003c/sub\u003e in Frankfurt, Germany using simulated lung fluids. Atmos Environ 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosenv.2014.02.055\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosenv.2014.02.055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, Yu Y, Yu J, Chen J, Niu Z, Yin L, Chen Y (2013) Spatial distribution and sources identification of elements in PM\u003csub\u003e2.5\u003c/sub\u003e among the coastal city group in the Western Taiwan Strait region, China. Sci Total Environ 442:77\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2012.10.045\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2012.10.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PM2.5, Toluca valley, metals and metalloids, seasonal and site variation","lastPublishedDoi":"10.21203/rs.3.rs-1517308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1517308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Toluca Valley Metropolitan Area (TVMA) is the fifth largest urban center in Mexico, located at high altitude, 2660 meters above sea level (m.a.s.l.) surrounded by mountain ranges. It is composed of heterogenous residential and industrial areas with dense vehicular traffic. This combination of geography and urbanization create a metropolitan area with high levels of air pollutants. The objective of this study was to provide evidence of the seasonal and spatial variation of metal(lloid)s in particulate matter minor to 2.5 microns (PM\u003csub\u003e2.5) \u003c/sub\u003ein Toluca Valley. Four sites were sampled between 2013-2014, that include urban and industrial areas, in the dry-cold (November-February) and hot-dry (March-May) season; PM\u003csub\u003e2.5 \u003c/sub\u003ewere collected using high and medium volume samplers. Metal and metalloids concentrations in PM\u003csub\u003e2.5 \u003c/sub\u003ewere analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Our results show the highest 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e concentration in the northern area, followed by the southern industrial area, and the lowest concentrations was observed in the southwest area independent of the season. Metals and metalloids with a recovery percentage above 80% were Cobalt (Co), Chrome (Cr), Copper (Cu), Manganese (Mn), and Antimony (Sb). The maximum concentrations of them were observed during the dry-cold season, in the urban-industrial sites found in the north and southern areas. Co, Cr, Cu, Mn, and Sb concentrations were up to one hundred or thousand folded in the dry-cold season compared to dry-hot season due to weather and geographical features of TVMA. The 24-hour PM\u003csub\u003e2.5\u003c/sub\u003e and metal(lloid)s concentrations exceed national and international guidelines to protect population health.\u003c/p\u003e","manuscriptTitle":"Seasonal and spatial variability of PM2.5 concentration, and associated metal(loid) content in the Toluca Valley, Mexico.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-04-06 16:18:38","doi":"10.21203/rs.3.rs-1517308/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f2411c65-3f16-4e3b-b49b-11ddf78485c4","owner":[],"postedDate":"April 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-04-15T19:31:02+00:00","versionOfRecord":[],"versionCreatedAt":"2022-04-06 16:18:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1517308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1517308","identity":"rs-1517308","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}