SciELO - Scientific Electronic Library Online

vol.62 número2Prevalencia de dislipidemias, diagnóstico previo, tratamiento y control: resultados de la Ensanut 2012Predictores de la función renal en una cohorte de adultos mexicanos índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO


Salud Pública de México

versión impresa ISSN 0036-3634

Salud pública Méx vol.62 no.2 Cuernavaca mar./abr. 2020  Epub 28-Feb-2022 

Artículos originales

Spatial distribution of manganese concentration and load in street dust in Mexico City

Distribución espacial de las concentraciones y carga de manganeso en el polvo urbano de la Ciudad de México

Anahí Aguilera1  2 

Francisco Bautista1 

Avto Gogichaichvili1 

Margarita Eugenia Gutiérrez-Ruiz3 

Águeda E Ceniceros-Gómez3 

Norma Ruth López-Santiago3 

1Laboratorio Universitario de Geofísica Ambiental, Centro de Investigaciones en Geografía Ambiental, Universidad Nacional Autónoma de México. Morelia, Mexico.

2Posgrado en Ciencias Biológicas, Universidad Nacional Autónoma de México. Mexico City, Mexico.

3Laboratorio de Biogeoquímica Ambiental, Facultad de Química, Universidad Nacional Autónoma de México. Mexito City, Mexico.



To obtain a first indication of the distribution and extent of manganese (Mn) contamination in Mexico City. Mn concentration and load in street dust were analyzed in order to reveal the most contaminated areas.

Materials and methods:

482 samples of street dust were analyzed through inductively coupled plasma-optical emission spectroscopy. The contamination factor (CF), the geoaccumulation index (Igeo) and the spatial interpolations of the kriging indicator were calculated.


A slight influence of anthropogenic activities is detected on the Mn content of street dust. The highest levels of pollution by concentration (Igeo=uncontaminatedtomoderately contaminated) are grouped towards the city’s north (industrial) and center (commercial and high traffic) areas. The areas with the highest Mn load were located towards the east and northwest areas (Igeo=moderately contaminated).


These findings will serve as a baseline to assess future variations in Mn content in Mexico City’s environment.

Keywords: manganese; environmental pollution; dust



Obtener una primera aproximación sobre la distribución espacial de la contaminación por manganeso (Mn) en la Ciudad de México. Se analizó la concentración y carga de Mn en el polvo de la calle para identificar las áreas más contaminadas.

Material y métodos:

482 muestras de polvo de la calle fueron analizadas con espectroscopía de emisión por plasma de acoplamiento inductivo. Se calculó el factor de contaminación, índice de geoacumulación, y las interpolaciones espaciales del indicador kriging.


Existe una ligera influencia de actividades antropogénicas en el contenido de Mn del polvo de la calle. Los niveles más altos de contaminación por concentración (Igeo=no contaminado a moderadamente contaminado) se agruparon en el norte (industrial) y centro (comercial y de alto tráfico) de la ciudad. Las áreas con las cargas de Mn más altas estuvieron al este y noroeste (Igeo=moderadamente contaminado), donde había más polvo.


Estos resultados servirán como punto de referencia para evaluar variaciones futuras en el contenido de Mn en la Ciudad de México.

Palabras clave: manganeso; contaminación ambiental; polvo


Scientific concern about the contamination due to manganese (Mn) has been raised because of its increasing presence in the environment, related to anthropogenic sources such as mining and smelting,1Mn alloy production,2,3welding, dry alkaline battery manufacturing, Mn salt production, ceramics, glass, aluminum cans and electronic components manufacture,4agrochemicals containing Mn such as fungicides and fertilizers,5as well as vehicle emissions.6

The phasing out of lead compounds from gasoline has led to the use of other compounds that have anti-knock characteristics, such as the methylcyclopentadienyl manganese tricarbonyl (MMT).4,7MMT is quickly converted to complex manganese phosphate, sulfate (MnSO4) and oxides (most likely Mn3O4). From these compounds, MnSO4generates the highest Mn concentration in the brain after inhalation, because it is the most soluble of all compounds.8

Under conditions of high exposure via ingestion or inhalation, the human system that regulates Mn levels appears to fail, thus Mn accumulates in the brain and other tissues, generating potentially toxic effects.6,8This accumulation can even occur at low levels of airborne Mn during long-term exposure because Mn clearance in the brain seems to be slow.4,9

The main effects of long-term exposure to inorganic Mn include neurological manifestations and biochemical alterations: in the initial stages of the disorder one may find reduced response speed, irritability, intellectual deficit, mood changes and compulsive behaviors to more prominent and irreversible extrapyramidal dysfunction resembling Parkinson disease upon protracted exposure.9

Recently, medical concern has been raised in Mexico City because children and youth are already exhibiting early Parkinson disease neuropathological hallmarks.10Mn could be a cause; however, there is no information on Mn concentration amounts in Mexico City’s environment.

Mexico City has more than 20 million inhabitants, 53 000 industries and approximately 5 million vehicles. Settled street dust constitutes a good surrogate for air pollution. It is easy to sample and manipulate, and it reflects the potential risk of population exposure.11,12Mn in street dust can be estimated as concentration (mg/kg), as load per area unit (mg/m2) or as rate load (mg/m2/30 days). All these measurements turn up important information.2,5

In order to obtain the first indicator of pollution extent and distribution in Mexico City’s urban environment, we chose to examine Mn concentration and load in the city’s street dust, as well as to identify the most contaminated areas.

Materials and methods


Systematic sampling was designed for a 703 km2area (figure 1). We collected 482 street dust samples during the dry season (March to April 2017) in a 1 m2area in the streets, next to the sidewalks. Stones, leaves, and branches were removed. Dust was gathered in four mounds at the ends of each street block. Then it was collected with plastic tools and deposited in labeled and georeferenced polyethylene bags. Dust samples were dried for two weeks under the shade at room temperature to avoid metal oxidation. They were sieved with a 250 µm mesh and then weighed.

Figure 1 Sampling sites of street dust in Mexico City, 2017 

Chemical analysis

Mn total concentration was determined in street dust by inductively coupled plasma-optical emission spectroscopy (ICP-OES). 0.4 g of dried and sieved dust was digested with 20 mL HNO3concentrated in an ETHOS EASY Microwave Digestion Platform (Milestone INC). The heating program was as follows: each sample’s temperature should rise to 175±5°C in approximately 5 min and remain at 175±5°C for 4.5 min. After cooling, samples were filtered with a Whatman No. 42 filter paper, transferred into a 50 mL flask and brought to volume with Type A water.

Quality controls included reagent blanks, duplicate samples, and spiked samples. The quality assurance and quality control (QA/QC) results showed no sign of contamination or losses in any of the analyses.The digests and the quality controls were triply analyzed with an ICP-OES spectrometer (Agilent Technologies 5100) using the USEPA method 6010C. Curve multi-element QCS-26R (High Purity) solutions were used for calibration preparations. The Radio Frequency potency (RF power) was 1.2 kW. Nebulization flow was 0.7 L/min and argon plasma flow was 12.0 L/min. Detection limit was 0.04 mg/L (5 mg/kg). Quantification limit was 0.12 mg/L (15 mg/kg). All samples were above the detection limit.

Contamination degree, spatial analysis, and human health risk assessment

Descriptive statistical analysis of both Mn concentration (mg/kg2) and Mn load (mg/m2) on street dust were carried out, as well as Spearman correlation coefficients for non-normal distribution. Mn load in street dust (mg/m2) was calculated by multiplying Mn concentration (mg/kg) and street dust load in kg/m2.

Furthermore, the contamination level could be estimated as the quotient of Mn concentration in each sample over the background value, which is known as the contamination factor):


Where Cnis Mn concentration in each sample point and Bnis background value. CF less than 1 indicatesinsignificant contamination, 1-3 ismoderate contamination, 3-6 isconsiderable contamination and more than 6 indicates high contamination.13

Usually, background value indicates an average concentration of natural values in a site. When there are no background values available, as in the case of Mexico City, the world background values for soils14or even the minimum value of the study data have been used.15To obtain the background value, we decided to normalize the data through log transformation and removal of outliers using Tukey Inner Fences (TIF=Q3+1.5 IQR or Q1-1.5 IQR), and use the first decile as the background value (170 mg/kg for concentration, and 4.5 mg/m2for load). Since Mexico City is a historic area, it is possible that all samples are impacted by anthropogenic Mn, however using the first decile instead of the minimum value, some variation is allowed.

The world background value of Mn in soils is very high (488 mg/kg);14we consider it is not suited to be used in this study due to the fact that soils are analyzed in the particle size below 2 000 μm, while we studied the street dust of Mexico City in the particle size below 250 μm, leaving a huge quantity of Mn contained in bigger particles outside the scope of this analysis.

In order to obtain another contamination index to produce a more robust contamination degree analysis, geoaccumulation index (Igeo) was also calculated. Igeo considers small variations in the background value using a 1.5 factor.


Igeo = log2 Cn1.5+Bn

Igeo can be interpreted as follows: Igeo< 0 (uncontaminated), 0-1 (uncontaminatedtomoderately contaminated), 1-2 (moderately contaminated), 2-3 (moderatelytohighly contaminated), 3-4 (highly contaminated), 4-5 (highlytovery highly contaminated), >5 (very highly contaminated).

The Kruskal-Wallis and Dunn nonparametric tests were used for variance analy sis by township. The null hypothesis is stochastic homogeneity, which is equivalent to equality of expected values of rank sample means. Kruskal-Wallis test examines if there is an observed tendency to be larger (or smaller) in at least one population than all the remaining populations together. The analyses were done with the R Project software, version 3.52.

CF and Igeo spatial distribution maps for Mn concentration and load were designed from a geostatistical analysis using GS+ software. The interpolation method was kriging, equations can be found elsewhere.13 To determine the spatial autocorrelation, the data semivariogram was calculated and adjusted to a theoretical model in order to estimate CF and Igeo at unsampled sites, using the kriging indicator method. It accepts data non-normality and converts the estimated values into indicator values that rank from 0 to 1. The output is the probabilities range of exceeding a cut-off value.

Different levels of contamination indexes were used as cut-off values to obtain several interpolations that were then overlapped in order to get a final map for each index. For the CF map, the cut-off values were: 1 (probability <50% meaninginsignificant contamination, >50% meaningmoderate contamination), 3 (probability >50% meaningconsiderable contamination) and 6 (probability >50% meaninghigh contamination).

For the Igeo map, cut-off values were 0 (probability <50% meaning uncontaminated, >50% meaninguncontaminatedtomoderately contaminated), and 1 (probability >50% meaningmoderately contaminated). We did not find Igeo values greater than 2, which is why the last cut-off value used was 1. For a study of this scale, we used the UTM projection, zone 14, horizontal datum ellipsoid and the World Geodetic System 84 (WGS84).

The non-carcinogenic hazard index (HI) developed by the USEPA was calculated for Mn exposure from street dust, both for children and adults (supplementary material). The estimated daily intake (EDI) in mg/kg per day by ingestion (EDI ing ), inhalation (EDI ing ), dermal contact (EDI dermal ) were obtained and divided into their corresponding reference dose (RfD) to obtain the hazard quotients for each exposure pathway (HQ ing/inh/derm ), and finally HI was found summing all those HQing/inh/derm. HI higher than 1 indicates that possible adverse effects to human health may occur.


Contamination degree

Mn concentration and load have been altered by human activities since their frequency’s distributions are positively skewed (figure 2a and d). Average contamination degree for Mn concentrations wasmoderate(CF=1.38), according to CF, which had an interval frominsignificant contamination(min=0.59) to considerable contamination (max=5.83) (figure 2b). On the other hand, if Igeo, which considers small natural variations in the background value, is considered, average pollution degree was uncontaminated (Igeo=-0.17); while the interval stood fromuncontaminatedtomoderately contaminated(min=-1.35, max=1.96) (figure 2c).

Figure 2 Frequency distribution for manganese (Mn) concentration and load (a and d), for their contamination factors (CF) (b and e) and geoaccumulation index (Igeo) (c and f) 

For Mn load, contamination average level wasmoderate(CF=2.38), according to CF, which varied frominsignificant contamination(min=0.2) tohigh contamination(max=7.46) (figure 2e). If Igeo was considered, contamination average degree wasuncontaminatedtomoderately contaminated(Igeo=0.43); while the interval went fromuncontaminatedtomoderately to highly contaminated(min=-2.88, max=2.31) (figure 2f).

On the other hand, street dust load had a greater impact than Mn concentration over Mn load, as the Spearman correlation coefficient between Mn load and street dust load was positive (r=0.87), while the correlation coefficient between Mn load and Mn concentration was weak (r=0.32). Street dust load averaged 46 g/m2with an interval from 5-173 g/m2and a variation coefficient of 50%.

Spatial distribution, municipal analysis, and human health risk assessment

CF showed amoderatedegree of contamination by Mn concentration in virtually all of Mexico City (figure 3a). Some small areas, mainly towards the south, showedinsignificant contamination. Only five isolated areas had aconsiderablelevel, in the municipalities of Miguel Hidalgo, Cuauhtémoc, Benito Juárez, Xochimilco and one between Iztacalco and Venustiano Carranza.

Figure 3 Spatial distribution of the contamination factor and geoaccumulation index for manganese (Mn) concentration and load 

Using Igeo, most of the city remains uncontaminated (figure 3b) by Mn concentration, especially towards the south.Uncontaminated to moderately contaminatedlevels were found in the center and north areas in the municipalities of Azcapotzalco, Gustavo A. Madero, Miguel Hidalgo, Cuauhtémoc, Venustiano Carranza, Benito Juárez, Iztacalco and the north of Iztapalapa. Only four areas aremoderately contaminated.

In terms of contamination by Mn load in street dust, more extensive areas were found compared to those of Mn concentration, with varying degrees of pollution. CF showedmoderate contaminationin most parts of the city (figure 3c). The most extensive areas withinsignificant contamination were located between the municipalities of Coyoacán and Tlalpan, while the most extensive areas withconsiderable contaminationsurrounded the city, mainly in the municipalities of Iztapalapa, Gustavo A. Madero and Azcapotzalco. Three hotspots with high contamination were located: two east of Iztpalapa and one north of Cuauhtémoc.

If Igeo is considered, the pattern remains practically the same as for the CF by Mn load, only much more defined (figure 3d). Theuncontaminated areaswere located to the southwest, between the municipalities of Coyoacán, Álvaro Obregón, Tlalpan and Xochimilco.Moderately contaminatedareas were located in the periphery, mainly in the municipalities of Iztapalapa, Gustavo A. Madero and Azcapotzalco. As for Igeo, hotspots were not differentiated.

The municipalities of Cuauhtémoc, Azcapotzalco, and Iztacalco had the highest concentrations (figure 4a). North municipalities showed the highest Mn concentrations, specially found in Cuauhtémoc. On the other hand, south municipalities had lower Mn concentrations. Furthermore, Tláhuac, Tlalpan, and Xochimilco in the south have the lowest concentrations of other potentially toxic elements also reported.13,16 Azcapotzalco and Iztapalapa were the municipalities with greater Mn load (figure 4b). Tlalpan and Coyoacán had the lowest Mn load.

Figure 4 Box and whisker plot for manganese (Mn) concentrations (a) and Mn load (b) by municipality, ordered from lower to higher according to Dunn’s non-parametric test 

No human health risk due to Mn exposure in street dust was found, neither for children nor for adults. Average HI (summing ingestion, inhalation and dermal contact) was 0.4 for children, and 0.005 for adults. The maximum value obtained was 0.6 for children and 0.08 for adults. All of them were below the safe level. The main exposure pathway was ingestion, which represented 86% of the HI for children, and 71% for adults.


Contamination degree

Our results showed that there is a slight influence of human activities on the Mn concentration and load in Mexico City’s street dust, since frequency distributions were positively skewed and average CF showedmoderate contamination. This type of distribution is common for anthropogenic elements because concentration depends on the distance to the source: the closer a sample is to the source, the higher the concentrations are.13,17,18

Contamination level undoubtedly is based on the background value selected. In this case, we used as background value of 170 mg/kg for Mn concentration, as was explained in the methods section. Other studies have used a background value for soils, for example Chinese background is 557 mg/kg.19,20Only three street dust samples in Mexico City surpassed this value. However, Mn may be retained in soils or absorbed by plants, while Mn particles in street dust are intemperized, until they become breathable particles smaller than 10 microns, and can be resuspended in the air. Thus, considering a background value for soil could lead us to underestimate the level of street dust contamination. Furthermore, soil background value depends on each study site and it has not yet been estimated for Mexico City.

In order to get a very clear idea of the contamination degree and its possible effects, this work’s results are compared with those found in other international studies (table I).

table I Comparison of manganese concentration and load in several international studies 

Study site Characteristic Sample type Concentration or load Mean Median Range Reference
Johannesburg, Africa MMT gasolines School dust Concentration (mg/kg) 404 314 17-959 6
Cape Town, Africa No MMT gasoline School dust 73 76 38-99 6
Salinas, USA Fungicides Maneb and Mancozeb House dust 171 2-414 5
Xi’an, China No contamination (Chinese background 557 mg/kg) Road dust 339.4 282-430 19
Dhaka, Bangladesh No contamination (Chinese background 557 mg/kg) Road dust 262 266 194-415 20
Mexico City Atmospheric contamination Street dust 235 224 100-990 This study
Simões Filho, Brazil Ferromanganese alloy plant Street dust Load rate (mg/m2/30 day) 12* 9.0-38 2
Salinas, USA Fungicides maneb and mancozeb House dust Load (mg/m2) 1.9 0.001-25 5
Mexico City Atmospheric contamination Street dust 10 11 0.9-34 This study

* Geometric mean

MMT: methylcyclopentadienyl manganese tricarbonyl

Mn concentrations in Mexico City´s street dust (100-990.5 mg/kg) were found within the range of the concentrations of an area widely using MMT gasoline (17-959 mg/kg). Furthermore, those concentrations were associated with Mn levels in children’s blood.6 This background information makes it advisable to analyze Mn bioavailability in street dust as well as a biomarker to Mn exposure, such as toenails or hair,2 in Mexico City’s children. Children are most vulnerable because they have higher absorption rates and have not fully developed Mn excretion mechanisms. 4,6

Compared to a site with wide use of Mn pesticides, average Mn concentration and load in Mexico City´s street dust were higher (235.2 mg/kg and 10.7 mg/m2) than those of Salinas Valley, California intramural dust (171 mg/kg and 1.9 mg/m2). 5Although it is expected that street dust has higher amounts of Mn, this antecedent is an reminder to pay more attention to Mn contamination.

Compared to a site adjacent to a Mn alloy production plant (less than 2 km), main Mn load in Mexico City was very close to main Mn load rate in Simões Filho, Brazil (12 mg/m2/30 days). 2 However, in Brazil, authors analyzed a 30-day rate, while in Mexico City we do not know how much accumulation is produced in that amount of time.

Spatial distribution, municipal analysis, and human health risk assessment

Maps showed that Igeo was more precise while demarcating contamination spatial distribution by Mn concentration and load in Mexico City´s street dust (figure 3). Igeo spatial distribution for Mn concentration corroborated human activities increase Mn levels in the city, as the most contaminated areas were concentrated towards the north and center of the city. These are areas where industrial and commercial activities predominate, and there is a lot of car traffic. Furthermore, it has been reported that residents from the north area have been exposed to higher PM10and PM2.5concentrations with high levels of the following metals: Mn, Zn, Cu, Pb, Ti, Sn, V and Ba.10

On the other hand, if Mn distribution was dominated by natural sources, it would be expected that the highest concentrations were located around the city, near the less urbanized sites or towards the southeast because of the active Popocatépetl volcano, as the volcanic activity is one of Mn main natural sources.4

By estimating the spatial distribution of Mn load contamination, a strong influence of street dust load was observed, as the areas with the highest Mn load were located towards the east and northwest of the city, where there was more dust. This influence was also exhibited by the high Spearman correlation coefficient between street dust load and Mn load (r=0.87). Other authors have also observed this.5

This shows the need to maintain cleaning plans in order to reduce exposure to Mn load and other potentially toxic elements. Street dust load can work as an indicator of the cleaning plans, the higher the street dust load, the lower the success of the cleaning plans. Gunier and colleagues5have reported that Mn load can be reduced by keeping the house clean and using rugs at the entrance. Indoor Mn concentrations can also be reduced by keeping the windows closed. 1

Although spatial analysis allows us to distinguish areas with greater pollution, municipal variance analysis allows us to identify municipalities with higher Mn concentration and load. Thanks to the Kruskal-Wallis test, it was possible to identify Azcapotzalco as the municipality with the worst contamination by Mn concentration and load. Nevertheless, this could not be distinguished in the Mn concentration pollution maps, so it is important to use different kinds of analyses to complement results.

On the other hand, it was possible to corroborate the spatial distribution patterns observed in Igeo Mn concentration and load maps. In the case of concentration, center and northern municipalities had higher values than those on the south, with statistically significant differences. As for the Mn load, periphery municipalities had higher values than those inside the city.

Even when no human health risk due to Mn exposure in street dust was found, it is important to keep monitoring this element since the maximum HI of the data for children (HIchildren=0.61) was close to the safe limit of 1. As this study demonstrated, street dust loading has an important influence on the Mn loading (total quantity of Mn in the street dust), therefore, local exposure factors (ingestion rate, particle emission factor, etc.) should be considered to obtain a more accurate human health risk assessment.


This study is the first to report the concentration and load of manganese in urban dust in Mexico City. There is a slight influence of human activities in Mn concentration and load in street dust. Mn concentration was 235.2 mg/kg and load average, 10.7 mg/m2. Although this is not a serious situation, caution is recommended due to lack of reference values. These findings can serve as a background to assess future potential changes in Mexico City’s Mn concentration in the environment.

Geoaccumulation index showed better resolution that the contamination factor in order to limit Mn pollution. The most contaminated areas by Mn concentration were found in central and northern Mexico City. As the Mn load is concerned, a strong influence of street dust load was observed, since a strong correlation was found between both, and the most contaminated areas were located towards the east and northwest of the city, where there was more dust.

Because the most contaminated areas by Mn concentration and Mn load were different, it is important to consider both variables in pollution and exposure studies of potentially toxic elements. The only municipality both with high Mn concentration and load was Azcapotzalco, which implies a double risk. An efficient street and house cleaning system may reduce exposure to a dust load of potentially toxic elements.


1. Cortez-Lugo M, Rodríguez-Dozal S, Rosas-Pérez I, Alamo-Hernández U, Riojas-Rodríguez H. Modeling and estimating manganese concentrations in rural households in the mining district of Molango, Mexico. Environ Monit Assess. 2015;187(12):752 [cited April, 2019]. Available from: [ Links ]

2. Menezes-Filho JA, Fraga de Souza KO, Gomes Rodrigues JL, Ribeiro dos Santos N, Bandeira M de J, Koin NL,et al. Manganese and lead in dust fall accumulation in elementary schools near a ferromanganese alloy plant. Environ Res. 2016;148:322-9. [ Links ]

3. Rodrigues JLG, Araújo CFS, dos Santos NR, Bandeira MJ, Anjos ALS, Carvalho CF,et al. Airborne manganese exposure and neurobehavior in school-aged children living near a ferro-manganese alloy plant. Environ Res . 2018;167:66-77. [ Links ]

4. Röllin HB, Nogueira CMCA. Manganese: Environmental Pollution and Health Effects. In: Nriagu JO, ed. Reference Module in Earth Systems and Environmental Sciences. Johannesburg, South Africa: Elsevier, 2019:617-29 [cited April, 2019]. Available from: Available from: [ Links ]

5.  >5. Gunier RB, Jerrett M, Smith DR, Jursa T, Yousefi P, Camacho J,et al. Determinants of manganese levels in house dust samples from the CHAMACOS cohort. Sci Total Environ. 2014;497-498:360-8 [cited April, 2019]. Available at: [ Links ]

6. Röllin H, Mathee A, Levin J, Theodorou P, Wewers F. Blood manganese concentrations among first-grade schoolchildren in two South African cities. Environ Res . 2005;97(1):93-9. [ Links ]

7. Abbott PJ. Methylcyclopentadienyl manganese tricarbonyl (MMT) in petrol: The toxicological issues. Sci Total Environ. 1987;67(2-3):247-55. [ Links ]

8. Smith D, Woodall GM, Jarabek AM, Boyes WK. Manganese testing under a clean air act test rule and the application of resultant data in risk assessments. Neurotoxicology. 2018;64:177-84. [ Links ]

9. Roth JA. Homeostatic and toxic mechanisms regulating manganese uptake, retention, and elimination. Biol Res. 2006;39(1):45-57. [ Links ]

10. Calderón-Garcidueñas L, Reynoso-Robles R, Pérez-Guillé B, Mukherjee PS, Gónzalez-Maciel A. Combustion-derived nanoparticles, the neuroenteric system, cervical vagus, hyperphosphorylated alpha synuclein and tau in young Mexico City residents. Environ Res . 2017;159:186-201. [ Links ]

11. Fulk F, Succop P, Hilbert TJ, Beidler C, Brown D, Reponen T,et al. Pathways of inhalation exposure to manganese in children living near a ferromanganese refinery: A structural equation modeling approach. Sci Total Environ. 2017;579:768-75. [ Links ]

12. Rodrigues JLG, Bandeira MJ, Araújo CFS, dos Santos NR, Anjos ALS, Koin NL,et al. Manganese and lead levels in settled dust in elementary schools are correlated with biomarkers of exposure in school-aged children. Environ Pollut. 2018;236:1004-13. [ Links ]

13. Ihl T, Bautista F, Cejudo Ruíz FR, Delgado M del C, Quintana Owen P, Aguilar D,et al. Concentration of toxic elements in topsoils of the metropolitan area of Mexico city: A spatial analysis using ordinary kriging and indicator kriging. Rev Int Contam Ambient. 2015;31(1):47-62 [cited April, 2019]. Available from: [ Links ]

14. Kabata-Pendias A. Trace elements in soils and plants. 4th ed. New York: CRC Press, 2011 [cited April, 2019]. Available from: [ Links ]

15. Declercq Y, Samson R, Castanheiro A, Spassov S, Tack FMG, Van De Vijver E,et al. Evaluating the potential of topsoil magnetic pollution mapping across different land use classes. Sci Total Environ. 2019;685:345-56. [ Links ]

16. Delgado C, Bautista F, Gogichaishvili A, Cortés JL, Quintana P, Aguilar D,et al. Identificación de las zonas contaminadas con metales pesados en el polvo urbano de la Ciudad de México. Rev Int Contam Ambie. 2019;35(1):81-100 [cited April, 2019]. Available from: [ Links ]

17. Guvenç N, Alagha O, Tuncel G. Investigation of soil multi-element composition in Antalya, Turkey. Environ Int. 2003(5):631-40. [ Links ]

18. Aguilera A, Armendariz C, Quintana P, García-Oliva F, Bautista F. Influence of Land Use and Road Type on the Elemental Composition of Urban Dust in a Mexican Metropolitan Area. Polish J Environ Stud. 2019;28(3):1535-47. [ Links ]

19. Shi D, Lu X, Wang Q. Evaluating Health Hazards of Harmful Metals in Roadway Dust Particles Finer than 100 μm. Polish J Environ Stud. 2018;27(6):2729-37. [ Links ]

20. Safiur Rahman M, Khan MDH, Jolly YN, Kabir J, Akter S, Salam A. Assessing risk to human health for heavy metal contamination through street dust in the Southeast Asian Megacity: Dhaka, Bangladesh. Sci Total Environ. 2019;660:1610-22. [ Links ]

Received: May 03, 2019; Accepted: October 07, 2019

Corresponding author: Dr. Francisco Bautista. Antigua Carretera a Pátzcuaro 8701, col. Ex Hacienda de San José de la Huerta. 58190 Morelia, Michoacán, Mexico. email:

Declaration of conflict of interests. The authors declare that they have no conflict of interests.

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License