El color del polvo urbano como indicador de contaminación por elementos potencialmente tóxicos: el caso de Ensenada, Baja California, México

José L. Cortés^1,3; Francisco Bautista^1,4*; Patricia Quintana⁵; Daniel Aguilar⁵; Avto Goguichaishvili²

¹ Laboratorio Universitario de Geofísica Ambiental, Centro de Investigaciones en Geografía Ambiental. Universidad Nacional Autónoma de México. Antigua Carretera a Pátzcuaro núm. 8701, col. Exhacienda de San José de la Huerta. C. P. 58190. Morelia, Michoacán, MÉXICO.

² Instituto de Geofísica, Universidad Nacional Autónoma de México. Antigua Carretera a Pátzcuaro núm. 8701, col. Exhacienda de San José de la Huerta. C. P. 58190. Morelia, Michoacán, MÉXICO. Correo-e: leptosol@ciga.unam.mx tel 52(443) 3223869 (*Autor para correspondencia).

³ Universidad Michoacana de San Nicolás de Hidalgo. Gral. Francisco J. Múgica s/n, Felícitas del Río. C. P. 58030. Morelia, Michoacán, MÉXICO.

⁵ Departamento de Física Aplicada, Centro de Investigaciones y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida. Antigua carretera a Progreso km 6. C. P. 97310. Cordemex, Mérida, Yucatán, MÉXICO.

Received: February 4, 2015.
Accepted: July 7, 2015.

ABSTRACT

Contamination by potentially toxic elements (PTE) is not periodically evaluated, given that the chemical analyses have a high cost. The ashes and combustion fumes give the ground a dark color, which could serve as a proxy indicator. In this study, a methodology was designed to prove the use of the color of urban dust as an indicator of contamination by PTE, and the most contaminated color was identified. 86 dust samples from Ensenada, Baja California were analyzed. The color of the samples was measured and the color indices (CI) were calculated using the RGB system. Nickel (Ni), Copper (Cu), Zinc (Zn), Lead (Pb), Rubidium (Rb), Vanadium (V), Strontium (Sr), and Yttrium (Y) were analyzed through x-ray fluorescence methods. The samples were grouped by color using the Munsell tables; the groups were validated with a discriminant analysis using the color indices. The multiple regressions indicated that there exists a relation between the CI and the PTE. The averages of the analyzed elements in the samples grouped by color were different (Kruskal-Wallis, P < 0.05). Gray dust contains higher concentrations of Pb, Cu, Zn and Ni. The color indices of urban dust can be considered a proxy methodology given their low cost, speed and reliability.

Keywords: Color indices, redness index; index; saturation index; hue index.

La contaminación por elementos potencialmente tóxicos (EPT) no se evalúa periódicamente, ya que los análisis químicos son de costo elevado. Las cenizas y humos de combustión otorgan color obscuro al suelo y afectan la salud de la población. El color podría funcionar como indicador proxy. En este trabajo se diseñó una metodología para probar el uso del color del polvo urbano como indicador de contaminación por EPT y se identificó el color más contaminado. Se analizaron 86 muestras de polvo de Ensenada, Baja California. El color de las muestras se midió y los índices de color (IC) se calcularon con el sistema RGB. El Níquel (Ni), Cobre (Cu), Zinc (Zn), Plomo (Pb), Rubidio (Rb), Vanadio (V), Estroncio (Sr) e Itrio (Y) se analizaron por fluorescencia de rayos X. Las muestras se agruparon por color con las tablas Munsell; los agrupamientos se validaron con un análisis discriminante utilizando los IC. Las regresiones múltiples indicaron que existe relación entre los IC y los EPT. Las medianas de los elementos analizados en las muestras agrupadas por color fueron diferentes (Kruskal-Wallis, P < 0.05). El polvo gris contiene mayores concentraciones de Pb, Cu, Zn y Ni. Los índices de color del polvo urbano pueden considerarse una metodología proxy debido al bajo costo, rapidez y confiabilidad.

Palabras clave: Índices de color; índice de rojez; índice de saturación; índice hue.

INTRODUCTION

Urban dust is a mixture of local soil and contaminating particles derived from the combustion of vehicles, chimneys and other wastes. It is formed by particles of various sizes; generally, a large portion corresponds to particles smaller than 10μ that contain heavy metals. These particles are considered harmful to human health because they can be inhaled and cause cancer (Sabath & Osorio, 2012). The quantity and types of contamination by potentially toxic elements (PTE) varies according to the activity of the populace (Aguilar et al., 2013a; Aguilar, Mejía, Bautista, Goguitchaichvili, & Morton, 2011; Guagliardi, Cicchela, & De Rosa, 2012; Wang, Xia, Yu, Jia, & Xu, 2014).

Diagnostics of the contamination of the dust by PTE in urban areas are not carried out periodically, given that this monitoring task has the following disadvantages: a) it requires a large number of samples, b) the chemical analyses have a high cost and take time to be carried out, and c) the chemical analysis of the samples generates dangerous residues. Because of this, it is necessary to look for fast, reliable and low cost indicators that would allow for the analysis of a large number of samples in an efficient and economical manner, such as the proxy methodologies; for example, the magnetic parameters (Aguilar et al., 2011; Aguilar et al., 2013b) and the color of the dust.

Color has been used in classification and soil formation studies (Dobos, Ciolkosz, & Waltman, 1990; Kumaravel, Sangode, Siva, & Kumar, 2010; IUSS Working Group WRB, 2014) and for the identification of greater fertility areas (Leirena-Alcocer & Bautista, 2014; Schulze et al., 1993), due to the high content of organic matter (Viscarra, Foaud, & Walter, 2008). Color has also been used to study the oxides of iron and their changes through edaphic processes (Levin, Ben-Dor, & Singer, 2005; Madeira, Bedidi, Cervelle, Pouget, & Flay, 1997; Schwertmann, 1993; Viscarra et al., 2008). Nevertheless, the relation between the color of the dust and the PTE in urban areas has not been studied, with this being the first case study. Some field observations have made it possible to infer that urban dust contains ashes and fine particles, product of the contamination by automobiles and chimneys. The contaminating particles stay on the surface giving it a certain color, changing drastically if observed only a few centimeters below the soil.

Recently, equipment that is capable of measuring the color of the soil in a numeric manner has been fabricated, eliminating subjectivity in the measurements (Leirena & Bautista, 2014; Levin et al., 2005). In 1861, James Clerk Maxwell created the RGB color system and demonstrated that any color can be generated from the trichrome structure that utilizes the primary colors red, green, and blue, basing the wavelength on a scale of 0 to 255. With these parameters, hue, redness, and saturation indices were obtained through which it is possible to carry out mathematical operations to find numerical relations between the color of an object and its chemical composition — in this case urban dust and the PTE. Based on this, the objectives of this study were to: a) design a methodology to prove the use of the color of urban dust as a proxy methodology of contamination with PTE, and b) identify the color of urban dust with greater concentrations of PTE.

Area of study

The area of study is the city of Ensenada, Baja California, Mexico, mainly located on the coastal plains, although it also includes foothills and mountainous areas (Figure 1). It is a city with tourism and is the route towards the south of the Baja California Peninsula through the transpeninsular highway. The samples of urban dust were collected from 1 m² above the surface of the streets (cement, asphalt or soil) from the city at 86 geographically located sites. The sampling was systematic in mesh to evaluate the different types of substrate and to try and obtain representativeness of the entire city. The samples were taken in the shade, were ground with an agate mortar, and were sifted with mesh 10 (2 mm of light). The sifted dust was divided into two parts: one to carry out the color measurements and a second for the chemical analysis.

Color analysis

The samples of urban dust were analyzed using the Munsell table (Munsell Color, 2000), and the color of the dust was measured using a Konica Minolta colorimeter (model CR400m, U.S.) which generates results in the X, Y, Z color system. The data was converted to the RGB decimal color system using the Color Slide Rule program; from this system the hue (HI), redness (RI), and saturation (SI) indices were obtained. The HI, RI, and SI of the urban dusts were obtained using the following equations (Levin et al., 2005):

In these equations, R, G, and B correspond to red, green, and blue, respectively. The color groups were formed using the Munsell table. Subsequently, the groups were validated using the measurements of the CI and a multivariate analysis.

Chemical analysis

The urban dust was analyzed by means of x-ray fluorescence using dispersed energy (FRX-ED), utilizing a Jordan Valley spectrometer (EX-6600, U.S.) equipped with a Si(Li) detector with an active area of 20 mm² and a resolution of 140 eV to 5.9 keV, operating at a maximum of 54 keV and 4,800 μA; international reference patterns were utilized for rocks and soils (Beckhoff, Kanngieβser, Langhoff, Wedell, & Wolff, 2007; Ihl et al., 2015; Lozano & Bernal, 2005). The analyzed elements were Chrome (Cr), Nickel (Ni), Copper (Cu), Zinc (Zn), Lead (Pb), Vanadium (V), Rubidium (Rb), Strontium (Sr), and Yttrium (Y).

Data analysis

A discriminant analysis is a classification and assignation technique for elements or a group, which allows to confirm the validation or not of the formation of sample groups based on a set of independent variables. The classification of the elements of a populace or group is carried out with lineal or quadratic functions (Lévy, Varela, Calvo, & Rodríguez, 2003).

Within each group of urban dust assigned by color, multiple correlations were made between the CI and the concentrations of Cr, Ni, Cu, Zn, Pb, V, and Rb utilizing Statgraphics plus 5.1 (Statpoint Technologies Inc., 1992). The multiple regression establishes the relation of a dependent variable Y with regard to a variable X (X₁ X₂... X_n) in a multidimensional space:

where:

Y = Dependent variable (indices of soil hue, redness, and saturation)

a = Regression parameter

β_i = Increase of Y in units when increasing X. The equation includes datum for each indicator β₁, β₂..., β_n (Aiken & West, 1991).

X_i = Explicative variable (concentrations of PTE)

e = Indication error

RESULTS AND DISCUSSION

Formation of color groups

Figure 2 shows the four color groups formed by the urban dust, based on the Munsell color card: I) Dark reds (2.5 YR), II) Grays (10 YR), III) Clear reddish browns (5 YR), and IV) Clear grayish-browns (10 YR). According to Table 1, the urban dust groups based on the Munsell tables coincided with the groups made in accordance to the HI, RI, and SI, since 100 % of the assignments were correct. On the other hand, the urban dust groups in accordance to color coincided 84.88 % with the groups made considering the concentration of the PTE (Table 2).

Correlation between color indices and potentially toxic elements

The multiple lineal regressions indicated an appropriate adjustment between the HI with regard to the PTE (Table 3). The best represented PTE in the equations were Pb and Zn in the four color groups of the urban dust samples. Regarding RI, the multiple regressions (Table 4) indicate that the best represented PTE in the equations were Cr, Ni, Cu, Pb, and V in the four groups. Finally, the regressions of the four groups based on the SI were significant (Table 5), which indicates that SI showed a high level of relation or association with the PTE. The best represented PTE in the equations were Cr, Ni, Cu, Zn, Pb, and V in the four color groups of the urban dust samples. The CI functioned as indicators for the concentration of PTE in all the cases or color groups; in general, they are related mainly with the concentrations of Pb, followed by Zn, Cr, Cu, Ni, and V.

Content of potentially toxic elements in urban dusts by color group

In line with the Kruskal-Wallis test, Ni, Cu, Zn, Pb, and Rb showed statistically significant differences (P < 0.05) between the averages of each color group. Figure 3 shows that the dark red color group of urban dust contains greater concentrations of V and Rb. On the other hand, the gray color urban dust samples have higher concentrations of Ni, Cu, Zn, and Pb, which indicate that this is the group with greater PTE contamination.

The ash and combustion fumes contributed by motor vehicles and chimneys generate grayish colorings when combined with the urban dust from the soil, especially when ash is a major component. The samples of urban dust of predominantly red or brown colors maintain the colors of the urban soils in the area; therefore, it can be said that they are less contaminated.

In subsequent studies, two conditions must be analyzed during the pre-treatment of the samples: a) the sieving and homogenization of the sample, since they can change the color of the urban dust, providing them with increased luminosity (Domínguez, Román, Prieto, & Acevedo, 2012; Matthias et al., 2000; Sánchez-Marañon, Delgado, Delgado, Pérez, & Melgosa, 1995), and b) the humidity content, given that humid dust can be darker than dried dust (Brooks, 1952; Kojima, 1958; Domínguez et al., 2012).

CONCLUSIONS

Gray colored urban dust contains greater concentrations of Pb, Cu, Zn, and Ni, potentially toxic elements (PTE) associated with environmental contamination of an anthropogenic origin. The color indices of urban dust for the case of Ensenada were shown to be a proxy indicator due to the low measurement cost, speed, and simplicity, as well as the fact that they do not generate harmful residues. In this study, a methodology has been established in order to identify the urban dust samples contaminated with PTE utilizing the color indices. The steps to follow are: a) analyze the color of the urban dust using the Munsell color card; b) measure the color of the urban dust and calculate the indices (HI, SI, and RI); c) group the urban dust samples by Munsell colors and validate the groups by means of a discriminant analysis with the color indices as independent variables; d) analyze the PTE in the urban dust samples; e) validate the formation of the color groups with a discriminant analysis considering the PTE as independent variables; and f) carry out the multiple regressions between the color indices and the PTE.

ACKNOWLEDGEMENTS

Thank you to the National Council of Science and Technology for the financial support of the project CB-2011-01-169915, and to A. García, C. Figueroa and D. Maldonado for their help in the field work. FBZ appreciates the economic support of DGAPA-UNAM for the realization of the sabbatical leave in CEBAS-CSIC.

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