Keywords
redness index
saturation index
Zinc
Munsel
How to Cite
##article.highlights##
- Color indices were mathematically related to heavy metals in urban dust.
- Heavy metals were generally found in the order Zn > Mn > Ba > Pb > Cu > Cr > Ni > V.
- Cr, Cu, Ni, Pb and Zn showed significant differences among the color groups.
- The concentration of metals by color groups allowed identifying the most contaminated urban dust.
- Urban dusts ranging from grey to black are indicators of high concentrations of heavy metals.
Abstract
Introduction: Urban dust contains heavy metals (HMs) that pose a risk to human health. Objective: To evaluate the color of urban dust as an indicator of HM pollution. Materials and methods: Color and HMs (Ba, Cr, Cu, Pb, Mn, Ni, V and Zn) were measured in 455 dust samples, and redness and saturation rates were calculated. Based on color, groups of samples were formed using cluster analysis. Multiple regression analysis between HMs and indices by color groups was performed, as well as a Kruskal-Wallis analysis of HMs by color groups. Results and discussion: Urban dust samples were classified as dark grayish brown (I), dark gray (II), dark olive brown (III), very dark gray (IV), grayish brown (V) and black (VI). Multiple linear regressions between color indices and HMs showed high and significant correlation (P < 0.05) in groups I, II, III and IV. Urban dust HMs were generally found in the order Zn > Mn > Ba > Pb > Cu > Cr > Ni > V. Also, Cr, Cu, Ni, Pb and Zn showed significant differences (P < 0.05) among the color groups; the samples from very dark gray to black were the most polluted, and those of dark grayish brown had lower HM contents. Conclusions: Urban dust color is an indicator of heavy metal pollution in Mexico City.References
Aguilar, B., Bautista, F., Goguichaishvili, A., Quintana, P., Carvallo, C., & Battu, J. (2013). Rock-magnetic properties of topsoils and urban dust from Morelia (>800,000 inhabitants), Mexico: Implications for anthropogenic pollution monitoring in Mexico’s medium size cities. Geofísica Internacional, 52(2), 121–133. doi: https://doi.org/10.19155/geofint.2013.052.2.1528
Barrón, V., & Torrent, J. (1986). Use of the Kubelka-Munk theory to study the influence of iron oxides on soil colour. Journal of Soil Science, 37(4), 499–510. doi: https://doi.org/10.1111/j.1365-2389.1986.tb00382.x
Bautista-Zúñiga, F., Cejudo-Reyes, B., Aguilar-Reyes, R., & Goguitchaichvili, A. (2014). El potencial del magnetismo en la clasificación de suelos: una revisión. Boletín de la Sociedad Geológica Mexicana, 66(2), 123–134. doi: https://doi.org/10.18268/BSGM2014v66n2a11
Buntley, G. J., & Westin, F. C. (1965). A comparative study of developmental color in Chestnut-Chernozem-Brunizem soil climosequence. Soil Science Society of America Journal Abstract, 29(5), 579–582. doi: https://doi.org/10.2136/sssaj1965.03615995002900050029x
Chen, H., Lu, X., Li, L.Y., Gao, T., & Chang, Y. (2014). Metal contamination in campus dust of Xi’an, China: A study based on multivariate statistics and spatial distribution. Science of the Total Environment, 484, 27–35. doi: https://doi.org/10.1016/j.scitotenv.2014.03.026
Cortés, J. L., Bautista, F., Quintana, P., Aguilar, D., & Goguichaishvili, A. (2015). The color of urban dust as indicator of contamination by the potentially toxic elements: the case of Ensenada, Baja California México. Chapingo Serie Ciencias Forestales y del Ambiente, 21(3), 255–266. doi: https://doi.org/10.5154/r.rchscfa.2015.02.003
Cortés, J. L., Bautista, F., Delgado, C., Quintana, P., Aguilar, D., García A., & Gogichaishvili, A. (2017). Spatial distribution of heavy metals in urban dust from Ensenada, Baja California, Mexico. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 23(1), 47–60. doi: https://doi.org/10.5154/r.rchscfa.2016.02.005
Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6(3), 241–252. doi: https://doi.org/10.2307/1266041
García-Rico, L., Meza-Figueroa, D., Gandolfi, A. J., Del Río-Salas, R., Romero, F. M., & Meza-Montenegro, M. (2016). Dust–metal sources in an urbanized arid zone: Implications for health-risk assessments. Archives of Environmental Contamination and Toxicology, 70(3), 522–533. doi: https://doi.org/10.1007/s00244-015-0229-5.
Hurst, V. J. (1977). Visual estimation of iron in saprolite. Geological Society of America Bulletin, 88(2), 174–176. doi: https://doi.org/10.1130/0016-7606(1977)88<174:VEOIIS>2.0.CO;2
Kirillova, N. P., Vodyanitskii, Y. N., & Sileva, T. M. (2015). Conversion of soil color parameters from the Munsell system to the CIE-L*a*b*. System, Genesis and Geography of Soils, 48(5), 468–475. doi: https://doi.org/10.1134/S1064229315050026
Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. doi: https://doi.org/10.2307/2280779
Levin, N., Ben-Dor, E., & Singer, A. (2005). A digital camera as a tool to measure colour indices and related properties of a sandy soils in semi-arid environments. International Journal of Remote Sensing, 26(24), 5475–5492. doi: https://doi.org/10.1080/01431160500099444
Liu, J. G., & Moore, McM. J. (1990). Hue image RGB colour composition. A simple technique to suppress shadow and enhance spectral signature. International Journal of Remote Sensing, 11(8), 1521–1530. doi: https://doi.org/10.1080/01431169008955110
Liu, Q., Liu, Y., Yin, J., Zhang, M., & Zhang, T. (2014). Chemical characteristics and source apportionment of PM10 during Asian dust storm and non-dust storm days in Beijing. Atmospheric Environment, 91, 85–94. doi: https://doi.org/10.1016/j.atmosenv.2014.03.057
Luo, X. S., Yu, S., Zhu, Y. G., & Li, X. D. (2012). Trace metal contamination in urban soils of China. Science of the Total Environment, 421–422, 17–30. doi: https://doi.org/10.1016/j.scitotenv.2011.04.020
Marín, P., Sánchez-Navarro, A., Díaz-Pereira, E., Bautista, F., Romero, M. M., & Delgado, I. M. J. (2018). Assessment of heavy metals and color as indicators of contamination of traffic intensity and sampling location. Sustainability, 10(11), 4105. doi: https://doi.org/10.3390/su10114105
Mathieu, R., Poujet, J., Cervelle, B., & Escafal, R. (1998). Relationships between satellite-based radiometric indices simulated using laboratory reflectance data and typic soil color of an arid environment. Remote Sensing of Environment, 66(1), 17–28. doi: https://doi.org/10.1016/S0034-4257(98)00030-3
Munsell Color Laboratory. (2000). Munsell soil color charts. New Windsor, Nueva York, USA: GretagMacbeth. Retrieved from https://www.worldcat.org/title/munsell-soil-color-charts/oclc/869844188?referer=di&ht=edition
Peña-García, L., Maciel-Flores, R., Rosas-Elguera, J., & Rentería-Tapia, V. (2016). Distribución de polvo urbano en la zona metropolitana de Guadalajara, México. Revista de Investigación y Desarrollo, 3(4), 16–23. Retrieved from http://www.ecorfan.org/spain/researchjournals/Investigacion_y_Desarrollo/vol2num4/Revista_de_Investigaci%C3%B3n_y_Desarrollo_V2_N4_4.pdf
Sabath, D. E., & Robles-Osorio, L. (2012). Medio ambiente y riñón: Nefrotoxicidad por metales pesados. Nefrología: Publicación oficial de la Sociedad Española de Nefrología, 32(3), 279–286. doi: https://doi.org/10.3265/Nefrologia.pre2012.Jan.10928
Sánchez-Duque, A., Bautista, F., Goguichaishvili, A., Cejudo-Ruiz, R., Reyes-López, J. A., Solís-Domínguez, F. A., & Morales-Contreras, J. J. (2015). Evaluación de la contaminación ambiental a partir del aumento magnético en polvos urbanos. Caso de estudio en la ciudad de Mexicali, México. Revista Mexicana de Ciencias Geológicas 32(3), 501–513. Retrieved from http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S1026-87742015000300501&lng=es&nrm=iso
Torrent, J., Schwertmann, U., Fechter, H., & Alferez, F. (1983). Quantitative relationships between soil color and hematite content. Soil Science, 136(6), 354–358. Retrieved from https://journals.lww.com/soilsci/Abstract/1983/12000/QUANTITATIVE_RELATIONSHIPS_BETWEEN_SOIL_COLOR_AND.4.aspx
Torrent, J., Schwertmann, U., & Schulze, D. G. (1980). Iron oxide mineralogy of some soils of two river terrace sequences in Spain. Geoderma, 23(3), 191–298. doi: https://doi.org/10.1016/0016-7061(80)90002-6
United States Environmental Protection Agency (U. S. EPA). (2007a). Method 3051A (SW-846): Microwave assisted acid digestion of sediments, sludges, and oils. Retrieved August 13, 2019 from https://www.epa.gov/homeland-security-research/us-epa-method-3051a-microwave-assisted-acid-digestion-sediments-sludges
United States Environmental Protection Agency (U. S. EPA). (2007b). Method 6010 C (SW-846). Inductively coupled plasma-atomic emission spectrometry. Retrieved from https://www.epa.gov/sites/production/files/2015-07/documents/epa-6010c.pdf
Viscarra-Rossel, R. A., Fouad, Y., & Walter, C. (2008). Using a digital camera to measure soil organic carbon and iron contents. Biosystems Engineering, 100(2), 149–159. doi: https://doi.org/10.1016/j.biosystemseng.2008.02.007
Vodyanitskii, Y. N., & Savichev, A. T. (2017). The influence of organic matter on soil color using the regression equations of optical parameters in the system CIE-L*a*b*, Annals of Agrarian Science, 15(3), 380–385. doi: https://doi.org/10.1016/j.aasci.2017.05.023
World Health Organization. (2014). 7 million premature deaths annually linked to air pollution. Retrieved April 12, 2018, from http://www.who.int/mediacentre/news/releases/2014/air-pollution/en/
Zafra, C. A., Temprano, J., & Tejero, M. J. M. (2011). Distribution of the concentration of heavy metals associated with the sediment particles accumulated on road surfaces. Environmental Technology, 32(9), 997–1008. doi: https://doi.org/10.1080/09593330.2010.523436
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2020 Revista Chapingo Serie Ciencias Forestales y del Ambiente