Keywords
geomática
geoestadística
interpolación espacial
Kriging
ozono
temperatura
compuestos orgánicos volátiles
precursores de ozono
distribución espacial
química atmosférica
problema ambiental
actividades antropogénicas
radiación solar
ozono troposférico
procesos de oxidación
incendios forestales
geoespacial atmospheric pollution
geomatic
geostatistics
spatial interpolation
Kriging
ozone
temperature
volatile organic compounds
ozone precursors
spatial distribution
atmospheric chemistry
environmental problem
anthropogenic activities
solar radiation
tropospheric ozone
oxidation processes
forest fires
geospatial
How to Cite
Abstract
Introduction: air pollution is an environmental problem caused by anthropogenic activities. One of the pollutants with the greatest impact on health is ozone, derived from nitrogen oxides (NOx) and volatile organic compounds that react with solar radiation to form tropospheric ozone. These oxidation processes in atmospheric chemistry are known as biogenic ozone precursors (biogenic Volatile Organic Compounds, VOCs) affecting the global carbon balance where forest fires are considered emitters of carbon dioxide and NOx (Radke et al. 1991). Plant tissues contain organic compounds that emitted in sufficient quantities can influence atmospheric chemistry (Main, 2003). One of the geospatial tools of geomatics that allows modeling and analysis of the distribution of pollutants in the air is the application of geostatistics through interpolation. The purpose of this analysis is to represent the spatial patterns of O3 concentrations by estimating values in non-sampled areas.
Methods: spatial interpolation stands out as a technique for the evaluation of atmospheric pollution, which makes it possible to identify areas exposed to risk levels of some pollutant. Regression and interpolation analysis, such as Kriging, allow the prediction of ozone in non-sampled areas of the city of Durango. It also allows the identification of population strata in urban areas and pollution hotspots, and it is also possible to evaluate the degree of exposure to ozone levels.
Results: the hourly average per month calculated at the three stations allowed determine the period in which the O3 concentration was at its maximum and minimum. With the interpolations performed with the ordinary Kriging method and by means of map algebra, the zone of influence of the pollutant was determined. There are higher concentrations in the western part of the city; this coincides with other studies where O3 levels were higher in the outskirts than in the central area, due to the vegetation, which provides O3 precursors. The zone of influence is distributed in areas where the average value of 0.032 ppm is exceeded from two to 11 times in 24 hours.
Conclusion: The analysis of the diurnal ozone cycle showed higher concentrations in May. The ordinary Kriging method performed an acceptable prediction according to the prediction errors presented in other research, considering the number of stations to determine the zone of influence where the average tropospheric ozone concentration was exceeded. Ozone showed a positive correlation with the temperature variable in the central and northwest zone, while it presented a positive and lower correlation in the west zone. This relationship indicates that O3 levels in the city depend significantly on temperature.
References
Avino, P. Manigrasso, M. (2008). Ten-year measurements of gaseous pollutants in urban air by an open-path analyzer. Atmospheric environment, 42(18), 4138–4148. https://doi.org/10.1016/j.atmosenv.2008.01.024
Basagaña, X., Rivera, M., Aguilera, I., Agis, D., Bouso, L., Elosua, R., y Künzli, N. (2012). Effect of the number of measurement sites on land use regression models in estimating local air pollution. Atmospheric Environment, 54, 634–642. https://doi.org/10.1016/j.atmosenv.2012.01.064
Beauchamp, M., Malherbe, L., de Fouquet, C., Létinois, L., y Tognet, F. (2018). A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model. Environmental Modelling & Software, 105, 132–152. https://doi.org/10.1016/j.envsoft.2018.03.033
Bello-Medina, PC., Rodríguez-Martínez, E., Prado-Alcalá, RA., y Rivas-Arancibia, S. (2019). Contaminación por ozono, estrés oxidativo, plasticidad sináptica y neurodegeneración. Neurología. https://doi.org/10.1016/j.nrl.2018.10.003
Bradley, FJ. (2007). Efecto genotóxico ya la salud en personal expuesto a contaminantes emitidos por ladrilleras en Durango, México. Maestro en Ciencias Ambientales. Victoria de Durango. Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Unidad-Durango del Instituto Politécnico Nacional. 90 p: http://tesis.ipn.mx/handle/123456789/843
Bravo, JL., Nava, M., y Muhlia, A. (2000). Relaciones entre la magnitud del valor máximo de ozono, la radiación solar y la temperatura ambiente en la Zona Metropolitana de la Ciudad de México. Revista Internacional de Contaminación Ambiental, 16(2). 45-54. https://www.revistascca.unam.mx/rica/index.php/rica/article/view/32542
Cano, YN., Morales, JA., Sánchez, LJ., Colina, MN., y Torres, JC. (2016). Evaluación de niveles de ozono en la ciudad de Maracaibo, Venezuela. Revista Internacional de Contaminación Ambiental, 32(1), 25-34. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0188-49992016000100025
Cañada, R., Vidal, MJ., y Moreno, A. (2010). Interpolación espacial y visualización cartográfica para el análisis de la justicia ambiental: ensayo metodológico sobre la contaminación por partículas atmosféricas en Madrid. Sevilla. Tecnologías de la Información Geográfica: La Información Geográfica al servicio de los ciudadanos. Secretariado de Publicaciones de la Universidad de Sevilla. 691-715. http://hdl.handle.net/10486/669117
Cañada, MR., Moreno, A., y González, H. (2015). Modelado de la calidad del aire urbano. Un experimento metodológico con técnicas de interpolación espacial. Boletín de la Asociación de Geógrafos Españoles, (65), 317-342. http://hdl.handle.net/10486/670056
Cheng, IK., McDonald, R., y Sandhu, H. (1998). Forest fire enhanced photochemical air pollution. A case study. Atmospheric Environment, 32. 673-681. https://doi.org/10.1016/S1352-2310(97)00319-1
DOF. (2003). Nom-036-Semarnat-1993. Que Establece Los Métodos De Medición Para Determinar La Concentración De Ozono En El Aire Ambiente Y Los Procedimientos Para La Calibración De Los Equipos De Medición. Diario Oficial de la Federación. https://sinaica.inecc.gob.mx/archivo/noms/NOM-036-SEMARNAT-1993.pdf
DOF. (2012). NOM-156-SEMARNAT-2012, Establecimiento y operación de sistemas de monitoreo de la calidad del aire. Diario Oficial de la Federación. https://sinaica.inecc.gob.mx/archivo/noms/NOM-156-SEMARNAT-2012.pdf
DOF. (2014). Nom-020-Ssa1-2014, Salud Ambiental. Valor límite permisible para la concentración de Ozono (O3) En El Aire Ambiente y Criterios para su Evaluación. Diario Oficial de la Federación. https://sinaica.inecc.gob.mx/archivo/noms/NOM-020-SSA1-2014%20O3.pdf
Giraldo, R. (2001). Propuesta de un indicador como variable auxiliar en el análisis cokriging. Revista Colombiana de Estadística, (24), 1-12. https://revistas.unal.edu.co/index.php/estad/article/view/28421
Gisgeography. (2018). Kriging Interpolation. https://gisgeography.com/kriging-interpolation-prediction/
Gorai, AK., Biswal, SS., y Mitra, G. (2017). Effects of meteorology on ground-level ozone (GLO) concentrations and identifying the hot spots having significantly higher GLO concentration in a semi-urban area. Environment, Development and Sustainability, 20(4), 1461–1481. https://doi.org/10.1007/s10668-017-9947-3
Guenther, A. (2000). The contribution of reactive carbon emissions from vegetation to the carbon 30 balance of terrestrial ecosystems, Chemosphere, 49, 837–844. https://doi.org/10.1016/s0045-6535(02)00384-3
Gunthe, SS., Beig, G., y Sahu, LK. (2016). Study of relationship between daily maxima in ozone and temperature in an urban site in India. Current Science (Bangalore), 110(10), 1994-1999. https://doi.org/10.18520/cs/v110/i10/1994-1999
Hernández-Cadena, L., Barraza-Villarreal, A., Ramírez-Aguilar, M., Moreno-Macías, H., Miller, P., Carbajal-Arroyo, LA., y Romieu, I. (2007). Morbilidad infantil por causas respiratorias y su relación con la contaminación atmosférica en Ciudad Juárez, Chihuahua, México. Salud Publica Mex, 49, 27-36. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0036-36342007000100005
Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P., y Briggs, D. (2008). A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment, 42(33), 7561–7578. https://doi.org/10.1016/j.atmosenv.2008.05.057
Janssen, S., Dumont, G., Fierens, F., y Mensink, C. (2008). Spatial interpolation of air pollution measurements using CORINE land cover data. Atmospheric Environment, 42(20), 4884–4903. https://doi.org/10.1016/j.atmosenv.2008.02.043
Jasaitis, D., Vasiliauskienė, V., Chadyšienė, R., y Pečiulienė, M. (2016). Surface Ozone Concentration and Its Relationship with UV Radiation, Meteorological Parameters and Radon on the Eastern Coast of the Baltic Sea. Atmosphere, 7(2), 27–. https://doi.org/10.3390/atmos7020027
Joseph, J., Sharif, HO., Sunil, T., y Alamgir, H. (2013). Application of validation data for assessing spatial interpolation methods for 8-h ozone or other sparsely monitored constituents. Environmental Pollution, 178, 411–418. https://doi.org/10.1016/j.envpol.2013.03.035
Jung, SW., Lee, K., Cho, YS., Choi, JH., Yang, W., Kang, TS., y Son, BS. (2016). Association by Spatial Interpolation between Ozone Levels and Lung Function of Residents at an Industrial Complex in South Korea. International Journal of Environmental Research and Public Health, 13(7), 728. https://doi.org/10.3390/ijerph13070728
Kethireddy, S., Tchounwou, P., Ahmad, H., Yerramilli, A., y Young, J. (2014). Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics. International Journal of Environmental Research and Public Health, 11(1), 983–1000. https://doi.org/10.3390/ijerph110100983
Kim, Sun-Young., Bechle, M., Hankey, S., Sheppard, L., Szpiro, AA., Marshall, JD., y Giorgi, E. (2020). Concentrations of criteria pollutants in the contiguous U.S: Role of prediction model parsimony in integrated empirical geographic regression. PLOS ONE, 15(2). https://doi.org/10.1371/journal.pone.0228535
Lee, EH. (2003). Use of auxiliary data for spatial interpolation of surface ozone patterns. Developments in Environmental Science, 165–194. https://doi.org/10.1016/S1474-8177(02)02007-7
Lopez-Rodríguez, F., Moral, FJ., y Valiente, P. (2009). Evaluation and analysis of the urban air pollution by means of the use of geostatistical techniques and geographical information systems (SIG). Selected Proceedings from the 13th International Congress on Project Engineering, 227-234. https://dialnet.unirioja.es/servlet/articulo?codigo=3309514
Malley, CS., Henze, DK., Kuylenstierna, JCI., Vallack, HW., Davila, Y., Anenberg, SC., y Ashmore, MR. (2017). Updated Global Estimates of Respiratory Mortality in Adults ≥30Years of Age Attributable to Long-Term Ozone Exposure. Environmental Health Perspectives, 125(8), 087021. https://doi.org/10.1289/EHP1390
Mauricio, JA. (2013). Introducción al análisis de series temporales. Universidad Complutense de Madrid. (En línea). Consultado 15 mayo, 2021. Disponible en https://www.ucm.es/data/cont/docs/518-2013-11-11-JAM-IAST-Libro.pdf
Michael, R., O’Lenick, CR., Monaghan, A., Wilhelmi, O., Wiedinmyer, C., Hayden, M., y Estes, M. (2018). Application of geostatistical approaches to predict the spatio-temporal distribution of summer ozone in Houston, Texas. Journal of Exposure Science & Environmental Epidemiology. https://doi.org/10.1038/s41370-018-0091-4
Miller, PR., Bauer, MDL., Nolasco, AQ., y Tejeda, TH. (1994). Comparison of ozone exposure characteristics in forested regions near Mexico City and Los Angeles. Atmospheric Environment, 28(1), 141-148. https//doi.org/10.1016/1352-2310(94)90029-9
Olaya, V. (2009). Sistemas de información geográfica. Cuadernos internacionales de tecnología para el desarrollo humano, (8), 15. http://hdl.handle.net/2099/7584
Organización Mundial de la Salud. (OMS). (2018). Salud y calidad del aire. http://www.who.int/es/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health
Rodríguez-Fernández, P., Prat-Aymerich, C., y Domínguez, J. (2019). Interaction Between Environmental Pollution and Respiratory Infections. Archivos de Bronconeumología (English Edition), 55(7), 351–352.
Team, RC. (2019). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL
Shukla, K., Kumar, P., Mann, GS., y Khare, M. (2019). Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2019.101997
Son, Y., Osornio-Vargas, ÁR., O’Neill, MS., Hystad, P., Texcalac-Sangrador, JL., Ohman-Strickland, P., y Schwander, S. (2018). Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters. Science of The Total Environment, 639, 40–48. https://doi.org/10.1016/j.scitotenv.2018.05.144
Tian, Y., Wu, Y., Liu, H., Si, Y., Wu, Y., Wang, X., y Wu, T. (2020). The impact of ambient ozone pollution on pneumonia: A nationwide time-series analysis. Environment International, 136, 105498. https://doi.org/10.1016/j.envint.2020.105498
Weather Spark. (2019). Weather Spark: El clima promedio en Victoria de Durango (en línea, sitio web). Consultado 23 de abril, 2020. Disponible en https://es.weatherspark.com/y/3659/Clima-promedio-en-Victoria-de-Durango-M%C3%A9xico-durante-todo-el-a%C3%B1o#Sections-Summary
Wang, X., Li, J., Zhang, Y., Xie, S., y Tang, X. (2009). Ozone source attribution during a severe photochemical smog episode in Beijing, China. Science in China Series B: Chemistry, 52(8), 1270-1280. https://doi.org/10.1007/s11426-009-0137-5
Xie, Y., Dai, H., Zhang, Y., Wu, Y., Hanaoka, T., y Masui, T. (2019). Comparison of health and economic impacts of PM2.5 and ozone pollution in China. Environment International, 130, 104881. https://doi.org/10.1016/j.envint.2019.05.075
Zhao, X., Cheng, H., He, S., Cui, X., Pu, X., y Lu, L. (2018). Spatial associations between social groups and ozone air pollution exposure in the Beijing urban area. Environmental Research, 164, 173–183. https://doi.org/10.1016/j.envres.2018.02.014
Zoran, M., Dida, MR., y Savastru, R. (2014). Ground level ozone (O3) associated with radon (222Rn) and particulate matter (PM) concentrations in Bucharest metropolitan area and adverse health effects. J Radioanal Nucl Chem, 300, 729–746. https://doi.org/10.1007/s10967-014-3041-1
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2021 Nova Scientia