Air temperature perturbation in La Malinche volcano area, Tlaxcala, Mexican Highland
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Abstract
Evaluating air temperature perturbation is important to know the anthropic activity's effect on the environmental system. The study case was La Malinche volcano, concerning the urban, agricultural, and forest environments. The air temperature data (average, maximum, minimum, standard deviation and range), was analyzed by prin- cipal components analysis (PCA), and the Kruskal-Wallis (K-W) test. Data analyses were made on a diurnal (warming and cooling rates), daily and monthly basis. The K-W test showed that warming and cooling rates are significantly different between the agricultural, urban, and forest zones, despite the north and south sides of La Malinche volcano had significant differences. The PCA indicated more perturbation concerning the cooling rates of air temperature among the environments than the warming rates. The average, maximum, and minimum air temperature of the urban environment and the standard deviation and range of the agricultural environment were the highest. The minimum air temperature changes more than the maximum in the volcano's urban, agricultural, and forest south side. The K-W test showed that the environmental conditions differed sig- nificantly based on average and maximum. The daily air temperature on the north side of La Malinche Volcano was very different from the south side. The PCA with average, maximum, minimum, standard deviation, and range showed that the environments are modified. The average monthly air temperature in the agricultural and forest areas was lower than average. Minimum air temperature increase was more accentuated in urban areas than in agriculture and forest areas and increased more than maximum.
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References
Abid A., Zhang M. J., Bagaria V. K., & Zou J. (2018). Exploring patterns enriched in a dataset with contrastive principal components. Nature Communications, 9(1), 1-7. doi: https://doi.org/10.1038/s41467-018-04608-8 DOI: https://doi.org/10.1038/s41467-018-04608-8
Ayandale A. (2017). Variations in urban surface temperature: an assessment of land use change impacts over Lagos metropolis. Weather. 72(10), 315-319. doi: https://doi.org/10.1002/wea.3178 DOI: https://doi.org/10.1002/wea.2925
Bernhardt J., Carleton A. M., & LaMagna C. (2018). A comparison of daily temperature-averaging methods: spatial variability and recent change for the CONUS. Journal of Climate, 31, 979-996. doi: https://doi.org/10.1175/JCLI-D-17-0089.1 DOI: https://doi.org/10.1175/JCLI-D-17-0089.1
Bethere L., Sennikovs J., & Batheres U. (2017). Climate indices for the Baltic states from principal component analysis. Earth System Dynamics, 8(4), 951-962. doi: https://doi.org/10.5194/esd-8-951-2017 DOI: https://doi.org/10.5194/esd-8-951-2017
Bloomer C, & Rehm G. (2014). Using Principal Component Analysis to find correlations and patterns at diamond light source (pp 19-21). Proceedings of IPAC, Dresden, Germany.
Braganza K, Karoly DJ, Arblaster. (2004). Diurnal temperature range as an index of global climate change during the twentieth century. Geophysical Research Letters, 31(13), L13217. doi: https://doi.org/10.1029/2004GL019998 DOI: https://doi.org/10.1029/2004GL019998
Castro-Govea R, Siebe C. (2007). Late Pleistocene–Holocene stratigraphy and radiocarbon dating of La Malinche volcano, Central Mexico. Journal of Volcanology and Geothermal Research. 162(1-2), 20-42. doi: https://doi.org/10.1016/j.jvolgeores.2007.01.002 DOI: https://doi.org/10.1016/j.jvolgeores.2007.01.002
Chen BX, Sun YF, Zhang HB, Han ZH, Wang JS, Li YK, Yang XL. (2018). Temperature change along elevation and its effect on the alpine timberline tree growth in the southeast of the Tibetan Plateau. Advances in Climate Change Research, 9(3), 185-191. doi: https://doi.org/10.1016/j.accre.2018.05.001 DOI: https://doi.org/10.1016/j.accre.2018.05.001
Chinchorkar SS, Vaidya VB, Vyas P. (2013). Monthly, seasonal and annual air temperature variability and trends- a case study to assess climate change on Anand (Gujarat State). Spring. 1(1), 20-25.
Colunga ML, Cambrón-Sandoval VH, Suzán-Azpiri H, Guevara-Escobar A, Luna-Soria H. (2015). The role of urban vegetation in temperature and heat island effects in Querétaro City, Mexico. Atmósfera. 28(3), 205-218. doi: https://doi.org/10.20937/ATM.2015.28.03.05 DOI: https://doi.org/10.20937/ATM.2015.28.03.05
Daultrey S. (1976). Principal components analysis: CATMOG Series. Geo Abstracts Ltd. University of East Anglia Norwich. https://alexsingleton.files.wordpress.com/2014/09/8-principle-components-analysis.pdf
De Frenne P, Zellweger F, Rodríguez-Sánchez F, Scheffers BR, Hylander K, Luoto M, Vellend M, Verheyen K, Lenoir J. (2019). Global buffering of temperatures under forest canopies. Nature Ecology and Evolution, 3, 744-749. doi: https://doi.org/10.1038/s41559-019-0842-1 DOI: https://doi.org/10.1038/s41559-019-0842-1
Duffy KA, Schwalm CR, Arcus VL, Koch GW, Liang LL, Schipper LA. (2021). How close are we to the temperature tipping point of the terrestrial biosphere? Science Advances, 7(3), 1-8. doi: https://doi.org/10.1126/sciadv.aay1052 DOI: https://doi.org/10.1126/sciadv.aay1052
Dutta PN, Karlo T, Dutta P. (2017). Some features of surface air temperature: a statistical viewpoint. Environment and Ecology Research, 5(5), 367-376. doi: https://doi.org/10.13189/eer.2017.050506 DOI: https://doi.org/10.13189/eer.2017.050506
Fernández EA, Romero CR, Zavala HJ. (2015). Redes de observación atmosférica y ambiental. Unidad de Informática para las Ciencias Atmosféricas y Ambientales (UNIATMOS). Red Universitaria de Observatorios Atmosféricos (RUOA). Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México. https://atlasclimatico.unam.mx/acdm/visualizador
Fuelner G, Rahmstorf S, Leverman A, Volkwardt S. (2013). On the origin of the surface air temperature difference between the Hemispheres in Earth's present-day climate. Journal of Climate, 26(18), 7136-7150. doi: https://doi.org/10.1175/JCLI-D-12-00636.1 DOI: https://doi.org/10.1175/JCLI-D-12-00636.1
García CF, Bestion E, Warfield R, Yvon-Durocher G. (2018). Changes in temperature alter the relationship between biodiversity and ecosystem functioning. Proceedings of the National Academy of Sciences of the United States of America. 115(43), 10989-10994. doi: https://doi.org/10.1073/pnas.1805518115 DOI: https://doi.org/10.1073/pnas.1805518115
Good E. (2015). Daily minimum and maximum surface air temperatures from geostationary satellite data. Journal of Geophysical Research: Atmospheres. Journal of Geophysical Research: Atmospheres, 120(6), 2306-2324. doi: https://doi.org/10.1002/2014JD022438 DOI: https://doi.org/10.1002/2014JD022438
Hajrya R, Mechbal N. (2013). Principal component analysis and perturbation theory-based robust damage detection of multifunctional aircraft structure. Structural Health Monitoring, 12(3), 263-277. doi: https://doi.org/10.1177/1475921713481015 DOI: https://doi.org/10.1177/1475921713481015
Hemond HF, Fechner EJ. (2015). The atmosphere. En Academic Press (Ed.), Chemical fate and transport in the environment (311-354 pp.). Academic Press. DOI: https://doi.org/10.1016/B978-0-12-398256-8.00004-9
Hu Y, Mskey S, Uhlenbrook S. (2012). Trends in temperature and rainfall extremes in the Yellow River source region, China. Climatic Change. 110, 403-429. doi: https://doi.org/10.1007/s10584-011-0056-2 DOI: https://doi.org/10.1007/s10584-011-0056-2
Hurth R and Pokorna L. (2005). Simultaneous analysis of climatic trends in multiple variables: an example of application of multivariate statistical methods. International Journal of Climatology (. 25, 469-484. doi: https://doi.org/10.1002/joc.1146 DOI: https://doi.org/10.1002/joc.1146
Imtiaz H, and Sarwate AD. (2016). Symmetric matrix perturbation for differentially-private principal component analysis. [Presentación de paper]. International Conference on Acoustics, Speech and Signal Processing, Shanghai, China. doi: https://doi.org/10.1109/ICASSP.2016.7472095. DOI: https://doi.org/10.1109/ICASSP.2016.7472095
Isaak DJ, Luce CH, Chandler GL, Horan DL, Wollrab SP. (2018). Principal components of thermal regimes in mountain river networks. Hydrology and Earth System Sciences, 22, 6225-6240. doi: https://doi.org/10.5194/hess-22-6225-2018 DOI: https://doi.org/10.5194/hess-22-6225-2018
Joliffe IT, Cadima J. (2016). Principal components analysis: a review and recent developments. Philosophical Transactions A, 374(2065), 2-16. doi: https://doi.org/10.1098/rsta.2015.0202 DOI: https://doi.org/10.1098/rsta.2015.0202
Lee X, Goulden ML, Hollinger DV, Barr A, Black TA, Bohrer G, et al. (2011). Observed increase in the local cooling effect of deforestation at higher latitudes. Nature, 479, 384-387. doi: https://doi.org/10.1038/nature10588 DOI: https://doi.org/10.1038/nature10588
Li D, Bou-Zeid E. (2013). Synergistic interactions between urban heat islands and heat waves: the impact in cities is larger than the sum of its parts. Journal of Applied Meteorology and Climatology, 52, 2051-2064. doi: https://doi.org/10.1175/JAMC-D-13-02.1 DOI: https://doi.org/10.1175/JAMC-D-13-02.1
Li Y, Zhao M, Motesharrei S, Mu Q, Kalnay E, Li S. (2015). Local cooling and warming effects of forest based on satellite observations. Nature Communications, 6, 6603. doi: https://doi.org/10.1038/ncomms7603 DOI: https://doi.org/10.1038/ncomms7603
López-Díaz F, Conde C, Sánchez O. (2013). Analysis of indices of extreme temperature events at Apizaco, Tlaxcala, Mexico: 1952-2003. Atmósfera. 26(3), 349-358. DOI: https://doi.org/10.1016/S0187-6236(13)71081-6
Machiwal D, Gupta A, Jha MK, Kamble T. (2019). Analysis of trend in temperature and rainfall time series of an Indian arid region: comparative evaluation of salient techniques. Theoretical and Applied Climatology, 136, 301-320. doi: https://doi.org/10.1007/s00704-018-2487-4 DOI: https://doi.org/10.1007/s00704-018-2487-4
Martínez R, Zambrano E, Nieto JJ, Costa F. (2017). Evolución, vulnerabilidad e impactos económicos y sociales de El Niño 2015-2016 en América Latina. Investigaciones Geográficas, 68, 65–78. DOI: https://doi.org/10.14198/INGEO2017.68.04
McBean EA and Rovers FA. (1998). Statistical procedures for analysis of environmental monitoring data and risk assessment. New Jersey: Prentice Hall.
Norma Mexicana: NMX-AA-166/1-SCFI-2013. (4 de septiembre 2013). Diario Oficial de la Federación, México. https://www.gob.mx/cms/uploads/attachment/file/166835/nmx-aa-166-1-scfi-2013_1_.pdf
Nwofor OK, Dike VN. (2010). Daytime surface air temperature variations at locations in Owerri Capital City: indications or urban heat island build-up? Advances in Science and Technology, 4(2), 91-97. https://advanscitech.com/nwofordike42.pdf
Oleson K. (2012). Contrasts between urban and rural climate in CCSM4 CMIP5 climate change scenarios. Journal of Climate, 25, 1390-1412. doi: https://doi.org/10.1175/JCLI-D-11-00098.1 DOI: https://doi.org/10.1175/JCLI-D-11-00098.1
Pascual RR, López QM, Chablé PLA, Espejo MAZ, Loranca DY, Ledesma LJI, Quintero VEY. (2019). Reporte del clima en México: Reporte anual 2019. CONAGUA, México. https://smn.conagua.gob.mx/es/reporte-del-clima-en-mexico
Pascual RR, López QM, Martínez SJN, Chablé PLA, Espejo MAZ, Ledezma LJI. (2018). Reporte del clima en México: Reporte anual 2018. CONAGUA, México. https://smn.conagua.gob.mx/es/reporte-del-clima-en-mexico
Peterson TC. (2003). Assessment of urban versus rural in situ surface temperatures in the Contiguous United States: No difference found. Journal of Climate, 26(18), 2941-2959. doi: https://doi.org/10.1175/1520-0442(2003)016<2941:AOUVRI>2.0.CO;2 DOI: https://doi.org/10.1175/1520-0442(2003)016<2941:AOUVRI>2.0.CO;2
Protsiv M, Ley C, Lankester J, Hastie T, Parsonnet J. (2020). Decreasing human body temperature in the United States since the Industrial Revolution. eLIFE, 9(e49555). doi: https://doi.org/10.7554/eLife.49555 DOI: https://doi.org/10.7554/eLife.49555
Qu M, Wan J, Hao X. (2014). Analysis of diurnal air temperature range in the continental United States. Weather and Climate Extremes, 4, 86-95. doi: https://doi.org/10.1016/j.wace.2014.05.002 DOI: https://doi.org/10.1016/j.wace.2014.05.002
Radons SZ, Heldwein AB, Loose LH, Bortoluzzi MP, Brand SI, Engers LBO. (2019). Modeling hourly air temperature based on internationally agreed times and the daily minimum temperature. Revista Brasileira de Engenharia Agrícola e Ambiental, 23 (11), 807-811 DOI: https://doi.org/10.1590/1807-1929/agriambi.v23n11p807-811
Rahman MA, Kang S, Nagabhatla N, Macnee R. (2017). Impacts of temperature and rainfall variations on rice productivity in major ecosystems of Bangladesh. Agriculture and Food Security, 6(10). doi: https://doi.org/10.1186/s40066-017-0089-5 DOI: https://doi.org/10.1186/s40066-017-0089-5
Rosenzweig C, Solecki W, Parshall L, Gaffin S, Lynn B, Goldberg R. et al. (2006). Mitigating New York City's heat island with urban forestry, living roofs, and light surfaces. 86th AMS Annual Meeting.
Roy and Balling Jr. (2005). Analysis of trends in maximum and minimum temperature, diurnal temperature range, and cloud cover over India. Geophysical Research Letters, 32(12), L12702. doi: https://doi.org/10.1029/2004GL022201 DOI: https://doi.org/10.1029/2004GL022201
Ruíz AO, Espejel TD, Ontiveros CRE, Enciso JM, Galindo RMA, Quesada PML et al. (2016). Monthly trend of maximum and minimum temperatures in Aguascalientes, Mexico. Revista Mexicana de Ciencias Agrícolas, 13, 2535-2549.
Rushayati SB, Shamila AD, Prasetyo. (2018). The role of vegetation in controlling air temperature resulting from urban heat island. Forum Geografi, 32(1), 1-11. doi: https://doi.org/10.23917/forgeo.v32i1.5289 DOI: https://doi.org/10.23917/forgeo.v32i1.5289
Schuenemeyer JH, Drew LJ. (2011). Statistics for earth and environmental scientists. New Jersey: John Wiley and Sons. https://www.wiley.com/en-us/Statistics+for+Earth+and+Environmental+Scientists-p-9780470584699 DOI: https://doi.org/10.1002/9780470650707
Shiflett SA, Liang LL, Crum SM, Feyisa GL, Wang J, Janerette GD. (2017). Variation in the urban vegetation, surface temperature, air temperature nexus. Science of the Total Environment, 579, 495-505. doi: https://doi.org/10.1016/j.scitotenv.2016.11.069 DOI: https://doi.org/10.1016/j.scitotenv.2016.11.069
Sun L. (2016). Distribution of the temperature field in a pavement structure. L. Sun (Ed.). In: Structural behavior of asphalt pavements (pp. 61-177). Butterworth-Heinemann. doi: https://doi.org/10.1016/B978-0-12-849908-5.00002-X. DOI: https://doi.org/10.1016/B978-0-12-849908-5.00002-X
Suomi J, Käyhkö J. (2012). The impact of environmental factors on urban temperature variability in the coastal city of Turku, SW Finland. International Journal of Climatology, 32(3): 451-463. doi: https://doi.org/10.1002/joc.2277 DOI: https://doi.org/10.1002/joc.2277
Vitt R, Gulyás Á, Matzarakis A. (2015). Temporal differences or urban-rural human biometeorological factors for planning and tourism in Szeged, Hungary. Advances in Meteorology, 25, 987576. doi: https://doi.org/10.1155/2015/987576 DOI: https://doi.org/10.1155/2015/987576
Vuckovic M, Kiesel K, Mahdavi A. (2017). The extent and implications of the microclimatic conditions in the urban environment: a Vienna case study. Sustainability. 9, 177. doi: https://doi.org/10.3390/su9020177 DOI: https://doi.org/10.3390/su9020177
Waldock C, Dornelas M, Bates AE. (2018). Temperature-driven biodiversity change: disentangling space and time. BioScience, 68(11), 873-884. doi: https://doi.org/10.1093/biosci/biy096 DOI: https://doi.org/10.1093/biosci/biy096
Wiesner S, Eschenbach A, Ament F. (2014). Urban air temperature anomalies and their relation to soil moisture observed in the city of Hamburg. Meteorologische Zeitschrift, 23(2), 143-157. doi: https://doi.org/10.1127/0941-2948/2014/0571 DOI: https://doi.org/10.1127/0941-2948/2014/0571
Wong N. H., & Peck T.T. (2005). The impact of vegetation on the environmental conditions of housing estates in Singapore. International Journal on Architectural Science, 6(1), 31-37. https://www.bse.polyu.edu.hk/researchCentre/Fire_Engineering/summary_of_output/journal/journal_AS.html
Yang Q., Huang X., & Li J. (2017). Assessing the relationship between surface urban heat islands and landscape patterns across climatic zones in China. Scientific Report, 7, 9337. doi: https://doi.org/10.1038/s41598-017-09628-w DOI: https://doi.org/10.1038/s41598-017-09628-w
Zeleňáková M., Purcz P., Hlavatá H., & Blišt’an. (2015). Climate change in urban versus rural areas. Procedia Engineering, 119, 1171-1180. doi: https://doi.org/10.1016/j.proeng.2015.08.968 DOI: https://doi.org/10.1016/j.proeng.2015.08.968
Zhuo B., Rybski D., & Kropp J. P. (2017). The role of city size and urban form in the surface urban heat island. Scientific Reports, 7, 4791. doi: https://doi.org/10.1038/s41598-017-04242-2 DOI: https://doi.org/10.1038/s41598-017-04242-2
Zuśka Z., Kopcińska J., Dacewicz E., Skowera B., Wojkowski J., & Ziernicka-Wojtaszek A. (2019). Application of the principal components analysis (PCA) method to assess the impact of meteorological elements on concentrations of particulate matter (PM10): A case study on the Mountain Valley (the Sącz Basin, Poland). Sustainability, 11, 6740. doi: https://doi.org/10.3390/su11236740 DOI: https://doi.org/10.3390/su11236740