Satellite and ground atmospheric particulate matter detection over Tucumán city, Argentina, space-time distribution, climatic and seasonal variability

María E. García; Lara S. Della Ceca; María I. Micheletti; Rubén D. Piacentini; Mariano Ordano; Nora J. F. Reyes; Sebastián Buedo; Juan A. González; María E. García; Lara S. Della Ceca; María I. Micheletti; Rubén D. Piacentini; Mariano Ordano; Nora J. F. Reyes; Sebastián Buedo; Juan A. González

doi:10.3934/environsci.2018.3.173

AIMS Environmental Science

2018, Volume 5, Issue 3: 173-194. doi: 10.3934/environsci.2018.3.173

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Research article

Satellite and ground atmospheric particulate matter detection over Tucumán city, Argentina, space-time distribution, climatic and seasonal variability

1.
Laboratory of Palynology, Miguel Lillo Foundation, Miguel Lillo 251, T4000JFE, San Miguel de Tucumán, Tucumán, Argentina
2.
Group of Atmospheric Physics, Solar Radiation and Astroparticles, Institute of Physics Rosario, CONICET—National University of Rosario, 27 de Febrero 210bis, S2000EZP, Rosario, Santa Fe, Argentina
3.
Faculty of Pharmaceutical and Biochemical Sciences, National University of Rosario, Suipacha 531, Rosario, Santa Fe, Argentina
4.
Laboratory of Energy Efficiency, Sustainability and Climate Change, IMAE, Faculty of Exact Sciences, Engineering and Surveying, National University of Rosario, Pellegrini 250, S2000BTP, Rosario, Santa Fe, Argentina
5.
Miguel Lillo Foundation and Lillo Executive Unit (UEL-FML-CONICET), Miguel Lillo 251, T4000JFE, San Miguel de Tucumán, Tucumán, Argentina
6.
Institute of Ecology, Miguel Lillo Foundation, Miguel Lillo 251, T4000JFE, San Miguel de Tucumán, Tucumán, Argentina

Received: 05 February 2018 Accepted: 08 June 2018 Published: 15 June 2018

The analysis of atmospheric particles (aerosols) is of special interest due to their potential effects on human health and other applications. In this paper the climatic and seasonal effects on aerosols have been characterized in Tucumán city (26°50’ S, 65° 13’ W,450 masl),Argentina, for the 2006–2013 period. The atmospheric aerosols in Tucumán city result from both stationary and mobile sources such as: industrial activity of sugar cane and alcohol distilleries, paper industry, biomass burning (mainly sugarcane waste crop and grasslands), household waste burning and transport emissions. The peak of industrial activity is seasonal, coincident with the austral winter (July-August-September), when accumulation of particles in the lower atmosphere occurs. In this region, there are no studies like the present one that evaluate, using “in situ” equipment, the temporal variation of aerosols and its causes, by applying modern analytical techniques. A continuous volumetric and isokinetic sampler of Hirst type (Burkard), was used for atmospheric particle sampling, in weekly records between 2006 and 2013. The particle concentration (number of particles per cubic meter) showed an increasing trend in the studied period. The monthly variation of: the particle concentration; the aerosol optical thickness at a wavelength of 550 nm (AOD₅₅₀) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard Aqua (NASA) satellite, and the AOD from different aerosol tracers (black and organic carbon, sea salt, sulfates, dust) obtained from the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), were also analyzed. The temporal variation in particle concentration was explained mostly by wind direction, while the corresponding variation for AOD₅₅₀(MODIS) was explained by temperature and seasonality (as by-product of climatic variation and anthropogenic particle emission sources). The variation in the AOD₅₅₀(MERRA-2) data series were explained by temperature, humidity, precipitation, and seasonality, with less effect of wind speed and direction. Particle concentration, AOD₅₅₀(MODIS), and AOD₅₅₀(MERRA-2) were highly variable. The cross-correlation between AOD₅₅₀(MODIS) and AOD₅₅₀(MERRA-2) time series was significantly positive at lag zero. Other contribution was the determination of the space-time distribution of aerosols on a monthly basis considering AOD₅₅₀ MODIS (3 km × 3 km) data. The present study suggests that these variables are affected by temperature and wind dynamics driven by seasonal and high-order autoregressive non-linear processes.
- Remote sensing,
- air pollution,
- seasonality,
- particulate matter quantification,
- environmental monitoring
Citation: María E. García, Lara S. Della Ceca, María I. Micheletti, Rubén D. Piacentini, Mariano Ordano, Nora J. F. Reyes, Sebastián Buedo, Juan A. González. Satellite and ground atmospheric particulate matter detection over Tucumán city, Argentina, space-time distribution, climatic and seasonal variability[J]. AIMS Environmental Science, 2018, 5(3): 173-194. doi: 10.3934/environsci.2018.3.173

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Abstract

The analysis of atmospheric particles (aerosols) is of special interest due to their potential effects on human health and other applications. In this paper the climatic and seasonal effects on aerosols have been characterized in Tucumán city (26°50’ S, 65° 13’ W,450 masl),Argentina, for the 2006–2013 period. The atmospheric aerosols in Tucumán city result from both stationary and mobile sources such as: industrial activity of sugar cane and alcohol distilleries, paper industry, biomass burning (mainly sugarcane waste crop and grasslands), household waste burning and transport emissions. The peak of industrial activity is seasonal, coincident with the austral winter (July-August-September), when accumulation of particles in the lower atmosphere occurs. In this region, there are no studies like the present one that evaluate, using “in situ” equipment, the temporal variation of aerosols and its causes, by applying modern analytical techniques. A continuous volumetric and isokinetic sampler of Hirst type (Burkard), was used for atmospheric particle sampling, in weekly records between 2006 and 2013. The particle concentration (number of particles per cubic meter) showed an increasing trend in the studied period. The monthly variation of: the particle concentration; the aerosol optical thickness at a wavelength of 550 nm (AOD₅₅₀) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard Aqua (NASA) satellite, and the AOD from different aerosol tracers (black and organic carbon, sea salt, sulfates, dust) obtained from the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2), were also analyzed. The temporal variation in particle concentration was explained mostly by wind direction, while the corresponding variation for AOD₅₅₀(MODIS) was explained by temperature and seasonality (as by-product of climatic variation and anthropogenic particle emission sources). The variation in the AOD₅₅₀(MERRA-2) data series were explained by temperature, humidity, precipitation, and seasonality, with less effect of wind speed and direction. Particle concentration, AOD₅₅₀(MODIS), and AOD₅₅₀(MERRA-2) were highly variable. The cross-correlation between AOD₅₅₀(MODIS) and AOD₅₅₀(MERRA-2) time series was significantly positive at lag zero. Other contribution was the determination of the space-time distribution of aerosols on a monthly basis considering AOD₅₅₀ MODIS (3 km × 3 km) data. The present study suggests that these variables are affected by temperature and wind dynamics driven by seasonal and high-order autoregressive non-linear processes.

References

[1]	Davies SJ, Unam L (1999) Smoke-haze from the 1997 Indonesian forest fires: effects on pollution levels, local climate, atmospheric CO2 concentrations, and tree photosynthesis. Forest Ecol Manag 124: 137–144. doi: 10.1016/S0378-1127(99)00060-2
[2]	Rai PK, Panda LLS (2014) Leaf dust deposition and its impact on biochemical aspect of some roadside plants of Aizawl, Mizoram, North East India. Int Res J Environ Sci 3: 14–19.
[3]	Pope CA III, Burnett RT, Michael D, et al. (2002) Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J Amer Med Assoc 287: 1132–1141. doi: 10.1001/jama.287.9.1132
[4]	Shah ASV, Lee KK, McAllister DA, et al. (2015) Short term exposure to air pollution and stroke: systematic review and meta-analysis. BMJ 350: h1295.
[5]	Sun X, Luo X, Zhao C, et al. (2015) The association between fine particulate matter exposure during pregnancy and preterm birth: a meta-analysis. BMC Pregnancy Childbirth 15: 300. doi: 10.1186/s12884-015-0738-2
[6]	Ye X, She B, Benya S (2018) Exploring Regionalization in the Network Urban Space. J Geovisual Spatial Analysis 2: 4. doi: 10.1007/s41651-018-0013-y
[7]	Atkinson RW, Mills IC, Walton HA, et al. (2015) Fine particle components and health-a systematic review and meta-analysis of epidemiological time series studies of daily mortality and hospital admissions. J Expo Sci Env Epid 25: 208–214. doi: 10.1038/jes.2014.63
[8]	Csavina J, Field J, Félix O, et al. (2014) Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates. Sci Total Environ 487: 82–90. doi: 10.1016/j.scitotenv.2014.03.138
[9]	Grobéty B, Gieré R, Dieze V, et al. (2010) Airborne particles in the urban environment. Elements 6: 229–234. doi: 10.2113/gselements.6.4.229
[10]	Kumar P, Hopke PK, Raja S, et al. (2012) Characterization and heterogeneity of coarse particles across an urban area. Atmos Environ 46: 449–459. doi: 10.1016/j.atmosenv.2011.09.018
[11]	Achad M, López ML, Ceppi S, et al. (2014) Assessment of ﬁne and sub-micrometer aerosols at an urban environment of Argentina. Atmos Environ 92: 522–532. doi: 10.1016/j.atmosenv.2014.05.001
[12]	Arkouli M, Ulke AG, Endlicher W, et al. (2010) Distribution and temporal behavior of particulate matter over the urban area ofBuenos Aires. Atmos Pollut Res 1: 1–8. doi: 10.5094/APR.2010.001
[13]	Escribano J, Gallardo L, Rondanelli R, et al. (2014) Satellite retrievals of aerosol optical depth over a subtropical urban area: the role of stratification and surface reflectance. Aerosol Air Qual Res 14: 596–607.
[14]	Garcia-Chevesich PA, Alvarado S, Neary DG, et al. (2014) Respiratory disease and particulate air pollution in Santiago Chile: Contribution of erosion particles from ﬁne sediments. Environ Pollut 187: 202–205. doi: 10.1016/j.envpol.2013.12.028
[15]	Ipiña A, Salum GM, Crino E, et al. (2012) Satellite and ground detection of very dense smoke clouds produced on the islands of the Paraná river delta that affected a large region inCentral Argentina. Adv Space Res 49: 966–977. doi: 10.1016/j.asr.2011.12.009
[16]	Piacentini RD, García B, Micheletti MI, et al. (2016) Selection of astrophysical/astronomical/solar sites at the Argentina East Andes range taking into account atmospheric components. Adv Space Res 57: 2559–2574. doi: 10.1016/j.asr.2016.03.027
[17]	Micheletti MI, Louedec K, Freire M, et al. (2017) Aerosol concentration measurements and correlations with particle trajectories at the Pierre Auger Observatory. Eur Phys J Plus 132: 245. doi: 10.1140/epjp/i2017-11503-0
[18]	Strawbridge KB, Snyder BJ (2004) Planetary boundary layer height determination during Pacific 2001 using the advantage of a scanning lidar instrument. Atmos Environ 38: 5861–5871. doi: 10.1016/j.atmosenv.2003.10.065
[19]	Zhang Q, Quan J, Tie X, et al. (2011) Impact aerosol particles on cloud formation: Aircraft measurements inBeijingChina. Atmos Environ 45: 665–672. doi: 10.1016/j.atmosenv.2010.10.025
[20]	Finlayson-Pitts BJ, Pitts Jr JN, Chemistry of the upper and lower atmosphere: theory, experiments, and applications. Elsevier, 1999.
[21]	Zhang Q, Ma X, Tie X, et al. (2009) Vertical distributions of aerosols under different weather conditions: Analysis of in-situ aircraft measurements inBeijing,China. Atmos Environ 43: 5526–5535. doi: 10.1016/j.atmosenv.2009.05.037
[22]	González JA, Prado FE, Piacentini RD (2014) Atmospheric dust accumulation on native and non-native species: Effects on gas exchange parameters. J Environ Qual 43: 801–808. doi: 10.2134/jeq2013.08.0308
[23]	Benedetti PE (2014) Seguimiento de fuegos en Tucumán. Informe Nº 1. Instituto Nacional de Tecnología Agropecuaria, Centro Regional Tucumán-Santiago del Estero, Estación Experimental Agropecuaria Famaillá, Tucumán, Argentina. Available from: http://inta.gob.ar/documentos/seguimiento-de-fuegos-en-tucuman [In Spanish].
[24]	Sayago J, Collantes M, Toledo M (1998) Geomorfología, In: Gianfrancisco J. et al., editors, Geología del Tucumán. Publicación Especial Colegio de Graduados en Ciencias Geológicas de Tucumán: 241–258.
[25]	Sesma P, Guido E, Puchulu M (1998) Clima de la provincia de Tucumán. In: J. Gianfrancisco et al., editors, Geología del Tucumán. Publicación Especial Colegio de Graduados en Ciencias Geológicas de Tucumán, 41–46.
[26]	Minetti JL (2005) Climatología de los vientos, In: Minetti, J.L. editor, El clima del noroeste argentino. Laboratorio Climatológico Sudamericano. Fundación Carl C. zon Caldenius, Tucumán, Argentina, 117–127.
[27]	Hirst J (1952) An automatic volumetric spore trap. Ann Appl Biol 39: 257–265. doi: 10.1111/j.1744-7348.1952.tb00904.x
[28]	Aira MJ, Jato V, Iglesias I (2005) Calidad del aire. Polen y esporas en la Comunidad Gallega. España: Eds. Xunta de Galicia, Consellería de Medio Ambiente.
[29]	Rasband W (1997) ImageJ. National Institutes ofHealth,USA. Available from: http://rsb.info.nih.gov/ij/.
[30]	Shumway RH, Stoffer DS (1982) An approach to time series smoothing and forecasting using the EM algorithm. J Time Ser Anal 3: 253–264. doi: 10.1111/j.1467-9892.1982.tb00349.x
[31]	Ordano M, Engelhard I, Rempoulakis P, et al. (2015) Olive fruit fly (Bactrocera oleae) population dynamics in theEastern Mediterranean: influence of exogenous uncertainty on a monophagous frugivorous insect. PloS one 10: e0127798. doi: 10.1371/journal.pone.0127798
[32]	Remer LA, Kaufman YJ, Tanré D, et al. (2005) The MODIS Aerosol algorithm, products, and validation. J Atmos Sci 62: 947–973. doi: 10.1175/JAS3385.1
[33]	GMAO-Global Modeling and Assimilation Office (2015) MERRA-2 tavgM_2d_aer_Nx: 2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).
[34]	Theil H (1950) A rank-invariant method of linear and polynomial regression analysis. Nederlandse Akademie Wetenchappen Series A 53: 386–392.
[35]	Sen PK (1968) Estimates of the regression coefficient based onKendall's tau. J Am Stat Assoc 63: 1379–1389. doi: 10.1080/01621459.1968.10480934
[36]	Box GEP, Jenkins GM, Reinsel GC (2008) Time series analysis: forecasting and control. Fourth edition,Hoboken,NJ: John Wiley & Sons, Inc.
[37]	Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-PLUS. New York: Springer-Verlag, Inc.
[38]	Venables WN, Ripley BD (2002) Modern applied statistics with S. New York: Springer.
[39]	Zuur AF, Ieno EN, Walker NJ, et al. (2009) Mixed effects models and extensions in ecology with R. New York: Springer Science+Business Media, LLC.
[40]	Gałecki AT, Burzykowski T (2013) Linear mixed-effects models using R. A step-by-step approach. New York: Springer Science & Business Media.
[41]	Fox J, Weisberg S (2011) An R companion to applied regression. Second edition,Thousand Oaks: Sage.
[42]	Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Boca Raton,FL: Chapman & Hall/CRC.
[43]	R Development Core Team (2017) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing,Vienna,Austria. Available from: http://www.R-project.org/
[44]	Agostinelli C, Lund U, R package 'circular': Circular Statistics (version 0.4-7)[Computer software], (2013). Available from: https://r-forge.r-project.org/projects/circular/
[45]	Højsgaard S, Halekoh U, Robison-Cox J, et al. (2013) doBy: doBy - Groupwise summary statistics, LSmeans, general linear contrasts, various utilities. R package version 4.5–10. Available from: http://CRAN.R-project.org/package=doBy
[46]	Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 26: 1–22.
[47]	Wickham H (2010) ggplot2: Elegant graphics for data analysis. J Stat Softw 35: 65–88.
[48]	Sarkar D, Andrews F, latticeExtra: Extra graphical utilities based on Lattice. R package version 0.6-26, 2013. Available from: http://CRAN.R-project.org/package=latticeExtra
[49]	Wood SN (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc 99: 673–686. doi: 10.1198/016214504000000980
[50]	Pinheiro J, Bates D, DebRoy S, et al. (2015) nlme: Linear and nonlinear mixed effects models. R package version 3.1–122, Available from: http://CRAN.R-project.org/package=nlme.
[51]	Carslaw DC, Ropkins K (2012) openair-an R package for air quality data analysis. Environ Modell Softw 27–28: 52–61.
[52]	Breheny P, Burchett W (2015) visreg: Visualization of regression models. R package version 2.2-0. Available from: http://CRAN.R-project.org/package=visreg
[53]	Christopher SA, Wang M, Berendes TA, et al. (1998) The 1985 Biomass Burning Season inSouth America: Satellite Remote Sensing of Fires, Smoke, and Regional Radiative Energy Budgets. J Appl Meteor 37: 661–678.
[54]	Castro Videla F, Barnaba F, Angelini F, et al. (2013) The relative role of Amazonian and non-Amazonian fires in building up the aerosol optical depth in South America: A five year study (2005–2009). Atmospheric Research 122: 298– 309. doi: 10.1016/j.atmosres.2012.10.026
[55]	Filonchyk M, Yan H, Yang S, et al. (2017) Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data. Adv Space Res 61: 1035–1046.
[56]	Filonchyk M, Yan H, Shareef TME, et al. (2018) Aerosol contamination survey during dust storm process inNorthwestern Chinausing ground, satellite observations and atmospheric modeling data. Theor Appl Climatol, 1–15.
[57]	Liu Y, Franklin M, Kahn R, et al. (2007) Using aerosol optical thickness to predict ground-level PM2.5 concentrations in theSt. Louisarea: A comparison between MISR and MODIS. Remote Sens Environ 107: 33–44.
[58]	Kahn RA, Gaitley BJ, Martonchik JV, et al. (2005) Multiangle Imaging Spectroradiometer (MISR) global aerosol optical depth validation based on 2 years of coincident Aerosol Robotic Network (AERONET) observations. J Geophys Res 110(D10).
[59]	Hu Z (2009) Spatial analysis of MODIS aerosol optical depth, PM 2.5, and chronic coronary heart disease. Int J Health Geogr 8: 27.
[60]	Li Y, Chen Q, Zhao H, et al. (2015) Variations in PM 10, PM 2.5 and PM 1.0 inan urban area of theSichuanBasinand their relation to meteorological factors. Atmosphere 6: 150–163.
[61]	van Donkelaar A, Martin RV, Park RJ. (2006) Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing. J Geophys Res Atmos 111(D21).
[62]	Tonatto MJ, Fernández de Ullivarri J, Alonso JM, et al. (2008) Sugarcane burning teledetection through MODIS system in Tucumán (Argentina). Revista Industrial y Agrícola de Tucumán 85: 31–35.
[63]	Carreras HA, Wannaz ED, Pignata ML (2009) Assessment of human health risk related to metals by the use of biomonitors in the province of Córdoba, Argentina. Environ Pollut 157: 117–122 doi: 10.1016/j.envpol.2008.07.018
[64]	García ME (2010) Aeropalinología de la ciudad de Yerba Buena, provincia de Tucumán (Argentina). Acta Botánica Malacitana 35: 115–131.
[65]	WHO (World Health Organization), Ambient air pollution: A global assessment of exposure and burden of disease. Published by WHO Document Production Services,Geneva,Switzerland. 2016. Available from: http://apps.who.int/iris/bitstream/10665/250141/1/9789241511353-eng.pdf.

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