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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

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.
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Keywords 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. AIMS Environmental Science, 2018, 5(3): 173-194. doi: 10.3934/environsci.2018.3.173


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