Use of satellite data for estimation of air pollution by particulate matter in Havana.

Main Article Content

Veronica Gutiérrez Quintero
Elieza Menezes Ruiz
Alina Roig Rassi
Marco Andrés Guevara Luna
Luis Carlos Belalcazar

Abstract

Measurements of a particulate material are important in air quality, public health,
epidemiological studies and decision making for short, and long, term measurements. In Cuba, there are no air quality monitoring networks. Only at some points are there air quality stations installed that measure the concentration of PM (Particulate Matter). Satellite measurements serve as an alternative approach to study air quality. The objective of this study is to evaluate the use of satellite data for the measurement of air pollution by particulate matter over Havana. Aerosol optical depth (AOD) recovered by the MODIS sensor from the TERRA and AQUA satellites, and PM2.5 at the surface of the MERRA-2 satellite product were used to compare with surface measurements of PM10 and PM2.5 during a campaign carried out in 2012 at three places in Havana. The linear correlation between the satellite and surface data was determined, as well as the three statisticians to evaluate the correspondence of the values between both samples. The relation of the satellite data with the measurements in situ concludes that: The data of the concentration of PM2.5 in the surface of the MERRA-2 satellite product are not optimal for the estimation of atmospheric contamination by PM2.5, as it behave in an opposite way, the PM10 concentration can be estimated by both MODIS-TERRA and AQUA, although MODIS-AQUA results are more accurate, the correlation of PM10 with MODIS-AQUA (rp=0.60) gave a higher value than TERRA (rp=0.53) and for the estimation of PM2.5, MODIS-TERRA information can be used as the calculated correlation is rp=0.42.

Downloads

Download data is not yet available.

Article Details

How to Cite
Gutiérrez QuinteroV., Menezes RuizE., Roig RassiA., Guevara LunaM. A., & BelalcazarL. C. (1). Use of satellite data for estimation of air pollution by particulate matter in Havana. Revista Cubana De Meteorología, 27(1). Retrieved from http://rcm.insmet.cu/index.php/rcm/article/view/550
Section
Original Articles

References

Duncan, b. et al., 2014. Satellite data of atmospheric pollution for u.s. air quality applications: examples of applications, summary of data end-user resources, answers to faqs, and common mistakes to avoid. Atmospheric environment, issue 94, pp. 647-662.
EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred General Purpose (Flat and Complex TERRAin) Dispersion Model and Other Revisions. 40 CFR Part 51. Rules and Regulations
Guevara-Luna, M., Guevara-Luna, F., Mendez, J. & Belalcazar, L., 2018. Spatial and temporal assessment of particulate matter using AOD data from MODIS and Surface Measurements in the Ambient Air of Colombia. Asian Journal of Atmospheric Environment, 2(12), p. 165.
Molina, E., Turtós, L., Meneses, E. & Alonso, D., 2017. Comportamiento de las fracciones PM10 y PM2.5 en tres zonas de La Habana (2012). Higiene y Sanidad Ambiental, 4(17), pp. 1553-1564.
Oficina Nacional de Normalización, 2014. NC 1059:2014 Calidad del aire ― metodología para modelar las afectaciones de la calidad del aire a escala local debido a las emisiones de contaminantes atmosféricos desde fuentes fijas. La Habana
Saunders, R., Kahl, J. & Ghorai, J., 2014. Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth. Atmospheric Environment, Issue 91, pp. 146-153.
Scire, J., Robe, F., Fernau, M. & Yamartino, R., 2000. A User’s Guide for the CALMET Dispersion Model (Version 5). EarthTech. Inc., Concord, MA.
Turtós, L., Meneses, E., Molina, E. & Alonso, D., 2012. Informe Final, Implementación y aplicación de modelos detallados para evaluar de la contaminación atmosférica regional (50-300 km)., Ciudad de La Habana: GIA, CUBAENERGIA.
Wald, L. & Baleynaud, J., 1999. Observing air quality over the city of Nantes by means of Landsat thermal infrared data. International Journal of Remote Sensing, 20(5), p. 947–959.

Most read articles by the same author(s)