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Remote sensing study of air pollution in the São Paulo Metropolitan Region: an intercomparison analysis with in situ measurements

Grant number: 13/21486-9
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): December 01, 2013
Effective date (End): November 30, 2015
Field of knowledge:Physical Sciences and Mathematics - Geosciences
Principal Investigator:Nilton Manuel Évora do Rosário
Grantee:Gabriela Resende D'Alessio
Home Institution: Instituto de Ciências Ambientais, Químicas e Farmacêuticas (ICAQF). Universidade Federal de São Paulo (UNIFESP). Campus Diadema. Diadema , SP, Brazil

Abstract

Air pollution monitoring is critical to assess its impacts. In spite of its accuracy and importance, in situ measurements are unable to provide an appropriate spatial characterization of air pollution in regional and global scales. In this context, atmosphere remote sensing (ARS) is an alternative monitoring tool able to attend the higher spatial coverage required by air pollution studies. However, remote sensing pollution products are still not officially integrated as air quality indicators by Air Quality Monitoring and Control Agencies, particularly in Brazil. The main reason for that is the difficulty to translate remote sensing measurements, which are integrated along the atmosphere column, to accurate surface information. Vertical distribution of pollution has been pointed out to play a major role on the application of remote sensing to assess surface air quality. The main goal of the present proposal is to investigate the relationship between Particulate Matter Optical Depth (Ä_(PM,» )), a remote sensing measurement of atmospheric particulate matter loading, and Particulate Matter concentration [PM] at the surface in São Paulo Metropolitan Region (SPMR). Focusing on the issue of the vertical distribution of particulate matter effect, the project aims to evaluate the correction of this effect basing on pollution vertical profile predicted by an air quality model. This synergy between measurements and modeling data is expected to improve remote sensing data as indicator of surface air quality conditions. To Brazil, a country with continental dimension, the integration of remote sensing pollution data is fundamental.