Assessing Spatial Variation of PBL Height and Aerosol Layer Aloft in São Paulo Megacity Using Simultaneously Two Lidar during Winter 2019

• Published in: Atmosphere Vol. 13; no. 4; p. 611
• Main Authors: Moreira, Gregori de Arruda, Oliveira, Amauri Pereira de, Codato, Georgia, Sánchez, Maciel Piñero, Tito, Janet Valdés, Silva, Leonardo Alberto Hussni e, Silveira, Lucas Cardoso da, Silva, Jonatan João da, Lopes, Fábio Juliano da Silva, Landulfo, Eduardo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2022
• Subjects: Aerosol optical depth; Aerosols; Algorithms; atmospheric boundary layer height; Biomass; Biomass burning; Breeze effects; Burning; Detection; Diurnal variations; elastic lidar; Height; horizontal homogeneity; Lidar; Megacities; Optical analysis; Optical thickness; Plumes; Radiation; Remote sensing systems; Satellite data; Sea breezes; Sea level; Spatial variations; Topographic effects; Winter
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13040611

This work presents the use of two elastic lidar systems to assess the horizontal variation of the PBL height (PBLH) and aerosol layer aloft in the São Paulo Megacity. These two lidars performed simultaneous measurements 10.7 km apart in a highly urbanized and relatively flat area of São Paulo for two winter months of 2019. The results showed that the PBLH differences display diurnal variation that depends on the PBL during daytime growth phases. Cloud and sea breeze effects control most of PBLH variation. In the absence of cloud and sea breeze, the maximum difference (~300 m) occurs in the rapid development stage and is due to topographic effects. When the PBL approaches its maximum daily value, it tends to level off with respect to the topography. In addition, it was presented a method that combines elastic lidar (to detect an aerosol layer) and satellite data (to classify such a layer from Aerosol Optical Depth (AOD) and Aerosol Index (AI) information) for the detection of biomass burning events. This methodology demonstrated that the variations caused by Biomass Burning in AOD and AI enable both the detection of aerosol plumes originating from biomass burning and the identification of their origin.