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An empirical model for estimating daily atmospheric column-averaged CO(2) concentration above São Paulo state, Brazil

BACKGROUND: The recent studies of the variations in the atmospheric column-averaged CO(2) concentration ([Formula: see text] ) above croplands and forests show a negative correlation between [Formula: see text] and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the m...

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Detalles Bibliográficos
Autores principales: da Costa, Luis Miguel, de Araújo Santos, Gustavo André, Panosso, Alan Rodrigo, de Souza Rolim, Glauco, La Scala , Newton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9188726/
https://www.ncbi.nlm.nih.gov/pubmed/35689700
http://dx.doi.org/10.1186/s13021-022-00209-7
Descripción
Sumario:BACKGROUND: The recent studies of the variations in the atmospheric column-averaged CO(2) concentration ([Formula: see text] ) above croplands and forests show a negative correlation between [Formula: see text] and Sun Induced Chlorophyll Fluorescence (SIF) and confirmed that photosynthesis is the main regulator of the terrestrial uptake for atmospheric CO(2). The remote sensing techniques in this context are very important to observe this relation, however, there is still a time gap in orbital data, since the observation is not daily. Here we analyzed the effects of several variables related to the photosynthetic capacity of vegetation on [Formula: see text] above São Paulo state during the period from 2015 to 2019 and propose a daily model to estimate the natural changes in atmospheric CO(2). RESULTS: The data retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) show that Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH) are the most significant factors for predicting the annual [Formula: see text] cycle. The daily model of [Formula: see text] estimated from Qg and RH predicts daily [Formula: see text] with root mean squared error of 0.47 ppm (the coefficient of determination is equal to 0.44, p < 0.01). CONCLUSION: The obtained results imply that a significant part of daily [Formula: see text] variations could be explained by meteorological factors and that further research should be done to quantify the effects of the atmospheric transport and anthropogenic emissions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13021-022-00209-7.