Modeling the Dynamics of Carbon Dioxide Over an Educational Institute

Abstract

Carbon dioxide is a major contributor to climate change. It absorbs the outgoing longwave radiation, thereby increasing the temperature in the atmosphere. This study examines the variables which contribute to the flux of CO2 over the 50-acre sprawling green campus of DA-IICT at Gandhinagar, Gujarat. The previous approach to this problem was to employ differential equations to model the CO2 emissions. We believe that a compartment-based model that incorporates fossil fuels, electricity, human emissions, and a Light Use Efficiency (LUE) model would provide a better approximation. The LUE based model computes the total carbon that is sequestered by plants. It uses the Primary Productivity Capacity ($$ \varepsilon $$) of plants and APAR (Absorbed Photosynthetically Active Radiation) to calculate the Gross Primary Productivity (GPP). Further, the Net Primary Productivity (NPP) is derived from the GPP. Three dedicated separate models using monthly MODIS NDVI, MODIS FPAR, and MODIS NPP time-series datasets were used to model this. To integrate the above, a Decision Tree-based algorithm was applied to compute the best fit curve and approximate it to the Keeling curve which is a graph of the accumulation of CO2 in the Earth’s atmosphere recorded at the Mauna Loa Observatory, Hawaii for all the three cases. The resultant curves indicated an MSE (Mean Square Error) close to zero and an upward trend was noticed for the future validation dataset.