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Modeling temperature and humidity effects on automobile fuel consumption and emissions

Janet Appiah Osei, Rabani Adamou, Amos T. Kabo–bah, Satyanarayana Narra, Adwoa Achiaah Osei · 2025 · 2 citationsRead the paper

Abstract Global warming is a major concern in the twenty-first century, with automobiles contributing significantly through the release of greenhouse gases that drive increases in global average temperature. The subsequent variations in weather particularly, temperature and relative humidity exert significant influence on vehicle fuel consumption and emission rates, yet these effects remain underexplored in Ghana. The study investigated this reinforcing relationship by applying an analytical model to predict the impacts of temperature and humidity on fuel consumption and CO 2 emissions, providing the first empirical assessment in the Ghanaian context. The model was developed based on three fundamental resistance forces to estimate vehicle mechanical power demand and ultimately, fuel consumption. Unlike conventional approaches, the model integrated weather factors and considered three representative speeds—aggressive (80 km/h), eco-speed (50 km/h), and traffic speed (15 km/h) to capture their combined effects on fuel use. CO 2 emissions were then estimated from the predicted fuel consumption using the IPCC default emission factors. The results depicted higher emissions as temperature and relative humidity reduced. Notably, emissions rose by approximately 3% as relative humidity decreased from higher to lower levels. Among the driving profiles, eco-speed consistently resulted in lower emissions compared to aggressive and traffic speeds, underscoring its relative efficiency under varying weather conditions. These findings underscore the substantial impacts of weather variability on vehicle CO 2 emissions, highlighting the need for policies that promote sustainable mobility practices to mitigate cumulative fleet carbon dioxide emissions under changing climatic conditions.

1 idea Seedlabs derived from this research

A navigation API that adjusts 'eco-friendly' route suggestions based on real-time local temperature and humidity data to minimize fuel consumption and CO2 emissions.

AI score 73/100