Spatiotemporal modeling of CO2 Emissions in Casablanca, Morocco, with Integrated Web Mapping Application for Interactive Data Sharing

Abstract. As urbanization accelerates, cities is grappling with rising CO2 emissions, largely driven by road traffic and residential electricity consumption. this study delves into the heart of this environmental challenge in Casablanca, as the largest city in Morocco using advanced modeling techniques to estimate emissions from household electricity consumption in 2020, a year marked by the COVID-19 lockdown as well as emissions from private cars, buses, trams, taxis during a study period between 2019 and 2023. By integrating diverse data sources such as CO2 emission factors, transportation data, climate variables remote sensing data, and additional datasets such as population density and electricity consumption data, this research captures the full scope of emissions across the city, considering factors like temperature, precipitation, and travel time index. The findings show important emission hotspots, with districts like Sidi Bernoussi contributing the highest emissions, totaling over 124 million kg of CO2 in 2020. This surge, particularly during the COVID-19 lockdown, highlights the direct link between population density, residential energy use, and emissions. In the other hand Al Fida Mers Sultan wins the top spot as the most polluted district due to traffic intensity and the dominance of diesel cars with approximately 3.5 million tonnes CO2 in 2023. Simultaneously the study sheds light on the disparities in emissions across different modes of public transportation. While taxis are the largest emitter among public transport options, contributing 4.4 million tonnes of CO2 during the study period, tramways stand out as the most environmentally friendly choice with just 535,342 tonnes of CO2 for the reason that uses electricity and solar energy instead of fuel fossil as source of energy, this reinforces the need for sustainable transportation policies, particularly as the city works to reduce its overall carbon footprint.

A standout feature of this study is comparison of the transportation emission estimation using ODIAC satellite data, confirming its contribution to overall co2 fuel fossil emission in the city. The research also introduces the creation of an innovative web mapping tool that allows users to explore emissions data interactively, offering spatial and temporal visualizations of the results. By combining mathematical modeling, GIS, and user-friendly mapping, the research offers valuable insights for tackling urban air pollution and provides a replicable framework for other rapidly growing cities.