Urban air pollution remains a critical environmental and public health challenge, with the World Health Organization estimating approximately 3.1 million deaths annually due to air quality degradation. While smart cities and green infrastructure initiatives have emerged as promising solutions, a significant research gap persists: there is limited quantitative guidance on the optimal balance between population density and green space requirements to maintain air quality in rapidly urbanizing areas. This research addresses this gap by introducing a novel theoretical model that establishes a mathematical relationship between three key urban parameters population density, total green space area, and non-green space area, to estimate and evaluate air pollution levels in smart cities. Unlike existing frameworks that primarily focus on qualitative benefits of green infrastructure or general urban design principles, this model provides a quantitative tool for city planners and policymakers to determine the minimum green space requirements necessary to maintain acceptable air quality standards relative to population size. The model is grounded in established ecological principles regarding carbon dioxide absorption through photosynthesis and oxygen production by vegetation, while acknowledging that human respiration contributes unavoidable CO2 emissions in urban environments. The theoretical framework is demonstrated through practical application examples, including Singapore and a hypothetical smart city scenario. The findings indicate that the proposed model can serve as a decision-support tool for sustainable urban planning and policy formulation, enabling governments to establish evidence-based regulations for green space preservation and urban development.
Mohammad Mahmoud Abu Omar (Sat,) studied this question.
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