Urban environmental problems are contributed by numerous factors, such as greenhouse gas emissions and air quality. Material combustion from vehicles, manufacturing plants, fossil fuel consumption, and many other industrial activities are the primary sources of atmospheric pollution. These noxious implications cause thousands of premature deaths, million years of life lost, and a contributor to climate change, primarily in developing countries. Therefore, pollution forecast and simulation before happening are the main focus of this paper. It helps address pollution levels, infrastructure installation of cities and underpins a range of environmental policies. Awareness of the characteristics of the atmospheric quality, including the mathematical basis for assessing pollution, allows building the pollution assessment models required to understand air quality and controls. In this paper, an effective model for simulating and analyzing atmospheric quality using cellular automata is proposed. Our model employs three novel underlying rules, e.g. rules of gravity, diffusion, and wind. Four experimental scenarios have been conducted to demonstrate the applicability of update rules necessitated to apprehend air pollution.