Air pollution is a pressing concern in urban environments, and this research highlights its connection to traffic emissions and weather conditions. By utilizing fuzzy inference systems, the study creates a predictive model that can help urban planners understand how meteorological data influences the amount and dispersion of pollutants.
The implications of this research are significant for policymakers aiming to improve air quality in cities. Effective urban transport management becomes possible by integrating environmental considerations into planning processes. However, reliance on predictive models also carries risks, such as potential inaccuracies in forecasting which could lead to ineffective policies if not carefully validated.
👉 Pročitaj original: arXiv AI Papers
