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Emission-free economy is an achievable target with the formulation of policies by the Governments around the globe. A major contribution is made by the transport industry. Delhi is one of the examples among the cities in India which is polluting the air by transportation which is about 20 percent. Towards achieving the Sustainable Development Goals, the nation has to take a different trajectory from concentrating on the technical aspect of reducing the hazardous emissions to refining the regulatory awareness. The research study is based on the statistical inputs provided by the government agencies and the judicial verdicts seeking a pollution free atmosphere. The Judiciary has been proactively recommending and directing the respective government to take necessary action towards controlling the pollution caused due to road transport. Nevertheless, the use of Artificial Intelligence has been an area which has not been strongly recommended by the apex court. The regulatory and judicial control over the emissions is towards a penalizing perspective and the technological assistance has not been resorted to. Though a one hundred percent emission-free economy is not a possibility, the areas which are still not explored or have not been brought under the purview of consideration have been the scope of study in the research paper. [ABSTRACT FROM AUTHOR]

India is one of the largest countries of the world with a high population density. Being a developing country, this nation is facing enormous challenges in regulating its air quality. Population growth, vehicular ownership, urbanization, demand of energy and industrialization process are driving the pollution level to an alarming state in Delhi which is the capital of India. Accurate air quality index (AQI) forecasting of Delhi will assist Government in making strategic decisions so as to avoid catastrophic deterioration in the quality of healthy air. Motivated from this, in this paper, for the first time a systematic study is made to assess the effectiveness of four promising univariate statistical forecasting models and twelve machine learning (ML) models in predicting the AQI of Delhi. Four accuracy measures including "mean absolute scaled error (MASE), symmetric mean absolute percentage error (SMAPE), root means square error (RMSE) and mean absolute error (MAE)" are used to assess the performance of statistical and ML models. Extensive statistical analyses on obtained results using Wilcoxon Signed-Rank test illustrate the superiority of multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) models than other considered models in predicting the Delhi AQI. [ABSTRACT FROM AUTHOR]