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This review paper is focusing on the investigations and studies on emissions of applying the Compressed Natural Gas (CNG) in the CNG-Diesel engines. As well as, the paper is highlighting studies that involved in the search for alternative fuels to be used for less emissions and good performance vehicles. Moreover, illustrating the developments occurred in this field for the previous five years, from year 2010 to year 2015. And, as observed, many researches indicate that there is a significant decreasing in emissions in the existence CNG-Diesel and other alternative fuels when comparing with the conventional pure diesel heavy duty vehicles engines. Furthermore, there are some attempts of finding alternative fuels were discussed in this paper.

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.