###### Abstract
Despite the relevant effects of cylinder-by-cylinder variation on SI engine performance and emissions, few technical papers are available in the literature. In addition, a reduced attention is devoted to understanding the causes that originates this phenomenon. As an example, [PERSON] [9] investigated the effect of cylinder-by-cylinder variation on indicated mean effective pressure (IMEP) in a radial nine-piston engine, founding differences in single cylinder IMEP up to 40%. [PERSON] et al. [10] conducted an experimental and numerical study on a four-cylinder spark ignition engine to evaluate the influence of cylinder-by-cylinder variation on the performance. They attribute this phenomenon to the non-uniformity of gas exchange between cylinders and estimated a relative deviation of individual cylinder IMEP larger than 30%. Recently, [PERSON] et al. [11] proposed a combustion variation control strategy, optimizing the thermal efficiency of a lean burn spark ignition engine by means of the reduction in the cylinder-by-cylinder variability. They reduced the combustion variation up to 28% with a maximum and an average increase in the brake thermal efficiency of 0.32% and 0.13%, respectively. Referring to the cylinder-out emissions, [PERSON] et al. [12] carried out individual cylinder measurements of emissions and pressure cycles on a six-cylinder lean burn natural gas engine. Mixture quality variations between cylinders were confirmed by the analysis of heat release and A/F ratio. Emission measurements in each cylinder were performed only at high/medium loads and low speeds. No clear trend between A/F ratio and HC emissions of single cylinders was observed, while lower NOx emissions were detected with the air/fuel mixture leaning. Concerning the numerical approach, a number of methodologies were adopted by worldwide researchers to explore the potentials of the models in reproducing both engine and cylinder performance. In the case of exhaust emissions, regression methods, Artificial Neural Network (ANN) models, Adaptive Neuro-Fuzzy Systems (ANFIS) and various phenomenological sub-models integrated into the fluid-dynamic codes are used. As an example, [PERSON] et al. [13] utilized an ANN approach to predict the overall performance, HCand CO emissions at the exhaust of an SI engine fueled with gasoline at different octane numbers. It was observed that the ANN model was able to reproduce the engine behavior at different speed/load points with very low root mean square errors. [PERSON] et al. [14] coupled a 1D code to a detailed chemical kinetics solver for the estimation of the engine-out NOx emissions of a direct injection spark ignition engine. The model was validated in a single operating point against the experimental findings and the outcomes of a developed 3D CFD model. Then, it was extended for the prediction of NOx emissions in the entire engine operating map, denoting a good agreement with the experimental data. In a previous work [15], the authors studied a turbocharged gasoline engine through a 1D code to predict the combustion and the emissions (HC, CO and NO) of a single cylinder under a limited set of operating points. A certain inaccuracy was found in reproducing the experimental trend of HC emission at part load and increasing the EGR content. [PERSON] et al. [16] analyzed the combustion process and the emissions of an original compression-ignition (CI) engine converted to an SI natural gas (NG) engine using 3D G-equation based RANS simulations. According to a unique set of model tuning parameters, 3D model was able to qualitatively predict the effect of NG composition on emissions over a reduced range of operating conditions. In the light of the above-discussed literature works, it emerges a lack of combined numerical/experimental studies on the main pollutant emissions of SI engines over different operating conditions (variation in speed/load point and EGR rate), also including the effects of cylinder-by-cylinder variation.