Quantifying the influence of inflow atmospheric characteristics on wind turbine's operation is fundamental for performance assessment, operational diagnosis, and the estimation of power production. While it is well-established that wind speed is the primary driver of power output, additional environmental factors such as turbulence intensity, atmospheric stability, shear and veer are also known to influence the rotor captured inflow and the turbine's response. However, isolating and interpreting the contribution of each of these factors remain a continuing effort. This study presents a framework using operational data, as nacelle-mounted lidar, SCADA and environmental data, to analyse the impact of these factors in a single wind turbine in a wind farm on the Belgian offshore zone. A machine-learning model is trained to represent the power production deviation behaviour, and SHapley Additive exPlanations (SHAP) are applied to quantify the contribution of each environmental variable to predicted power output. Results show that, excluding wind speed, turbulence intensity and air density are the biggest predictors in the transition of torque-control to pitch-control of the wind turbine operation, with respective contributions that influence the power deviation prediction of 16.8 % and 14.5 %. As for higher wind speeds, air density appears as the main influential factors, contributing up to 32.8 % for the above-rated wind speed of the power curve.