Nowadays electronic noses (e-noses) are often prescribed in the permits of new or existing plants for a continuous monitoring of ambient air quality at receptors or plant fenceline. To do this, the e-nose has to guarantee a stable classification and quantification performance over time. However, the problem of drift (i.e., progressive deviation over time of sensor responses) becomes critical in the case of continuous odour monitoring in the field. Thus, specific strategies for drift compensation need to be defined. This paper focuses on the development a specific drift correction model based on OPLS to mitigate drift effects on e-nose data relevant to three olfactometric campaigns carried out at a landfill over three years. The paper aims to reduce costs associated to the recalibration of the decisional model to be performed before every seasonal e-nose monitoring campaign prescribed in the permit of the landfill. The OPLS model was built on the data collected in the first two campaigns, while data relevant to the most recent campaign were used to test its efficacy in compensating drift. The results achieved pointed out the potentialities of the OPLS model to mitigate drift effects, thereby allowing to extend the applicability of the model developed within an olfactometric campaign to the subsequent ones. The classification performance achieved involving the OPLS correction (i.e., 75%) was considerably higher than the one achieved on non-corrected data (i.e., about 55%).