4.2. Integrated Limitation Correction Strategy 4.2.1 Correction of metrological disturbances Mobile air quality monitoring systems operate under dynamic environmental conditions that may introduce additional measurement uncertainty compared with fixed monitoring stations. Sensors mounted on moving vehicles are exposed to me-chanical vibrations, rapid fluctuations in ambient temperature, and variations in relative humidity. These disturbances may alter sensor responses and affect measurement sta-bility, particularly when compact or low-cost sensors are used. As a result, raw meas-urements obtained from mobile platforms may deviate from the true ambient pollutant concentrations. To mitigate these metrological disturbances, a dynamic correction model can be introduced to compensate for the influence of environmental and operational factors on sensor measurements. The proposed correction approach assumes that the response of the sensor can be influenced by several variables during mobile monitoring operations, including temperature, humidity, airflow disturbances associated with vehicle speed, and mechanical vibrations generated by vehicle movement. Under moderate environmental variations, the influence of these factors on sensor response can be approximated using a first-order linear sensitivity model. This model-ing approach is commonly applied in environmental sensor calibration studies, where the measured concentration is corrected by introducing adjustment terms proportional to the deviations of environmental parameters from reference conditions. The corrected concentration can therefore be expressed as:

4.2. Integrated Limitation Correction Strategy 4.2.1 Correction of metrological disturbances Mobile air quality monitoring systems face unique challenges when operating in constantly changing environmental conditions, which can create greater measurement uncertainty than what occurs at stationary monitoring locations. When sensors are attached to moving vehicles, they encounter mechanical vibrations, sudden changes in surrounding temperature, and shifting humidity levels. These disruptions can change how sensors respond and impact the reliability of measurements, especially with compact or budget-friendly sensor types. Consequently, the raw data collected from mobile platforms may not accurately reflect the actual pollutant levels in the air. A dynamic correction model can help address these metrological disruptions by adjusting for how environmental and operational conditions affect sensor readings. The correction method we propose recognizes that sensor performance during mobile monitoring can be influenced by multiple factors, such as temperature, humidity, air movement disturbances caused by vehicle speed, and mechanical vibrations from vehicle motion. When environmental conditions change moderately, these factors' impact on sensor performance can be estimated through a first-order linear sensitivity model. This modeling technique is frequently used in environmental sensor calibration research, where measured concentrations are adjusted by adding correction terms that correspond to how much environmental parameters differ from standard reference conditions. The adjusted concentration can be calculated as: