## 1 Introduction  
With the advancement of industrialization and urbanization, air pollution has become a global environmental issue, directly impacting human health and quality of life. Prolonged exposure to air pollutants has been linked to a wide range of diseases, including cardiovascular [1], respiratory [2], and neurological disorders [3], among others, making comprehensive air quality monitoring and timely reporting essential [4]. Many cities have established national monitoring stations to release reliable Air Quality Index (AQI) data, keeping the public informed about air quality and managing pollutants. While these stations provide accurate pollutant measurements, their high installation and maintenance costs limit widespread use. To address this, regions are adopting low-cost monitoring devices with lower installation and maintenance expenses. However, these devices are more susceptible to environmental factors, such as fluctuations in temperature and humidity, which can lead to sensor drift or variations in sensitivity. Additionally, interference from other pollutants, such as ozone or carbon monoxide, can affect sensor readings, causing cross-sensitivity or inaccurate measurements. Unlike national control stations, which use calibrated, high-precision instruments and operate in controlled environments, low-cost devices may experience greater variability in their readings due to these environmental influences, ultimately resulting in less accurate data for air quality assessment compared to national control stations. To address the issue of low accuracy in measurement data from low-cost air monitoring sensors, calibration methods are generally divided into hardware and software approaches. Hardware compensation involves building circuits to adjust for factors such as temperature and humidity that can influence sensor data [5]. However, this approach lacks flexibility and often leads to significant errors under varying environmental conditions, making it impractical for real-world calibration applications. To improve precision, software compensation methods rely on reference data collected from public air monitoring stations, often using on-site techniques to adjust sensor outputs [6]. Traditional software compensation methods, including interpolation [7], multiple linear regression [8], etc., are widely employed in this context. [PERSON] et al. [9] utilized interpolation to estimate missing values and smooth sensor outputs, while [PERSON] et al. [10] applied multiple linear regression to account for temperature and humidity effects, improving calibration accuracy. However, traditional methods assume a linear relationship [11] between errors and environmental factors, which often fails to capture complex error patterns and adapt to sensor drift or environmental changes, leading to unstable performance in dynamic conditions. These limitations [12] have driven the adoption of machine learning-based approaches for more accurate sensor calibration. Recently, machine learning techniques have emerged as promising alternatives to overcome these challenges. Random Forest Regression (RFR) effectively addresses cross-sensitivity [13], while [PERSON] and [PERSON] [14] employed methods like SVM, k-nearest neighbors, and random forests for calibration using government-approved data. [PERSON] [15] demonstrated the efficiency of multi-layer perceptrons for PM\({}_{2.5}\) time calibration. Recurrent Neural Networks (RNNs) model historical time series but face challenges like pre-determining time delays and capturing long-term dependencies. Long Short-Term Memory (LSTM) networks [16] address these issues, as shown by studies like [PERSON] [17] using HypeAIR for real-time calibration and [PERSON] et al. 's [18] demonstration of CNN and RNN effectiveness for calibrating CO and O\({}_{3}\) sensors, surpassing traditional regression techniques. Linear regression is often regarded as a straightforward and intuitive approach for improving sensor accuracy. However, as noted by [PERSON] et al.