###### Abstract
This model employs interpolation to transform irregular spatial air quality and weather data into a uniform grid for CNN processing. The ICNN demonstrated high accuracy with an R-squared value over 0.97 and a root mean square error (RMSE) around 16% of the standard deviation. It also effectively forecasted high PM concentrations, with detection probabilities and critical success indices both exceeding 0.90 and 0.85, respectively [11]. In 2023, [PERSON] and [PERSON] developed a novel spatiotemporal model, the Label Distribution Spatiotemporal Prediction Model (LDSPM), employing the K-Core algorithm concept combined with label distribution learning. This model integrates K-Core techniques with label distribution support vector regression to assess the impact of meteorological factors on PM2.5 levels. Each factor is analyzed using complete ensemble empirical mode decomposition with adaptive noise and predicted through a long short-term memory neural network. The final predictions are refined using a particle swarm optimization extreme learning machine, demonstrating superior performance compared to existing models and offering innovative approaches for PM2.5 prediction [12]. During the period from 1998 to 2012, there were some studies on air quality forecasting in Macau. In 1998, [PERSON] and [PERSON] utilized machine learning methods to predict future five-day concentrations of SO2 in Macau using ANNs as the prediction model. The results indicated that the accuracy of the ANN models was within 14.45% and 13.71% for two testing periods, suggesting the potential of ANNs in air quality forecasting [13]. In 2008, [PERSON] et al. applied the Kalman filter algorithm to predict winter PM10 concentrations in Macau. The algorithm was implemented on AR(2) and AREX models, with the latter incorporating meteorological data such as wind speed and direction as external inputs on the basis of the AR(2) model. The study showed that the Kalman filter algorithm could be used for forecasting and that the AREX model outperformed the AR(2) model in terms of prediction accuracy [14]. In 2012, [PERSON] et al. constructed five different models, including the linear and radial basis function models of SVM, to predict daily environmental air pollutant concentrations in Macau and compared the prediction accuracy of these models. The study revealed that both the linear and radial basis function models of SVM demonstrated good performance. Future efforts could explore the combination of genetic algorithms with SVM to improve the accuracy and efficiency of the models [15]. Since 2020, three research papers focusing on the prediction of air quality in Macau have been published, rekindling interest in this area. In 2020, [PERSON] et al. utilized long short-term memory (LSTM) recurrent neural networks to predict future concentrations of air pollution substances (APSs) in Macau. The study also incorporated pre-trained neural networks using transfer learning to assist in constructing high-accuracy neural network models. The results indicated that LSTM networks initialized with pre-trained neural networks achieved a higher level of prediction accuracy and required fewer training iterations [16]. In 2022, to predict the levels of PM10 and PM2.5 in Macau, [PERSON] et al. employed machine learning methods such as Random Forest (RF), Gradient Boosting (GB), support vector regression (SVR), and Multiple Linear Regression (MLR). The study found that Random Forest (RF) was a reliable method for predicting pollutant concentrations in Macau, especially during periods of dramatic changes in air quality due to large-scale pandemic-related lockdowns [17]. In 2023, [PERSON] et al. utilized Artificial Neural Networks (ANNs), Random Forest (RF), Extreme Gradient Boosting (GBX), support vector regression (SVR), and Multiple Linear Regression (MLR) to predict 24 h and 48 h concentrations of PM10, PM2.5, and CO in Macau. The results demonstrated that RF and SVM performed best in predicting concentrations of PM10, PM2.5, and CO [18].