Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model’s results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns is crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5 related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components - feature selection, clustering, and integrated prediction, the model first employs the non-dominated sorting Genetic Algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the Adaboost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.