A new scheme of PM2.5 and O3 control strategies with the integration of SOM, GA and WRF-CAMx

Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China

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.

Emission inventory and air quality impact of non-road construction equipment in different emission stages

Non-road construction equipment (NRCE) is an important source of air pollution, and it is crucial to fully understand the impact of NRCE on atmospheric PM2.5 and O3 pollution. However, systematic assessment of the impact of NRCE emissions on the atmosphere is lacking, especially with the latest implementation of the Stage IV Standard, and current research progress is insufficient for the development of effective control measures. This study estimated NRCE emission inventories at different emission standard stages and their impact on the atmosphere, using the "2 + 26" cities as the case study area. The results showed that the total NRCE emissions of CO, NOx, VOC, and PM2.5 were 387, 418, 82, and 24 kt in 2015 and 319, 262, 62, and 15 kt in 2020 and are predicted to be 270, 226, 48, and 10 kt in 2025, respectively. Simulation results showed that the contributions of NRCE to NO3-, NO2, PM2.5, and O3 were 16.7 %, 18.9 %, 7.7 %, and 8.2 % in 2015 to 13.6 %, 18.4 %, 6.5 %, and 6.7 % in 2020, respectively. In both 2015 and 2020, NRCE emissions in southern cities showed greater impacts on the average concentrations in the "2 + 26" cities than those in northern cities. The contributions of local NRCE emissions to local PM2.5 and O3 concentrations in the 28 cities ranged from 30 %-59 % and 13 %-39 %, respectively. The O3 sensitivity estimated by the HDDM illustrated that nonlinear characteristics highlighted the importance of coordinated control of NOx and VOC and can inspire development of post-processing technology and electricity substitution. The belt-like area connecting Zhengzhou to Beijing showed higher exposure concentrations of PM2.5 and O3, and the concentration exposure in urban areas was much higher than that in the rural and other areas. The environmental impact assessment of NRCE emissions can provide guidance for its management and development.