College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
Int J Environ Res Public Health. 2019 Sep 20;16(19):3505. doi: 10.3390/ijerph16193505.
The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn't thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.
空气污染问题是人类面临的一个长期问题，近年来变得越来越严重，引起了全球的关注。建立一个科学有效的空气质量预警系统非常重要。遗憾的是，以前的研究不仅没有彻底探索空气污染物的预测，也没有彻底探索空气质量评估，相关的研究工作仍然很少，尤其是在中国。因此，本研究开发了一种由预测和评估组成的新型空气质量预警系统。首先，先进的数据预处理技术改进的完全集合经验模态分解自适应噪声（ICEEMDAN）与强大的群体智能算法鲸鱼优化算法（WOA）和高效的人工神经网络极限学习机（ELM）相结合，形成了预测模型。然后，通过模糊综合评价的方法对预测结果进行进一步分析，提供直观的空气质量信息和相应的措施。该系统在中国的京津冀地区进行了测试，该地区是世界上具有代表性的研究区域，使用了北京、天津和石家庄两年间的六种主要空气污染物的日浓度数据来验证其准确性和效率。结果表明，与其他基准模型相比，预测模型在污染物浓度预测方面具有优势，与实际情况相比，评价模型在空气质量水平报告方面令人满意。因此，该系统有望在未来在全球范围内在空气污染控制和智慧城市建设中发挥重要作用。
