Yang Zhongshan, Wang Jian
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China.
Environ Res. 2017 Oct;158:105-117. doi: 10.1016/j.envres.2017.06.002. Epub 2017 Jun 14.
Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM and PM respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability.
随着工业化和城市化进程，许多国家的空气污染正在恶化，导致气候变化并影响人们的健康，因此，这使得政策制定者的工作更加困难。因此，建立一个更科学的空气质量监测和预警系统，以客观评估空气污染程度并准确预测污染物浓度，既紧迫又必要。然而，将空气质量评估与空气污染物浓度预测相结合以建立空气质量系统的情况并不常见。在本文中，我们提出了一种新的空气质量监测和预警系统，包括评估模块和预测模块。在空气质量评估模块中，采用模糊综合评价法来确定主要污染物，并更科学地评估空气污染程度。在空气污染物浓度预测模块中，提出了一种新颖的混合模型，该模型将互补总体经验模态分解、改进的布谷鸟搜索和差分进化算法与埃尔曼神经网络相结合，以提高六种主要空气污染物浓度的预测精度。为了验证该系统的有效性，使用了中国两个城市的污染物数据。模糊综合评价结果表明，西安和济南的主要空气污染物分别为PM和PM，且西安的空气质量优于济南。预测结果表明，所提出的混合模型由于其更高的预测精度和稳定性，明显优于所有基准模型。
