A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health

PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance.

[2]  Lei Yu,et al.  New Intelligent Control Strategy Hybrid Grey–RCMAC Algorithm for Ocean Wave Power Generation Systems , 2020 .

[3]  Jianzhou Wang,et al.  A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting , 2019, Applied Energy.

[4]  Jianzhou Wang,et al.  Impacts of haze pollution on China's tourism industry: A system of economic loss analysis. , 2021, Journal of environmental management.

[5]  G. de Gennaro,et al.  PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set , 2014, Chemistry Central Journal.

[6]  Chengshi Tian,et al.  Modelling of carbon price in two real carbon trading markets , 2020 .

[7]  Wei-Chiang Hong,et al.  A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back propagation neural network for mid‐short term load forecasting , 2020 .

[8]  Jianzhou Wang,et al.  A novel hybrid model for short-term wind power forecasting , 2019, Appl. Soft Comput..

[9]  Maitreyee Dutta,et al.  Fuzzy Inference System Tree with Particle Swarm Optimization and Genetic Algorithm: A novel approach for PM10 forecasting , 2021, Expert Syst. Appl..

[10]  Li Li,et al.  Evaluation of future energy consumption on PM2.5 emissions and public health economic loss in Beijing , 2018, Journal of Cleaner Production.

[11]  Yongmei Fang,et al.  Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices , 2020 .

[12]  Zeshui Xu,et al.  Comprehensive Economic Loss Assessment of Disaster Based on CGE Model and IO model—A Case Study on Beijing “7.21 Rainstorm” , 2019, Economic Impacts and Emergency Management of Disasters in China.

[13]  Zheng-Xin Wang,et al.  Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model , 2019, Journal of Cleaner Production.

[14]  Wenqing Wu,et al.  Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model , 2019, Renewable Energy.

[15]  P. J. García Nieto,et al.  PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study. , 2018, The Science of the total environment.

[16]  Hao Li,et al.  Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening , 2017 .

[17]  B. Ostro,et al.  Air pollution and health effects: A study of medical visits among children in Santiago, Chile. , 1999, Environmental health perspectives.

[18]  Liying Wang,et al.  Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications , 2020, Eng. Appl. Artif. Intell..

[19]  Yu Jin,et al.  The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model , 2018, Inf. Sci..

[20]  Qing Yang,et al.  The impact of trade on fuel-related mercury emissions in Beijing—evidence from three-scale input-output analysis , 2017 .

[21]  Lifeng Wu,et al.  Using FGM(1,1) model to predict the number of the lightly polluted day in Jing-Jin-Ji region of China , 2019, Atmospheric Pollution Research.

[22]  Haiyan Lu,et al.  An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting , 2018, Appl. Soft Comput..

[23]  Jie Xia,et al.  Application of a new information priority accumulated grey model with time power to predict short-term wind turbine capacity , 2019, Journal of Cleaner Production.

[24]  Yunzhen Xu,et al.  Air quality early-warning system for cities in China , 2017 .

[25]  Li Li,et al.  The health economic loss of fine particulate matter (PM2.5) in Beijing , 2017 .

[26]  Chaoqing Yuan,et al.  On novel grey forecasting model based on non-homogeneous index sequence , 2013 .

[27]  Chao Chen,et al.  Improved pollution forecasting hybrid algorithms based on the ensemble method , 2019, Applied Mathematical Modelling.

[28]  Yanjun Shi,et al.  Two-step-hybrid model based on data preprocessing and intelligent optimization algorithms (CS and GWO) for NO2 and SO2 forecasting , 2019, Atmospheric Pollution Research.

[29]  Lifeng Wu,et al.  Prediction of air quality indicators for the Beijing-Tianjin-Hebei region , 2018, Journal of Cleaner Production.

[30]  V. Silva,et al.  Probabilistic earthquake and flood loss assessment in the Middle East , 2020, International Journal of Disaster Risk Reduction.

[31]  Yan Hao,et al.  The study and application of a novel hybrid system for air quality early-warning , 2019, Appl. Soft Comput..

[32]  Yan Hao,et al.  Point and interval forecasting for carbon price based on an improved analysis-forecast system , 2020, Applied Mathematical Modelling.

[33]  Xin Ma,et al.  A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China , 2019, Energy.

[34]  C. Pope,et al.  Long-term ambient fine particulate matter air pollution and lung cancer in a large cohort of never-smokers. , 2011, American journal of respiratory and critical care medicine.

[35]  Hsiao-Tien Pao,et al.  Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model , 2012 .

[36]  Huiru Zhao,et al.  An optimized grey model for annual power load forecasting , 2016 .

[37]  Xiang Xu Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning , 2020 .