Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved.

[1]  Jiaying Li,et al.  Equilibrium Selection under the Bayes-Based Strategy Updating Rules , 2020, Symmetry.

[2]  Jiang Liu,et al.  Modeling the impact of soundscape drivers on perceived birdsongs in urban forests , 2020 .

[3]  Jianjun He,et al.  Spatial and temporal distribution of open bio-mass burning in China from 2013 to 2017 , 2019, Atmospheric Environment.

[4]  K. Tan,et al.  Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems , 2020, IEEE Transactions on Cybernetics.

[5]  Investigating impact of emission inventories on PM2.5 simulations over North China Plain by WRF-Chem , 2018, Atmospheric Environment.

[6]  Qi Yue,et al.  Two-sided matching for hesitant fuzzy numbers in smart intelligent technique transfer , 2020 .

[7]  Jingkuang Liu,et al.  Exploring factors influencing construction waste reduction: A structural equation modeling approach , 2020 .

[8]  Jun Xu,et al.  Real-Time Prediction of Taxi Demand Using Recurrent Neural Networks , 2018, IEEE Transactions on Intelligent Transportation Systems.

[9]  Ling Zhang,et al.  A high spatial-temporal resolution emission inventory of multi-type air pollutants for Wuxi city , 2019, Journal of Cleaner Production.

[10]  A. Asundi,et al.  High-resolution transport-of-intensity quantitative phase microscopy with annular illumination , 2017, Scientific Reports.

[11]  Sik Chung Tam,et al.  Short-term prediction of SO2 concentration in Macau with artificial neural networks , 1998 .

[12]  Branka Vucetic,et al.  A Tutorial on Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions , 2020, IEEE Communications Surveys & Tutorials.

[13]  Hongwei Lu,et al.  Analysis of microplastics in a remote region of the Tibetan Plateau: Implications for natural environmental response to human activities. , 2020, The Science of the total environment.

[14]  Yu Sun,et al.  Video Coding Optimization for Virtual Reality 360-Degree Source , 2020, IEEE Journal of Selected Topics in Signal Processing.

[15]  Wei-wei Chen,et al.  [Spatial and Temporal Characteristics of Air Quality and Cause Analysis of Heavy Pollution in Northeast China]. , 2019, Huan jing ke xue= Huanjing kexue.

[16]  C. Zender,et al.  Projected changes in dust emissions and regional air quality due to the shrinking Salton Sea , 2018 .

[17]  Weiqiang Zhang,et al.  Progress of ethylene action mechanism and its application on plant type formation in crops , 2020, Saudi journal of biological sciences.

[18]  Dan Chen,et al.  Impacts of aerosol–radiation interaction on meteorological forecasts over northern China by offline coupling of the WRF-Chem-simulated aerosol optical depth into WRF: a case study during a heavy pollution event , 2020 .

[19]  Haibo Gao,et al.  Definition and Application of Variable Resistance Coefficient for Wheeled Mobile Robots on Deformable Terrain , 2020, IEEE Transactions on Robotics.

[20]  Xiaowei Deng,et al.  Dynamic response of saddle membrane structure under hail impact , 2020 .

[21]  Roy M. Harrison,et al.  Characterization and source apportionment of carbonaceous PM2.5 particles in China - A review , 2018, Atmospheric Environment.

[22]  K. Yadav,et al.  USE OF RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK APPROACH FOR METHYLENE BLUE REMOVAL BY ADSORPTION ONTO WATER HYACINTH , 2020 .

[23]  Jun Liu,et al.  Efficient Deployment With Geometric Analysis for mmWave UAV Communications , 2020, IEEE Wireless Communications Letters.

[24]  Jun Yao,et al.  Multifidelity Genetic Transfer: An Efficient Framework for Production Optimization , 2021 .

[25]  Liu Ruitin.  Analysis of Meteorological Characteristics during Winter Haze Events in Beijing , 2014 .

[26]  Lanhe Zhang,et al.  Fate of NaClO and membrane foulants during in-situ cleaning of membrane bioreactors: Combined effect on thermodynamic properties of sludge , 2019, Biochemical Engineering Journal.

[27]  Junyi Zhang,et al.  Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering , 2021, Inf. Sci..

[28]  Hui Zhao,et al.  History Matching of Naturally Fractured Reservoirs Using a Deep Sparse Autoencoder , 2021 .

[29]  Jingkuang Liu,et al.  An environmental assessment model of construction and demolition waste based on system dynamics: a case study in Guangzhou , 2019, Environmental Science and Pollution Research.

[30]  Fengkai Gao,et al.  Energy Efficiency Modeling of Integrated Energy System in Coastal Areas , 2020, Journal of Coastal Research.

[31]  Zifa Wang,et al.  Explaining the spatiotemporal variation of fine particle number concentrations over Beijing and surrounding areas in an air quality model with aerosol microphysics. , 2017, Environmental pollution.

[32]  Ying Li,et al.  Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8 , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Gangyi Jiang,et al.  Optimizing Multistage Discriminative Dictionaries for Blind Image Quality Assessment , 2018, IEEE Transactions on Multimedia.

[34]  Meigen Zhang,et al.  MICS-Asia III: multi-model comparison and evaluation of aerosol over East Asia , 2019, Atmospheric Chemistry and Physics.

[35]  Lanhe Zhang,et al.  New insights into the rapid formation of initial membrane fouling after in-situ cleaning in a membrane bioreactor , 2019, Process Biochemistry.

[36]  Jianjun He,et al.  Uncertainty analysis of spatiotemporal characteristics of haze pollution from 1961 to 2017 in China , 2020 .

[37]  Lu Zhang,et al.  hPSD: A Hybrid PU-Learning-Based Spammer Detection Model for Product Reviews , 2020, IEEE Transactions on Cybernetics.

[38]  Li He,et al.  Ecological vulnerability assessment for ecological conservation and environmental management. , 2018, Journal of environmental management.

[39]  Zhong-ren Peng,et al.  Investigating the Role of Meteorological Factors in the Vertical Variation in PM2.5 by Unmanned Aerial Vehicle Measurement , 2019, Aerosol and Air Quality Research.

[40]  Patricio Perez,et al.  PM2.5 forecasting in a large city: Comparison of three methods , 2008 .

[41]  Li He,et al.  Sustainability appraisal of desired contaminated groundwater remediation strategies: an information-entropy-based stochastic multi-criteria preference model , 2020, Environment, Development and Sustainability.

[42]  B. Hu,et al.  Emission reduction decision of agricultural supply chain considering carbon tax and investment cooperation , 2021 .

[43]  Shu-lei Tian,et al.  Effects of Al3+ on the microstructure and bioflocculation of anoxic sludge. , 2020, Journal of environmental sciences.

[44]  L. Tian,et al.  Transport of intensity phase retrieval and computational imaging for partially coherent fields: The phase space perspective , 2015 .

[45]  Jakub Montewka,et al.  A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions , 2021, Reliab. Eng. Syst. Saf..

[46]  Lanhe Zhang,et al.  Effect of Fe3+ on the sludge properties and microbial community structure in a lab-scale A2O process. , 2021, The Science of the total environment.

[47]  S. Iglauer,et al.  Dynamic Pore‐Scale Dissolution by CO2‐Saturated Brine in Carbonates: Impact of Homogeneous Versus Fractured Versus Vuggy Pore Structure , 2020, Water Resources Research.

[48]  Yimiao Huang,et al.  Large group activity security risk assessment and risk early warning based on random forest algorithm , 2021, Pattern Recognit. Lett..