Yongmei Zhang^{1, *}, Jianzhe Ma², Lei Hu³, Keming Yu⁴, Lihua Song^{1, 5}, Huini Chen¹

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1929-1944, 2020, DOI:10.32604/cmc.2020.010556

Abstract The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract… More >

Rashmi Gupta¹, Manju Khari¹, Vipul Gupta¹, Elena Verdú², Xing Wu³, Enrique Herrera-Viedma⁴, Rubén González Crespo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.3, pp. 1175-1192, 2020, DOI:10.32604/cmes.2020.010092

Abstract The images capture in a bad environment usually loses its fidelity and contrast. As the light rays travel towards its destination they get scattered several times due to the tiny particles of fog and pollutants in the environment, therefore the energy gets lost due to multiple scattering till it arrives its destination, and this degrades the images. So the images taken in bad weather appear in bad quality. Therefore, single image haze removal is quite a bit tough task. Significant research has been done in the haze removal algorithm but in all the techniques, the coefficient of scattering is taken… More >

Lijuan Kong^1,2, Haiye Yu^1,2, Meichen Chen^1,2, Zhaojia Piao^1,2, Jingmin Dang¹, Yuanyuan Sui^1,2,*

Phyton-International Journal of Experimental Botany, Vol.88, No.4, pp. 367-378, 2019, DOI:10.32604/phyton.2019.09017

Abstract The particle matter, particularly the suspended particle matter (PM ≤ 2.5) in the air is not only a risk factor for human health, but also affects the survival and physiological features of plants. Plants show advantages in the adsorption of particle matter, while the factors, such as the leaf shape, leaf distribution density and leaf surface microstructure, such as grooves, folds, stomata, flocculent projections, micro-roughness, long fuzz, short pubescence, wax and secretory products, appeared to play an important role determing their absorption capacity. In this paper, the research progress on the capture or adsorption of atmospheric particles was summarized, and… More >

Qiang Liu^1,*, Yanyun Zou^2,3, Xiaodong Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.3, pp. 617-637, 2019, DOI:10.32604/cmes.2019.06272

Abstract Haze-fog, which is an atmospheric aerosol caused by natural or man-made factors, seriously affects the physical and mental health of human beings. PM_2.5 (a particulate matter whose diameter is smaller than or equal to 2.5 microns) is the chief culprit causing aerosol. To forecast the condition of PM_2.5, this paper adopts the related the meteorological data and air pollutes data to predict the concentration of PM_2.5. Since the meteorological data and air pollutes data are typical time series data, it is reasonable to adopt a machine learning method called Single Hidden-Layer Long Short-Term Memory Neural Network (SSHL-LSTMNN) containing memory capability… More >