Minh Thanh Vo¹, Anh H. Vo², Huong Bui³, Tuong Le^4,5,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3029-3041, 2023, DOI:10.32604/iasc.2023.034636 - 15 March 2023

Abstract Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone’s health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in… More >

Narendran Sobanapuram Muruganandam, Umamakeswari Arumugam^*

Computer Systems Science and Engineering, Vol.44, No.2, pp. 979-989, 2023, DOI:10.32604/csse.2023.024943 - 15 June 2022

Abstract In forecasting real time environmental factors, large data is needed to analyse the pattern behind the data values. Air pollution is a major threat towards developing countries and it is proliferating every year. Many methods in time series prediction and deep learning models to estimate the severity of air pollution. Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality. This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter (PM) PM2.5. To perform experimental analysis the… More >

Himanshu¹, Kunwar Pal², Sanjeev Jain³, S. K. Tyagi^1,*

Energy Engineering, Vol.118, No.5, pp. 1237-1251, 2021, DOI:10.32604/EE.2021.016489 - 16 July 2021

Abstract A disruptive approach to a fundamental process has been applied in a biomass combustion device with two variable speed fans to supply air for gasification and another for combustion processes, separately. Besides, the preheating of secondary air, required for combustion process was also ensured through annulus chamber before being fed into the combustion chamber. The turbulent flow and homogenous mixing were also ensured by controlling the flow rate resulting in the reduced emissions of carbon monoxide (CO) and fine particulate matter (PM 2.5, particulate matter having aerodynamic diameter <2.5 micron). The design approach applied here… More >

R. Mangayarkarasi¹, C. Vanmathi^1,*, Mohammad Zubair Khan², Abdulfattah Noorwali³, Rachit Jain⁴, Priyansh Agarwal⁴

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3363-3380, 2021, DOI:10.32604/cmc.2021.014991 - 01 March 2021

Abstract Urbanization affects the quality of the air, which has drastically degraded in the past decades. Air quality level is determined by measures of several air pollutant concentrations. To create awareness among people, an automation system that forecasts the quality is needed. The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India. The overall air quality index (AQI) at any particular time is given as the maximum band for any pollutant. PM2.5 is a fine particulate matter of a size less… More >

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 - 30 June 2020

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 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… More >

Chen Ding¹, Guizhi Wang^1,*, Qi Liu²

Journal on Big Data, Vol.1, No.2, pp. 55-69, 2019, DOI:10.32604/jbd.2019.06110

Abstract Haze concentration prediction, especially PM2.5, has always been a significant focus of air quality research, which is necessary to start a deep study. Aimed at predicting the monthly average concentration of PM2.5 in Beijing, a novel method based on Monte Carlo model is conducted. In order to fully exploit the value of PM2.5 data, we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively. The results show that these data are both approximately normal distribution. On the basis… More >