Song Yu^*, Chen Wang

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5555-5571, 2023, DOI:10.32604/cmc.2023.038162 - 29 April 2023

Abstract PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such… More >

Muhammad Zulqarnain¹, Rozaida Ghazali^1,*, Habib Shah², Lokman Hakim Ismail¹, Abdullah Alsheddy³, Maqsood Mahmud⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3051-3068, 2022, DOI:10.32604/cmc.2022.021629 - 07 December 2021

Abstract Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM_2.5) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM_2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial… More >

Xiaorui Shao, Chang Soo Kim^*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5143-5160, 2022, DOI:10.32604/cmc.2022.020689 - 11 October 2021

Abstract

Accurate multi-step PM_2.5 (particulate matter with diameters ≤2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM_2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM_2.5 concentration series. In which, CNN and LSTM are used to

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Xiangli Wang¹, Peiyong Ni^2,*

FDMP-Fluid Dynamics & Materials Processing, Vol.16, No.2, pp. 237-243, 2020, DOI:10.32604/fdmp.2020.09469 - 21 April 2020

Abstract PM_2.5 and formaldehyde are two main indoor pollutants potentially threatening the health of human beings. In this paper, the concentrations of PM_2.5 and formaldehyde inside an office were measured under different conditions. The effects of temperature on the formaldehyde originating from the decoration materials, including flooring, gypsum powder, joint mixture and corestock, were also assessed. The results show that window ventilation can produce the same PM_2.5 purification as an air cleaner. The concentration of formaldehyde released from the decoration materials is highly correlated to the indoor temperature, but it is not significantly influenced by humidity. In particular, 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)… More >