scholarly journals Predicting air pollutant emissions from a medical incinerator using grey model and neural network

2015 ◽  
Vol 39 (5-6) ◽  
pp. 1513-1525 ◽  
Author(s):  
Tzu-Yi Pai ◽  
Huang-Mu Lo ◽  
Terng-Jou Wan ◽  
Li Chen ◽  
Pei-Shan Hung ◽  
...  
2017 ◽  
Vol 16 (4) ◽  
pp. 809-819 ◽  
Author(s):  
Gabriel Lazar ◽  
Iulia Carmen Ciobotici Terryn ◽  
Andreea Cocarcea

2020 ◽  
pp. 1-11
Author(s):  
Zhiqi Jiang ◽  
Xidong Wang

This paper conducts in-depth research and analysis on the commonly used models in the simulation process of air pollutant diffusion. Combining with the actual needs of air pollution, this paper builds an air pollution system model based on neural network based on neural network algorithm, and proposes an image classification method based on deep learning and Gaussian aggregation coding. Moreover, this paper proposes a Gaussian aggregation coding layer to encode image features extracted by deep convolutional neural networks. Learn a fixed-size dictionary to represent the features of the image for final classification. In addition, this paper constructs an air pollution monitoring system based on the actual needs of the air system. Finally, this article designs a controlled experiment to verify the model proposed in this article, uses mathematical statistics to process data, and scientifically analyze the statistical results. The research results show that the model constructed in this paper has a certain effect.


2021 ◽  
Vol 7 (3) ◽  
pp. eabd6696
Author(s):  
Zongbo Shi ◽  
Congbo Song ◽  
Bowen Liu ◽  
Gongda Lu ◽  
Jingsha Xu ◽  
...  

The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2 concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2 concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3 increased by 2 to 30% (except for London), the total gaseous oxidant (Ox = NO2 + O3) showed limited change, and PM2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.


2014 ◽  
Vol 14 (17) ◽  
pp. 8849-8868 ◽  
Author(s):  
Y. Zhao ◽  
J. Zhang ◽  
C. P. Nielsen

Abstract. To examine the efficacy of China's actions to control atmospheric pollution, three levels of growth of energy consumption and three levels of implementation of emission controls are estimated, generating a total of nine combined activity-emission control scenarios that are then used to estimate trends of national emissions of primary air pollutants through 2030. The emission control strategies are expected to have more effects than the energy paths on the future emission trends for all the concerned pollutants. As recently promulgated national action plans of air pollution prevention and control (NAPAPPC) are implemented, China's anthropogenic pollutant emissions should decline. For example, the emissions of SO2, NOx, total suspended particles (TSP), PM10, and PM2.5 are estimated to decline 7, 20, 41, 34, and 31% from 2010 to 2030, respectively, in the "best guess" scenario that includes national commitment of energy saving policy and implementation of NAPAPPC. Should the issued/proposed emission standards be fully achieved, a less likely scenario, annual emissions would be further reduced, ranging from 17 (for primary PM2.5) to 29% (for NOx) declines in 2015, and the analogue numbers would be 12 and 24% in 2030. The uncertainties of emission projections result mainly from the uncertain operational conditions of swiftly proliferating air pollutant control devices and lack of detailed information about emission control plans by region. The predicted emission trends by sector and chemical species raise concerns about current pollution control strategies: the potential for emissions abatement in key sectors may be declining due to the near saturation of emission control devices use; risks of ecosystem acidification could rise because emissions of alkaline base cations may be declining faster than those of SO2; and radiative forcing could rise because emissions of positive-forcing carbonaceous aerosols may decline more slowly than those of SO2 emissions and thereby concentrations of negative-forcing sulfate particles. Expanded control of emissions of fine particles and carbonaceous aerosols from small industrial and residential sources is recommended, and a more comprehensive emission control strategy targeting a wider range of pollutants (volatile organic compounds, NH3 and CO, etc.) and taking account of more diverse environmental impacts is also urgently needed.


Energy ◽  
2021 ◽  
pp. 121724
Author(s):  
Geng Liu ◽  
Shida Sun ◽  
Chao Zou ◽  
Bo Wang ◽  
Lin Wu ◽  
...  

1986 ◽  
Vol 12 (1-4) ◽  
pp. 351-362 ◽  
Author(s):  
Ken Sexton ◽  
Lurance M. Webber ◽  
Steven B. Hayward ◽  
Richard G. Sextro

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bing Liu ◽  
Qingbo Zhao ◽  
Yueqiang Jin ◽  
Jiayu Shen ◽  
Chaoyang Li

AbstractIn this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.


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