IoT enabled environmental toxicology for air pollution monitoring using AI techniques

2021 ◽  
pp. 112574
Author(s):  
P. Asha ◽  
L. Natrayan ◽  
B.T. Geetha ◽  
J. Rene Beulah ◽  
R. Sumathi ◽  
...  
2016 ◽  
Vol 5 (1) ◽  
pp. 30
Author(s):  
HASAN MOHD. TAHSEENUL ◽  
CHOURASIA VIJAY S. ◽  
ASUTKAR SANJAY M. ◽  
◽  
◽  
...  

Data in Brief ◽  
2021 ◽  
pp. 107127
Author(s):  
Jose M. Barcelo-Ordinas ◽  
Pau Ferrer-Cid ◽  
Jorge Garcia-Vidal ◽  
Mar Viana ◽  
Ana Ripoll

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.


Author(s):  
B.H. Sudantha ◽  
Manchanayaka MALSK ◽  
Nilantha Premakumara ◽  
Chamani Shiranthika ◽  
C. Premachandra ◽  
...  

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 290
Author(s):  
Akvilė Feiferytė Skirienė ◽  
Žaneta Stasiškienė

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.


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