Large Data Technology-Based Analysis Method of Sudden Eco-Environmental Toxic Pollution
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-02
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Sudden environmental toxic pollution accidents occur from time to time at home and abroad, seriously affecting the safety of the ecological environment.
Different environmental factors affect the use of manual inspection and analysis methods, causing inaccurate results of inspection and analysis.
In view of this problem, a large data technology-based analysis of sudden ecological environmental toxic pollution is proposed.
Method.
The genome and proteome in different environments were analyzed, and the target organisms were strictly defined to determine the effect of the molecular toxicity of pollution factors on the ecological environment.
According to the molecular toxicity, the sudden eco-environmental toxicity pollution was analyzed using large data technology.
Under the action of different particle sizes, dosages, and adsorption times of activated carbon, the experiments confirmed that the results of large data technology analysis are more accurate, which have provided necessary means for the protection of the ecological environment.
American Psychological Association (APA)
He, Guilan& Yao, Junping. 2020. Large Data Technology-Based Analysis Method of Sudden Eco-Environmental Toxic Pollution. Journal of Chemistry،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1182328
Modern Language Association (MLA)
He, Guilan& Yao, Junping. Large Data Technology-Based Analysis Method of Sudden Eco-Environmental Toxic Pollution. Journal of Chemistry No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1182328
American Medical Association (AMA)
He, Guilan& Yao, Junping. Large Data Technology-Based Analysis Method of Sudden Eco-Environmental Toxic Pollution. Journal of Chemistry. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1182328
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1182328