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Atmosphere 2018, 9(12), 463; https://doi.org/10.3390/atmos9120463

Random Forest Algorithm for the Relationship between Negative Air Ions and Environmental Factors in an Urban Park

1,2,†
,
1,2,3,†
,
4
,
4
,
1,2,3
and
1,2,3,*
1
School of Agriculture and Biology and Research Centre for Low Carbon Agriculture, Shanghai Jiao Tong University, 800 Dongchuan Rd., Shanghai 200240, China
2
Shanghai Urban Forest Ecosystem Research Station, National Forestry and Grassland Administration, 800 Dongchuan Rd., Shanghai 200240, China
3
Key Laboratory for Urban Agriculture, Ministry of Agriculture and Rural Affairs, 800 Dongchuan Rd., Shanghai 200240, China
4
Shanghai Forest Station, 1053-7 Hutai Rd., Shanghai 200072, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 27 October 2018 / Revised: 21 November 2018 / Accepted: 22 November 2018 / Published: 26 November 2018
Full-Text   |   PDF [2752 KB, uploaded 26 November 2018]   |  

Abstract

Negative air ions (NAIs) are a natural component of air and have a positive impact on the health of urban residents. Few studies have focused on the relationship between NAI concentration (NAIC) in the urban atmosphere and environmental factors, such as meteorological factors and air pollutants. Therefore, we established observation points in Zhongshan Park in downtown Shanghai, China, and continuously measured and recorded changes in NAIC for one year. We also monitored nine meteorological factors and six atmospheric pollutants. Through correlation analysis and multiple linear regression analysis, the key factors influencing NAIC were screened, and the effects of those factors on NAIC were explored using the random forest algorithm. The results show that NAIC is most sensitive to humidity, followed by radiation and temperature, and finally to PM2.5. Humidity is the most critical factor, primarily because it directly affects the formation of NAIs from both the environment and vegetation. Furthermore, our results reveal that the mechanisms through which NAIC is influenced by the same factor varies seasonally. We analyzed the relationship between NAIC in an urban atmosphere and environmental factors by using big data, which is a new method for studying the relationships between NAIs and environmental factors. Our results indicate potential explanations for the mechanisms underlying NAI response to various environmental factors. View Full-Text
Keywords: negative air ion; environmental influencing factor; random forest algorithm; effects ranking negative air ion; environmental influencing factor; random forest algorithm; effects ranking
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Miao, S.; Zhang, X.; Han, Y.; Sun, W.; Liu, C.; Yin, S. Random Forest Algorithm for the Relationship between Negative Air Ions and Environmental Factors in an Urban Park. Atmosphere 2018, 9, 463.

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