DOI: 10.1016/j.atmosenv.2018.04.019
Scopus记录号: 2-s2.0-85045629852
论文题名: Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment
作者: Johnson N ; E ; , Bonczak B ; , Kontokosta C ; E
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 184 起始页码: 9
结束页码: 16
语种: 英语
英文关键词: Air quality
; Calibration
; Low-cost sensing
; Machine learning
; Urban
Scopus关键词: Aerosols
; Air quality
; Artificial intelligence
; Calibration
; Costs
; Developing countries
; Learning systems
; Meteorology
; Urban planning
; Air quality monitoring
; Complex urban environments
; Low costs
; Machine learning methods
; Machine learning techniques
; Performance and reliabilities
; Reference instruments
; Urban
; Quality control
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: The increased availability and improved quality of new sensing technologies have catalyzed a growing body of research to evaluate and leverage these tools in order to quantify and describe urban environments. Air quality, in particular, has received greater attention because of the well-established links to serious respiratory illnesses and the unprecedented levels of air pollution in developed and developing countries and cities around the world. Though numerous laboratory and field evaluation studies have begun to explore the use and potential of low-cost air quality monitoring devices, the performance and stability of these tools has not been adequately evaluated in complex urban environments, and further research is needed. In this study, we present the design of a low-cost air quality monitoring platform based on the Shinyei PPD42 aerosol monitor and examine the suitability of the sensor for deployment in a dense heterogeneous urban environment. We assess the sensor's performance during a field calibration campaign from February 7th to March 25th 2017 with a reference instrument in New York City, and present a novel calibration approach using a machine learning method that incorporates publicly available meteorological data in order to improve overall sensor performance. We find that while the PPD42 performs well in relation to the reference instrument using linear regression (R2 = 0.36–0.51), a gradient boosting regression tree model can significantly improve device calibration (R2 = 0.68–0.76). We discuss the sensor's performance and reliability when deployed in a dense, heterogeneous urban environment during a period of significant variation in weather conditions, and important considerations when using machine learning techniques to improve the performance of low-cost air quality monitors. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82832
Appears in Collections: 气候变化事实与影响
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作者单位: University of Warwick, United Kingdom; Center for Urban Science and Progress, New York University, United States; Department of Civil and Urban Engineering, New York University, United States
Recommended Citation:
Johnson N,E,, Bonczak B,et al. Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment[J]. Atmospheric Environment,2018-01-01,184