Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques-Reference-Cited by-同舟云学术

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Evaluation and calibration of low‐cost off‐the‐shelf particulate matter sensors using machine learning techniques

Published:2022-10 Issue:5-6 Volume:12 Page:134-148
ISSN:2043-6386
Container-title:IET Wireless Sensor Systems
language:en
Short-container-title:IET Wireless Sensor Systems

Author:

Ghamari Mohammad¹^ORCID,Kamangir Hamid²,Arezoo Keyvan³,Alipour Khalil³

Affiliation:

1. Department of Electrical and Computer Engineering Kettering University Flint Michigan USA

2. University of California‐Davis, Biological and Agricultural Engineering Davis California USA

3. Department of Mechatronics Engineering University of Tehran Tehran Iran

Publisher

Institution of Engineering and Technology (IET)

Subject

Industrial and Manufacturing Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/wss2.12043

Reference57 articles.

1. Long-term exposure to low-level ambient air pollution and incidence of stroke and coronary heart disease: a pooled analysis of six European cohorts within the ELAPSE project

2. Effects of air pollutants on the transmission and severity of respiratory viral infections

3. Characterization of PM2.5, PM2.5–10 and PM>10 in ambient air, Yokohama, Japan

4. Estimates of global mortality attributable to particulate air pollution using satellite imagery

5. Acute Effects of Ambient Particulate Matter on Mortality in Europe and North America: Results from the APHENA Study

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1. Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights;Environmental Monitoring and Assessment;2024-09-04

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