Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

Nicholas E. Johnson, Bartosz Bonczak, Constantine Kontokosta

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)9-16
Number of pages8
JournalAtmospheric Environment
Volume184
DOIs
StatePublished - Jul 1 2018

Fingerprint

aerosol
sensor
calibration
cost
air quality
atmospheric pollution
developing world
city
machine learning
air quality monitoring
method
evaluation
laboratory
weather condition
world
developed country

Keywords

  • Air quality
  • Calibration
  • Low-cost sensing
  • Machine learning
  • Urban

ASJC Scopus subject areas

  • Environmental Science(all)
  • Atmospheric Science

Cite this

Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment. / Johnson, Nicholas E.; Bonczak, Bartosz; Kontokosta, Constantine.

In: Atmospheric Environment, Vol. 184, 01.07.2018, p. 9-16.

Research output: Contribution to journalArticle

@article{2d0a7e6d92f549668414cd998aad6277,
title = "Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment",
abstract = "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.",
keywords = "Air quality, Calibration, Low-cost sensing, Machine learning, Urban",
author = "Johnson, {Nicholas E.} and Bartosz Bonczak and Constantine Kontokosta",
year = "2018",
month = "7",
day = "1",
doi = "10.1016/j.atmosenv.2018.04.019",
language = "English (US)",
volume = "184",
pages = "9--16",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment

AU - Johnson, Nicholas E.

AU - Bonczak, Bartosz

AU - Kontokosta, Constantine

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

AB - 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.

KW - Air quality

KW - Calibration

KW - Low-cost sensing

KW - Machine learning

KW - Urban

UR - http://www.scopus.com/inward/record.url?scp=85045629852&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045629852&partnerID=8YFLogxK

U2 - 10.1016/j.atmosenv.2018.04.019

DO - 10.1016/j.atmosenv.2018.04.019

M3 - Article

AN - SCOPUS:85045629852

VL - 184

SP - 9

EP - 16

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

ER -