Spatiotemporal distribution of indoor particulate matter concentration with a low-cost sensor network

Jiayu Li, Haoran Li, Yehan Ma, Yang Wang, Ahmed A. Abokifa, Chenyang Lu, Pratim Biswas

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

Real-time measurement of particulate matter (PM) is important for the maintenance of acceptable air quality. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with sufficient spatial resolution. In this study, a wireless network of low-cost particle sensors that can be deployed indoors was developed. To overcome the well-known limitations of low sensitivity and poor signal quality associated with low-cost sensors, a sliding window and a low pass filter were developed to enhance the signal quality. Utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes.

Original languageEnglish (US)
Pages (from-to)138-147
Number of pages10
JournalBuilding and Environment
Volume127
DOIs
StatePublished - Jan 2018
Externally publishedYes

Bibliographical note

Funding Information:
This work was partially supported by Fullgraf Foundation ; and McDonnell Academy Global Energy and Environmental Partnership (MAGEEP) at Washington University in St. Louis. One of the authors (J.L.) would also like to acknowledge the McDonnell International Scholars Academy at Washington University in St. Louis for their support.

Publisher Copyright:
© 2017

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Kriging
  • Low-cost sensor
  • Machine learning
  • Spatial temporal distribution
  • Wireless

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