Development of novel methodologies for utilising low-cost sensors for ambient Particulate Matter measurement
type: CreativeWork
description: There is increasing concern about the impact of ambient Particulate Matter (PM) exposure on human health. Existing ambient air quality monitoring networks, because of their sparseness, are not able to capture the spatial-temporal variation of ambient PM concentration. Recent studies have shown that although low-cost PM monitors, such as Optical Particles Counters (OPCs), can be utilised for both personal exposure and high-density sensor network applications, these sensors can be subject to several limitations such as relative humidity (RH) interferences, the inability to measure smaller (e.g. ultrafine) particles and the assumption made about the constant aerosol density value used in the conversion from number concentration to mass concentration. Water can condense on aerosol particles resulting, if uncorrected for, in particle size growth and thus overestimation of the measured PM concentration in a manner which depends on both RH and the chemical composition of particles. In contrast, failing to account for particles below the sensors’ size detection limit leads to potentially large underestimations of PM concentrations. To account for changes in the aerosol density values assumed by OPCs, the PM readings calculated from the particle size and concentration measurements require the application of an additional, often empirical, factor. Not accounting for this factor, results in a poor agreement between PM measurements from an OPC and a reference gravimetric instrument. To address these issues, this work describes the development, application and assessment of a three-stage correction process which allows low-cost sensors to provide quantitative PM measurements. The first step is to account for the particle hygroscopic growth under high RH conditions. This led to the development of two correction methodologies: 1) a particle size distribution correction algorithm (based on κ-Köhler theory) which corrects for the particle hygroscopic growth after the measurements, a…
abstract: There is increasing concern about the impact of ambient Particulate Matter (PM) exposure on human health. Existing ambient air quality monitoring networks, because of their sparseness, are not able to capture the spatial-temporal variation of ambient PM concentration. Recent studies have shown that although low-cost PM monitors, such as Optical Particles Counters (OPCs), can be utilised for both personal exposure and high-density sensor network applications, these sensors can be subject to several limitations such as relative humidity (RH) interferences, the inability to measure smaller (e.g. ultrafine) particles and the assumption made about the constant aerosol density value used in the conversion from number concentration to mass concentration. Water can condense on aerosol particles resulting, if uncorrected for, in particle size growth and thus overestimation of the measured PM concentration in a manner which depends on both RH and the chemical composition of particles. In contrast, failing to account for particles below the sensors’ size detection limit leads to potentially large underestimations of PM concentrations. To account for changes in the aerosol density values assumed by OPCs, the PM readings calculated from the particle size and concentration measurements require the application of an additional, often empirical, factor. Not accounting for this factor, results in a poor agreement between PM measurements from an OPC and a reference gravimetric instrument. To address these issues, this work describes the development, application and assessment of a three-stage correction process which allows low-cost sensors to provide quantitative PM measurements. The first step is to account for the particle hygroscopic growth under high RH conditions. This led to the development of two correction methodologies: 1) a particle size distribution correction algorithm (based on κ-Köhler theory) which corrects for the particle hygroscopic growth after the measurements, a…
author: Di Antonio, Andrea
language: eng
keywords: Air quality, Low-cost sensor, OPC, environmental monitoring, particulate matter (PM)
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/a258a6cf-d1d2-4605-92eb-8cf3cf796480/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2020-09-28
date modified: 2026-07-22T22:24:31Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.68376