Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments

Lia Chatzidiakou*, Anika Krause, Olalekan Popoola, Andrea Di Antonio, Mike Kellaway, Yiqun Han, Freya Squires, Teng Wang, Hanbin Zhang, Qi Wang, Yunfei Fan, Shiyi Chen, Min Hu, Jennifer Quint, Benjamin Barratt, Frank Kelly, Tong Zhu, Roderic Jones

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

73 Citations (Scopus)

Abstract

The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (<span classCombining double low line"inline-formula">NOx</span>), carbon monoxide (CO), ozone (<span classCombining double low line"inline-formula">O3</span>) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean <span classCombining double low line"inline-formula"><math xmlnsCombining double low line"http://www.w3.org/1998/Math/MathML" idCombining double low line"M3" displayCombining double low line"inline" overflowCombining double low line"scroll" dspmathCombining double low line"mathml"><mrow><msup><mover accentCombining double low line"true"><mi>R</mi><mo mathvariantCombining double low line"normal">ĝ€3/4</mo></mover><mn mathvariantCombining double low line"normal">2</mn></msup><mo>Combining double low line</mo><mn mathvariantCombining double low line"normal">0.93</mn></mrow></math><span><svg:svg xmlns:svgCombining double low line"http://www.w3.org/2000/svg" widthCombining double low line"49pt" heightCombining double low line"16pt" classCombining double low line"svg-formula" dspmathCombining double low line"mathimg" md5hashCombining double low line"bee94480a783d1340ee26eb64ec38285"><svg:image xmlns:xlinkCombining double low line"http://www.w3.org/1999/xlink" xlink:hrefCombining double low line"amt-12-4643-2019-ie00001.svg" widthCombining double low line"49pt" heightCombining double low line"16pt" srcCombining double low line"amt-12-4643-2019-ie00001.png"/></svg:svg></span></span>, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (mean <span classCombining double low line"inline-formula"><math xmlnsCombining double low line"http://www.w3.org/1998/Math/MathML" idCombining double low line"M4" displayCombining double low line"inline" overflowCombining double low line"scroll" dspmathCombining double low line"mathml"><mrow><msup><mover accentCombining double low line"true"><mi>R</mi><mo mathvariantCombining double low line"normal">ĝ€3/4</mo></mover><mn mathvariantCombining double low line"normal">2</mn></msup><mo>Combining double low line</mo><mn mathvariantCombining double low line"normal">0.82</mn></mrow></math><span><svg:svg xmlns:svgCombining double low line"http://www.w3.org/2000/svg" widthCombining double low line"49pt" heightCombining double low line"16pt" classCombining double low line"svg-formula" dspmathCombining double low line"mathimg" md5hashCombining double low line"4a2ee0dd20d927b8a2e5751bbd94cd8e"><svg:image xmlns:xlinkCombining double low line"http://www.w3.org/1999/xlink" xlink:hrefCombining double low line"amt-12-4643-2019-ie00002.svg" widthCombining double low line"49pt" heightCombining double low line"16pt" srcCombining double low line"amt-12-4643-2019-ie00002.png"/></svg:svg></span></span>, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.

Original languageEnglish
Pages (from-to)4643-4657
Number of pages15
JournalAtmospheric Measurement Techniques
Volume12
Issue number8
DOIs
Publication statusPublished - 30 Aug 2019

Fingerprint

Dive into the research topics of 'Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments'. Together they form a unique fingerprint.

Cite this