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

• Main Authors: L. Chatzidiakou, A. Krause, O. A. M. Popoola, A. Di Antonio, M. Kellaway, Y. Han, F. A. Squires, T. Wang, H. Zhang, Q. Wang, Y. Fan, S. Chen, M. Hu, J. K. Quint, B. Barratt, F. J. Kelly, T. Zhu, R. L. Jones
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-08-01
• Series: Atmospheric Measurement Techniques
• Online Access: https://www.atmos-meas-tech.net/12/4643/2019/amt-12-4643-2019.pdf
• ISSN: 1867-1381; 1867-8548

<p>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 class="inline-formula">NO_<i>x</i></span>), carbon monoxide (CO), ozone (<span class="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 class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mover accent="true"><mi>R</mi><mo mathvariant="normal">‾</mo></mover><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.93</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="49pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="bee94480a783d1340ee26eb64ec38285"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-12-4643-2019-ie00001.svg" width="49pt" height="16pt" src="amt-12-4643-2019-ie00001.png"/></svg:svg></span></span>, min–max: 0.80–1.00) and excellent agreement with standard instrumentation (mean <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><msup><mover accent="true"><mi>R</mi><mo mathvariant="normal">‾</mo></mover><mn mathvariant="normal">2</mn></msup><mo>=</mo><mn mathvariant="normal">0.82</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="49pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="4a2ee0dd20d927b8a2e5751bbd94cd8e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="amt-12-4643-2019-ie00002.svg" width="49pt" height="16pt" src="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.</p>