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. The inaccurate quantification of personal exposure to airpollution introduces error and bias in health estimations, severely limitingcausal inference in epidemiological research worldwide. Rapid advancementsin affordable, miniaturised air pollution sensor technologies offer thepotential to address this limitation by capturing the high variability ofpersonal exposure during daily life in large-scale studies withunprecedented spatial and temporal resolution. However, concerns remainregarding the suitability of novel sensing technologies for scientific andpolicy purposes. In this paper we characterise the performance of a portablepersonal air quality monitor (PAM) that integrates multiple miniaturisedsensors for nitrogen oxides (NOx), carbon monoxide (CO), ozone(O3) and particulate matter (PM) measurements along with temperature,relative humidity, acceleration, noise and GPS sensors. Overall, the airpollution sensors showed high reproducibility (mean R‾2=0.93, min–max: 0.80–1.00) and excellent agreement withstandard instrumentation (mean R‾2=0.82, min–max: 0.54–0.99) in outdoor, indoor and commutingmicroenvironments across seasons and different geographical settings. Animportant outcome of this study is that the error of the PAM issignificantly smaller than the error introduced when estimating personalexposure based on sparsely distributed outdoor fixed monitoring stations.Hence, novel sensing technologies such as the ones demonstrated here canrevolutionise health studies by providing highly resolved reliable exposuremetrics at a large scale to investigate the underlying mechanisms of theeffects of air pollution on health.

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