Abstract. Low-cost particulate matter (PM) sensors have been under
investigation as it has been hypothesized that the use of low-cost and
easy-to-use sensors could allow cost-efficient extension of the currently
sparse measurement coverage. While the majority of the existing literature
highlights that low-cost sensors can indeed be a valuable addition to the
list of commonly used measurement tools, it often reiterates that the risk
of sensor misuse is still high and that the data obtained from the sensors
are only representative of the specific site and its ambient conditions. This
implies that there are underlying reasons for
inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to
investigate the particle-size selectivity of low-cost sensors. Evaluated
sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp
GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of
size selectivity was carried out in the laboratory using a novel reference
aerosol generation system capable of steadily producing monodisperse
particles of different sizes (from ∼0.55 to 8.4 µm)
on-line. The results of the study show that none of the low-cost sensors
adhered to the detection ranges declared by the manufacturers; moreover,
cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020)
indicates that the sensors can only achieve independent responses for one or
two size bins, whereas the spectrometer can sufficiently characterize
particles with 15 different size bins. These observations provide insight into
and evidence of the notion that particle-size selectivity has an essential
role in the analysis of the sources of errors in sensors.
This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.
Timonen (2020) Long-term sensor measurements of lung deposited surface area of particulate matter emitted from local vehicular and residential wood combustion sources,
High-resolution large-eddy simulation (LES) is exploited to study indoor air turbulence and its effect on the dispersion of respiratory virus-laden aerosols and subsequent transmission risks. The LES modeling is carried out with unprecedented accuracy and subsequent analysis with novel mathematical robustness. To substantiate the physical relevance of the LES model under realistic ventilation conditions, a set of experimental aerosol concentration measurements are carried out, and their results are used to successfully validate the LES model results. The obtained LES dispersion results are subjected to pathogen exposure and infection probability analysis in accordance with the Wells–Riley model, which is here mathematically extended to rely on LES-based space- and time-dependent concentration fields. The methodology is applied to assess two dissimilar approaches to reduce transmission risks: a strategy to augment the indoor ventilation capacity with portable air purifiers and a strategy to utilize partitioning by exploiting portable space dividers. The LES results show that use of air purifiers leads to greater reduction in absolute risks compared to the analytical Wells–Riley model, which fails to predict the original risk level. However, the two models do agree on the relative risk reduction. The spatial partitioning strategy is demonstrated to have an undesirable effect when employed without other measures, but may yield desirable outcomes with targeted air purifier units. The study highlights the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies.
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