Opportunities and limitations of aerosol sensors to urban air quality monitoring

Ilmakehän pienhiukkaset ovat yksi keskeisimmistä kuolleisuuden riskitekijöistä kansainvälisessä taudin rasittavuuden analyysissä. Useat epidemiologiset tutkimukset ovat osoittaneet pienhiukkasten ja sydän- ja verisuoni- sekä hengitystiesairauksien yhteyden, ja eri arvioiden mukaan useita miljoonia ennenaikaisia kuolemia tapahtuu joka vuosi pienhiukkasaltistumisen seurauksena. Jotta pienhiukkasten negatiivisiin terveysvaikutuksiin voitaisiin vaikuttaa, tulee niiden lähteet ja dynaamiset prosessit, kuten alueellinen leviäminen, tuntea hyvin. Viimeaikainen aerosolisensoreiden esilletulo ja kehittyminen ovat avanneet uusia mahdollisuuksia ilmanlaadun seurantaan. Sensorit, jotka ovat tyypillisesti pienikokoisia, suhteellisen edullisia ja helppokäyttöisiä, mahdollistavat alueellisesti kattavien sensoriverkkomittausten toteuttamisen ja siten pienhiukkasten tarkemman tutkimisen. Sensoreiden edullisempi ja siten yksinkertaisempi mittaustekniikka saattaa toisaalta johtaa suurempaan mittausepätarkkuuteen ja huonompaan laatuun. Tämän työn tavoitteena oli arvioida ja luonnehtia aerosolisensoreiden tarkkuutta ja soveltuvuutta kaupunkialueiden ilmanlaadun seurantaan. Tutkimus keskittyi kahteen mittaustekniikkaan, jotka ovat parhaiten sovellettavissa sensorityyppisiin mittauksiin; optiseen ja diffuusiovarautumiseen perustuvaan tekniikkaan. Optisia sensoreita testattiin sekä ulkoilmassa että laboratoriossa, missä niiden hiukkaskokovalikoivuutta arvioitiin tutkimalla sensorin vastetta keinotekoisesti tuotetuilla erikokoisilla referenssihiukkasilla. Diffuusiovarautumiseen perustuvia sensoreita, jotka mittaavat niin kutsuttua keuhkodeposoituvaa pinta-ala, testattiin ulkoilmassa, missä niiden suorituskykyä arvioitiin erityisesti erittäin pienten nanohiukkasten, kuten liikenteen pakokaasun sekä puunpolton päästöjen, näkökulmasta. Tutkimustulosten perusteella optiset aerosolisensorit eivät toistaiseksi ole soveltuvia pitkäaikaiseen viranomaisvalvonnassa tehtävään ilmanlaadun seurantaan. Tämä johtuu niiden virheellisestä kalibroinnista, jonka seurauksena sensorit eivät mittaa hiukkaskokoluokkia, joita niiden tekniset tuoteselosteet antavat olettaa. Riski mittausdatan väärin tulkinnalle on täten ilmeinen. Toisaalta, kun mitattu hiukkasten kokojakauma rajattiin vastaamaan sensorin ominaista vastealuetta, sensorin mittaustarkkuus oli hyvä ja toistettava. Tämän perusteella, vaikkakin virheellinen kalibrointi rajoittaa optisten sensoreiden käytettävyyttä, konsepti ja visio sensoripohjaisesta mittausverkosta on mahdollinen ja saavutettavissa. Diffuusiovarautumiseen perustuvat sensorit osoittivat olevan teknisesti kehittyneempiä kuin optiset sensorit. Testatut sensorit olivat tarkkoja ja stabiileja kaikissa kenttämittauskampanjoissa, ja ne olivat erityisen hyvin soveltuvia liikenteen pakokaasujen sekä puunpolton päästöjen mittaamiseen. Tämän vuoksi diffuusiovaraukseen perustuvat sensorit olisivat arvokas lisä muiden mittaustekniikoiden rinnalle, varsinkin kun nanohiukkasten osuus kaupunki-ilmassa on merkittävä.Atmospheric particles are one of the leading mortality risk factors in the Global Burden of Disease study (GBD). The association between particulate mass of particles smaller than 2.5 μm in diameter (PM2.5) and cardiovascular and pulmonary diseases has been characterized by multiple epidemiological studies, and varying estimates suggest that several million premature death occur globally each year due to PM2.5 exposure. Mitigation of the adverse health effects of particulate matter requires comprehensive understanding of their sources and dynamic processes, such as spatial dispersion. Recent emergence and development of aerosol sensors, which are typically characterized as small, relatively low cost and easy to use, have enabled new opportunities in air quality monitoring. As a result of their practical convenience, sensors can be deployed to the field in high quantities which, consequently, enables network-type, spatially comprehensive measurements. However, with more simplified and less expensive measurement approach, less accurate and reliable results may be expected. This study aimed to evaluate and characterize the accuracy and usability of aerosol sensor to urban air quality measurements. The investigation focused on two of the most prominent measurement techniques applicable to sensor type monitoring; optical and diffusion chargingbased techniques. Sensors utilizing optical technique were evaluated in laboratory and field studies for their error sources and particle size-selectivity, specifically. Diffusion charging-based sensors, which measure lung deposited surface area of particles, were evaluated in the field for their suitability to measure combustion emitted particles, such as vehicular exhaust and residential wood combustion emissions. Results of the study indicated that optical aerosol sensors are unlikely to be fit for long-term regulatory monitoring. The main issues preventing this arise from their improper calibration which poses a significant risk of data misinterpretation; none of the laboratory evaluated sensors measured particle sizes which their technical specifications implied. On the other hand, field tests showed that when the measured size fraction was targeted to match the true detection range of the sensor, highly accurate and repeatable results were obtained. This implies that, while the usability of optical sensors is limited in their current form, the concept and vision of a sensor driven air quality monitoring network remains valid and achievable. In comparison to optical sensors, diffusion charging-based sensors were found to be more mature in terms of their technological development. The evaluated sensors exhibited accurate and stable performance throughout the test campaigns and were shown to be particularly well-suited the measurement of combustion emitted particles. Hence, diffusion charger sensors would be a valuable addition to be used alongside other measurement techniques as urban air quality is heavily affected by nanoparticles

High-resolution large-eddy simulation of indoor turbulence and its effect on airborne transmission of respiratory pathogens - Model validation and infection probability analysis

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

Cantilever-enhanced photoacoustic measurement of light-absorbing aerosols

Photoacoustic detection is a sensitive method for measurement of light-absorbing particles directly in the aerosol phase. In this article, we demonstrate a new sensitive technique for photoacoustic aerosol absorption measurements using a cantilever microphone for the detection of the photoacoustic signal. Compared to conventional diaphragm microphones, a cantilever offers increased sensitivity by up to two orders of magnitude. The measurement setup uses a photoacoustic cell from Gasera PA201 gas measurement system, which we have adapted for aerosol measurements. Here we reached a noise level of 0.013 Mm(-1) (one standard deviation) with a sampling time of 20 s, using a simple single-pass design without a need for a resonant acoustic cell. The sampling time includes 10 s signal averaging time and 10 s sample exchange, since the photoacoustic cell is designed for closed cell operation. We demonstrate the method in measurements of size-selected nigrosin particles and ambient black carbon. Due to the exceptional sensitivity, the technique shows great potential for applications where low detection limits are required, for example size-selected absorption measurements and black carbon detection in ultra clean environments.Peer reviewe

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

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 similar to 0.55 to 8.4 mu m) 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.Peer reviewe

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality 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.Peer reviewe

Input-Adaptive Proxy for Black Carbon as a Virtual Sensor

Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20–80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86–0.94; urban background: adjR2 = 0.74–0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future

Input-Adaptive Proxy for Black Carbon as a Virtual Sensor

Missing data has been a challenge in air quality measurement. In this study, we develop an input-adaptive proxy, which selects input variables of other air quality variables based on their correlation coefficients with the output variable. The proxy uses ordinary least squares regression model with robust optimization and limits the input variables to a maximum of three to avoid overfitting. The adaptive proxy learns from the data set and generates the best model evaluated by adjusted coefficient of determination (adjR2). In case of missing data in the input variables, the proposed adaptive proxy then uses the second-best model until all the missing data gaps are filled up. We estimated black carbon (BC) concentration by using the input-adaptive proxy in two sites in Helsinki, which respectively represent street canyon and urban background scenario, as a case study. Accumulation mode, traffic counts, nitrogen dioxide and lung deposited surface area are found as input variables in models with the top rank. In contrast to traditional proxy, which gives 20–80% of data, the input-adaptive proxy manages to give full continuous BC estimation. The newly developed adaptive proxy also gives generally accurate BC (street canyon: adjR2 = 0.86–0.94; urban background: adjR2 = 0.74–0.91) depending on different seasons and day of the week. Due to its flexibility and reliability, the adaptive proxy can be further extend to estimate other air quality parameters. It can also act as an air quality virtual sensor in support with on-site measurements in the future

Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets

Lung-deposited surface area (LDSA) has been considered to be a better metric to explain nanoparticle toxicity instead of the commonly used particulate mass concentration. LDSA concentrations can be obtained either by direct measurements or by calculation based on the empirical lung deposition model and measurements of particle size distribution. However, the LDSA or size distribution measurements are neither compulsory nor regulated by the government. As a result, LDSA data are often scarce spatially and temporally. In light of this, we developed a novel statistical model, named the input-adaptive mixed-effects (IAME) model, to estimate LDSA based on other already existing measurements of air pollutant variables and meteorological conditions. During the measurement period in 2017–2018, we retrieved LDSA data measured by Pegasor AQ Urban and other variables at a street canyon (SC, average LDSA = 19.7 ± 11.3 µm2 cm−3) site and an urban background (UB, average LDSA = 11.2 ± 7.1 µm2 cm−3) site in Helsinki, Finland. For the continuous estimation of LDSA, the IAME model was automatised to select the best combination of input variables, including a maximum of three fixed effect variables and three time indictors as random effect variables. Altogether, 696 submodels were generated and ranked by the coefficient of determination (R2), mean absolute error (MAE) and centred root-mean-square difference (cRMSD) in order. At the SC site, the LDSA concentrations were best estimated by mass concentration of particle of diameters smaller than 2.5 µm (PM2.5), total particle number concentration (PNC) and black carbon (BC), all of which are closely connected with the vehicular emissions. At the UB site, the LDSA concentrations were found to be correlated with PM2.5, BC and carbon monoxide (CO). The accuracy of the overall model was better at the SC site (R2=0.80, MAE = 3.7 µm2 cm−3) than at the UB site (R2=0.77, MAE = 2.3 µm2 cm−3), plausibly because the LDSA source was more tightly controlled by the close-by vehicular emission source. The results also demonstrated that the additional adjustment by taking random effects into account improved the sensitivity and the accuracy of the fixed effect model. Due to its adaptive input selection and inclusion of random effects, IAME could fill up missing data or even serve as a network of virtual sensors to complement the measurements at reference stations.Peer reviewe

Combining Phi6 as a surrogate virus and computational large-eddy simulations to study airborne transmission of SARS-CoV-2 in a restaurant

COVID-19 has highlighted the need for indoor risk-reduction strategies. Our aim is to provide information about the virus dispersion and attempts to reduce the infection risk. Indoor transmission was studied simulating a dining situation in a restaurant. Aerosolized Phi6 viruses were detected with several methods. The aerosol dispersion was modeled by using the Large-Eddy Simulation (LES) technique. Three risk-reduction strategies were studied: (1) augmenting ventilation with air purifiers, (2) spatial partitioning with dividers, and (3) combination of 1 and 2. In all simulations infectious viruses were detected throughout the space proving the existence long-distance aerosol transmission indoors. Experimental cumulative virus numbers and LES dispersion results were qualitatively similar. The LES results were further utilized to derive the evolution of infection probability. Air purifiers augmenting the effective ventilation rate by 65% reduced the spatially averaged infection probability by 30%-32%. This relative reduction manifests with approximately 15 min lag as aerosol dispersion only gradually reaches the purifier units. Both viral findings and LES results confirm that spatial partitioning has a negligible effect on the mean infection-probability indoors, but may affect the local levels adversely. Exploitation of high-resolution LES jointly with microbiological measurements enables an informative interpretation of the experimental results and facilitates a more complete risk assessment.Peer reviewe

Opinion: Insights into updating Ambient Air Quality Directive 2008/50/EC

As evidence of adverse health effects due to air pollution continues to increase, the World Health Organization (WHO) recently published its latest edition of the global air quality guidelines (World Health Organization, 2021). Although not legally binding, the guidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based air quality management strategies. In the light of this, the European Union has stated its intent to revise the current ambient air quality directive (2008/50/EC) to more closely resemble the newly published WHO guidelines (European Commission, 2020). This article provides an informed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The selected features include discussion about (1) air quality sensors as a part of a hierarchical observation network, (2) the number of minimum sampling points and their siting criteria, and (3) new target air pollution parameters for future consideration.Peer reviewe