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Topical Collection "Sensors for Air Quality Monitoring"

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Physical Sensors".

Editors

Prof. Dr. Klaus Schäfer
E-Mail Website
Collection Editor
Atmospheric Physics Consultant, 82467 Garmisch-Partenkirchen, Germany
Interests: meteorological influences upon air pollution; air pollution formation processes; emissions of air pollutants; remote sensing of the atmosphere
Special Issues, Collections and Topics in MDPI journals
Dr. Matthias Budde
E-Mail Website
Collection Editor
Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Interests: environmental sensing; mobile computing; Internet of Things (IoT); human-computer-interaction (HCI); pervasive games/gamification; context and activity recognition

Topical Collection Information

Dear Colleagues,

New sensors to detect air pollutants like fine dust (PM10, PM2.5), O3, NO2, or CO as well as greenhouse gases like CO2 are available and applied in different areas of atmospheric observations. These sensors are not only small, lightweight, fast, and cheap, but also relatively unstable and inaccurate. It is time to provide an overview about

- The possibilities and shortcomings of the new sensing techniques and applications;

- The methodologies to overcome their disadvantages;

- The solutions to integrate networks of these sensors into the existing, well-calibrated air-quality monitoring networks;

- The solutions to use them for air-quality monitoring; and

- Their application to new tasks such as the detection of air pollution hot spots or the evaluation of emission inventories and numerical air pollution simulations.

Further, it is necessary to extend our knowledge about harmful compounds in the atmosphere. This is possible by measurements in the atmosphere, but also at the source of emissions into the atmosphere. Emission measurements are required because some air pollutants are secondary (i.e., these compounds are formed in the atmosphere under certain meteorological conditions and together with other atmospheric compounds).

So, we ask physicists, chemists, engineers, information scientists, and corresponding researchers to send in their papers for this Special Issue.

Otherwise, the requirements to develop new sensors are defined by environmental physicians and epidemiologists and their working results originate the development of new sensors. The way is now open to detect personal air pollution exposure and maybe in the future for personal pollen and fungi exposure as a basis for new measures to improve human health. Papers from this research are very welcome.

Prof. Dr. Klaus Schäfer
Dr. Matthias Budde
Collection Editors

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Keywords

  • air pollution measurements
  • air quality networks
  • new air pollutants
  • emission inventory evaluation
  • air pollution hot spots
  • air quality simulation evaluation
  • personal air pollution exposure
  • epidemiology
  • environmental medicine

Published Papers (13 papers)

2022

Jump to: 2021, 2020

Article
Enhanced Ambient Sensing Environment—A New Method for Calibrating Low-Cost Gas Sensors
Sensors 2022, 22(19), 7238; https://doi.org/10.3390/s22197238 - 24 Sep 2022
Viewed by 345
Abstract
Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The [...] Read more.
Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0–120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time. Full article
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Article
Assessment of Individual-Level Exposure to Airborne Particulate Matter during Periods of Atmospheric Thermal Inversion
Sensors 2022, 22(19), 7116; https://doi.org/10.3390/s22197116 - 20 Sep 2022
Viewed by 213
Abstract
Air pollution exposure is harmful to human health and reducing it at the level of an individual requires measurements and assessments that capture the spatiotemporal variability of different microenvironments and the influence of specific activities. In this paper, activity-specific and general indoor and [...] Read more.
Air pollution exposure is harmful to human health and reducing it at the level of an individual requires measurements and assessments that capture the spatiotemporal variability of different microenvironments and the influence of specific activities. In this paper, activity-specific and general indoor and outdoor exposure during and after a period of high concentrations of particulate matter (PM), e.g., an atmospheric thermal inversion (ATI) in the Ljubljana subalpine basin, Slovenia, was assessed. To this end, personal particulate matter monitors (PPM) were used, worn by participants of the H2020 ICARUS sampling campaigns in spring 2019 who also recorded their hourly activities. ATI period(s) were determined based on data collected from two meteorological stations managed by the Slovenian Environmental Agency (SEA). Results showed that indoor and outdoor exposure to PM was significantly higher during the ATI period, and that the difference between mean indoor and outdoor exposure to PM was much higher during the ATI period (23.0 µg/m3) than after (6.5 µg/m3). Indoor activities generally were associated with smaller differences, with cooking and cleaning even having higher values in the post-ATI period. On the other hand, all outdoor activities had higher PM values during the ATI than after, with larger differences, mostly >30.0 µg/m3. Overall, this work demonstrated that an individual-level approach can provide better spatiotemporal resolution and evaluate the relative importance of specific high-exposure events, and in this way provide an ancillary tool for exposure assessments. Full article
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Article
Development of Air Quality Boxes Based on Low-Cost Sensor Technology for Ambient Air Quality Monitoring
Sensors 2022, 22(10), 3830; https://doi.org/10.3390/s22103830 - 18 May 2022
Cited by 2 | Viewed by 784
Abstract
Analyses of the relationships between climate, air substances and health usually concentrate on urban environments because of increased urban temperatures, high levels of air pollution and the exposure of a large number of people compared to rural environments. Ongoing urbanization, demographic ageing and [...] Read more.
Analyses of the relationships between climate, air substances and health usually concentrate on urban environments because of increased urban temperatures, high levels of air pollution and the exposure of a large number of people compared to rural environments. Ongoing urbanization, demographic ageing and climate change lead to an increased vulnerability with respect to climate-related extremes and air pollution. However, systematic analyses of the specific local-scale characteristics of health-relevant atmospheric conditions and compositions in urban environments are still scarce because of the lack of high-resolution monitoring networks. In recent years, low-cost sensors (LCS) became available, which potentially provide the opportunity to monitor atmospheric conditions with a high spatial resolution and which allow monitoring directly at vulnerable people. In this study, we present the atmospheric exposure low-cost monitoring (AELCM) system for several air substances like ozone, nitrogen dioxide, carbon monoxide and particulate matter, as well as meteorological variables developed by our research group. The measurement equipment is calibrated using multiple linear regression and extensively tested based on a field evaluation approach at an urban background site using the high-quality measurement unit, the atmospheric exposure monitoring station (AEMS) for meteorology and air substances, of our research group. The field evaluation took place over a time span of 4 to 8 months. The electrochemical ozone sensors (SPEC DGS-O3: R2: 0.71–0.95, RMSE: 3.31–7.79 ppb) and particulate matter sensors (SPS30 PM1/PM2.5: R2: 0.96–0.97/0.90–0.94, RMSE: 0.77–1.07 µg/m3/1.27–1.96 µg/m3) showed the best performances at the urban background site, while the other sensors underperformed tremendously (SPEC DGS-NO2, SPEC DGS-CO, MQ131, MiCS-2714 and MiCS-4514). The results of our study show that meaningful local-scale measurements are possible with the former sensors deployed in an AELCM unit. Full article
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2021

Jump to: 2022, 2020

Article
Evaluation of Solid Particle Number Sensors for Periodic Technical Inspection of Passenger Cars
Sensors 2021, 21(24), 8325; https://doi.org/10.3390/s21248325 - 13 Dec 2021
Cited by 4 | Viewed by 902
Abstract
Following the increase in stringency of the European regulation limits for laboratory and real world automotive emissions, one of the main transport related aspects to improve the air quality is the mass scale in-use vehicle testing. Solid particle number (SPN) emissions have been [...] Read more.
Following the increase in stringency of the European regulation limits for laboratory and real world automotive emissions, one of the main transport related aspects to improve the air quality is the mass scale in-use vehicle testing. Solid particle number (SPN) emissions have been drastically reduced with the use of diesel and gasoline particulate filters which, however, may get damaged or even been tampered. The feasibility of on-board monitoring and remote sensing as well as of the current periodical technical inspection (PTI) for detecting malfunctioning or tampered particulate filters is under discussion. A promising methodology for detecting high emitters is SPN testing at low idling during PTI. Several European countries plan to introduce this method for diesel vehicles and the European Commission (EC) will provide some guidelines. For this scope an experimental campaign was organized by the Joint Research Centre (JRC) of the EC with the participation of different instrument manufacturers. Idle SPN concentrations of vehicles without or with a malfunctioning particulate filter were measured. The presence of particles under the current cut-off size of 23 nm as well as of volatile particles during idling are presented. Moreover, the extreme case of a well performing vehicle tested after a filter regeneration is studied. In most of the cases the different sensors used were in good agreement, the high sub-23 nm particles existence being the most challenging case due to the differences in the sensors’ efficiency below the cut-off size. Full article
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Article
The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland
Sensors 2021, 21(15), 5208; https://doi.org/10.3390/s21155208 - 31 Jul 2021
Cited by 15 | Viewed by 1956
Abstract
In this paper, we present a detailed analysis of the public data provided by low-cost sensors (LCS), which were used for spatial and temporal studies of air quality in Krakow. A PM (particulate matter) dataset was obtained in spring in 2021, during which [...] Read more.
In this paper, we present a detailed analysis of the public data provided by low-cost sensors (LCS), which were used for spatial and temporal studies of air quality in Krakow. A PM (particulate matter) dataset was obtained in spring in 2021, during which a fairly strict lockdown was in force as a result of COVID-19. Therefore, we were able to separate the effect of solid fuel heating from other sources of background pollution, mainly caused by urban transport. Moreover, we analyzed the historical data of PM2.5 from 2010 to 2019 to show the effect of grassroots efforts and pro-clean-air legislation changes in Krakow. We designed a unique workflow with a time-spatial analysis of PM1, PM2.5, and PM10, and temperature data from Airly(c) sensors located in Krakow and its surroundings. Using geostatistical methods, we showed that Krakow’s neighboring cities are the main sources of air pollution from solid fuel heating in the city. Additionally, we showed that the changes in the law in Krakow significantly reduced the PM concentration as compared to neighboring municipalities without a fossil fuel prohibition law. Moreover, our research demonstrates that informative campaigns and education are important initiating factors in order to bring about cleaner air in the future. Full article
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Article
An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis
Sensors 2021, 21(14), 4781; https://doi.org/10.3390/s21144781 - 13 Jul 2021
Cited by 6 | Viewed by 941
Abstract
Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples [...] Read more.
Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring. Full article
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Article
Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study
Sensors 2021, 21(12), 4214; https://doi.org/10.3390/s21124214 - 19 Jun 2021
Cited by 8 | Viewed by 1256
Abstract
We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly [...] Read more.
We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference. Full article
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Article
Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning
Sensors 2021, 21(10), 3338; https://doi.org/10.3390/s21103338 - 11 May 2021
Cited by 5 | Viewed by 1344
Abstract
Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are [...] Read more.
Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided. Full article
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Article
Investigating a Low-Cost Dryer Designed for Low-Cost PM Sensors Measuring Ambient Air Quality
Sensors 2021, 21(3), 804; https://doi.org/10.3390/s21030804 - 26 Jan 2021
Cited by 7 | Viewed by 1350
Abstract
Air pollution in urban areas is a huge concern that demands an efficient air quality control to ensure health quality standards. The hotspots can be located by increasing spatial distribution of ambient air quality monitoring for which the low-cost sensors can be used. [...] Read more.
Air pollution in urban areas is a huge concern that demands an efficient air quality control to ensure health quality standards. The hotspots can be located by increasing spatial distribution of ambient air quality monitoring for which the low-cost sensors can be used. However, it is well-known that many factors influence their results. For low-cost Particulate Matter (PM) sensors, high relative humidity can have a significant impact on data quality. In order to eliminate or reduce the impact of high relative humidity on the results obtained from low-cost PM sensors, a low-cost dryer was developed and its effectiveness was investigated. For this purpose, a test chamber was designed, and low-cost PM sensors as well as professional reference devices were installed. A vaporizer regulated the humid conditions in the test chamber. The low-cost dryer heated the sample air with a manually adjustable intensity depending on the voltage. Different voltages were tested to find the optimum one with least energy consumption and maximum drying efficiency. The low-cost PM sensors with and without the low-cost dryer were compared. The experimental results verified that using the low-cost dryer reduced the influence of relative humidity on the low-cost PM sensor results. Full article
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2020

Jump to: 2022, 2021

Article
Comparisons of Laboratory and On-Road Type-Approval Cycles with Idling Emissions. Implications for Periodical Technical Inspection (PTI) Sensors
Sensors 2020, 20(20), 5790; https://doi.org/10.3390/s20205790 - 13 Oct 2020
Cited by 7 | Viewed by 1110
Abstract
For the type approval of compression ignition (diesel) and gasoline direct injection vehicles, a particle number (PN) limit of 6 × 1011 p/km is applicable. Diesel vehicles in circulation need to pass a periodical technical inspection (PTI) test, typically every two years, [...] Read more.
For the type approval of compression ignition (diesel) and gasoline direct injection vehicles, a particle number (PN) limit of 6 × 1011 p/km is applicable. Diesel vehicles in circulation need to pass a periodical technical inspection (PTI) test, typically every two years, after the first four years of circulation. However, often the applicable smoke tests or on-board diagnostic (OBD) fault checks cannot identify malfunctions of the diesel particulate filters (DPFs). There are also serious concerns that a few high emitters are responsible for the majority of the emissions. For these reasons, a new PTI procedure at idle run with PN systems is under investigation. The correlations between type approval cycles and idle emissions are limited, especially for positive (spark) ignition vehicles. In this study the type approval PN emissions of 32 compression ignition and 56 spark ignition vehicles were compared to their idle PN concentrations from laboratory and on-road tests. The results confirmed that the idle test is applicable for diesel vehicles. The scatter for the spark ignition vehicles was much larger. Nevertheless, the proposed limit for diesel vehicles was also shown to be applicable for these vehicles. The technical specifications of the PTI sensors based on these findings were also discussed. Full article
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Article
Application of Machine Learning for the in-Field Correction of a PM2.5 Low-Cost Sensor Network
Sensors 2020, 20(17), 5002; https://doi.org/10.3390/s20175002 - 03 Sep 2020
Cited by 10 | Viewed by 1793
Abstract
Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. [...] Read more.
Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM2.5 levels were compared with those of GRIMM-calibrated PM2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 μg/m3, reduced from 18.4 ± 6.5 μg/m3 before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM2.5 sensor networks. Full article
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Article
Wildfire Smoke Adjustment Factors for Low-Cost and Professional PM2.5 Monitors with Optical Sensors
Sensors 2020, 20(13), 3683; https://doi.org/10.3390/s20133683 - 30 Jun 2020
Cited by 34 | Viewed by 6694
Abstract
Air quality monitors using low-cost optical PM2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements [...] Read more.
Air quality monitors using low-cost optical PM2.5 sensors can track the dispersion of wildfire smoke; but quantitative hazard assessment requires a smoke-specific adjustment factor (AF). This study determined AFs for three professional-grade devices and four monitors with low-cost sensors based on measurements inside a well-ventilated lab impacted by the 2018 Camp Fire in California (USA). Using the Thermo TEOM-FDMS as reference, AFs of professional monitors were 0.85 for Grimm mini wide-range aerosol spectrometer, 0.25 for TSI DustTrak, and 0.53 for Thermo pDR1500; AFs for low-cost monitors were 0.59 for AirVisual Pro, 0.48 for PurpleAir Indoor, 0.46 for Air Quality Egg, and 0.60 for eLichens Indoor Air Quality Pro Station. We also compared public data from 53 PurpleAir PA-II monitors to 12 nearby regulatory monitoring stations impacted by Camp Fire smoke and devices near stations impacted by the Carr and Mendocino Complex Fires in California and the Pole Creek Fire in Utah. Camp Fire AFs varied by day and location, with median (interquartile) of 0.48 (0.44–0.53). Adjusted PA-II 4-h average data were generally within ±20% of PM2.5 reported by the monitoring stations. Adjustment improved the accuracy of Air Quality Index (AQI) hazard level reporting, e.g., from 14% to 84% correct in Sacramento during the Camp Fire. Full article
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Article
Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data
Sensors 2020, 20(5), 1406; https://doi.org/10.3390/s20051406 - 04 Mar 2020
Cited by 4 | Viewed by 1869
Abstract
Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used [...] Read more.
Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6–22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research. Full article
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