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Special Issue "Sensors for Air Quality Monitoring"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 20 January 2022.

Special Issue Editors

Prof. Dr. Klaus Schäfer
E-Mail
Guest 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 and Collections in MDPI journals
Dr. Matthias Budde
E-Mail Website
Guest 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

Special Issue 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
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (6 papers)

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Research

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
Viewed by 427
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
(This article belongs to the Special Issue Sensors for Air Quality Monitoring)
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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 1 | Viewed by 573
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
(This article belongs to the Special Issue Sensors for Air Quality Monitoring)
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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 2 | Viewed by 740
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
(This article belongs to the Special Issue Sensors for Air Quality Monitoring)
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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 4 | Viewed by 975
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
(This article belongs to the Special Issue Sensors for Air Quality Monitoring)
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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 13 | Viewed by 4364
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
(This article belongs to the Special Issue Sensors for Air Quality Monitoring)
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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
Viewed by 1251
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
(This article belongs to the Special Issue Sensors for Air Quality Monitoring)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.


 

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