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Sensors for Particulate Matter and Air Pollution

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

Deadline for manuscript submissions: closed (14 February 2022) | Viewed by 46182

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Special Issue Editors

Dr. Angela Maria Stortini

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Guest Editor

Department of Molecular Sciences and Nanosystems, University Ca’ Foscari of Venice, via Torino 155, 30172 Mestre-Venezia, Italy
Interests: nanostructured electrodes; bio and biomimetic sensors; environmental monitoring; water and air quality; sea surface microlayer
Special Issues, Collections and Topics in MDPI journals

Dr. Alessandra Cincinelli

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Department of Chemistry, Università degli Studi di Firenze, 50121 Florence, Italy
Interests: waste water treatment plants; persistent organic pollutants; emerging compounds; microplastics; heavy metals; human health
Special Issues, Collections and Topics in MDPI journals

Prof. Dragana Đorđević

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Guest Editor

ICTM-Center of excellence in environmental chemistry and engineering, University of Belgrade, Belgrade, Serbia
Interests: air pollution, particulate matter, aerosol chemistry, measurements methods, sensors calibrations

Dr. Milija Sarajlic

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Guest Editor

IHTM Center for Microelectronic Technologies, Njegoseva 12, 11000 Belgrade, Serbia
Interests: chemical sensors, microelectronics, system on chip, temperature sensors, X-ray detectors

Special Issue Information

Dear Colleagues,

Air quality is a matter of strong interest for authorities and citizens. Bad air quality implies damage to the health of citizens, with a considerable number of deaths, inconvenience in the daily life of population, damage to the cultural heritage of cities, and other unpleasant effects.

The use of new technologies, suitable for monitoring air quality and particulate matter (PM) in the atmosphere, both in the outdoor environment and in the indoor environment, pushes the development of new and sustainable technologies that are able to provide reliable information.

The aim of this Special Issue is to integrate the results of theoretical and experimental science and sensors technology for particulate matter and air pollution sensors.

Scope

-Sensor technologies for PM and air pollution (outdoor and indoor);
-Wireless PM sensors;
-Intelligent PM sensors;
-Low-cost sensor technologies;
-Sensor calibrations;
-Sensor networks;
-Bioaerosol sensors;
-Case studies for air pollution monitoring performed by sensors;
-Air quality management with the help of chemical sensors;
-Wearable chemical sensors.

Manuscript, reviews, and letters are welcome.

Dr. Angela Maria Stortini
Prof. Alessandra Cincinelli
Prof. Dragana Đorđević
Dr. Milija Sarajlic
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2600 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 sensors
Indoor and outdoor PM detection
Intercalibration and data validation

Published Papers (10 papers)

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Research

20 pages, 25635 KiB

Open AccessArticle

Performance and Deployment of Low-Cost Particle Sensor Units to Monitor Biomass Burning Events and Their Application in an Educational Initiative

by Fabienne Reisen, Jacinta Cooper, Jennifer C. Powell, Christopher Roulston and Amanda J. Wheeler

Sensors 2021, 21(21), 7206; https://doi.org/10.3390/s21217206 - 29 Oct 2021

Cited by 3 | Viewed by 2399

Abstract

Biomass burning smoke is often a significant source of airborne fine particles in regional areas where air quality monitoring is scarce. Emerging sensor technology provides opportunities to monitor air quality on a much larger geographical scale with much finer spatial resolution. It can also engage communities in the conversation around local pollution sources. The SMoke Observation Gadget (SMOG), a unit with a Plantower dust sensor PMS3003, was designed as part of a school-based Science, Technology, Engineering and Mathematics (STEM) project looking at smoke impacts in regional areas of Victoria, Australia. A smoke-specific calibration curve between the SMOG units and a standard regulatory instrument was developed using an hourly data set collected during a peat fire. The calibration curve was applied to the SMOG units during all field-based validation measurements at several locations and during different seasons. The results showed strong associations between individual SMOG units for PM_2.5 concentrations (r² = 0.93–0.99) and good accuracy (mean absolute error (MAE) < 2 μg m⁻³). Correlations of the SMOG units to reference instruments also demonstrated strong associations (r² = 0.87–95) and good accuracy (MAE of 2.5–3.0 μg m⁻³). The PM_2.5 concentrations tracked by the SMOG units had a similar response time as those measured by collocated reference instruments. Overall, the study has shown that the SMOG units provide relevant information about ambient PM_2.5 concentrations in an airshed impacted predominantly by biomass burning, provided that an adequate adjustment factor is applied. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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24 pages, 11327 KiB

Open AccessArticle

Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments

by Lara P. Clark, V. Sreekanth, Bujin Bekbulat, Michael Baum, Songlin Yang, Pao Baylon, Timothy R. Gould, Timothy V. Larson, Edmund Y. W. Seto, Chris D. Space and Julian D. Marshall

Sensors 2020, 20(12), 3417; https://doi.org/10.3390/s20123417 - 17 Jun 2020

Cited by 4 | Viewed by 4409

Abstract

We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1–3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving “gold standard” measurements is needed to investigate accuracy. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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33 pages, 16896 KiB

Open AccessArticle

How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations

by Erika Brattich, Alessandro Bracci, Alessandro Zappi, Pietro Morozzi, Silvana Di Sabatino, Federico Porcù, Francesca Di Nicola and Laura Tositti

Sensors 2020, 20(11), 3073; https://doi.org/10.3390/s20113073 - 29 May 2020

Cited by 28 | Viewed by 5283

Abstract

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30′ N, 11°21′ E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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16 pages, 1985 KiB

Open AccessArticle

Sensitivity Analysis for Predicting Sub-Micron Aerosol Concentrations Based on Meteorological Parameters

by Martha A. Zaidan, Ola Surakhi, Pak Lun Fung and Tareq Hussein

Sensors 2020, 20(10), 2876; https://doi.org/10.3390/s20102876 - 19 May 2020

Cited by 6 | Viewed by 2661

Abstract

Sub-micron aerosols are a vital air pollutant to be measured because they pose health effects. These particles are quantified as particle number concentration (PN). However, PN measurements are not always available in air quality measurement stations, leading to data scarcity. In order to compensate this, PN modeling needs to be developed. This paper presents a PN modeling framework using sensitivity analysis tested on a one year aerosol measurement campaign conducted in Amman, Jordan. The method prepares a set of different combinations of all measured meteorological parameters to be descriptors of PN concentration. In this case, we resort to artificial neural networks in the forms of a feed-forward neural network (FFNN) and a time-delay neural network (TDNN) as modeling tools, and then, we attempt to find the best descriptors using all these combinations as model inputs. The best modeling tools are FFNN for daily averaged data (with R

^{2} = 0.77

) and TDNN for hourly averaged data (with R

^{2} = 0.66

) where the best combinations of meteorological parameters are found to be temperature, relative humidity, pressure, and wind speed. As the models follow the patterns of diurnal cycles well, the results are considered to be satisfactory. When PN measurements are not directly available or there are massive missing PN concentration data, PN models can be used to estimate PN concentration using available measured meteorological parameters. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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44 pages, 19617 KiB

Open AccessArticle

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

by Florentin Michel Jacques Bulot, Hugo Savill Russell, Mohsen Rezaei, Matthew Stanley Johnson, Steven James Johnston Ossont, Andrew Kevin Richard Morris, Philip James Basford, Natasha Hazel Celeste Easton, Gavin Lee Foster, Matthew Loxham and Simon James Cox

Sensors 2020, 20(8), 2219; https://doi.org/10.3390/s20082219 - 15 Apr 2020

Cited by 55 | Viewed by 8627

Abstract

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1

\min

, and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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15 pages, 5808 KiB

Open AccessArticle

Vertical Profiles of Pollution Particle Concentrations in the Boundary Layer above Paris (France) from the Optical Aerosol Counter LOAC Onboard a Touristic Balloon

by Jean-Baptiste Renard, Vincent Michoud and Jérôme Giacomoni

Sensors 2020, 20(4), 1111; https://doi.org/10.3390/s20041111 - 18 Feb 2020

Cited by 18 | Viewed by 3141

Abstract

Atmospheric pollution by particulate matter represents a significant health risk and needs continuous monitoring by air quality networks that provide mass concentrations for PM10 and PM2.5 (particles with diameter smaller than 10 μm and 2.5 μm, respectively). We present here a new approach to monitor the urban particles content, using six years of aerosols number concentration measurements for particles in the 0.2−50 μm size range. These measurements are performed by the Light Optical Aerosols Counter (LOAC) instrument onboard the tethered touristic balloon “Ballon de Paris Generali”, in Paris, France. Such measurements have allowed us first to detect at ground a seasonal variability in the particulate matter content, due to the origin of the particles (anthropogenic pollution, pollens), and secondly, to retrieve the mean evolution of particles concentrations with height above ground up to 150 m. Measurements were also conducted up to 300 m above ground during major pollution events. The vertical evolution of concentrations varies from one event to another, depending on the origin of the pollution and on the meteorological conditions. These measurements have shown the interest of performing particle number concentrations measurements for the air pollution monitoring in complement with regulatory mass concentrations measurement, to better evaluate the intensity of the pollution event and to better consider the effect of smallest particles, which are more dangerous for human health. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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17 pages, 9112 KiB

Open AccessArticle

In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection

by Andi Setiono, Maik Bertke, Wilson Ombati Nyang’au, Jiushuai Xu, Michael Fahrbach, Ina Kirsch, Erik Uhde, Alexander Deutschinger, Ernest J. Fantner, Christian H. Schwalb, Hutomo Suryo Wasisto and Erwin Peiner

Sensors 2020, 20(3), 618; https://doi.org/10.3390/s20030618 - 22 Jan 2020

Cited by 18 | Viewed by 5497

Abstract

In this study, we investigate the performance of two piezoresistive micro-electro-mechanical system (MEMS)-based silicon cantilever sensors for measuring target analytes (i.e., ultrafine particulate matters). We use two different types of cantilevers with geometric dimensions of 1000 × 170 × 19.5 µm³ and 300 × 100 × 4 µm³, which refer to the 1st and 2nd types of cantilevers, respectively. For the first case, the cantilever is configured to detect the fundamental in-plane bending mode and is actuated using a resistive heater. Similarly, the second type of cantilever sensor is actuated using a meandering resistive heater (bimorph) and is designed for out-of-plane operation. We have successfully employed these two cantilevers to measure and monitor the changes of mass concentration of carbon nanoparticles in air, provided by atomizing suspensions of these nanoparticles into a sealed chamber, ranging from 0 to several tens of µg/m³ and oversize distributions from ~10 nm to ~350 nm. Here, we deploy both types of cantilever sensors and operate them simultaneously with a standard laboratory system (Fast Mobility Particle Sizer, FMPS, TSI 3091) as a reference. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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18 pages, 5052 KiB

Open AccessArticle

Evaluation of a 10 nm Particle Number Portable Emissions Measurement System (PEMS)

by Barouch Giechaskiel, Athanasios Mamakos, Joseph Woodburn, Andrzej Szczotka and Piotr Bielaczyc

Sensors 2019, 19(24), 5531; https://doi.org/10.3390/s19245531 - 14 Dec 2019

Cited by 30 | Viewed by 4632

Abstract

On-board portable emissions measurement systems (PEMS) are part of the type approval, in-service conformity, and market surveillance aspects of the European exhaust emissions regulation. Currently, only solid particles >23 nm are counted, but Europe will introduce a lower limit of 10 nm. In this study, we evaluated a 10-nm prototype portable system comparing it with laboratory systems measuring diesel, gasoline, and CNG (compressed natural gas) vehicles with emission levels ranging from approximately 2 × 10¹⁰ to 2 × 10¹² #/km. The results showed that the on-board system differed from the laboratory 10-nm system on average for the tested driving cycles by less than approximately 10% at levels below 6 × 10¹¹ #/km and by approximately 20% for high-emitting vehicles. The observed differences were similar to those observed in the evaluation of portable >23 nm particle counting systems, despite the relatively small size of the emitted particles (with geometric mean diameters <42 nm) and the additional challenges associated with sub-23 nm measurements. The latter included the presence of semivolatile sub-23 nm particles, the elevated concentration levels during cold start, and also the formation of sub-23 nm artefacts from the elastomers that are used to connect the tailpipe to the measurement devices. The main conclusion of the study is that >10 nm on-board systems can be ready for introduction in future regulations. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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22 pages, 3380 KiB

Open AccessArticle

Measuring Spatial and Temporal PM_2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors

by Anondo Mukherjee, Steven G. Brown, Michael C. McCarthy, Nathan R. Pavlovic, Levi G. Stanton, Janice Lam Snyder, Stephen D’Andrea and Hilary R. Hafner

Sensors 2019, 19(21), 4701; https://doi.org/10.3390/s19214701 - 29 Oct 2019

Cited by 26 | Viewed by 4479

Abstract

Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) sensors were deployed from December 2016 to January 2017 to determine the spatial variability of PM_2.5 in Sacramento, California. Prior to, and after, the study, the 19 sensors were deployed and collocated at a regulatory air monitoring site. The sensors demonstrated a high degree of precision during all collocated measurement periods (Pearson R² = 0.98 − 0.99 across all sensors), with little drift. A sensor-specific correction factor was developed such that each sensor reported a comparable value. Sensors had a moderate degree of correlation with regulatory monitors during the study (R² = 0.60 − 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM_2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM_2.5 spatial variability, finding an average pairwise coefficient of divergence of 0.22 and a range of 0.14 to 0.33, indicating mostly homogeneous distributions. No significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities (p value = 0.24) was observed. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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12 pages, 1685 KiB

Open AccessArticle

Identification of Bicycling Periods Using the MicroPEM Personal Exposure Monitor

by Robert Chew, Jonathan Thornburg, Darby Jack, Cara Smith, Qiang Yang and Steven Chillrud

Sensors 2019, 19(21), 4613; https://doi.org/10.3390/s19214613 - 23 Oct 2019

Cited by 11 | Viewed by 2863

Abstract

Exposure assessment studies are the primary means for understanding links between exposure to chemical and physical agents and adverse health effects. Recently, researchers have proposed using wearable monitors during exposure assessment studies to obtain higher fidelity readings of exposures actually experienced by subjects. However, limited research has been conducted to link a wearer’s actions to periods of exposure, a necessary step for estimating inhaled dosage. To aid researchers in these settings, we developed a machine learning model for identifying periods of bicycling activity using passively collected data from the RTI MicroPEM wearable exposure monitor, a lightweight device capable of continuously sampling both air pollution levels and accelerometry parameters. Our best performing model identifies biking activity with a mean leave-one-session-out (LOSO) cross-validation F1 score of 0.832 (unweighted) and 0.979 (weighted). Accelerometer derived features contributed greatly to the model performance, as well as temporal smoothing of the predicted activities. Additionally, we found competitive activity recognition can occur with even relatively low sampling rates, suggesting suitability for exposure assessment studies where continuous data collection for long periods (without recharge) are needed to capture realistic daily routines and exposures. Full article

(This article belongs to the Special Issue Sensors for Particulate Matter and Air Pollution)

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