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The Role of Low-Cost Air Pollution Sensors in Urban Air...
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The Role of Low-Cost Air Pollution Sensors in Urban Air Quality, Source Apportionment, and Health Exposure

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 6383

Special Issue Editors

Dr. Jai Prakash

E-Mail Website
Guest Editor
Assistant Professor, Department of Atmospheric Science, Central University of Rajasthan, Ajmer, India
Interests: aerosol; black and brown carbon; secondary organic aerosol (SOA); emission inventory; source apportionment; radiative forcing
Dr. Ravi Kant Pathak

E-Mail Website
Guest Editor
Department of Chemistry and Molecular Biology, Atmospheric Science, University of Gothenburg, SE-412 96 Gothenburg, Sweden
Interests: gaseous and aerosol emissions; secondary aerosol; atmospheric chemistry; brown carbon chromophores; low-cost sensors

Special Issue Information

Dear Colleagues,

The increase of the automobile sectors, various industries, and rapid deforestation has led to an increase in air pollution and environmental deterioration in cities. These characteristics of urban cities can be assessed by low-cost sensors. Such characteristics are desirable in larger urban cities, and especially in cities with poor air quality. Low-cost sensors are technologies that have the ability for groundbreaking air quality monitoring, through massive increases in spatial-temporal resolution, thus offering answers to scientific questions and applications for end users. The low-cost sensors are manufactured using micro-fiber technology and micro-electronics that have optical and nano-structure elements. This makes them compact, light-weight, cost effective, with low-power consumption, and with communication devices to utilize the cloud for visualizing the data.  Recently, there has been a proliferation in the use of pollution sensors that offer advantages of low cost, compact size, and high portability. Low-cost sensors monitoring and source apportionment studies of pollution and gases suggest their potential impact on poor air quality, hot-spot location, possible sources, and human health. Due to decreased air quality  during smog or other episodic events in cities, we have an urgent need to visualize air quality parameters in real-time to understand its processes and the factors affecting its spatial-temporal variability. Real-time high resolution monitoring pollution and gases is vital in order to assess their health impacts, source apportionment, evaluation of source emission control improvements, regulatory emission norms, and decision-making of new policies in the long run. Yet, limited real life-based low-cost network systems across urban cities are impeding our understanding of the evolution, distribution, atmospheric transformation mechanisms, and fate of air pollutants.

This Special Issue hopes to discuss urban air pollution, source emissions, source apportionment, and its impact on health using low-cost sensors in urban cities with a synergy of field-based and remote sensing observations and modelling. We are especially interested in articles based on low-cost air pollution sensors, development of network systems, laboratory and field evaluation, and modelling using machine learning technique where the results show scientific novelty. We would also like to cover the association of air pollutants and health exposure data, which represent a major scientific concern. Articles submitted to this issue can also include studies based on source apportionment modelling, long-term observations, short-term campaigns, and chamber-based experiments. They should have a highly focused objective and demonstrate advances in this area of research.

We invite authors to contribute to the following topics:

  • Air pollution and public health in urban cities using low-cost sensors;
  • Development and field evaluation of low-cost sensors of PM, gases, VOC, and radicals;
  • Development low-cost sensor network in cities for helping smart cities and environmental sustainability;
  • Low-cost sensors and satellite observation, source apportionment, and chemistry between PM and gases;
  • Indoor air quality measurement and development of indoor sensors 

Dr. Jai Prakash
Dr. Ravi Kant Pathak
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. Atmosphere is an international peer-reviewed open access monthly 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 2400 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

  •  low-cost sensors
  •  urban air pollution
  •  source apportionment
  •  smart cities and environmental sustainably
  •  air quality and health effects

Published Papers (2 papers)

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Research

25 pages, 8592 KiB  
Article
Air Quality Sensor Networks for Evidence-Based Policy Making: Best Practices for Actionable Insights
by Jelle Hofman, Jan Peters, Christophe Stroobants, Evelyne Elst, Bart Baeyens, Jo Van Laer, Maarten Spruyt, Wim Van Essche, Elke Delbare, Bart Roels, Ann Cochez, Evy Gillijns and Martine Van Poppel
Atmosphere 2022, 13(6), 944; https://doi.org/10.3390/atmos13060944 - 9 Jun 2022
Cited by 5 | Viewed by 2967
Abstract
(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout [...] Read more.
(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (Uexp < 50% for PM and <25% for NO2). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO2 and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities. Full article
(This article belongs to the Special Issue The Role of Low-Cost Air Pollution Sensors in Urban Air Quality, Source Apportionment, and Health Exposure)
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12 pages, 2432 KiB  
Article
Significance of Meteorological Feature Selection and Seasonal Variation on Performance and Calibration of a Low-Cost Particle Sensor
by Vikas Kumar, Vasudev Malyan and Manoranjan Sahu
Atmosphere 2022, 13(4), 587; https://doi.org/10.3390/atmos13040587 - 6 Apr 2022
Cited by 2 | Viewed by 2327
Abstract
Poor air quality is a major environmental concern worldwide, but people living in low- and middle-income countries are disproportionately affected. Measurement of PM2.5 is essential for establishing regulatory standards and developing policy frameworks. Low-cost sensors (LCS) can construct a high spatiotemporal resolution [...] Read more.
Poor air quality is a major environmental concern worldwide, but people living in low- and middle-income countries are disproportionately affected. Measurement of PM2.5 is essential for establishing regulatory standards and developing policy frameworks. Low-cost sensors (LCS) can construct a high spatiotemporal resolution PM2.5 network, but the calibration dependencies and subject to biases of LCS due to variable meteorological parameters limit their deployment for air-quality measurements. This study used data collected from June 2019 to April 2021 from a PurpleAir Monitor and Met One Instruments’ Model BAM 1020 as a reference instrument at Alberta, Canada. The objective of this study is to identify the relevant meteorological parameters for each season that significantly affect the performance of LCS. The meteorological features considered are relative humidity (RH), temperature (T), wind speed (WS) and wind direction (WD). This study applied Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Random Forest (RF) and Gradient Boosting (GB) models with varying features in a stepwise manner across all the seasons, and only the best results are presented in this study. Improvement in the performance of calibration models is observed by incorporating different features for different seasons. The best performance is achieved when RF is applied but with different features for different seasons. The significant meteorological features are PM2.5_LCS in Summer, PM2.5_LCS, RH and T in Autumn, PM2.5_LCS, T and WS in Winter and PM2.5_LCS, RH, T and WS in Spring. The improvement in R2 for each season (values in parentheses) is Summer (0.66–0.94), Autumn (0.73–0.96), Winter (0.70–0.95) and Spring (0.70–0.94). This study signifies selecting the right combination of models and features to attain the best results for LCS calibration. Full article
(This article belongs to the Special Issue The Role of Low-Cost Air Pollution Sensors in Urban Air Quality, Source Apportionment, and Health Exposure)
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