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Data Fusion/Assimilation of Low-Cost Sensors for Air Pollution Exposure
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Data Fusion/Assimilation of Low-Cost Sensors for Air Pollution Exposure

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Air".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 6726

Special Issue Editors

Prof. Dr. Tareq Hussein

E-Mail Website
Guest Editor
1. Department of Physics, School of Science, University of Jordan, Amman 11942, Jordan
2. Institute for Atmospheric and Earth System Research (INAR / Physics), University of Helsinki, PL 64, FI-00014 Helsinki, Finland
Interests: atmospheric and environmental sciences; air pollution; urban and indoor air quality; dynamics and physical characterization of aerosol particles; emissions and fate of atmospheric aerosols, dry deposition; exposure; modeling, analytical, and numerical methods
Special Issues, Collections and Topics in MDPI journals
Dr. Martha A. Zaidan

E-Mail Website1 Website2
Guest Editor
Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, FI-00560 Helsinki, Finland.
Interests: : atmospheric sciences, air pollution, low-cost sensors, data sciences, data mining, machine learning

Special Issue Information

Air pollution is a major issue in urban areas. High population density is related to excess anthropogenic emissions, impacting upon the environment and health. According to the World Health Organization (WHO), each year approximately 7 million human deaths are attributed to exposure to ambient air pollution. To perform air quality monitoring in urban areas, there is a need to measure the pollution with high resolution. Indeed, highly accurate and reliable air quality monitoring stations have been established to continuously monitor air quality. However, these monitoring stations are expensive and complex to establish, operate, and maintain. Therefore, it is not feasible to deploy these stations massively in urban areas. Alternatively, the dense deployment of low-cost air quality sensors in urban areas enables the detection of the pollution hot spots in real-time. Low-cost air quality sensors are also beneficial to be deployed indoors, where official monitoring stations are not able to perform measurements.

Due to the impact of air pollution on human health, as well as the increasing popularity of low-cost air quality sensors, this Special Issue aims to publish new research and reviews to assess and examine the adverse health effects via the assimilation of data from low-cost air quality sensors both indoors and outdoors. The scope of this Special Issue also includes Internet of things technology and several data science methods, such as data mining and machine learning for processing and analyzing air quality data for investigating health exposure. We are also interested in air quality data fusion and its association to human health as well as environmental epidemiology.

Prof. Tareq Hussein
Dr. Martha A. Zaidan
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. International Journal of Environmental Research and Public Health 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 2500 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

  • Urban air pollution 
  • Environmental epidemiology 
  • Data fusion 
  • Data assimilation 
  • Health exposure 
  • Indoor measurements 
  • Internet of things 
  • Air quality low-cost sensors 
  • Data sciences 
  • Data mining 
  • Machine learning

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Published Papers (2 papers)

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Research

18 pages, 5108 KiB  
Article
Harmonization and Visualization of Data from a Transnational Multi-Sensor Personal Exposure Campaign
by Rok Novak, Ioannis Petridis, David Kocman, Johanna Amalia Robinson, Tjaša Kanduč, Dimitris Chapizanis, Spyros Karakitsios, Benjamin Flückiger, Danielle Vienneau, Ondřej Mikeš, Céline Degrendele, Ondřej Sáňka, Saul García Dos Santos-Alves, Thomas Maggos, Demetra Pardali, Asimina Stamatelopoulou, Dikaia Saraga, Marco Giovanni Persico, Jaideep Visave, Alberto Gotti and Dimosthenis Sarigiannisadd Show full author list remove Hide full author list
Int. J. Environ. Res. Public Health 2021, 18(21), 11614; https://doi.org/10.3390/ijerph182111614 - 4 Nov 2021
Cited by 9 | Viewed by 3234
Abstract
Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European [...] Read more.
Use of a multi-sensor approach can provide citizens with holistic insights into the air quality of their immediate surroundings and their personal exposure to urban stressors. Our work, as part of the ICARUS H2020 project, which included over 600 participants from seven European cities, discusses the data fusion and harmonization of a diverse set of multi-sensor data streams to provide a comprehensive and understandable report for participants. Harmonizing the data streams identified issues with the sensor devices and protocols, such as non-uniform timestamps, data gaps, difficult data retrieval from commercial devices, and coarse activity data logging. Our process of data fusion and harmonization allowed us to automate visualizations and reports, and consequently provide each participant with a detailed individualized report. Results showed that a key solution was to streamline the code and speed up the process, which necessitated certain compromises in visualizing the data. A thought-out process of data fusion and harmonization of a diverse set of multi-sensor data streams considerably improved the quality and quantity of distilled data that a research participant received. Though automation considerably accelerated the production of the reports, manual and structured double checks are strongly recommended. Full article
(This article belongs to the Special Issue Data Fusion/Assimilation of Low-Cost Sensors for Air Pollution Exposure)
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11 pages, 1570 KiB  
Article
Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure Information
by JinSoo Park and Sungroul Kim
Int. J. Environ. Res. Public Health 2020, 17(18), 6573; https://doi.org/10.3390/ijerph17186573 - 9 Sep 2020
Cited by 7 | Viewed by 2356
Abstract
The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM2.5. However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to bring a [...] Read more.
The activity pattern is a significant factor in identifying hotspots of personal exposure to air pollutants, such as PM2.5. However, the recording process of an activity pattern can be annoying to study participants, because they are often asked to bring a diary or a tracking recorder to write or validate their activity patterns when they change their activity profiles. Furthermore, the accuracy of the records of activity patterns can be lower, because people can mistakenly record them. Thus, this paper proposes an idea to overcome these problems and make the whole data-collection process easier and more reliable. Our idea was based on transforming training data using the statistical properties of the children’s personal exposure level to PM2.5, temperature, and relative humidity and applying the properties to a decision tree algorithm for classification of activity patterns. From our final machine-learning modeling processes, we observed that the accuracy for activity-pattern classification was more than 90% in both the training and test data. We believe that our methodology can be used effectively in data-collection tasks and alleviate the annoyance that study participants may feel. Full article
(This article belongs to the Special Issue Data Fusion/Assimilation of Low-Cost Sensors for Air Pollution Exposure)
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