Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Atmosphere
Special Issues
Opportunities and Challenges in Air Pollution Exposure Assessment
share announcement

Opportunities and Challenges in Air Pollution Exposure Assessment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality and Health".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 10605

Special Issue Editor

Dr. Juana Maria Delgado-Saborit

E-Mail Website
Guest Editor
Instituto de Salud Global de Barcelona (ISGLOBAL), Barcelona, Spain
Interests: exposure assessment; personal monitoring; sensors; biomarkers; exposure modeling; dispersion models; land use regression; machine learning models; cognition; neurodevelopment; neurodegeneration; pregnancy outcomes; environmental health

Special Issue Information

Dear Colleagues,

Assessment of air pollution exposures is a critical component of epidemiological studies investigating the associations between air pollution and health effects. However, the lack of accurate, quantitative measures of exposures applicable to large populations is the greatest source of uncertainty in many epidemiological studies, limiting the power of such studies to enable definitive conclusions about the associations between exposure and disease. Accurate characterization of exposure to air pollution is also critical in risk assessment to enable appropriate measures for risk management.

Last but not least, the global burden of disease requires information on exposure to air pollution at a global scale. Many developing countries lack an air quality monitoring program due to financial pressures, limited infrastructure, and low institutional capacity. This results in a scarcity of exposure data for a large proportion of the global population and affects the global burden of disease calculations.

Novel tools such as miniaturized sensors, remote sensing, geographic information systems, and information and communications technologies, in combination with traditional methodologies, such as personal and biological monitoring, and innovative modeling approaches provide an opportunity to characterize human exposure to air pollution accurately. These approaches can be implemented to assess air pollution exposure in a wide variety of settings, including exposures in large populations and in developing countries. The availability of big data also represents an untapped source of information to characterize exposure to air pollution.

We invite you to consider submitting your research for publication in this Special Issue of Atmosphere, focusing on “Opportunities and Challenges in Air Pollution Exposure Assessment”. We encourage contributions on novel approaches to model large-scale exposures, including deterministic, statistical, land use regression and Bayesian models, as well as models exploiting machine learning techniques and hybrid models. Contributions providing new insights into the use of sensors, monitors, and biomarkers for large cohorts and in developing countries are welcome.  Research work using mixed methods, questionnaires, and proxies to characterize exposures in challenging settings would be considered. We also invite contributions implementing remote sensing, geographical information systems, information and communications technologies, and use of big data to characterize air pollution exposures.

Dr. Juana Maria Delgado-Saborit
Guest Editor

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

  • Sensors
  • Remote sensing
  • Deterministic models
  • Statistical models
  • Land use regression models
  • Bayesian models
  • Machine learning models
  • Hybrid models
  • Large-scale population
  • Developing countries
  • Geographical information systems
  • Information and communications technologies
  • Big data

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 3365 KiB  
Article
Using Low-Cost Measurement Systems to Investigate Air Quality: A Case Study in Palapye, Botswana
by William Lassman, Jeffrey R. Pierce, Evelyn J. Bangs, Amy P. Sullivan, Bonne Ford, Gizaw Mengistu Tsidu, James P. Sherman, Jeffrey L. Collett, Jr. and Solomon Bililign
Atmosphere 2020, 11(6), 583; https://doi.org/10.3390/atmos11060583 - 9 Jun 2020
Cited by 6 | Viewed by 4829
Abstract
Exposure to particulate air pollution is a major cause of mortality and morbidity worldwide. In developing countries, the combustion of solid fuels is widely used as a source of energy, and this process can produce exposure to harmful levels of particulate matter with [...] Read more.
Exposure to particulate air pollution is a major cause of mortality and morbidity worldwide. In developing countries, the combustion of solid fuels is widely used as a source of energy, and this process can produce exposure to harmful levels of particulate matter with diameters smaller than 2.5 microns (PM2.5). However, as countries develop, solid fuel may be replaced by centralized coal combustion, and vehicles burning diesel and gasoline may become common, changing the concentration and composition of PM2.5, which ultimately changes the population health effects. Therefore, there is a continuous need for in-situ monitoring of air pollution in developing nations, both to estimate human exposure and to monitor changes in air quality. In this study, we present measurements from a 5-week field experiment in Palapye, Botswana. We used a low-cost, highly portable instrument package to measure surface-based aerosol optical depth (AOD), real-time surface PM2.5 concentrations using a third-party optical sensor, and time-integrated PM2.5 concentration and composition by collecting PM2.5 onto Teflon filters. Furthermore, we employed other low-cost measurements of real-time black carbon and time-integrated ammonia to help interpret the observed PM2.5 composition and concentration information during the field experiment. We found that the average PM2.5 concentration (9.5 µg∙m−3) was below the World Health Organization (WHO) annual limit, and this concentration closely agrees with estimates from the Global Burden of Disease (GBD) report estimates for this region. Sulfate aerosol and carbonaceous aerosol, likely from coal combustion and biomass burning, respectively, were the main contributors to PM2.5 by mass (33% and 27% of total PM2.5 mass, respectively). While these observed concentrations were on average below WHO guidelines, we found that the measurement site experienced higher concentrations of aerosol during first half our measurement period (14.5 µg∙m−3), which is classified as “moderately unhealthy” according to the WHO standard. Full article
(This article belongs to the Special Issue Opportunities and Challenges in Air Pollution Exposure Assessment)
►▼ Show Figures

Figure 1

19 pages, 4933 KiB  
Article
A Random Forest Approach to Estimate Daily Particulate Matter, Nitrogen Dioxide, and Ozone at Fine Spatial Resolution in Sweden
by Massimo Stafoggia, Christer Johansson, Paul Glantz, Matteo Renzi, Alexandra Shtein, Kees de Hoogh, Itai Kloog, Marina Davoli, Paola Michelozzi and Tom Bellander
Atmosphere 2020, 11(3), 239; https://doi.org/10.3390/atmos11030239 - 29 Feb 2020
Cited by 50 | Viewed by 4982
Abstract
Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate [...] Read more.
Air pollution is one of the leading causes of mortality worldwide. An accurate assessment of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal models for particulate matter (PM) have been developed in several countries, estimates of daily nitrogen dioxide (NO2) and ozone (O3) concentrations at high spatial resolution are lacking, and no such models have been developed in Sweden. We collected data on daily air pollutant concentrations from routine monitoring networks over the period 2005–2016 and matched them with satellite data, dispersion models, meteorological parameters, and land-use variables. We developed a machine-learning approach, the random forest (RF), to estimate daily concentrations of PM10 (PM<10 microns), PM2.5 (PM<2.5 microns), PM2.5–10 (PM between 2.5 and 10 microns), NO2, and O3 for each squared kilometer of Sweden over the period 2005–2016. Our models were able to describe between 64% (PM10) and 78% (O3) of air pollutant variability in held-out observations, and between 37% (NO2) and 61% (O3) in held-out monitors, with no major differences across years and seasons and better performance in larger cities such as Stockholm. These estimates will allow to investigate air pollution effects across the whole of Sweden, including suburban and rural areas, previously neglected by epidemiological investigations. Full article
(This article belongs to the Special Issue Opportunities and Challenges in Air Pollution Exposure Assessment)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop