Special Issue "Statistical Approaches to Investigate Air Quality"

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

Deadline for manuscript submissions: closed (15 October 2020).

Special Issue Editors

Dr. Sivaraman Balachandran
E-Mail Website
Guest Editor
College of Engineering and Applied Science, University of Cincinnati, OH 45221, USA
Interests: source apportionment; receptor modeling; air quality and health; statistical modeling
Prof. Dr. Piero Di Carlo
E-Mail Website
Guest Editor
Department of Psychological, Health & Territorial Sciences, University "G. d'Annunzio" of Chieti-Pescara, Via dei Vestini, 31, 66100 Chieti, Italy
Interests: physics–chemistry of the atmosphere and climatology; impact of atmospheric composition changes on climate, air quality, and human health; climate change adaptation and mitigation
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Special Issue Information

Dear Colleagues,

We invite you to contribute to a special issue of Atmosphere, dedicated to statistical approaches to investigate air quality. Encompassing a variety of techniques, these statistical approaches are an important tool for air quality management.  For example, they can be used to forecast air pollutant concentrations, identify sources and quantify their contributions to air quality, and, estimate exposure and associated health impacts. As the trend in air quality management continues toward increased use of portable instrumentation, including low-cost sensors, the research community is utilizing sophisticated techniques to analyze large volumes of data, as well as forecast air pollution at fine spatial and temporal scales. Recent advancements in statistical techniques, including data mining and deep learning are currently being utilized and can offer a more robust picture of air quality and support air quality management efforts. At the same time, traditional methods such as receptor models continue to be utilized – especially in regions that have only recently acquired the necessary speciation data. To bring together the research community, we invite researchers in a broad array of fields, including environmental engineering, environmental science and public health to submit original research work this special issue of Atmosphere devoted to statistical approaches to investigate air quality.

Dr. Sivaraman Balachandran
Prof. Piero Di Carlo
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 papers will be 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 2000 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

  • Statistical models
  • Air quality
  • Source apportionment
  • Forecast models
  • Spatiotemporal models
  • Health
  • Exposure

Published Papers (4 papers)

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Research

Article
Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components
Atmosphere 2020, 11(11), 1233; https://doi.org/10.3390/atmos11111233 - 16 Nov 2020
Cited by 1 | Viewed by 985
Abstract
Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of [...] Read more.
Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical transport model. In cross-validation experiments, BART demonstrated good out-of-sample prediction performance at monitoring locations (R2 from 0.62 to 0.73). More importantly, prediction intervals associated with concentration estimates from BART showed good coverage probability at locations with and without monitoring data. In our case study, major PM2.5 components could be estimated with good accuracy, especially when collocated PM2.5 total mass observations were available. In conclusion, BART is an attractive approach for modeling ambient air pollution levels, especially for its ability to provide uncertainty in estimates that may be useful for subsequent health impact and health effect analyses. Full article
(This article belongs to the Special Issue Statistical Approaches to Investigate Air Quality)
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Article
Determining the Impact of Wildland Fires on Ground Level Ambient Ozone Levels in California
Atmosphere 2020, 11(10), 1131; https://doi.org/10.3390/atmos11101131 - 21 Oct 2020
Viewed by 899
Abstract
Wildland fire smoke is visible and detectable with remote sensing technology. Using this technology to assess ground level pollutants and the impacts to human health and exposure is more difficult. We found the presence of satellite derived smoke plumes for more than a [...] Read more.
Wildland fire smoke is visible and detectable with remote sensing technology. Using this technology to assess ground level pollutants and the impacts to human health and exposure is more difficult. We found the presence of satellite derived smoke plumes for more than a couple of hours in the previous three days has significant impact on the chances of ground level ozone values exceeding the norm. While the magnitude of the impact will depend on characteristics of fires such as size, location, time in transport, or ozone precursors produced by the fire, we demonstrate that information on satellite derived smoke plumes together with site specific regression models provide useful information for supporting causal relationship between smoke from fire and ozone exceedances of the norm. Our results indicated that fire seasons increasing the median ozone level by 15 ppb. However, they seem to have little impact on the metric used for regulatory compliance, in particular at urban sites, except possibly during the 2008 forest fires in California. Full article
(This article belongs to the Special Issue Statistical Approaches to Investigate Air Quality)
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Article
Statistical Analysis of the CO2 and CH4 Annual Cycle on the Northern Plateau of the Iberian Peninsula
Atmosphere 2020, 11(7), 769; https://doi.org/10.3390/atmos11070769 - 21 Jul 2020
Cited by 3 | Viewed by 834
Abstract
Outliers are frequent in CO2 and CH4 observations at rural sites. The aim of this paper is to establish a procedure based on the lag-1 autocorrelation to form measurement groups, some of which include outliers, and the rest include regular measurements. [...] Read more.
Outliers are frequent in CO2 and CH4 observations at rural sites. The aim of this paper is to establish a procedure based on the lag-1 autocorrelation to form measurement groups, some of which include outliers, and the rest include regular measurements. Once observations are classified, a second objective is to determine the number of harmonics in order to suitably describe the annual evolution of both gases. Monthly CO2 and CH4 percentiles were calculated over a six-year period. Linear trends for most of the percentiles were around 2.24 and 0.0097 ppm year−1, and the interquartile ranges of residuals calculated from detrended concentrations were 6 and 0.02 ppm for CO2 and CH4, respectively. Five concentration groups were proposed for CO2 and six were proposed for CH4 from the lag-1 autocorrelation applied to detrended observations. Monthly medians were calculated in each group, and combinations of harmonics were applied in an effort to fit the annual cycle. Finally, adding annual and semi-annual harmonics successfully described the cycle where one step was observed in the concentration decrease in spring, not only for high CO2 percentiles but also for low CH4 percentiles. Full article
(This article belongs to the Special Issue Statistical Approaches to Investigate Air Quality)
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Article
Statistical Learning of the Worst Regional Smog Extremes with Dynamic Conditional Modeling
Atmosphere 2020, 11(6), 665; https://doi.org/10.3390/atmos11060665 - 22 Jun 2020
Cited by 3 | Viewed by 1116
Abstract
This paper is concerned with the statistical learning of the extreme smog (PM 2.5 ) dynamics of a vast region in China. Differently from classical extreme value modeling approaches, this paper develops a dynamic model of conditional, exponentiated Weibull distribution modeling and analysis [...] Read more.
This paper is concerned with the statistical learning of the extreme smog (PM 2.5 ) dynamics of a vast region in China. Differently from classical extreme value modeling approaches, this paper develops a dynamic model of conditional, exponentiated Weibull distribution modeling and analysis of regional smog extremes, particularly for the worst scenarios observed in each day. To gain higher modeling efficiency, weather factors will be introduced in an enhanced model. The proposed model and the enhanced model are illustrated with temporal/spatial maxima of hourly PM 2.5 observations each day from smog monitoring stations located in the Beijing–Tianjin–Hebei geographical region between 2014 and 2019. The proposed model performs more precisely on fittings compared with other previous models dealing with maxima with autoregressive parameter dynamics, and provides relatively accurate prediction as well. The findings enhance the understanding of how severe extreme smog scenarios can be and provide useful information for the central/local government to conduct coordinated PM 2.5 control and treatment. For completeness, probabilistic properties of the proposed model were investigated. Statistical estimation based on the conditional maximum likelihood principle is established. To demonstrate the estimation and inference efficiency of studies, extensive simulations were also implemented. Full article
(This article belongs to the Special Issue Statistical Approaches to Investigate Air Quality)
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