Special Issue "Air Quality and Health Predictions"

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

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editor

Dr. M. Talat Odman
E-Mail Website
Guest Editor
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: air pollution meteorology and atmospheric chemistry; wildland fires; air quality modeling at local, urban, regional, and global scales; computational fluid dynamics in environmental applications; numerical methods and algorithms, high performance computing; visualization, decision support, and expert systems

Special Issue Information

Dear Colleagues,

Exposure to ambient air pollution is associated with a wide array of adverse health outcomes: From chronic bronchitis to myocardial infarctions, from low birth weight to premature mortality. Studies suggest that air pollution is one of the largest environmental health risks and a leading factor for burden of disease. In the past, we primarily relied on limited ground-level observations of certain pollutant concentrations from sparse networks to estimate exposures. Recent advances in both measurement and modelling technologies are providing new opportunities for developing high-resolution spatiotemporal fields for more pollutants. Exposures can be estimated much more accurately due to these high-resolution pollutant fields and even individualized by using geolocation technologies that allow the tracking of people in real time. These developments have major implications for public health tracking/surveillance and health impact assessment purposes. Air quality and public health scientists around the globe are striving to find novel ways to best use available data and technologies. The plethora of new approaches being proposed for estimating exposures requires comparative evaluations to determine best approaches while setting new directions for the future of exposure science.

This Special Issue focuses on the state-of-the-science in the prediction of air quality, exposure, and health impacts, including:

  • Air quality model development and applications geared towards generating high resolution pollutant concentration fields;
  • Novel uses of satellite observations in predicting ground-level concentrations;
  • Field studies using low-cost sensors to build dense air pollutant observation networks;
  • Data fusion methods that bring together modelled and observed data to take advantage of their best characteristics;
  • Comparative evaluations of different approaches to estimating exposures and predicting health impacts;
  • Short and medium range forecasts of air quality, exposures, and burdens of disease;
  • Wildfire and prescribed fire smoke impacts;
  • Health impact assessments based on novel approaches to predicting air quality

Dr. M. Talat Odman
Guest Editor

Manuscript Submission Information

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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

  • air pollution
  • nitrogen dioxide
  • ozone
  • particulate matter
  • air quality modeling
  • monitoring networks
  • satellite observations
  • low-cost sensors
  • exposure estimation
  • health impact assessments

Published Papers (8 papers)

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Research

Open AccessArticle
Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone
Int. J. Environ. Res. Public Health 2019, 16(18), 3468; https://doi.org/10.3390/ijerph16183468 - 18 Sep 2019
Cited by 2
Abstract
Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and [...] Read more.
Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application (“app”) that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessArticle
Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005–2014
Int. J. Environ. Res. Public Health 2019, 16(18), 3314; https://doi.org/10.3390/ijerph16183314 - 09 Sep 2019
Abstract
Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses [...] Read more.
Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality (CMAQ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, NO3, NH4+, EC, OC, SO42−) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessFeature PaperArticle
African American Exposure to Prescribed Fire Smoke in Georgia, USA
Int. J. Environ. Res. Public Health 2019, 16(17), 3079; https://doi.org/10.3390/ijerph16173079 - 24 Aug 2019
Abstract
Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM2.5) in the U.S. state of Georgia for 2018. (1) Background: African Americans constitute 32.4% of Georgia’s population, making it [...] Read more.
Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM2.5) in the U.S. state of Georgia for 2018. (1) Background: African Americans constitute 32.4% of Georgia’s population, making it the largest racial/ethnic minority group in the state followed by Hispanic Americans at 9.8%. African Americans, Hispanic Americans, and lower wealth groups are more likely than most middle and upper income White Americans to be exposed to environmental pollutants. This is true because racial and ethnic minorities are more likely to live in urban areas where pollution is more concentrated. As a point of departure, we examine PM2.5 concentrations specific to prescribed fire smoke, which typically emanates from fires occurring in rural or peri-urban areas. Two objectives are specified: a) examine the association between percent African American and PM2.5 concentrations at the census tract level for Georgia, and b) identify emitters of PM2.5 concentrations that exceed National Ambient Air Quality Standards (NAAQS) for the 24-h average, i. e., >35 µg/m3. (2) Methods: For the first objective, we estimate a spatial Durbin error model (SDEM) where pollution concentration (PM2.5) estimates for 1683 census tracts are regressed on percent of the human population that is African American or Hispanic; lives in mobile homes; and is employed in agriculture and related occupations. Also included as controls are percent evergreen forest, percent mixed evergreen/deciduous forest, and variables denoting lagged explanatory and error variables, respectively. For the second objective, we merge parcel and prescribed burn permit data to identify landowners who conduct prescribed fires that produce smoke exceeding the NAAQS. (3) Results: Percent African American and mobile home dweller are positively related to PM2.5 concentrations; and government and non-industrial private landowners are the greatest contributors to exceedance levels (4) Conclusions: Reasons for higher PM2.5 concentrations in areas with higher African American and mobile home percent are not clear, although we suspect that neither group is a primary contributor to prescribed burn smoke but rather tend to live proximate to entities, both public and private, that are. Also, non-industrial private landowners who generated prescribed burn smoke exceeding NAAQS are wealthier than others, which suggests that African American and other environmental justice populations are less likely to contribute to exceedance levels in the state. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessArticle
The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA
Int. J. Environ. Res. Public Health 2019, 16(13), 2312; https://doi.org/10.3390/ijerph16132312 - 29 Jun 2019
Cited by 1
Abstract
Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was [...] Read more.
Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessArticle
Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment
Int. J. Environ. Res. Public Health 2019, 16(12), 2137; https://doi.org/10.3390/ijerph16122137 - 17 Jun 2019
Abstract
Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September [...] Read more.
Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessArticle
Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM
Int. J. Environ. Res. Public Health 2019, 16(12), 2133; https://doi.org/10.3390/ijerph16122133 - 17 Jun 2019
Abstract
Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, [...] Read more.
Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessArticle
Development of a WebGIS-Based Analysis Tool for Human Health Protection from the Impacts of Prescribed Fire Smoke in Southeastern USA
Int. J. Environ. Res. Public Health 2019, 16(11), 1981; https://doi.org/10.3390/ijerph16111981 - 04 Jun 2019
Cited by 1
Abstract
We have developed the Southern Integrated Prescribed Fire Information System (SIPFIS) to disseminate prescribed fire information, including daily forecasts of potential air quality impacts for southeastern USA. SIPFIS is a Web-based Geographic Information Systems (WebGIS) assisted online analysis tool that provides easy access [...] Read more.
We have developed the Southern Integrated Prescribed Fire Information System (SIPFIS) to disseminate prescribed fire information, including daily forecasts of potential air quality impacts for southeastern USA. SIPFIS is a Web-based Geographic Information Systems (WebGIS) assisted online analysis tool that provides easy access to air quality and fire-related data products, and it facilitates visual analysis of exposure to smoke from prescribed fires. We have demonstrated that the information that SIPFIS provides can help users to accomplish several fire management activities, especially those related to assessing environmental and health impacts associated with prescribed burning. SIPFIS can easily and conveniently assist tasks such as checking residential community-level smoke exposures for personal use, pre-screening for fire-related exceptional events that could lead to air quality exceedances, supporting analysis for air quality forecasts, and the evaluation of prescribed burning operations, among others. The SIPFIS database is currently expanding to include social vulnerability and human health information, and this will evolve to bring more enhanced interactive functions in the future. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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Open AccessFeature PaperArticle
A Metamodeling Framework for Quantifying Health Damages of Power Grid Expansion Plans
Int. J. Environ. Res. Public Health 2019, 16(10), 1857; https://doi.org/10.3390/ijerph16101857 - 26 May 2019
Abstract
In this paper, we present an analytical framework to establish a closed-form relationship between electricity generation expansion planning decisions and the resulting negative health externalities. Typical electricity generation expansion planning models determine the optimal technology–capacity–investment strategy that minimizes total investment costs as well [...] Read more.
In this paper, we present an analytical framework to establish a closed-form relationship between electricity generation expansion planning decisions and the resulting negative health externalities. Typical electricity generation expansion planning models determine the optimal technology–capacity–investment strategy that minimizes total investment costs as well as fixed and variable operation and maintenance costs. However, the relationship between these long-term planning decisions and the associated health externalities is highly stochastic and nonlinear, and it is computationally expensive to evaluate. Thus, we developed a closed-form metamodel by executing computer-based experiments of a generation expansion planning model, and we analyzed the resulting model outputs in a United States Environmental Protection Agency (EPA) screening tool that approximates the associated human health externalities. Procedural guidance to verify the accuracy and to select key metamodel parameters to enhance its prediction capability is presented. Specifically, the metamodel presented in this paper can predict the resulting health damages of long-term power grid expansion decisions, thus, enabling researchers and policy makers to quickly assess the health implications of power grid expansion decisions with a high degree of certainty. Full article
(This article belongs to the Special Issue Air Quality and Health Predictions)
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