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The Use of Earth Observations for Exposure Assessment in Epidemiological Studies

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 30853

Special Issue Editor

Prof. Dr. Itai Kloog

E-Mail Website
Guest Editor
The Department of Geography and Environmental Development,Ben-Gurion University of the Negev, Beer Sheva P.O.B. 653, Israel
Interests: GIS; exposure assessment; environmental epidemiology; geo-statistics; air pollution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite data such as land surface temperature, aerosol optical depth (AOD), and other products have been used to model multiple environmental pollutants, such as air temperature and air pollution across large spatial areas at high spatiotemporal resolutions. These models enable the exposure assessment of entire populations and have been shown to reduce error in exposure estimates, thus mitigating downward bias in health effect estimates. Recent advances in satellite remote sensing have lifted some of the limitations of previous satellite data, such as relatively coarse spatial and temporal resolutions, thus improving exposure assessment modeling. This Special Issue focuses on these new advances in relation to environmental exposure modeling and their application in epidemiological studies.

Research studies and reviews on the topic from around the world are encouraged to provide a more profound understanding of the topic and provide new insights.

Dr. Itai Kloog
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • remote sensing
  • AOD
  • temperature

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Published Papers (5 papers)

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Research

19 pages, 2698 KiB  
Article
A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain
by Rochelle Schneider, Ana M. Vicedo-Cabrera, Francesco Sera, Pierre Masselot, Massimo Stafoggia, Kees de Hoogh, Itai Kloog, Stefan Reis, Massimo Vieno and Antonio Gasparrini
Remote Sens. 2020, 12(22), 3803; https://doi.org/10.3390/rs12223803 - 20 Nov 2020
Cited by 57 | Viewed by 10666 | Correction
Abstract
Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This [...] Read more.
Epidemiological studies on the health effects of air pollution usually rely on measurements from fixed ground monitors, which provide limited spatio-temporal coverage. Data from satellites, reanalysis, and chemical transport models offer additional information used to reconstruct pollution concentrations at high spatio-temporal resolutions. This study aims to develop a multi-stage satellite-based machine learning model to estimate daily fine particulate matter (PM2.5) levels across Great Britain between 2008–2018. This high-resolution model consists of random forest (RF) algorithms applied in four stages. Stage-1 augments monitor-PM2.5 series using co-located PM10 measures. Stage-2 imputes missing satellite aerosol optical depth observations using atmospheric reanalysis models. Stage-3 integrates the output from previous stages with spatial and spatio-temporal variables to build a prediction model for PM2.5. Stage-4 applies Stage-3 models to estimate daily PM2.5 concentrations over a 1 km grid. The RF architecture performed well in all stages, with results from Stage-3 showing an average cross-validated R2 of 0.767 and minimal bias. The model performed better over the temporal scale when compared to the spatial component, but both presented good accuracy with an R2 of 0.795 and 0.658, respectively. These findings indicate that direct satellite observations must be integrated with other satellite-based products and geospatial variables to derive reliable estimates of air pollution exposure. The high spatio-temporal resolution and the relatively high precision allow these estimates (approximately 950 million points) to be used in epidemiological analyses to assess health risks associated with both short- and long-term exposure to PM2.5. Full article
(This article belongs to the Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies)
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22 pages, 9919 KiB  
Article
Estimation of Hourly near Surface Air Temperature Across Israel Using an Ensemble Model
by Bin Zhou, Evyatar Erell, Ian Hough, Alexandra Shtein, Allan C. Just, Victor Novack, Jonathan Rosenblatt and Itai Kloog
Remote Sens. 2020, 12(11), 1741; https://doi.org/10.3390/rs12111741 - 28 May 2020
Cited by 17 | Viewed by 3643
Abstract
Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely [...] Read more.
Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies. Full article
(This article belongs to the Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies)
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16 pages, 2578 KiB  
Article
Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function
by Khang Chau, Meredith Franklin and W. James Gauderman
Remote Sens. 2020, 12(6), 1028; https://doi.org/10.3390/rs12061028 - 23 Mar 2020
Cited by 13 | Viewed by 4410
Abstract
Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical [...] Read more.
Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM 2.5 , and PM 2.5 speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM 2.5 (test R 2 = 0.68 ) and its chemical components (test R 2 = –0.71). In single-pollutant models, FEV 1 decreased by 131 mL (95% CI: − 232 , − 35 ) per 10.7-µg/m 3 increase in PM 2.5 , by 158 mL (95% CI: − 273 , − 43 ) per 1.2-µg/m 3 in sulfates (SO 4 2 − ), and by 177 mL (95% CI: − 306 , − 56 ) per 1.6-µg/m 3 increase in dust; FVC decreased by 175 mL (95% CI: − 310 , − 29 ) per 1.2-µg/m 3 increase in SO 4 2 − and by 212 mL (95% CI: − 391 , − 28 ) per 2.5-µg/m 3 increase in nitrates (NO 3 − ). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates. Full article
(This article belongs to the Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies)
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18 pages, 3138 KiB  
Article
Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods
by Mahdieh Danesh Yazdi, Zheng Kuang, Konstantina Dimakopoulou, Benjamin Barratt, Esra Suel, Heresh Amini, Alexei Lyapustin, Klea Katsouyanni and Joel Schwartz
Remote Sens. 2020, 12(6), 914; https://doi.org/10.3390/rs12060914 - 12 Mar 2020
Cited by 93 | Viewed by 7904
Abstract
Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and [...] Read more.
Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km × 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R2 of 0.882. However, its ability to predict spatial variability was weaker, with a R2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area. Full article
(This article belongs to the Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies)
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16 pages, 5168 KiB  
Article
Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley
by Meytar Sorek-Hamer, Meredith Franklin, Khang Chau, Michael Garay and Olga Kalashnikova
Remote Sens. 2020, 12(4), 685; https://doi.org/10.3390/rs12040685 - 19 Feb 2020
Cited by 8 | Viewed by 3283
Abstract
Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We [...] Read more.
Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R2 = 0.35, RMSE = 10.38 μg/m3) were significantly improved when spatial (R2 = 0.45, RMSE = 9.54 μg/m3), temporal (R2 = 0.62, RMSE = 8.30 μg/m3), and spatiotemporal (R2 = 0.65, RMSE = 7.58 μg/m3) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R2 = 0.60, RMSE = 8.42 μg/m3). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications. Full article
(This article belongs to the Special Issue The Use of Earth Observations for Exposure Assessment in Epidemiological Studies)
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