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Remote Sensing for Environmental Health: From Fine-Scale Measurement towards Dynamic Exposure Assessment

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 9150

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Special Issue Editors

Dr. Yimeng Song

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Guest Editor

Department of the Environment, Yale University, New Haven, CT 06520, USA
Interests: geographic information science; remote sensing; urban environment; human mobility; exposure assessment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bo Huang

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Guest Editor

Department of Geography and Resource Management, Chinese University of Hong Kong, Hong Kong, China
Interests: spatio-temporal data analytics; unified satellite image fusion; spatial statistics for land use/land cover change modeling; multi-objective optimization for sustainable land use planning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mei-Po Kwan

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Guest Editor

Institute of Space and Earth Information Science & Department of Geography and Resource Management, The Chinese University of Hong Kong, New Territories, Sha Tin, Hong Kong
Interests: environmental health; human mobility; healthy cities; social justice; GIScience
Special Issues, Collections and Topics in MDPI journals

Dr. Bin Chen

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Guest Editor

Division of Landscape and Architecture, The University of Hong Kong, Hong Kong, China
Interests: remote sensing; data fusion and applications; geospatial big data analysis; environmental health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Living environments have been well demonstrated to have significant impacts on human health. In the context of climate change and urbanization, scientific evidence to aid a comprehensive understanding of how people are exposed to their ambient physical and social environments is a critical requirement for preventing disease and reducing environmental health inequalities. The advances in remote sensing, combined with social sensing and GeoAI techniques, provide additional insights to investigate patterns of human–environment interaction through fine spatial–temporal scale and dynamic ways.

This Special Issue of Remote Sensing aims to gather original articles and reviews showing practical applications of remote sensing in different areas of environmental epidemiology/health studies as well as exposure assessment studies. The expected topics include, but are not limited to, population exposure to environmental factors, dynamic environmental exposure assessment, fine spatial–temporal scale environmental factor mapping, real-time environmental exposure monitoring and modelling, multi- and hyperspectral techniques for environmental analysis, remote sensing-derived social factors in health, land-cover change and public health, climate change and public health, environmental justice, and health disparities.

Dr. Yimeng Song
Prof. Dr. Bo Huang
Prof. Dr. Mei-Po Kwan
Dr. Bin Chen
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. 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

environmental health
environmental exposure
environmental justice
climate change
air pollution
green/blue spaces
land-cover change

Published Papers (4 papers)

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Research

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17 pages, 3028 KiB

Open AccessArticle

Quantifying Spatiotemporal Heterogeneities in PM_2.5-Related Health and Associated Determinants Using Geospatial Big Data: A Case Study in Beijing

by Yanrong Zhu, Juan Wang, Bin Meng, Huimin Ji, Shaohua Wang, Guoqing Zhi, Jian Liu and Changsheng Shi

Remote Sens. 2022, 14(16), 4012; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14164012 - 18 Aug 2022

Cited by 4 | Viewed by 1784

Abstract

Air pollution has brought about serious challenges to public health. With the limitations of available data, previous studies overlooked spatiotemporal heterogeneities in PM_2.5-related health (PM_2.5-RH) and multiple associated factors at the subdistrict scale. In this research, social media Weibo data was employed to extract PM_2.5-RH based on the Bidirectional Encoder Representations from Transformers (BERT) model, in Beijing, China. Then, the relationship between PM_2.5-RH and eight associated factors was qualified based on multi-source geospatial big data using Geographically Weighted Regression (GWR) models. The results indicate that the PM_2.5-RH in the study area showed a spatial pattern of agglomeration to the city center and seasonal variation in the spatially non-stationary effects. The impacts of varied factors on PM_2.5-RH were also spatiotemporally heterogeneous. Specifically, nighttime light (NTL), population density (PD) and the normalized difference built-up index (NDBI) had outstanding effects on PM_2.5-RH in the four seasons, but with spatial disparities. The impact of the normalized difference vegetation index (NDVI) on PM_2.5-RH was significant in summer, especially in the central urban areas, while in winter, the contribution of the air quality index (AQI) was increased. This research further demonstrates the feasibility of using social media data to indicate the effect of air pollution on public health and provides new insights into the seasonal impacts of associated driving factors on the health effects of air pollution. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Health: From Fine-Scale Measurement towards Dynamic Exposure Assessment)

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18 pages, 5423 KiB

Open AccessArticle

Measuring PM_2.5 Concentrations from a Single Smartphone Photograph

by Shiqi Yao, Fei Wang and Bo Huang

Remote Sens. 2022, 14(11), 2572; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112572 - 27 May 2022

Cited by 3 | Viewed by 2245

Abstract

PM_2.5 participates in light scattering, leading to degraded outdoor views, which forms the basis for estimating PM_2.5 from photographs. This paper devises an algorithm to estimate PM_2.5 concentrations by extracting visual cues and atmospheric indices from a single photograph. While air quality measurements in the context of complex urban scenes are particularly challenging, when only a single atmospheric index or cue is given, each one can reinforce others to yield a more robust estimator. Therefore, we selected an appropriate atmospheric index in various outdoor scenes to identify reasonable cue combinations for measuring PM_2.5. A PM_2.5 dataset (PhotoPM-daytime) was built and used to evaluate performance and validate efficacy of cue combinations. Furthermore, a city-wide experiment was conducted using photographs crawled from the Internet to demonstrate the applicability of the algorithm in large-area PM_2.5 monitoring. Results show that smartphones equipped with the developed method could potentially be used as PM_2.5 sensors. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Health: From Fine-Scale Measurement towards Dynamic Exposure Assessment)

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21 pages, 2205 KiB

Open AccessArticle

Estimating High-Resolution PM_2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

by Fei Wang, Shiqi Yao, Haowen Luo and Bo Huang

Remote Sens. 2022, 14(6), 1515; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061515 - 21 Mar 2022

Cited by 7 | Viewed by 2622

Abstract

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM_2.5) concentrations. However, existing approaches to estimate PM_2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM_2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM_2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM_2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R² values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM_2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM_2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM_2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM_2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Health: From Fine-Scale Measurement towards Dynamic Exposure Assessment)

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Other

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14 pages, 3814 KiB

Open AccessTechnical Note

Identifying PM_2.5-Related Health Burden in the Context of the Integrated Development of Urban Agglomeration Using Remote Sensing and GEMM Model

by Lili Xu, Binjie Chen, Chenhao Huang, Mengmeng Zhou, Shucheng You, Fangming Jiang, Weirong Chen and Jinsong Deng

Remote Sens. 2023, 15(11), 2770; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112770 - 26 May 2023

Cited by 1 | Viewed by 1173

Abstract

Integrated development of urban agglomeration is emerging as the main pattern of China’s new modernization. Yet, atmospheric pollution continues to have an adverse impact on public health, challenging efforts to promote coordinated regional development. To better understand the interaction between atmospheric pollution-related health burdens and urbanization, this study employed deep learning technology to obtain high-resolution satellite-derived PM_2.5 concentration data across the Yangtze River Delta (YRD) region. Using the Global Exposure Mortality Model (GEMM), this study estimated premature mortality resulting from long-term exposure to PM_2.5 and innovatively incorporated exposure factors to improve accuracy. Results indicated that while PM_2.5 concentrations decreased by 16.13% from 2015 to 2019, the region still experienced 239,000 premature mortalities in 2019, with notable disparities among cities of different economic levels and sizes. Furthermore, it was found through correlation analysis that residential density and GDP per capita were highly associated with premature mortality. In conclusion, these findings highlight the continuing challenge of achieving equitable effectiveness of joint air pollution control across regions in the context of integrated development of urban agglomeration. Full article

(This article belongs to the Special Issue Remote Sensing for Environmental Health: From Fine-Scale Measurement towards Dynamic Exposure Assessment)

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