Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing for Urban Human Health
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing for Urban Human Health

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 13509

Special Issue Editor

Dr. Panagiotis Kosmopoulos

E-Mail Website
Guest Editor
Institute for Environmental Research and Sustainable Development & Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, 15236 Penteli, Greece
Interests: sustainable development; renewable energy; environmental research; earth observation; atmospheric impacts on solar irradiance and human health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, 55% of the world’s population lives in urban areas—a proportion that is expected to increase to 68% by 2050. Urban living and well-being is affected by the air quality (AQ) and solar radiation standards. AQ standards for human health include ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM), while spectral solar radiation (SSR) levels in terms of spectrally-weighted indices form human health standards related to the ultraviolet index (UVI) and the vitamin D effective dose (VDED). Exposure to high O3 and/or NO2 concentrations can reduce lung function and trigger asthma, causing lung diseases and breathing problems, and can increase symptoms of bronchitis in asthmatic children. In addition to lung diseases, PM affects the central nervous system and the reproductive system, and causes cancer, heart attacks, arrhythmias, and cardiovascular diseases. At the same time, the prolonged exposure to UV radiation causes immunosuppression, and DNA and eye damage (e.g., skin cancer, aging, and eye cataract). On the other hand, VDED through exposure to sunlight is related to fertility and pregnancy, cardiovascular health, weight management, and musculoskeletal support. As a result, the continuous monitoring of the aforementioned AQ and SSR modulators and indicators using current remote sensing techniques is critical for urban human health quality and standards adoption. This Special Issue aims to review methodologies for AQ and SSR measurements, observations, and modelling using remote sensing technologies and data sources. Satellite remote sensing provides better spatial coverage, and various methods have been developed for AQ and SSR issues, with the main disadvantages being the increased uncertainties and the required validations against ground-based measurements or modelling data. Accurate knowledge, monitoring, and analysis of the AQ and SSR at the urban scale is very important in order to cover the multivariable topic of urban human health and the adaptable urban environment.

Dr. Panagiotis Kosmopoulos
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

  • air quality
  • spectral solar radiation
  • urban human health
  • remote sensing techniques
  • UV-Index
  • vitamin D
  • ozone
  • particulate matter
  • nitrogen dioxide
  • atmospheric monitoring
Dr. Panagiotis Kosmopoulos
Guest Editor

Published Papers (3 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

17 pages, 11155 KiB  
Article
High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013
by Hong Wang, Jiawen Li, Zhiqiu Gao, Steve H.L. Yim, Huanfeng Shen, Hung Chak Ho, Zhiyuan Li, Zhaoliang Zeng, Chao Liu, Yubin Li, Guicai Ning and Yuanjian Yang
Remote Sens. 2019, 11(23), 2724; https://doi.org/10.3390/rs11232724 - 20 Nov 2019
Cited by 26 | Viewed by 3247
Abstract
To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it [...] Read more.
To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5. Full article
(This article belongs to the Special Issue Remote Sensing for Urban Human Health)
►▼ Show Figures

Graphical abstract

24 pages, 6677 KiB  
Article
Urban Health Related Air Quality Indicators over the Middle East and North Africa Countries Using Multiple Satellites and AERONET Data
by Maram El-Nadry, Wenzhao Li, Hesham El-Askary, Mohamed A. Awad and Alaa Ramadan Mostafa
Remote Sens. 2019, 11(18), 2096; https://doi.org/10.3390/rs11182096 - 8 Sep 2019
Cited by 16 | Viewed by 4540
Abstract
Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps [...] Read more.
Air pollution is reported as one of the most severe environmental problems in the Middle East and North Africa (MENA) region. Remotely sensed data from newly available TROPOMI - TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, shows an annual mean of high-resolution maps of selected air quality indicators (NO2, CO, O3, and UVAI) of the MENA countries for the first time. The correlation analysis among the aforementioned indicators show the coherency of the air pollutants in urban areas. Multi-year data from the Aerosol Robotic Network (AERONET) stations from nine MENA countries are utilized here to study the aerosol optical depth (AOD) and Ångström exponent (AE) with other available observations. Additionally, a total of 65 different machine learning models of four categories, namely: linear regression, ensemble, decision tree, and deep neural network (DNN), were built from multiple data sources (MODIS, MISR, OMI, and MERRA-2) to predict the best usable AOD product as compared to AERONET data. DNN validates well against AERONET data and proves to be the best model to generate optimized aerosol products when the ground observations are insufficient. This approach can improve the knowledge of air pollutant variability and intensity in the MENA region for decision makers to operate proper mitigation strategies. Full article
(This article belongs to the Special Issue Remote Sensing for Urban Human Health)
►▼ Show Figures

Graphical abstract

24 pages, 12381 KiB  
Article
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements
by Wenzhao Li, Elham Ali, Islam Abou El-Magd, Moustafa Mohamed Mourad and Hesham El-Askary
Remote Sens. 2019, 11(17), 1998; https://doi.org/10.3390/rs11171998 - 24 Aug 2019
Cited by 11 | Viewed by 5121
Abstract
This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and [...] Read more.
This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations of multiple parameters, including solar radiation, air temperature, humidity, and UV exposure. Scarce ground PM10 data were collected and compared against satellite products, yet owed to their discrete nature of availability, our approach made use of the Random Decision Forest (RDF) model to convert satellite-based AOD and other meteorological parameters to predict PM10. The RDF model with inputs from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) and Global Land Data Assimilation System (GLDAS) datasets improves the performance of using AOD products to estimate PM10 values. The connection between climate variability and aerosol intensity, as well as their impact on health-related PM2.5 over Egypt is also demonstrated. Full article
(This article belongs to the Special Issue Remote Sensing for Urban Human Health)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop