Special Issue "Remote Sensing Applications for Urban Air Quality Research: The Continuing Challenge"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editor

Dr. Alexandra Chudnovsky
E-Mail Website
Guest Editor
Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel

Special Issue Information

Dear Colleagues,

One of the main questions and challenges in the current research is how increased urbanization impacts air quality and how it will affect future environmental conditions. To answer this question, we need not only a deep knowledge and realistic consideration of the weather, climate and models, but also a set of continuous monitoring observations from the ground and space of vital environmental parameters. This knowledge is especially needed by policy- and decision-makers to moderate the risks and to regulate urban developments.

Remote-sensing assessments from satellite instruments have become increasingly important for assessing ground or tropospheric conditions. These methods have evolved rapidly over the past 10 years, with numerous applications relevant to environmental and public health. In this Special Issue we would like to provide a state-of-the-art synthesis of these methods and their applications for sensing ground-level conditions.

 Potential topics include, but are not limited to:

  • Air monitoring
  • Green space assessment
  • Traffic assessment
  • Data sufficiency for air quality monitoring using wide range of measurements
  • Sources of air pollution
  • Passive and active sensing of air pollution
  • Human exposure
  • Urban heat island vs air pollution

Dr. Alexandra Chudnovsky
Guest Editor

Manuscript Submission Information

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Keywords

  • Particulate Matter (PM)
  • Aerosol Optical Depth
  • Vegetation
  • Satellite Imagery
  • Air Pollution
  • Exposure Estimates
  • Urban Climate

Published Papers (2 papers)

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Research

Open AccessArticle
Optimal Band Analysis of a Space-Based Multispectral Sensor for Urban Air Pollutant Detection
Atmosphere 2019, 10(10), 631; https://doi.org/10.3390/atmos10100631 - 19 Oct 2019
Abstract
Air pollution continues to attract more and more public attention. Space-based infrared sensors provide a measure to monitor air quality in large areas. In this paper, a band selection procedure of space-based infrared sensors is proposed for urban air pollutant detection, in which [...] Read more.
Air pollution continues to attract more and more public attention. Space-based infrared sensors provide a measure to monitor air quality in large areas. In this paper, a band selection procedure of space-based infrared sensors is proposed for urban air pollutant detection, in which observation geometry, ground and atmosphere radiant characteristics, and sensor system noise are integrated. The physics-based atmospheric radiative transfer model is reviewed and used to calculate total spectral radiance at the sensor aperture. Spectral filters with different central wavelength and bandwidth are designed to calculate contrasts in various bands, which can be presented as a two-dimensional matrix. Minimal available bandwidth and signal-to-noise ratio threshold are set to characterize the impacts of the sensor system. In this way, the band with higher contrast is assumed to have better detection performance. The proposed procedure is implemented to analyze an optimal band for detecting four types of gaseous pollutants and discriminating aerosol particle pollution to demonstrate usefulness. Simulation results show that narrower bands tend to achieve better performance while the optimal band is related to the available minimal bandwidth and pollutant density. In addition, the bands that are near optimal can achieve similar performance. Full article
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Open AccessArticle
Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain
Atmosphere 2019, 10(5), 245; https://doi.org/10.3390/atmos10050245 - 03 May 2019
Abstract
Monitoring particulate matter with aerodynamic diameters of less than 2.5 μm (PM2.5) is of great importance to assess its adverse effects on human health, especially densely populated regions. In this paper, an improved linear mixed effect model (LMEM) was developed. The [...] Read more.
Monitoring particulate matter with aerodynamic diameters of less than 2.5 μm (PM2.5) is of great importance to assess its adverse effects on human health, especially densely populated regions. In this paper, an improved linear mixed effect model (LMEM) was developed. The model introduced meteorological variable, column water vapor (CWV), which has as the same resolution as satellite-derived aerosol optical thickness (AOT), to enhance PM2.5 estimation accuracy by considering spatiotemporal consistency of CWV and AOT. The model was implemented to urban agglomeration of Chengdu Plain during 2015. The results show that model accuracy has been improved significantly compared to linear regression model (R2 = 0.49), with R2 of 0.81 and root mean squared prediction error (RMSPE) of 15.47 μg/m3, mean prediction error (MPE) of 11.09 μg/m3, and effectively revealed the characteristics of spatiotemporal variations PM2.5 level across the study area: The PM2.5 level is higher in the central and southern areas with dense population, while it is lower in the northwest and southwest mountain areas; and the PM2.5 level is higher during autumn and winter, while it is lower during spring and summer. The product data in this paper are valuable for local government pollution monitoring, public health research, and urban air quality control. Full article
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