Special Issue "Air Quality Research Using Remote Sensing"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Prof. Dr. Maria João Costa
E-Mail Website
Guest Editor
Department of Physics, Institute of Earth Sciences, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal
Interests: atmospheric remote sensing; cloud and aerosol properties; radiative transfer modeling; radiative forcing; cloud–aerosol interactions; cloud–aerosol radiative effects; air and water quality remote sensing
Special Issues and Collections in MDPI journals
Prof. Dr. Daniele Bortoli
E-Mail Website
Guest Editor
Department of Physics, Institute of Earth Sciences, School of Science and Technology, University of Évora, 7000-671 Évora, Portugal
Interests: atmospheric sciences; air pollution control; differential optical absorption spectroscopy (DOAS); ozone hole; optoelectronic remote sensing instrumentation

Special Issue Information

Dear Colleagues,

Air pollution is a worldwide environmental hazard with serious consequences not only for health and climate, but also for agriculture, ecosystems, and cultural heritage, among others. According to the WHO, there are 8 million premature deaths every year resulting from exposure to ambient air pollution. In addition, more than 90% of the world’s population lives in places where air quality is poor, exceeding the recommended limits, most of them in low- or middle-income countries. On the other hand, air pollution and climate influence each other through complex physicochemical interactions in the atmosphere, altering the Earth’s energy balance, with implications in climate change and air quality.

It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze the different scenarios aiming to assess air pollution levels and develop early warning and forecast systems as means to improve air quality and assure public health, in favor of a reduction in air pollution casualties and mitigation of climate change phenomena. This Special Issue invites contributions dealing with remote sensing of air quality, including combination with in situ data, modeling approaches, and synergy of different instrumentations and techniques.

Prof. Dr. Maria João Costa
Prof. Dr. Daniele Bortoli
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 papers will be 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 2400 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
  • Air quality
  • Aerosols
  • Trace gases
  • Air pollution
  • Climate

Published Papers (5 papers)

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Research

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Article
Change of CO Concentration Due to the COVID-19 Lockdown in China Observed by Surface and Satellite Observations
Remote Sens. 2021, 13(6), 1129; https://doi.org/10.3390/rs13061129 - 16 Mar 2021
Viewed by 411
Abstract
The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument [...] Read more.
The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Infrared Atmospheric Sounding Interferometer (IASI)). It is found that the average CO surface concentration in 2020 was close to that in 2019 before the lockdown, and became 18.7% lower as compared to 2019 during the lockdown. The spatial variation of the change in the CO surface concentration is high, with an 8–27% reduction observed for Beijing, Shanghai, Chengdu, Zhengzhou, and Guangzhou, and almost no change in Wuhan. The TROPOMI and IASI satellite observations show that the CO columns decreased by 2–13% during the lockdown in most regions in China. However in South China, there was an 8.8% increase in the CO columns observed by TROPOMI and a 36.7% increase observed by IASI, which is contrary to the 23% decrease in the surface CO concentration. The enhancement of the CO column in South China is strongly affected by the fire emissions transported from Southeast Asia. This study provides an insight into the impact of COVID-19 on CO concentrations both at the surface and in the columns in China, and it can be extended to evaluate other areas using the same approach. Full article
(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)
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Article
A Satellite-Based Land Use Regression Model of Ambient NO2 with High Spatial Resolution in a Chinese City
Remote Sens. 2021, 13(3), 397; https://doi.org/10.3390/rs13030397 - 24 Jan 2021
Viewed by 551
Abstract
Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few [...] Read more.
Previous studies have reported that intra-urban variability of NO2 concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO2 concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO2 concentrations at a city scale by including satellite-retrieved NO2 tropospheric column density, population density, traffic indicators, and NOx emission data. The R2 of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO2 variations at a 100 m × 100 m resolution and demonstrated that NO2 pollution in winter is 1.46 times higher than that in summer. NO2 concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO2 concentrations exceeding the annual-mean standard at 40 μg/m3 in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control. Full article
(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)
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Article
Global-Scale Patterns and Trends in Tropospheric NO2 Concentrations, 2005–2018
Remote Sens. 2020, 12(21), 3526; https://doi.org/10.3390/rs12213526 - 28 Oct 2020
Cited by 2 | Viewed by 639
Abstract
Nitrogen dioxide (NO2) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO2 concentrations globally using data from the satellite-based Ozone Monitoring [...] Read more.
Nitrogen dioxide (NO2) is an important air pollutant with both environmental and epidemiological effects. The main aim of this study is to analyze spatial patterns and temporal trends in tropospheric NO2 concentrations globally using data from the satellite-based Ozone Monitoring Instrument (OMI). Additional aims are to compare the satellite data with ground-based observations, and to find the timing and magnitude of greatest breakpoints in tropospheric NO2 concentrations for the time period 2005–2018. The OMI NO2 concentrations showed strong relationships with the ground-based observations, and inter-annual patterns were especially well reproduced. Eastern USA, Western Europe, India, China and Japan were identified as hotspot areas with high concentrations of NO2. The global average trend indicated slightly increasing NO2 concentrations (0.004 × 1015 molecules cm−2 y−1) in 2005–2018. The contribution of different regions to this global trend showed substantial regional differences. Negative trends were observed for most of Eastern USA, Western Europe, Japan and for parts of China, whereas strong, positive trends were seen in India, parts of China and in the Middle East. The years 2005 and 2007 had the highest occurrence of negative breakpoints, but the trends thereafter in general reversed, and the highest tropospheric NO2 concentrations were observed for the years 2017–2018. This indicates that the anthropogenic contribution to air pollution is still a major issue and that further actions are necessary to reduce this contribution, having a substantial impact on human and environmental health. Full article
(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)
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Technical Note
Aerial Mapping of Odorous Gases in a Wastewater Treatment Plant Using a Small Drone
Remote Sens. 2021, 13(9), 1757; https://doi.org/10.3390/rs13091757 - 30 Apr 2021
Viewed by 498
Abstract
Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. [...] Read more.
Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. Current monitoring methodologies based on fixed gas detectors and sporadic olfactometric measurements (human panels) do not allow for an accurate spatial representation of such emissions. In this paper we use a small drone equipped with an array of electrochemical and metal oxide (MOX) sensors for mapping odorous gases in a mid-sized WWTP. An innovative sampling system based on two (10 m long) flexible tubes hanging from the drone allowed near-source sampling from a safe distance with negligible influence from the downwash of the drone’s propellers. The proposed platform is very convenient for monitoring hard-to-reach emission sources, such as the plant’s deodorization chimney, which turned out to be responsible for the strongest odor emissions. The geo-localized measurements visualized in the form of a two-dimensional (2D) gas concentration map revealed the main emission hotspots where abatement solutions were needed. A principal component analysis (PCA) of the multivariate sensor signals suggests that the proposed system can also be used to trace which emission source is responsible for a certain measurement. Full article
(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)
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Letter
Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada
Remote Sens. 2020, 12(24), 4112; https://doi.org/10.3390/rs12244112 - 16 Dec 2020
Cited by 4 | Viewed by 698
Abstract
A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO2) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and CHemistry), was used together with TROPOMI to determine expected NO2 columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO2 emissions due to the lockdown were seen, with an average 40% (roughly 10 kt[NO2]/yr) in Toronto and Mississauga and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20–30%, which demonstrates the importance of taking meteorology into account. A model run with reduced emissions (from 65 kt[NO2]/yr to 40 kt[NO2]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO2 columns. Full article
(This article belongs to the Special Issue Air Quality Research Using Remote Sensing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Assessment of WRF-Chem-RTFDDA dust analyses and forecasts using Meteosat and CALIPSO remote sensing observations
Authors: Dorita Rostkier-Edelstein; Yongxin Zhang; Rong-Shyang Sheu; Yubao Liu; Amit Yunker; Pavel Kunin; Adam Pietrkowski
Affiliation: Israel Institute for Biological Research, Israel The Hebrew University of Jerusalem, Israel National Center for Atmospheric Research, USA Life Science Research Institute, Israel IAF Meteo Center, Israel
Abstract: Immediate impacts of dust storms include, among others, degradation of air quality and increase in respiratory illness in people and livestock. Their forecast is of extreme importance. In this study we combine WRF-Chem model and RTFDDA (Real-Time Four-Dimensional Data Assimilation), WRF-Chem-RTFDDA, to simulate and forecasts dust storms in the Middle East and North Africa region (MENA). WRF-Chem is capable of simulating the emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with the meteorology. RTFDDA continuously assimilates both conventional and nonconventional meteorological observations to provide improved initial conditions for dust analyses and forecasts. We assessed the skill of WRF-Chem-RTFFDA in forecasting dust storms in the MENA region during a spring period by comparing its results to remote sensing observations, Meteosat SEVIRI dust images and backscatter-attenuation profiles from the CALIPSO mission. WRF-Chem-RTFDDA was run at a horizontal resolution of 9 km grid size, including mineral dust only without the inclusion of anthropogenic aerosols and chemical reactions. The synoptic conditions of the storms were characterized by a cold front at the low level and an upper-level low-pressure system over the Western Mediterranean. Strong westerly and southwesterly winds associated with the cold fronts and the low-pressure systems are behind the development and evolution of the dust storms. WRF-Chem-RTFDDA was run in continuous assimilation mode, assimilating meteorological observations only, and launching 48 hours free forecasts every 6 hours. Two cold starts were performed during the studied period. Initial and lateral boundary conditions were provided by GFS global analyses and forecasts. No global dust model was used for initialization and no dust observations were assimilated into the model. We note that meteorological observations are sparse in large areas of the MENA region. These limitations present a significant challenge to the forecasting system. We analyzed the skill of the WRF-Chem-RTFDDA analyses and forecasts to reproduce the horizontal spatial distribution of the dust by comparing them to Meteosat SEVIRI dust images. The model vertical dust distribution was assessed by comparison of model backscatter attenuation profiles to those retrieved from the CALIPSO mission. The skill was analyzed as function of forecast lead time and as a function of the overall time from cold start of the system. Statistical verification of model backscatter attenuation with respect to CALIPSO retrievals included calculation of RMSE, bias and correlation scores. Same scores were calculated as part of the verification of the meteorological WRF-Chem-RTFDDA analysis and forecasts against ECMWF operations analyses for the same period. Our results show that WRF-Chem-RTFDDA reproduced the main features of the dust storms during the studied period. In the present system, time from cold start plays a more significant role in dust-forecast skill than free-forecast lead time does. Since no external dust information is provided to the model, dust emissions spin-up simulated by WRF-Chem plays a most relevant role in our system. The vertical extent of the attenuated backscatter is fairly well reproduced once model emissions are spined-up. However, the model vertical distribution of attenuation values shows more noticeable differences with respect to CALIPSO retrievals. We analyze these differences and relate them to skill of the model to simulate horizontal and vertical wind speeds. Our study shows the feasibility of dust forecasts using minimal input data.

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