Special Issue "Air Quality Research Using Remote Sensing"
Deadline for manuscript submissions: 30 June 2021.
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
Interests: atmospheric sciences; air pollution control; differential optical absorption spectroscopy (DOAS); ozone hole; optoelectronic remote sensing instrumentation
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
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.
- Remote sensing
- Air quality
- Trace gases
- Air pollution
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.