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Urban Air Quality Monitoring using Remote Sensing

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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 September 2020) | Viewed by 25231

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Special Issue Editors

Dr. Pawan Gupta

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Guest Editor

Universities Space Research Association (USRA), National Aeronautics and Space Administration (NASA), Huntsville, AL 21046, USA
Interests: remote sensing of atmosphere; air quality; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Sagnik Dey

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Guest Editor

Centre for Atmospheric Sciences and Centre of Excellence for Research on Clean Air (CERCA), IIT Delhi, Hauz Khas, New Delhi 110016, India
Interests: air pollution and its impact on climate; aerosol-cloud-climate interaction; remote sensing of Earth’s environment; health impacts of air pollution and climate change
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jason Blake Cohen

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Guest Editor

School of Environment and Spatial Informatics, China Univesity of Mining and Technology, Xuzhou 221116, China
Interests: integration of data across multiple satellites; remote sensing and modeling of aerosols; inverse modeling of atmospheric composition and emissions sources; remote sensing of air quality extremes; remote sensing of short lived climate forcers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution around the world is a growing problem, and achieving clean air for breathing is one of the top priorities of the United Nation’s Sustainable Development Goals (SDGs). Remote sensing methods from space or ground, over the last two decades, have advanced, and can provide useful information on the state of the air. The focus of this Special Issue is on the monitoring and forecasting of surface air quality using the remote sensing observations of aerosols and trace gases at local, regional, and global scales. We encourage authors to submit contributions that describe original research methods, data, and the results of studies conducted on aerosols and trace gases (i.e., NO2, SO2, O3, HCHO, CH4, NH3, etc.) products from ground- and space-based remote sensing sensors. The specific topics include (but are not limited to) the following: PM2.5/PM10 measurements and estimates from satellite and surface; regional trends of atmospheric composition; assimilation of satellite data into regional and global models; transport of aerosols; role of biomass burning; dust aerosols and anthropogenic emissions in air quality; boundary layer processes and their impact on satellite estimations; and the physical and statistical modeling of air quality, population health, and ecological impact assessments driven by satellite data. Air quality product development, validation, and inter-comparison with models from current satellite and sensors in LEO (TROPOMI, MODIS, MISR, OMI, VIIRS, and OMPS), GEO (GOES-R, GOES-S, Himawari-8/9, GOCI, anf INSAT), and L1 (EPIC) orbits are encouraged. Studies discussing upcoming satellite missions (i.e., TEMPO, MAIA, GEMS, and 3MI) are also welcome.

Dr. Pawan Gupta
Prof. Dr. Sagnik Dey
Dr. Jason Blake Cohen
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

air pollution
air quality
satellite
space
particulate matter
trace gases

Published Papers (6 papers)

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Research

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33 pages, 6364 KiB

Open AccessArticle

A Framework to Predict High-Resolution Spatiotemporal PM_2.5 Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China

by Guangyuan Zhang, Haiyue Lu, Jin Dong, Stefan Poslad, Runkui Li, Xiaoshuai Zhang and Xiaoping Rui

Remote Sens. 2020, 12(17), 2825; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172825 - 31 Aug 2020

Cited by 27 | Viewed by 4520

Abstract

Air-borne particulate matter, PM_2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM_2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM_2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m³ and the highest coefficient of determination regression score function (R²) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m³ compared to SARIMA’s 17.41 µg/m³. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM_2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA. Full article

(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)

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25 pages, 14415 KiB

Open AccessArticle

Spatial and Temporal Distribution of PM_2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets

by Johana M. Carmona, Pawan Gupta, Diego F. Lozano-García, Ana Y. Vanoye, Fabiola D. Yépez and Alberto Mendoza

Remote Sens. 2020, 12(14), 2286; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142286 - 16 Jul 2020

Cited by 19 | Viewed by 4441

Abstract

Aerosol and meteorological remote sensing data could be used to assess the distribution of urban and regional fine particulate matter (PM_2.5), especially in locations where there are few or no ground-based observations, such as Latin America. The objective of this study is to evaluate the ability of Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRA-2) aerosol components to represent PM_2.5 ground concentrations and to develop and validate an ensemble neural network (ENN) model that uses MERRA-2 aerosol and meteorology products to estimate the monthly average of PM_2.5 ground concentrations in the Monterrey Metropolitan Area (MMA), which is the main urban area in Northeastern Mexico (NEM). The project involves the application of the ENN model to a regional domain that includes not only the MMA but also other municipalities in NEM in the period from January 2010 to December 2014. Aerosol optical depth (AOD), temperature, relative humidity, dust PM_2.5, sea salt PM_2.5, black carbon (BC), organic carbon (OC), and sulfate (SO₄²⁻) reanalysis data were identified as factors that significantly influenced PM_2.5 concentrations. The ENN estimated a PM_2.5 monthly mean of 25.62 μg m⁻³ during the entire period. The results of the comparison between the ENN and ground measurements were as follows: correlation coefficient R ~ 0.90; root mean square error = 1.81 μg m⁻³; mean absolute error = 1.31 μg m⁻³. Overall, the PM_2.5 levels were higher in winter and spring. The highest PM_2.5 levels were located in the MMA, which is the major source of air pollution throughout this area. The estimated data indicated that PM_2.5 was not distributed uniformly throughout the region but varied both spatially and temporally. These results led to the conclusion that the magnitude of air pollution varies among seasons and regions, and it is correlated with meteorological factors. The methodology developed in this study could be used to identify new monitoring sites and address information gaps. Full article

(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)

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13 pages, 3678 KiB

Open AccessArticle

Global Distribution of Column Satellite Aerosol Optical Depth to Surface PM_2.5 Relationships

by Sundar Christopher and Pawan Gupta

Remote Sens. 2020, 12(12), 1985; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121985 - 20 Jun 2020

Cited by 22 | Viewed by 4560

Abstract

Using a combined Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) mid-visible aerosol optical depth (AOD) product at 0.1 × 0.1-degree spatial resolution and collocated surface PM_2.5 (particulate matter with aerodynamic diameter smaller than 2.5 μm) monitors, we provide a global five-year (2015–2019) assessment of the spatial and seasonal AOD–PM_2.5 relationships of slope, intercepts, and correlation coefficients. Only data from ground monitors accessible through an open air-quality portal that are available to the worldwide community for air quality research and decision making are used in this study. These statistics that are reported 1 × 1-degree resolution are important since satellite AOD is often used in conjunction with spatially limited surface PM_2.5 monitors to estimate global distributions of surface particulate matter concentrations. Results indicate that more than 3000 ground monitors are now available for PM_2.5 studies. While there is a large spread in correlation coefficients between AOD and PM_2.5, globally, averaged over all seasons, the correlation coefficient is 0.55 with a unit AOD producing 54 μgm⁻³ of PM_2.5 (Slope) with an intercept of 8 μgm⁻³. While the number of surface PM_2.5 measurements has increased by a factor of 10 over the last decade, a concerted effort is still needed to continue to increase these monitors in areas that have no surface monitors, especially in large population centers that will further leverage the strengths of satellite data. Full article

(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)

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24 pages, 11928 KiB

Open AccessArticle

Pollution Trends in China from 2000 to 2017: A Multi-Sensor View from Space

by Jing Li

Remote Sens. 2020, 12(2), 208; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020208 - 08 Jan 2020

Cited by 36 | Viewed by 3919

Abstract

Satellite sensors can provide unique views of global pollution information from space. In particular, a series of aerosol and trace gas monitoring instruments have been operating for more than a decade, providing the opportunity to analyze temporal trends of major pollutants on a large scale. In this study, we integrate aerosol products from MODIS (MODIS Resolution Imaging Spectroradiometer, all abbreviations and their definitions are listed alphabetically in Abbreviations) and MISR (Multi-angle Imaging Spectroradiometer), the AAI (Absorbing Aerosol Index) product from OMI (Ozone Monitoring Instrument), column SO₂ and NO₂ concentrations from OMI, and tropospheric column ozone concentration from OMI/MLS (Microwave Limb Sounder) to study temporal changes in major pollutants over China. MODIS and MISR consistently revealed that column AOD (Aerosol Optical Depth) increased from 2000, peaked around 2007, and started to decline afterward, except for northwest and northeast China, where a continuous upward trend was found. Extensive negative trends in both SO₂ and NO₂ have also been found over major pollution source regions since ~2005. On the other hand, the OMI AAI exhibited significant increases over north China, especially the northeast and northwest regions. These places also have a decreased Angstrom exponent as revealed by MISR, indicating an increased fraction of large particles. In general, summer had the largest AOD, SO₂, and NO₂ trends, whereas AAI trends were strongest for autumn and winter. A multi-regression analysis showed that much of the AOD variance over major pollution source regions could be explained by SO₂, NO₂, and AAI combined, and that the SO₂ and NO₂ reduction was likely to be responsible for the negative AOD trends, while the AOD increase over NE and NW China may be associated with an increase of coarse particles revealed by increased AAI and decreased AE. In contrast to aerosols, tropospheric ozone exhibited a steady increase from 2005 throughout China. This indicates that although the recent emission control effectively reduced aerosol pollutants, ozone remains a challenging issue and may dominate future air pollution. Full article

(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)

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19 pages, 5472 KiB

Open AccessArticle

Decomposing the Long-term Variation in Population Exposure to Outdoor PM_2.5 in the Greater Bay Area of China Using Satellite Observations

by Changqing Lin, Alexis K. H. Lau, Jimmy C. H. Fung, Qianshan He, Jun Ma, Xingcheng Lu, Zhiyuan Li, Chengcai Li, Renguang Zuo and Andromeda H. S. Wong

Remote Sens. 2019, 11(22), 2646; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11222646 - 13 Nov 2019

Cited by 5 | Viewed by 2896

Abstract

The Greater Bay Area (GBA) of China is experiencing a high level of exposure to outdoor PM_2.5 pollution. The variations in the exposure level are determined by spatiotemporal variations in the PM_2.5 concentration and population. To better guide public policies that aim to reduce the population exposure level, it is essential to explicitly decompose and assess the impacts of different factors. This study took advantage of high-resolution satellite observations to characterize the long-term variations in population exposure to outdoor PM_2.5 for cities in the GBA region during the three most-recent Five-Year Plan (FYP) periods (2001–2015). A new decomposition method was then used to assess the impact of PM_2.5 variations and demographic changes on the exposure variation. Within the decomposition framework, an index of pollution-population-coincidence–induced PM_2.5 exposure (PPCE) was introduced to characterize the interaction of PM_2.5 and the population distribution. The results showed that the 15-year average PPCE levels in all cities were positive (e.g., 6 µg/m³ in Guangzhou), suggesting that unfavorable city planning had led to people dwelling in polluted areas. An analyses of the spatial differences in PM_2.5 changes showed that urban areas experienced a greater decrease in PM_2.5 concentration than did rural areas in most cities during the 11th (2006–2010) and 12th (2011–2015) FYP periods. These spatial differences in PM_2.5 changes reduced the PPCE levels of these cities and thus reduced the exposure levels (by as much as -0.58 µg/m³/year). The population migration resulting from rapid urbanization, however, increased the PPCE and exposure levels (by as much as 0.18 µg/m³/year) in most cities during the three FYP periods considered. Dongguan was a special case in that the demographic change reduced the exposure level because of its rapid development of residential areas in cleaner regions adjacent to Shenzhen. The exposure levels in all cities remained high because of the high mean PM_2.5 concentrations and their positive PPCE. To better protect public health, control efforts should target densely populated areas and city planning should locate more people in cleaner areas. Full article

(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)

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16 pages, 5702 KiB

Open AccessEditor’s ChoiceLetter

Variability of Major Aerosol Types in China Classified Using AERONET Measurements

by Lu Zhang and Jing Li

Remote Sens. 2019, 11(20), 2334; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202334 - 09 Oct 2019

Cited by 17 | Viewed by 3267

Abstract

Aerosol type is a critical piece of information in both aerosol forcing estimation and passive satellite remote sensing. However, the major aerosol types in China and their variability is still less understood. This work uses direct sun measurements and inversion derived parameters from 47 sites within the Aerosol Robotic Network (AERONET) in China, with more than 39,000 records obtained between April 1998 and January 2017, to identify dominant aerosol types using two independent methods, namely, K means and Self Organizing Map (SOM). In total, we define four aerosol types, namely, desert dust, scattering mixed, absorbing mixed and scattering fine, based on their optical and microphysical characteristics. Seasonally, dust aerosols mainly occur in the spring and over North and Northwest China; scattering mixed are more common in the spring and summer, whereas absorbing aerosols mostly occur in the autumn and winter during heating period, and scattering fine aerosols have their highest occurrence frequency in summer over East China. Based on their spatial and temporal distribution, we also generate seasonal aerosol type maps that can be used for passive satellite retrieval. Compared with the global models used in most satellite retrieval algorithms, the unique feature of East Asian aerosols is the curved single scattering albedo spectrum, which could be related to the mixing of black carbon with dust or organic aerosols. Full article

(This article belongs to the Special Issue Urban Air Quality Monitoring using Remote Sensing)

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