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

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 59646

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Special Issue Editors

Prof. Dr. Gerrit de Leeuw

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Guest Editor

Royal Netherlands Meteorological Institute (KNMI), R & D Satellite Observations, 3731 GA De Bilt, The Netherlands
Interests: aerosols; satellite remotes sensing; air quality; climate; aerosol-cloud interaction; sea spray aerosol
Special Issues, Collections and Topics in MDPI journals

Prof. Ronald van Der A

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Guest Editor

Royal Netherlands Meteorological Institute (KNMI), R&D Satellite Observations, Utrechtseweg 297, 3731GA De Bilt, The Netherlands
Interests: emissions from satellite observations; air quality; satellite remote sensing

Special Issue Information

Dear Colleagues,

Air quality is determined by atmospheric aerosols and trace gases, which have adverse effects on, e.g., health, visibility and climate. In particular, the near-surface concentrations of NO₂, SO₂, O₃, NH₃, Volatile Organic Compounds (VOCs) and aerosol properties, for air quality purposes often expressed as PM2.5 or PM10, are important. The vertical column densities (VCDs) of trace gases and the column-integrated aerosol extinction coefficients (i.e., the aerosol optical depth, or AOD) can be determined from satellite observations, using the same method globally. However, to determine the near-surface concentrations and emissions of trace gases and aerosols (PM) requires the use of a model taking into account processes affecting the vertical profile. In addition, the determination of emissions of aerosols and trace gases requires inverse modeling in which the concentrations are constrained by satellite observations. This top-down approach allows for the determination of near surface concentrations and emissions with high temporal resolution and reveals emission and concentration changes on very short time scales (~1 month).

This Special Issue seeks contributions on the use of remote sensing to determine concentrations and emissions of aerosols and trace gases, including effects of meteorological parameters and large scale circulation on the PM2.5/AOD relationships.

Prof. Gerrit de Leeuw
Prof. Ronald van der A
Guest Editors

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Keywords

Satellite Remote Sensing
Air quality
Emissions
Inverse Modeling
Data Assimilation

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Published Papers (12 papers)

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20 pages, 4236 KiB

Open AccessArticle

Contrasting Aerosol Optical Characteristics and Source Regions During Summer and Winter Pollution Episodes in Nanjing, China

by Jing Wang, Gerrit de Leeuw, Shengjie Niu and Hanqing Kang

Remote Sens. 2019, 11(14), 1696; https://doi.org/10.3390/rs11141696 - 17 Jul 2019

Cited by 10 | Viewed by 3318

Abstract

Two episodes with heavy air pollution in Nanjing, China, one in the summer and another one in the winter of 2017, were selected to study aerosol properties using sun photometer and ground-based measurements, together with source region analysis. The aerosol properties, the meteorological conditions, and the source regions during these two episodes were very different. The episodes were selected based on the air quality index (AQI), which reached a maximum value of 193 during the summer episode (26 May–3 June) and 304 during the winter episode (21–31 December). The particulate matter (PM) concentrations during the winter episode reached maximum values for PM_2.5/10 of 254 μg m⁻³ and 345 μg m⁻³, much higher than those during the summer (73 and 185 μg m⁻³). In contrast, the value of aerosol optical depth (AOD) at 500 nm was higher during the summer episode (2.52 ± 0.19) than during that in the winter (1.38 ± 0.18). A high AOD value does not necessarily correspond to a high PM concentration but is also affected by factors, such as wind, Planetary Boundary Layer Height (PBLH), and relative humidity. The mean value of the Ångström Exponent (AE) varied from 0.91–1.42, suggesting that the aerosol is a mixture of invaded dust and black carbon. The absorption was stronger during the summer than during the winter, with a minimum value of the single scattering albedo (SSA) at 440 nm of 0.86 on 28 May. Low values of asymmetry factor (ASY) (0.65 at 440 nm and 0.58 at 1020 nm) suggest a large number of anthropogenic aerosols, which are absorbing fine-mode particles. The Imaginary part of the Refractive Index (IRI) was higher during the summer than during the winter, indicating there was absorbing aerosol during the summer. These differences in aerosol properties during the summer and winter episodes are discussed in terms of meteorological conditions and transport. The extreme values of PM and AOD were reached during both episodes in conditions with stable atmospheric stratification and low surface wind speed, which are conducive for the accumulation of pollutants. Potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analysis show that fine mode absorbing aerosols dominate during the summer season, mainly due to emissions of local and near-by sources. In the winter, part of the air masses was arriving from arid/semi-arid regions (Shaanxi, Ningxia, Gansu, and Inner Mongolia provinces) covering long distances and transporting coarse particles to the study area, which increased the scattering characteristics of aerosols. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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17 pages, 2905 KiB

Open AccessArticle

Hyperspectral Imaging Retrieval Using MODIS Satellite Sensors Applied to Volcanic Ash Clouds Monitoring

by Luis Arias, Jose Cifuentes, Milton Marín, Fernando Castillo and Hugo Garcés

Remote Sens. 2019, 11(11), 1393; https://doi.org/10.3390/rs11111393 - 11 Jun 2019

Cited by 6 | Viewed by 5060

Abstract

In this paper, we present a method for hyperspectral retrieval using multispectral satellite images. The method consists of the use of training spectral data with a compressive capability. By using principal component analysis (PCA), a proper number of basis vectors are extracted. These vectors are properly combined and weighted by the sensors’ responses from visible MODIS channels, achieving as a result the retrieval of hyperspectral images. Once MODIS channels are used for hyperspectral retrieval, the training spectra are projected over the recovered data, and the ground-based process used for training can be reliably detected. To probe the method, we use only four visible images from MODIS for large-scale ash clouds’ monitoring from volcanic eruptions. A high-spectral resolution data of reflectances from ash was measured in the laboratory. Using PCA, we select four basis vectors, which combined with MODIS sensors responses, allows estimating hyperspectral images. By comparing both the estimated hyperspectral images and the training spectra, it is feasible to identify the presence of ash clouds at a pixel-by-pixel level, even in the presence of water clouds. Finally, by using a radiometric model applied over hyperspectral retrieved data, the relative concentration of the volcanic ash in the cloud is obtained. The performance of the proposed method is compared with the classical method based on temperature differences (using infrared MODIS channels), and the results show an excellent match, outperforming the infrared-based approach. This proposal opens new avenues to increase the potential of multispectral remote systems, which can be even extended to other applications and spectral bands for remote sensing. The results show that the method could play an essential role by providing more accurate information of volcanic ash spatial dispersion, enabling one to prevent several hazards related to volcanic ash where volcanoes’ monitoring is not feasible. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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24 pages, 5420 KiB

Open AccessArticle

Properties of Arctic Aerosol Based on Sun Photometer Long-Term Measurements in Ny-Ålesund, Svalbard

by Sandra Graßl and Christoph Ritter

Remote Sens. 2019, 11(11), 1362; https://doi.org/10.3390/rs11111362 - 6 Jun 2019

Cited by 15 | Viewed by 3905

Abstract

On the basis of sun photometer measurements located at the German-French polar research base AWIPEV in Ny-Ålesund (

{78.923}^{°}

{11.928}^{°}

E), Svalbard, long-term changes (2001–2017) of aerosol properties in the European Arctic are analyzed with the main focus on physical [...] Read more.

On the basis of sun photometer measurements located at the German-French polar research base AWIPEV in Ny-Ålesund (

{78.923}^{°}

{11.928}^{°}

E), Svalbard, long-term changes (2001–2017) of aerosol properties in the European Arctic are analyzed with the main focus on physical aerosol properties like Aerosol Optical Depth (AOD) and the Ångström exponent during the Arctic haze season in spring compared with summer and autumn months. In order to gain more information from the photometer data and to reduce the error of fitting the data to the Ångström law, a new approach with an Ångström exponent, which depends linearly on wavelength, is presented in this paper. With the Mie program of libRadtran, a calculator for long- and short-wave radiation through the Earth’s atmosphere, artificial aerosol size distributions were created to extend the physical understanding of this modified Ångström law. Monthly means of the measured AOD of the years 1994–2017 are presented to analyze long-term changes of aerosol properties and its load. Because photometer data in general have no height information, a comparison with a Lidar located at the same site is presented. The so-obtained data are then compared with the previous Mie calculus. More homogeneous aerosol properties were found during spring and more heterogeneous in summer. To study possible aerosol sources and sinks, five-day back-trajectories were calculated with the FLEXPART model at three different arriving heights at 11 UTC in the village Ny-Ålesund. Besides the pollution pathway of the aerosol into the European Arctic based on the calculated back-trajectories, the influence of the boundary layer parameterized by the lowermost 100 hPa atmospheric layer is analyzed and compared to the measured aerosol load by the photometer in Ny-Ålesund additionally. During spring, the open ocean acts as a sink for aerosols, whereas sea ice clearly reduces their sinks. Hence, trajectories over sea ice are correlated to higher aerosol loads. Thus, both sources and sinks must be considered to understand aerosol occurrences in the Arctic. Full article

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20 pages, 22238 KiB

Open AccessArticle

Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

by Wenhao Zhang, Hui Xu and Lili Zhang

Remote Sens. 2019, 11(9), 1108; https://doi.org/10.3390/rs11091108 - 9 May 2019

Cited by 27 | Viewed by 4940

Abstract

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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18 pages, 10526 KiB

Open AccessArticle

Multi-Time Scale Analysis of Regional Aerosol Optical Depth Changes in National-Level Urban Agglomerations in China Using Modis Collection 6.1 Datasets from 2001 to 2017

by Lei Zhang, Ming Zhang and Yibin Yao

Remote Sens. 2019, 11(2), 201; https://doi.org/10.3390/rs11020201 - 21 Jan 2019

Cited by 15 | Viewed by 4241

Abstract

With the rapid development of China’s economy and industry, characterizing the spatial and temporal changes of aerosols in China has attracted widespread attention from researchers. The national-level urban agglomerations are the most concentrated areas of China’s economic, population and resource. Studying the spatial and temporal changes of aerosol optical depth (AOD) in these regions has practical guiding significance for effective monitoring of atmospheric particulate pollution. This paper analyzed the spatial and temporal variations of AOD in China’s urban agglomerations during 2001–2017 by using Terra Moderate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Level 2 aerosol products (MOD04_L2). Five national-level urban agglomerations were chosen: Yangtze River Delta (YRD), Pearl River Delta (PRD), Beijing-Tianjin-Hebei (BTH), Yangtze River Middle-Reach (YRMR) and Cheng-Yu (CY). We analyzed the change patterns of AOD in different urban agglomerations at multi-time scales and built a time series decomposition model to mine the long-term trend, seasonal variation and abnormal change information of AOD time series. The result indicated that averaged AOD values in the five urban agglomerations were basically increased first and then decreased at the annual time scale during 2001–2017. The averaged AOD showed strong seasonal differences and AOD values in spring and summer were typically higher than those in autumn and winter. At the monthly time scale, the AOD typically varied from low in cold months to high in warm months and then decreased during the rainy periods. Time series decompositions revealed that a notable transition around 2007–2008 dominated the long-term overall trend over the five selected urban agglomerations and an initial upward tendency followed by a downward tendency was observed during 2001–2017. This study can be utilized to provide decision-making basis for atmospheric environmental governance and future development of urban agglomerations. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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20 pages, 7636 KiB

Open AccessArticle

Retrieval of Vertical Mass Concentration Distributions—Vipava Valley Case Study

by Longlong Wang, Samo Stanič, Klemen Bergant, William Eichinger, Griša Močnik, Luka Drinovec, Janja Vaupotič, Miloš Miler, Mateja Gosar and Asta Gregorič

Remote Sens. 2019, 11(2), 106; https://doi.org/10.3390/rs11020106 - 9 Jan 2019

Cited by 19 | Viewed by 3984

Abstract

Aerosol vertical profiles are valuable inputs for the evaluation of aerosol transport models, in order to improve the understanding of aerosol pollution ventilation processes which drive the dispersion of pollutants in mountainous regions. With the aim of providing high-accuracy vertical distributions of particle mass concentration for the study of aerosol dispersion in small-scale valleys, vertical profiles of aerosol mass concentration for aerosols from different sources (including Saharan dust and local biomass burning events) were investigated over the Vipava valley, Slovenia, a representative hot-spot for complex mixtures of different aerosol types of both anthropogenic and natural origin. The analysis was based on datasets taken between 1–30 April 2016. In-situ measurements of aerosol size, absorption, and mass concentration were combined with lidar remote sensing, where vertical profiles of aerosol concentration were retrieved. Aerosol samples were characterized by SEM-EDX, to obtain aerosol morphology and chemical composition. Two cases with expected dominant presence of different specific aerosol types (mineral dust and biomass-burning aerosols) show significantly different aerosol properties and distributions within the valley. In the mineral dust case, we observed a decrease of the elevated aerosol layer height and subsequent spreading of mineral dust within the valley, while in the biomass-burning case we observed the lifting of aerosols above the planetary boundary layer (PBL). All uncertainties of size and assumed optical properties, combined, amount to the total uncertainty of aerosol mass concentrations below 30% within the valley. We have also identified the most indicative in-situ parameters for identification of aerosol type. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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23 pages, 4360 KiB

Open AccessArticle

The Global Spatiotemporal Distribution of the Mid-Tropospheric CO₂ Concentration and Analysis of the Controlling Factors

by Liangzhong Cao, Xi Chen, Chi Zhang, Alishir Kurban, Jin Qian, Tao Pan, Zuozhong Yin, Xiugong Qin, Friday Uchenna Ochege and Philippe De Maeyer

Remote Sens. 2019, 11(1), 94; https://doi.org/10.3390/rs11010094 - 8 Jan 2019

Cited by 25 | Viewed by 5060

Abstract

The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO₂ globally. In this paper, we use the AIRS CO₂ product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO₂ and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO₂ concentrations (20°S–5°N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO₂ emission areas. The areas with high mid-tropospheric CO₂ concentrations (30°N–70°N) (382.1 ppm) are formed due to high CO₂ emissions. The global mid-tropospheric CO₂ concentrations increased gradually (the annual average rate of increase in CO₂ concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO₂ and related mechanisms. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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17 pages, 2912 KiB

Open AccessArticle

Evaluating MODIS Dust-Detection Indices over the Arabian Peninsula

by Sarah Albugami, Steven Palmer, Jeroen Meersmans and Toby Waine

Remote Sens. 2018, 10(12), 1993; https://doi.org/10.3390/rs10121993 - 8 Dec 2018

Cited by 23 | Viewed by 6011

Abstract

Sand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31–32); (c) BTD (20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31–32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20–31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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27 pages, 25325 KiB

Open AccessArticle

Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms—Part 1: Global Evaluation

by Yahui Che, Linlu Mei, Yong Xue, Jie Guang, Lu She, Ying Li, Andreas Heckel and Peter North

Remote Sens. 2018, 10(9), 1414; https://doi.org/10.3390/rs10091414 - 6 Sep 2018

Cited by 5 | Viewed by 7551 | Correction

Abstract

The European Space Agency’s (ESA’s) Aerosol Climate Change Initiative (CCI) project intends to exploit the robust, long-term, global aerosol optical thickness (AOT) dataset from Europe’s satellite observations. Newly released Swansea University (SU) aerosol products include ATSR-2 (1995-2003) and AATSR(2002-2012) retrieval with a spatial resolution of 10 km. Recently an experimental version of a retrieval using AATSR/MERIS synergy was developed to provide four months of data for initial testing. In this study, both AATSR retrieval (SU/AATSR) and AATSR/MERIS synergy retrieval (SU/synergy) datasets are validated globally using Aerosol Robotic Network (AERONET) observations for March, June, September, and December 2008, as suggested by the Aerosol-CCI project. The analysis includes the impacts of cloud screening, surface parameterization, and aerosol type selections for two datasets under different surface and atmospheric conditions. The comparison between SU/AATSR and SU/synergy shows very accurate and consistent global patterns. The global evaluation using AERONET shows that the SU/AATSR product exhibits slightly better agreement with AERONET than the SU/synergy product. SU/synergy retrieval overestimates AOT for all surface and aerosol conditions. SU/AATSR data is much more stable and has better quality; it slightly underestimates fine-mode dominated and absorbing AOTs yet slightly overestimates coarse-mode dominated and non-absorbing AOTs. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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16 pages, 2623 KiB

Open AccessArticle

An Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska

by Alyson McPhetres and Srijan Aggarwal

Remote Sens. 2018, 10(9), 1384; https://doi.org/10.3390/rs10091384 - 31 Aug 2018

Cited by 15 | Viewed by 3896

Abstract

The air quality monitoring network in Alaska is currently limited to ground-based observations in urban areas and national parks, leaving a large proportion of the state unmonitored. The use of Moderate Resolution Imaging Spectroradiometer MODIS aerosol optical depth (AOD) to estimate ground-level particulate pollution concentrations has been successfully demonstrated around the world and could potentially be used in Alaska. In this work, MODIS AOD measurements at 550 nm were validated against AOD derived from two ground-based Aerosol Robotic Network (AERONET) sunphotometers in Alaska, located at Utqiagvik (previously known as Barrow) and Bonanza Creek, to determine if MODIS AOD from the Terra and Aqua satellites could be used to estimate ground-level particulate pollution concentrations. The MODIS AOD was obtained from MODIS collection 6 using the dark target Land and Ocean algorithms from years 2000 to 2014. MODIS data could only be obtained between the months of April and October; therefore, it was only evaluated for those months. Individual and combined Terra and Aqua MODIS data were considered. The results showed that MODIS collection 6 products at 10-km resolution for Terra and Aqua combined are not valid over land but are valid over the ocean. Note that the individual Terra and Aqua MODIS collection 6 AOD products at 10-km resolution are valid over land individually but not when combined. Results also suggest the MODIS collection 6 AOD products at 3-km resolution are valid over land and ocean and perform better over land than the 10-km product. These findings indicate that MODIS collection 6 AOD products can be used quantitatively in air quality applications in Alaska during the summer months. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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17 pages, 4500 KiB

Open AccessArticle

An Analysis of Factors Influencing the Relationship between Satellite-Derived AOD and Ground-Level PM10

by Roland Stirnberg, Jan Cermak and Hendrik Andersen

Remote Sens. 2018, 10(9), 1353; https://doi.org/10.3390/rs10091353 - 25 Aug 2018

Cited by 30 | Viewed by 6157

Abstract

Air pollution can endanger human health, especially in urban areas. Assessment of air quality primarily relies on ground-based measurements, but these provide only limited information on the spatial distribution of pollutants. In recent years, satellite derived Aerosol Optical Depth (AOD) has been used to approximate particulate matter (PM) with varying success. In this study, the relationship between hourly mean concentrations of particulate matter with a diameter of 10 micrometers or less (PM10) and instantaneous AOD measurements is investigated for Berlin, Germany, for 2001–2015. It is found that the relationship between AOD and PM10 is rarely linear and strongly influenced by ambient relative humidity (RH), boundary layer height (BLH), wind direction and wind speed. Generally, when a moderately dry atmosphere (30% < RH ≤ 50%) coincides with a medium BLH (600–1200 m), AOD and PM10 are in the same range on a semi-quantitative scale. AOD increases with ambient RH, leading to an overestimation of the dry particle concentration near ground. However, this effect can be compensated if a low boundary layer (<600 m) is present, which in turn significantly increases PM10, eventually leading to satellite AOD and PM10 measurements of similar magnitude. Insights of this study potentially influence future efforts to estimate near-ground PM concentrations based on satellite AOD. Full article

(This article belongs to the Special Issue Remote Sensing of Air Quality)

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3 pages, 164 KiB

Open AccessCorrection

Correction: Yahui, Che et al. Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms—Part 1: Global Evaluation. Remote Sensing 2018, 10, 1414

by Yahui Che, Linlu Mei, Yong Xue, Jie Guang, Lu She, Ying Li, Andreas Heckel and Peter North

Remote Sens. 2019, 11(5), 568; https://doi.org/10.3390/rs11050568 - 8 Mar 2019

Viewed by 2443

Abstract

The authors wish to make the following corrections to this paper [...] Full article

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