E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Remote Sensing of Atmospheric Pollution"

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

Deadline for manuscript submissions: closed (30 June 2017)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Assoc. Prof. Dr. Yang Liu

Rollins School of Public Health, Emory University 1518 Clifton Road NE Atlanta, GA 30322 USA
E-Mail
Phone: 1 404 7272131
Interests: application of satellite aerosol remote sensing in air quality modeling
Guest Editor
Prof. Dr. Jun Wang

Center for Global and Environmental Research & Dept. of Chemical and Biochemical Engineering 4133 Seamans Center Iowa City, IA 52242, USA
Website | E-Mail
Interests: satellite remote sensing, particle scattering, radiative transfer, observation-based atmospheric modeling
Guest Editor
Prof. Dr. Omar Torres

Physical Scientist, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
Website | E-Mail
Phone: 1-301-614- 6667
Interests: Radiative Transfer Modeling, Remote Sensing of aerosols, satellite retrieval algorithms, aerosol optical and physical properties

Special Issue Information

Dear Colleagues,

The launch of the NASA EOS Terra satellite in 1999 marked the beginning of an exciting era of satellite remote sensing of the Earth’s atmosphere. Since then, measurements from spaceborne platforms, such as Terra, Aqua, Aura, ENVISAT, CALIPSO, and Suomi NPP have enabled us to characterize a wide range of chemical species in the troposphere. Global observations of aerosols and gas pollutants, such as ozone, carbon monoxide, sulfur dioxide, nitrogen dioxide, and formaldehyde, are available at increasingly high precision and spatial resolution. Satellite observations of air pollutants offer wide spatial coverage which cannot be matched by any ground-based monitoring networks. This strength is particularly valuable for the developing world where regular ground monitoring is sparse or nonexistent. Enhanced retrieval algorithms also allow continued improvement and expansion of satellite data during the mission lifetime. Thus far, satellite data have been used to tracking pollutant plumes, support air quality forecasting, provide evidence during exceptional air pollution events, evaluate model performance, estimate pollutant emissions, and study long-term air pollution trends. However, major challenges still need to be answered in order to effectively apply satellite data in air pollution monitoring. For example, how do we effectively mitigate the impact of substantial data missingness due to cloud cover and high surface reflectance? How do we account for the satellite retrieval errors, both random and systematic, which often vary with and surface types and meteorological conditions? These and many other research questions are being actively investigated by both the satellite science teams and the applied air quality community.
This Special Issue of Remote Sensing invites research articles in the following (but not limited to) topics:
•    Validation and evaluation of the latest data products and technologies of air pollution remote sensing
•    Innovative approach to quantify how satellite retrieval errors impact air pollution exposure assessment
•    Applications of satellite data in air pollution modeling
•    Characterization of the trend of air pollution at the local, regional, and global scales
•    Use of satellite data to improve our understanding of air pollutant emissions

Dr. Yang Liu
Dr. Jun Wang
Dr. Omar Torres
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 1800 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.

Published Papers (20 papers)

View options order results:
result details:
Displaying articles 1-20
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model
Remote Sens. 2017, 9(9), 950; https://doi.org/10.3390/rs9090950
Received: 3 July 2017 / Revised: 16 August 2017 / Accepted: 9 September 2017 / Published: 13 September 2017
Cited by 5 | PDF Full-text (15585 KB) | HTML Full-text | XML Full-text
Abstract
People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO2 based on satellite data. In this paper, a one-year dataset of tropospheric NO2 columns from [...] Read more.
People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO2 based on satellite data. In this paper, a one-year dataset of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) together with ambient monitoring station measurements and meteorological data from May 2013 to April 2014, are used to estimate the ground level NO2. The mean values of OMI tropospheric NO2 columns show significant geographical and seasonal variation when the ambient monitoring stations record a certain range. Hence, a geographically and temporally weighted regression (GTWR) model is introduced to treat the spatio-temporal non-stationarities between tropospheric-columnar and ground level NO2. Cross-validations demonstrate that the GTWR model outperforms the ordinary least squares (OLS), the geographically weighted regression (GWR), and the temporally weighted regression (TWR), produces the highest R2 (0.60) and the lowest values of root mean square error mean (RMSE), absolute difference (MAD), and mean absolute percentage error (MAPE). Our method is better than or comparable to the chemistry transport model method. The satellite-estimated spatial distribution of ground NO2 shows a reasonable spatial pattern, with high annual mean values (>40 μg/m3), mainly over southern Hebei, northern Henan, central Shandong, and southern Shaanxi. The values of population-weight NO2 distinguish densely populated areas with high levels of human exposure from others. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Figure 1

Open AccessArticle Linear and Non-Linear Trends for Seasonal NO2 and SO2 Concentrations in the Southern Hemisphere (2004−2016)
Remote Sens. 2017, 9(9), 891; https://doi.org/10.3390/rs9090891
Received: 30 June 2017 / Revised: 3 August 2017 / Accepted: 24 August 2017 / Published: 28 August 2017
Cited by 1 | PDF Full-text (4083 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
In order to address the behaviour of nitrogen dioxide (NO2) and sulphur dioxide (SO2) in the context of a changing climate, linear and non-linear trends for the concentrations of these two trace gases were estimated over their seasonal standardised [...] Read more.
In order to address the behaviour of nitrogen dioxide (NO2) and sulphur dioxide (SO2) in the context of a changing climate, linear and non-linear trends for the concentrations of these two trace gases were estimated over their seasonal standardised variables in the Southern Hemisphere—between the Equator and 60° S—using data retrieved by the Ozone Monitoring Instrument, for the period 2004–2016. A rescaling was applied to the calculated linear trends so that they are expressed in Dobson units (DU) per decade. Separately, the existence of monotonic—not necessarily linear—trends was addressed by means of the Mann-Kendall test. Results indicate that the SO2 exhibits significant linear trends in the planetary boundary layer only; they are present in all the analysed seasons but just in a small number of grid cells that are generally located over the landmasses or close to them. The SO2 concentrations in the quarterly time series exhibit, on average, a linear trend that is just below 0.08 DU decade−1 when significant and not significant values are considered altogether, but this figure increases to 0.80 DU decade−1 when only the significant trends are included. On the other hand, an important number of pixels in the lower troposphere, the middle troposphere, and the lower stratosphere have significant monotonic upward or downward trends. As for the NO2, no significant linear trends were found either in the troposphere or in the stratosphere, yet monotonic upward and downward trends were observed in the former and latter layers, respectively. Unlike the linear trends, semi-linear and non-linear trends were seen over the continents and in remote regions over the oceans. This suggests that pollutants are transported away from their sources by large-scale circulation and redistributed hemispherically. The combination of regional meteorological phenomena with atmospheric chemistry was raised as a possible explanation for the observed trends. If extrapolated, these trends are in an overall contradiction with the projected emissions of both gases for the current century. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
Remote Sens. 2017, 9(8), 858; https://doi.org/10.3390/rs9080858
Received: 10 June 2017 / Revised: 14 August 2017 / Accepted: 16 August 2017 / Published: 19 August 2017
Cited by 10 | PDF Full-text (3859 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for [...] Read more.
Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle How Do Aerosol Properties Affect the Temporal Variation of MODIS AOD Bias in Eastern China?
Remote Sens. 2017, 9(8), 800; https://doi.org/10.3390/rs9080800
Received: 21 June 2017 / Revised: 27 July 2017 / Accepted: 1 August 2017 / Published: 3 August 2017
Cited by 6 | PDF Full-text (3272 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and [...] Read more.
The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and coarse mode aerosols) in East Asia. Here, using ground-based observations in six AERONET sites, we characterize typical aerosol optical properties (including their spatiotemporal variation) in eastern China, and evaluate their impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrieval bias. Both the SSA and fine-mode particle sizes increase from northern to southern China in winter, reflecting the effect of relative humidity on particle size. The SSA is ~0.95 in summer regardless of the AEROENT stations in eastern China, but decreases to 0.85 in polluted winter in northern China. The dominance of larger and highly scattering fine-mode particles in summer also leads to the weakest phase function in the backscattering direction. By focusing on the analysis of high aerosol optical depth (AOD) (>0.4) conditions, we find that the overestimation of the AOD in Dark Target (DT) retrieval is prevalent throughout the whole year, with the bias decreasing from northern China, characterized by a mixture of fine and coarse (dust) particles, to southern China, which is dominated by fine particles. In contrast, Deep Blue (DB) retrieval tends to overestimate the AOD only in fall and winter, and underestimates it in spring and summer. While the retrievals from both the DT and DB algorithms show a reasonable estimation of the fine-mode fraction of AOD, the retrieval bias cannot be attributed to the bias in the prescribed SSA alone, and is more due to the bias in the prescribed scattering phase function (or aerosol size distribution) in both algorithms. In addition, a large yearly change in aerosol single scattering properties leads to correspondingly obvious variations in the time series of MODIS AOD bias. Our results reveal that the aerosol single scattering properties in the MODIS algorithm are insufficient to describe a large variation of aerosol properties in eastern China (especially change of particle size), and can be further improved by using newer AERONET data. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Attributing Accelerated Summertime Warming in the Southeast United States to Recent Reductions in Aerosol Burden: Indications from Vertically-Resolved Observations
Remote Sens. 2017, 9(7), 674; https://doi.org/10.3390/rs9070674
Received: 10 April 2017 / Revised: 26 June 2017 / Accepted: 26 June 2017 / Published: 1 July 2017
Cited by 15 | PDF Full-text (13260 KB) | HTML Full-text | XML Full-text
Abstract
During the twentieth century, the southeast United States cooled, in direct contrast with widespread global and hemispheric warming. While the existing literature is divided on the cause of this so-called “warming hole,” anthropogenic aerosols have been hypothesized as playing a primary role in [...] Read more.
During the twentieth century, the southeast United States cooled, in direct contrast with widespread global and hemispheric warming. While the existing literature is divided on the cause of this so-called “warming hole,” anthropogenic aerosols have been hypothesized as playing a primary role in its occurrence. In this study, unique satellite-based observations of aerosol vertical profiles are combined with a one-dimensional radiative transfer model and surface temperature observations to diagnose how major reductions in summertime aerosol burden since 2001 have impacted surface temperatures in the southeast US. We show that a significant improvement in air quality likely contributed to the elimination of the warming hole and acceleration of the positive temperature trend observed in recent years. These reductions coincide with a new EPA rule that was implemented between 2006 and 2010 that revised the fine particulate matter standard downward. Similar to the southeast US in the twentieth century, other regions of the globe may experience masking of long-term warming due to greenhouse gases, especially those with particularly poor air quality. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Figure 1

Open AccessArticle Study of PBLH and Its Correlation with Particulate Matter from One-Year Observation over Nanjing, Southeast China
Remote Sens. 2017, 9(7), 668; https://doi.org/10.3390/rs9070668
Received: 29 April 2017 / Revised: 15 June 2017 / Accepted: 23 June 2017 / Published: 28 June 2017
Cited by 9 | PDF Full-text (5532 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Planetary Boundary Layer Height (PBLH) plays an important role in the formation and development of air pollution events. Particulate Matter is one of major pollutants in China. Here, we present the characteristics of PBLH through three-methods of Lidar data inversion and show [...] Read more.
The Planetary Boundary Layer Height (PBLH) plays an important role in the formation and development of air pollution events. Particulate Matter is one of major pollutants in China. Here, we present the characteristics of PBLH through three-methods of Lidar data inversion and show the correlation between the PBLH and the PM2.5 (PM2.5 with the diameter <2.5 μm) in the period of December 2015 through November 2016, over Nanjing, in southeast China. We applied gradient method (GRA), standard deviation method (STD) and wavelet covariance transform method (WCT) to calculate the PBLH. The results show that WCT is the most stable method which is less sensitive to the signal noise. We find that the PBLH shows typical seasonal variation trend with maximum in summer and minimum in winter, respectively. The yearly averaged PBLH in the diurnal cycle show the minimum of 570 m at 08:00 and the maximum of 1089 m at 15:00 Beijing time. Furthermore, we investigate the relationship of the PBLH and PM2.5 concentration under different particulate pollution conditions. The correlation coefficient is about −0.70, which is negative correlation. The average PBLH are 718 m and 1210 m when the PM2.5 > 75 μg/m3 and the PM2.5 < 35 μg/m3 in daytime, respectively. The low PBLH often occurs with condition of the low wind speed and high relative humidity, which will lead to high PM2.5 concentration and the low visibility. On the other hand, the stability of PBL is enhanced by high PM concentration and low visibility. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Estimation of Surface NO2 Volume Mixing Ratio in Four Metropolitan Cities in Korea Using Multiple Regression Models with OMI and AIRS Data
Remote Sens. 2017, 9(6), 627; https://doi.org/10.3390/rs9060627
Received: 25 April 2017 / Revised: 15 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
Cited by 4 | PDF Full-text (4303 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Surface NO2 volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO2 VMRST) and monthly mean surface NO2 VMR (NO2 VMRM) are estimated for the first time using three regression models with Ozone [...] Read more.
Surface NO2 volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO2 VMRST) and monthly mean surface NO2 VMR (NO2 VMRM) are estimated for the first time using three regression models with Ozone Monitoring Instrument (OMI) data in four metropolitan cities in South Korea: Seoul, Gyeonggi, Daejeon, and Gwangju. Relationships between the surface NO2 VMR obtained from in situ measurements (NO2 VMRIn-situ) and tropospheric NO2 vertical column density obtained from OMI from 2007 to 2013 were developed using regression models that also include boundary layer height (BLH) from Atmospheric Infrared Sounder (AIRS) and surface pressure, temperature, dew point, and wind speed and direction. The performance of the regression models is evaluated via comparison with the NO2 VMRIn-situ for two validation years (2006 and 2014). Of the three regression models, a multiple regression model shows the best performance in estimating NO2 VMRST and NO2 VMRM. In the validation period, the average correlation coefficient (R), slope, mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), and percent difference between NO2 VMRIn-situ and NO2 VMRST estimated by the multiple regression model are 0.66, 0.41, −1.36 ppbv, 6.89 ppbv, 8.98 ppbv, and 31.50%, respectively, while the average corresponding values for the other two models are 0.75, 0.41, −1.40 ppbv, 3.59 ppbv, 4.72 ppbv, and 16.59%, respectively. All three models have similar performance for NO2 VMRM, with average R, slope, MB, MAE, RMSE, and percent difference between NO2 VMRIn-situ and NO2 VMRM of 0.74, 0.49, −1.90 ppbv, 3.93 ppbv, 5.05 ppbv, and 18.76%, respectively. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle MODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water
Remote Sens. 2017, 9(6), 595; https://doi.org/10.3390/rs9060595
Received: 23 March 2017 / Revised: 26 May 2017 / Accepted: 7 June 2017 / Published: 12 June 2017
Cited by 6 | PDF Full-text (8772 KB) | HTML Full-text | XML Full-text
Abstract
We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow [...] Read more.
We present a new approach to retrieve Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently does not conduct AOD retrieval in shallow waters that have visible sediments or sea-floor (i.e., Class 2 waters). Over the global coastal water regions in cloud-free conditions, coastal screening leads to ~20% unavailability of AOD retrievals. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm to be negligible regardless of water turbidity, and therefore the 2.1 μm reflectance at the top of the atmosphere is sensitive to both change of fine-mode and coarse-mode AODs. By assuming that the aerosol single scattering properties over coastal turbid water are similar to those over the adjacent open-ocean pixels, the new algorithm can derive AOD over these shallow waters. The test algorithm yields ~18% more MODIS-AERONET collocated pairs for six AERONET stations in the coastal water regions. Furthermore, comparison of the new retrieval with these AERONET observations show that the new AOD retrievals have equivalent or better accuracy than those retrieved by the MODIS operational algorithm’s over coastal land and non-turbid coastal water product. Combining the new retrievals with the existing MODIS operational retrievals yields an overall improvement of AOD over those coastal water regions. Most importantly, this refinement extends the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides. This expanded coverage is crucial for better understanding of impact of anthropogenic aerosol particles on coastal air quality and climate. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Ground-Level NO2 Concentrations over China Inferred from the Satellite OMI and CMAQ Model Simulations
Remote Sens. 2017, 9(6), 519; https://doi.org/10.3390/rs9060519
Received: 9 March 2017 / Revised: 15 May 2017 / Accepted: 19 May 2017 / Published: 24 May 2017
Cited by 6 | PDF Full-text (6018 KB) | HTML Full-text | XML Full-text
Abstract
In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO2) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO2 columns rather than ground-level NO [...] Read more.
In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO2) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO2 columns rather than ground-level NO2 concentrations, but actually ground-level NO2 concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO2 concentrations using the total column of NO2 observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year’s worth of data from 2014 was processed and the results compared with ground-based NO2 measurements from a network of China’s National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO2 concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m3, which was improved to a standard deviation of 18.90 μg/m3 between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO2 concentrations over China. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Plume Segmentation from UV Camera Images for SO2 Emission Rate Quantification on Cloud Days
Remote Sens. 2017, 9(6), 517; https://doi.org/10.3390/rs9060517
Received: 27 March 2017 / Revised: 7 May 2017 / Accepted: 21 May 2017 / Published: 24 May 2017
Cited by 4 | PDF Full-text (11274 KB) | HTML Full-text | XML Full-text
Abstract
We performed measurements of SO2 emissions with a high UV sensitive dual-camera optical system. Generally, in order to retrieve the two-dimensional SO2 emission rates of a source, e.g., the slant column density of a plume emitted by a stack, one needs [...] Read more.
We performed measurements of SO2 emissions with a high UV sensitive dual-camera optical system. Generally, in order to retrieve the two-dimensional SO2 emission rates of a source, e.g., the slant column density of a plume emitted by a stack, one needs to acquire four images with UV cameras: two images including the emitting stack at wavelengths with high and negligible absorption features (λon/off), and two additional images of the background intensity behind the plume, at the same wavelengths as before. However, the true background intensity behind a plume is impossible to obtain from a remote measurement site at rest, and thus, one needs to find a way to approximate the background intensity. Some authors have presented methods to estimate the background behind the plume from two emission images. However, those works are restricted to dealing with clear sky, or almost homogeneously illuminated days. The purpose of this work is to present a new approach using background images constructed from emission images by an automatic plume segmentation and interpolation procedure, in order to estimate the light intensity behind the plume. We compare the performance of the proposed approach with the four images method, which uses, as background, sky images acquired at a different viewing direction. The first step of the proposed approach involves the segmentation of the SO2 plume from the background. In clear sky days, we found similar results from both methods. However, when the illumination of the sky is non homogeneous, e.g., due to lateral sun illumination or clouds, there are appreciable differences between the results obtained by both methods. We present results obtained in a series of measurements of SO2 emissions performed on a cloudy day from a stack of an oil refinery in Montevideo City, Uruguay. The results obtained with the UV cameras were compared with scanning DOAS measurements, yielding a good agreement. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Ground Ammonia Concentrations over China Derived from Satellite and Atmospheric Transport Modeling
Remote Sens. 2017, 9(5), 467; https://doi.org/10.3390/rs9050467
Received: 27 March 2017 / Revised: 2 May 2017 / Accepted: 7 May 2017 / Published: 15 May 2017
Cited by 2 | PDF Full-text (14035 KB) | HTML Full-text | XML Full-text
Abstract
As a primary basic gas in the atmosphere, atmospheric ammonia (NH3) plays an important role in determining air quality, environmental degradation, and climate change. However, the limited ground observation currently presents a barrier to estimating ground NH3 concentrations on a [...] Read more.
As a primary basic gas in the atmosphere, atmospheric ammonia (NH3) plays an important role in determining air quality, environmental degradation, and climate change. However, the limited ground observation currently presents a barrier to estimating ground NH3 concentrations on a regional scale, thus preventing a full understanding of the atmospheric processes in which this trace gas is involved. This study estimated the ground NH3 concentrations over China, combining the Infrared Atmospheric Sounding Interferometer (IASI) satellite NH3 columns and NH3 profiles from an atmospheric chemistry transport model (CTM). The estimated ground NH3 concentrations showed agreement with the variability in annual ground NH3 measurements from the Chinese Nationwide Nitrogen Deposition Monitoring Network (NNDMN). Great spatial heterogeneity of ground NH3 concentrations was found across China, and high ground NH3 concentrations were found in Northern China, Southeastern China, and some areas in Xinjiang Province. The maximum ground NH3 concentrations over China occurred in summer, followed by spring, autumn, and winter seasons, which were in agreement with the seasonal patterns of NH3 emissions in China. This study suggested that a combination of NH3 profiles from CTMs and NH3 columns from satellite obtained reliable ground NH3 concentrations over China. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Intercomparison of Ozone Vertical Profile Measurements by Differential Absorption Lidar and IASI/MetOp Satellite in the Upper Troposphere–Lower Stratosphere
Remote Sens. 2017, 9(5), 447; https://doi.org/10.3390/rs9050447
Received: 10 February 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 8 May 2017
Cited by 3 | PDF Full-text (2116 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces the technique of retrieving the profiles of vertical distribution of ozone considering temperature and aerosol correction in DIAL sounding of the atmosphere. The authors determine wavelengths, which are promising for measurements of ozone profiles in the upper troposphere–lower stratosphere. An [...] Read more.
This paper introduces the technique of retrieving the profiles of vertical distribution of ozone considering temperature and aerosol correction in DIAL sounding of the atmosphere. The authors determine wavelengths, which are promising for measurements of ozone profiles in the upper troposphere–lower stratosphere. An ozone differential absorption lidar is designed for the measurements. The results of applying the developed technique to the retrieval of the vertical profiles of ozone considering temperature and aerosol correction in the altitude range 6–15 km in DIAL sounding of the atmosphere confirm the prospects of ozone sounding at selected wavelengths of 341 and 299 nm with the proposed lidar. The 2015 ozone profiles retrieved were compared with satellite IASI data and the Kruger model. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain
Remote Sens. 2017, 9(5), 432; https://doi.org/10.3390/rs9050432
Received: 13 February 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 2 May 2017
Cited by 9 | PDF Full-text (1885 KB) | HTML Full-text | XML Full-text
Abstract
The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical [...] Read more.
The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The “MODIS-like” VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the “dark-target” algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012–31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04–0.06 and the correlation of 0.83–0.86, with the largest MB (0.10–0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13–0.14 and a higher correlation (0.93–0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02–0.04 for fine mode, 0.06–0.12 for coarse mode and 0.03–0.05 for bi-mode at 440 nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g., surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan
Remote Sens. 2017, 9(5), 403; https://doi.org/10.3390/rs9050403
Received: 22 February 2017 / Revised: 15 April 2017 / Accepted: 21 April 2017 / Published: 25 April 2017
Cited by 16 | PDF Full-text (1915 KB) | HTML Full-text | XML Full-text
Abstract
Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change. [...] Read more.
Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change. In this study, three-year (from 1 January 2014 to 31 December 2016) records of VIIRS Intermediate Product (IP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals on aerosol optical depth (AOD) at 550 nm were evaluated by comparing them to ground sun photometer measurements over Wuhan. Results indicated that VIIRS IP retrievals were underestimated by 5% for the city. A comparison of VIIRS IP retrievals and ground sun photometer measurements showed a lower R2 of 0.55 (0.79 for Terra-MODIS and 0.76 for Aqua-MODIS), with only 52% of retrievals falling within the expected error range established by MODIS over land (i.e., ±(0.05 + 0.15AOD)). Bias analyses with different Ångström exponents (AE) demonstrated that land aerosol model selection of the VIIRS retrieval over Wuhan was appropriate. However, the larger standard deviations (i.e., uncertainty) of VIIRS AODs than MODIS AODs could be attributed to the less robust retrieval algorithm. Monthly variations displayed largely underestimated AODs of VIIRS in winter, which could be caused by a large positive bias in surface reflectance estimation due to the sparse vegetation and greater surface brightness of Wuhan in this season. The spatial distribution of VIIRS and MODIS AOD observations revealed that the VIIRS IP AODs over high-pollution areas (AOD > 0.8) with sparse vegetation were underestimated by more than 20% in Wuhan, and 40% in several regions. Analysis of several clear rural areas (AOD < 0.2) with native vegetation indicated an overestimation of about 20% in the northeastern region of the city. These findings showed that the VIIRS IP AOD at 550 nm can provide a solid dataset with a high resolution (750 m) for quantitative scientific investigations and environmental monitoring over Wuhan. However, the performance of dark target algorithms in VIIRS was associated with aerosol types and ground vegetation conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Interference of Heavy Aerosol Loading on the VIIRS Aerosol Optical Depth (AOD) Retrieval Algorithm
Remote Sens. 2017, 9(4), 397; https://doi.org/10.3390/rs9040397
Received: 13 February 2017 / Revised: 12 April 2017 / Accepted: 19 April 2017 / Published: 23 April 2017
Cited by 5 | PDF Full-text (17366 KB) | HTML Full-text | XML Full-text
Abstract
Aerosol optical depth (AOD) has been widely used in climate research, atmospheric environmental observations, and other applications. However, high AOD retrieval remains challenging over heavily polluted regions, such as the North China Plain (NCP). The Visible Infrared Imaging Radiometer Suite (VIIRS), which was [...] Read more.
Aerosol optical depth (AOD) has been widely used in climate research, atmospheric environmental observations, and other applications. However, high AOD retrieval remains challenging over heavily polluted regions, such as the North China Plain (NCP). The Visible Infrared Imaging Radiometer Suite (VIIRS), which was designed as a successor to the Moderate Resolution Imaging Spectroradiometer (MODIS), will undertake the aerosol observations mission in the coming years. Using the VIIRS AOD retrieval algorithm as an example, we analyzed the influence of heavy aerosol loading through the 6SV radiative transfer model (RTM) with a focus on three aspects: cloud masking, ephemeral water body tests, and data quality estimation. First, certain pixels were mistakenly screened out as clouds and ephemeral water bodies because of heavy aerosols, resulting in the loss of AOD retrievals. Second, the greenness of the surface could not be accurately identified by the top of atmosphere (TOA) index, and the quality of the aggregation data may be artificially high. Thus, the AOD retrieval algorithm did not perform satisfactorily, indicated by the low availability of data coverage (at least 37.97% of all data records were missing according to ground-based observations) and overestimation of the data quality (high-quality data increased from 63.42% to 80.97% according to radiative simulations). To resolve these problems, the implementation of a spatial variability cloud mask method and surficial index are suggested in order to improve the algorithm. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Modelling Seasonal GWR of Daily PM2.5 with Proper Auxiliary Variables for the Yangtze River Delta
Remote Sens. 2017, 9(4), 346; https://doi.org/10.3390/rs9040346
Received: 9 December 2016 / Revised: 26 February 2017 / Accepted: 1 April 2017 / Published: 5 April 2017
Cited by 7 | PDF Full-text (4128 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy [...] Read more.
Over the past decades, regional haze episodes have frequently occurred in eastern China, especially in the Yangtze River Delta (YRD). Satellite derived Aerosol Optical Depth (AOD) has been used to retrieve the spatial coverage of PM2.5 concentrations. To improve the retrieval accuracy of the daily AOD-PM2.5 model, various auxiliary variables like meteorological or geographical factors have been adopted into the Geographically Weighted Regression (GWR) model. However, these variables are always arbitrarily selected without deep consideration of their potentially varying temporal or spatial contributions in the model performance. In this manuscript, we put forward an automatic procedure to select proper auxiliary variables from meteorological and geographical factors and obtain their optimal combinations to construct four seasonal GWR models. We employ two different schemes to comprehensively test the performance of our proposed GWR models: (1) comparison with other regular GWR models by varying the number of auxiliary variables; and (2) comparison with observed ground-level PM2.5 concentrations. The result shows that our GWR models of “AOD + 3” with three common meteorological variables generally perform better than all the other GWR models involved. Our models also show powerful prediction capabilities in PM2.5 concentrations with only slight overfitting. The determination coefficients R2 of our seasonal models are 0.8259 in spring, 0.7818 in summer, 0.8407 in autumn, and 0.7689 in winter. Also, the seasonal models in summer and autumn behave better than those in spring and winter. The comparison between seasonal and yearly models further validates the specific seasonal pattern of auxiliary variables of the GWR model in the YRD. We also stress the importance of key variables and propose a selection process in the AOD-PM2.5 model. Our work validates the significance of proper auxiliary variables in modelling the AOD-PM2.5 relationships and provides a good alternative in retrieving daily PM2.5 concentrations from remote sensing images in the YRD. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Aerosol Retrieval Sensitivity and Error Analysis for the Cloud and Aerosol Polarimetric Imager on Board TanSat: The Effect of Multi-Angle Measurement
Remote Sens. 2017, 9(2), 183; https://doi.org/10.3390/rs9020183
Received: 22 November 2016 / Revised: 29 January 2017 / Accepted: 16 February 2017 / Published: 22 February 2017
Cited by 6 | PDF Full-text (4297 KB) | HTML Full-text | XML Full-text
Abstract
Aerosol scattering is an important source of error in CO2 retrievals from satellite. This paper presents an analysis of aerosol information content from the Cloud and Aerosol Polarimetric Imager (CAPI) onboard the Chinese Carbon Dioxide Observation Satellite (TanSat) to be launched in [...] Read more.
Aerosol scattering is an important source of error in CO2 retrievals from satellite. This paper presents an analysis of aerosol information content from the Cloud and Aerosol Polarimetric Imager (CAPI) onboard the Chinese Carbon Dioxide Observation Satellite (TanSat) to be launched in 2016. Based on optimal estimation theory, aerosol information content is quantified from radiance and polarization observed by CAPI in terms of the degrees of freedom for the signal (DFS). A linearized vector radiative transfer model is used with a linearized Mie code to simulate observation and sensitivity (or Jacobians) with respect to aerosol parameters. In satellite nadir mode, the DFS for aerosol optical depth is the largest, but for mode radius, it is only 0.55. Observation geometry is found to affect aerosol DFS based on the aerosol scattering phase function from the comparison between different viewing zenith angles or solar zenith angles. When TanSat is operated in target mode, we note that multi-angle retrieval represented by three along-track measurements provides additional 0.31 DFS on average, mainly from mode radius. When adding another two measurements, the a posteriori error decreases by another 2%–6%. The correlation coefficients between retrieved parameters show that aerosol is strongly correlated with surface reflectance, but multi-angle retrieval can weaken this correlation. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle High Resolution Aerosol Optical Depth Retrieval Using Gaofen-1 WFV Camera Data
Remote Sens. 2017, 9(1), 89; https://doi.org/10.3390/rs9010089
Received: 5 December 2016 / Revised: 10 January 2017 / Accepted: 15 January 2017 / Published: 19 January 2017
Cited by 5 | PDF Full-text (8194 KB) | HTML Full-text | XML Full-text
Abstract
Aerosol Optical Depth (AOD) is crucial for urban air quality assessment. However, the frequently used moderate-resolution imaging spectroradiometer (MODIS) AOD product at 10 km resolution is too coarse to be applied in a regional-scale study. Gaofen-1 (GF-1) wide-field-of-view (WFV) camera data, with high [...] Read more.
Aerosol Optical Depth (AOD) is crucial for urban air quality assessment. However, the frequently used moderate-resolution imaging spectroradiometer (MODIS) AOD product at 10 km resolution is too coarse to be applied in a regional-scale study. Gaofen-1 (GF-1) wide-field-of-view (WFV) camera data, with high spatial and temporal resolution, has great potential in estimation of AOD. Due to the lack of shortwave infrared (SWIR) band and complex surface reflectivity brought from high spatial resolution, it is difficult to retrieve AOD from GF-1 WFV data with traditional methods. In this paper, we propose an improved AOD retrieval algorithm for GF-1 WFV data. The retrieved AOD has a spatial resolution of 160 m and covers all land surface types. Significant improvements in the algorithm include: (1) adopting an improved clear sky composite method by using the MODIS AOD product to identify the clearest days and correct the background atmospheric effect; and (2) obtaining local aerosol models from long-term CIMEL sun-photometer measurements. Validation against MODIS AOD and ground measurements showed that the GF-1 WFV AOD has a good relationship with MODIS AOD (R2 = 0.66; RMSE = 0.27) and ground measurements (R2 = 0.80; RMSE = 0.25). Nevertheless, the proposed algorithm was found to overestimate AOD in some cases, which will need to be improved upon in future research. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Open AccessArticle Evaluation of Aqua MODIS Collection 6 AOD Parameters for Air Quality Research over the Continental United States
Remote Sens. 2016, 8(10), 815; https://doi.org/10.3390/rs8100815
Received: 18 July 2016 / Revised: 19 September 2016 / Accepted: 26 September 2016 / Published: 1 October 2016
Cited by 11 | PDF Full-text (3863 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Satellite-retrieved aerosol optical depth (AOD) has become an important predictor of ground-level particulate matter (PM) and greatly empowered air pollution research worldwide. We evaluated the AOD parameters included in the Collection 6 aerosol product of the Moderate Resolution Imaging Spectroradiometer (MODIS) for two [...] Read more.
Satellite-retrieved aerosol optical depth (AOD) has become an important predictor of ground-level particulate matter (PM) and greatly empowered air pollution research worldwide. We evaluated the AOD parameters included in the Collection 6 aerosol product of the Moderate Resolution Imaging Spectroradiometer (MODIS) for two key factors affecting their applications in air quality research—coverage and accuracy—over the continental US. For the high confidence retrievals (QAC 3), the 10 km DB-DT combined AOD has the best coverage nationwide (29.7% of the days in a year in any given 12 km grid cell). While the Eastern US generally had more successful AOD retrievals, the highest spatial coverage of AOD parameters were found in California (>55%) and other vegetated parts of the Western US. If lower QAC retrievals were included, the coverage of the 10 km DB AOD was dramatically increased to 49.6%. In the Eastern US, the QAC 3 retrievals of all four AOD parameters are highly correlated with AERONET observations (correlation coefficients between 0.80 and 0.92). In the Western US, positive retrieval errors existed in all MODIS AOD parameters, resulting in lower correlations with AERONET. AOD retrieval errors showed significant dependence on flight geometry, land cover type, and weather conditions. To ensure appropriate use of these AOD values, air quality researchers should carefully balance the needs for coverage and accuracy, and develop additional data screening criteria based on their study design. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Other

Jump to: Research

Open AccessTechnical Note Performance of MODIS C6 Aerosol Product during Frequent Haze-Fog Events: A Case Study of Beijing
Remote Sens. 2017, 9(5), 496; https://doi.org/10.3390/rs9050496
Received: 22 March 2017 / Revised: 4 May 2017 / Accepted: 14 May 2017 / Published: 18 May 2017
Cited by 4 | PDF Full-text (6875 KB) | HTML Full-text | XML Full-text
Abstract
The newly released MODIS Collection 6 aerosol products have been widely used to evaluate fine particulate matter with a 10 km Dark Target aerosol optic depth (DT AOD) product, a new 3 km DT AOD product and an enhanced Deep Blue (DB) AOD [...] Read more.
The newly released MODIS Collection 6 aerosol products have been widely used to evaluate fine particulate matter with a 10 km Dark Target aerosol optic depth (DT AOD) product, a new 3 km DT AOD product and an enhanced Deep Blue (DB) AOD product. However, the representativeness of MODIS AOD products under different air quality conditions remains unclear. In this study, we obtained all three types of MODIS Terra AOD from 2001 to 2015 and Aqua AOD from 2003 to 2015 for the Beijing region to study the performance of the different AOD products (Collection 6) under different air quality situations. The validation of three MODIS AOD products suggests that DB AOD has the highest accuracy with an expected error (EE) envelope (containing at least 67% of the matchups on a scatter plot) of 0.05 + 0.15τ, followed by 10 km DT AOD (0.08 + 0.2τ) and 3 km DT AOD (0.35 + 0.15τ), specifically for Beijing. Near-surface PM2.5 concentrations during the passage of MODIS from 2013 to 2015 were also obtained to categorize air quality as unpolluted, moderately, and heavily polluted, as well as to analyze the performance of the different AOD products under different air quality conditions. Very few MODIS 3 km DT retrievals appeared on heavily polluted days, making it almost impossible to play an effective role in air quality applications in Beijing. While the DB AOD allowed for considerable retrievals under all air quality conditions, it had a coarse spatial resolution. These results demonstrate that the MODIS 3 km DT AOD product may not be the appropriate proxy to be used in the satellite retrieval of surface PM2.5, especially for those areas with frequent haze-fog events like Beijing. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top