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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 December 2022) | Viewed by 14513

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Prof. Dr. Cheng Liu

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Guest Editor

School of Engineering Science, University of Science and Technology of China, 96 Jinzhai Road, Hefei 230026, China
Interests: satellite remote sensing; ground based remote sensing (MAX-DOAS, FTS, Lidar); deep learning
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Dear Colleagues,

Monitoring air pollution is crucial to understand the relationship between emissions and air pollution levels, support air quality management, and reduce human exposure. Current monitoring networks are unfortunately woefully inadequate to fully understand the formation of air pollution and attribution of sources. We thus highlight the need for the development of an international stereoscopic monitoring strategy that can depict three-dimensional (3D) distribution of atmospheric composition to beat uncertainties and to advance diagnostic understanding and prediction of air pollution. The stereoscopic monitoring strategy based on satellite and ground-based remote sensing techniques will help us to better characterize the formation of air pollution, optimize air quality management, and protect human health.

This Special Issue aims at studies on remote sensing of atmospheric pollutions by different sensors and platforms. Topics can cover all aspects of sensor design, new techniques for atmospheric pollutant measurements, and applications of remote sensing.

Fields of research and review articles include but are not limited to the following topics:

Development of retrieval algorithm for trace gases, greenhouse gases, aerosol, and cloud; satellite observation; ground-based measurements; unmanned aerial vehicle observation; aircraft observations; design of sensor; application of remote sensing; machine learning.

Dr. Cheng Liu
Guest Editor

Manuscript Submission Information

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Keywords

satellite remote sensing
ground-based remote sensing
trace gas
aerosol
chemistry
machine learning

Published Papers (7 papers)

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22 pages, 11154 KiB

Open AccessArticle

Seasonal and Diurnal Characteristics of the Vertical Profile of Aerosol Optical Properties in Urban Beijing, 2017–2021

by Xinglu Zhang, Yu Zheng, Huizheng Che, Ke Gui, Lei Li, Hujia Zhao, Yuanxin Liang, Wenrui Yao, Xindan Zhang, Hengheng Zhao, Yanting Lu and Xiaoye Zhang

Remote Sens. 2023, 15(2), 475; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020475 - 13 Jan 2023

Cited by 4 | Viewed by 1583

Abstract

Seasonal and diurnal characteristics of the vertical profiles of aerosol properties are essential for detecting the regional transport and the climatic radiative effects of aerosol particles. We have studied the seasonal and diurnal characteristics of the vertical distribution of aerosols in urban Beijing from 2017 to 2021 based on long-term Raman–Mie LiDAR observations. The influence of the vertical distribution of aerosols, the meteorological conditions within the boundary layer, the optical–radiometric properties of aerosols, and their interconnections, were investigated during a heavy haze pollution event in Beijing from 8 to 15 February 2020 using both meteorological and sun photometer data. The aerosol extinction coefficient was highest in summer (0.4 km⁻¹), followed by winter (0.35 km⁻¹), and roughly equal in spring and autumn (0.3 km⁻¹). The aerosol extinction coefficient showed clear daily variations and was different in different seasons as a result of the variation in the height of the boundary layer. During the haze pollution event, the particulate matter mainly consisted of scattered spherical fine particles and the accumulation time of pollutants measured via the AOD_440nm and PM_2.5 mass concentration was different as a result of the hygroscopic growth of the aerosol particles. This growth increased scattering and led to an increase in the aerosol optical depth. The vertical transport of particulate matter also contributed to the increase in the aerosol optical depth. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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22 pages, 9955 KiB

Open AccessArticle

Quantitative Evaluation of Dust and Black Carbon Column Concentration in the MERRA-2 Reanalysis Dataset Using Satellite-Based Component Retrievals

by Lei Li, Huizheng Che, Xin Su, Xindan Zhang, Ke Gui, Yu Zheng, Hujia Zhao, Hengheng Zhao, Yuanxin Liang, Yadong Lei, Lei Zhang, Junting Zhong, Zhili Wang and Xiaoye Zhang

Remote Sens. 2023, 15(2), 388; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15020388 - 8 Jan 2023

Cited by 4 | Viewed by 2031

Abstract

The aerosol optical property products of Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis dataset have been extensively investigated on a global or regional scale. However, the understanding of MERRA-2 aerosol component products on an extensive temporal and spatial scale is inadequate. Recently, the aerosol component products have been derived from the observations of Polarization and Directionality of the Earth’s Reflectances/Polarization and Anisotropy of Reflectance for Atmospheric Science coupled with observations from a Lidar (POLDER/PARASOL). This study presents a quantitative evaluation of the MERRA-2 reanalysis dust and black carbon (BC) column concentration using independent satellite-based aerosol component concentration retrievals. Both GRASP/Component and MERRA-2 reanalysis products can capture well the temporal variation in dust column concentration over the dust emission resource and downwind dust-dominated regions with the correlation coefficient (R) varying from 0.80 to 0.98. MERRA-2 reanalysis dust products present higher column concentration than GRASP/Component dust retrievals with relative differences of about 20~70%, except in the Taklamakan Desert and Bay of Bengal, where the relative differences can be negative. The differences in dust column concentration over the African dust regions are larger than that over the Asian dust regions. Similar temporal variations in BC column concentration are characterized by both GRASP/Component BC retrievals and MERRA-2 BC products with R of about 0.70~0.90, except in the North China Plain region. We should pay more caution with the regional applicability of MERRA-2 component products when large differences and high correlation coefficients are obtained simultaneously. The results are favorable for identifying the behavior of MERRA-2 reanalysis component estimation in a new view and demonstrate a practical application of the satellite-based component retrievals, which could make more contributions to the improvement of model estimation in the near future. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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17 pages, 5481 KiB

Open AccessArticle

Estimating Regional PM_2.5 Concentrations in China Using a Global-Local Regression Model Considering Global Spatial Autocorrelation and Local Spatial Heterogeneity

by Heng Su, Yumin Chen, Huangyuan Tan, Annan Zhou, Guodong Chen and Yuejun Chen

Remote Sens. 2022, 14(18), 4545; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184545 - 11 Sep 2022

Cited by 4 | Viewed by 1977

Abstract

Linear regression models are commonly used for estimating ground PM_2.5 concentrations, but the global spatial autocorrelation and local spatial heterogeneity of PM_2.5 distribution are either ignored or only partially considered in commonly used models for estimating PM_2.5 concentrations. Therefore, taking both global spatial autocorrelation and local spatial heterogeneity into consideration, a global-local regression (GLR) model is proposed for estimating ground PM_2.5 concentrations in the Yangtze River Delta (YRD) and in the Beijing, Tianjin, Hebei (BTH) regions of China based on the aerosol optical depth data, meteorological data, remote sensing data, and pollution source data. Considering the global spatial autocorrelation, the GLR model extracts global factors by the eigenvector spatial filtering (ESF) method, and combines the fraction of them that passes further filtering with the geographically weighted regression (GWR) method to address the local spatial heterogeneity. Comprehensive results show that the GLR model outperforms the ordinary GWR and ESF models, and the GLR model has the best performance at the monthly, seasonal, and annual levels. The average adjusted R² of the monthly GLR model in the YRD region (the BTH region) is 0.620 (0.853), which is 8.0% and 7.4% (6.8% and 7.0%) higher than that of the monthly ESF and GWR models, respectively. The average cross-validation root mean square error of the monthly GLR model is 7.024 μg/m³ in the YRD region, and 9.499 μg/m³ in the BTH region, which is lower than that of the ESF and GWR models. The GLR model can effectively address the spatial autocorrelation and spatial heterogeneity, and overcome the shortcoming of the ordinary GWR model that overfocuses on local features and the disadvantage of the poor local performance of the ordinary ESF model. Overall, the GLR model with good spatial and temporal applicability is a promising method for estimating PM_2.5 concentrations. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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22 pages, 6614 KiB

Open AccessArticle

Preliminary Assessment and Verification of the Langley Plots Calibration of the Sun Photometer at Mt Foyeding Observatory, Beijing

by Yu Zheng, Huizheng Che, Ke Gui, Xiangao Xia, Hujia Zhao, Lei Li, Lei Zhang, Xinglu Zhang, Hengheng Zhao, Yuanxin Liang, Hong Wang, Yaqiang Wang and Xiaoye Zhang

Remote Sens. 2022, 14(17), 4321; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174321 - 1 Sep 2022

Cited by 1 | Viewed by 1453

Abstract

An assessment and verification of the Langley calibration method of the Sun photometer at Mt Foyeding (MFYD) Observatory in Beijing was performed. We explored whether the Langley plot calibration is practicable for this mountainous site by analyzing the aerosol climatology and carrying out a case study. Then, the aerosol optical depth (AOD) results were verified under the reference of AERONET AOD. The results showed that satisfactory atmospheric conditions are present on winter mornings, characterized by a smaller average AOD (~0.09–0.14) and a lower range ratio (~36.97–63.38%) than in the afternoons and over a whole day. The six days selected as the case study all showed stable atmospheric conditions characterized by daily average triplets of <2% for all wavelengths. The residual sum of squares for

V_{0 λ}

at all wavelengths was <0.0002 and the residual standard deviation was <0.2%. A large improvement was found in the linear regression at morning relative to the statistics obtained over the whole day, when the coefficient of determination and residual standard deviation were promoted by 0.22–2.90% and ~2.76–23.32, respectively. The final

V_{0 λ}

value was derived from 31 days of observation and the deviations from the reference

V_{0 λ}

were about −1.69, −1.29, −0.81, −0.42, −0.34, −0.22, −0.63 and −0.36% at 340, 380, 440, 500, 675, 870, 1020 and 1640 nm, respectively. The regression analysis of the AOD validation showed a perfect AOD performance, with 100% of the retrievals lying within the expected error (0.05 ± 10%) from 380 to 1640 nm and 99.99% for the 340 nm band. Good AOD agreement (correlation coefficients > 0.998) and residual standard deviation values ranging from ~0.006 to 0.011 were observed, with the relative mean bias varying from 0.999 to 1.066. The mean biases were concentrated within ±0.02 for the ultraviolet bands and within ±0.01 for the other bands; therefore, the results of this preliminary assessment and verification indicated that the Langley plots method is suitable for photometer calibration at the MFYD Observatory. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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21 pages, 8350 KiB

Open AccessArticle

Investigating the Relationship between Air Pollutants and Meteorological Parameters Using Satellite Data over Bangladesh

by Md Masudur Rahman, Wang Shuo, Weixiong Zhao, Xuezhe Xu, Weijun Zhang and Arfan Arshad

Remote Sens. 2022, 14(12), 2757; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122757 - 8 Jun 2022

Cited by 8 | Viewed by 2788

Abstract

Understanding of the relationship between air pollutants and meteorological parameters on the regional scale is a prerequisite for setting up air pollution prevention and control strategies; however, there is a lack of methodical investigations, particularly in the context of Bangladesh’s deficiency of information on air pollution. This study represents the first attempt to investigate the relationship between air pollutants (NO₂, O₃, SO₂, and CO) and meteorological parameters over Bangladesh using satellite data (OMI and MOPITT) during the period from 2015 to 2020. Geographically weighted regression (GWR) modelling was utilized to assess the relationship between air pollutants and weather variables. The spatial representation and average values of geographically varying coefficients showed that the column densities of air pollutants were affected by the meteorological parameters. For example, NO₂ was positively associated with temperature in most of the studied regions, with an average geographically varying coefficient value of 0.12 Dobson units (DU, 1 DU = 2.687 × 10¹⁶ molecules/cm²), indicating that NO₂ concentrations increase by 0.12 DU/year with every unit increase in temperature. The sources of NO₂ and SO₂ in Dhaka were identified through emission inventory analysis, and transportation and industry emissions were the most significant influencing factors for NO₂ and SO₂, respectively. Temperature and pressure showed a higher degree of relationship with all four air pollutants compared with other parameters. The results and discussion presented in this study can be of benefit for policy makers in developing air pollution control strategies in Bangladesh. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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12 pages, 11753 KiB

Open AccessTechnical Note

Local-Scale Horizontal CO₂ Flux Estimation Incorporating Differential Absorption Lidar and Coherent Doppler Wind Lidar

by Bin Yue, Saifen Yu, Manyi Li, Tianwen Wei, Jinlong Yuan, Zhen Zhang, Jingjing Dong, Yue Jiang, Yuanjian Yang, Zhiqiu Gao and Haiyun Xia

Remote Sens. 2022, 14(20), 5150; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205150 - 14 Oct 2022

Cited by 8 | Viewed by 1817

Abstract

A micro-pulse lidar system incorporating differential absorption lidar (DIAL) and coherent Doppler wind lidar (CDWL) is proposed and demonstrated. Due to the high signal-to-noise ratio (SNR) of the superconducting nanowire single-photon detector (SNSPD), the DIAL channel achieves high sensitivity in CO₂ measurement. Meanwhile, the CDWL channel is used to obtain the horizontal wind field. In the process of the optimization and calibration of the DIAL receiver, specifically, mode scrambling and temperature control of the connecting fiber between the telescope and the SNSPD enhance the stability and robustness of the system. Horizontal scanning of the CO₂ concentration and the wind field is carried out in a 6 km range over a scanning span of 60° with a radial resolution of 150 m and 15 s. The results show that the hybrid lidar system captures the spatial distribution of CO₂ concentration and the wind field simultaneously. The horizontal net CO₂ flux in a radius of 6 km is estimated by integrating the CO₂ concentration and the wind transport vector, indicating different characteristics of horizontal net CO₂ fluxes in an industrial area, a university campus, and a park. During most of the experiment, CO₂ flux remained positive in the industrial area, but balances fell to nearly zero on the campus and in the park. The horizontal net fluxes averaged over 24 h in the three areas are 3.5 × 10⁵ ppm·m²·s⁻¹, 0.7 × 10⁵ ppm·m²·s⁻¹, and 0.1 × 10⁵ ppm·m²·s⁻¹. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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11 pages, 16893 KiB

Open AccessTechnical Note

Real-Time Synchronous 3-D Detection of Air Pollution and Wind Using a Solo Coherent Doppler Wind Lidar

by Jinlong Yuan, Yunbin Wu, Zhifeng Shu, Lian Su, Dawei Tang, Yuanjian Yang, Jingjing Dong, Saifen Yu, Zhen Zhang and Haiyun Xia

Remote Sens. 2022, 14(12), 2809; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122809 - 11 Jun 2022

Cited by 11 | Viewed by 1872

Abstract

The monitoring and tracking of urban air pollution is a challenging environmental issue. The approach of synchronous 3-D detection of wind and pollution using a solo coherent Doppler wind lidar (CDWL) is developed and demonstrated. The 3-D distribution of pollutant is depicted by the backscatter coefficient based on signal intensity of CDWL. Then, a high-resolution wind field is derived to track the local air pollution source with its diffusion and to analyze transboundary air pollution episodes. The approach is experimentally implemented in a chemical industry park. Smoke plumes caused by point source pollutions are captured well using plan position indicator (PPI) scanning with low elevation. A typical source of pollution is located, combining the trajectory of the smoke plume and the horizontal wind vector. In addition, transboundary air pollution caused by the transport of dust storms is detected in a vertical profile scanning pattern, which is consistent with the results of national monitoring stations and backward trajectory models. Our present work provides a significant 3-D detection approach to air pollution monitoring with its sources, paths, and heights by using a solo-CDWL system. Full article

(This article belongs to the Special Issue Stereoscopic Remote Sensing of Air Pollutants and Applications)

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