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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 13981

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Special Issue Editors

Dr. Xuewei Hou

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Guest Editor

School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Interests: the source, sink and transport of tropospheric ozone; the impact of meteorological parameters on air quality
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bin Zhu

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Guest Editor

School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Interests: atmospheric chemistry and atmospheric environment; air quality; climate change

Dr. Kanike Raghavendra Kumar

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Guest Editor

Department of Physics, Koneru Lakshmaiah Education Foundation (KLEF), Vaddeswaram, Guntur 522502, Andhra Pradesh, India
Interests: remote sensing of aerosols; air pollution; aerosol radiative forcing; climate change

Dr. Alok Pandey

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Guest Editor

Lancaster Environment Centre, Lancaster University, Lancaster, UK
Interests: air pollution; urban climate; atmospheric science; atmospheric modelling; remote sensing; GIS

Dr. Kainan Zhang

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Guest Editor

School of Earth Sciences and Resources, Chang’an University, Xi’an 710054, China
Interests: air quality; remote sensing of aerosols; urban air pollution monitoring; particulate matter modelling

Special Issue Information

Dear Colleagues,

Human activities have had significant impacts on the global environment. Rapid economic growth, industrialization, urbanization, and extensive transportation networks have resulted in a substantial deterioration of air quality all over the world. Clean air is an essential requirement for the healthy existence of humans. In view of the health implications of air pollution and the regulations on air quality enacted by various countries and international organizations, it has become important to monitor ambient air quality in order to devise ameliorative strategies to tackle the problem of air pollution. Conventionally, monitoring the ambient air quality at different locations has depended on the information from ground-based observations. However, a major constraint with ground-based observations is that they are location-specific and do not give much information about the spatial distribution of the pollutant being monitored. The emergence of satellite-based methods of monitoring air pollutant levels in the atmosphere during recent decades has been of definite advantage in capturing spatio-temporal air quality trends. The use of satellite remote sensing to map air pollutants along with climatology will improve our understanding of the emission sources and air pollution–climate interaction.

This Special Issue will provide a greater understanding of the distributions and trends in air pollutants, helping us to use models to better understand air pollution problems from scales ranging from urban to regional and global. Further, it will help us to provide a guideline for better air-pollution control policies to improve the health of human beings, as well as that of the Earth’s environment.

This research topic calls for papers that can improve our understanding of the characteristics of air pollution using satellite remote sensing and modelling. Potential research topics include but are not limited to the following:

Impacts of meteorological parameters on air pollution;
Trends and mechanisms of tropospheric ozone over different atmospheric layers;
Regional air quality and climate studies using satellite data;
Vertical distribution of aerosol and black carbon;
Effect of extreme weather on air quality;
Characteristics of dust transport;
Urbanization and air pollution climatology;
Urban photochemical pollution;
Air pollution impacts on human and environmental health.

Dr. Xuewei Hou
Prof. Dr. Bin Zhu
Dr. Kanike Raghavendra Kumar
Dr. Alok Pandey
Dr. Kainan Zhang
Guest Editors

Manuscript Submission Information

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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.

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Keywords

air pollution
satellite remote sensing
atmospheric chemistry
aerosols
urban climate
extreme weather
air quality and health
climate change
air pollution and climate policies

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Published Papers (8 papers)

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25 pages, 40565 KiB

Open AccessArticle

Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches

by Prabuddha M. H. Dewage, Lakitha O. H. Wijeratne, Xiaohe Yu, Mazhar Iqbal, Gokul Balagopal, John Waczak, Ashen Fernando, Matthew D. Lary, Shisir Ruwali and David J. Lary

Remote Sens. 2024, 16(13), 2454; https://doi.org/10.3390/rs16132454 - 3 Jul 2024

Cited by 2 | Viewed by 1138

Abstract

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM

_{2.5}

levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM

_{2.5}

concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM

_{2.5}

surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM

_{2.5}

analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM

_{2.5}

to 9

μ

g/m

^{3}

, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM

_{2.5}

distribution across the entire United States for our study period, we found that the entire nation encountered PM

_{2.5}

levels that exceeded 9

μ

g/m

^{3}

for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9

μ

g/m

^{3}

for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM

_{2.5}

concentrations below 9

μ

g/m

^{3}

. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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23 pages, 17106 KiB

Open AccessArticle

Vertical Features of Volatile Organic Compounds and Their Potential Photochemical Reactivities in Boundary Layer Revealed by In-Situ Observations and Satellite Retrieval

by Siqi Yang, Bin Zhu, Shuangshuang Shi, Zhuyi Jiang, Xuewei Hou, Junlin An and Li Xia

Remote Sens. 2024, 16(8), 1403; https://doi.org/10.3390/rs16081403 - 16 Apr 2024

Viewed by 926

Abstract

Based on in-situ vertical observations of volatile organic compounds (VOCs) in the lower troposphere (0–1.0 km) in Nanjing, China, during the summer and autumn, we analyzed the VOCs vertical profiles, diurnal variation, and their impact factors in meteorology and photochemistry. The results showed that almost all the concentrations of VOC species decreased with height, similar to the profiles of primary air pollutants, as expected. However, we found the ratios of inactive species (e.g., acetylene) and secondary VOCs (e.g., ketones and aldehydes) in total VOCs (TVOCs) increased with height. Combined with satellite-retrieved data, we found the average HCHO tropospheric column concentrations were 2.0 times higher in the summer than in the autumn. While the average of tropospheric NO₂ column concentrations was 3.0 times lower in the summer than in the autumn, the seasonal differences in the ratio of oxygenated VOCs (OVOCs) to NO₂ (e.g., HCHO/NO₂) shown in TROPOMI satellite-retrieved data were consistent with in-situ observations (e.g., acetone/NO₂). On average, during autumn daytime, the mixing layer (ML), stable boundary layer (SBL), and residual layer (RL) had OH loss rates (LOH) of 6.9, 6.3, and 5.5 s⁻¹, respectively. The LOH of alkenes was the largest in the ML, while the LOH of aromatics was the largest in the SBL and RL. At autumn night, the NO₃ loss rates (LNO₃) in the SBL and RL were 2.0 × 10⁻² and 1.6 × 10⁻² s⁻¹, respectively, and the LNO₃ of aromatics was the largest in the SBL and RL. In the daytime of summer, the LOH of VOCs was ~40% lower than that in autumn in all layers, while there was no significant difference in LNO₃ at night between the two seasons. This study provides data support and a theoretical basis for VOC composite pollution control in the Nanjing region. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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21 pages, 44272 KiB

Open AccessArticle

Three-Dimensional Distribution and Transport Features of Dust and Polluted Dust over China and Surrounding Areas from CALIPSO

by Xiaofeng Xu, Yudi Yang, Zixu Xiong, Jianming Gong and Tianyang Luo

Remote Sens. 2023, 15(24), 5734; https://doi.org/10.3390/rs15245734 - 15 Dec 2023

Cited by 1 | Viewed by 1085

Abstract

Dust plays a very important role in the Earth’s climate system by its direct and indirect effects. Deserts in northwestern China contribute a large amount of dust particles, both inland and outside, while the vertical distribution and transport mechanism of dust still have many uncertainties. Using Level 3 cloud-free monthly aerosol products of the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) system from 2007 to 2020, we analyzed the spatial and temporal variations and transport features for dust and polluted dust aerosols over China and the surrounding areas. The results show that the Taklimakan Desert (TD) and the Thar Desert (TRD) always act as the high-value centers of dust optical depth (DOD), while the centers of polluted dust optical depth (PDOD) are located in eastern China, the Sichuan Basin and the Indian subcontinent. The DOD shows an increasing trend in most areas, while the PDOD presents a significant decrease and increase in eastern China and central India, respectively. The largest DOD appears in spring over the TD and the Gobi Desert (GD), while the largest DOD in summer is over the TRD. Although most dusts in the TD and TRD are concentrated below 4 km, they may be higher over the TD. Most of the polluted dusts are confined to under 2 km. The dust input to the Tibetan Plateau (TP) could come from both the TD and TRD and occurs mostly in spring and summer, respectively. The polluted dusts of South Asia and the Indian subcontinent are mostly contained in the boundary layer in winter, but they could extend much higher in spring and summer, which favors their transport into southwestern China. The dust layer shows apparent seasonality. Its top reaches a higher level in spring and summer, while the base stays at a similar height in all seasons. The dust layer appears to be the thickest in spring over most areas, while the thickest layer in summer is over the TD and TRD. The polluted dust layer is thickest in the Indian subcontinent in spring. The overlapping of dust and polluted dust layers present different patterns in different regions, which suggests diverse mixture processes of dusts and pollutants. Finally, we compared and found different influences of meteorological factors, such as wind field, boundary layer height and precipitation, on the variations in DOD and PDOD over dust sources and other areas. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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17 pages, 12674 KiB

Open AccessArticle

Quasi-Biweekly Oscillation of PM_2.5 in Winter over North China and Its Leading Circulation Patterns

by Xinsheng Zhu and Chenyu Yao

Remote Sens. 2023, 15(16), 4069; https://doi.org/10.3390/rs15164069 - 17 Aug 2023

Viewed by 1095

Abstract

Persistent pollution often occurs in North China in winter. The study of the sub-seasonal evolution characteristics of fine particles (PM_2.5) can provide a theoretical basis for the prediction and prevention of persistent pollution. Based on the high-resolution gridded data of PM_2.5 and NCEP/NCAR reanalysis, the sub-seasonal variation in PM_2.5 in North China in winter and its dominant circulation patterns from 1960/61 to 2019/20 were analyzed. The results show that, in winter, PM_2.5 in North China shows a dominant period of 10–20 days, and persistent heavy pollution occurs at the active phase of oscillation. Based on the PM_2.5 quasi-biweekly oscillation (QBWO) events, the 850 hPa wave train can be classified into four categories. It was found that, during the active phase of PM_2.5 QBWO, the wind speed is weak and humidity is high in the low-troposphere for all of the four event types, while the quasi-biweekly 850 hPa wave train and the track of geopotential height anomaly are significantly different. Based on the characteristics of circulation evolution, these four types of events can be named as eastward, split southward, southeastward, and merged event. The energy conversion between the basic flow and the quasi-biweekly disturbance, and the mean flow difference are responsible for the circulation diversity for different PM_2.5 QBWO events. The above research results can provide a theoretical basis for pollutant prediction. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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21 pages, 7463 KiB

Open AccessArticle

Characteristics of Air Pollutant Distribution and Sources in the East China Sea and the Yellow Sea in Spring Based on Multiple Observation Methods

by Yucheng Wang, Guojie Xu, Liqi Chen and Kui Chen

Remote Sens. 2023, 15(13), 3262; https://doi.org/10.3390/rs15133262 - 25 Jun 2023

Cited by 4 | Viewed by 2196

Abstract

The composition of marine aerosol is quite complex, and its sources are diverse. Across the East China Sea (ECS) and the Yellow Sea (YS), multi-dimensional analysis of marine aerosols was conducted. The characteristics of carbonaceous aerosols and gaseous pollutants were explored through in situ ship-based observation, MERRA-2 reanalysis datasets and TROPOMI data from Sentinel-5P satellite. Black carbon (BC)’s average concentration is 1.35 ± 0.78 μg/m³, with high-value BC observed during the cruise. Through HYSPLIT trajectory analysis, sources of BC were from the northern Eurasian continent, the Shandong Peninsula, the ECS and Northwest Pacific Ocean (NWPO). The transport of marine sources like ship emissions cannot be ignored. According to the absorption Angstrom exponent (AAE), BC originates from biomass burning (BB) in the shortwave band (~370 nm) and from fossil fuel combustion in the longwave band (~660 nm). Organic carbon (OC), sulfate (SO₄²⁻) and BC report higher Angstrom exponent (AE) while dust and sea salt reveal lower AE, which can be utilized to classify the aerosols as being fine- or coarse-mode, respectively. OC has the highest AE (ECS: 1.98, YS: 2.01), indicating that anthropogenic activities could be a significant source. The process of biomass burning aerosol (BBA) mixed with sea salt could contribute to the decline in BBA’s AE. Ship emissions may affect the distribution of tropospheric nitrogen dioxide (NO₂) in the ECS, especially during the COVID-19 pandemic. Tropospheric NO₂ over the YS has the highest value (up to 12 × 10¹⁵ molec/cm²). Stratospheric NO₂ has a ladder-like distribution from north to south, and the variation gradient was lower than that in the troposphere. Carbon monoxide (CO) accumulates in the south and east of the ECS and the east of the YS, while the variation over the eastern YS is relatively frequent. Seas near the Korean Peninsula have extremely high CO concentration (up to 1.35 × 10¹⁷ molec/cm²). Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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21 pages, 32564 KiB

Open AccessArticle

Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling

by Yongwei Wang, Danmeng Zhao and Qian Ma

Remote Sens. 2023, 15(12), 3111; https://doi.org/10.3390/rs15123111 - 14 Jun 2023

Viewed by 2497

Abstract

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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18 pages, 6437 KiB

Open AccessArticle

Characteristics of Optical Properties and Heating Rates of Dust Aerosol over Taklimakan Desert and Tibetan Plateau in China Based on CALIPSO and SBDART

by Xiaofeng Xu, Shixian Pan, Tianyang Luo, Yudi Yang and Zixu Xiong

Remote Sens. 2023, 15(3), 607; https://doi.org/10.3390/rs15030607 - 19 Jan 2023

Cited by 1 | Viewed by 2048

Abstract

The spatial and temporal distributions of dust aerosol and its radiative heating effect over Taklimakan Desert (TD) and Tibetan Plateau (TP) were analyzed using the CALIPSO aerosol products and the SBDART model during 2007–2020. The annual dust aerosol optical depths (DAOD at 532 nm) ranged from 0.266 to 0.318 over TD and 0.086 to 0.108 over TP, with means of 0.286 ± 0.015 and 0.097 ± 0.006, respectively. The regional mean DAODs of TD (TP) from spring to winter were 0.375 ± 0.020 (0.107 ± 0.010), 0.334 ± 0.028 (0.110 ± 0.010), 0.235 ± 0.026 (0.071 ± 0.008), and 0.212 ± 0.045 (0.083 ± 0.011), respectively. The maximal (minimal) seasonal DAOD of TD appeared in spring (winter), while that of TP appeared in summer (autumn). Although neither the annual nor the seasonal DAODs showed a statistically significant trend over both TD and TP, their yearly fluctuations were apparent, showing coefficients of variation of 0.053 and 0.065 over TD and TP, respectively. The profile of dust extinction coefficient (σ_D) showed the maximum in spring and summer over TD and TP, respectively. It showed a weak increasing trend of σ_D over both TD and TP in spring, but a decreasing trend in autumn. The dust of TD is concentrated within 1–4 km, where the annual averaged shortwave (SW) dust heating rates (DHRs) were larger than 2 K·day⁻¹ from March to September. Over TP, the dust heating layer with SW DHR > 2 K·day⁻¹ ranged from 3 to 4 km during March to June. The SW DHR was much larger in spring and summer than in the other two seasons over both regions, with the maximum in spring. A relatively strong dust heating layer with top >5 km appeared along the north slope of the TP, indicating an important energy transport channel from TD to TP, especially in spring and summer. It showed an increasing trend of the SW DHR over both TD and TP in spring and winter, but a decreasing trend in summer and autumn. Over TD, the most powerful heating appeared within 2–4 km, but the strength and the area of high-value DHR reduced from spring to winter. The highest SW DHR of TP appeared over the Qaidam Basin, acting as an important transmission channel of dust and its heating. For the columnar mean of lower than 10 km, the annual mean DHRs of TD and TP were 0.93 and 0.48 K⋅day⁻¹, respectively. Although the DAOD and DHR of TP were both lower, its shortwave dust heating efficiency (DHE) was 1.7 times that of TD, which suggested that the same amount of dust imported to TP could generate a stronger heating effect than it did at the source. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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13 pages, 4561 KiB

Open AccessTechnical Note

The Impact of Meteorological Conditions and Emissions on Tropospheric Column Ozone Trends in Recent Years

by Xuewei Hou, Yifan Zhang, Xin Lv and James Lee

Remote Sens. 2023, 15(22), 5293; https://doi.org/10.3390/rs15225293 - 9 Nov 2023

Cited by 3 | Viewed by 1435

Abstract

Based on OMI/MLS data (2005–2020) and Community Earth System Model (CESM2) simulated results (2001–2020), annual variation trends of tropospheric column ozone (TCO) in the recent two decades are explored, and the separate impacts of meteorological conditions and emissions on TCO are quantified. The stratospheric ozone tracer (O₃S) is used to quantify the contribution of stratospheric ozone to the trend of TCO. The evaluation shows that the simulated results capture the spatial-temporal distributions and the trends of tropospheric column ozone well. Over the East Asia and Southeast Asia regions, TCO is increasing, with a rate of ~0.2 DU/yr, which is primarily attributed to the emission changes in ozone precursors, nitrogen oxide (NOx) and volatile organic chemicals (VOCs). But the changes in meteorological conditions weaken the increase in TCO, even leading to a decrease in East Asia in spring and summer. TCO is decreasing in the middle and high latitudes of the southern hemisphere, which is mainly attributed to the changes in meteorological conditions. The increasing rates are the highest in autumn, especially over North America, East Asia, Europe and South of East Asia, with rate values of 0.20, 0.31, 0.17, and 0.32 DU/yr, respectively. Over the equatorial region, the contribution of stratospheric ozone to TCO is below 10 DU, and shows a weak positive trend of ~0.2 DU/yr. In the latitude of ~30°N/S, the stratospheric contribution is high, ~25 DU, and is affected by the sinking branch of the Brewer–Dobson circulation and stratosphere–troposphere exchange in the vicinity of tropical jet stream. The stratospheric contribution to TCO in the north of 30°N is significantly decreasing (~0.6 DU/yr) under the influence of meteorological conditions. Changes in emissions weaken the decrease in stratospheric contributions in the north of 30°N and enhance the increase in 30°S–30°N significantly. The trends of stratospheric contributions on TCO partly explain the trends of TCO which are mostly affected by the change in emissions. To control the increasing TCO, actions to reduce emissions are urgently needed. Full article

(This article belongs to the Special Issue Air Quality Mapping via Satellite Remote Sensing)

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