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Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment

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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 (30 September 2024) | Viewed by 9621

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Special Issue Editors

Dr. Ying Li

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Guest Editor

Associate Professor, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Interests: air pollution; ocean remote sensing; air–sea interactions; satellite remote sensing

Prof. Dr. Ismail Gultepe

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Guest Editor

Faculty of Engineering and Applied Science, Ontario Technical University, Oshawa, ON L1G 0C5, Canada
Interests: clouds; cold weather systems; cloud microphysics; precipitation; arctic weather; aviation meteorology; aircraft and ground based in-situ and remote sensing observations of the atmosphere, including satellites, radars, lidars, as well as microwave radiometers
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Dr. Pradeep Khatri

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Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai City 980-8578, Japan
Interests: field observation; remote sensing; atmospheric radiation; cloud; aerosol

Special Issue Information

Dear Colleagues,

Air pollution is an increasingly important topic of global concern. It harms ecological protection and the advancement of human health, while also inducing global climate change, etc. Therefore, this Special Issue emphasizes the importance of atmospheric particulate matter and the measurement of its components (PM, such as PM2.5, PM10, dust, dirt, soot, or smoke, black carbon, organic carbon, etc.) to provide decision support to improve air quality, understand regional climate impact and ensure sustainable development.

In recent years, remote sensing technologies have been widely used in atmospheric composition retrieval and air quality monitoring. Given their characteristics of high resolution, full-time, etc., remote sensing can provide more comprehensive, timely, and accurate data sets for use to assess air pollution. Meanwhile, with the development of artificial intelligence (AI), remote sensing data can be analyzed more efficiently and accurately to estimate and assess air pollution. This Special Issue aims to measure and estimate the PM and its components in the atmosphere and assess air pollution using satellite remote sensing, whether alone or together with AI and numerical models.

We encourage authors to submit articles describing original research results from measuring and estimating PM and its components in the atmosphere and assessing the environmental and climatic impacts of air pollution using satellite remote sensing, AI, and numerical models.

Topics include (but are not limited to):

New algorithms for PM and its component remote sensing from different satellites (such as MODIS, MISR, OMI, VIIRS, OMPS, GOES-R, GOES-S, Himawari-8/9 GOCI, GEMS );
New method for the estimation of PM and its component, applying satellite remote sensing, AI, and numerical models.
Innovative approaches for analyzing remote sensing data for air quality and air pollution assessment;
Air quality product development, validation, and inter-comparison;
Research into important aspects of air pollution control based on remote sensing data (remote sensing observation data analysis, source emission inventory estimation, air quality model improvement, source resolution, health exposure, ).

Dr. Ying Li
Prof. Dr. Ismail Gultepe
Dr. Pradeep Khatri
Guest Editors

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

remote sensing
aerosol components
numerical models
machine learning
deep learning

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22 pages, 6359 KiB

Open AccessArticle

Comparative Study on the Vertical Column Concentration Inversion Algorithm of Tropospheric Trace Gas Based on the MAX-DOAS Measurement Spectrum

by Haoyue Wang, Yuehua Lu, Ke Yu, Feihong Xiao, Rongzhi Guo, Naicong Yan and Weiguo Wang

Remote Sens. 2024, 16(18), 3359; https://doi.org/10.3390/rs16183359 - 10 Sep 2024

Viewed by 487

Abstract

The tropospheric vertical column concentration (VCDtrop) of NO₂, SO₂, and HCHO was retrieved, respectively, by employing the geometric method (Geomtry), simplified model method (Model), and look-up table method (Table) with the observation spectra of the multi-axis differential absorption spectroscopy instrument (MAX-DOAS). The correlation and relative differences of the inversion results obtained by these three algorithms, as well as the changes in quantiles, were explored. The comparative analysis reveals that the more concentrated the vertical distribution height of gas components is in the near-surface layer, the better the conformity of the VCDtrop retrieved by different algorithms. However, the increase in relative differences is also related to the diurnal variation of gas components. The influence of aerosols on the inversion of the VCDtrop is greater than the change in the vertical distribution height of the gas component itself. The near-surface concentration and distribution height of gas components are the internal factors that give rise to relative differences in the inversion of the VCDtrop by different algorithms, while aerosols are one of the extremely important external reasons. The VCDtrop inverted by Geomtry without considering the influence of aerosols is generally larger except for NO₂. Model sets up aerosols in accordance with the height and meteorological conditions of the atmospheric environment. Table can invert the aerosol profile in real time. Compared with Model, it shows a significant improvement in the refined setting of aerosols. Moreover, while obtaining the vertical distribution of aerosols, it can invert the diurnal variation of the VCDtrop. The VCDtrop inverted by Table is the smallest, and the relative difference with Model is on average about 10% smaller. The relative difference of the VCDtrop for the same height (aerosol optical thickness) quantile is 7–15% (about 25% lower on average). When comparing the inversion results of Table with the Ozone Monitoring Instrument (OMI) satellite product, the MAX-DOAS inversion results of NO₂, SO₂, and HCHO are all larger than the OMI product. This is related to the different observation methods of the MAX-DOAS and OMI and the configuration between the aerosol layer and the distribution height of gas components. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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18 pages, 6972 KiB

Open AccessArticle

The Accurate Inversion of the Vertical Ozone Profile in High-Concentration Aerosols Based on a New DIAL-A Case Study

by Na Ma, Jie Wang, Chenglei Pei, Sipeng Yang, Tianshu Zhang, Yujun Zhang, Jianing Wan and Yiwei Xu

Remote Sens. 2024, 16(16), 2997; https://doi.org/10.3390/rs16162997 - 15 Aug 2024

Viewed by 487

Abstract

Recently, in China, during the period of transition between spring and summer, the combination of sandstorms and ozone (O₃) pollution has posed a significant challenge to the strategy of coordinated control of fine particulate matters (PM_2.5) and O₃. On the one hand, the dust invasion brings many primary aerosols and causes a large range of transboundary transport. On the other hand, the high concentration of aerosol causes a severe disturbance to the distribution of O₃. Traditionally, high-resolution assessments of the spatial distribution of aerosols and O₃ can be carried out using LiDAR technology. However, the negligence of the influence of aerosols in the process of O₃ retrieval in traditional differential absorption lidar (DIAL) leads to an error in the accuracy of ozone concentration. Especially when dust transit occurs, the errors become bigger. In this study, a self-customized four-wavelength differential-absorption LiDAR system was used to synchronously obtain the accurate vertical distributions of ozone and high-concentration aerosol. The wavelength index of concentrated aerosol was inverted and applied to the differential equation framework for O₃ calculation. This novel approach to retrieving the vertical profile of O₃ was proposed and verified by applying it to a dust pollution event that occurred from April to May 2021 in Anyang City Henan Province, which is located in Northern China. It was found that the extinction coefficient of aerosol reached 2.5 km⁻¹ during the dust period, and O₃ was mainly distributed between 500 m and 1500 m. The O₃ error exceeded over 10% arising from the high-concentration aerosol below 1.5 km during the dust storm event. By employing the inversion algorithm while considering the aerosol effects, the ozone concentration error was improved by over 10% compared with the error recorded without considering the aerosol influence especially in dust events. Through this study, it was found that the algorithm could effectively realize the synchronous and accurate inversion of high-concentration aerosols and O₃ and can provide key technical support for air pollution control in China in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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18 pages, 14889 KiB

Open AccessArticle

Random Forest Model-Based Inversion of Aerosol Vertical Profiles in China Using Orbiting Carbon Observatory-2 Oxygen A-Band Observations

by Xiao-Qing Zhou, Hai-Lei Liu, Min-Zheng Duan, Bing Chen and Sheng-Lan Zhang

Remote Sens. 2024, 16(13), 2497; https://doi.org/10.3390/rs16132497 - 8 Jul 2024

Viewed by 699

Abstract

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O₂ A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O₂ A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km⁻¹. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km⁻¹. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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24 pages, 6525 KiB

Open AccessArticle

Parameterization of Dust Emissions from Heaps and Excavations Based on Measurement Results and Mathematical Modelling

by Karol Szymankiewicz, Michał Posyniak, Piotr Markuszewski and Paweł Durka

Remote Sens. 2024, 16(13), 2447; https://doi.org/10.3390/rs16132447 - 3 Jul 2024

Viewed by 673

Abstract

Assessment of the concentrations of dust pollution resulting from both measurements at reference stations and those determined using mathematical modelling requires accurate identification of the sources of emission. Although the concentration of dust results from several complex transport processes, as well as chemical and microphysical transformations of aerosols, sources of emissions may have a significant impact on the local level of pollution. This pilot study aimed to use measurements of the concentrations of dust (with the specification of the PM10 and PM2.5 fractions) made over a heap/excavation and its surroundings using an airship equipped with equipment for testing the optical and microphysical properties of atmospheric aerosols, and a ground station located at the facility. On the basis of the measurements, the function of the source of emissions of dust was estimated. According to our study, the yearly emission of dust varies between 42,470 and 886,289 kg for PM10, and between 42,470 and 803,893 for PM2.5 (minimum and maximum values). A model of local air quality was also used, which allowed us to verify the parameterization of emissions of dust pollutants for the PM10 and PM2.5 fractions from heaps and excavations based on the modelling results. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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29 pages, 17646 KiB

Open AccessArticle

Dust Events over the Urmia Lake Basin, NW Iran, in 2009–2022 and Their Potential Sources

by Abbas Ranjbar Saadat Abadi, Karim Abdukhakimovich Shukurov, Nasim Hossein Hamzeh, Dimitris G. Kaskaoutis, Christian Opp, Lyudmila Mihailovna Shukurova and Zahra Ghasabi

Remote Sens. 2024, 16(13), 2384; https://doi.org/10.3390/rs16132384 - 28 Jun 2024

Viewed by 682

Abstract

Nowadays, dried lake beds constitute the largest source of saline dust storms, with serious environmental and health issues in the surrounding areas. In this study, we examined the spatial–temporal distribution of monthly and annual dust events of varying intensity (dust in suspension, blowing dust, dust storms) in the vicinity of the desiccated Urmia Lake in northwestern (NW) Iran, based on horizontal visibility data during 2009–2022. Dust in suspension, blowing dust and dust storm events exhibited different monthly patterns, with higher frequencies between March and October, especially in the southern and eastern parts of the Urmia Basin. Furthermore, the intra-annual variations in aerosol optical depth at 500 nm (AOD₅₅₀) and Ångström exponent at 412/470 nm (AE) were investigated using Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Urmia Lake Basin (36–39°N, 44–47°E). Monthly distributions of potential coarse aerosol (AE < 1) sources affecting the lower troposphere over the Urmia Basin were reconstructed, synergizing Terra/Aqua MODIS AOD₅₅₀ for AE < 1 values and HYSPLIT_4 backward trajectories. The reconstructed monthly patterns of the potential sources were compared with the monthly spatial distribution of Terra MODIS AOD₅₅₀ in the Middle East and Central Asia (20–70°E, 20–50°N). The results showed that deserts in the Middle East and the Aral–Caspian arid region (ACAR) mostly contribute to dust aerosol load over the Urmia Lake region, exhibiting higher frequency in spring and early summer. Local dust sources from dried lake beds further contribute to the dust AOD, especially in the western part of the Urmia Basin during March and April. The modeling (DREAM8-NMME-MACC) results revealed high concentrations of near-surface dust concentrations, which may have health effects on the local population, while distant sources from the Middle East are the main controlling factors to aerosol loading over the Urmia Basin. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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20 pages, 4738 KiB

Open AccessArticle

Trans-Boundary Dust Transport of Dust Storms in Northern China: A Study Utilizing Ground-Based Lidar Network and CALIPSO Satellite

by Zhisheng Zhang, Zhiqiang Kuang, Caixia Yu, Decheng Wu, Qibing Shi, Shuai Zhang, Zhenzhu Wang and Dong Liu

Remote Sens. 2024, 16(7), 1196; https://doi.org/10.3390/rs16071196 - 29 Mar 2024

Viewed by 1075

Abstract

During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station, the HYSPLIT model, ground-based polarized Lidar networks, AGRI payload data from Fengyun satellites and CALIPSO satellite Lidar data to jointly explore and scrutinize the three-dimensional spatial and temporal characteristics of aerosol transport. Firstly, by integrating meteorological data for PM2.5 and PM10, the air quality is assessed across six stations within the Lidar network during the dust storm. Secondly, employing a backward trajectory tracking model, the study elucidates sources of dust at the Lidar network sites. Thirdly, deploying a newly devised portable infrared 1064 nm Lidar and a pulsed 532 nm Lidar, a ground-based Lidar observation network is established for vertical probing of transboundary dust transport within the observed region. Finally, by incorporating cloud imagery from Fengyun satellites and CALIPSO satellite Lidar data, this study revealed the classification of dust and the height distribution of dust layers at pertinent sites within the Lidar observation network. The findings affirm that the eastward movement and southward compression of the intensifying Mongolian cyclone led to severe dust storm weather in western and southern Mongolia, as well as Inner Mongolia, further transporting dust into northern, northwestern, and northeastern parts of China. This dust event wielded a substantial impact on a broad expanse in northern China, manifesting in localized dust storms in Inner Mongolia, Beijing, Gansu, and surrounding areas. In essence, the dust emanated from the deserts in Mongolia and northwest China, encompassing both deserts and the Gobi region. The amalgamation of ground-based and spaceborne Lidar observations conclusively establishes that the distribution height of dust in the source region ranged from 3 to 5 km. Influenced by high-pressure systems, the protracted transport of dust over extensive distances prompted a gradual reduction in its distribution height owing to sedimentation. The comprehensive analysis of pertinent research data and information collectively affirms the precision and efficacy of the three-dimensional aerosol monitoring conducted by the ground-based Lidar network within the region. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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23 pages, 9387 KiB

Open AccessArticle

Cloud–Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data

by Xingzhao Zhou, Bin Chen, Qia Ye, Lin Zhao, Zhihao Song, Yixuan Wang, Jiashun Hu and Ruming Chen

Remote Sens. 2024, 16(5), 904; https://doi.org/10.3390/rs16050904 - 4 Mar 2024

Cited by 1 | Viewed by 1387

Abstract

Precise cloud and aerosol identification hold paramount importance for a thorough comprehension of atmospheric processes, enhancement of meteorological forecasts, and mitigation of climate change. This study devised an automatic denoising cloud–aerosol classification deep learning algorithm, successfully achieving cloud–aerosol identification in atmospheric vertical profiles utilizing CALIPSO L1 data. The algorithm primarily consists of two components: denoising and classification. The denoising task integrates an automatic denoising module that comprehensively assesses various methods, such as Gaussian filtering and bilateral filtering, automatically selecting the optimal denoising approach. The results indicated that bilateral filtering is more suitable for CALIPSO L1 data, yielding SNR, RMSE, and SSIM values of 4.229, 0.031, and 0.995, respectively. The classification task involves constructing the U-Net model, incorporating self-attention mechanisms, residual connections, and pyramid-pooling modules to enhance the model’s expressiveness and applicability. In comparison with various machine learning models, the U-Net model exhibited the best performance, with an accuracy of 0.95. Moreover, it demonstrated outstanding generalization capabilities, evaluated using the harmonic mean F1 value, which accounts for both precision and recall. It achieved F1 values of 0.90 and 0.97 for cloud and aerosol samples from the lidar profiles during the spring of 2019. The study endeavored to predict low-quality data in CALIPSO VFM using the U-Net model, revealing significant differences with a consistency of 0.23 for clouds and 0.28 for aerosols. Utilizing U-Net confidence and a 532 nm attenuated backscatter coefficient to validate medium- and low-quality predictions in two cases from 8 February 2019, the U-Net model was found to align more closely with the CALIPSO observational data and exhibited high confidence. Statistical comparisons of the predicted geographical distribution revealed specific patterns and regional characteristics in the distribution of clouds and aerosols, showcasing the U-Net model’s proficiency in identifying aerosols within cloud layers. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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18 pages, 2907 KiB

Open AccessArticle

Emission-Based Machine Learning Approach for Large-Scale Estimates of Black Carbon in China

by Ying Li, Sijin Liu, Reza Bashiri Khuzestani, Kai Huang and Fangwen Bao

Remote Sens. 2024, 16(5), 837; https://doi.org/10.3390/rs16050837 - 28 Feb 2024

Cited by 1 | Viewed by 1165

Abstract

Tremendous efforts have been made to construct large-scale estimates of aerosol components. However, Black Carbon (BC) estimates over large spatiotemporal scales are still limited. We proposed a novel approach utilizing machine-learning techniques to estimate BC on a large scale. We leveraged a comprehensive gridded BC emission database and auxiliary variables as inputs to train various machine learning (ML) models, specifically a Random Forest (RF) algorithm, to estimate high spatiotemporal BC concentration over China. Different ML algorithms have been applied to a large number of potential datasets and detailed variable importance and sensitivity analysis have also been carried out to explore the physical relevance of variables on the BC estimation model. RF algorithm showed the best performance compared with other ML models. Good predictive performance was observed for the training cases (R² = 0.78, RMSE = 1.37 μgm⁻³) and test case databases (R² = 0.77, RMSE = 1.35 μgm⁻³) on a daily time scale, illustrating a significant improvement compared to previous studies with remote sensing and chemical transport models. The seasonal variation of BC distributions was also evaluated, with the best performance observed in spring and summer (R² ≈ 0.7–0.76, RMSE ≈ 0.98–1.26 μgm⁻³), followed by autumn and winter (R² ≈ 0.7–0.72, RMSE ≈ 1.37–1.63 μgm⁻³). Variable importance and sensitivity analysis illustrated that the BC emission inventories and meteorology showed the highest importance in estimating BC concentration (R² = 0.73, RMSE = 1.88 μgm⁻³). At the same time, albedo data and some land cover type variables were also helpful in improving the model performance. We demonstrated that the emission-based ML model with an appropriate auxiliary database (e.g., satellite and reanalysis datasets) could effectively estimate the spatiotemporal BC concentrations at a large scale. In addition, the promising results obtained through this approach highlight its potential to be utilized for the assessment of other primary pollutants in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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15 pages, 6006 KiB

Open AccessTechnical Note

Satellite-Based Estimation of Near-Surface NO₂ Concentration in Cloudy and Rainy Areas

by Fuliang Deng, Yijian Chen, Wenfeng Liu, Lanhui Li, Xiaojuan Chen, Pravash Tiwari and Kai Qin

Remote Sens. 2024, 16(10), 1785; https://doi.org/10.3390/rs16101785 - 17 May 2024

Viewed by 835

Abstract

Satellite-based remote sensing enables the quantification of tropospheric NO₂ concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO₂ observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO₂ column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO₂ concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO₂ measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R² = 0.87) and precision (RMSE = 5.36 μg/m³). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO₂ mapping in cloudy and rainy areas. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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14 pages, 11225 KiB

Open AccessTechnical Note

3-D Changes of Tropospheric O₃ in Central and Eastern China Induced by Tropical Cyclones over the Northwest Pacific: Recent-Year Characterization with Multi-Source Observations

by Yongcheng Jiang, Tianliang Zhao, Kai Meng, Xugeng Cheng and Qiaoyi Lv

Remote Sens. 2024, 16(7), 1178; https://doi.org/10.3390/rs16071178 - 28 Mar 2024

Cited by 1 | Viewed by 853

Abstract

In this study, the multi-year data of meteorology and O₃ from remote sensing and ground observations are applied to characterize the 3-D changes of O₃ in the troposphere over central and eastern China (CEC) induced by the tropical cyclones (TCs) in the tropical and subtropical ocean regions over Northwest Pacific. The CEC-regional average of near-surface O₃ levels is significantly elevated with 6.0 ppb in the large coverage by the TCs in the subtropical ocean, while the TCs in the tropical ocean alter near-surface O₃ weakly, indicating the latitudinal-located TCs in the subtropical offshore ocean could largely influence the O₃ variations over CEC. The sub-seasonal change with the positive and negative anomalies of near-surface O₃ is induced by the tropical TCs from June to July and from August to October. The peripheral circulation of TCs in the subtropical offshore ocean persistently enhances the O₃ concentrations over CEC during the season of East Asian summer monsoons. The positive O₃ anomalies maintain from the entire troposphere to the lower stratosphere over CEC in the peripheries of subtropical TCs, while the tropical TCs cause the positive O₃ anomalies merely in the lower troposphere. The O₃ transport and accumulation, photochemical production and stratospheric intrusion are climatologically confirmed as the major meteorological mechanisms of TCs affecting the O₃ variations. This study reveals that the downward transport of stratospheric O₃ of TCs in the subtropical ocean exerts a large impact on the atmospheric environment over CEC, while the regional O₃ transport and photochemical productions dominate the lower troposphere over CEC with less impact of stratospheric intrusion from the TCs in the tropical ocean region. These results present the climatology of tropospheric O₃ anomalies in China induced by the TCs over the Northwest Pacific with enhancing our comprehension of the meteorological impact on O₃ variations over the East Asian monsoon region. Full article

(This article belongs to the Special Issue Remote Sensing of Particulate Matter, Its Components and Air Pollution Assessment)

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