Article
Geochemistry & Geophysics
Mengdan Cao, Ming Zhang, Xin Su, Lunche Wang
Summary: This study proposed a new two-stage machine learning algorithm for retrieving aerosol properties over land using satellite observations. The algorithm showed good performance and was able to accurately estimate aerosol optical depth, Angstrom exponent, fine mode fraction, and fine mode aerosol optical depth. The results were validated using independent site network validation, and compared favorably to the validation metrics of MODIS operational products. The spatial patterns of the retrieved aerosol properties were also found to be in agreement with those of MODIS and POLDER products.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Wonei Choi, Hyeongwoo Kang, Dongho Shin, Hanlim Lee
Summary: In the study of aerosol types in Asian capital cities, researchers found that pollution-sourced aerosols, especially non-absorbing aerosols, predominated, and Asian cities are typically influenced by natural dust aerosols, particularly in East Asia and South Asia. No specific seasonal effects on aerosol type were detected in Southeast Asia.
Article
Environmental Sciences
Igor B. Konovalov, Nikolai A. Golovushkin, Matthias Beekmann, Solene Turquety
Summary: This study examined the feasibility of statistically characterizing the evolution of BrC absorption and related parameters in smoke plumes from intense wildfires in Siberia using a combination of data from three satellite instruments. The results showed a significant decrease in BrC absorption over time, but it remained considerable. This study provides valuable insights into the atmospheric evolution of BrC absorption and the partitioning of BrC and BC contributions to the total light absorption by BB aerosol.
Article
Environmental Sciences
Wonei Choi, Hanlim Lee, Daewon Kim, Serin Kim
Summary: The study improved the spatial coverage of satellite aerosol classification using a random forest model trained with AERONET aerosol-type dataset as the target variables. By excluding satellite input variables with many missing data or low accuracy, good performance in aerosol-type classification was achieved. The RF-based model allowed for improved spatial coverage in satellite aerosol classification, with performance similar to previously developed models.
Article
Meteorology & Atmospheric Sciences
Xin Nie, Qianjun Mao
Summary: This paper successfully implemented the parameterization of aerosol particle shape based on satellite and ground observation data and analyzed the polarization characteristics and distribution of aerosol depolarization ratio in different regions. The results show that the aerosol depolarization ratio data is mainly distributed below 6 km, with lower values in urban regions and higher values in desert regions. The aspect ratio and roundness of aerosols obtained by inversion also vary with region, with larger values in desert regions. The parameterized data of aerosol nonsphericity can provide an effective shape input for aerosol optical modeling, as confirmed by the calculation results of solar radiance.
ATMOSPHERIC RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Tao Huang, Yannian Zhu, Daniel Rosenfeld, Yuanjian Yang, David H. Y. Lam, W. H. Leung, Harry F. Lee, Jack C. H. Cheng, Steve H. L. Yim
Summary: The regime dependence of aerosol-cloud interaction has been explored through idealized cloud-resolving models and observations. The activation of cloud condensation nuclei (CCN) is limited by particle numbers in clean conditions, while updraft velocities play a crucial role in polluted regimes. Warm rain suppression is significantly enhanced over inland areas. These findings are supported by satellite retrievals and LiDAR observations.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Environmental Sciences
Nivedita Sanwlani, Reshmi Das
Summary: This study derived the mineral composition of airborne dust aerosols using satellite optical measurements and compared it with chemically analyzed elemental concentrations. The results showed a high correlation between the derived mineral concentrations and the elemental concentrations, and the derived mineral concentrations were consistent with other monitoring data.
Article
Environmental Sciences
S. Sabetghadam, O. Alizadeh, M. Khoshsima, A. Pierleoni
Summary: Analyses of aerosol optical properties over the Middle East from 2001 to 2019 reveal significant seasonal and regional variability. The study shows that AOD values are highest over the Arabian Peninsula and southeastern Iran to western Pakistan, while lowest values are found over countries in the northern part of the region. The study also identifies dominance of mixed type aerosols in all seasons, with desert dust playing a significant role in spring and summer.
ATMOSPHERIC ENVIRONMENT
(2021)
Article
Geosciences, Multidisciplinary
Henri Taskinen, Arttu Vaisanen, Lauri Hatakka, Timo H. Virtanen, Timo Lahivaara, Antti Arola, Ville Kolehmainen, Antti Lipponen
Summary: A post-process correction and downscaling approach for satellite remote sensing of aerosols has been developed and validated. The approach is suitable for deriving downscaled, high-resolution aerosol optical depth (AOD) estimates and significantly improves the accuracy of the retrievals, as demonstrated in the evaluation over the Washington D.C.-Baltimore area.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Rheumatology
Ezgi Deniz Batu, Seher Sener, Elif Arslanoglu Aydin, Emil Aliyev, Ilknur Bagrul, Seyma Turkmen, Ozlem Akgun, Zeynep Balik, Ayse Tanatar, Yagmur Bayindir, Zehra Kizildag, Ruya Torun, Aybuke Gunalp, Taner Coskuner, Rana Isguder, Tuncay Aydin, Fatih Haslak, Muserref Kasap Cuceoglu, Esra Esen, Ulas Akcay, Oezge Basaran, Aysenur Pac Kisaarslan, Fuat Akal, Deniz Yuce, Semanur Ozdel, Mehmet Bulbul, Yelda Bilginer, Nuray Aktay Ayaz, Betul Sozeri, Ozgur Kasapcopur, Erbil Unsal, Seza Ozen
Summary: The study aimed to compare the characteristics of colchicine-resistant and colchicine-responsive patients with familial Mediterranean fever (FMF) and develop a score for predicting colchicine resistance at the time of FMF diagnosis. The researchers found that colchicine-resistant patients had longer, more frequent attacks and were younger at symptom onset. By developing a score that includes age at symptom onset, attack frequency, arthritis, chest pain, and two exon 10 gene mutations, colchicine resistance can be predicted more accurately.
Review
Geochemistry & Geophysics
Ralph A. A. Kahn, Elisabeth Andrews, Charles A. A. Brock, Mian Chin, Graham Feingold, Andrew Gettelman, Robert C. C. Levy, Daniel M. M. Murphy, Athanasios Nenes, Jeffrey R. R. Pierce, Thomas Popp, Jens Redemann, Andrew M. M. Sayer, Arlindo M. M. da Silva, Larisa Sogacheva, Philip Stier
Summary: Aerosol forcing uncertainty remains the largest climate forcing uncertainty and has not diminished significantly in the past 20 years. This review summarizes the contributions made by satellite observations, atmospheric measurements, modeling, and data assimilation to reduce the uncertainty in aerosol forcing of climate. The review highlights the need for systematic aircraft in situ measurements, suborbital programs, and integration of satellite observations, measurements, and modeling to reduce the persistent uncertainty in aerosol climate forcing.
REVIEWS OF GEOPHYSICS
(2023)
Article
Environmental Sciences
Yang Ou, Lei Li, Zhengqiang Li, Ying Zhang, Oleg Dubovik, Yevgeny Derimian, Cheng Chen, David Fuertes, Yisong Xie, Anton Lopatin, Fabrice Ducos, Zongren Peng
Summary: Remote sensing observations were used to analyze aerosol components in the North China Plain, showing higher black carbon mass concentration in Shanxi compared to Beijing, while brown carbon mass concentrations were higher in Beijing. Additionally, fine ammonium sulfate-like particles were three times lower in Beijing than in Shanxi.
Article
Environmental Sciences
Edward Gryspeerdt, Tom Goren, Tristan W. P. Smith
Summary: The response of clouds to aerosol perturbations varies in time scales, with instantaneous effects and longer-term adjustments. Ship emissions of aerosols can affect cloud properties within a few hours, while cloud adjustments can continue for more than 10 hours. The temporal evolution and background cloud field are crucial in understanding the aerosol impact on clouds.
ATMOSPHERIC CHEMISTRY AND PHYSICS
(2021)
Article
Meteorology & Atmospheric Sciences
Tamanna Subba, Mukunda M. Gogoi, K. Krishna Moorthy, Pradip K. Bhuyan, Binita Pathak, Anirban Guha, Manoj Kumar Srivastava, B. M. Vyas, Karamjit Singh, Jayabala Krishnan, T. V. Lakshmi Kumar, S. Suresh Babu
Summary: This study estimates the regional aerosol direct radiative forcing (ARF) in the Indian region using multi-year observations and satellite data. The synergistic approach improves the accuracy of ARF estimates and avoids overestimation or underestimation of atmospheric forcing. The study also reveals that aerosols in the atmosphere reduce the surface solar radiation flux, especially during autumn and winter.
ATMOSPHERIC RESEARCH
(2022)
Article
Environmental Sciences
Seoyoung Lee, Myungje Choi, Jhoon Kim, Young-Je Park, Jong-Kuk Choi, Hyunkwang Lim, Jeewoo Lee, Minseok Kim, Yeseul Cho
Summary: In this study, the YAER algorithm was extended to GOCI-II data and the first results of aerosol optical properties retrieved from GOCI-II data were presented. The results showed that GOCI-II aerosol retrievals have a consistent spatial distribution with those from GOCI and provide AOD at a higher spatial resolution, revealing finer changes in aerosol concentrations.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
Article
Environmental Sciences
Xing Yan, Zhou Zang, Yize Jiang, Wenzhong Shi, Yushan Guo, Dan Li, Chuanfeng Zhao, Letu Husi
Summary: The development of a new Spatial-Temporal Interpretable Deep Learning Model (SIDLM) has been detailed in this study to enhance the interpretability and predictive accuracy of satellite-based PM2.5 measurements. The SIDLM demonstrated higher accuracy than five machine learning inversion methods and showed strong influence of certain districts on PM2.5 concentrations in urban areas such as Beijing. Overall, the new model has promising applications in deep learning-based predictions and spatiotemporal analysis of other earth observation data.
ENVIRONMENTAL POLLUTION
(2021)
Article
Environmental Sciences
Xing Yan, Zhou Zang, Chen Liang, Nana Luo, Rongmin Ren, Maureen Cribb, Zhanqing Li
Summary: This study generated and analyzed a 10-year global FMF product using satellite data, revealing global patterns and interannual/seasonal variations. Different countries showed different linear trends in FMF, with a particularly strong upward trend in Australia since 2008.
ENVIRONMENTAL POLLUTION
(2021)
Article
Environmental Sciences
Pai Zheng, Zhangjian Chen, Yonghong Liu, Hongbin Song, Chieh-Hsi Wu, Bingying Li, Moritz U. G. Kraemer, Huaiyu Tian, Xing Yan, Yuxin Zheng, Nils Chr Stenseth, Guang Jia
Summary: Long-term exposure to air pollutants may increase the number of COVID-19 cases, but the association may be influenced by population size based on stratified analysis.
ENVIRONMENTAL POLLUTION
(2021)
Article
Geochemistry & Geophysics
Chen Liang, Zhou Zang, Zhanqing Li, Xing Yan
Summary: Researchers have developed an improved global land-scale fAOD product by combining different algorithms and MODIS aerosol products, with validation results showing high accuracy and reliability. The improved product eliminates multiple zero values in traditional methods, providing more accurate data, especially showing high fAOD loading in eastern China and northern India.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Environmental Sciences
Zhou Zang, Dan Li, Yushan Guo, Wenzhong Shi, Xing Yan
Summary: This study demonstrates the importance of considering aerosol size properties like fine mode fraction (FMF) in satellite-based PM2.5 modeling, showing that FMF&AOT-PM2.5 performs better than AOT in estimating PM2.5 concentrations, especially on dust and haze days in China. Including FMF led to better linear correlation between PM2.5 and fAOT, emphasizing the need for a more accurate FMF product for superior PM2.5 retrieval and estimation.
Article
Remote Sensing
Zhou Zang, Yushan Guo, Yize Jiang, Chen Zuo, Dan Li, Wenzhong Shi, Xing Yan
Summary: A semi-SILDM model was proposed for O-3 prediction in China, showing high accuracy and interpretability at both national and urban scales through time-based validation and data analysis. The model further revealed the spatiotemporal characteristics of O-3 pollution.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Nana Luo, Zhou Zang, Chuan Yin, Mingyuan Liu, Yize Jiang, Chen Zuo, Wenji Zhao, Wenzhong Shi, Xing Yan
Summary: In this study, a two-stage deep learning model, combining CNN, DNN, and IG, was proposed to accurately estimate sparse ground-truth data of O-3. The model showed higher accuracy compared to individual CNN and DNN, with a higher R-2 and lower RMSE. Using the model, the contribution of nearby cities to O-3 in Beijing during extreme weather and clean days was interpreted. The model allowed for a finer-scale analysis of O-3 pollution and demonstrated greater temporal and spatial accuracy.
ATMOSPHERIC ENVIRONMENT
(2022)
Article
Environmental Sciences
Xing Yan, Chen Zuo, Zhanqing Li, Hans W. Chen, Yize Jiang, Bin He, Huiming Liu, Jiayi Chen, Wenzhong Shi
Summary: This study proposes a new deep learning model called SOPiNet, which allows for simultaneous monitoring and coverage of PM2.5 and O3 concentrations at a spatial resolution of 5 km. Using surface observations across China from 2014-2021, a strong relationship between PM2.5 and O3 was found. The results suggest that simultaneous retrieval of different but related pollutants can improve near-real time satellite-based air quality monitoring.
ENVIRONMENTAL POLLUTION
(2023)
Article
Environmental Sciences
Chen Zuo, Jiayi Chen, Yue Zhang, Yize Jiang, Mingyuan Liu, Huiming Liu, Wenji Zhao, Xing Yan
Summary: This study evaluates four meteorological reanalysis datasets in China and finds that ERA5 is the most accurate in terms of temperature, relative humidity, wind speed, and boundary layer height, while FNL has the highest uncertainty. The spatial accuracy of all datasets is higher in the eastern region compared to the western region due to complex terrain and limited ground-based observations. ERA5 performs the best in retrieving PM2.5 in China, providing a useful guideline for subsequent satellite-based PM2.5 retrieval studies in the country.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Engineering, Environmental
Zhou Zang, Yue Zhang, Chen Zuo, Jiayi Chen, Bin He, Nana Luo, Junxiao Zou, Wenji Zhao, Wenzhong Shi, Xing Yan
Summary: This study proposes a new deep learning model (SCAM) for retrieving global land coarse-mode aerosol optical depths (cAOD). Compared to traditional models, SCAM considers the impact of spatiotemporal feature interactions and can describe both linear and nonlinear relationships, resulting in significantly improved accuracy and coverage of the retrieval results.
ENVIRONMENTAL SCIENCE & TECHNOLOGY
(2023)
Article
Geosciences, Multidisciplinary
Xing Yan, Zhou Zang, Zhanqing Li, Nana Luo, Chen Zuo, Yize Jiang, Dan Li, Yushan Guo, Wenji Zhao, Wenzhong Shi, Maureen Cribb
Summary: This study developed a new satellite-based global land daily fine-mode fraction (FMF) dataset, which is more reliable compared to existing products. The dataset combines the advantages of physical and deep learning methods to accurately capture the fine-mode characteristics of aerosols. Comparison with AERONET measurements shows high accuracy of the dataset. Additionally, the study identified different trends in FMF changes across regions and countries.
EARTH SYSTEM SCIENCE DATA
(2022)
