1660 citations as recorded by crossref.

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895. Variability of mineral dust deposition in the western Mediterranean basin and south-east of France J. Vincent et al. https://doi.org/10.5194/acp-16-8749-2016
896. Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach T. Li et al. https://doi.org/10.1002/2017GL075710
897. Evaluation of WRF-Chem-RTFDDA dust forecasts over the MENA region using in-situ and remote-sensing observations D. Rostkier-Edelstein et al. https://doi.org/10.3389/fenvs.2022.981852
898. Aerosols under heatwaves: Interactions between pollution and heatwave extremes M. Caushaj et al. https://doi.org/10.1051/epjconf/202636504004
899. Study of optical scattering properties and direct radiative effects of high-altitude cirrus clouds in Barcelona, Spain, with 4 years of lidar measurements C. Gil-Díaz et al. https://doi.org/10.5194/acp-25-3445-2025
900. Assessing the performance of the Copernicus Atmosphere Monitoring Service reanalysis: a comprehensive analysis of aerosol optical depth over India K. Shukla et al. https://doi.org/10.1007/s11356-025-37286-3
901. Statistics and Meteorology of Air Pollution Episodes over the South African Highveld Based on Satellite–Model Datasets M. Jury https://doi.org/10.1175/JAMC-D-16-0354.1
902. Long-term Changes in Solar Radiation in Northern Eurasia during the Warm Season According to Measurements and Reconstruction Model E. Volpert & N. Chubarova https://doi.org/10.3103/S1068373921080021
903. ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set A. Gkikas et al. https://doi.org/10.5194/amt-14-309-2021
904. Spaceborne observations of aerosol vertical distribution over Indian mainland (2009–2018) M. Mehta et al. https://doi.org/10.1016/j.atmosenv.2020.117902
905. Effects of COVID-19 pandemic lockdown: A satellite data-based appraisal of air quality in Guwahati, Assam J. Biswas & B. Pathak https://doi.org/10.1016/j.matpr.2022.06.218
906. Landscape determinants of spatio-temporal patterns of aerosol optical depth in the two most polluted metropolitans in the United States C. Wang et al. https://doi.org/10.1016/j.scitotenv.2017.07.273
907. Technical note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers – first steps toward assessing the feasibility of a global observation strategy D. Anderson et al. https://doi.org/10.5194/acp-23-6319-2023
908. Spatiotemporal variability of aerosol-cloud interactions in Northwestern Pacific and impact on NPP X. Qiu et al. https://doi.org/10.1016/j.atmosenv.2025.121151
909. Estimation of the Hourly Aerosol Optical Depth From GOCI Geostationary Satellite Data: Deep Neural Network, Machine Learning, and Physical Models J. Yeom et al. https://doi.org/10.1109/TGRS.2021.3107542
910. An exploratory study on the aerosol height retrieval from OMI measurements of the 477  nm O2 − O2 spectral band using a neural network approach J. Chimot et al. https://doi.org/10.5194/amt-10-783-2017
911. CALIOP-Based Quantification of Central Asian Dust Transport Y. Han et al. https://doi.org/10.3390/rs14061416
912. Assessments of Satellite-Based Aerosol Optical Depth for Monitoring Air Quality of the Large Port of Busan, Korea U. Jeong et al. https://doi.org/10.3390/atmos16101123
913. Spatial and seasonal variations in atmospheric aerosols over Nigeria: Assessment of influence of intertropical discontinuity movement A. Ayanlade et al. https://doi.org/10.1177/1759313118820306
914. The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations Q. Yang et al. https://doi.org/10.1016/j.envpol.2019.02.071
915. Continental aerosol properties and absorption retrieval using random forest machine learning method specific to geostationary remote sensing F. Bao et al. https://doi.org/10.1016/j.rse.2024.114275
916. Spatio-temporal variations in fine particulate matter and evaluation of associated health risk over Pakistan A. Mariam et al. https://doi.org/10.1002/ieam.4446
917. Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS G. Wind et al. https://doi.org/10.5194/gmd-15-1-2022
918. Retrieval of global aerosol and surface properties from the Gaofen-5 Directional Polarimetric Camera measurements Z. Zhang et al. https://doi.org/10.5194/amt-19-2555-2026
919. Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya A. Kumar et al. https://doi.org/10.1016/j.rse.2017.12.019
920. Impact of atmospheric circulation types on southwest Asian dust and Indian summer monsoon rainfall D. Kaskaoutis et al. https://doi.org/10.1016/j.atmosres.2017.11.002
921. Impacts of Sun Glint Off Ice Clouds on DSCOVR EPIC Cloud Products T. Várnai et al. https://doi.org/10.1109/TGRS.2024.3400253
922. Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation A. Sayer et al. https://doi.org/10.5194/amt-8-5277-2015
923. The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data Y. Wu et al. https://doi.org/10.3390/rs8090765
924. SI-traceable solar irradiance measurements for aerosol optical depth retrieval N. Kouremeti et al. https://doi.org/10.1088/1681-7575/ac6cbb
925. The regime of aerosol asymmetry parameter over Europe, the Mediterranean and the Middle East based on MODIS satellite data: evaluation against surface AERONET measurements M. Korras-Carraca et al. https://doi.org/10.5194/acp-15-13113-2015
926. Estimation of particulate matter (PM2.5, PM10) concentration and its variation over urban sites in Bangladesh A. Gupta et al. https://doi.org/10.1007/s42452-020-03829-1
927. Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types R. Yousefi et al. https://doi.org/10.1016/j.scitotenv.2020.137906
928. High-Resolution Satellite-Based PM2.5 Concentration Data Acquired During the COVID-19 Outbreak Throughout China: Model, Variations, and Reasons H. Guo et al. https://doi.org/10.1109/JSTARS.2021.3119383
929. SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. https://doi.org/10.3390/rs9030253
930. Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China Y. Che et al. https://doi.org/10.5194/acp-16-9655-2016
931. Global validation and comparison of MODIS DT and POLDER-3 GRASP aerosol products over ocean X. Pei et al. https://doi.org/10.1016/j.atmosres.2022.106480
932. Global and regional evaluation of a global model simulated AODs with AERONET and MODIS observations V. Sheel et al. https://doi.org/10.1002/joc.5367
933. Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2) S. Tilmes et al. https://doi.org/10.5194/gmd-16-6087-2023
934. A critical view of long-term AVHRR aerosol data record in China: Retrieval frequency and heavy pollution M. Tao et al. https://doi.org/10.1016/j.atmosenv.2019.117246
935. Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012 S. Loría-Salazar et al. https://doi.org/10.1016/j.atmosenv.2016.08.070
936. Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China M. Zhang et al. https://doi.org/10.3390/atmos10080426
937. Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm T. Zhang et al. https://doi.org/10.1080/15481603.2022.2060596
938. Increase of surface solar irradiance across East China related to changes in aerosol properties during the past decade J. Li et al. https://doi.org/10.1088/1748-9326/aaa35a
939. Models transport Saharan dust too low in the atmosphere: a comparison of the MetUM and CAMS forecasts with observations D. O'Sullivan et al. https://doi.org/10.5194/acp-20-12955-2020
940. Satellite-based PM2.5 estimation directly from reflectance at the top of the atmosphere using a machine learning algorithm J. Liu et al. https://doi.org/10.1016/j.atmosenv.2019.04.002
941. GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia M. Choi et al. https://doi.org/10.5194/amt-11-385-2018
942. Dual‐Isotope Constraints on Seasonally Resolved Source Fingerprinting of Black Carbon Aerosols in Sites of the Four Emission Hot Spot Regions of China W. Fang et al. https://doi.org/10.1029/2018JD028607
943. Mitigating Satellite‐Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF‐Chem Model Over the Northern sub‐Saharan African Region J. Wang et al. https://doi.org/10.1002/2017JD026840
944. Coupling meteorological variables with Moderate Resolution Imaging Spectroradiometer atmospheric products for estimating global solar radiation J. Chen et al. https://doi.org/10.1016/j.enconman.2019.112383
945. Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa A. Merdji et al. https://doi.org/10.1007/s11356-022-22997-8
946. 准东煤田吸光气溶胶特性及辐射强迫模拟 张. Zhang Zikang et al. https://doi.org/10.3788/AOS241868
947. Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India B. Tyagi et al. https://doi.org/10.1016/j.envpol.2020.116354
948. High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al. https://doi.org/10.1029/2022GL099733
949. Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak A. Rudke et al. https://doi.org/10.1016/j.rse.2023.113514
950. Optimal estimation of spectral surface reflectance in challenging atmospheres D. Thompson et al. https://doi.org/10.1016/j.rse.2019.111258
951. The relationship between cloud condensation nuclei (CCN) concentration and light extinction of dried particles: indications of underlying aerosol processes and implications for satellite-based CCN estimates Y. Shinozuka et al. https://doi.org/10.5194/acp-15-7585-2015
952. Study of Chemical and Optical Properties of Biomass Burning Aerosols during Long-Range Transport Events toward the Arctic in Summer 2017 T. Zielinski et al. https://doi.org/10.3390/atmos11010084
953. Aerosols impact on the convective and non-convective rain distribution over the Indian region: Results from WRF-Chem simulation S. Kedia et al. https://doi.org/10.1016/j.atmosenv.2019.01.020
954. Long term trend in aerosol direct radiative effects over Indian Ocean region from multi- satellite observations S. Jose et al. https://doi.org/10.1080/2150704X.2021.1957509
955. An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model F. Li et al. https://doi.org/10.1016/j.chemosphere.2024.142820
956. Role of aerosol–cloud–radiation interactions in modulating summertime quasi-biweekly rainfall intensity over South China H. Chen et al. https://doi.org/10.5194/acp-25-15765-2025
957. Spatiotemporal Trends of Aerosols over Urban Regions in Pakistan and Their Possible Links to Meteorological Parameters G. Ali et al. https://doi.org/10.3390/atmos11030306
958. Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: the TROPOspheric Monitoring Instrument (TROPOMI) NO2 product and the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol optical depth (AOD) data fusion product and its proxy J. Park et al. https://doi.org/10.5194/amt-16-3039-2023
959. Verification of dust forecast over the Indian region with satellite and ground based observations P. Das et al. https://doi.org/10.1080/01431161.2016.1239285
960. Observations of the Interaction and Transport of Fine Mode Aerosols With Cloud and/or Fog in Northeast Asia From Aerosol Robotic Network and Satellite Remote Sensing T. Eck et al. https://doi.org/10.1029/2018JD028313
961. Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements A. Sayer et al. https://doi.org/10.1002/2017JD026934
962. Seasonality of the mass concentration and chemical composition of aerosols around an urbanized basin in East Asia C. Chou et al. https://doi.org/10.1002/2016JD025728
963. Large air quality and human health impacts due to Amazon forest and vegetation fires E. Butt et al. https://doi.org/10.1088/2515-7620/abb0db
964. Error characterization and analysis of aerosol products from the geostationary environment monitoring spectrometer (GEMS) G. Du et al. https://doi.org/10.1016/j.atmosres.2025.108682
965. Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite L. Xu et al. https://doi.org/10.3390/rs14174427
966. Evaluation of the MISR fine resolution aerosol product using MODIS, MISR, and ground observations over China Y. Si et al. https://doi.org/10.1016/j.atmosenv.2019.117229
967. Impact of boundary layer schemes in RegCM-Chem on East Asian climate and its response to the aerosol-radiation interaction H. Cao et al. https://doi.org/10.1016/j.atmosres.2026.108951
968. Development and evaluation of the Aerosol Forecast Member in the National Center for Environment Prediction (NCEP)'s Global Ensemble Forecast System (GEFS-Aerosols v1) L. Zhang et al. https://doi.org/10.5194/gmd-15-5337-2022
969. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation M. Tao et al. https://doi.org/10.1016/j.atmosenv.2019.06.004
970. Hourly Aerosol Assimilation of Himawari‐8 AOT Using the Four‐Dimensional Local Ensemble Transform Kalman Filter T. Dai et al. https://doi.org/10.1029/2018MS001475
971. Seasonality biases arise from the interplay of retrieval quality and solar zenith angle effects in passive sensor AOD products S. Smith et al. https://doi.org/10.5194/acp-25-14333-2025
972. The influence of simulated surface dust lofting and atmospheric loading on radiative forcing S. Saleeby et al. https://doi.org/10.5194/acp-19-10279-2019
973. A strengthened and southward-shifted westerly jet mitigates warming-induced drying across Asian drylands J. Jiang & T. Zhou https://doi.org/10.1126/sciadv.aed7890
974. Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models H. Lee et al. https://doi.org/10.5194/acp-16-6627-2016
975. Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD S. Kim et al. https://doi.org/10.3390/app112210755
976. Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions G. Saponaro et al. https://doi.org/10.5194/acp-20-1607-2020
977. Investigating the relationship of aerosols with enhanced vegetation index and meteorological parameters over Pakistan S. Tariq et al. https://doi.org/10.1016/j.apr.2021.101080
978. Evaluation of the multi‐angle implementation of atmospheric correction (MAIAC) aerosol algorithm through intercomparison with VIIRS aerosol products and AERONET S. Superczynski et al. https://doi.org/10.1002/2016JD025720
979. A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI: Development and Verification over Asia D. Fu et al. https://doi.org/10.1016/j.eng.2023.09.023
980. Long-Term Trends in Aerosol Single Scattering Albedo Cause Bias in MODIS Aerosol Optical Depth Trends Z. Zhang et al. https://doi.org/10.1109/TGRS.2024.3424981
981. Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations M. Haghighatnasab et al. https://doi.org/10.5194/acp-22-8457-2022
982. Bias in modeled solar radiation by non-resolved intra-daily AOD variability J. Ruiz-Arias https://doi.org/10.1016/j.solener.2020.04.082
983. Long-term MAX-DOAS measurements of NO2, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain V. Kumar et al. https://doi.org/10.5194/acp-20-14183-2020
984. Fusion of the Arctic Aerosol Optical Depth Based on Improved Bayesian Maximum Entropy Y. Han et al. https://doi.org/10.1109/TGRS.2026.3654145
985. Assessment of Aerosol Radiative Forcing with 1-D Radiative Transfer Modeling in the U. S. South-East E. Alston & I. Sokolik https://doi.org/10.3390/atmos9070271
986. Effect of Reduced Emissions from Thermal Power Plants in China on Local Air Quality Improvement K. Lee & S. Shin https://doi.org/10.5572/KOSAE.2022.38.2.304
987. Assessing the relationship between atmospheric aerosols and maximum surface air temperature over the Indian region T. Sarin & V. Vinoj https://doi.org/10.1038/s41598-026-40641-0
988. Extended aerosol and surface characterization from S5P/TROPOMI with GRASP algorithm. Part I: Conditions, approaches, performance and new possibilities P. Litvinov et al. https://doi.org/10.1016/j.rse.2024.114355
989. Global changes in aerosol single scattering albedo during COVID-19 K. Ansari & S. Ramachandran https://doi.org/10.1016/j.atmosenv.2024.120649
990. Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China Q. Ma et al. https://doi.org/10.1109/JSTARS.2017.2650144
991. Large Contribution of Coarse Mode to Aerosol Microphysical and Optical Properties: Evidence from Ground-Based Observations of a Transpacific Dust Outbreak at a High-Elevation North American Site E. Kassianov et al. https://doi.org/10.1175/JAS-D-16-0256.1
992. A global aerosol classification algorithm incorporating multiple satellite data sets of aerosol and trace gas abundances M. Penning de Vries et al. https://doi.org/10.5194/acp-15-10597-2015
993. Impact of Chinese air pollutants on a record-breaking PMs episode in the Republic of Korea for 11–15 January 2019 H. Oh et al. https://doi.org/10.1016/j.atmosenv.2020.117262
994. Aerosol optical depth trend over the Middle East K. Klingmüller et al. https://doi.org/10.5194/acp-16-5063-2016
995. The Uncharacteristic Occurrence of the June 2013 Biomass-Burning Haze Event in Southeast Asia: Effects of the Madden-Julian Oscillation and Tropical Cyclone Activity Y. Oozeer et al. https://doi.org/10.3390/atmos11010055
996. Assessing the radiative impacts of an extreme desert dust outbreak and the potential improvements on short-term weather forecasts: The case of February 2015 A. Gkikas et al. https://doi.org/10.1016/j.atmosres.2019.04.020
997. Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols G. Wind et al. https://doi.org/10.5194/gmd-9-2377-2016
998. Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing Q. Xu et al. https://doi.org/10.1016/j.apr.2020.03.007
999. Improving aerosol optical depth retrievals from Himawari-8 with ensemble learning enhancement: Validation over Asia D. Fu et al. https://doi.org/10.1016/j.atmosres.2023.106624
1000. Monitoring cyanobacterial harmful algal blooms at high spatiotemporal resolution by fusing Landsat and MODIS imagery Y. Zhao et al. https://doi.org/10.1016/j.envadv.2020.100008
1001. Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain N. Singh et al. https://doi.org/10.5194/acp-18-14197-2018
1002. An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, part 1: Algorithm development X. Yan et al. https://doi.org/10.1016/j.rse.2017.02.005
1003. Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning J. Fuchs et al. https://doi.org/10.5194/acp-18-16537-2018
1004. Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea S. Park et al. https://doi.org/10.5194/acp-19-1097-2019
1005. Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China W. Wang et al. https://doi.org/10.3390/rs9080858
1006. An Enhanced Aerosol Optical Depth Retrieval Algorithm for Particulate Observing Scanning Polarimeter (POSP) Data Over Land Z. Ji et al. https://doi.org/10.1109/TGRS.2024.3514170
1007. A global land aerosol fine-mode fraction dataset (2001–2020) retrieved from MODIS using hybrid physical and deep learning approaches X. Yan et al. https://doi.org/10.5194/essd-14-1193-2022
1008. Aerosol Optical Depth Over the Nepalese Cryosphere Derived From an Empirical Model B. Bhattarai et al. https://doi.org/10.3389/feart.2019.00178
1009. The global spatial-temporal distribution and EOF analysis of AOD based on MODIS data during 2003–2021 X. Tian et al. https://doi.org/10.1016/j.atmosenv.2023.119722
1010. Integration of GEMS and MODIS AOD for enhanced long-term aerosol monitoring over East Asia H. Jeon et al. https://doi.org/10.1080/01431161.2026.2632162
1011. Extreme levels of Canadian wildfire smoke in the stratosphere over central Europe on 21–22 August 2017 A. Ansmann et al. https://doi.org/10.5194/acp-18-11831-2018
1012. Nocturnal, seasonal and intra-annual variability of tropospheric aerosols observed using ground-based and space-borne lidars over a tropical location of India P. Prasad et al. https://doi.org/10.1016/j.atmosenv.2019.06.008
1013. Coupling Hourly Land Surface Models With Spatiotemporal Variability Characteristics: A Study on FY-4B/AGRI Land Aerosol Retrieval Algorithm Y. Si et al. https://doi.org/10.1109/TGRS.2025.3591646
1014. Annual and Seasonal Variations in Aerosol Optical Characteristics in the Huai River Basin, China from 2007 to 2021 X. Deng et al. https://doi.org/10.3390/rs16091571
1015. Climatological variations in aerosol optical depth and aerosol type identification in Liaoning of Northeast China based on MODIS data from 2002 to 2019 H. Zhao et al. https://doi.org/10.1016/j.scitotenv.2021.146810
1016. A synergic study on estimating surface downward shortwave radiation from satellite data D. Wang et al. https://doi.org/10.1016/j.rse.2021.112639
1017. A Multi-Temporal and Multi-Spectral Method to Estimate Aerosol Optical Thickness over Land, for the Atmospheric Correction of FormoSat-2, LandSat, VENμS and Sentinel-2 Images O. Hagolle et al. https://doi.org/10.3390/rs70302668
1018. A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing A. Sayer et al. https://doi.org/10.5194/amt-13-373-2020
1019. The impact of moderately absorbing aerosol on surface sensible, latent, and net radiative fluxes during the summer of 2015 in Central Europe K. Markowicz et al. https://doi.org/10.1016/j.jaerosci.2020.105627
1020. Scattering and absorbing aerosols in the climate system J. Li et al. https://doi.org/10.1038/s43017-022-00296-7
1021. Advanced Time series Multi-wavelength AOD Transformer (TMAT) model: joint retrieval of aerosol optical and microphysical properties using Himawari-8 AHI data L. She et al. https://doi.org/10.1016/j.jag.2026.105252
1022. A deep hybrid CNN–BiLSTM–attention framework with metaheuristic optimization and uncertainty quantification for solar power forecasting in dust-affected arid climates M. Bou-Rabee et al. https://doi.org/10.3389/fenrg.2026.1750871
1023. Towards gridded nighttime aerosol optical thickness retrievals using VIIRS day–night band observations over regions with artificial light sources J. Zhang et al. https://doi.org/10.5194/amt-18-1787-2025
1024. Climatology of Aerosols over the Caribbean Islands: Aerosol Types, Synoptic Patterns, and Transport A. Rodríguez Vega et al. https://doi.org/10.1175/JAMC-D-21-0015.1
1025. Aerosol Optical Depth Retrieval From Landsat 8 OLI Images Over Urban Areas Supported by MODIS BRDF/Albedo Data X. Tian et al. https://doi.org/10.1109/LGRS.2018.2827200
1026. Retrieval of nighttime aerosol optical depth by simultaneous consideration of artificial and natural light sources Y. Meng et al. https://doi.org/10.1016/j.scitotenv.2023.166354
1027. Characterizing Smoke Haze Events in Australia Using a Hybrid Approach of Satellite-Based Aerosol Optical Depth and Chemical Transport Modeling M. Sowden et al. https://doi.org/10.3390/rs16071266
1028. Analysis of spatial-temporal heterogeneity in remotely sensed aerosol properties observed during 2005–2015 over three countries along the Gulf of Guinea Coast in Southern West Africa M. Aklesso et al. https://doi.org/10.1016/j.atmosenv.2018.03.062
1029. Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea P. Ettehadi Osgouei et al. https://doi.org/10.1016/j.atmosenv.2021.118784
1030. Aerosol monitoring in Siberia using an 808 nm automatic compact lidar G. Ancellet et al. https://doi.org/10.5194/amt-12-147-2019
1031. Anthropogenic emissions from South Asia reverses the aerosol indirect effect over the northern Indian Ocean S. Jose et al. https://doi.org/10.1038/s41598-020-74897-x
1032. Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. https://doi.org/10.5194/amt-15-895-2022
1033. Identification of Aerosol Pollution Hotspots in Jiangsu Province of China Y. Wang et al. https://doi.org/10.3390/rs13142842
1034. Assessing the Challenges of Surface‐Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States K. Kaku et al. https://doi.org/10.1029/2017JD028074
1035. Tracking Smoke from a Prescribed Fire and Its Impacts on Local Air Quality Using Temporally Resolved GOES-16 ABI Aerosol Optical Depth (AOD) A. Huff et al. https://doi.org/10.1175/JTECH-D-20-0162.1
1036. Aerosol optical depth retrieval over the city of Abu Dhabi, United Arab Emirates (UAE) using Landsat-8 OLI images K. Omari et al. https://doi.org/10.1016/j.apr.2019.01.015
1037. Consistency of Aerosol Optical Properties between MODIS Satellite Retrievals and AERONET over a 14-Year Period in Central–East Europe L. Deaconu et al. https://doi.org/10.3390/rs16101677
1038. Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns H. Lim et al. https://doi.org/10.5194/amt-14-4575-2021
1039. Comparison of aerosol optical depth between observation and simulation from MIROC-SPRINTARS: Effects of temporal inhomogeneous sampling S. Park et al. https://doi.org/10.1016/j.atmosenv.2018.05.021
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1491. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions P. Gupta et al. https://doi.org/10.5194/amt-11-3145-2018
1492. Evaluating the potential for application of MODIS satellite data for urban air quality monitoring in Vietnam B. Hieu et al. https://doi.org/10.1088/1757-899X/869/2/022035
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1495. Smoke emissions from agricultural fires in Mexico and Central America B. Rios & G. Raga https://doi.org/10.1117/1.JRS.13.036509
1496. Assessment of urban aerosol pollution over the Moscow megacity by the MAIAC aerosol product E. Zhdanova et al. https://doi.org/10.5194/amt-13-877-2020
1497. The role of aerosol layer height in quantifying aerosol absorption from ultraviolet satellite observations J. Sun et al. https://doi.org/10.5194/amt-12-6319-2019
1498. The observational evidence of association between types of aerosol mode-cloud-precipitation interaction over Iran M. Rezaei et al. https://doi.org/10.1016/j.apr.2023.101760
1499. Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China A. Fan et al. https://doi.org/10.3390/rs9121269
1500. EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product E. Proestakis et al. https://doi.org/10.5194/acp-19-11743-2019
1501. Comprehensive Evaluation of Spatial Distribution and Temporal Trend of NO2, SO2 and AOD Using Satellite Observations over South and East Asia from 2011 to 2021 M. Rahman et al. https://doi.org/10.3390/rs15205069
1502. Combining low-cost, surface-based aerosol monitors with size-resolved satellite data for air quality applications P. deSouza et al. https://doi.org/10.5194/amt-13-5319-2020
1503. Satellite Remote Sensing for Estimating PM2.5 and Its Components Y. Li et al. https://doi.org/10.1007/s40726-020-00170-4
1504. Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder L. Liang et al. https://doi.org/10.1038/s41597-023-02696-w
1505. Merging regional and global aerosol optical depth records from major available satellite products L. Sogacheva et al. https://doi.org/10.5194/acp-20-2031-2020
1506. Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis S. Falah et al. https://doi.org/10.1016/j.atmosenv.2021.118659
1507. Estimation of daily photosynthetically active radiation (PAR) in presence of low to high aerosol loads: application to OLCI-like satellite data T. Harmel & M. Chami https://doi.org/10.1364/OE.24.0A1390
1508. Change in precipitation pattern over South Asia in response to the trends in regional warming and free-tropospheric aerosol loading S. Jana et al. https://doi.org/10.1038/s41598-024-64842-7
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1512. Microphysics and Radiation Effect of Dust on Saharan Air Layer: An HS3 Case Study Z. Tao et al. https://doi.org/10.1175/MWR-D-17-0279.1
1513. Aerosol Optical Depth from MODIS satellite data above the Pierre Auger Observatory N. Borodai et al. https://doi.org/10.1051/epjconf/201919702011
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1517. Particulate matter variability in Kathmandu based on in-situ measurements, remote sensing, and reanalysis data S. Becker et al. https://doi.org/10.1016/j.atmosres.2021.105623
1518. Multi-dimensional satellite observations of aerosol properties and aerosol types over three major urban clusters in eastern China Y. Liu et al. https://doi.org/10.5194/acp-21-12331-2021
1519. Characterisation of dust aerosols and their absorbing efficiency over South Asia using infrared observations from INSAT-3D Imager C. Lima et al. https://doi.org/10.1080/01431161.2024.2431176
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1522. Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain J. Zhu et al. https://doi.org/10.3390/rs9050432
1523. Spatial and temporal variability in dust storms in the Middle East, 2002–2018: three case studies in July 2009 N. Hamzeh et al. https://doi.org/10.1007/s12517-021-06859-0
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1527. Description and evaluation of the tropospheric aerosol scheme in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS-AER, cycle 45R1) S. Rémy et al. https://doi.org/10.5194/gmd-12-4627-2019
1528. Multi-Sensor Retrieval of Aerosol Optical Properties for Near-Real-Time Applications Using the Metop Series of Satellites: Concept, Detailed Description, and First Validation M. Grzegorski et al. https://doi.org/10.3390/rs14010085
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1530. Improved simulations of biomass burning aerosol optical properties and lifetimes in the NASA GEOS Model during the ORACLES-I campaign S. Das et al. https://doi.org/10.5194/acp-24-4421-2024
1531. Investigating the frequency and interannual variability in global above-cloud aerosol characteristics with CALIOP and OMI R. Alfaro-Contreras et al. https://doi.org/10.5194/acp-16-47-2016
1532. Satellite-Derived Correlation of SO2, NO2, and Aerosol Optical Depth with Meteorological Conditions over East Asia from 2005 to 2015 C. Lin et al. https://doi.org/10.3390/rs11151738
1533. Simultaneous Monitoring of Wildfire and Smoke Using Geostationary Satellite Infrared Channel Observations K. Lee et al. https://doi.org/10.5572/KOSAE.2024.40.3.337
1534. Large-scale MODIS AOD products recovery: Spatial-temporal hybrid fusion considering aerosol variation mitigation Y. Wang et al. https://doi.org/10.1016/j.isprsjprs.2019.08.017
1535. On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties V. Arya et al. https://doi.org/10.1007/s12040-020-01526-6
1536. Performance of the Semi-Empirical Precipitable Water Vapor Retrieval Algorithm Developed for Polarized Scanning Atmospheric Corrector (PSAC) in the Presence of Sensor Decay Y. Xie et al. https://doi.org/10.1109/TGRS.2023.3304129
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1538. The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness M. Salzmann et al. https://doi.org/10.1029/2021MS002699
1539. Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS) M. Kim et al. https://doi.org/10.5194/acp-16-1789-2016
1540. Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations J. Zhao et al. https://doi.org/10.1016/j.atmosres.2020.104978
1541. Surface Reflectance and Aerosol Retrieval from SPOT-VGT and PROBA-V in the Mission Exploitation Platform Environment M. Luffarelli et al. https://doi.org/10.3390/rs15215109
1542. Circular polarization in atmospheric aerosols S. Gassó & K. Knobelspiesse https://doi.org/10.5194/acp-22-13581-2022
1543. Continuing the MODIS Dark Target Aerosol Time Series with VIIRS V. Sawyer et al. https://doi.org/10.3390/rs12020308
1544. Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa W. Tang et al. https://doi.org/10.5194/gmd-16-6001-2023
1545. Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China X. Tian et al. https://doi.org/10.3390/rs10020197
1546. Effect of Aerosols, Tropospheric NO2 and Clouds on Surface Solar Radiation over the Eastern Mediterranean (Greece) G. Alexandri et al. https://doi.org/10.3390/rs13132587
1547. Emission Reductions Significantly Reduce the Hemispheric Contrast in Cloud Droplet Number Concentration in Recent Two Decades Y. Cao et al. https://doi.org/10.1029/2022JD037417
1548. Results From the Deep Convective Clouds-Based Response Versus Scan-Angle Characterization for the MODIS Reflective Solar Bands A. Angal et al. https://doi.org/10.1109/TGRS.2017.2759660
1549. Estimation of Net Surface Shortwave Radiation From Remotely Sensed Data Under Dust Aerosol Conditions Y. Sun & B. Tang https://doi.org/10.1109/ACCESS.2021.3069791
1550. Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model P. Kim et al. https://doi.org/10.5194/acp-15-10411-2015
1551. Clouds and the Earth’s Radiant Energy System (CERES) Cloud Radiative Swath (CRS) Edition 4 Data Product R. Scott et al. https://doi.org/10.1175/JTECH-D-22-0021.1
1552. Contrasting changes in snow cover and its sensitivity to aerosol optical properties in Hindukush-Karakoram-Himalaya region M. Ahmad et al. https://doi.org/10.1016/j.scitotenv.2019.134356
1553. Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China W. Xue et al. https://doi.org/10.1016/j.scitotenv.2019.134577
1554. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. https://doi.org/10.1016/j.rse.2021.112827
1555. Development of an Ozone Monitoring Instrument (OMI) aerosol index (AI) data assimilation scheme for aerosol modeling over bright surfaces – a step toward direct radiance assimilation in the UV spectrum J. Zhang et al. https://doi.org/10.5194/gmd-14-27-2021
1556. Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances R. Houborg & M. McCabe https://doi.org/10.1016/j.rse.2017.03.013
1557. Accuracy assessment of MODIS land aerosol optical thickness algorithms using AERONET measurements over North America H. Jethva et al. https://doi.org/10.5194/amt-12-4291-2019
1558. Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS L. Wang et al. https://doi.org/10.1016/j.isprsjprs.2024.06.022
1559. Two decades of high-resolution aerosol product over the Sierra Nevada Mountain region (SE Spain): Spatio-temporal distribution and impact on ecosystems A. del Águila et al. https://doi.org/10.1016/j.atmosres.2024.107515
1560. Investigation on spatiotemporal distribution of aerosol optical properties over two oceanic regions surrounding Indian subcontinent during summer monsoon season C. Vachaspati et al. https://doi.org/10.1007/s11356-018-2682-y
1561. Lower tropospheric ozone over the North China Plain: variability and trends revealed by IASI satellite observations for 2008–2016 G. Dufour et al. https://doi.org/10.5194/acp-18-16439-2018
1562. Advancing the quantification of aerosol-cloud interactions with the CALIPSO-CloudSat-Aqua/MODIS record Z. Li et al. https://doi.org/10.5194/acp-26-7705-2026
1563. Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion G. Geng et al. https://doi.org/10.1021/acs.est.1c01863
1564. Physical-Guided Transfer Deep Neural Network for High-Resolution AOD Retrieval D. Chen et al. https://doi.org/10.3390/rs17213606
1565. Analysis of Atmospheric Aerosol Optical Properties in the Northeast Brazilian Atmosphere with Remote Sensing Data from MODIS and CALIOP/CALIPSO Satellites, AERONET Photometers and a Ground-Based Lidar A. de Oliveira et al. https://doi.org/10.3390/atmos10100594
1566. Decreased dust particles amplify the cloud cooling effect by regulating cloud ice formation over the Tibetan Plateau J. Chen et al. https://doi.org/10.1126/sciadv.ado0885
1567. Seasonal evolution of aerosol loading and its vertical distribution in northeastern China from long-term satellite observations and model reanalysis H. Zhao et al. https://doi.org/10.1016/j.atmosenv.2023.119720
1568. Cloud condensation nuclei concentrations derived from the CAMS reanalysis K. Block et al. https://doi.org/10.5194/essd-16-443-2024
1569. Analysis and Validation of the Aerosol Optical Depth of MODIS Products in Gansu Province, Northwest China F. Huang et al. https://doi.org/10.3390/rs15122972
1570. Impact of California Fires on Local and Regional Air Quality: The Role of a Low‐Cost Sensor Network and Satellite Observations P. Gupta et al. https://doi.org/10.1029/2018GH000136
1571. Evaluating the consistency between Sentinel-2 and Planet constellations at field scale: illustration over winter wheat Y. Ma et al. https://doi.org/10.1007/s11119-025-10226-4
1572. 融合MODIS和OMI卫星数据的准东煤田吸光气溶胶辐射强迫估算研究 王. Wang Shuo et al. https://doi.org/10.3788/AOS250830
1573. Assessment of Satellite AOD during the 2020 Wildfire Season in the Western U.S. X. Ye et al. https://doi.org/10.3390/rs14236113
1574. Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8 Y. Xie et al. https://doi.org/10.1109/TGRS.2019.2944949
1575. Temporal variation of surface reflectance and cloud fraction used to identify background aerosol retrieval information over East Asia S. Park et al. https://doi.org/10.1016/j.atmosenv.2023.119916
1576. Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information X. Wang et al. https://doi.org/10.3389/fenvs.2022.925979
1577. A Decadal Data Set of Global Atmospheric Dust Retrieved From IASI Satellite Measurements L. Clarisse et al. https://doi.org/10.1029/2018JD029701
1578. The Sub-Daily Variability of Aerosol Loading and Associated Radiative Forcing Over the Indian Region T. Mukherjee & V. Vinoj https://doi.org/10.3389/feart.2021.727169
1579. PM2.5 mapping using integrated geographically temporally weighted regression (GTWR) and random sample consensus (RANSAC) models H. Chu & M. Bilal https://doi.org/10.1007/s11356-018-3763-7
1580. Long-term study of desert dust deposition effects on phytoplankton biomass in the Persian Gulf using Google Earth Engine H. Asgari & A. Soleimany https://doi.org/10.1016/j.marpolbul.2023.115564
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1582. An Ensemble Machine-Learning Model To Predict Historical PM2.5Concentrations in China from Satellite Data Q. Xiao et al. https://doi.org/10.1021/acs.est.8b02917
1583. The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean S. Gassó & O. Torres https://doi.org/10.5194/amt-9-3031-2016
1584. High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images H. Lin et al. https://doi.org/10.1016/j.atmosenv.2021.118591
1585. Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015 C. Jung et al. https://doi.org/10.1016/j.envpol.2017.11.016
1586. A study of 15-year aerosol optical thickness and direct shortwave aerosol radiative effect trends using MODIS, MISR, CALIOP and CERES R. Alfaro-Contreras et al. https://doi.org/10.5194/acp-17-13849-2017
1587. Improving aerosol retrieval from FY-3D/MERSI by parameterizing a binary quadratic spectral surface reflectance model with urban percentage and vegetation index W. Ji et al. https://doi.org/10.1016/j.atmosres.2025.108291
1588. Source-Specific PM2.5 Exposure and Associated Health Risks During Beijing Winter X. Liu et al. https://doi.org/10.3390/toxics13121081
1589. Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign A. Vaishya et al. https://doi.org/10.5194/acp-18-17669-2018
1590. Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties S. Moparthy et al. https://doi.org/10.3390/su11051410
1591. MISR empirical stray light corrections in high-contrast scenes J. Limbacher & R. Kahn https://doi.org/10.5194/amt-8-2927-2015
1592. Top-of-atmosphere hyper and multispectral signatures of submerged plastic litter with changing water clarity and depth S. Garaba & T. Harmel https://doi.org/10.1364/OE.451415
1593. A New Chemistry-Climate Model GRIMs-CCM: Model Evaluation of Interactive Chemistry-Meteorology Simulations S. Lee et al. https://doi.org/10.1007/s13143-022-00281-6
1594. Impact of mineral dust photocatalytic heterogeneous chemistry on the formation of the sulfate and nitrate: A modelling study over East Asia X. Li et al. https://doi.org/10.1016/j.atmosenv.2023.120166
1595. In-Flight Relative Radiometric Calibration of a Wide Field of View Directional Polarimetric Camera Based on the Rayleigh Scattering over Ocean S. Zhu et al. https://doi.org/10.3390/rs14051211
1596. Terra and Aqua MODIS TEB intercomparison using Himawari-8/AHI as reference T. Chang et al. https://doi.org/10.1117/1.JRS.13.017501
1597. Decadal simulation and comprehensive evaluation of CESM/CAM5.1 with advanced chemistry, aerosol microphysics, and aerosol‐cloud interactions J. He et al. https://doi.org/10.1002/2014MS000360
1598. Trends and Variability in Aerosol Optical Depth over North China from MODIS C6 Aerosol Products during 2001–2016 P. Wang et al. https://doi.org/10.3390/atmos8110223
1599. Southern Hemisphere mid- and high-latitudinal AOD, CO, NO2, and HCHO: spatiotemporal patterns revealed by satellite observations D. Ahn et al. https://doi.org/10.1186/s40645-019-0277-y
1600. Spatial Variation and Relation of Aerosol Optical Depth with LULC and Spectral Indices V. Sharma et al. https://doi.org/10.3390/atmos13121992
1601. Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies A. Sayer et al. https://doi.org/10.1002/2015JD024729
1602. The Influences of Indian Monsoon Phases on Aerosol Distribution and Composition over India P. Khan et al. https://doi.org/10.3390/rs16173171
1603. Variability in air-pollutants, aerosols, and associated meteorology over peninsular India and neighboring ocean regions during COVID-19 lockdown to unlock phases A. Sarkar et al. https://doi.org/10.1016/j.apr.2021.101231
1604. Understanding MODIS dark-target collection 5 and 6 aerosol data over China: Effect of surface type, aerosol loading and aerosol absorption L. Mei et al. https://doi.org/10.1016/j.atmosres.2019.05.023
1605. Satellites See the World’s Atmosphere S. Ackerman et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0009.1
1606. Validation of MODIS C6 Dark Target Aerosol Products at 3 km and 10 km Spatial Resolutions Over the China Seas and the Eastern Indian Ocean X. Shen et al. https://doi.org/10.3390/rs10040573
1607. Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation L. She et al. https://doi.org/10.3390/rs10040490
1608. Numerical simulations of dust storms originated from dried lakes in central and southwest Asia: The case of Aral Sea and Sistan Basin S. Karami et al. https://doi.org/10.1016/j.aeolia.2021.100679
1609. Multiconstituent Data Assimilation With WRF‐Chem/DART: Potential for Adjusting Anthropogenic Emissions and Improving Air Quality Forecasts Over Eastern China C. Ma et al. https://doi.org/10.1029/2019JD030421
1610. Transport of aerosols over the French Riviera – link between ground-based lidar and spaceborne observations P. Chazette et al. https://doi.org/10.5194/acp-19-3885-2019
1611. Impact of satellite viewing-swath width on global and regional aerosol optical thickness statistics and trends P. Colarco et al. https://doi.org/10.5194/amt-7-2313-2014
1612. The Evaluation of the North Atlantic Climate System in UKESM1 Historical Simulations for CMIP6 J. Robson et al. https://doi.org/10.1029/2020MS002126
1613. Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data X. Ceamanos et al. https://doi.org/10.5194/amt-16-2575-2023
1614. April 2008 Saharan dust event: Its contribution to PM10 concentrations over the Anatolian Peninsula and relation with synoptic conditions B. Kabatas et al. https://doi.org/10.1016/j.scitotenv.2018.03.150
1615. Description and evaluation of the tropospheric aerosol scheme in the Integrated Forecasting System (IFS-AER, cycle 47R1) of ECMWF S. Rémy et al. https://doi.org/10.5194/gmd-15-4881-2022
1616. Evaluating Aerosol Optical Depth From Himawari‐8 With Sun Photometer Network W. Wang et al. https://doi.org/10.1029/2018JD028599
1617. Assimilation and Forecasting Experiment for Heavy Siberian Wildfire Smoke in May 2016 with Himawari-8 Aerosol Optical Thickness K. YUMIMOTO et al. https://doi.org/10.2151/jmsj.2018-035
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