1734 citations as recorded by crossref.

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41. Estimates of the aerosol indirect effect over the Baltic Sea region derived from 12 years of MODIS observations G. Saponaro et al. https://doi.org/10.5194/acp-17-3133-2017
42. Large Air Quality and Public Health Impacts due to Amazonian Deforestation Fires in 2019 E. Butt et al. https://doi.org/10.1029/2021GH000429
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45. Extreme dust storm over the eastern Mediterranean in September 2015: satellite, lidar, and surface observations in the Cyprus region R. Mamouri et al. https://doi.org/10.5194/acp-16-13711-2016
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48. Retrieval of desert dust and carbonaceous aerosol emissions over Africa from POLDER/PARASOL products generated by the GRASP algorithm C. Chen et al. https://doi.org/10.5194/acp-18-12551-2018
49. City-level variations in aerosol optical properties and aerosol type identification derived from long-term MODIS/Aqua observations in the Sichuan Basin, China H. Cai et al. https://doi.org/10.1016/j.uclim.2021.100886
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52. Simulation of a Dust‐And‐Rain Event Across the Red Sea Using WRF‐Chem S. Parajuli et al. https://doi.org/10.1029/2022JD038384
53. Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds D. Rosenfeld et al. https://doi.org/10.1126/science.aav0566
54. Spatial and Temporal Distribution of PM2.5 Pollution over Northeastern Mexico: Application of MERRA-2 Reanalysis Datasets J. Carmona et al. https://doi.org/10.3390/rs12142286
55. Validation, analysis, and comparison of MISR V23 aerosol optical depth products with MODIS and AERONET observations R. Fan et al. https://doi.org/10.1016/j.scitotenv.2022.159117
56. Decadal changes and extreme events of aerosol over China: Spatiotemporal characteristics revealed by multi-satellite observations L. Chen et al. https://doi.org/10.1016/j.atmosres.2025.108643
57. Source profiling of air pollution and its association with acute respiratory infections in the Himalayan-bound region of India M. Wani et al. https://doi.org/10.1007/s11356-021-15413-0
58. Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product H. Ștefănie et al. https://doi.org/10.3390/rs15123072
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62. Lidar Profiling of Aerosol Vertical Distribution in the Urbanized French Alpine Valley of Annecy and Impact of a Saharan Dust Transport Event P. Chazette & J. Totems https://doi.org/10.3390/rs15041070
63. Evaluation of MODIS Deep Blue Aerosol Algorithm in Desert Region of East Asia: Ground Validation and Intercomparison M. Tao et al. https://doi.org/10.1002/2017JD026976
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65. Status update: is smoke on your mind? Using social media to assess smoke exposure B. Ford et al. https://doi.org/10.5194/acp-17-7541-2017
66. Satellite observations of smoke–cloud–radiation interactions over the Amazon rainforest R. Herbert & P. Stier https://doi.org/10.5194/acp-23-4595-2023
67. Aerosol Distributions and Transport over Southern Morocco from Ground-Based and Satellite Observations (2004–2020) T. Millet et al. https://doi.org/10.3390/atmos13060923
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83. Improving the confidence in retrievals of vertical distributions of cloud condensation nuclei number concentration from ARM supported by aircraft in situ observations J. Tian et al. https://doi.org/10.5194/amt-19-5051-2026
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896. Can MODIS detect trends in aerosol optical depth over land? X. Fan et al. https://doi.org/10.1007/s00376-017-7017-2
897. Observational evidence of elevated smoke layers during crop residue burning season over Delhi: Potential implications on associated heterogeneous PM2.5 enhancements A. Mhawish et al. https://doi.org/10.1016/j.rse.2022.113167
898. Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China L. Yang et al. https://doi.org/10.3390/rs14081935
899. Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties F. Bender et al. https://doi.org/10.16993/tellusb.1868
900. Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014 M. Bilal et al. https://doi.org/10.3390/rs11050486
901. Investigation of Aerosol Climatology, Optical Characteristics and Variability over Egypt Based on Satellite Observations and In-Situ Measurements I. Abou El-Magd et al. https://doi.org/10.3390/atmos11070714
902. Identification of dust aerosols, their sources, and the effect of soil moisture in Central Asia J. Liu et al. https://doi.org/10.1016/j.scitotenv.2023.161575
903. A generalized land surface reflectance reconstruction method for aerosol retrieval: Application to the Particulate Observing Scanning Polarimeter (POSP) onboard GaoFen-5 (02) satellite Z. Shi et al. https://doi.org/10.1016/j.rse.2023.113683
904. Satellite-Based Optimization and Planning of Urban Ventilation Corridors for a Healthy Microclimate Environment D. Gong et al. https://doi.org/10.3390/su152115653
905. Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017) L. Sogacheva et al. https://doi.org/10.5194/acp-18-11389-2018
906. Preliminary Retrieval and Validation of Aerosol Optical Depths from FY-4B Advanced Geostationary Radiation Imager Images D. Zhou et al. https://doi.org/10.3390/rs16020372
907. Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces H. Wang et al. https://doi.org/10.1016/j.jqsrt.2018.08.011
908. National-Scale Estimates of Ground-Level PM2.5 Concentration in China Using Geographically Weighted Regression Based on 3 km Resolution MODIS AOD W. You et al. https://doi.org/10.3390/rs8030184
909. Trends in aerosol optical properties over Eastern Europe based on MODIS-Aqua M. Filonchyk et al. https://doi.org/10.1016/j.gsf.2020.03.014
910. Potential responses of vegetation to atmospheric aerosols in arid and semi-arid regions of Asia L. Jiao et al. https://doi.org/10.1007/s40333-021-0005-5
911. Evaluation of biomass burning aerosols in the HadGEM3 climate model with observations from the SAMBBA field campaign B. Johnson et al. https://doi.org/10.5194/acp-16-14657-2016
912. Systematic Evaluation of Four Satellite AOD Datasets for Estimating PM2.5 Using a Random Forest Approach J. Handschuh et al. https://doi.org/10.3390/rs15082064
913. Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning H. Choi et al. https://doi.org/10.1016/j.scitotenv.2025.180535
914. Estimation and Validation of Collection 6 Moderate Resolution Imaging Spectroradiometer Aerosol Products for East Asia K. Lee https://doi.org/10.5572/ajae.2018.12.3.193
915. A robust aerosol impact on clouds along the subtropical to tropical transition N. Yeheskel et al. https://doi.org/10.5194/acp-26-8765-2026
916. Spatial-Temporal Variation of AOD Based on MAIAC AOD in East Asia from 2011 to 2020 P. Wang et al. https://doi.org/10.3390/atmos13121983
917. Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark Target and Deep Blue Combined Aerosol Product M. Bilal & J. Nichol https://doi.org/10.1109/JSTARS.2017.2693289
918. Variability in the correlation between satellite-derived liquid cloud droplet effective radius and aerosol index over the northern Pacific Ocean S. Tan et al. https://doi.org/10.1080/16000889.2017.1391656
919. The impact of resolution on meteorological, chemical and aerosol properties in regional simulations with WRF-Chem P. Crippa et al. https://doi.org/10.5194/acp-17-1511-2017
920. Precipitable water vapor over oceans from the Maritime Aerosol Network: Evaluation of global models and satellite products under clear sky conditions D. Pérez-Ramírez et al. https://doi.org/10.1016/j.atmosres.2018.09.007
921. Radiative Impacts of Aerosols During COVID-19 Lockdown Period Over the Indian Region R. Bhawar et al. https://doi.org/10.3389/fenvs.2021.746090
922. Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation X. Li et al. https://doi.org/10.1016/j.jclepro.2019.03.121
923. Trends in the types and absorption characteristics of ambient aerosols over the Indo-Gangetic Plain and North China Plain in last two decades S. Ramachandran & M. Rupakheti https://doi.org/10.1016/j.scitotenv.2022.154867
924. Improving XCO2 retrieval under high aerosol loads with fused satellite aerosol Data: Advancing understanding of anthropogenic emissions H. Zhu et al. https://doi.org/10.1016/j.isprsjprs.2025.03.009
925. Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX‐AQ Y. Shi et al. https://doi.org/10.1029/2023JD039085
926. A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula A. Alban et al. https://doi.org/10.1007/s41748-024-00398-w
927. A satellite-measured view of aerosol component content and optical property in a haze-polluted case over North China Plain L. Li et al. https://doi.org/10.1016/j.atmosres.2021.105958
928. A hybrid physics-ML framework for aerosol optical depth retrieval from OCM-2 on Oceansat-2 satellite M. Gogoi et al. https://doi.org/10.1016/j.jqsrt.2026.110139
929. Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign P. Gupta et al. https://doi.org/10.5194/amt-12-6557-2019
930. A hybrid method for reconstructing the historical evolution of aerosol optical depth from sunshine duration measurements W. Wandji Nyamsi et al. https://doi.org/10.5194/amt-13-3061-2020
931. Spatiotemporal analysis of hydrological and environmental changes in lake Urmia (2000–2024) using google earth engine H. Li et al. https://doi.org/10.1007/s12145-026-02089-8
932. 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
933. 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
934. 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
935. Aerosols under heatwaves: Interactions between pollution and heatwave extremes M. Caushaj et al. https://doi.org/10.1051/epjconf/202636504004
936. 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
937. 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
938. 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
939. 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
940. 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
941. Spaceborne observations of aerosol vertical distribution over Indian mainland (2009–2018) M. Mehta et al. https://doi.org/10.1016/j.atmosenv.2020.117902
942. 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
943. 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
944. 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
945. Forcing dataset updates impact regional but not global Earth radiation budget trends in the GFDL AM4 climate model C. Fan et al. https://doi.org/10.1073/pnas.2611210123
946. 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
947. 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
948. 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
949. CALIOP-Based Quantification of Central Asian Dust Transport Y. Han et al. https://doi.org/10.3390/rs14061416
950. 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
951. 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
952. 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
953. 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
954. 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
955. Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS G. Wind et al. https://doi.org/10.5194/gmd-15-1-2022
956. 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
957. 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
958. 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
959. Impacts of Sun Glint Off Ice Clouds on DSCOVR EPIC Cloud Products T. Várnai et al. https://doi.org/10.1109/TGRS.2024.3400253
960. 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
961. 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
962. SI-traceable solar irradiance measurements for aerosol optical depth retrieval N. Kouremeti et al. https://doi.org/10.1088/1681-7575/ac6cbb
963. 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
964. 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
965. 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
966. 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
967. SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm L. She et al. https://doi.org/10.3390/rs9030253
968. 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
969. 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
970. 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
971. 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
972. 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
973. 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
974. 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
975. 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
976. 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
977. 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
978. 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
979. Integrated assessment of aerosol optical properties over West Africa: multivariate classification, seasonality, and long-term variability N. Yusuf et al. https://doi.org/10.3389/fenvs.2026.1948463
980. 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
981. 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
982. 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
983. 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
984. 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
985. 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
986. 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
987. High‐Resolution Post‐Process Corrected Satellite AOD H. Taskinen et al. https://doi.org/10.1029/2022GL099733
988. 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
989. Optimal estimation of spectral surface reflectance in challenging atmospheres D. Thompson et al. https://doi.org/10.1016/j.rse.2019.111258
990. 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
991. 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
992. 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
993. 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
994. 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
995. 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
996. 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
997. 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
998. 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
999. 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
1000. Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements A. Sayer et al. https://doi.org/10.1002/2017JD026934
1001. 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
1002. 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
1003. 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
1004. Synchronous Atmospheric Correction of High Spatial Resolution Images from Gao Fen Duo Mo Satellite L. Xu et al. https://doi.org/10.3390/rs14174427
1005. 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
1006. 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
1007. 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
1008. 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
1009. 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
1010. 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
1011. 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
1012. 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
1013. 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
1014. 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
1015. 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
1016. 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
1017. 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
1018. 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
1019. 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
1020. 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
1021. 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
1022. 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
1023. 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
1024. 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
1025. 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
1026. 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
1027. 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
1028. Global changes in aerosol single scattering albedo during COVID-19 K. Ansari & S. Ramachandran https://doi.org/10.1016/j.atmosenv.2024.120649
1029. Impacts of South Asian aerosol inflow over Mount Qomolangma on downstream cloud–precipitation processes through a long-range ice-crystal “seeding” effect X. Xu et al. https://doi.org/10.5194/acp-26-13581-2026
1030. Long Temporal Analysis of 3-km MODIS Aerosol Product Over East China Q. Ma et al. https://doi.org/10.1109/JSTARS.2017.2650144
1031. 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
1032. 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
1033. 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
1034. Aerosol optical depth trend over the Middle East K. Klingmüller et al. https://doi.org/10.5194/acp-16-5063-2016
1035. Error Budget Analysis of a Sun Glint Based In-Flight Polarization Calibration Method from Blue to SWIR Spectral Bands L. Qie et al. https://doi.org/10.3390/rs18183068
1036. 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
1037. 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
1038. 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
1039. 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
1040. 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
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1045. 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
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1120. Characteristics of Ethiopia Air Chemistry and Its Meteorological Context M. Jury https://doi.org/10.1175/EI-D-17-0009.1
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1178. Modeling dust sources, transport, and radiative effects at different altitudes over the Tibetan Plateau Z. Hu et al. https://doi.org/10.5194/acp-20-1507-2020
1179. Exploration of Machine Learning Techniques in Emulating a Coupled Soil–Canopy–Atmosphere Radiative Transfer Model for Multi-Parameter Estimation From Satellite Observations H. Shi et al. https://doi.org/10.1109/TGRS.2019.2921392
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1725. Variation of Atmospheric Properties over the Central Himalaya During a Low-Intensity Dust Episode: Insights from Lidar and Auxiliary Observations S. Singh et al. https://doi.org/10.1007/s41810-026-00395-8
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1727. An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India J. Singh et al. https://doi.org/10.1007/s10661-022-10573-z
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1730. An Evaluation of MODIS-Retrieved Aerosol Optical Depth over AERONET Sites in Alaska A. McPhetres & S. Aggarwal https://doi.org/10.3390/rs10091384
1731. Assessing populations at risk: Air pollution and extreme heat in the Louisville Metropolitan Statistical Area P. Coleman & A. Gaughan https://doi.org/10.1080/23754931.2026.2692136
1732. Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product M. Garay et al. https://doi.org/10.5194/amt-13-593-2020
1733. Evaluating PM2.5 Exposure Disparities Through Agent-Based Geospatial Modeling in an Urban Airshed D. Johnson et al. https://doi.org/10.3390/air3040033
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