A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

Surface NO₂ volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO₂ VMR_ST) and monthly mean surface NO₂ VMR (NO₂ VMR_M) are estimated for the first time using three regression models with Ozone Monitoring Instrument (OMI) data in four metropolitan cities in South Korea: Seoul, Gyeonggi, Daejeon, and Gwangju. Relationships between the surface NO₂ VMR obtained from in situ measurements (NO₂ VMR_In-situ) and tropospheric NO₂ vertical column density obtained from OMI from 2007 to 2013 were developed using regression models that also include boundary layer height (BLH) from Atmospheric Infrared Sounder (AIRS) and surface pressure, temperature, dew point, and wind speed and direction. The performance of the regression models is evaluated via comparison with the NO₂ VMR_In-situ for two validation years (2006 and 2014). Of the three regression models, a multiple regression model shows the best performance in estimating NO₂ VMR_ST and NO₂ VMR_M. In the validation period, the average correlation coefficient (R), slope, mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), and percent difference between NO₂ VMR_In-situ and NO₂ VMR_ST estimated by the multiple regression model are 0.66, 0.41, −1.36 ppbv, 6.89 ppbv, 8.98 ppbv, and 31.50%, respectively, while the average corresponding values for the other two models are 0.75, 0.41, −1.40 ppbv, 3.59 ppbv, 4.72 ppbv, and 16.59%, respectively. All three models have similar performance for NO₂ VMR_M, with average R, slope, MB, MAE, RMSE, and percent difference between NO₂ VMR_In-situ and NO₂ VMR_M of 0.74, 0.49, −1.90 ppbv, 3.93 ppbv, 5.05 ppbv, and 18.76%, respectively.

An investigation into the diurnal characteristics of vertical formaldehyde (HCHO) profiles was conducted based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements in Beijing during the CAREBEIJING campaign, covering a month-long period through August and September 2006. Vertical HCHO profiles were retrieved based on a combined differential optical absorption spectroscopy (DOAS) technique and an optimal estimation method (OEM). The HCHO volume-mixing ratio (VMR) was found to be highest in the layer from the surface up to an altitude of 1 km and to decrease with altitude above this layer. In all retrieved profiles, HCHO was not detected in the layer from 3–4 km. Over the diurnal cycle, the HCHO VMR values were generally highest at 15:00 local time (LT) and were lower in the morning and late afternoon. The mean HCHO VMRs were 6.17, 1.82, and 0.80 ppbv for the 0–1, 1–2, and 2–3-km layers, respectively, at 15:00 LT, whereas they were 3.54 (4.79), 1.06 (1.43), and 0.46 (0.63) ppbv for the 0–1, 1–2, and 2–3-km layers, respectively, at 09:00 (17:00) LT. The HCHO VMRs reached their highest values at 15:00 LT on August 19, which were 17.71, 5.20, and 2.31 ppbv for the 0–1, 1–2, and 2–3-km layers, respectively. This diurnal pattern implies that the photo-oxidation of volatile organic compounds (VOCs) was most active at 15:00 LT for several days during the campaign period. In a comparison of the derived HCHO VCDs with those obtained from the Ozone Monitoring Instrument (OMI) measurements, the HCHO vertical column density (VCD) values obtained from the OMI measurements tend to be smaller than those from the MAX-DOAS.

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

We retrieved aerosol effective height (AEH) values from TROPOspheric monitoring instrument (TROPOMI) measurements based on the O ₄ absorption at 477 nm. As the first step in the AEH retrieval algorithm, TROPOMI carbon monoxide and aerosol index products were used for the first time to determine aerosol type. An online radiative transfer calculation method was introduced to the AEH retrieval process. The AEHs were retrieved over northeast Asia (various aerosol types), South Africa (biomass-burning smoke), and the Sahara Desert (dust). The retrieved AEHs were compared with AEHs derived using aerosol extinction profiles from ground and satellite-based lidar measurements. A preliminary evaluation based on comparison with lidar suggests reasonable performance, but shows that cloud masking is the main source of uncertainty. The errors associated with the online-based AEH retrieval algorithm were calculated using synthetic radiance. The total AEH retrieval errors were 0.91 km (0.4 ≤ AOD < 1) and 0.30 km (2 ≤ AOD < 3), highlighting the increased sensitivity of the AEH retrieval algorithm under high AOD conditions. This study demonstrates that the O ₄ absorption band at 477 nm is applicable to aerosol height retrieval from TROPOMI with a high spatial resolution.

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.