Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China

Characteristics and optical properties of atmospheric aerosols based on long-term AERONET investigations in an urban environment of Pakistan

Radiative balance, local climate, and human health are all significantly influenced by aerosol. Recent severe air pollution over Lahore, a city in Pakistan calls for more thorough research to determine the negative impacts brought on by too many aerosols. To study regional aerosol characteristics and their differences from various aspects, in-depth and long-term (2007-2020) investigations of the columnar aerosol properties over the urban environment of Lahore were carried out by using AERONET data. The Aerosol Optical Depth (AOD400) and Angstrom Exponent (AE400-870) vary from low values of 0.10 to a maximum value of 4.51 and from 0.03 to 1.81, respectively. The huge differences in the amount of AOD440 as well as AE440-870 show the large fluctuation of aerosol classes because of various sources of their emission. During the autumn and winter seasons, the decreasing trend of the optical parameters of aerosols like Single Scattering Albedo (SSA) and Asymmetry Parameter (ASY) with increasing wavelength from 675 to 1020 nm indicates the dominance of light-absorbing aerosols (biomass burning (BB) and industrial/urban (UI). Due to the long-distance dust movement during spring, summer, and autumn, coarse mode particles predominated in Lahore during the study period. Dust type (DD) aerosols are found to be the dominant one during spring (46.92%), summer (54.31%), and autumn (57.46%) while urban industry (BB/UI) was dominant during the winter season (53.21%). During each season, the clean continental (CC) aerosols are found to be in negligible amounts, indicating terrible air quality in Lahore City. The present research work fills up the study gap in the optical properties of aerosols in Lahore and will help us understand more fully how local aerosol fluctuation affects regional climate change over the urban environment of Lahore.

Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China

The Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) algorithm was developed for aerosol retrieval on bright surfaces. Although the global validation accuracy of the DB product is satisfactory, there are still some regions found to have very low accuracy. To this end, DB has updated the surface database in the latest version of the Collection 6.1 (C6.1) algorithm. Some studies have shown that DB aerosol optical depth (AOD) of the old version Collection 6 (C6) has been seriously underestimated in Northwestern China. However, the status of the new version of the C6.1 product in this region is still unknown. This study aims to comprehensively evaluate the performance of the MODIS DB product in Northwestern China. The DB AOD with high quality (Quality Flag = 2 or 3) was selected to validate against the 23 sites from the China Aerosol Remote Sensing Network (CARSNET) and Aerosol Robotic Network (AERONET) during the period 2002–2014. By the overall analysis, the results indicate that both C6 and C6.1 show significant underestimation with a large fraction of more than 54% of collocations falling below the Expected Error (EE = ±(0.05 + 20% AODground)) envelope and with a large negative Mean Bias (MB) of less than −0.14. Furthermore, the new C6.1 products failed to achieve reasonable improvements in the region of Northwestern China. Besides, C6.1 has slightly fewer collocations than C6 due that some pixels with systematic biases have been removed from the new surface reflectance database. From the analysis of the site scale, the scatter plot of C6.1 is similar to that of C6 in most sites. Furthermore, a significant underestimation of DB AOD was observed at most sites, with the most severe underestimation at two sites located in the Taklimakan Desert region. Among 23 sites in Northwestern China, there are only two sites where C6.1 has largely improved the underestimation of C6. Furthermore, it is interesting to note that there are also two sites where the accuracy of the new C6.1 has declined. Moreover, it is surprising that there is one site where a large overestimation was observed in C6 and improved in C6.1. Additionally, we found a constant value of about 0.05 for both C6 and C6.1 at several sites with low aerosol loading, which is an obvious artifact. The significant improvements of C6.1 were observed in the Middle East and Central Asia but not in most sites of Northwestern China. The results of this study will be beneficial to further improvements in the MODIS DB algorithm.

Variation and Driving Factor of Aerosol Optical Depth over the South China Sea from 1980 to 2020

Spatial and temporal variation of aerosol optical depth (AOD) and optical depth of different aerosol types derived from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) over the South China Sea (SCS) between 1980 and 2020 were studied. AOD distribution showed different characteristics throughout the entire SCS. Sulfate Aerosol Optical Depth (SO4AOD) and Sea Salt Aerosol Optical Depth (SSAOD) mainly contributed to the spatial and temporal variation of AOD over the SCS. A significant increasing trend followed by a decreasing trend of AOD could be observed in the north of the SCS from 1980 to 2020. Mean MERRA-2 AOD between 1980 and 2020 showed that AOD was high in the north and low in the south and that AOD gradually decreased from north to south over the SCS. AOD after 2000 was obviously higher than that of the 1980s and 1990s. Higher AOD appeared in the spring and winter, and low AOD appeared in the summer. The spatial distribution of scattering aerosol optical depth (SAOD) was similar to AOD distribution over the SCS. SO4AOD and SSAOD were obviously higher than black carbon aerosol optical depth (BCAOD), organic carbon aerosol optical depth (OCAOD), and dust aerosol optical depth (DUAOD) over the SCS. SO4AOD accounted for over 50% of total AOD (TAOD) over the north of the SCS, while BCAOD and DUAOD accounted for less than 10% of TAOD over the entire SCS. An obvious annual mean TAOD increase between 1980 and 2007 could be observed over the northern part of the SCS (NSCS), while a TAOD decrease happened from 2008 to 2020 in this region. The correlation coefficient between TAOD and SO4AOD over NSCS from 1980 to 2020 was about 0.93, indicating SO4AOD was the driving factor of TAOD variation in this area. Different AOD variation trends over the different areas of the SCS could be observed during the two periods including 1980–2007 and 2008–2020. AOD increase appeared over most of the SCS during the period from 1980 to 2007, while AOD decrease could be observed over most of the SCS from 2008 to 2020.

Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET

This study provided a comprehensive evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 006 (C6) and 061 (C6.1) Dark Target (DT) 10 km aerosol optical depth (AOD) over China during 2002-2014. Considering that sparse Aerosol Robotic Network (AERONET) sites are available in China, 18 sites from China Aerosol Remote Sensing Network (CARSNET) were also used to conduct this validation. The results showed that C6.1 DT outperform C6 with 59.03% of the retrievals falling within the expected error (EE) compared to C6 (54.94%). Meanwhile, C6.1 DT achieved a reduced RMSE of 0.171, a higher R of 0.901 and a bias closer to 0 relative to C6 (RMSE: 0.185; R: 0.890). When the validation was conducted over different underlying surfaces, C6 DT overestimated AOD by 19.8%, with only 45.01% of the retrievals within the EE over urban sites, whereas C6.1 showed clear improvements, with 11.8% more data falling within the EE. Hardly any improvement was observed in C6.1 over forest, cropland, and grassland sites. The C6.1 DT exhibited more significant improvements over Beijing area and northern China than southern China. The highest retrieval accuracy of 61.05% among the four Beijing sites was achieved at Beijing_CARSNET, but the improvements were lower than other Beijing sites. The extent of the improvements was positively correlated with the percentage of urban pixels over the sites in Beijing and northern China in terms of the retrieval accuracy. Moreover, C6.1 DT had a little effect on improvements over southern China and showed reduced collocation over coastal cities.

A Climatological Satellite Assessment of Absorbing Carbonaceous Aerosols on a Global Scale

A global climatology of absorbing carbonaceous aerosols (ACA) for the period 2005–2015 is obtained by using satellite MODIS (Moderate Resolution Imaging Spectroradiometer)-Aqua and OMI (Ozone Monitoring Instrument)-Aura aerosol optical properties and by applying an algorithm. The algorithm determines the frequency of presence of ACA (black and brown carbon) over the globe at 1° × 1° pixel level and on a daily basis. The results of the algorithm indicate high frequencies of ACA (up to 19 days/month) over world regions with extended biomass burning, such as the tropical forests of southern and central Africa, South America and equatorial Asia, over savannas, cropland areas or boreal forests, as well as over urban and rural areas with intense anthropogenic activities, such as the eastern coast of China or the Indo-Gangetic plain. A clear seasonality of the frequency of occurrence of ACA is evident, with increased values during June–October over southern Africa, during July–November over South America, August–November over Indonesia, November–March over central Africa and November–April over southeastern Asia. The estimated seasonality of ACA is in line with the known annual patterns of worldwide biomass-burning emissions, while other features such as the export of carbonaceous aerosols from southern Africa to the southeastern Atlantic Ocean are also successfully reproduced by the algorithm. The results indicate a noticeable interannual variability and tendencies of ACA over specific world regions during 2005–2015, such as statistically significant increasing frequency of occurrence over southern Africa and eastern Asia.

Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta during the 2014-2017 Period

Large amounts of aerosol particles suspended in the atmosphere pose a serious challenge to the climate and human health. In this study, we produced a dataset through merging the Moderate Resolution Imaging Spectrometers (MODIS) Collection 6.1 3-km resolution Dark Target aerosol optical depth (DT AOD) with the 10-km resolution Deep Blue aerosol optical depth (DB AOD) data by linear regression and made use of it to unravel the spatiotemporal characteristics of aerosols over the Pan Yangtze River Delta (PYRD) region from 2014 to 2017. Then, the geographical detector method and multiple linear regression analysis were employed to investigate the contributions of influencing factors. Results indicate that: (1) compared to the original Terra DT and Aqua DT AOD data, the average daily spatial coverage of the merged AOD data increased by 94% and 132%, respectively; (2) the values of four-year average AOD were high in the north-east and low in the south-west of the PYRD; (3) the annual average AOD showed a decreasing trend from 2014 to 2017 while the seasonal average AOD reached its maximum in spring; and that (4) Digital Elevation Model (DEM) and slope contributed most to the spatial distribution of AOD, followed by precipitation and population density. Our study highlights the spatiotemporal variability of aerosol optical depth and the contributions of different factors over this large geographical area in the four-year period, and can, therefore, provide useful insights into the air pollution control for decision makers.

An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data

The long satellite aerosol data record enables assessments of historical PM_2.5 level in regions where routine PM_2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM_2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM_2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM_2.5 well with the cross-validation (CV) R² (RMSE) of 0.79 (21 μg/m³). The cluster-based subregion models outperformed national models and improved the CV R² by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level ( R² = 0.58, RMSE = 29 μg/m³) and monthly level ( R² = 0.76, RMSE = 16 μg/m³). Our hindcast modeling system allows for the construction of unbiased historical PM_2.5 levels.