Theoretical Uncertainty Analysis of Satellite Retrieved Aerosol Optical Depth Associated with Surface Albedo and Aerosol Optical Properties

Particulate matter estimation using satellite datasets: a machine learning approach

In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM10) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. INSAT-3D and Moderate Resolution Imaging Spectroradiometer (MODIS) for the period of 7 years (2014 to 2020). Ground datasets of AOD are taken from the Aerosol Robotic Network (AERONET) for the validation of satellite-retrieved AOD. The observation of particulate matter (PM) data is acquired from the Central Pollution Control Board (CPCB) station across India. Analysis has been performed on a monthly basis for the given time period. The result shows that AOD products of MODIS exhibit good correlation with AERONET AOD whereas INSAT-3D AOD is not well correlated with AERONET AOD. However, after applying an error envelope and threshold-based filtering technique, we have found that INSAT-3D shows significant correlation with ground-level AOD with approximate correlation of 0.66 for Jaipur and 0.57 for Kanpur exhibiting almost similar performance as MODIS-derived AOD. Satellite AOD data together with ground PM concentration data is used to train the machine learning model (random forest) for the estimation of the PM distribution across India for the year 2020. An encouraging correlation of R-squared (R2) value 0.78 has been observed between the estimated and observed PM10 concentrations. The model demonstrates effective training, mitigating huge overestimation and underestimation. However, despite closely tracking the trends of estimated PM10 with observed PM10, few instances of overestimation persist. This suggests the need for an expanded training dataset to further refine and enhance the model's accuracy. Finally, the machine learning model used for PM10 estimation is found to be optimal for a calibrated satellite AOD product.

Dust in Western Iran: the emergence of new sources in response to shrinking water bodies

We detected sources of dust in the Middle East that contribute to dust events in Western Iran in different seasons. By the analysis of the synoptic data, we identified 309 dusty days in Western Iran during the period 2000-2016. A dusty day is diagnosed if under low horizontal visibility (< 1 km), the dust in suspension is reported at least once a day in at least three synoptic stations. We identified dust sources in the Middle East based on the analysis of the MOD04L2 data from MODIS, the backward HYSPLIT trajectory model, and synoptic conditions. The most influential sources affecting Western Iran are located on the shore and northwest of Lake Tharthar, Hour-al-Azim Marsh, the shore of Razzaza, Habbaniyah Lakes, and West Hammar Marsh, which contributed to 110, 79, 59, 56, and 51 dusty days, respectively. The fluctuation of the surface water area largely contributes to the variability of dusty days in Western Iran. Indeed, the peak dust activity in Western Iran was during the period 2008-2012 in response to the substantial shrinkage of the main water bodies in Iraq. The main sources of dust influencing Western Iran are located in northern and eastern Saudi Arabia in spring, Deir ez-Zur in Syria's Aleppo and Raqqa in summer, and Syria's Homs and Al-Hasakah in winter and spring. Sources of dust in Western Iraq and in most parts of entire Iraq have, respectively, led to the formation of summer and spring dust events in Western Iran. Decreased precipitation in the Middle East from autumn 2007 to 2012 and the occurrence of severe droughts have also contributed to the shrinkage of lakes and wetlands, as well as the reduced agricultural productivity in the Middle East, all of which contributed to the intensification of dust activity in Western Iran in recent decades.

Physical-Optical Properties of Marine Aerosols over the South China Sea: Shipboard Measurements and MERRA-2 Reanalysis

Aerosols play an important role in the Earth–atmosphere system. Their impacts on the weather and climate are highly dependent on spatiotemporal distributions as well as physical-optical properties. Physical-optical properties of the aerosols over the Asian continent have been widely investigated, but there are relatively few observations in maritime locations, especially the South China Sea (SCS). Here, with the combination of in situ ship-based observations from June and July 2019 as well as long-term MERRA-2 reanalysis datasets from January 2012 to December 2021, the physical and optical properties of marine aerosols in the SCS are explored. The impacts of meteorological factors, particularly frontal systems, on the aerosol properties are further analyzed based on detailed observations. The observed results show that aerosols are vertically concentrated below 3 km and the extinction coefficient reaches the maximum value of 0.055 km−1 near 480 m. Moreover, the particles are composed of an accumulation and a coarse particle mode, and they conform to the lognormal distribution. The synoptic-scale case study demonstrates that both the cold front and stationary front lead to an increase in aerosol optical thickness (AOD), which is due to the enhanced wind speed and the hygroscopic growth of fine particles, respectively. The long-term analysis indicates that AOD decreases from northwest to southeast with the increasing distance away from the continent, and it reflects higher values in spring and winter than in summer and autumn. Sulfate and sea salt dominate AOD in this region when compared with other components. The overall AOD shows a significant negative trend of −0.0027 year−1. This work will help us further understand the physical and optical properties of marine aerosols over the SCS and then contribute to quantifying the aerosol radiative forcing in the future.

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.