The Impact of COVID-19 Lockdowns on Satellite-Observed Aerosol Optical Thickness over the Surrounding Coastal Oceanic Areas of Megacities in the Coastal Zone

Nearly 40 years of aerosol optical thickness (AOT) climate data record (CDR) derived from NOAA operational satellite Advanced Very High Resolution Radiometer (AVHRR) observation over the global oceans is used to study the AOT changes due to the COVID-19 lockdown over the surrounding coastal oceanic areas of 18 megacities in the coast zone (MCCZ). The AOT difference between the annual mean AOT values of 2020 with COVID-19 lockdown and 2019 without the lockdown along with the 2020 AOT annual anomaly are used to effectively identify the AOT changes that are a result of the lockdown. We found that for most of the 18 MCCZ, the COVID-19 lockdowns implemented to contain the spread of the coronavirus resulted in a decrease between 1% and 30% in AOT due to reduced anthropogenic emissions associated with the lockdowns. However, the AOT long-term trend and other aerosol interannual variations due to favorable or unfavorable meteorological conditions may mask AOT changes due to the lockdown effect in some MCCZ. Different seasonal variations of aerosol amount in 2020 relative to 2019 due to other natural aerosol emission sources not influenced by the lockdown, such as dust storms and natural biomass burning and smoke, may also conceal a limited reduction in the annual mean AOT due to the lockdown in MCCZ with relatively loose lockdown. This study indicates that the use of long-term satellite observation is helpful for studying and monitoring the aerosol changes due to the emission reduction associated with the COVID-19 lockdown in the surrounding coastal oceanic areas of MCCZ, which will benefit the future development of the mitigation strategy for air pollution and emissions in megacities.

Variation of aerosol optical thickness with atmospheric water vapour : A case study over a continental station Mysore, India

Atmospheric measurements in a continental, low latitude station Mysore (12.3° N) has been carried out, for the period December 2003 to June 2006. Measurements were made using a sunphotometer with five bands in the visible and near-infrared range of the solar spectrum. To bring out the wavelength dependence of Aerosol Optical Thickness (AOT) on atmospheric water vapour, typically two wavelength channels are being used, one at 500 nm and the other at 1020 nm. A linear dependence between AOT and water vapour on meteorologically calm days is the important observation made. Growth rate of AOT is found to be larger at shorter wavelength (500 nm) than that of the longer wavelength (1020 nm). A mass-plot representation is followed on monthly basis, which is nothing but the graphical plot of spectral AOT versus water vapour of the scans for all the clear sky days of a particular month. Further investigations reveal that some months exhibit a single trend of growth of AOT with water vapour whereas double trend is the scenario for other months. These results provide insight into the changes in the atmospheric aerosol characteristics with precipitable water vapour, which is the subject matter of this paper.

Spatial and Temporal Variations of Aerosol Optical Thickness over the China Seas from Himawari-8

Six years of hourly aerosol optical thickness (AOT) data retrieved from Himawari-8 were used to investigate the spatial and temporal variations, especially diurnal variations, of aerosols over the China Seas. First, the Himawari-8 AOT data were consistent with the AERONET measurements over most of the China Seas, except for some coastal regions. The spatial feature showed that AOT over high latitude seas was generally larger than over low latitude seas, and it is distributed in strips along the coastline and decreases gradually with increasing distance from the coastline. AOT undergoes diurnal variation as it decreases from 9:00 a.m. local time, reaching a minimum at noon, and then begins to increase in the afternoon. The percentage daily departure of AOT over the East China Seas generally ranged ±20%, increasing sharply in the afternoon; however, over the northern part of the South China Sea, daily departure reached a maximum of >40% at 4:00 p.m. The monthly variation in AOT showed a pronounced annual cycle. Seasonal variations of the spatial pattern showed that the largest AOT was usually observed in spring and varies in other seasons for different seas.

THE VARIABILITY OF AEROSOL OPTICAL THICKNESS IN MOUNTAINOUS AND FOOTHILL AREAS OF THE NORTH OSSETIA ACCORDING TO SATELLITE MEASUREMENTS

Atmospheric aerosol is one of the indicators of air quality that affects the environmental situation. The aerosol optical thickness (AOT) of the atmosphere is studied in the mountainous, foothill, and plain regions of Republic of North Ossetia-Alania (Russian Federation) based on satellite data EOS Terra, Aqua. Several geographical locations at different heights were selected for the analysis. Daily series of AOT, air temperature and precipitation data were obtained on period over 20 years for each point. The results of statistical analysis of long-term values of AOT, temperature and precipitation are shown. There is a statistically significant relationship between the averaged values, and there is also a dependence on the height of the location and the proximity of mountains. Long-term average dependencies provide a basis for predicting the AOT value based on the measured temperature at height of 2 m for territories located in a complex landscape at different altitudes.

Retrieval of Aerosol Optical Thickness with Custom Aerosol Model Using SKYNET Data over the Chiba Area

The retrieval of the aerosol optical thickness (AOT) from remotely-sensed data relies on the adopted aerosol model. However, the method of this technique has been rather limited because of the high variability of the surface albedo, in addition to the spatial variability in the aerosol properties over the land surfaces. To overcome unsolved problems, we proposed a method for the visibility-derived AOT estimation from SKYNET-based measurement and daytime satellite images with a custom aerosol model over the Chiba area (35.62° N, 140.10° E), which is located in the greater Tokyo metropolitan area in Japan. Different from conventionally-used aerosol models for the boundary layer, we created a custom aerosol model by using sky-radiometer observation data of aerosol volume size distribution and refractive indices, coupled with spectral response functions (SPFs) of satellite visible bands to alleviate the wide range of path-scattered radiance. We utilized the radiative transfer code 6S to implement the radiative transfer calculation based on the created custom aerosol model. The concurrent data from ground-based measurement are used in the radiative analysis, namely the temporal variation of AOT from SKYNET. The radiative estimation conducted under clear-sky conditions with minimum aerosol loading is used for the determination of the surface albedo, so that the 6S simulation yields a well-defined relation between total radiance and surface albedo. We made look-up tables (LUTs) pixel-by-pixel over the Chiba area for the custom aerosol model to retrieve the satellite AOT distribution based on the surface albedo. Therefore, such a reference of surface albedo generated from clear-sky conditions, in turn, can be employed to retrieve the spatial distribution of AOT on both clear and relatively turbid days. The value for the AOTs retrieved using the custom aerosol model is found to be stable than conventionally-used typical aerosol models, indicating that our method yields substantially better performance.

Comparison of three aerosol representations of NHM-Chem (v1.0) for the simulations of air quality and climate-relevant variables

This study provides comparisons of aerosol representation methods incorporated into a regional-scale nonhydrostatic meteorology–chemistry model (NHM-Chem). Three options for aerosol representations are currently available: the five-category non-equilibrium (Aitken, soot-free accumulation, soot-containing accumulation, dust, and sea salt), three-category non-equilibrium (Aitken, accumulation, and coarse), and bulk equilibrium (submicron, dust, and sea salt) methods. The three-category method is widely used in three-dimensional air quality models. The five-category method, the standard method of NHM-Chem, is an extensional development of the three-category method and provides improved predictions of variables relating to aerosol–cloud–radiation interaction processes by implementing separate treatments of light absorber and ice nuclei particles, namely, soot and dust, from the accumulation- and coarse-mode categories (implementation of aerosol feedback processes to NHM-Chem is still ongoing, though). The bulk equilibrium method was developed for operational air quality forecasting with simple aerosol dynamics representations. The total CPU times of the five-category and three-category methods were 91 % and 44 % greater than that of the bulk method, respectively. The bulk equilibrium method was shown to be eligible for operational forecast purposes, namely, the surface mass concentrations of air pollutants such as O3, mineral dust, and PM2.5. The simulated surface concentrations and depositions of bulk chemical species of the three-category method were not significantly different from those of the five-category method. However, the internal mixture assumption of soot/soot-free and dust/sea salt particles in the three-category method resulted in significant differences in the size distribution and hygroscopicity of the particles. The unrealistic dust/sea salt complete mixture of the three-category method induced significant errors in the prediction of the mineral dust-containing cloud condensation nuclei (CCN), which alters heterogeneous ice nucleation in cold rain processes. The overestimation of soot hygroscopicity by the three-category method induced errors in the BC-containing CCN, BC deposition, and light-absorbing aerosol optical thickness (AAOT). Nevertheless, the difference in AAOT was less pronounced with the three-category method because the overestimation of the absorption enhancement was compensated by the overestimation of hygroscopic growth and the consequent loss due to in-cloud scavenging. In terms of total properties, such as aerosol optical thickness (AOT) and CCN, the results of the three-category method were acceptable.

Consistent retrieval of cloud/aerosol single scattering properties and surface reflectance

<p>The CISAR (Combined Inversion of Surface and AeRosols) algorithm is exploited in the framework of the ESA Aerosol Climate Change Initiatiave (CCI) project, aiming at providing a set of atmospheric (cloud and aerosol) and surface reflectance products derived from S3A/SLSTR observations using the same radiative transfer physics and assumptions. CISAR is an advance algorithm developed by Rayference originally designed for the retrieval of aerosol single scattering properties and surface reflectance from both geostationary and polar orbiting satellite observations.  It is based on the inversion of a fast radiative transfer model (FASTRE). The retrieval mechanism allows a continuous variation of the aerosol and cloud single scattering properties in the solution space.</p><p> </p><p>Traditionally, different approaches are exploited to retrieve the different Earth system components, which could lead to inconsistent data sets. The simultaneous retrieval of different atmospheric and surface variables over any type of surface (including bright surfaces and water bodies) with the same forward model and inversion scheme ensures the consistency among the retrieved Earth system components. Additionally, pixels located in the transition zone between pure clouds and pure aerosols are often discarded from both cloud and aerosol algorithms. This “twilight zone” can cover up to 30% of the globe. A consistent retrieval of both cloud and aerosol single scattering properties with the same algorithm could help filling this gap.</p><p> </p><p>The CISAR algorithm aims at overcoming the need of an external cloud mask, discriminating internally between aerosol and cloud properties. This approach helps reducing the overestimation of aerosol optical thickness in cloud contaminated pixels. The surface reflectance product is delivered both for cloud-free and cloudy observations.  </p><p> </p><p>Global maps obtained from the processing of S3A/SLSTR observations will be shown. The SLSTR/CISAR products over events such as, for instance, the Australian fire in the last months of 2019, will be discussed in terms of aerosol optical thickness, aerosol-cloud discrimination and fine/coarse mode fraction.</p>

Trends of Aerosol Optical Thickness Using VIIRS S-NPP during Fog Episodes in Pakistan and India

Aerosol Optical Thickness (AOT) is one of the important parameters for assessing regional and global level of climate change. Fog episodes have considerably increased in south Asia because of environmental factors, and the burning of agricultural residue leads to major social and economic problems. In present study, Mann-Kendall trend analysis of AOT and active fire events was done, and their significance were assessed using long-term (October 2012–February 2020) remote sensing data derived smog maps. Visible Infrared Imaging Radiometer Suite National Polar Partnership (VIIRS N-PP) was used to map AOT episodes over the northern region of Pakistan and India. Results reveal that AOT displays a significantly decreasing trend over the northern and eastern region of Pakistan and a similar decreasing trend from the Western to Eastern region of India. Furthermore, active fire events have a significantly increasing trend at the Northern region of Pakistan. However, fire events have a significantly decreasing trend over the southern and southeastern region of India. Additionally, statistically significant decreasing trends were observed for AOT over Chakwal (p-value = 0.2, Z_MK = −2.3) and Patiala (p-value = 0.15, Z_MK = −3.2). Fire events have a significantly increasing trend for Dera Ismail Khan (p-value = 0.01, Z_MK = 1.9), Jhang (p-value = 0.01, Z_MK = 1.9), and Chakwal (p-value = 0.01, Z_MK = 1.8), while they are significantly decreasing trend near New Delhi (p-value = 0.2, Z_MK = −0.9), Aligarh (p-value = 0.15, Z_MK = −0.9) and Patiala (p-value = 0.2, Z_MK = −0.8).