Understanding Haze: Modeling Size-Resolved Mineral Aerosol from Satellite Remote Sensing

Mineral dust aerosols are composed of a complex mixture of silicates, carbonates, oxides, and sulfates. The minerals’ chemical composition and size distribution are vital parameters to evaluate dust environmental impacts. However, the quantification of minerals remains a challenge due to the sparse in situ measurements of dust samples. Here we derive the size-resolved mineralogical composition of airborne dust aerosols from MODIS (Terra and Aqua) satellite-acquired optical measurements and compare it with chemically analyzed elemental (Al, Fe, Ca, Mg) concentrations of aerosols for PM2.5 and PM10 from Chonburi, Chiang Rai, and Bangkok in Thailand, and from Singapore. MODIS-derived mineral retrievals exhibited high correlations with elemental concentrations with R2 ≥ 0.84 for PM2.5 and ≥0.96 for PM10. High mineral dust activity was detected in the vicinity of biomass-burning areas with gypsum and calcite exhibiting tracer characteristics of combustion. The spatiotemporal pattern of the MODIS-derived minerals matched with Ozone Monitoring Instrument (OMI)-derived dust, sulfates, and carbonaceous aerosols, indicating the model’s consistency. Variation in aerosol loading by ±90% led to deviation in the mineral concentration by <10%. An uncertainty of 6.4% between AERONET-measured and MODIS-derived AOD corresponds to a < ± 2% uncertainty in MODIS-derived mineral concentration, demonstrating the robustness of the model.

Classification and transformation of aerosols over selected Indian cities during reduced emissions under Covid-19 lockdown

Abstract

Studies in the recent past show improved air quality over India during the Covid-19 lockdown. This research attempts to characterize atmospheric aerosols in terms of α and AOD and their transformation over India during the pandemic lockdown. The type and particle distribution of aerosols, including gaseous species for five Indian regions were considered. Fine to coarse particle shift was observed in most regions. The northern region observed high fire counts, implying crop residue burning season during the stringent lockdown. Thiruvananthapuram, in the south, showed an increase in PM, owing to the resumption of mobility post-lockdown. Hyderabad, however; observed increased PM2.5 (2.79%) and AOD (37.23%) during Phase 1. Maritime (MT) aerosol predominated over Thiruvananthapuram, whereas urban/biomass burning (UBB) type decreased over the eastern region. Contributions from continental average (CA), maritime continental average (MCA), and MT were observed over Hyderabad, post-lockdown. In the central region, MCA was replaced by UBB and mixed type, with isolated episodes of clean continental (CC) and desert dust (DD). During lockdown phases, an increase in O3 over western, northern, and central regions is attributed to increased temperature and decreased NO2. A significant correlation with population density (PD) exists with NO2 (R 2 = 0.75; p < 0.05), suggesting human mobility as a major contributor to NO2 in the atmosphere during the lockdown period.

Highlights

Characterization of atmospheric aerosols during Covid-19 lockdown over India. General shift from fine to coarse particles size in most regions. Crop residue burning increased pollutants in North during lockdown. Forest fire season in central and south-central region increased PM, NO2 concentrations during lockdown. Maritime origin aerosols dominate over Thiruvananthapuram. Decline in pollutants in post-lockdown due to meteorology (early monsoon, cyclone Amphan, and Nisarga).

Supplementary Information

The online version contains supplementary material available at 10.1007/s12040-022-01916-y.

Accuracy and error cause analysis, and recommendations for usage of Himawari-8 aerosol products over Asia and Oceania

The Himawari-8 aerosol algorithm was updated to version 3 (V30). However, no study has evaluated its performance. The purpose of this study is to verify and to compare version 2.1 (V21) and V30 aerosol products, to explain which factor dominates the aerosol optical depth (AOD) error, and to provide recommendations for aerosol product usage. The AOD accuracy of V30 was better than that of V21, with a higher correlation coefficient (R) and a higher expected error (EE_DT). The V30 AOD metrics (including R, EE_DT, and the root mean square error) exceeded those of V21 on more than 69% of the AERONET sites and its bias from MODIS AOD was smaller than that of V21 AOD. However, the V30 AOD does not meet the metric of EE_DT > 0.66. The analysis results suggest that aerosol type parameters (primarily the Ångström exponent (AE)) may be the dominant factor determining the AOD error. This reveals the direction of H8 algorithm improvement. More than 59% of the H8 AE value meets the expected error but they do not capture the variety (R < 0.3). The FMF and SSA retrieved by H8 performed poorly. The V30 AOD performs best in Japan and South Korea (83.3% of AERONET sites meet the EE_DT > 0.66 requirement) and has better data accuracy in the morning. Therefore, we recommend V30 AOD morning data to users in Japan and South Korea regions.

Numerical Assessment of Downward Incoming Solar Irradiance in Smoke Influenced Regions—A Case Study in Brazilian Amazon and Cerrado

Smoke aerosol plumes generated during the biomass burning season in Brazil suffer long-range transport, resulting in large aerosol optical depths over an extensive domain. As a consequence, downward surface solar irradiance, and in particular the direct component, can be significantly reduced. Accurate solar energy assessments considering the radiative contribution of biomass burning aerosols are required to support Brazil’s solar power sector. This work presents the 2nd generation of the radiative transfer model BRASIL-SR, developed to improve the aerosol representation and reduce the uncertainties in surface solar irradiance estimates in cloudless hazy conditions and clean conditions. Two numerical experiments allowed to assess the model’s skill using observational or regional MERRA-2 reanalysis AOD data in a region frequently affected by smoke. Four ground measurement sites provided data for the model output validation. Results for DNI obtained using δ-Eddington scaling and without scaling are compared, with the latter presenting the best skill in all sites and for both experiments. An increase in the relative error of DNI results obtained with δ-Eddington optical depth scaling as AOD increases is evidenced. For DNI, MBD deviations ranged from −2.3 to −0.5%, RMSD between 2.3 and 4.7% and OVER between 0 and 5.3% when using in-situ AOD data. Overall, our results indicate a good skill of BRASIL-SR for the estimation of both GHI and DNI.

Black Carbon over Wuhan, China: Seasonal Variations in Its Optical Properties, Radiative Forcing and Contribution to Atmospheric Aerosols

As an important fraction of light-absorbing particles, black carbon (BC) has a significant warming effect, despite accounting for a small proportion of total aerosols. A comprehensive investigation was conducted on the characteristics of atmospheric aerosols and BC particles over Wuhan, China. Mass concentration, optical properties, and radiative forcing of total aerosols and BC were estimated using multi-source observation data. Results showed that the BC concentration monthly mean varied from 2.19 to 5.33 μg m−3. The BC aerosol optical depth (AOD) maximum monthly mean (0.026) occurred in winter, whereas the maximum total AOD (1.75) occurred in summer. Under polluted-air conditions, both aerosol radiative forcing (ARF) and BC radiative forcing (BCRF) at the bottom of the atmosphere (BOA) were strongest in summer, with values of −83.01 and −11.22 W m−2, respectively. In summer, ARF at BOA on polluted-air days was more than two-fold that on clean-air days. In addition, compared with clean-air days, BCRF at BOA on polluted-air days was increased by 76% and 73% in summer and winter, respectively. The results indicate an important influence of particulate air pollution on ARF and BCRF. Furthermore, the average contribution of BCRF to ARF was 13.8%, even though the proportion of BC in PM2.5 was only 5.1%.

Impact of Aerosol and Cloud on the Solar Energy Potential over the Central Gangetic Himalayan Region

We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of ground-based AERONET Sun Photometer and satellite observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat Second Generation (MSG), with radiative transfer model (RTM) simulations and 1 day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Clouds and aerosols are one of the most common sources of solar irradiance attenuation and hence causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The outputs of RTM results presented with high accuracy under clear, cloudy sky and dust conditions for global horizontal (GHI) and beam horizontal irradiance (BHI). On an annual basis the total aerosol attenuation was found to be up to 105 kWh m−2 for the GHI and 266 kWh m−2 for BHI, respectively, while the cloud effect is much stronger with an attenuation of 245 and 271 kWh m−2 on GHI and BHI. The results of this study will support the Indian solar energy producers and electricity handling entities in order to quantify the energy and financial losses due to cloud and aerosol presence.

Cloud, Aerosol, and Radiative Properties Over the Western North Atlantic Ocean

This study examines the atmospheric properties of weather states (WSs) derived from the International Satellite Cloud Climatology Project over the Western North Atlantic Ocean. In particular, radiation and aerosol data corresponding to two sites in the study domain, Pennsylvania State University and Bermuda, were examined to characterize the atmospheric properties of the various satellite-derived WSs. At both sites, the fair weather WS was most prevalent, followed by the cirrus WS. Differences in the seasonality of the various WSs were observed at the two sites. Fractional sky cover and effective shortwave cloud transmissivity derived from ground-based radiation measurements were able to capture differences among the satellite-derived WSs. Speciated aerosol optical thicknesses (AOT) from the Modern-Era Retrospective Analysis for Research and Applications, version 2 were used to investigate potential differences in aerosol properties among the WSs. The clear sky WS exhibited below-average seasonal values of AOT at both sites year-round, as well as relatively high rates of occurrence with low AOT events. In addition, the clear sky WS showed above-average contributions from dust and black carbon to the total AOT year-round. Finally, transitions between various WSs were examined under low, high, and midrange AOT conditions. The most common pathway was for the WSs to remain in the same state after a 3 h interval. Some WSs, such as mid latitude storms, deep convection, middle top, and shallow cumulus, were more prevalent as ending states under high AOT conditions. This work motivates examining differences in aerosol properties between WSs in other regions.

Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models

Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMF&AOT-PM2.5) data were tested as input to a deep learning model and four classical machine learning models. The results showed that FMF&AOT-PM2.5 performed better than AOT (AOT-PM2.5) in modelling PM2.5 estimations. The FMF was then applied in satellite-based PM2.5 retrieval over China during 2020, and FMF&AOT-PM2.5 was found to have a better agreement with ground-level PM2.5 than AOT-PM2.5 on dust and haze days. The better linear correlation between PM2.5 and fAOT on both haze and dust days (dust days: R = 0.82; haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM2.5. This study demonstrates the importance of including the FMF to improve PM2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM2.5 retrieval.

Spatio-Temporal Variability of Aerosol Components, Their Optical and Microphysical Properties over North China during Winter Haze in 2012, as Derived from POLDER/PARASOL Satellite Observations

Pollution haze is a frequent phenomenon in the North China Plain (NCP) appearing during winter when the aerosol is affected by various pollutant sources and has complex distribution of the aerosol properties, while different aerosol components may have various critical effects on air quality, human health and radiative balance. Therefore, large-scale and accurate aerosol components characterization is urgently and highly desirable but hardly achievable at the regional scale. In this respect, directional and polarimetric remote sensing observations have great potential for providing information about the aerosol components. In this study, a state-of-the-art GRASP/Component approach was employed for attempting to characterize aerosol components in the NCP using POLDER/PARASOL satellite observations. The analysis was done for January 2012 in Beijing (BJ) and Shanxi (SX). The results indicate a peak of the BC mass concentration in an atmospheric column of 82.8 mg/m2 in the SX region, with a mean of 29.2 mg/m2 that is about four times higher than one in BJ (8.9 mg/m2). The mean BrC mass concentrations are, however, higher in BJ (up to ca. 271 mg/m2) than that in SX, which can be attributed to a higher anthropogenic emission. The mean amount of fine ammonium sulfate-like particles observed in the BJ region was three times lower than in SX (131 mg/m2). The study also analyzes meteorological and air quality data for characterizing the pollution event in BJ. During the haze episode, the results suggest a rapid increase in the fine mode aerosol volume concentration associated with a decrease of a scale height of aerosol down to 1500 m. As expected, the values of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD) and fine mode aerosol optical depth (AODf) are much higher on hazy days. The mass fraction of ammonium sulfate-like aerosol increases from about 13% to 29% and mass concentration increases from 300 mg/m2 to 500 mg/m2. The daily mean PM2.5 concentration and RH independently measured during these reported pollution episodes reach up to 425 g/m3 and 80% correspondingly. The monthly mean mass concentrations of other aerosol components in the BJ are found to be in agreement with the results of previous research works. Finally, a preliminary comparison of these remote sensing derived results with literature and in situ PM2.5 measurements is also presented.

Synergistic aircraft and ground observations of transported wildfire smoke and its impact on air quality in New York City during the summer 2018 LISTOS campaign

Air pollution associated with wildfire smoke transport during the summer can significantly affect ozone (O₃) and particulate matter (PM) concentrations, even in heavily populated areas like New York City (NYC). Here, we use observations from aircraft, ground-based lidar, in-situ analyzers and satellite to study and assess wildfire smoke transport, vertical distribution, optical properties, and potential impact on air quality in the NYC urban and coastal areas during the summer 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We investigate an episode of dense smoke transported and mixed into the planetary boundary layer (PBL) on August 15-17, 2018. The horizontal advection of the smoke is shown to be characterized with the prevailing northwest winds in the PBL (velocity > 10 m/s) based on Doppler wind lidar measurements. The wildfire sources and smoke transport paths from the northwest US/Canada to northeast US are identified from the NOAA hazard mapping system (HMS) fires and smoke product and NOAA-HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory analysis. The smoke particles are distinguished from the urban aerosols by showing larger lidar-ratio (70-sr at 532-nm) and smaller depolarization ratio (0.02) at 1064-nm using the NASA High Altitude Lidar Observatory (HALO) airborne high-spectral resolution lidar (HSRL) measurements. The extinction-related angstrom exponents in the near-infrared (IR at 1020-1640 nm) and Ultraviolet (UV at 340-440 nm) from NASA-Aerosol Robotic Network (AERONET) product show a reverse variation trend along the smoke loadings, and their absolute differences indicate strong correlation with the smoke-Aerosol Optical Depth (AOD) (R > 0.94). We show that the aloft smoke plumes can contribute as much as 60-70% to the column AOD and that concurrent high-loadings of O₃, carbon monoxide (CO), and black carbon (BC) were found in the elevated smoke layers from the University of Maryland (UMD) aircraft in-situ observations. Meanwhile, the surface PM_2.5 (PM with diameter ≤ 2.5 μm), organic carbon (OC) and CO measurements show coincident and sharp increase (e.g., PM_2.5 from 5 μg/m³ before the plume intrusion to ~30 μg/m³) with the onset of the plume intrusions into the PBL along with hourly O₃ exceedances in the NYC region. We further evaluate the NOAA-National Air Quality Forecasting Capability (NAQFC) model PBL-height, PM_2.5, and O₃ with the observations and demonstrate good consistency near the ground during the convective PBL period, but significant bias at other times. The aloft smoke layers are sometimes missed by the model.

Comparison of Aerosol Optical Depth from MODIS Product Collection 6.1 and AERONET in the Western United States

Recent observations reveal that dust storms are increasing in the western USA, posing imminent risks to public health, safety, and the economy. Much of the observational evidence has been obtained from ground-based platforms and the visual interpretation of satellite imagery from limited regions. Comprehensive satellite-based observations of long-term aerosol records are still lacking. In an effort to develop such a satellite aerosol dataset, we compared and evaluated the Aerosol Optical Depth (AOD) from Deep Blue (DB) and Dark Target (DT) product collection 6.1 with the Aerosol Robotic Network (AERONET) program in the western USA. We examined the seasonal and monthly average number of Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua DB AOD retrievals per 0.1∘×0.1∘ from January 2003 to December 2017 across the region’s different topographic, climatic, and land cover conditions. The number of retrievals in the southwest United States was on average greater than 37 days per 90 days for all seasons except summer. Springtime saw the highest number of AOD retrievals across the southwest, consistent with the peak season for synoptic-scale dust events. The majority of Arizona, New Mexico, and western Texas showed the lowest number of retrievals during the monsoon season. The majority of collocating domains of AOD from the Aqua sensor showed a better correlation with AERONET AOD than AOD from Terra, and the correlation coefficients exhibited large regional variability across the study area. The correlation coefficient between the couplings Aqua DB AOD-AERONET AOD and Terra DB AOD-AERONET AOD ranges from 0.1 to 0.94 and 0.001 to 0.94, respectively. In the majority of the sites that exhibited less than a 0.6 correlation coefficient and few matched data points at the nearest single pixel, the correlations gradually improved when the spatial domain increased to a 50 km × 50 km box averaging domain. In general, the majority of the stations revealed significant correlation between MODIS and AERONET AOD at all spatiotemporal aggregating domains, although MODIS generally overestimated AOD compared to AERONET. However, the correlation coefficient in the southwest United States was the lowest and in stations from a higher latitude was the highest. The difference in the brightness of the land surface and the latitudinal differences in the aerosol inputs from the forest fires and solar zenith angles are some of the factors that manifested the latitudinal correlation differences.

2019‒2020 Australian bushfire air particulate pollution and impact on the South Pacific Ocean

During late 2019 and early 2020, Australia experienced one of the most active bushfire seasons that advected large emissions over the adjacent ocean. Herein, we present a comprehensive research on mixed atmospheric aerosol particulate pollution emitted by wildfires in the atmosphere and the ocean. Based on a wide range of physical and biochemical data, including the Aerosol Robotic Network, multi-satellite observations, and Argo floats, we investigated the spatio-temporal variations and mixed compositions of aerosol particles, deposition in the coastal waters of eastern Australia and the South Pacific Ocean, and biogeochemical responses in the water column. Four types of wildfire-derived mixed particles were classified by using the optical properties of aerosols into four types, including the background aerosols, mineral dust, wildfire smoke particles, and residual smoke. The coarse particles accounted for more than 60% of the mineral dust on 22 November 2019 in the Tasman Sea; afterwards, during the wildfire smoke episode from December 2019 to January 2020, the particles affected large areas of the atmosphere such as eastern Australia, the South Pacific Ocean, and South America. The maximum value of the aerosol optical depth reached 2.74, and the proportion of fine particles accounted for 98.9% in the smoke episode. Mineral dust and smoke particles from the fire emissions changed the particle composition in the surface ocean. Particle deposition accounted for increases in chlorophyll-a concentration (Chla) standardized anomaly up to maximum of 23.3 with a lag time of less than 8 days. In the vertical direction, float observations showed the impact of exogenous particles on the water column could up to 64.7 m deep, resulting in Chla of 1.85 mg/m3. The high Chla lasted for a minimum period of two months until it returned to normal level.

An Aerosol Climatology and Implications for Clouds at a Remote Marine Site: Case Study Over Bermuda

Aerosol characteristics and aerosol-cloud interactions remain uncertain in remote marine regions. We use over a decade of data (2000-2012) from the NASA AErosol RObotic NETwork, aerosol and wet deposition samples, satellite remote sensors, and models to examine aerosol and cloud droplet number characteristics at a representative open ocean site (Bermuda) over the Western North Atlantic Ocean (WNAO). Annual mean values were as follows: aerosol optical depth (AOD) = 0.12, Ångström Exponent (440/870 nm) = 0.95, fine mode fraction = 0.51, asymmetry factor = 0.72 (440 nm) and 0.68 (1020 nm), and Aqua-MODIS cloud droplet number concentrations = 51.3 cm-3. The winter season (December-February) was characterized by high sea salt optical thickness and the highest aerosol extinction in the lowest 2 km. Extensive precipitation over the WNAO in winter helps contribute to the low FMFs in winter (~0.40-0.50) even though air trajectories often originate over North America. Spring and summer had more pronounced influence from sulfate, dust, organic carbon, and black carbon. Volume size distributions were bimodal with a dominant coarse mode (effective radii: 1.85-2.09 μm) and less pronounced fine mode (0.14-0.16 μm), with variability in the coarse mode likely due to different characteristic sizes for transported dust (smaller) versus regional sea salt (larger). Extreme pollution events highlight the sensitivity of this site to long-range transport of urban emissions, dust, and smoke. Differing annual cycles are identified between AOD and cloud droplet number concentrations, motivating a deeper look into aerosol-cloud interactions at this site.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

New global aerosol fine-mode fraction data over land derived from MODIS satellite retrievals

The space-borne measured fine-mode aerosol optical depth (fAOD) is a gross index of column-integrated anthropogenic particulate pollutants, especially over the populated land. The fAOD is the product of the AOD and the fine-mode fraction (FMF). While there exist numerous global AOD products derived from many different satellite sensors, there have been much fewer, if any, global FMF products with a quality good enough to understand their spatiotemporal variations. This is key to understanding the global distribution and spatiotemporal variations of air pollutants, as well as their impacts on global environmental and climate changes. Modifying our newly developed retrieval algorithm to the latest global-scale Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product (Collection 6.1), a global 10-year FMF product is generated and analyzed here. We first validate the product through comparisons with the FMF derived from Aerosol Robotic Network (AERONET) measurements. Among our 169,313 samples, the satellite-derived FMFs agreed with the AERONET spectral deconvolution algorithm (SDA)-retrieved FMFs with a root-mean-square error (RMSE) of 0.22. Analyzed using this new product are the global patterns and interannual and seasonal variations of the FMF over land. In general, the FMF is large (>0.80) over Mexico, Myanmar, Laos, southern China, and Africa and less than 0.5 in the Sahelian and Sudanian zones of northern Africa. Seasonally, higher FMF values occur in summer and autumn. The linear trend in the satellite-derived and AERONET FMFs for different countries was explored. The upward trend in the FMFs was particularly strong over Australia since 2008. This study provides a new global view of changes in FMFs using a new satellite product that could help improve our understanding of air pollution around the world.

Assessment of Normalized Water-Leaving Radiance Derived from GOCI Using AERONET-OC Data

The geostationary ocean color imager (GOCI), as the world’s first operational geostationary ocean color sensor, is aiming at monitoring short-term and small-scale changes of waters over the northwestern Pacific Ocean. Before assessing its capability of detecting subdiurnal changes of seawater properties, a fundamental understanding of the uncertainties of normalized water-leaving radiance (nLw) products introduced by atmospheric correction algorithms is necessarily required. This paper presents the uncertainties by accessing GOCI-derived nLw products generated by two commonly used operational atmospheric algorithms, the Korea Ocean Satellite Center (KOSC) standard atmospheric algorithm adopted in GOCI Data Processing System (GDPS) and the NASA standard atmospheric algorithm implemented in Sea-Viewing Wide Field-of-View Sensor Data Analysis System (SeaDAS/l2gen package), with Aerosol Robotic Network Ocean Color (AERONET-OC) provided nLw data. The nLw data acquired from the GOCI sensor based on two algorithms and four AERONET-OC sites of Ariake, Ieodo, Socheongcho, and Gageocho from October 2011 to March 2019 were obtained, matched, and analyzed. The GDPS-generated nLw data are slightly better than that with SeaDAS at visible bands; however, the mean percentage relative errors for both algorithms at blue bands are over 30%. The nLw data derived by GDPS is of better quality both in clear and turbid water, although underestimation is observed at near-infrared (NIR) band (865 nm) in turbid water. The nLw data derived by SeaDAS are underestimated in both clear and turbid water, and the underestimation worsens toward short visible bands. Moreover, both algorithms perform better at noon (02 and 03 Universal Time Coordinated (UTC)), and worse in the early morning and late afternoon. It is speculated that the uncertainties in nLw measurements arose from aerosol models, NIR water-leaving radiance correction method, and bidirectional reflectance distribution function (BRDF) correction method in corresponding atmospheric correction procedure.

Spectral Derivatives of Optical Depth for Partitioning Aerosol Type and Loading

Quantifying aerosol compositions (e.g., type, loading) from remotely sensed measurements by spaceborne, suborbital and ground-based platforms is a challenging task. In this study, the first and second-order spectral derivatives of aerosol optical depth (AOD) with respect to wavelength are explored to determine the partitions of the major components of aerosols based on the spectral dependence of their particle optical size and complex refractive index. With theoretical simulations from the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) model, AOD spectral derivatives are characterized for collective models of aerosol types, such as mineral dust (DS) particles, biomass-burning (BB) aerosols and anthropogenic pollutants (AP), as well as stretching out to the mixtures among them. Based on the intrinsic values from normalized spectral derivatives, referenced as the Normalized Derivative Aerosol Index (NDAI), a unique pattern is clearly exhibited for bounding the major aerosol components; in turn, fractions of the total AOD (fAOD) for major aerosol components can be extracted. The subtlety of this NDAI method is examined by using measurements of typical aerosol cases identified carefully by the ground-based Aerosol Robotic Network (AERONET) sun–sky spectroradiometer. The results may be highly practicable for quantifying fAOD among mixed-type aerosols by means of the normalized AOD spectral derivatives.

Climatology and trends of aerosol optical depth with different particle size and shape in northeast China from 2001 to 2018

Aerosol generated from the economic development and extensive urbanization in northeast China (NEC) could influence aerosol optical properties and affect the regional air quality. The level 3 aerosol optical depth (AOD) of different particle size and shape (spherical or nonspherical) obtained by Multiangle Imaging Spectroradiometer (MISR) version 23 were used to estimate their seasonal, annual, and decadal distribution and contribution in NEC from 2001 to 2018. The highest AOD of approximately 0.3 was found in the central Liaoning urban agglomeration, and the lowest AOD occurred in the mountainous area of NEC; the proportion of spherical AOD in NEC region was more than 90%. The contribution of large AOD was higher in spring, ranging from 28.8% to 29.8%. In spring and summer, small and medium AODs were concentrated in central Liaoning (approximately 0.2-0.3 and 0.06-0.08, respectively). The annual variation in the AOD of different particle size was significantly higher in Liaoning than in Jilin and Heilongjiang. The annual proportions of small and spherical AODs were approximately 60% and 90%, respectively. The annual occurrence of clean conditions with AOD < 0.05 was most common in northern Heilongjiang (approximately 20%). In NEC, the annual occurrence frequencies of 0.05 < AOD < 0.15 and AOD > 0.6 were the highest (approximately 50%) and the lowest (less than 1%), respectively. Interdecadal AOD revealed a positive trend from 2001 to 2008 and a negative trend from 2009 to 2018. The frequency of occurrence trend at different AOD levels also changed from positive to negative between these two periods. The findings in this study are based on the first aerosol retrieval of the newly released MISR in NEC. The results provide a comprehensive understanding of the regional and climatological aerosol extinction with different AOD of size and shape as well as various level bins in NEC.

Improved Algorithms for Remote Sensing-Based Aerosol Retrieval during Extreme Biomass Burning Events

This study proposed an aerosol characterization process using satellites for severe biomass burning events. In general, these severely hazy cases are labeled as “undecided” or “hazy.” Because atmospheric aerosols are significantly affected by factors such as air quality, global climate change, local environmental risk, and human and biological health, efficient and accurate algorithms for aerosol retrieval are required for global satellite data processing. Our previous classification of aerosol types was based primarily on near-ultraviolet (UV) data, which facilitated subsequent aerosol retrieval. In this study, algorithms for aerosol classification were expanded to events with serious biomass burning aerosols (SBBAs). Once a biomass burning event is identified, the appropriate radiation simulation method can be applied to characterize the SBBAs. The second-generation global imager (SGLI) on board the Japanese mission JAXA/Global Change Observation Mission-Climate contains 19 channels, including red (674 nm) and near-infrared (869 nm) polarization channels with a high resolution of 1 km. Using the large-scale wildfires in Kalimantan, Indonesia in 2019 as an example, the complementarity between the polarization information and the nonpolarized radiance measurements from the SGLI was demonstrated to be effective in radiation simulations for biomass burning aerosol retrieval. The retrieved results were verified using NASA/AERONET ground-based measurements, and then compared against JAXA/SGLI/L2-version-1 products, and JMA/Himawari-8/AHI observations.

Aerosol properties in the atmosphere of Natal/Brazil measured by an AERONET Sun-photometer

We analyzed data measured by a Sun-photometer of the RIMA-AERONET network with the purpose to characterize the aerosol properties in the atmosphere over Natal, state capital of Rio Grande do Norte, at the coast of Northeast Brazil. Aerosol Optical Depth, Ångström Exponent, Volume Size Distribution, Single Scattering Albedo, Complex Refractive Index, Asymmetry Factor, and Precipitable Water were analyzed from August 2017 to March 2018. In addition, MODIS and CALIOP observations, local Lidar measurements, and modeled backward trajectories were analyzed in a case study on February 9, 2018, that consistently confirmed the identification of a persistent aerosol layer below 4 km agl. Aerosols present in the atmosphere of Natal showed monthly mean Aerosol Optical Depth at 500 nm below 0.15 (~ 75%), monthly means of the Ångström Exponent at 440-670 nm between 0.30 and 0.70 (~ 69%), bimodal Volume Size Distribution is dominantly coarse mode, Single Scattering Albedo at 440 nm is 0.80, Refractive Index - Real Part around 1.50, Refractive Index - Imaginary Part ranging from 0.01 to 0.04, and the Asymmetry Factor ranged from 0.73 to 0.80. The aerosol typing during the measurement period showed that atmospheric aerosol over Natal is mostly composed of mixed aerosol (58.10%), marine aerosol (34.80%), mineral dust (6.30%), and biomass burning aerosols (0.80%). Backward trajectories identified that 51% of the analyzed air masses over Natal originated from the African continent.

Evolution of Aerosols in the Atmospheric Boundary Layer and Elevated Layers during a Severe, Persistent Haze Episode in a Central China Megacity

Aerosol vertical profiling is crucial to understand the formation mechanism and evolution processes of haze, which have not yet been comprehensively clarified. In this study, we investigated a severe, persistent haze event in Wuhan (30.5° N, 114.4° E), China during 5–18 January 2013 by the use of a polarization lidar, a Cimel sun photometer, meteorological datasets, and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model, focusing on the time–height evolution of aerosols in both the atmospheric boundary layer (ABL) and elevated layers. During the haze period, the integrated particle depolarization ratio was 0.05 ± 0.02, and the fine mode fraction reached 0.91 ± 0.03, indicating haze particles were rather spherical and predominately submicron, that is, of anthropogenic nature. Compared with the clear period, columnar aerosol optical depth at 500 nm tripled to 1.32 ± 0.31, and the strongest enhancement in aerosol concentration occurred from near the ground to an altitude of 1.2 km during the haze period. The daytime evolution of aerosol vertical distribution in the ABL exhibited a distinct pattern under haze weather. Abundant particles accumulated below 0.5 km in the morning hours due to stable meteorological conditions, including a strong surface-based inversion (4.4–8.1 °C), late development (from 1000–1100 LT) of the convective boundary layer, and weak wind (<4 m∙s−1) in the lowermost troposphere. In the afternoon, improved ventilation delivered an overall reduction in boundary layer aerosols but was insufficient to eliminate haze. Particularly, the morning residual layer had an optical depth of 0.29–0.56. It influenced air quality indirectly by weakening convective activities in the morning and directly through the fumigation process around noon, suggesting it may be an important element in aerosol–ABL interactions during consecutive days with haze. Our lidar also captured the presence of the elevated aerosol layers (EALs) embodying regional/long-range transport. Most of the EALs were observed to subside to <1.2 km and exacerbate the pollution level. Backward trajectory analysis and lidar data revealed the EALs originated from the transport of anthropogenic pollutants from the Sichuan Basin, China, and of dust from the deserts in the northwest. They were estimated to contribute ~19% of columnar aerosol-loading, pointing to a non-negligible role of transport during the intense pollution episode. The results could benefit the complete understanding of aerosol–ABL interactions under haze weather and air quality forecasting and control in Wuhan.

Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations

To better understand air quality issues in South Korea, it is essential to identify the main contributors of air pollution and to quantify the effects of transboundary transport. In this study, geostationary satellite measurements were used to assess the effects of aerosol transport on air quality in South Korea. This study proposes a method to define the long-range transport (LRT) of aerosols into the Korean Peninsula using remote sensing obervations and back-trajectories and estimates the LRT effects on air quality in Seoul using in-situ particulate matter (PM) measurements. Aerosol optical depths (AODs) are obtained from the Geostationary Ocean Color Imager (GOCI), and the back-trajectories are from the National Ocean and Atmospheric Administration (NOAA) HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. For LRT events, satellite observations showed high AOD plumes over the Yellow Sea, a pathway between Eastern China and South Korea, and the movements of aerosol plumes transported to South Korea were also detected. PM_2.5 concentrations, PM₁₀ concentrations, and AOD during LRT increased by 52%, 49%, and 81%, respectively, relative to their average values for 2015-2018. To quantitatively characterize the LRT of aerosols, the effects of LRT on PM_2.5 concentrations were estimated for each PM concentration category. The contribution of LRT to PM_2.5 concentrations was estimated to be 33% during 2015-2018. When high concentrations of PM_2.5 were observed in Seoul, they were likely to be associated with LRT events.

Impact of Shutdown due to COVID-19 Pandemic on Aerosol Characteristics in Kanpur, India

BACKGROUND

Since March 2020, the number of confirmed COVID-19 positive cases have steadily risen in India. Various preventive measures have been taken to contain the spread of COVID-19. With restrictions on human activities, anthropogenic emissions driving air pollution levels have seen a reduction since March 23, 2020, when the government imposed the first nationwide shutdown. The landlocked Indo-Gangetic Plain (IGP) has many densely-populated cities, witnessing high levels of particulate matter due to both nature-driven and anthropogenic elements. Kanpur is an urban metropolis in the IGP with high aerosol loading, and this paper explores the impact of restricted anthropogenic activities on aerosol characteristics in Kanpur.

OBJECTIVES

This study aims to investigate the change in aerosol optical depth level and its related parameters during the shutdown phases in Kanpur city compared to the same time periods in 2017-2019.

METHODS

Aerosol optical properties such as aerosol optical depth (AOD) at 500 nm, Angstrom exponent (AE), fine mode fraction (FMF) of AOD at 500 nm and single scattering albedo (SSA) at 440 nm were obtained from the Aerosol Robotic Network (AERONET) station operating in Kanpur from the 1st March to the 30th April for 2017-2020.

RESULTS

A significant decrease in aerosol loading was observed during the shutdown period compared to the pre-and partial shutdown periods in 2020 as well as during the same time periods of 2017-2019. Mean AOD, FMF and SSA were 0.37, 0.43 and 0.89, respectively, during the shutdown period in 2020. A 20-35% reduction in mean AOD levels was observed during the shutdown period in 2020 as compared to the same period in 2017-2019.

CONCLUSIONS

The shutdown led to an improvement in air quality due to decreases in anthropogenic emissions. As fine particles, typically from urban and industrial emissions, dominate episodic air pollution events, this study can be further utilized by the scientific community and regulators to strengthen the emergency response action plan to check high pollution episodes in Kanpur city until cleaner technologies are in place.

COMPETING INTERESTS

The authors declare no completing financial interests.

Estimation of Surface Concentrations of Black Carbon from Long-Term Measurements at Aeronet Sites over Korea

We estimated fine-mode black carbon (BC) concentrations at the surface using AERONET data from five AERONET sites in Korea, representing urban, rural, and background. We first obtained the columnar BC concentrations by separating the refractive index (RI) for fine-mode aerosols from AERONET data and minimizing the difference between separated RIs and calculated RIs using a mixing rule that can represent a real aerosol mixture (Maxwell Garnett for water-insoluble components and volume average for water-soluble components). Next, we acquired the surface BC concentrations by establishing a multiple linear regression (MLR) between in-situ BC concentrations from co-located or adjacent measurement sites, and columnar BC concentrations, by linearly adding meteorological parameters, month, and land-use type as the independent variables. The columnar BC concentrations estimated from AERONET data using a mixing rule well reproduced site-specific monthly variations of the in-situ measurement data, such as increases due to heating and/or biomass burning and long-range transport associated with prevailing westerlies in the spring and winter, and decreases due to wet scavenging in the summer. The MLR model exhibited a better correlation between measured and predicted BC concentrations than those based on columnar concentrations only, with a correlation coefficient of 0.64. The performance of our MLR model for BC was comparable to that reported in previous studies on the relationship between aerosol optical depth and particulate matter concentration in Korea. This study suggests that the MLR model with properly selected parameters is useful for estimating the surface BC concentration from AERONET data during the daytime, at sites where BC monitoring is not available.

Investigating the Long-Range Transport of Aerosol Plumes Following the Amazon Fires (August 2019): A Multi-Instrumental Approach from Ground-Based and Satellite Observations

Despite a number of studies on biomass burning (BB) emissions in the atmosphere, observation of the associated aerosols and pollutants requires continuous efforts. Brazil, and more broadly Latin America, is one of the most important seasonal sources of BB, particularly in the Amazon region. Uncertainty about aerosol loading in the source regions is a limiting factor in terms of understanding the role of aerosols in climate modelling. In the present work, we investigated the Amazon BB episode that occurred during August 2019 and made the international headlines, especially when the smoke plumes plunged distant cities such as São Paulo into darkness. Here, we used satellite and ground-based observations at different locations to investigate the long-range transport of aerosol plumes generated by the Amazon fires during the study period. The monitoring of BB activity was carried out using fire related pixel count from the moderate resolution imaging spectroradiometer (MODIS) onboard the Aqua and Terra platforms, while the distribution of carbon monoxide (CO) concentrations and total columns were obtained from the infrared atmospheric sounding interferometer (IASI) onboard the METOP-A and METOP-B satellites. In addition, AERONET sun-photometers as well as the MODIS instrument made aerosol optical depth (AOD) measurements over the study region. Our datasets are consistent with each other and highlight AOD and CO variations and long-range transport of the fire plume from the source regions in the Amazon basin. We used the Lagrangian transport model FLEXPART (FLEXible PARTicle) to simulate backward dispersion, which showed good agreement with satellite and ground measurements observed over the study area. The increase in Rossby wave activity during the 2019 austral winter the Southern Hemisphere may have contributed to increasing the efficiency of large-scale transport of aerosol plumes generated by the Amazon fires during the study period.

The Ångström Exponent and Single-Scattering Albedo of Black Carbon: Effects of Different Coating Materials

In this work, the absorption Ångström exponent (AAE), extinction Ångström exponent (EAE), and single-scattering albedo (SSA) of black carbon (BC) with different coating materials are numerically investigated. BC with different coating materials can provide explanations for the small AAE, small EAE, and large AAE observed in the atmosphere, which is difficult to be explained by bare BC aggregate models. The addition of organic carbon (OC) does not necessarily increase AAE due to the transformation of BC morphologies and the existence of non-absorbing OC. The addition of coating materials does also not necessarily decrease EAE. While the addition of coating materials can increase the total size of BC-containing particles, the effective refractive index can be modified by introducing the coating materials, so increases the EAE. We found that it is not possible to differentiate between thinly- and heavily-coated BC based on EAE or AAE alone. On the other hand, SSA is much less sensitive to the size and can provide much more information for distinguishing heavily-coated BC from thinly-coated BC. For BC with different coating materials and mixing states, AAE, EAE, and SSA show rather different sensitivities to particle size and composition ratios, and their spectral-dependences also exhibit distinct differences. Different AAE and EAE trends with BC/OC ratio were also found for BC with different coating materials and mixing states. Furthermore, we also found empirical fittings for AAE, EAE, SSA, and optical cross-sections, which may be useful for retrieving the size information based on the optical measurements.

Aerosol optical properties and its type classification based on multiyear joint observation campaign in north China plain megalopolis

Since haze and other air pollution are frequently seen in the North China Plain (NCP), detail information on aerosol optical and radiative properties and its type classification is demanded for the study of regional environmental pollution. Here, a multiyear ground-based synchronous sun photometer observation at seven sites on North China Plain megalopolis from 2013 to 2018 was conducted. First, the annual and seasonal variation of these characteristics as well as the intercomparsion were analyzed. Then the potential relationships between these properties with meteorological factors and the aerosol type classification were discussed. The results show: Particle volume exhibited a decreasing trend from the urban downtown to suburban and the rural region. The annual average aerosol optical depth at 440 nm (AOD440) varied from ∼0.43 to 0.86 over the NCP. Annual average single-scattering albedo at 440 nm (SSA440) varied from ∼0.89 to 0.93, indicating a moderate to slight absorption capacity. Average absorption aerosol optical depth at 440 nm (AAOD440) varied from ∼0.07 to 0.10. The absorption Ångström exponent (AAE) (∼0.89-1.40) indicated the multi-types of absorptive matters originated form nature and anthropogenic emission. The discussion of aerosol composition showed a smaller particle size of aerosol from biomass burning and/or fossil foil consumption with enhanced aerosol scattering and enlarged light extinction. Aerosol classification indicated a large percentage of mixed absorbing aerosol (∼20%-49%), which showed increasing trend between relative humidity (RH) with aerosol scattering and dust was an important environmental pollutant compared to southern China.

Long-term (2003-2018) trends in aerosol chemical components at a high-altitude background station in the western North Pacific: Impact of long-range transport from continental Asia

This study examined the long-term trends in chemical components in PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) samples collected at Lulin Atmospheric Background Station (LABS) located on the summit of Mt. Lulin (2862 m above mean sea level) in Taiwan in the western North Pacific during 2003-2018. High ambient concentrations of PM_2.5 and its chemical components were observed during March and April every year. This enhancement was primarily associated with the long-range transport of biomass burning (BB) smoke emissions from Indochina, as revealed from cluster analysis of backward air mass trajectories. The decreasing trends in ambient concentrations of organic carbon (-0.67% yr^-1; p = 0.01), elemental carbon (-0.48% yr^-1; p = 0.18), and non-sea-salt (nss) K⁺ (-0.71% yr^-1; p = 0.04) during 2003-2018 indicated a declining effect of transported BB aerosol over the western North Pacific. These findings were supported by the decreasing trend in levoglucosan (-0.26% yr^-1; p = 0.20) during the period affected by the long-range transport of BB aerosol. However, NO₃^- displayed an increasing trend (0.71% yr^-1; p = 0.003) with considerable enhancement resulting from the air masses transported from the Asian continent. Given that the decreasing trends were for the majority of the chemical components, the columnar aerosol optical depth (AOD) also demonstrated a decreasing trend (-1.04% yr^-1; p = 0.0001) during 2006-2018. Overall decreasing trends in ambient (carbonaceous aerosol and nss-K⁺) as well as columnar (e.g., AOD) aerosol loadings at the LABS may influence the regional climate, which warrants further investigations. This study provides an improved understanding of the long-term trends in PM_2.5 chemical components over the western North Pacific, and the results would be highly useful in model simulations for evaluating the effects of BB transport on an area.

Spectral Aerosol Optical Depth Retrievals by Ground-Based Fourier Transform Infrared Spectrometry

Aerosol Optical Depth (AOD) and the Ångström Exponent (AE) have been calculated in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions over a period of one year (May 2019–May 2020) at the high-mountain Izaña Observatory (IZO) from Fourier Transform Infrared (FTIR) solar spectra. The high-resolution FTIR measurements were carried out coincidentally with Cimel CE318-T photometric observations in the framework of the Aerosol Robotic Network (AERONET). A spectral FTIR AOD was generated using two different approaches: by means of the selection of seven narrow FTIR micro-windows (centred at 1020.90, 1238.25, 1558.25, 1636.00, 2133.40, 2192.00, and 2314.20 nm) with negligible atmospheric gaseous absorption, and by using the CE318-AERONET’s response function in the near-coincident bands (1020 nm and 1640 nm) to degrade the high-resolution FTIR spectra. The FTIR system was absolutely calibrated by means of a continuous Langley–Plot analysis over the 1-year period. An important temporal drift of the calibration constant was observed as a result of the environmental exposure of the FTIR’s external optical mirrors (linear degradation rate up to 1.75% month−1). The cross-validation of AERONET-FTIR databases documents an excellent agreement between both AOD products, with mean AOD differences below 0.004 and root-mean-squared errors below 0.006. A rather similar agreement was also found between AERONET and FTIR convolved bands, corroborating the suitability of low-resolution sunphotometers to retrieve high-quality AOD data in the NIR and SWIR domains. In addition, these results demonstrate that the methodology developed here is suitable to be applied to other FTIR spectrometers, such as portable and low-resolution FTIR instruments with a potentially higher spatial coverage. The spectral AOD dependence for the seven FTIR micro-windows have been also examined, observing a spectrally flat AOD behaviour for mineral dust particles (the typical atmospheric aerosols presented at IZO). A mean AE value of 0.53 ± 0.08 for pure mineral dust in the 1020–2314 nm spectral range was retrieved in this paper. A subsequent cross-validation with the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package has ensured the reliability of the FTIR dataset, with AE values between 0.36 to 0.60 for a typical mineral dust content at IZO of 100 cm−3 and water-soluble particle (WASO) content ranging from 600 to 6000 cm−3. The new database generated in this study is believed to be the first long-term time series (1-year) of aerosol properties generated consistently in the NIR and SWIR ranges from ground-based FTIR spectrometry. As a consequence, the results presented here provide a very promising tool for the validation and subsequent improvement of satellite aerosol products as well as enhance the sensitivity to large particles of the existing databases, required to improve the estimation of the aerosols’ radiative effect on climate.

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

High-Resolution Gridded Level 3 Aerosol Optical Depth Data from MODIS

The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce Level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and Level 3 data at 1 degree. The local and global applications have benefited from the coarse resolution gridded data sets (i.e., Level 3, 1 degree), as it is easier to use since data volume is low, and several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we created a high spatial resolution (high-resolution gridded (HRG), 0.1 × 0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meet the accuracy requirements of Level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open an Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications.

Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal

This study presents the spectral monthly and seasonal variation of aerosol optical depth (τAOD), single scattering albedo (SSA), and aerosol absorption optical depth (AAOD) between 2010 and 2018 obtained from the Aerosol Robotic Network (AERONET) over Pokhara, Nepal. The analysis of these column-integrated aerosol optical data suggests significant monthly and seasonal variability of aerosol physical and optical properties. The pre-monsoon season (March to May) has the highest observed τAOD(0.75 ± 0.15), followed by winter (December to February, 0.47 ± 0.12), post-monsoon (October and November, 0.39 ± 0.08), and monsoon seasons (June to September, 0.27 ± 0.13), indicating seasonal aerosol loading over Pokhara. The variability of Ångström parameters, α, and β, were computed from the linear fit line in the logarithmic scale of spectral τAOD, and used to analyze the aerosol physical characteristics such as particle size and aerosol loading. The curvature of spectral τAOD, α’, computed from the second-order polynomial fit, reveals the domination by fine mode aerosol particles in the post-monsoon and winter seasons, with coarse mode dominating in monsoon, and both modes contributing in the pre-monsoon. Analysis of air mass back trajectories and observation of fire spots along with aerosol optical data and aerosol size spectra suggest the presence of mixed types of transboundary aerosols, such as biomass, urban-industrial, and dust aerosols in the atmospheric column over Pokhara.

Development of a coupled aerosol lidar data quality assurance and control scheme with Monte Carlo analysis and bilateral filtering

Mie-scatter lidar can capture the vertical distribution of aerosols, and a high degree of quantification of lidar data would be capable of coupling with a chemical transport model (CTM). Thus, we develop a data quality assurance and control scheme for aerosol lidar (TRANSFER) that mainly includes a Monte Carlo uncertainty analysis (MCA) and bilateral filtering (BF). The AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) is utilized as the ground truth to evaluate the validity of TRANSFER, and the result exhibits a sharp 41% (0.36) decrease in root mean square error (RMSE), elucidating an acceptable overall performance of TRANSFER. The maximum removal of uncertainties appears in MCA with an RMSE of 0.08 km^-1, followed by denoising (DN) with 50% of MCA in RMSE. BF can smooth interior data without destroying the edge of the structure. The most noteworthy correction occurs in summer with an RMSE of 0.15 km^-1 and Pearson correlation coefficient of 0.8, and the least correction occurs in winter with values of 0.07 km^-1 and 0.93, respectively. Overestimations of raw data are mostly identified, and representative values occur with weak southerly winds, low visibility, high relative humidity (RH) and high concentrations of both ground fine particulate matter (PM_2.5) and ozone. Apart from long-term variations, the intuitional variation in a typical overestimated pollution episode, especially represented by vertical profiles, shows a favorable performance of TRANSFER during stages of transport and local accumulation, as verified by backward trajectories. Few underestimation cases are mainly attributed to BF smoothing data with a sudden decrease. The main limitation of TRANSFER is the zigzag profiles found in a few cases with very small extinction coefficients. As a supplement to the research community of aerosol lidar and an exploration under complicated pollution in China, TRANSFER can aid in the preprocessing of lidar data-powered applications.

Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

An Investigation of Vertically Distributed Aerosol Optical Properties over Pakistan Using CALIPSO Satellite Data

The vertically distributed aerosol optical properties are investigated over Pakistan utilizing the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 products from 2007 to 2014. For a better understanding of the spatiotemporal characteristics of vertical aerosol layers, the interannual and seasonal variations of nine selected aerosol parameters such as the AOD of the lowest aerosol layer (AODL), the base height of the lowest aerosol layer (HL), the top height of the highest aerosol layer (HH), the volume depolarization ratio of the lowest aerosol layer (DRL), the color ratio of the lowest aerosol layer (CRL), total AOD of all the aerosol layers (AODT), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (TL), the AOD proportion for the lowest aerosol layer (PAODL) for both day and night times are analyzed. The results show AODT increased slightly from 2007 to 2014 over Pakistan, and relatively high AODT exists over the Punjab and Sindh (southern region), which might be owing to the high level of economic development, frequent dust storms, and profound agricultural activities (anthropogenic emissions). AODT increases from north to south. The reason may be that the southern region is rapidly urbanized and is near the desert. The northern region is dominated by agricultural land, and cities are usually semi-urbanized. The highest AODT appears in summer compared to the other seasons, and during daytime compared to nighttime. The HL and HH vary significantly, owing to the topography of Pakistan. The N is relatively large over Punjab and Sindh compared to the other regions, which might be caused by relatively stronger atmospheric convections. The spatial distribution of the TL showed an inverse relationship with the topography as lower values are observed over elevated regions such as Gilgit-Baltistan and Jammu-Kashmir. The value of the PAODL indicates that 77% of the total aerosols are mainly concentrated in the lowest layer of the atmosphere over Pakistan. The higher values of DRL and CRL indicate non-spherical and large particles over Balochistan and Sindh, which might be related to the proximity to the desert. This study provides very useful information about vertically distributed aerosol optical properties which could help researchers and policymakers to regulate and mitigate air pollution issues of Pakistan.

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)

Exposure to outdoor fine particulate matter (PM_2.5) is a leading risk factor for mortality. We develop global estimates of annual PM_2.5 concentrations and trends for 1998-2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM_2.5 concentrations using geophysical relationships between surface PM_2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM_2.5 estimates are highly consistent with globally distributed ground monitors (R² = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM_2.5 estimates to predict and account for the residual bias with PM_2.5 monitors, yielding even higher cross validated agreement (R² = 0.90-0.92; slope = 0.90-0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (-0.28 ± 0.03 μg/m³/yr), Europe (-0.15 ± 0.03 μg/m³/yr), India (1.13 ± 0.15 μg/m³/yr), and globally (0.04 ± 0.02 μg/m³/yr). The positive trend (2.44 ± 0.44 μg/m³/yr) for India over 2005-2013 and the negative trend (-3.37 ± 0.38 μg/m³/yr) for China over 2011-2018 are remarkable, with implications for the health of billions of people.

Investigation of Aerosol Climatology, Optical Characteristics and Variability over Egypt Based on Satellite Observations and In-Situ Measurements

Egypt experiences high rates of air pollution, which is a major threat to human health and the eco-environment and therefore needs to be tackled by defining major causes to hinder or mitigate their impacts. The major driving forces of air pollution are either of local and/or regional origin. In addition, seasonal aerosols may be natural, such as dust particles transported from the western desert, or anthropogenic aerosols which are transported from industrial areas and smoke particles from seasonal biomass burning. Monitoring the optical properties of aerosols and their pattern in the atmosphere on a daily basis requires a robust source of information and professional analytical tools. This research explored the potential of using time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Aerosol Robotic Network (AERONET) data to comprehensively investigate the aerosol optical depth (AOD) and variability for the period 2012–2018 on a daily basis. The data show that spring, summer and autumn seasons experienced the highest anomaly originating from regional and national sources. The high AOD in spring associated with a low Ångström exponent (AE) indicates the presence of coarse particles which naturally originate from desert dust or sea spray. In contrast, the high AE in summer and autumn demonstrated the dominance of fine anthropogenic aerosols such as smoke particles from local biomass burning. The observation of a high number of fire incidents over Egypt in October and November 2018, during the months of rice crop harvesting, showed that these incidents contribute to the presence of autumn aerosols across the country. In-situ measurements of Particulate Matter (PM10) from local stations from an environmental based network as well as the AERONET AOD were used to validate the MODIS AOD, providing a high correlation coefficient of r = 0.73.

Air Quality Degradation by Mineral Dust over Beijing, Chengdu and Shanghai Chinese Megacities

Air pollution in Chinese megacities has reached extremely hazardous levels, and human activities are responsible for the emission or production of large amounts of particulate matter (PM). In addition to PM from anthropogenic sources, natural phenomena, such as dust storms over Asian deserts, may also emit large amounts of PM, which lead episodically to poor air quality over Chinese megacities. In this paper, we quantify the degradation of air quality by dust over Beijing, Chengdu and Shanghai megacities using the three dimensions (3D) chemistry transport model CHIMERE, which simulates dust emission and transport online. In the first part of our work, we evaluate dust emissions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Infrared Atmospheric Sounding Interferometer (IASI) satellite observations of aerosol optical depth, respectively, in the visible and the thermal infrared over source areas. PM simulations were also evaluated compared to surface monitoring stations. Then, mineral dust emissions and their impacts on particle composition of several Chinese megacities were analyzed. Dust emissions and transport over China were simulated during three years (2011, 2013 and 2015). Annual dust contributions to the PM 10 budget over Beijing, Chengdu and Shanghai were evaluated respectively as 6.6%, 9.5% and 9.3%. Dust outbreaks largely contribute to poor air quality events during springtime. Indeed it was found that dust significantly contribute for 22%, 52% and 43% of spring PM 10 events (for Beijing, Chengdu and Shanghai respectively).

Evaluation of Himawari-8/AHI, MERRA-2, and CAMS Aerosol Products over China

Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.

Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties.

Near-Real-Time Application of SEVIRI Aerosol Optical Depth Algorithm

Within the framework of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project, the near-real-time (NRT) operation has been documented for an in-house developed algorithm used for the retrieval of aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG). With the frequency of 15 min at a spatial resolution of roughly 5.5 × 5.5 km the AOD maps are provided for the country domains of Poland, the Czech Republic, Romania, and Southern Norway. A significant improvement has been reported in terms of modification of the existing prototype algorithm that it suits the operational NRT AOD retrieval for an extended area. This is mainly due to the application of the optimal interpolation method for the AOD estimation on reference days with the use of ground-based measurements of the Aerosol Robotic Network (AERONET) and the Aerosol Research Network (PolandAOD-NET) as well as simulations of the Copernicus Atmosphere Monitoring Service (CAMS). The main issues that have been addressed regarding surface reflectance estimation, cloud screening and uncertainty calculation. Exemplary maps of the NRT retrieval have been presented.

Interdecadal variation in aerosol optical properties and their relationships to meteorological parameters over northeast China from 1980 to 2017

Northeast China has undergone rapid urbanisation with increased anthropogenic emissions, and the types and absorption properties of aerosols may affect regional climate change. MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, version 2) distributions of aerosol optical depth (AOD), Ångström exponent (AE), and absorption aerosol optical depth (AAOD) from 1980 to 2017 was studied to estimate the climatology of aerosol optical properties over Northeast China. The highest AOD and AAOD occurred in Liaoning Province range from 0.3 to 0.4 and 0.02-0.03, respectively. The spacial distribution of black carbon (BC) AOD was similar to AAOD with maximum value in Liaoning province about 0.04 related to the emission sources and human activities. The seasonal interdecadal distribution indicated larger dust (DU) AOD in Liaoning (0.12) and organic carbon (OC) AOD in Heilongjiang (0.18). The contribution of SO₄ to total AOD was significant in autumn and winter, and BC particles contributed 70% to total AAOD in all seasons. The decadal change in AOD was positive for 2000-2009 (0.2/decadal) due to the increased dust events happening in spring. The positive correlation between AOD and relative humidity (RH) at surface was about 0.4-0.6; the negative correlation between AOD and surface wind speed (WS) (-0.6), planetary boundary layer height (PBLH) (-0.2 to -0.6), sea level pressure (SLP) (-0.2) was found over the study period. This study's findings enable more comprehensive understanding of the distribution of aerosols optical properties and regional climatology in Northeast China.

Column Integrated Water Vapor and Aerosol Load Characterization with the New ZEN-R52 Radiometer

The study shows the first results of the column-integrated water vapor retrieved by the new ZEN-R52 radiometer. This new radiometer has been specifically designed to monitor aerosols and atmospheric water vapor with a high degree of autonomy and robustness in order to allow the expansion of the observations of these parameters to remote desert areas from ground-based platforms. The ZEN-R52 device shows substantial improvements compared to the previous ZEN-R41 prototype: a smaller field of view, an increased signal-to-noise ratio, better stray light rejection, and an additional channel (940 nm) for precipitable water vapor (PWV) retrieval. PWV is inferred from the ZEN-R52 Zenith Sky Radiance (ZSR) measurements using a lookup table (LUT) methodology. The improvement of the new ZEN-R52 in terms of ZSR was verified by means of a comparison with the ZEN-R41, and with the Aerosol Robotic Network (AERONET) Cimel CE318 (CE318-AERONET) at Izaña Observatory, a Global Atmosphere Watch (GAW) high mountain station (Tenerife, Canary Islands, Spain), over a 10-month period (August 2017 to June 2018). ZEN-R52 aerosol optical depth (AOD) was extracted by means of the ZEN–AOD–LUT method with an uncertainty of ±0.01 ± 0.13*AOD. ZEN-R52 PWV extracted using a new LUT technique was compared with quasi-simultaneous (±30 s) Fourier Transform Infrared (FTIR) spectrometer measurements as reference. A good agreement was found between the two instruments (PWV means a relative difference of 9.1% and an uncertainty of ±0.089 cm or ±0.036 + 0.061*PWV for PWV <1 cm). This comparison analysis was extended using two PWV datasets from the same CE318 reference instrument at Izaña Observatory: one obtained from AERONET (CE318-AERONET), and another one using a specific calibration of the 940-nm channel performed in this work at Izaña Atmospheric Research Center Observatory (CE318-IARC), which improves the PWV product.

Five Years of Dust Episodes at the Southern Italy GAW Regional Coastal Mediterranean Observatory: Multisensors and Modeling Analysis

The Mediterranean area is a climate-change hotspot because of the natural and anthropogenic pollution pressure. The presence of natural aerosols, such as dust, influences solar radiation and contributes to the detection, in storm episodes, of significant concentrations of PM10 in Southern Italy, where generally fresh and clean air is due to local circulation, and particulate matter concentrations are very low. We present the results of medium-term observations (2015–2019) at Lamezia Terme GAW (Global Atmospheric Watch) Regional Observatory, with the purpose of identifying the dust incursion events by studying the aerosol properties in the site. To achieve this goal, the experimental data, collected by several instruments, have been also correlated with the large-scale atmospheric patterns derived by the ERA5 reanalysis dataset, in order to study the meteorological conditions that strongly influence dust outbreaks and their spatio-temporal behavior. An intense dust-outbreak episode, which occurred on 23–27 April 2019, was chosen as a case study; a detailed analysis was carried out considering surface and column optical properties, chemical properties, large-scale pattern circulation, air-quality modeling/satellite products, and back-trajectory analysis, to confirm the capability of the modeled large-scale atmospheric fields to correctly simulate the conditions mainly related to the desert dust-outbreak events.

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

Tropospheric Dust and Associated Atmospheric Circulations over the Mediterranean Region with Focus on Romania’s Territory

The aim of this study is to assess the distribution of dust over the Mediterranean region, with a special focus on the territory of Romania. Two parameters are analyzed—Dust Load (DL) and Aerosol Optical Depth (AOD), the data is obtained from the dust forecast model BSC-DREAM8b v2.0, for the period between December 2015 and February 2019. The main geographical features of dust occurrence in the Mediterranean region are presented at the monthly and annual scale. The results show that, for Romania, the dust load is high from February to June, when it reaches its annual maximum. The atmospheric circulation inducing intense dust events over Romania have also been assessed using an objective classification method. A key element for the dust transport from the Sahara toward South-Eastern Europe is represented by the development of a deep cyclone South of Italy, following thereafter a North-East path towards the Balkan peninsula. The results at the regional scale are analyzed in connection with the aerosol optical properties at the local scale (e.g., aerosol optical depth at 440 nm, Absorption Ångström Exponent and Scattering Ångström Exponent at 440 nm and 675 nm, respectively) retrieved from the Aerosol Robotic Network (AERONET-NASA) for Romania, using data from ACTRIS-RO monitoring sites from Iași, Cluj–Napoca, and Bucharest. The differences between the forecast model and the observational data are also explored. Our results also show that the contribution of the natural mineral dust to air pollution in Romania is small, representing not more than 10% of all kinds of aerosols detected over the observation points from the ACTRIS-RO network.

Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods

This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three-dimensional variational (3D-Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48 hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30 hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D-Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter.

Aerosol Optical Properties and Contribution to Differentiate Haze and Haze-Free Weather in Wuhan City

Haze is an atmospheric phenomenon in which different types of particulates obscure the sky, and hence affect almost all human activities. Over a couple of recent decades, China has witnessed increasingly worse air quality as well as atmospheric haziness in its cities. There are various haze contributing factors including the rapid industrialization, excessive biomass burning, and an increase in the number of vehicles. This study proposes a methodology based on the aerosols scattering and absorption properties, to predict the likelihood of an episode of hazy days. This case study employs the aerosol optical properties data from integrated nephelometer and aethalometer sensors from December 2009 to September 2014 over Wuhan. The role and contribution of each aerosol optical parameter (e.g., aerosol scattering and absorption coefficients, single scattering albedo, scattering, and absorption Ångström exponents, backscatter ratio, and asymmetry factor) in distinguishing haze and haze-free conditions has been quantitatively determined based on a machine learning approach. Each aerosol optical parameter was classified independently by the support vector machine (SVM) algorithm, and the aerosol scattering (85.37%) and absorption (74.53%) coefficients were found to be primary potential indicators. Through the Kolmogorov-Smirnov test and traditional statistical analysis, the aerosol scattering and absorption coefficients were then verified as important indicators in distinguishing haze and haze-free days. Finally, through a probability density diagram and frequency histogram, we propose a simple quantitative standard to distinguish between haze and haze-free conditions based on the aerosol scattering coefficient and absorption coefficient in Wuhan City. The accuracy of the standard was determined to be 81.49% after testing, which indicates an accurate result. An error in aerosol optical properties may lead to an error in the calculation of aerosol radiative forcing, the earth’s energy budget, and climate prediction. Therefore, understanding of the aerosol properties during haze-free and haze-days will help policymakers to make new policies to control urban pollution and their effects on human health.