Long-term quantification of springtime aerosols over Saudi Arabia using multi-satellite remotely sensed data

A comprehensive analysis of aerosol characteristics over Saudi Arabia from 2005 to 2022, utilizing high-resolution satellite-based observations and reanalysis datasets, examining the distribution of aerosols and their subtypes across the three dimensions (temporal, spatial, and altitude based) for March, April, and May. This study focuses on the analysis of parameters such as aerosol optical depth (AOD), angstrom exponent (AE), absorption aerosol optical depth (AAOD), and Ultraviolet Aerosol Index (UVAI), revealing significant spatial disparities, with elevated aerosol concentrations in the central and eastern regions and comparatively lower concentrations along the western coastal areas. In this study, the spatial patterns and temporal trends are analyzed through trajectory modeling. The study also investigates the composition of aerosols in various Saudi cities. Aerosols prevailing in a dozen Saudi Arabian cities were systematically categorized into six sub-types, considering their particle size and UV-absorbing properties. Notably, two major aerosol sub-types, absorbing coarse (AC) aerosols (UVAI > 0.25, AE < 0.70) treated as mineral dust and absorbing mixed (AM) aerosols (0.70 < AE < 1.25) along with neutral fine (NF) particles (- 0.5 < UVAI < 0.25, AE > 1.25) treated as urban, predominate across the Kingdom of Saudi Arabia.

Accounting for the aerosol type and additional satellite-borne aerosol products improves the prediction of PM2.5 concentrations

Fine airborne particles (diameter <2.5 μm; PM_2.5) are recognized as a major threat to human health due to their physicochemical properties: composition, size, shape, etc. However, normally only size-fraction-specific particle concentrations are monitored. Interestingly, although the aerosol type is reported as part of the aerosol optical depth retrieval from satellite observations, it has not been utilized, to date, as an auxiliary information/co-variate for PM_2.5 prediction. We developed Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models that account for this information when predicting surface PM_2.5. The models take as input only widely available data: satellite aerosol products with full cover and surface meteorological data. Distinct models were developed for AOD of specific aerosol types. Both the RF and XGBoost models performed well, showing moderate-to-high cross-validated adjusted R² (RF: 0.753-0.909; XGBoost: 0.741-0.903), depending on the aerosol type and other covariates. The weighted performance of the specific aerosol-type models was higher than of the RF and XGBoost baseline models, where all the AOD retrievals were used together (the common practice). Our approach can provide improved risk estimates due to exposure to PM_2.5, better resolved radiative forcing calculations, and tailored abatement surveillance of specific pollutants/sources.

Study of aerosol optical properties at two urban areas in the north of Vietnam with the implication for biomass burning impacts

The spatiotemporal distribution and characterization of aerosol optical properties in the north of Vietnam were investigated extensively using the long-term measurements obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) (from 2010 to 2019) and two Aerosol Robotic Network (AERONET) stations (Nghia Do, Hanoi City, from 2010 to 2019 and Son La from 2012 to 2019) located in fast-developing urban areas. This study also analyzed the tendency of AOD over different wavelengths as well as the influences of meteorological parameters and boundary layer height (BLH) on AOD and Ångström exponent (AE). The annual mean AOD₅₀₀ at Nghia Do and Son La stations were 0.81 and 0.78, respectively. Our results revealed the existence of particles emitted from anthropogenic activities in Hanoi and Son La with the dominance of fine particles (more than 90% of the AE_440-870 were larger than 1). Besides, a high percentage of AE_440-870 larger than or approximately 1.5 during the dry and transition months in Son La indicated the existence of biomass-burning aerosol particles due to forest fires and burning of agriculture residuals. The classification results for aerosol types showed the presence of both biomass burning and urban/industrial aerosol types at Nghia Do and Son La sites. Among the investigated meteorological parameters, surface solar radiation expressed a significant correlation with AE_440-870 in all three seasons at the two sites due to the secondary formation of fine particles induced by the high solar radiation condition. The impacts of meteorological parameters and BLH on AOD were not observed simultaneously during three seasons at Nghia Do and Son La stations.

Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number (ACINd) and effective radius (ACIreff) of the droplets with altitude. Furthermore, ACINd and ACIreff for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum ACINd values are obtained when the activation of new droplets stops, while ACIreff reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30% at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18% cause an ACI variability of 30% while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining ACIreff uncertainty below 16% for the uncertainty of all LARSS input parameters of 10%. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index (ACIRs), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the ACIreff, which depends on the Angstrom exponent. The use of ACIRs to derive ACIreff presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.

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.

Inferring wavelength dependence of AOD and Ångström exponent over a sub-tropical station in South Africa using AERONET data: influence of meteorology, long-range transport and curvature effect

Aerosol optical properties over a southern sub-tropical site Skukuza, South Africa were studied to determine the variability of the aerosol characteristics using CIMEL Sunphotometer data as part of the AErosol RObotic NETwork (AERONET) from December 2005 to November 2006. Aerosol optical depth (AOD), Ångström exponent (α), and columnar water vapor (CWV) data were collected, analyzed, and compiled. Participating in this network provided a unique opportunity for understanding the sources of aerosols affecting the atmosphere of South Africa (SA) and the regional radiation budget. The meteorological patterns significantly (p<0.05) influenced the amount and size distribution of the aerosols. Results showed that seasonal variation of AOD at 500 nm (AOD500) over the observation site were characterized by low values (0.10-0.13) in autumn, moderate values (0.14-0.16) in summer and winter seasons, and high to very high values (0.18-0.40) during the spring, with an overall mean value of 0.18±0.12. Ångström exponent α(440-870), varied from 0.5 to 2.89, with significant (p<0.0001) seasonal variability. CWV showed a strong annual cycle with maximum values in the summer and autumn seasons. The relationship between AOD, Ångström exponent (α), and CWV showed a strong dependence (p<0.0001) of α on AOD and CWV, while there was no significant correlation between AOD and CWV. Investigation of the adequacy of the simple use of the spectral AOD and Ångström exponent data was used in deriving the curvature (a2) showed to obtain information for determining the aerosol-particle size. The negative a2 values are characterized by aerosol-size dominated by fine-mode (0.1-1 μm), while the positive curvatures indicate abundance of coarse particles (>1 μm). Trajectory cluster analyses revealed that the air masses during the autumn and winter seasons have longer advection pathways, passing over the ocean and continent. This is reflected in the aerosol properties that are derived from the ocean, desert, and anthropogenic activities that include biomass burning and industrial pollution.

Synergetic technique combining elastic backscatter lidar data and sunphotometer AERONET inversion for retrieval by layer of aerosol optical and microphysical properties

We present a so-called lidar and almucantar (LidAlm) algorithm that combines information provided by standard elastic backscatter lidar (i.e., calibrated attenuated backscatter coefficient profile at one or two wavelengths) and sunphotometer AERONET inversion of almucantar like measurements (i.e., column-integrated aerosol size distribution and refractive index). The purpose of the LidAlm technique is to characterize the atmospheric column by its different aerosol layers. These layers may be distinct or partially mixed, and they may contain different aerosol species (e.g., urban, desert, or biomass burning aerosols). The LidAlm synergetic technique provides the extinction and backscatter coefficient profiles, particle size distributions, and backscatter-to-extinction ratios for each aerosol layer. We present the LidAlm procedure and sensitivity studies. The applications are illustrated with examples of actual atmospheric conditions encountered in the Paris area.

Characterization of aerosols from biomass burning--a case study from Mizoram (Northeast), India

Physical and optical properties of biomass burning aerosols in Northeastern region, India analyzed based on measurements made during February 2002. Large spatial extent of Northeastern Region moist tropical to moist sub-tropical forests in India have high frequency of burning in annual dry seasons. Characterization of resultant trace gases and aerosols from biomass burning is important for the atmospheric radiative process. Aerosol optical depth (AOD) observed to be high during burning period compared to pre- and post-burning days. Peak period of biomass burning is highly correlated with measured AOD and total columnar water vapor. Size distribution of aerosols showed bimodal size distribution during burning day and unimodal size distribution during pre- and post-burning days. Size distribution retrievals from biomass burning aerosols show dominance of accumulation mode particles. Weighted mean radius is high (0.22 microm) during burning period. Columnar content of aerosols observed to be high during burning period in addition to the drastic reduction of visibility. During the burning day Anderson sampler measurements showed dominance of accumulation mode particles. The diurnal averaged values of surface shortwave aerosol radiative forcing af biomass burning aerosols varies from -59 to -87 Wm(-2) on different days. Measured and modeled solar irradiances are also discussed in the paper.