Large Salt Dust Storms Follow a 30-Year Rainfall Cycle in the Mar Chiquita Lake (Córdoba, Argentina)

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin.

A westerly wind dominated Puna Plateau during deposition of upper Pleistocene loessic sediments in the subtropical Andes, South America

The Tafí del Valle depression (~27° S) in the eastern Andes of Argentina provides a record of late Pleistocene dust deposition in the subtropics of South America. We present large-n U-Pb geochronology data for detrital zircons from upper Pleistocene loess-paleosol deposits. When compared to regional data, the age spectra from the Tafí del Valle samples are most like the southern Puna Plateau, supporting derivation largely from the west and northwest. This runs counter to hypotheses suggesting these loessic sediments were derived from the low elevation plains to the east or extra-Andean Patagonia. Mapping of linear wind erosion features on the Puna Plateau yield a mean orientation of 125.7° (1 s.d. = 12.4°). These new data and existing records are consistent with a westerly-northwesterly dominated (upper- and lower-level) wind system over the southern Puna Plateau (to at least ~27° S) during periods of high dust accumulation in Tafí del Valle.

Detrital zircon ages in Pleistocene sediments and wind erosion patterns indicate the Puna Plateau was dominated by westerly winds during intervals of high dust accumulation in the eastern subtropical Andes.

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.