Contamination assessment and source identification of metals and metalloids in submicron road dust (PM) in Moscow Megacity.

The content of 39 metals and metalloids (MMs) in submicron road dust (PM fraction) was studied in the traffic zone, residential courtyards with parking lots, and on pedestrian roads in parks in Moscow. The geochemical profiles of PM vary slightly between different types of roads and courtyards but differ significantly from those in parks. In Moscow, compared to other cities worldwide, submicron road dust contains less As, Sb, Mo, Cr, Cd, Sn, Tl, Ca, Rb, La, Y, U, but more Cu, Zn, Co, Fe, Mn, Ti, Zr, Al, V.

Metals and metalloids pollution levels, partitioning, and sources in the environmental compartments of a small urban catchment in Moscow megacity.

This study examines the contamination levels and sources of 32 metals and metalloids (MMs) in environmental compartments (roadside soil, road dust, and river suspended sediments) of a small urbanized river catchment located in Moscow megacity. MMs partitioning between particle size fractions (PM, PM, and PM) was analyzed by ICP-MS and ICP-AES methods. The pollution level of particle size fractions with MMs decreases in the following series: road dust > suspended sediments > soils.

Memristor-based spiking neural network with online reinforcement learning.

Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure.

Elemental composition of atmospheric PM during COVID-19 lockdown and recovery periods in Moscow (April-July 2020).

Changes in the concentrations of PM-bound potentially toxic elements (PTEs) during the COVID-19 lockdown period and after the revocation of restrictions were analyzed using the data received at the Aerosol Complex of Moscow State University in April-July 2020. During the lockdown, the input of biomass combustion products enriched in PTEs from the Moscow region hindered the decrease in pollutant concentrations. After the introduction of the self-isolation regime, lower concentrations of most PTEs occurred due to the decrease in anthropogenic activity and the rainy meteorological conditions.

Contamination levels and source apportionment of potentially toxic elements in size-fractionated road dust of Moscow.

The distributions of potentially toxic elements (PTEs) among PM, PM, PM, and PM fractions of the road dust were studied in the western and eastern parts of Moscow, impacted mainly by the road transport and the industrial sector, respectively. The partitioning of PTEs in road dust can provide more precise information on pollution sources and its further interpretation regarding human health risks. The concentrations of PTEs were analyzed by mass and atomic emission inductively coupled plasma spectrometry.