Assessment of Smoke Pollution Caused by Wildfires in the Baikal Region (Russia)

Effects of Daily Peat Smoke Exposure on Present and Next Generations

This study aimed to follow the neurotoxic effect of peat smoke on adult outbred rats and its influence on central nervous system (CNS) parameters in first-generation offspring. Under experimental conditions, exposure to peat smoke was carried out on adult male Wistar rats for 24 h. After the end of the exposure, an open field test (OFT), electroencephalography (EEG), and histological analysis of the testes and brains of smoke-exposed males were performed, after which they were mated with intact females to obtain F1 offspring. Stillbirth, neonatal mortality, and body weight at 4, 7, 14, and 21 postnatal days, as well as behavior in the OFT and EEG parameters during puberty (3 months), were assessed. The results of the examination of F0 males showed a significant increase in motor activity and anxiety in the open field test and a violation of EEG parameters. Histopathologically, peat smoke caused a sharp increase in shadow cells (homogeneous cells with pale-stained cytoplasm, in which the cell and nuclear membranes are not visualized) and degeneratively altered neurons in the brain; we found no changes in the testicles. Peat smoke exposure during preconception did not affect neonatal mortality and weight gain in F1 offspring. Adult females born to peat-smoke-exposed males showed an increase in locomotor activity, and the behavior of adult F1 males did not differ from the control. In F1 males, a statistically significant increase in slow-wave activity indices in the delta band was observed.

Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018

The easternmost Amazon, located in the Maranhão State, in Brazil, has suffered massive deforestation in recent years, which has devastated almost 80% of the original vegetation. We aim to characterize hot spots, hot moments, atmospheric carbon dioxide anomalies (Xco₂, ppm), and their interactions with climate and vegetation indices in eastern Amazon, using data from NASA's Orbiting Carbon Observatory-2 (OCO-2). The study covered the period from January 2015 to December 2018. The data were subjected to regression, correlation, and temporal analysis, identifying the spatial distribution of hot/cold moments and hot/cold spots. In addition, anomalies were calculated to identify potential CO₂ sources and sinks. Temporal changes indicate atmospheric Xco₂ in the range from 362.2 to 403.4 ppm. Higher Xco₂ values (hot moments) were concentrated between May and September, with some peaks in December. The lowest values (cold moments) were concentrated from November to April. SIF 771 W m^-2 sr^-1 μm^-1 explained the temporal changes of Xco₂ in 58% (R² adj = 0.58; p < 0.001) and precipitation in 27% (R² adj = 0.27; p ≤ 0.001). Spatial hot spots with 90% confidence were more representative in 2016. The maximum and minimum Xco₂ (ppm) anomalies were 6.19 ppm (source) and -6.29 ppm (sink), respectively. We conclude that the hot moments of Xco₂ in the eastern Amazon rainforest are concentrated in the dry season of the year. Xco₂ spatial hot spots and anomalies are concentrated in the southern region and close to protected areas of the Amazon rainforest.

The Emission Characteristics of Pollutants from Thermal Desorption of Soil Contaminated by Transformer Oil

In order to reduce pollutant emission from substation fires into the soil environment, developing technology to remove pollutants in soil after substation fires is necessary. Among the soil remediation technologies, thermal desorption has been proven to be an effective soil remediation method. In this paper, the effects of thermal desorption temperature and atmosphere on the releasing characteristics of pollutants in the soil polluted by waste oil in the accident oil pool in the substation were examined. The results showed that when the thermal desorption temperature was 500 °C, a large number of macromolecular organics decompose from the solid phase and change into long-chain alkanes and macromolecular acids under pure N2 atmosphere. When the thermal desorption temperature was higher than 500 °C the pollutants in the soil were further decomposed into small molecular organics. In addition, the organics were transformed to CO2, SO2, NOx, and CO under 20% O2/N2 atmosphere when the temperature was above 500 °C.

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.