Catastrophic PM2.5 emissions from Siberian forest fires: Impacting factors analysis

Synoptic weather patterns during fire spread events in Siberia

Several recent studies have indicated a strong relationship between extensive wildfires in Siberia and synoptic-scale weather processes. In this study, we used the concept of fire spread events to investigate the relationships between synoptic and surface-level weather conditions and extensive wildfires in Siberia during 2001-2022 using the MODIS and ERA5 reanalysis products. We analyzed the spatio-temporal features and seasonality of fire spread events in the region and found that most of them occurred in the central part of Eastern Siberia (ES) during the summer months, following the wildfire season in the region. A significant positive trend in the annual count of fire spread events was found in ES, coinciding with non-significant negative trends in cloud cover and precipitation and non-significant positive trends in air temperature and the fire weather index. Results show that in the ES region, which accounts for 46 % of the total number of considered events, the main driver of fire spread events is the formation of a positive geopotential height anomaly, which, based on the pattern of the meridional wind component, indicates the presence of an anticyclone above the area of fire spread events. The presence of a high-pressure zone causes a decrease in cloud cover over regions with fires, leading to increases in the amount of incoming solar radiation and surface air temperature and a decrease in precipitation. These conditions contribute to the drying of fuel and an increase in the overall fire hazard level, which in turn leads to an intensification of the combustion process, as evidenced by an increase in the radiative power of fires.

Wildfire spreading prediction using multimodal data and deep neural network approach

Predicting wildfire spread behavior is an extremely important task for many countries. On a small scale, it is possible to ensure constant monitoring of the natural landscape through ground means. However, on the scale of large countries, this becomes practically impossible due to remote and vast forest territories. The most promising source of data in this case that can provide global monitoring is remote sensing data. Currently, the main challenge is the development of an effective pipeline that combines geospatial data collection and the application of advanced machine learning algorithms. Most approaches focus on short-term fire spreading prediction and utilize data from unmanned aerial vehicles (UAVs) for this purpose. In this study, we address the challenge of predicting fire spread on a large scale and consider a forecasting horizon ranging from 1 to 5 days. We train a neural network model based on the MA-Net architecture to predict wildfire spread based on environmental and climate data, taking into account spatial distribution features. Estimating the importance of features is another critical issue in fire behavior prediction, so we analyze their contribution to the model's results. According to the experimental results, the most significant features are wind direction and land cover parameters. The F1-score for the predicted burned area varies from 0.64 to 0.68 depending on the day of prediction (from 1 to 5 days). The study was conducted in northern Russian regions and shows promise for further transfer and adaptation to other regions. This geospatial data-based artificial intelligence (AI) approach can be beneficial for supporting emergency systems and facilitating rapid decision-making.

Wildfire and smoke association with COVID-19 cases in the Pantanal wetland, Brazil

OBJECTIVES

In 2020, Brazil experienced two concurrent public health challenges related to respiratory disease: wildfires and increased mortality due to the coronavirus (COVID-19) pandemic. Smoke from these wildfires contributed to a variety of air pollutants, including fine particulate matter (PM2.5). The present study aims to investigate the effects of environmental and socio-economic factors on COVID-19 hospitalisation in the Pantanal.

STUDY DESIGN

Ecological retrospective study.

METHODS

We applied a multilevel negative binomial model to relate monthly hospitalisation data with environmental variables.

RESULTS

We showed that monthly PM2.5 concentration levels had the greatest influence on the increase in hospitalisations by COVID-19 in the elderly (23 % increase). The Gini index, a coefficient that reflects income inequalities, also had a positive association with COVID-19 hospitalisations (18 % increase). Higher temperatures and humidity were protective factors, showing a 15 % and 14 % decrease in hospitalisations, respectively. The results of the present study suggest that high PM2.5 exposure contributed to the increase in COVID-19 hospitalisations, as did the social inequalities of each municipality.

CONCLUSIONS

The present study highlights the importance of gathering evidence supported by multiple information sources to guide decision-making and identify populations needing better public health systems.

MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks

Metal-organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility-a MOF key performance indicator-of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host-guest interaction studies.

Chemical characteristics and spatiotemporal variation of marine fine particles for clustered channels of air masses transporting toward remote background sites in East Asia

This study investigated the chemical characteristics, spatiotemporal distribution, and source apportionment of marine fine particles (PM_2.5) for clustered transport channels/routes of air masses moving toward three remote sites in East Asia. Six transport routes in three channels were clustered based on backward trajectory simulation (BTS) in the order of: West Channel > East Channel > South Channel. Air masses transported toward Dongsha Island (DS) came mainly from the West Channel, while those transported toward Green Island (GR) and Kenting Peninsula (KT) came mostly from the East Channel. High PM_2.5 commonly occurred from late fall to early spring during the periods of Asian Northeastern Monsoons (ANMs). Marine PM_2.5 was dominated by water-soluble ions (WSIs) which were predominated by secondary inorganic aerosols (SIAs). Although the metallic content of PM_2.5 was predominated by crustal elements (Ca, K, Mg, Fe, and Al), enrichment factor clearly showed that trace metals (Ti, Cr, Mn, Ni, Cu, and Zn) came mainly from anthropogenic sources. Organic carbon (OC) was superior to elemental carbon (EC), while OC/EC and SOC/OC ratios in winter and spring were higher than those in other two seasons. Similar trends were observed for levoglucosan and organic acids. The mass ratio of malonic acid and succinic acid (M/S) was commonly higher than unity, showing the influences of biomass burning (BB) and secondary organic aerosols (SOAs) on marine PM_2.5. We resolved that the main sources of PM_2.5 were sea salts, fugitive dust, boiler combustion, and SIAs. Boiler combustion and fishing boat emissions at DS had higher contribution than those at GR and KT. The highest/lowest contribution ratios of cross-boundary transport (CBT) were 84.9/29.6% in winter and summer, respectively.

Composition of inorganic elements in fine particulate matter emitted during surface fire in relation to moisture content of forest floor combustibles

The moisture content of combustible material on the forest floor is constantly changing due to environmental factors, which have a direct impact on the composition and emission intensity of particulate matter released during fire. In this study, an indoor biomass combustion analysis device was used to analyze the emission characteristics of fine particulate matter (PM_2.5) from combustion of herbaceous combustible materials with different moisture contents (0%, 15%, and 30%). The composition of inorganic elements in PM_2.5 (Zn, K, Mg, Ca, and other 13 measurable elements) were determined by inductively coupled plasma-mass spectrometer (ICP-MS). The results showed that the PM_2.5 emission factor increased significantly with the increase of moisture content of combustible materials in the range of 11.63 ± 0.55 for dry samples to 36.71 ± 1.21 g/kg for samples with 30% moisture content. The main elemental components of PM_2.5 were K, Zn, Ca, Mg, and Na and K, Ca, Mg, and Na emission factors increased with the increase of moisture content of combustibles. The proportion of macronutrients in PM_2.5 released by combustion of each herb increased as the moisture content increased, but the proportion of trace elements gradually decreased. There was a good correlation between elemental composition of PM_2.5 and that of herbaceous combustibles. The results provide evidence that the moisture content of combustible materials has a significant effect on the emission of inorganic elements in particulate matter, and hence cautions should be exercised during fuel reduction treatments, such as early prescribed fire.

Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2001-2021

Russia has the largest forest area on earth. Its boreal forests officially store about 97 Pg C, which significantly affect the global carbon cycle. In recent years, forest fires have been intensifying on the planet, leading to increased carbon emissions. Here we review how differences in fire control management of Russian forests affect fire related emissions. Carbon emissions due to fire were estimated using satellite data and compared to official reports for 2001-2021. We found that the relative areas affected by fire did differ between different fire protection zones, and 89 % of the area burnt was in forests controlled by fire-fighting aircraft or areas without protection. As a result, 417.7 Mha of poor or unprotected Russian forests (42 % of total) account about a half of total carbon emissions. According to our estimates, the average area of burnt forests in Russia was about 8.3 Mha per year between 2016 and 2021, resulting in annual carbon emission of 193 million metric tons (Mt) C emissions, and 53 % of them were from unprotected forest. These estimated carbon emissions are significantly higher than official national reports (79 Mt C yr^-1). We estimated that net carbon uptake for Russia for 2015-2021 was about 333 ± 37 Mt C, which is roughly double the official estimates. Our results highlight large spatial differences in fire protection and prevention strategies in fire related emissions. The so-called control zone which stretches across large parts of Eastern Russia has no fire control and is the region of major recent fires. Our study shows that to estimate the Russian forest carbon balance it is critical to include this area. Implementation of some forest management in the remote areas (i.e., control zone) would help to decrease forest loss and resulting carbon emissions.