Global fire cases in recent years and their vast damages are vivid reasons to study the wildfires more deeply. A 25-year period natural wildfire database and a wide array of environmental variables are used in this study to develop an artificial neural network model with the aim of predicting potential fire spots. This study focuses on non-human reasons of wildfires (natural) to compute global warming effects on wildfires. Among the environmental variables, this study shows the significance of temperature for predicting wildfire cases while other parameters are presented in a next study. The study area of this study includes all natural forest fire cases in United States from 1992 to 2015. The data of eight days including the day fire occurred and 7 previous days are used as input to the model to forecast fire occurrence probability of that day. The climatic inputs are extracted from ECMWF. The inputs of the model are temperature at 2 meter above surface, relative humidity, total pressure, evaporation, volumetric soil water layer, snow melt, Keetch–Byram drought index, total precipitation, wind speed, and NDVI. The results show there is a transient temperature span for each forest type which acts like a threshold to predict fire occurrence. In temperate forests, a 0.1-degree Celsius increase in temperature relative to 7-day average temperature before a fire occurrence results in prediction model output of greater than 0.8 for 4.75% of fire forest cases. In Boreal forests, the model output for temperature increase of less than 1 degree relative to past 7-day average temperature represents no chance of wildfire. But the non-zero fire forest starts at 2 degrees increase of temperature which ends to 2.62% of fire forest cases with model output of larger than 0.8. It is concluded that other variables except temperature are more determinant to predict wildfires in temperate forests rather than in boreal forests.

Wildfires are occurring worldwide with greater frequency and intensity. Wildfires, as well as other sources of air pollution including environmental tobacco smoke, household biomass combustion, agricultural burning, and vehicular emissions, release large amounts of toxic substances into the atmosphere. The ocular surface is constantly exposed to the ambient air and is hence vulnerable to damage from air pollutants. This review describes the detrimental effects of wildfire smoke and air pollution on the ocular surface and resultant signs and symptoms. The latest relevant evidence is synthesised and critically evaluated. A mechanism for the pathophysiology of ocular surface damage will be proposed considering the existing literature on respiratory effects of air pollution. Current strategies to reduce human exposure to air pollutants are discussed and specific possible approaches to protect the ocular surface and manage air pollution induced ocular surface damage are suggested. Further avenues of research are suggested to understand how acute and chronic air pollution exposure affects the ocular surface including the short and long-term implications.

In this short communication, we estimate that California's wildfire carbon dioxide equivalent (CO₂e) emissions from 2020 are approximately two times higher than California's total greenhouse gas (GHG) emission reductions since 2003. Without considering future vegetation regrowth, CO₂e emissions from the 2020 wildfires could be the second most important source in the state above either industry or electrical power generation. Regrowth may partly of fully occur over a long period, but due to exigencies of the climate crisis most of the regrowth will not occur quickly enough to avert greater than 1.5 degrees of warming. Global monetized damages caused by CO₂e from in 2020 wildfire emissions amount to some $7.1 billion USD. Our analysis suggests that significant societal benefits could accrue from larger investments in improved forest management and stricter controls on new development in fire-prone areas at the wildland-urban interface.

Wildfires are a complex environmental problem worldwide. The ashes produced during the fire bear metals and PAHs with high toxicity and environmental persistence. These are mobilized into downhill waterbodies, where they can impair water quality and human health. In this context, the present study aimed at assessing the toxicity of mimicked wildfire runoff to human skin cells, providing a first view on the human health hazardous potential of such matrices. Human keratinocytes (HaCaT) were exposed to aqueous extracts of ashes (AEA) prepared from ash deposited in the soil after wildfires burned a pine or a eucalypt forest stand. Cytotoxicity (MTT assay) and changes in cell cycle dynamics (flow cytometry) were assessed. Cell viability decreased with increasing concentrations of AEA, regardless of the ash source, the extracts preparation method (filtered or unfiltered to address the dissolved or the total fractions of contaminants, respectively) or the exposure period (24 and 48 h). The cells growth was also negatively affected by the tested AEA matrices, as evidenced by a deceleration of the progress through the cell cycle, namely from phase G0/G1 to G2. The cytotoxicity of AEA could be related to particulate and dissolved metal content, but the particles themselves may directly affect the cell membrane. Eucalypt ash was apparently more cytotoxic than pine ash due to differential ash metal burden and mobility to the water phase. The deceleration of the cell cycle can be explained by the attempt of cells to repair metal-induced DNA damage, while if this checkpoint and repair pathways are not well coordinated by metal interference, genomic instability may occur. Globally, our results trigger public health concerns since the burnt areas frequently stand in slopes of watershed that serve as recreation sites and sources of drinking water, thus promoting human exposure to wildfire-driven contamination.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia’s most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series’ of nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide CO and particulate matter with diameter < 2.5 μm (PM₂.₅), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM₂.₅ (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM₂.₅ 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6–343.) excess deaths and 3490.0 (95% CI 1325.9–5653.5) additional hospitalisations were likely as a result of PM₂.₅ and O₃ exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO₂ decreased by 21.5 and 8% in Victoria and NSW respectively. O₃ and PM₂.₅ remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.

In this review, global change processes have been linked to polycyclic aromatic compounds (PACs) in Canada and a first national budget of sources and sinks has been derived. Sources are dominated by wildfire emissions that affect western and northern regions of Canada disproportionately due to the location of Pacific and boreal forests and the direction of prevailing winds. Wildfire emissions are projected to increase under climate warming along with releases from the thawing of glaciers and permafrost. Residential wood combustion, domestic transportation and industry contribute the bulk of anthropogenic emissions, though they are substantially smaller than wildfire emissions and are not expected to change considerably in coming years. Other sources such as accidental spills, deforestation, and re-emission of previous industrial deposition are expected to contribute anthropogenic and biogenic PACs to nearby ecosystems. PAC sinks are less well-understood. Atmospheric deposition is similar in magnitude to anthropogenic sources. Considerable knowledge gaps preclude the estimation of environmental transformations and transboundary flows, and assessing the importance of climate change relative to shifts in population distribution and energy production is not yet possible. The outlook for PACs in the Arctic is uncertain due to conflicting assessments of competing factors and limited measurements, some of which provide a baseline but have not been followed up in recent years. Climate change has led to an increase in primary productivity in the Arctic Ocean, but PAC-related impacts on marine biota appear to be modest. The net effect of changes in ecological exposure from changing emissions and environmental conditions throughout Canada remains to be seen. Evidence suggests that the PAC budget at the national scale does not represent impacts at the local or regional level. The ability to assess future trends depends on improvements to Canada’s environmental measurement strategy and biogeochemical modelling capability.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

Reconstructions of 250 years historical inputs of two distinct types of black carbon (soot/graphitic black carbon (GBC) and char-BC) were conducted on sediment cores from two basins of the Puget Sound, WA. Signatures of polycyclic aromatic hydrocarbons (PAHs) were also used to support the historical reconstructions of BC to this system. Down-core maxima in GBC and combustion-derived PAHs occurred in the 1940s in the cores from the Puget Sound Main Basin, whereas in Hood Canal such peak was observed in the 1970s, showing basin-specific differences in inputs of combustion byproducts. This system showed relatively higher inputs from softwood combustion than the northeastern U.S. The historical variations in char-BC concentrations were consistent with shifts in climate indices, suggesting an influence of climate oscillations on wildfire events. Environmental loading of combustion byproducts thus appears as a complex function of urbanization, fuel usage, combustion technology, environmental policies, and climate conditions.

Forest fires can threaten amphibians because ash-associated contaminants transported by post-fire runoff impact both terrestrial and aquatic ecosystems. Still, the effects of these contaminants on the skin microbiome of amphibians have been overlooked. Thus, the main objective of this study was to assess the effects of ash from different severity wildfires (moderate and high) on the skin microbiome of the Iberian frog (Rana iberica). Bacterial isolates sampled from R. iberica skin microbiome were tested for their antimicrobial activity against the pathogen Aeromonas salmonicida. The isolates with antimicrobial activity were identified and further exposed to several concentrations (0, 6.25, 12.5, 25, 50, 75, and 100%) of Eucalypt (Eucalyptus globulus) aqueous extracts (AAEs) of ash from both a moderate and a high severity wildfire. The results showed that 53% of the bacterial isolates presented antimicrobial activity, with Pseudomonas being the most common genus. Exposure to AAEs had diverse effects on bacterial growth since a decrease, an increase or no effects on growth were observed. For both ash types, increasing AAEs concentrations led to an increase in the number of bacteria whose growth was negatively affected. Ash from the high severity fire showed more adverse effects on bacterial growth than those from moderate severity, likely due to the higher metal concentrations of the former. This study revealed that bacteria living in Iberian frogs' skin could be impaired by ash-related contaminants, potentially weakening the individual's immune system. Given the foreseen increase in wildfires' frequency and severity under climate change, this work raises awareness of the risks faced by amphibian communities in fire-prone regions, emphasising the importance of a rapid implementation of post-fire emergency measures for the preservation and conservation of this group of animals.

Wildfires may play a role in redistributing radionuclides in the environment in combination with hydrological processes such as surface runoff and soil erosion. We investigated plot-scale radionuclide wash-off at forest sites affected by wildfires in the Chernobyl Exclusion Zone (CEZ). We also compared speciation of the washed-off radionuclides with those in previous studies conducted just after the accident in 1986. We observed the surface runoff and the radionuclide wash-off with a soil erosion plot at forest and post-fire sites during May–September 2018. In the post-fire site, 2.81 mm of surface runoff was observed in at least three flow events resulting from 285.8 mm total rainfall. The fluxes of dissolved and particulate ¹³⁷Cs were estimated as 4.9 and 161 Bq m⁻², respectively. The dissolved phase ⁹⁰Sr flux was estimated as 214 Bq m⁻². At the forest site, a single surface runoff (0.67 mm) event was generated by rainfall of 182.2 mm. The fluxes of dissolved and particulate ¹³⁷Cs wash-off values were 6.2 and 8.6 Bq m⁻², respectively. The flux of dissolved ⁹⁰Sr wash-off from the forest was estimated as 45.1 Bq m⁻². The distribution coefficient, which indicates the dissolved-particulate form of radionuclides, in the post-fire site was 30 times higher than that in the forest site, indicating the importance of particulate ¹³⁷Cs wash-off after fire in the CEZ. The entrainment coefficients for dissolved and particulate ¹³⁷Cs concentrations were around 50 times lower than those obtained in the corresponding position within the CEZ immediately after the accident in 1987. The effect of downward migration of ¹³⁷Cs over 30 years led to decreased entrainment coefficients for dissolved and particulate ¹³⁷Cs. The effect of downward migration of radionuclides was considered sufficient to indicate changes in normalized liquid and solid radionuclides wash-off entrainment coefficient and the distribution coefficient in this study.