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.

The San Joaquin Valley (SJV) of California has suffered persistent particulate matter (PM) pollution despite many years of control efforts. To further understand the chemical drivers of this problem and to support the development of State Implementation Plan for PM, a time-of-flight aerosol chemical speciation monitor (ToF-ACSM) outfitted with a PM₂.₅ lens and a capture vaporizer has been deployed at the Fresno-Garland air monitoring site of the California Air Resource Board (CARB) since Oct. 2018. The instrument measured non-refractory species in PM₂.₅ continuously at 10-min resolution. In this study, the data acquired from Oct. 2018 to May 2019 were analyzed to investigate the chemical characteristics, sources and atmospheric processes of PM₂.₅ in the SJV. Comparisons of the ToF-ACSM measurement with various co-located aerosol instruments show good agreements. The inter-comparisons indicated that PM₂.₅ in Fresno was dominated by submicron particles during the winter whereas refractory species accounted for a major fraction of PM₂.₅ mass during the autumn associated with elevated PM₁₀ loadings. A rolling window positive matrix factorization analysis was applied to the organic aerosol (OA) mass spectra using the Multilinear Engine (ME-2) algorithm. Three distinct OA sources were identified, including vehicle emissions, local and regional biomass burning, and formation of oxygenated species. There were significant seasonal variations in PM₂.₅ composition and sources. During the winter, residential wood burning and oxidation of nitrogen oxides were major contributors to the occurrence of haze episodes with PM₂.₅ dominated by biomass burning OA and nitrate. In autumn, agricultural activities and wildfires were found to be the main cause of PM pollution. PM₂.₅ concentrations decreased significantly after spring and were dominated by oxygenated OA during March to May. Our results highlight the importance of using seasonally dependent control strategies to mitigate PM pollution in the SJV.

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.

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.

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.

In the Mediterranean ecosystem, wildfires are very frequent and the predicted future with a probable increase of fires could drastically modify the vegetation scenarios. Vegetation fires are an important source of gases and primary emissions of fine carbonaceous particles in the atmosphere. In this paper, we present gaseous and particulate emissions data from the combustion of different plant tissues (needles/leaves, branches and needle/leaf litter), obtained from one conifer (Pinus halepensis) and one deciduous broadleaf tree (Quercus pubescens). Both species are commonly found throughout the Mediterranean area, often subject to wildfires. Experiments were carried out in a combustion chamber continuously sampling emissions throughout the different phases of a fire (pre-ignition, flaming and smoldering). We identified and quantified 83 volatile organic compounds including important carcinogens that can affect human health. CO and CO₂ were the main gaseous species emitted, benzene and toluene were the dominant aromatic hydrocarbons, methyl-vinyl-ketone and methyl-ethyl-ketone were the most abundant measured oxygenated volatile organic compounds. CO₂ and methane emissions peaked during the flaming phase, while the peak of CO emissions occurred during the smoldering phase. Overall, needle/leaf combustion released a greater amount of volatile organic compounds into the atmosphere than the combustion of branches and litter. There were few differences between emissions from the combustion of the two tree species, except for some compounds. The combustion of P. halepensis released a great amount of monoterpenes as α-pinene, β-pinene, p-cymene, sabinene, 3-carene, terpinolene and camphene that are not emitted from the combustion of Q. pubescens. The combustion of branches showed the longest duration of flaming and peak of temperature. Data presented appear crucial for modeling with the intent of understanding the loss of C during different phases of fire and how different typologies of biomass can affect wildfires and their speciation emissions profile.

As the climate in California warms and wildfires become larger and more severe, satellite-based observational tools are frequently used for studying impact of those fires on air quality. However little objective work has been done to quantify the skill these satellite observations of smoke plumes have in predicting impacts to PM2.5 concentrations at ground level monitors, especially those monitors used to determine attainment values for air quality under the Clean Air Act. Using PM2.5 monitoring data from a suite of monitors throughout the Central California area, we found a significant, but weak relationship between satellite-observed smoke plumes and PM2.5 concentrations measured at the surface. However, when combined with an autoregressive statistical model that uses weather and seasonal factors to identify thresholds for flagging unusual events at these sites, we found that the presence of smoke plumes could reliably identify periods of wildfire influence with 95% accuracy.

monitoring sites operated by the National Park Service in Sierra Nevada, California, are used to develop an ozone forecasting model and to estimate the contribution of wildland fires on ambient ozone levels. The analyses of weather and ozone data pointed to the transport of ozone precursors from the Central Valley as an important source of pollution in these National Parks. Comparisons of forecasted and observed values demonstrated that accurate forecasts of next-day hourly ozone levels may be achieved by using a time series model with historic averages, expected local weather and modeled PM values as explanatory variables. Results on fire smoke influence indicated occurrence of significant increases in average ozone levels with increasing fire activity. The overall effect on diurnal ozone values, however, was small when compared with the amount of variability attributed to sources other than fire. We have demonstrated that it is possible to produce accurate forecasts of next-day hourly ozone levels in the Sierra Nevada, CA, during fire season.

House dust is an important medium for exposure to persistent pollutants, such as metals. Detailed characterization of metal composition is needed to identify sources and potential health impacts of exposure. In this study we show that specific metals in dust dominate in different locations within residential homes in a mid-size Canadian city (Fort McMurray, Alberta), up to two years after a major wildfire event in 2016. Dust samples were collected in high-traffic (e.g. bedroom, N = 186), low-traffic (e.g. basement, N = 158), and entranceway areas (N = 171) of residential homes (N = 125), and analyzed for 25 trace metal elements using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The profile of metal concentrations in the entranceway resembled that of outdoor soils, especially for crustal elements. On the other hand, Cu, Zn, and Pb concentrations in dust sampled in indoor living areas were on average three to six times higher than in other indoor locations indicating indoor sources for these elements. In general, Pb concentrations were similar or lower than in an average Canadian residence, but a substantial fraction showed anomalously high concentrations in the low-traffic areas, particularly on concrete surfaces in basements. Notably, the 2016 wildfires showed limited influence on metal concentrations in indoor dust, despite the widespread concerns in the community about long term exposure. Enrichment factor ratio calculations and principal component analysis showed two classes of sources of metals in dust that represent geogenic-outdoor sources and anthropogenic-indoor sources. Overall, we demonstrate that outdoor and indoor sources of dust impact its composition, and these influences are reflected in the different areas of a home.

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.