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.

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.

The low-cost and compact size of light-scattering-based particulate matter (PM) sensors provide an opportunity for improved spatiotemporally resolved PM measurements. However, these inexpensive sensors have limitations and need to be characterized under realistic conditions. This study evaluated two Plantower PMS (particulate matter sensor) 1003s and two PMS 5003s outdoors in Salt Lake City, Utah over 320 days (1/2016–2/2016 and 12/2016–10/2017) through multiple seasons and a variety of elevated PM2.5 events including wintertime cold-air pools (CAPs), fireworks, and wildfires. The PMS 1003/5003 sensors generally tracked PM2.5 concentrations compared to co-located reference air monitors (one tapered element oscillating microbalance, TEOM, and one gravimetric federal reference method, FRM). The different PMS sensor models and sets of the same sensor model exhibited some intra-sensor variability. During winter 2017, the two PMS 1003s consistently overestimated PM2.5 by a factor of 1.89 (TEOM PM2.5<40 μg/m3). However, compared to the TEOM, one PMS 5003 overestimated PM2.5 concentrations by a factor of 1.47 while the other roughly agreed with the TEOM. The PMS sensor response also differed by season. In two consecutive winters, the PMS PM2.5 measurements correlated with the hourly TEOM measurements (R2 > 0.87) and 24-h FRM measurements (R2 > 0.88) while in spring (March–June) and wildfire season (June–October) 2017, the correlations were poorer (R2 of 0.18–0.32 and 0.48–0.72, respectively). The PMS 1003s maintained high intra-sensor agreement after one year of deployment during the winter seasons, however, one PMS 1003 sensor exhibited a significant drift beginning in March 2017 and continued to deteriorate through the end of the study. Overall, this study demonstrated good correlations between the PMS sensors and reference monitors in the winter season, seasonal differences in sensor performance, some intra-sensor variability, and drift in one sensor. These types of factors should be considered when using measurements from a network of low-cost PM sensors.

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ2 (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

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.

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.

Here we present the chemical characterization of the water-soluble organic carbon fraction of atmospheric aerosol collected during a prescribed fire burn in relation to soil organic matter and biomass combustion. Using nuclear magnetic resonance spectroscopy, we observed that humic-like substances in fire emissions have been associated with soil organic matter rather than biomass. Using a chemical mass balance model, we estimated that soil organic matter may contribute up to 41% of organic hydrogen and up to 27% of water-soluble organic carbon in fire emissions. Dust particles, when mixed with fresh combustion emissions, substantially enhances the atmospheric oxidative capacity, particle formation and microphysical properties of clouds influencing the climatic responses of atmospheric aeroso. Owing to the large emissions of combustion aerosol during fires, the release of dust particles from soil surfaces that are subjected to intense heating and shear stress has, so far, been lacking.

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.