Explore the vast range of titles available.

Fuel mapping is key to fire propagation risk assessment and regeneration potential. Previous studies have mapped fuel types using remote sensing data, mainly at local-regional scales, while at smaller scales fuel mapping has been based on general-purpose global databases. This work aims to develop a methodology for producing fuel maps across European regions to improve wildland fire risk assessment. A methodology to map fuel types on a regional-continental scale is proposed, based on Sentinel-3 images, horizontal vegetation continuity, biogeographic regions, and biomass data. A vegetation map for the Iberian Peninsula and the Balearic Islands was generated with 85% overall accuracy (category errors between 3% and 28%). Two fuel maps were generated: (1) with 45 customized fuel types, and (2) with 19 fuel types adapted to the Fire Behaviour Fuel Types (FBFT) system. The mean biomass values of the final parameterized fuels show similarities with other fuel products, but the biomass values do not present a strong correlation with them (maximum Spearman’s rank correlation: 0.45) because of the divergences in the existing products in terms of considering the forest overstory biomass or not.

The suggestion has been made within the wildland fire community that the rate of spread in the upper portion of the fire danger spectrum is largely independent of the physical fuel characteristics in certain forest ecosystem types. Our review and analysis of the relevant scientific literature on the subject suggest that fuel characteristics have a gradual diminishing effect on the rate of fire spread in forest and shrubland fuel types with increasing fire danger, with the effect not being observable under extreme fire danger conditions. Empirical-based fire spread models with multiplicative fuel functions generally do not capture this effect adequately. The implications of this outcome on fire spread modelling and fuels management are discussed.

BackgroundThe Australian Fire Danger Rating System (AFDRS) was implemented operationally throughout Australia in September 2022, providing calculation of fire danger forecasts based on peer-reviewed fire behaviour models. The system is modular and allows for ongoing incorporation of new scientific research and improved datasets.AimsPrior to operational implementation of the AFDRS, a Research Prototype (AFDRSRP), described here, was built to test the input data and systems and evaluate the performance and potential outputs.MethodsFire spread models were selected and aligned with fuel types in a process that captured bioregional variation in fuel characteristics. National spatial datasets were created to identify fuel types and fire history in alignment with existing spatial weather forecast layers.Key resultsThe AFDRSRP demonstrated improvements over the McArthur Forest and Grass Fire Danger systems due to its use of improved fire behaviour models, as well as more accurately reflecting the variation in fuels.ConclusionsThe system design was robust and allowed for the incorporation of updates to the models and datasets prior to implementation of the AFDRS.

Background In low- and middle-income countries, households mainly use solid fuels like wood, charcoal, dung, agricultural residues, and coal for cooking. This poses significant public health concerns due to the emission of harmful particles and gases. To address these issues and support Sustainable Development Goals (SDGs), adopting cleaner cooking fuels like electricity and gas are acknowledged as a viable solution. However, access to these cleaner fuels is limited, especially in rural areas. Methods This study conducted a face-to-face survey with 1240 individuals in rural Bangladesh to explore the link between health issues and cooking fuel type, as well as barriers to transitioning to clean cooking. Using a convenient sampling technique across four divisions/regions, the survey gathered socio-demographic and health data, along with information on clean cooking barriers through a semi-structured questionnaire. Binary and multivariable logistic regression analyses were then employed to identify significant associations between cooking fuel type and health problems. Results The study revealed that a majority of participants (73.3%) relied on solid fuel for cooking. The use of solid fuel was significantly correlated with factors such as lower education levels, reduced family income, location of residence, and the experience of health issues such as cough, chest pressure while breathing, eye discomfort, diabetes, asthma, and allergies. Economic challenges emerged as the foremost obstacle to the adoption of clean cooking, accompanied by other contributing factors. Conclusion The use of solid fuel in rural Bangladeshi households poses substantial health risks, correlating with respiratory, eye, cardiovascular, and metabolic issues. Lower education and income levels, along with specific residential locations, were associated with higher solid fuel usage. Economic challenges emerged as the primary obstacle to adopting clean cooking practices. These findings emphasize the need for implementing strategies to promote clean cooking, address barriers, and contribute to achieving Sustainable Development Goal targets for health and sustainable energy access in Bangladesh.

In the face of growing concerns about greenhouse gas emissions, there is increasing interest in forecasting the likely demand for alternative fuel vehicles. This paper presents an analysis carried out on stated preference survey data on California consumer responses to a joint vehicle type choice and fuel type choice experiment. Our study recognises the fact that this choice process potentially involves high correlations that an analyst may not be able to adequately represent in the modelled utility components. We further hypothesise that a cross-nested logit structure can capture more of the correlation patterns than the standard nested logit model structure in such a multi-dimensional choice process. Our empirical analysis and a brief forecasting exercise produce evidence to support these assertions. The implications of these findings extend beyond the context of the demand for alternative fuel vehicles to the analysis of multi-dimensional choice processes in general. Finally, an extension verifies that further gains can be made by using mixed GEV structures, allowing for random heterogeneity in addition to the flexible correlation structures.

This study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022‑139156OB‑C21, PID2022‑139156OB‑C22) within the National Program for the Promotion of Scientific‑Technical Research (2021–2023); by the Spanish Ministry of Science and Innovation and the Next‑Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021‑130925B‑I00); by the Regional Government of the Principality of Asturias, the Foundation for the Promotion of Applied Scientific Research and Technology in Asturias (FICYT) and the European Regional Development Fund (ERDF) in the framework of the REWILDING project (AYUD/2021/51261) (...)

Despite that commuters spend only 5.5% of their time in cabin vehicles, their exposure to harmful air pollutants, originated from the vehicle itself, and traffic emission is considered significant. In this study, two passenger cars with different type of fuels were investigated in terms of air quality and thermal comfort of their cabin. Investigation was performed in the city of Kozani, Northern Greece. Moreover, air samples near the exhausts were taken, in order to compare concentration of compounds found indoors. Twelve volatile organic compounds and CO 2 were measured inside the cabin when the cars were stopped, when idle and when they were cruising in medium and heavy traffic roads, under various ventilated conditions. Thermal comfort was investigated while driving the cars through the city traffic. Results showed that the air around the diesel exhaust is less affected by emissions from the engine compared to LPG fuel. This is reflected to the TVOC measured into the cabin. Results also revealed that the air quality of a diesel fuel moving car with open windows is only affected by the traffic emissions from neighbouring vehicles, while for the car with LPG fuel, the self-pollution from its own exhaust might contribute together with the outdoor air.

Live fuel moisture content (LFMC) is a critical variable influencing wildfire behavior, ignition potential, and suppression difficulty, yet it remains challenging to monitor consistently across landscapes due to sparse field observations, rapid temporal changes, and vegetation heterogeneity. This study presents a comprehensive review of satellite-based approaches for estimating LFMC, with emphasis on methods applicable to New Zealand, where wildfire risk is increasing due to climate change. We assess the suitability of different remote sensing data sources, including multispectral, thermal, and microwave sensors, and evaluate their integration for characterizing both LFMC and fuel types. Particular attention is given to the trade-offs between data resolution, revisit frequency, and spectral sensitivity. As knowledge of fuel type and structure is critical for understanding wildfire behavior and LFMC, the review also outlines key limitations in existing land cover products for fuel classification and highlights opportunities for improving fuel mapping using remotely sensed data. This review lays the groundwork for the development of an operational LFMC prediction system in New Zealand, with broader relevance to fire-prone regions globally. Such a system would support real-time wildfire risk assessment and enhance decision-making in fire management and emergency response.

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests.