Wood combustion in small-scale stoves remains an important heat source, yet it is also associated with the emission of particulate matter and other pollutants. A CFD model of beech wood combustion was previously developed and validated for the reference fuel condition and subsequently used to perform a parametric analysis at different fuel moisture contents. The present study experimentally validates these previously published CFD parametric predictions using additional combustion experiments with beech wood at different moisture levels. The main objective of the present study is therefore to experimentally validate the CFD parametric predictions reported in the previous work, with particular attention to particulate matter emissions. To verify these parametric predictions, additional combustion experiments were conducted using beech wood at different moisture levels on an as-received basis (12.5
This study analyzes trends in water availability using the surface water balance at the sub-basin scale in the Amazon biome. The analysis considered a reference period (2009–2021) and a future period (2022–2100). Two climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were established: SSP2-4.5 and SSP5-8.5. Precipitation projections were obtained from the Global Climate Model (GCM) GFDL-ESM4, while temperature projections were estimated using the mean of a five-model ensemble (MM-5), composed of KIOST-ESM, FGOALS-g3, BCC-CSM2-MR, NESM3, and GFDL-ESM4. Streamflow was simulated using a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) recurrent neural network. Reference evapotranspiration was estimated using the Hargreaves–Samani equation, calibrated for local conditions, and regional crop coefficients were applied to improve evapotranspiration estimates. Trend analyses were performed using the Mann–Kendall test and Sen’s slope estimator for water balance and components. The calibration of the Hargreaves–Samani equation improved the estimation of reference evapotranspiration, whereas the original formulation tended to overestimate ETo under humid tropical conditions. The results also indicate a significant increasing trend in evapotranspiration (Z = 7.92), accompanied by decreasing precipitation (Z = − 3.96), reductions in specific discharge (Z = − 3.61), and a pronounced decline in the water balance (Z = − 4.77), particularly under the SSP5-8.5 scenario.
Understanding the dynamics of high-intensity rainfall is crucial for assessing hydrological risks and informing climate adaptation strategies, particularly in regions vulnerable to climate change and variability, such as South Australia. This study examines the temporal variations in high-intensity rainfall events across four meteorological stations in Greater Adelaide, South Australia, Australia: Adelaide Airport, Kent Town, Hindmarsh, and Parafield. Daily rainfall data obtained from the Scientific Information for Land-Owners (SILO) database were analysed using the Bivariate Truncated Logistic-Geometric (BTLG) model to examine joint relationships between episode duration and peak intensity. Change-point analysis using the nonparametric Pettitt test was conducted to identify the time at which the effect of climate change on the intensity of daily rainfall became evident. The results indicate noticeable changes in the frequency and rainfall episodes, with key shifts around 1985 toward shorter-duration, generally lower-intensity events at several locations with daily data. The joint probability analysis p (peak > 20 mm, duration > 5 days) indicated a reduction in long-duration extreme events. This study highlights emerging trends of concentrated rainfall and their potential effects on urban runoff, flooding, and infrastructure design.
Furrow irrigation is widely practiced in arid and semi-arid regions but suffers from low efficiency due to infiltration variability and water losses. This systematic review analysed 92 peer-reviewed studies (2017–2024) on short-cycle crops, identified through a reproducible search of five bibliographic databases (Scopus, Web of Science, IEEE Xplore, SpringerLink, ScienceDirect) supplemented by a manually directed search of publisher platforms, following Kitchenham Charters’ protocol. Study quality was assessed narratively, based on validation against field data and comparison with established simulation software (SIRMOD, WinSRFR), rather than through a formal quality-appraisal tool. The narrative synthesis indicates that optimized irrigation techniques achieve efficiency gains ranging from 20
The literature on the underlying factors of environmental sustainability has flourished in recent decades. Despite the increasing attention given to improving environmental quality, few studies have focused on the catalysts for environmental balance. This research aims to explore the spatial spillover effects of financial technology (FINT) on load capacity factor dynamics (LOCAFD) as a proxy for environmental balance dynamics. The present study applied the panel smooth threshold regression (PSTR) approach and the dynamic Spatial Durbin model (SDM) with Lee-Yu transformation in order to identify the determining factors of LOCAFD in the case of 10 emerging market economies. The outcomes of the study reveal that only under the high threshold of natural resources, Fintech innovation positively affects the LOCAFD. The results from both PSTR and SDM indicated the absence of beneficial effects of natural resources on the LOCAFD. Likewise, the results support the positive moderating effect of FinTech on the relationship between financial development and the load factor dynamics. Although FinTech and financial development individually exert significant negative direct effects on the LOCAFD, the results disclose that, using the spatial economic weighting matrix, the interaction term between FinTech (FINT) and financial development (FD) is positive and highly significant for direct, indirect, and total effects. Finally, the findings reveal that the spatial spillovers of Fintech have a beneficial impact on the environmental balance dynamics for economically similar countries, while their effects become negative for geographically closed economies. The integration of an approach combining both spatial dynamics, as well as the non-linear methods, can provide clarification to decision-makers regarding the most appropriate financial technologies and strategic management of natural resources to be implemented to accelerate environmental balance dynamics for the case of emerging market economies.
