Diffuse pollution from agricultural activities is a major environmental problem. The extent of the impacts is driven, e.g., by local topography due to the influence on hydrological processes. This study aims to investigate the use of different scale topographic data in assessing runoff-erosion processes. The TopAGNPS module, a component of the AnnAGNPS model (Annualized Agriculture Nonpoint Source), was used to assess the impact of the vertical resolution of the Digital Elevation Model (DEM) on the topographic and hydrological configuration of a basin, and on the simulation of soil erosion. The study focuses on a small agro-forestry basin (190 ha) located in the municipality of Idanha-a-Nova, Portugal. A georeferenced survey of the basin’s surface drainage network was carried out and then compared with the results of the simulated drainage networks generated by the TopAGNPS module using two DEMs with vertical resolutions of 1 and 5 m. The DEM with a 5 m vertical resolution produced unsatisfactory results, as evidenced by significant discrepancies between the simulated and observed natural drainage networks. On the other hand, the drainage network generated with the DEM of 1 m resolution was very similar to the observed drainage network. The use of distinct topographic configurations modeled based on different vertical resolution DEMs on the estimation of soil erosion by water using the RUSLE model (Revised Universal Soil Loss Equation), resulted in significant differences considering the values of 5.85 and 4.17 ton/ha year for the DEM with 1 m and 5 m vertical resolution, respectively. Considering that soil erosion by water and other processes, such as the transport of pollutants, are distributed processes, it is of great relevance to consider good topographic and hydrologic configurations to achieve more reliable simulations and better support decision-making.

Soil is a complex environment, where microorganisms play a crucial role in the maintenance of soil structure, nutrient cycling and thus soil quality, relevant for croplands productivity. Soil microbial activity, however, is largely determined by water availability, and both are affected by agricultural management. This study aims to investigate the impact of irrigation, i.e. different amount of water and application scheduling, on soil microbial biomass and functional structure of soil microbiome (number of microorganisms in various ecological-trophic groups) in a Mediterranean maize farm. In 2023, six irrigation treatments were applied in a maize farm located in the Mondego Agricultural Valey, centre region of mainland Portugal. The irrigation treatments included three different amounts of water per week applied with drip irrigation: i) 100 mm, the average optimal amount over the last 30 years, based on APSIM crop model), ii) 55 mm, selected to simulate water scarcity conditions, and iii) the amount recommended by the local farmers’ association, based on weekly weather forecast and water balance modelling (ranged between 24 mm and 66 mm over the study period). The water was applied once or split in two applications during the week in different treatments. Each treatment was applied in triplicated plots, each plot covering five maize rows and extending over 10 m length. Nine composite soil samples (0-15 cm depth) per treatment were collected immediately before and after the irrigation period (~ 6 to 17 weeks after sowing). The soil samples were analyzed in sterile conditions using solid growth media: Nutrient Agar, Agar-Agar, Jensens Medium, Soil Agar, and Czapek-Dox Medium. The serial dilutions of the samples were provided until the suspension contained a microorganism titer within the range of 10−3–10−5 CFU/mL. The content of general microbial biomass (Сmic) in the soil was determined using the rehydration method. The results show that after the irrigation period, Cmic increased between 14% to 48%. The number of different nitrogen fixing bacteria and ammonifiers (nitrogen-mineralizing bacteria) increased, whereas the number of micromycetes, spore forming bacteria, oligotrophic, and pedotrophic bacteria groups decreased in all the treatments, which are good indicators about soil quality. Generally, these changes are slightly higher in the treatments where irrigation was applied twice instead of once a week (e.g. 28-31% vs 20-34% increase in nitrogen fixing bacteria, and 33-50% vs 12-36% decrease in oligotrophic bacteria - often associated to nutrient-poor soils). This highlights the relevance of providing a more uniform soil moisture content over the crop season (through smaller amounts of water, applied more often) to support soil microbial communities. Microbial biomass was lowest in plots receiving the less water (55 mm per week), but it was similar between plots receiving 100 mm of water per week and adjusting the amount of water to the recommendations of local farmers’ association. Long term average data would be useful to support decision on the amount of water to apply in areas where technical recommendations are not available. Adequate irrigation management in croplands can support soil biodiversity.

Heatwaves can greatly impact societies, underscoring the need to extend current heatwave prediction lead times. This study investigates multiple machine learning (ML) model approaches for heatwave occurrence prediction with long lead times of one to five months. Five ML classifiers, built using Google Earth Engine remote sensing datasets, are developed and tested for heatwave prediction for the national scale (case example of Sweden) over time period 1989–2019. The ML modelling is based on 13 final explanatory atmospheric and landscape features. The balanced random forest model exhibits the consistently best performance among the tested ML models, stable across all investigated lead times (from one to five months) with balanced accuracy of around 0.77, even though not overall identifying actual heatwave occurrence (decreased recall for heatwave occurrence from 0.87 to 0.81). Application of SHapley Additive exPlanations technique for model interpretation shows increasing importance of model output with increasing lead time for landscape features such as runoff and soil water. Overall, more frequent heatwave occurrence emerges for places characterized by lower values of geopotential height, evaporation, precipitation, and topographical slope, and higher values of temperature, runoff, and sea level pressure. The study also exemplifies how the developed ML modelling approach could be used to identify and warn for early signs of forthcoming heatwave occurrence, and further step-wise improve the identification and warning toward less uncertainty for shorter lead times. This can facilitate earlier warning in support of better planning of measures to mitigate adverse heatwave impacts, up to several months ahead of their possible occurrence.

Despite the growing research interest regarding the perceptions of forest-related stakeholders about forest functions and values, the views of social actors not directly involved in forestry have been largely overlooked, particularly in Southern European countries like Portugal. This article addresses this gap by analyzing the social perceptions regarding the functions of the Matas do Litoral, public-managed forests in the Centre Region of Portugal, severely impacted by the mega-fires of October 2017. Through a hierarchical cluster analysis of survey results collected from 1000 inhabitants of the adjacent parishes of the Matas, three groups were identified-"The Moderately Interested," "The Comprehensively Interested" and "The Environmentally Interested"-based on significant differences regarding their perceptions of forest functions, the future of these areas, essential factors for effective forest management, the mega-fires and their consequences. Sociodemographic characteristics-particularly age and literacy levels-forest ownership, knowledge and familiarity with these forests help to explain the differences between the three clusters.

Measurement, monitoring, and prediction of soil organic carbon (SOC) are fundamental to supporting climate change mitigation efforts and promoting sustainable agricultural management practices. This review discusses recent advances in methodologies and technologies for SOC quantification, including remote sensing (RS), proximal soil sensing (PSS), artificial intelligence (AI) for SOC modelling (in particular, machine learning (ML) and deep learning (DL)), biogeochemical modelling, and data fusion. Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo-climatic and land management, and the use of data processing and modelling frameworks. We also found that the accuracy of AI-driven SOC prediction improves when RS covariates are included. Although DL often outperforms classical ML, there is no single best AI algorithm. By incorporating simulated outputs from biogeochemical model as additional training data for AI, causal relationships in SOC turnover can be incorporated into empirical modelling, while maintaining predictive accuracy. In conclusion, SOC prediction can be enhanced through 1) integrating sensing technologies, 2) applying AI, notably DL, 3) addressing biogeochemical model limitations (assumptions, parameterization, structure), 4) expanding SOC data availability, 5) improving mathematical representation of microbial influences on SOC, and 6) strengthening interdisciplinary cooperation between soil scientists and model developers.

The energy and power sectors are among the main contributors to greenhouse gas emissions (GHG) and are responsible for global warming. Anthropogenic emissions of carbon dioxide (CO2), the most important GHG, result primarily from the combustion of fossil fuels. A major portion is associated with energy consumption that is at the center of the climate change debate. Renewable energy technology (e.g. wind, solar) has been used and incorporated into the energy sector since the Clean Development Mechanism (CDM) was introduced. Hydroelectricity has an important place in generation of electricity aided by the mechanism mentioned above. The contribution of hydropower to avoid or mitigate GHG is uncertain because there is a possibility that hydroelectric dams may produce and discharge biomass-based methane (CH4), and it is known that methane is about 25 times stronger than carbon dioxide (CO2) in the greenhouse effect. As a certain amount of organic matter is accumulated in hydropower dams, biomethane and CO2 are generated from the anaerobic decomposition of the accumulated organic matter. Therefore, it seems a worthwhile subject for assessing whether hydropower really and effectively contributes to reducing GHG. A field survey was carried out in the middle of Japan's main island (so-called Kansai area) by chromatography analysis during five months, and the obtained data show a seasonal variation of dissolved CH4 ranging from 20 ppm to 10 ppm.

Soil erosion is a major degradation process affecting many regions worldwide and impairing the ability of soil to provide ecosystem services. This chapter provides a brief overview of the soil erosion processes and their susceptibility in different land uses, including agricultural, forest, and urban land, as well as different soil management practices that have been implemented to mitigate the problem. Additionally, this chapter synthesizes information regarding the use of remote sensing to support soil erosion assessments, providing examples of remote sensing data used in different studies to support soil erosion modeling.

Fish have common neurotransmitter pathways with humans, exhibiting a significant degree of conservation and homology. Thus, exposure to fluoxetine makes fish potentially susceptible to biochemical and physiological changes, similarly to what is observed in humans. Over the years, several studies demonstrated the potential effects of fluoxetine on different fish species and at different levels of biological organization. However, the effects of parental exposure to unexposed offspring remain largely unknown. The consequences of 15-day parental exposure to relevant concentrations of fluoxetine (100 and 1000 ng/L) were assessed on offspring using zebrafish as a model organism. Parental exposure resulted in offspring early hatching, non-inflation of the swimming bladder, increased malformation frequency, decreased heart rate and blood flow, and reduced growth. Additionally, a significant behavioral impairment was also found (reduced startle response, basal locomotor activity, and altered non-associative learning during early stages and a negative geotaxis and scototaxis, reduced thigmotaxis, and anti-social behavior at later life stages). These behavior alterations are consistent with decreased anxiety, a significant increase in the expression of the monoaminergic genes slc6a4a (sert), slc6a3 (dat), slc18a2 (vmat2), mao, tph1a, and th2, and altered levels of monoaminergic neurotransmitters. Alterations in behavior, expression of monoaminergic genes, and neurotransmitter levels persisted until offspring adulthood. Given the high conservation of neuronal pathways between fish and humans, data show the possibility of potential transgenerational and multigenerational effects of pharmaceuticals’ exposure. These results reinforce the need for transgenerational and multigenerational studies in fish, under realistic scenarios, to provide realistic insights into the impact of these pharmaceuticals.

The functional conservation of important selective serotonin reuptake inhibitor (SSRI) targets in non-target organisms raises concerns about their potential adverse effects on the ecosystems. Although the environmental levels of SSRIs like paroxetine (PAR) have risen, the knowledge regarding the effects of long-term exposure to PAR is limited. This study investigated the impact of sub-chronic exposure (21 days) to two sublethal concentrations of PAR (40 and 400 mu g/L) on the behaviour of adult zebrafish in different scenarios: basal activity (under dark and light conditions), stress response (evoked by sudden light transitions) and stress response recovery. A new framework was employed for the integrative study of fish's swimming performance based on their innate ability to respond to light shifts. Several swimming-associated parameters (e.g., total swimming distance, time of inactivity, swimming angles) and thigmotaxis were monitored for an integrated analysis in each scenario. Data revealed reduced swimming activity, impaired behavioural response to stress and alterations in stress recovery of PAR-exposed fish. An anxiolytic effect was particularly noticeable in fish basal swimming activity in the dark at 400 mu g/L and in the behavioural response to stress (from dark to light) and stress recovery (from light to dark) for organisms exposed to 40 mu g/L. The detected PAR-induced behavioural modifications suggest a disruption of brain glucocorticoid signalling that may have implications at the individual level (e.g., changing behavioural responses to predators), with

In this case study pharmaceuticals were analysed in the Mondego river (Portugal) and their environmental risk assessed by means of risk quotients based on an extensive retrieval of ecotoxicological data for freshwater and saltwater species. The Mondego river crosses Coimbra, the most populated city in the Portuguese Centro Region hosting a complex of regional hospitals. Environmentally relevant and prioritised pharmaceuticals were investigated in this study and their potential hazards were evaluated by conducting a separate risk assessment for the freshwater and estuary parts of the examined river section.A target analysis approach with method detection limits down to 0.01 ng L−1 was used to determine pharmaceuticals. Twenty-one prioritised target analytes out of seven therapeutical classes (antibiotics, iodinated X-ray contrast media (ICM), analgesics, lipid reducers, antiepileptics, anticonvulsants, beta-blockers) were investigated by applying ultra-high pressure liquid chromatography coupled to a triple quadrupole mass spectrometer equipped with an electrospray ionisation source.The relative pattern of pharmaceuticals along the middle to the lower Mondego showed a quite uniform picture while an approximately 40fold increase of absolute concentrations was observed downstream of the wastewater treatment plant (WWTP) discharge of Coimbra. The most frequently measured substance groups were the ICM, represented by the non-ionic ICM iopromide (βmin: 3.03 ng L−1 - βmax: 2,810 ng L−1). Environmentally more critical substances such as carbamazepine, diclofenac, and bezafibrate, with concentrations up to and 52.6 ng L−1, 59.8 ng L−1, and 10.2 ng L−1 respectively, may potentially affect aquatic wildlife. Carbamazepine revealed elevated risk quotients (RQs >1) along the middle and lower Mondego with a maximum RQ of 53 downstream of Coimbra. Especially for saltwater species, carbamazepine and clarithromycin pose high potential risks.Especially in periods of low water discharge of the Mondego river, other pharmaceuticals as diclofenac and bezafibrate may pose additional risks downstream of the WWTP.

Dripping rainfall simulators for soil research generate water drops with different types of drippers, such as plastic and glass tubes, drippers in a form of holes, irrigation drippers and hanging yarn, among which metal tubes are the most commonly used. Metal tubes appear in the form of capillaries with a flat tip or in the form of hypodermic needles. Hypodermic needles are suitable for this purpose because their diameter size is standardized with relatively small deviations from the standardized dimensions, they are available on the market, relatively cheap, made of stainless material and have a threaded connector (Luer taper). However, very often the descriptions of the rainfall simulators were not complete, nor was the calibration of the needles. In order to conduct calibration and define water drops diameter for different hypodermic needles size and dripping speed, it was constructed rainfall simulator with one dripper in a form of hypodermic needle. Simulator was designed in accordance to research of Rončević et al., (2022), needle calibration research and future soil research requirements. Simulator was intended primarily for laboratory use. It consists of structural support, water tank, water pump, mechanism of water flow regulation and simulator operation, water tank with dripper and dripper. Calibration was conducted for 11 different needles, ranging in size from 16 G to 32 G (G – gauge number), at different dripping speed, using drop counting sensor. Water drops are measured using weight measuring method. Obtained water drops size ranging from 3.70 to 1.48 mm. Based on research data, two nomograms were made for determination of interconnected parameters of potentially simulated rainfall: water drops size under different dripping speed, drippers discharge, number of drippers, and kinetic energy of water drops for the given falling height. The results of research provide the data necessary for the design of future dripping rainfall simulators for soil research and use of hypodermic needles as drippers.

Large lakes face considerable challenges due to human activities and climate change, impacting local weather conditions and ecosystem sustainability. Lake Urmia, Iran’s largest lake and the world’s second-largest saltwater lake, has undergone a substantial reduction in water levels, primarily due to drought, climate change, and excessive irrigation. This study focuses on the potential repercussions on local climate conditions, particularly investigating the impact of moisture sources, evaporation from lake surfaces, and evapotranspiration from agricultural activities, on local convection rainfall. The prevailing westerly winds in the basin suggest a hypothesis that this moisture is transported eastward within the basin, potentially leading to local precipitation as it ascends to higher altitudes near the eastern basin border. To validate this hypothesis, climate data from 1986 to 2017 from the Sarab meteorological station (east of the lake basin, influenced by local precipitation) and Saqez meteorological station (south of the basin, unaffected by local precipitation) were analyzed. The impact of lake water level reduction was assessed by categorizing data into periods of normal lake conditions (1986–1995) and water level reduction (1996–2017). Additionally, the MSWEP global precipitation product was used to examine the precipitation distribution in the entire basin over the entire period and sub-periods. The findings indicate Lake Urmia’s significant influence on convective rainfall in the eastern basin, especially during the summer. Despite decreasing lake levels from 1996 to 2017, convective rainfall in the eastern basin increased during the summer, suggesting intensified agricultural irrigation, particularly in hot seasons.

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.