Artificial light at night (ALAN) has become a problem for fireflies because it disrupts their natural processes and threatens the conservation of their populations. In this regard, the aim of the study was to determine the effects of ALAN on firefly species through a systematic review. The PRISMA 2020 statement was fundamental for the review of the databases, and the inclusion and exclusion criteria for specifying the subject of study. On the other hand, the annual growth of scientific production was determined using the digital tool (Calcuvio). The year and country with the highest scientific production were 2021 and the United States, respectively, and the annual growth (2005−2025) was 16%. The most studied species was Lampyris noctiluca, and the effect of ALAN on the most common fireflies was a change in the intensity and frequency of their flashes in females. It is concluded that investment should be made in research in countries with abundant and diverse populations of fireflies. Furthermore, studies should be conducted on trophic interactions or sublethal physiological effects of fireflies, as well as on diversifying the species under study.

Groundwater contamination, exacerbated by improper solid waste disposal, is a critical environmental problem, especially in regions such as the Toluca Valley, where water supply depends on aquifers. This study quantified leachate generation at the Metepec landfill in the State of Mexico using the Water Balance Model (WBM) and the Hydrologic Evaluation of Landfill Performance model (HELP 3). Three hydrological years (dry, average, and wet) were analyzed to represent contrasting scenarios. The results showed that leachate generation is directly related to precipitation. In dry months, infiltration represented less than 17% of annual rainfall, while in some months evapotranspiration was higher. The HELP 3 model estimated higher volumes due to the moisture generated by waste decomposition. Comparative analysis revealed that, in the absence of barriers, percolation exceeded 12% of annual precipitation. When a clay layer was implemented, infiltration was practically eliminated, and with a geomembrane it was reduced to approximately 1% of total precipitation. These results demonstrate the effectiveness of structural configurations in reducing leachate generation. The study concludes that the implementation of impermeable barriers and post-closure monitoring programs is essential to mitigate environmental impacts and protect groundwater resources.

The study goal was to evaluate the effectiveness of school eco-clubs and digital technologies as instruments for fostering environmental responsibility amid the environmental challenges in rural areas of Southeast Kazakhstan. A descriptive-analytical design with mixed methods was applied. The sample consisted of 186 “student–parent” pairs from 10 schools in the Almaty Region and 30 experts. Data was collected from September 2023 to April 2024 using validated questionnaires, focus groups, and expert interviews. A statistically significant relationship was identified between the duration of participation in eco-clubs and the level of students’ environmental knowledge ($p$ $<$ 0.01) and between the intensity of intergenerational communication and changes in family practices: safe handling of agrochemicals (62.2% of families), composting organic waste (55.1%), and the use of organic fertilizers (38.8%). Regression analysis showed that practical demonstrations ($\beta$ = 0.42, Cohen’s d = 0.89) and virtual reality (VR) technologies ($\beta$ = 0.35, Cohen’s d = 0.74) are the strongest predictors of changes in environmental behavior ($R^2$ = 0.51, $p$ $<$ 0.001). VR outperforms traditional methods: knowledge acquisition is 35% higher (4.6 versus 3.4 points), and practical application is twice as high (89.3% and 45.2%, respectively). The study confirms the effectiveness of school eco-clubs as a catalyst for environmental changes in rural families. The authors demonstrate the potential of virtual technologies in compensating for the limitations of environmental education in remote agricultural regions.

Landslide susceptibility modeling (LSM) in data scarce environments remains limited by weak interpretability and the absence of calibrated predictive uncertainty, reducing its reliability for operational decision making. This study introduces a cloud-native LSM framework that unifies SHapley Additive exPlanations (SHAP) for transparent model interpretation with Conformal Prediction (CP) for statistically valid uncertainty quantification, implemented end-to-end within Google Earth Engine (GEE). Multi-sensor remote sensing datasets were processed in GEE, and four machine learning (ML) classifiers, Random Forest (RF), Gradient Boosted Trees (GBT), Support Vector Machine (SVM), and Classification and Regression Trees (CART) were trained using a spatially stratified, balanced inventory of 238 landslide and 227 non-landslide locations in Pune District, India. CART achieved the highest predictive performance (Accuracy = 0.8655; Kappa = 0.6142; Precision = 0.8055), while RF was selected for SHAP and CP analyses due to its smoother probability outputs. SHAP identified rainfall, elevation, slope, normalized difference vegetation index (NDVI), and topographical wetness index (TWI) as the dominant predictors. CP mapped high confidence ($<$5% uncertainty) susceptibility in steep, high rainfall escarpments and elevated uncertainty ($>$30%) in data sparse plains. The resulting explainable, uncertainty aware hazard maps, deployed via a public GEE application, provide policy ready insights and offer a transferable template for monsoon prone, low inventory regions.

The purpose of this work was to study purolate (porous carbons from the Kuzbass deposits, Russia). Thermograms in the temperature range 150−700 ℃ showed an up to 8.7% mass loss in the purolate samples. It was proven that purolate has a large range of particle size (from 0.1 to 3 mm) and pH (8.0−9.0) and a low total pore volume in water (0.5 cm$^3$/g). It was found that in addition to C and O$_2$, Zn (5,346.8 mg/kg), Ba (256 mg/kg), Sr (304 mg/kg), Cu (541 mg/kg), and MnO (119 mg/kg) are present in significant amounts in purolate; it does not contain Al$_2$O$_3$, SiO$_2$, Rb, and Zr. It was established that the service life of the sorbent layer is 380 min at an adsorption temperature of 28−30 ℃ (analysis of the adsorption breakthrough curve). The final degree of purification from the model mixture ranged from 35.4% for manganese ions to 98.1% for iron ions. Analysis of the kinetic curves of ion extraction found that the highest adsorption (0.07 g/g) for 250 min was observed during the extraction of manganese ions, the lowest (0.045 g/g) for 300 min, during the extraction of nitrite ions. The development of a new technology using anthracite-based adsorbents for treating water from coal mining operations would help address environmental concerns in resource-dependent areas and contribute to the rehabilitation and revitalization of aquatic ecosystems.

Industrialization and population growth pose significant environmental issues, particularly in water quality, making many sources unsuitable for domestic use. Natural organic compounds and metal nanoparticles (NPs) are used as wastewater adsorbents. The current research investigated the adsorption kinetics, isotherms, and reusability of the manganese oxide NPs synthesized from star anise (SA) (Illicium verum) extract (MnO@SE) to aid in the creation of environmentally friendly water purification solutions. MnO@SE was prepared with SA extract and manganese acetate (II) tetrahydrate solution. The green-synthesized biosorbent was characterized employing methods including Fourier transform infrared spectroscopy, X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). These evaluations offered good information on surface shape and surface-available functional groups. The influences of pH, adsorbent dosage, ion concentration, and contact time on metal ion adsorption were all examined. The results revealed that model solutions with a pH of 2.0, a biosorbent dosage of 0.8 g/L, an initial concentration of 25 mg/L, and a contact time of 50 minutes produced the best removal efficiency (96.34% for Cr(VI) and 87.01% for Pb(II)). The adsorption processes of both metal ions occurred in a multilayer fashion on the heterogeneous surface of the biosorbent through diffusion kinetics, according to the isotherm and kinetic findings. The adsorption process is endothermic and spontaneous, according to thermodynamic analysis. The study revealed that the green-synthesized MnO@SE effectively removed 96.34% Cr(VI) and 87.01% Pb(II) under optimal conditions, promoting eco-friendly water purification through multilayer, endothermic, spontaneous, and diffusion-driven adsorption.