Search Results (5,670)

To combat climate change, farmers want to develop sustainable agriculture that enhances food production while strengthening their capacity to cope with extreme weather events and pest and disease pressures. Promoting agroecological farming practices is a promising approach in enhancing sustainability and strengthening the climate-resilient farming systems. Recent research often overlooks to what extent the agroecological farming practices (AFP) provide a measurable advantage over non-AFP methods under increasing environmental challenges. In this regard, this study compares the extent of climate resilience between AFP mango-based farming systems and non-AFP mango-based farming systems in southern Ethiopia. AFP adopters applied ecological principles like intercropping, integrated pest management, agroforestry, canopy management, varietal diversity, and water and soil preservation to enhance biodiversity and soil health, and boost productivity and ecosystem services. The study employed a mixed-method design, drawing on the data from 395 selected households. The resilience of AFP and non-AFP farming systems was assessed by computing the 13 agroecosystem indicators of climate resilience using the Self-evaluation and Holistic Assessment of Climate Resilience of Farmers and Pastoralists (SHARP+) tool. Households in AFP mango-based farming system demonstrated greater diversification in agricultural production system compared to those in non-AFP mango farming system. The analysis of climate resilience indicators showed that the mango production systems under the AFP were more climate-robust than their conventional systems. Both the compound resilience score and the household resilience index showed that the mango farming systems under AFP substantially enhanced climate resilience. Hence, coordinated supports from the extension services, NGOs, and researchers are needed to scale up these benefits of AFP. Strengthening the AFP mango farming requires addressing the key barriers such as market access, input availability, and crop diversification strategies. This paper identifies important avenues for further AFP research in Sub-Saharan African countries. Full article

Climate change-induced variability in temperature and precipitation increasingly constrains crop production on sandy-textured soils with low water-holding capacity and limited nutrient retention. Such soils, classified as Brunic Arenosols, are widespread across the temperate climate zone of Central Europe, particularly in post-glacial landscapes, where they constitute a significant proportion of marginal agricultural lands. This study evaluated the relative influence of growing-season weather conditions and selected soil physicochemical properties on the yield of Camelina sativa and Brassica carinata cultivated under low-input management on Brunic Arenosols in northwestern Poland during the 2023 season. Yields varied markedly among sites. Camelina sativa produced yields from 300 to 930 kg ha⁻¹, with the highest yield recorded at the site characterized by higher BS and phosphorus availability. Brassica carinata produced yields from 0 to 370 kg ha⁻¹, including complete yield loss at one location due to severe pathogen infestation. Spearman’s correlation analysis revealed that temperature was a key determinant for both crops (r = 0.77 for C. sativa; r = 0.82 for B. carinata). For Camelina sativa, yield was strongly associated with BS (r = 0.80) and available P (r = 0.69), whereas Brassica carinata was more sensitive to climatic variability, showing a negative relationship with precipitation (r = −0.63). The results indicate species-specific responses to soil fertility and weather conditions under water- and nutrient-limited conditions typical of Central European sandy soils. While Camelina sativa performance was more closely linked to soil chemical status, Brassica carinata appeared predominantly climate-driven. These findings highlight the broader relevance of the study for temperate regions of Central Europe and support the integration of soil fertility management with climate-adaptive strategies when introducing alternative oilseed crops to marginal lands. Full article

Fog is a near-surface weather phenomenon with low visibility that significantly threatens transportation safety. Understanding the spatiotemporal evolution and formation mechanisms of fog is essential for improving fog forecasting and warning services to reduce related casualties and economic losses. This study examines consecutive dense fog events with long duration and high intensity that occurred along the Ma-Zhao Expressway in northeastern Yunnan from 24 to 30 October 2022. Yunnan is a typical low-latitude plateau region in southwestern China with complex terrain and diverse climates, leading to particularly complicated fog formation processes. Correlation analysis indicates that thermal and vapor factors show stronger correlations with visibility, with correlation coefficients reaching 0.68 for vertical temperature difference and −0.63 for surface relative humidity, both significant at the 99% confidence level. These values are notably higher than those of dynamic factors such as near-surface wind speed, which yields a correlation coefficient of 0.47. The results confirm the dominant role of thermal and vapor conditions in the formation and maintenance of these dense fog events, together with favorable conditions including near-surface air saturation, weak dynamic processes, and a temperature inversion in the lower troposphere. Standardized anomaly analysis reveals obvious atmospheric anomalies during the fog episodes. A strong southerly wind anomaly appears in the lower troposphere, driven by a cyclone over the Philippines and an anomalous anticyclone east of Yunnan. This southerly transport delivers warm and moist air toward the Ma-Zhao Expressway, accompanied by a positive temperature anomaly of 1.7, standard deviations near 700 hPa and a positive specific humidity anomaly of more than 2 standard deviations in the lower troposphere. These conditions favor the development of temperature inversions and atmospheric saturation, further promoting the occurrence and persistence of consecutive dense fog events. This study clarifies the key effects of thermal and vapor conditions as well as low-level southerly wind anomalies on dense fog over the Yunnan low-latitude plateau. These conclusions deepen the understanding of fog formation mechanisms in complex plateau terrain and provide a scientific reference for fog forecasting and early warning along mountain expressways in similar low-latitude plateau regions. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article

Soiling in photovoltaic systems is a recurring problem that reduces energy generation and demands efficient operation and maintenance (O&M) strategies. In this context, this paper proposes a machine learning-based approach to identify dirt levels and generate cleaning alerts using operational and weather data. Initially, the models were evaluated with a decision threshold ranging from 0.5 to 0.7, using only operational features. Subsequently, the inclusion of weather features was tested, which improved the models’ performance and enabled the selection of the best models for the exhaustive features search step. The models analyzed in this step were Extra Trees, Histogram-based Gradient Boosting, Extreme Gradient Boosting, and Random Forest. Exhaustive analysis further improved model performance, as indicated by global metrics and ROC curves. The Extra Trees model with a threshold of 0.5 showed the best performance and was selected as the final configuration, achieving an accuracy of 0.9884 and an AUC-ROC of 0.9957. Finally, the selected model was applied to determine daily soiling levels and trigger alerts based on temporal persistence, indicating its potential to support predictive O&M decisions and cleaning actions in PV systems. Full article

As a representative form of extreme weather, typhoons inflict widespread and systemic damage, posing a severe threat to the livestock industry. The stress they induce, typhoon stress (TS), is an unavoidable and complex environmental challenge that severely disrupts the ovarian function of Wenchang chickens. In this preliminary study, we employed a two-group comparison design (n = 6 per group) integrating behavioral observations, serum biochemical assays, histopathological examinations, and molecular analyses (qPCR, 16S rDNA sequencing, and transcriptome sequencing) to explore the role of the microbiota–gut–brain–ovarian axis (MGBOA) in this process. The findings revealed that TS markedly reduced water intake and locomotor activity, while it elevated serum corticosterone (CORT) and oxidative stress markers. It also induced shifts in gut microbiota composition, including a decrease in Bacteroides and an increase in Escherichia–Shigella. Furthermore, TS compromises duodenal intestinal barrier integrity, as evidenced by downregulation of the tight junction proteins TJP1 and CLDN1, structural damage to intestinal villi, and a reduced villus-to-crypt ratio. In the hypothalamus, VIP mRNA expression was upregulated, while GHSR expression was downregulated; the expression of the tight junction protein CLDN5 was also reduced. In the ovary, reproductive potential was suppressed, manifested by a reduction in follicle number and downregulation of STAR expression. Ovarian transcriptome analysis highlighted enrichments in pathways associated with inflammation (e.g., Toll-like receptor signaling) and lipid metabolism (e.g., PPAR signaling). These results support the hypothesis that TS impairs egg production via the MGBOA, providing preliminary mechanistic insights into how environmental stressors might disrupt animal productivity through MGBOA-mediated pathways. Full article

Atmospheric corrosion of carbon and low-alloy steels causes direct economic losses that are estimated at around 3.4% of the global GDP, and its accurate multi-year prediction is essential for protective coating selection, service-life estimation, and infrastructure maintenance scheduling. In this study, machine learning (ML) algorithms, including gradient boosting regressor (GBR), eXtreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and ridge regression, were trained on a 600-sample physics-grounded dataset to predict the cumulative atmospheric corrosion loss (µm) of low-alloy steels over 1–10 years of exposure. The dataset was constructed using the exact ISO 9223:2012 dose–response function (DRF) for a first-year corrosion rate and the ISO 9224:2012 power-law multi-year kinetic model (C(t) = C₁·t0.5), spanning ISO 9223 corrosivity categories C2–CX across 11 environmental and material input features. All models were evaluated on the original (untransformed) corrosion scale under an 80/20 train/test split and five-fold cross-validation. Gradient boosting achieved the best overall performance with test set R² = 0.968, CV-R² = 0.969, RMSE = 10.58 µm, MAE = 5.99 µm, and MAPE = 12.6%. XGBoost was a close second (R² = 0.958, CV-R² = 0.960). RF achieved an R² of 0.944. SHAP (SHapley Additive exPlanations) analysis identified SO₂ deposition rate, exposure time, relative humidity, Cl⁻ deposition rate, and temperature as the five most influential predictors. The dominance of the SO₂ deposition rate (mean |SHAP| = 26.37 µm) and the high second-place ranking of exposure time (13.67 µm) are fully consistent with the ISO 9223:2012 dose–response function and ISO 9224:2012 power-law kinetics, respectively, while among the material features, Cu and Cr contents showed the strongest negative SHAP contributions, confirming their corrosion-inhibiting roles in weathering steels. These results establish a physics-consistent, interpretable ML benchmark exceeding R² = 0.90 for multi-year cumulative corrosion loss prediction and provide a quantitative tool for alloy screening, coating selection in aggressive atmospheric environments, and service-life planning. Full article

The operation of driver assistance systems and autonomous vehicles requires a sensor system and a control algorithm. Sensors provide information to detect people, vehicles and objects in the vehicle’s environment; however, their performance can be degraded by adverse environmental conditions and contamination. This literature review identified factors that reduce sensor visibility, such as weather conditions and external contamination. In this study, the detection efficiency of state-of-the-art neural network-based object detectors was examined in a simulation environment using a synthetic dataset. A custom dataset comprising six urban and suburban traffic scenarios was created, including clean images and ten contaminated variants per scene with increasing mud coverage. The results show that contamination leads to a measurable reduction in detection performance across all models. Smaller variants are more sensitive to degradation, while medium-complexity models provide a favorable balance between robustness and computational cost. Increasing model size yields limited additional robustness, and performance differences between architectures highlight the importance of model design. Furthermore, the spatial distribution of contamination, particularly near the image center, has a significant impact on performance in addition to its overall extent. Full article

This study presents a structured methodology for mid-term electricity demand forecasting in the Greek interconnected power system, incorporating climate-sensitive and socio-economic variables. A set of linear regression models was developed to produce forecasts at both monthly and daily resolutions, aiming to balance accuracy with transparency and computational efficiency. Monthly demand was modeled using macro-trend variables such as GDP, population, and energy prices, while daily demand was approached through a disaggregated modeling structure, assigning a distinct regression model to each day of the week. Temperature effects were introduced at both levels using cooling and heating degree days, estimated based on seasonal weather forecasts provided by 51 meteorological models. The modeling approach developed shows a high predictive value. The monthly electricity demand forecast over a six-month horizon exhibits a mean absolute percentage error and a maximum error of approximately 1.4% and 3.9%, respectively, when actual meteorological data are employed, and 3.7% and 8.5%, respectively, when seasonal meteorological forecasts are used for the entire year 2022, in which it has been tested. Adjusting the model for projecting, the monthly peak load in the same time horizon, presents less accurate yet satisfactory results, with a mean and maximum error of 2.9% and 9.6%, respectively, when actual meteorological data are used, and 5.3% and 12.9%, respectively, when seasonal meteorological forecasts are employed. Full article

Opuntia ficus-indica (L.) Mill., also named Cactus pear, is a crop widespread in many countries with Mediterranean and subtropical climates, where it represents a valuable source of food. However, in southern Europe, this fruit market is limited to a few months, from summer to autumn. The possibility to extend the ripening period of fruit is represented by the special pruning of the first bloom flush and consequent new development of late flowers and fruits. Extending the cultivation period would allow farmers to maximize the crop’s potential, thereby extending the Cactus pear market season throughout much of the year. In this study, conducted in southern Sardinia (Italy), progressive pruning was applied with the aim of evaluating the fruit characteristics in relation to this type of cultivation, also considering the weather conditions during the experimental period. Morphological traits and physicochemical compositions of fruit picked in four harvests during two sampling seasons from August 2022 to March 2023, and from August 2023 to March 2024 were compared. According to principal component analysis (PCA), most of the observed characters showed significant differences among harvest periods but also between the two seasons of cultivation (year of cultivation: r = 0.722 on PC1), suggesting that the meteorological trend strongly modulated fruit traits. Some fruit qualities were partially lost during the winter months, such as juice acidity and total soluble solids (TSS). October was the month with the highest TSS levels (13.5 ± 0.25), followed by August, January and March. On the other hand, juiciness and fresh weight remained unchanged or even improved in fruit harvested out-of-season. As observed in the redundancy analysis (RDA) a contribution of 54% due to weather variability emerged. In Particular, TSS levels, pH and juice dry matter were associated with high temperatures, solar radiation, and wind intensity. Wind speed was also moderately linked with betalain content. Moreover, high relative humidity was associated with lower pH values, higher water content, and higher fruit fresh weight. A significant difference was found between the two years in betalains content (80.0 ± 3.7 µg·mL⁻¹ in 2022–2023 and 28.2 ± 2.5 µg·mL⁻¹ in 2023–2024). The breakdown in the 2023–2024 season was likely due to the strong heat wave of July 2023 (up to 47 °C), which caused their partial degradation. In light of seasonal variability, this work provides some useful insights for future management of Cactus pear, also considering the possibility of usefully extending the period of cultivation and harvesting. Full article

To identify the sources and driving mechanisms of nitrate contamination in pore water around Nansi Lake, 54 pore water samples were analyzed via hydrogeochemical analysis, Gibbs diagrams, ionic ratios, and principal component analysis (PCA). The pore water is predominantly slightly alkaline, with dominant cations Ca²⁺ and Na⁺, and anions HCO₃⁻ and SO₄²⁻. Nitrate-nitrogen (NO₃⁻-N) concentrations range from 0.82 to 54.31 mg·L⁻¹, with a coefficient of variation of 1.41 and an exceedance rate of 18.52%, indicating significant external inputs. A positive correlation between NO₂⁻ and NO₃⁻ suggests denitrification in some areas. Nitrate concentrations exhibit distinct spatial heterogeneity: high concentrations occur in agricultural/aquaculture lakeside plains and urban areas, low concentrations near coal mining subsidence zones, and transitional zones showing outward diffusion. Nitrate sources are predominantly anthropogenic. High Cl⁻ and low NO₃⁻/Cl⁻ ratios indicate domestic and aquaculture wastewater infiltration, whereas low Cl⁻ and high NO₃⁻/Cl⁻ ratios indicate agricultural fertilizer input. Industrial and natural sources are minor. PCA identified three controlling factors (cumulative variance 69.81%): coal mining and industrial/domestic pollution (39.82%), carbonate rock weathering (19.44%), and agricultural activities (10.55%). Health risk assessment shows no significant risk for adults (hazard quotient (HQ) < 1), but children face localized risks at nine sites (HQs of 1.25–2.26) in intensive farming, urban, and transitional zones. Excessive fertilizer application and sewage leakage are the primary causes, posing methemoglobinemia risks to infants. This study provides a scientific basis for nitrate pollution control and sustainable water management in the Nansi Lake Basin and offers methodological insights for similar lacustrine plain regions. Full article

This study evaluates GNSS water-vapor tomography across the Lisbon metropolitan area and explores how increasing network density with low-cost receivers improves three-dimensional humidity fields for meteorological applications. Three configurations were tested for December 2022, a month characterized by several rainfall events, including a severe urban-impacting one: (i) a hybrid setup combining permanent and low-cost stations (TOMO_PL), (ii) a dense network of only low-cost stations (TOMO_L), (iii) a sparse arrangement using only permanent stations (TOMO_P). Tomographic water vapor density fields were compared with independent references from the Weather Research and Forecasting (WRF) model, ERA 5 reanalysis, and radiosonde data. All products show the expected exponential decline in water vapor with increasing altitude. Tomography consistently underestimates moisture in the lowest 2.0 to 2.5 km and tends to overestimate it at higher levels, with a weaker correlation above mid-tropospheric heights. Vertical RMSE remains below 2 g m⁻³ for all solutions, but TOMO_P performs the worst due to weak and uneven spatial geometry. Time–height analysis reveals that densified setups capture the changing moisture in the lower atmosphere, including increased near-surface humidity during December 11–13, when rainfall exceeded 120 mm in 24 h, although mid-level intrusions and dry layers observed by radiosondes are not captured. Mean PWV patterns show realistically low points over the Sintra mountain range and align best with TOMO_PL (spatial RMSE 0.6 g m⁻³, bias 0.4 g m⁻³, correlation 0.9), while TOMO_P creates artifacts that mimic mesoscale gradients. Categorized skill analysis shows the highest accuracy under high-moisture conditions and limited ability to detect dry conditions, with TOMO_PL showing the best overall performance against both ERA5 and WRF. Overall, low-cost densification significantly enhances boundary-layer humidity and PWV retrievals, supporting their use for urban heavy-rain monitoring and, with error-aware integration, for short-term forecasting. Full article

Traditional pipe roof support design methods generally assume horizontal ground conditions and treat the pipe roof as a monolithic beam, thereby neglecting the differential stress distribution among individual steel pipes under unsymmetrical loading. To address this gap, this paper presents two main contributions: a low-carbon cement-based grouting material suitable for pipe roof reinforcement, and a new mechanical model that simultaneously accounts for biased pressure conditions and the inter-pipe micro-arch effect. First, the working performance of limestone calcined clay cement (LC³) grout was systematically tested at a water–cement ratio of 1:1, and the optimal mix ratio was determined. Grout–soil reinforcement tests on weathered granite show that, for grout-to-soil volume ratios between 0.2 and 0.8, the compressive strength of the reinforced material exceeds 10 MPa and the elastic modulus exceeds 600 MPa. Second, a mechanical model for the pipe roof was established based on the Pasternak two-parameter foundation theory, incorporating both biased pressure conditions and the inter-pipe micro-arch effect. The model predictions were compared with existing field monitoring data in the literature, showing consistent trends and good agreement in peak deflection values. Parametric analysis reveals that under horizontal ground conditions, the pipe roof response is symmetric, with the vault as the most critical area. As the bias angle increases, the maximum response shifts toward the higher side of the terrain, and the stress difference between pipes on both sides increases significantly. Theoretical analysis of the low-carbon grouting material shows that pipe roof deflection is moderately reduced compared to traditional grouting materials, but at the cost of increasing bending moment and shear force within the steel pipes. The proposed low-carbon grouting material and the validated mechanical model provide theoretical support for the design optimization of pipe roof support in shallow unsymmetrical loading tunnels. Full article

Sub-sag 21 in the eastern Yangjiang Sag, Pearl River Mouth Basin, South China, contains a thick lacustrine source-rock interval within the lower Wenchang Formation and is a major exploration target on the northern margin of the South China Sea. However, the timing of deposition during the early to middle Eocene remains poorly constrained, and the applicability of quantitative palaeoclimate reconstruction methods in low-latitude lacustrine basins requires further evaluation. In this study, we analyzed mudstones from the lower Wenchang Formation in Well E1. Using cyclostratigraphic constraints, we applied AstroGeoFit to construct an astronomically tuned age model, and combined palynological coexistence analysis with geochemical weathering proxies and linear–regression calibration to quantitatively reconstruct and cross-validate mean annual temperature and mean annual precipitation. Within this time-calibrated framework, we further quantified organic-carbon burial to evaluate the relationship between palaeoclimate evolution and organic-matter enrichment. The AstroGeoFit results indicate that the top of the lower Wenchang Formation in Well E1 is constrained to 44.563 Ma, and that the studied succession spans 50.249–44.563 Ma. Palynological coexistence analysis identifies three palaeoclimate phases within this interval. Method evaluation shows that the temperature reconstruction based on major-element geochemistry agrees well with the pollen-based temperature record, whereas one precipitation reconstruction based on weathering proxies shows the most robust agreement and stability relative to the pollen-based precipitation record. Reconstructed mean annual temperature ranges from 10.77 to 22.20 °C, and reconstructed mean annual precipitation ranges from 1188.27 to 1871.89 mm. Correlation analyses on the tuned timescale show that precipitation is more strongly associated than temperature with organic-matter accumulation parameters, including total organic carbon and organic carbon accumulation rate, indicating that organic carbon burial in the eastern Yangjiang Sag lake basin was mainly controlled by hydrological forcing. During the Early Eocene Climatic Optimum, carbon burial in low-latitude lakes was, therefore, not a simple response to elevated temperature, but instead reflected the integrated effects of precipitation, runoff, stratification, material supply, transport, and preservation. The evolutionary sequence further suggests that early high productivity was diluted by rapid sedimentation, reducing total organic carbon; subsequent cooling, lake deepening, and strengthened stratification enhanced organic matter preservation; and finally, tectonic subsidence together with regional humidification promoted the development and long-term preservation of high-quality lacustrine source rocks. Full article

Brazilian soils are prone to a gradual decline in fertility due to intensive agricultural activity combined with natural weathering, which increases the demand for chemical fertilizers. Among potential alternatives, soil remineralization using crushed rock is a promising strategy. Silicate agrominerals (SAs) applied as soil remineralizers have attracted attention due to their ability to supply plant-available nutrients while reducing dependence on conventional mineral fertilizers. This study evaluated the potential of residues from six quarries in Brazil as soil remineralizers as a regulatory screening assessment. Samples were subjected to mineralogical, petrological, and chemical characterization using an integrated approach, including X-ray diffraction (XRD), Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), and leaching experiments. XRD analysis revealed that anorthite and augite were the major minerals present in the mining waste. These minerals are less resistant to weathering, which enhances the release of macro- and micronutrients, essential for the development of various crops. Chemically, the samples were dominated by SiO₂, Fe₂O₃, and Al₂O₃, with the sum of bases (K₂O + CaO + MgO) ranging from 11.92% to 16.85%, meeting Brazilian standards for use as a soil remineralizer. Leaching results revealed that pH responses varied significantly among the studied samples for the filler particles, with an alkaline shift reaching values above 9.0 after 72 h. In contrast, the powder particle size samples showed no significant variation between the different materials tested, maintaining nearly constant pH levels throughout the period. This preliminary evaluation demonstrates that mining tailings from Brazilian quarries have potential as a sustainable soil remineralizer. This approach not only offers an alternative for soil fertilization but also promotes waste management and circular economy practices, although further studies are needed to assess long-term effectiveness and safety. Full article