Abstract: Seasonal precipitation forecasting remains challenging in regions with complex topography and high climatic variability, such as the state of Minas Gerais, Brazil. This study evaluates the performance of an Artificial Intelligence (AI)–based ensemble approach for seasonal precipitation prediction during 2024 and compares its results with those obtained from the NCEP Climate Forecast System version 2 (NCEP-CFSv2), a model from the North American Multi-Model Ensemble (NMME). The AI model was trained using high-resolution precipitation data from the MERGE-CPTEC dataset and applied to generate seasonal forecasts. Model performance was assessed using Root Mean Square Error (RMSE), Mean Square Error (MSE), and Relative Error (RE). Observed seasonal precipitation anomalies for 2024 were also examined to contextualize forecast skill under different climatic conditions. The results show that the AI-based forecasts consistently outperform the NCEP-CFSv2 from NMME across all seasons, exhibiting lower error metrics and improved representation of spatial precipitation patterns. The highest forecast skill was observed during winter (JJA), when atmospheric conditions are more stable and precipitation variability is low. During the wet seasons (DJF and SON), despite increased convective activity and spatial heterogeneity, the AI model maintained greater spatial coherence and closer agreement with observations than the dynamical forecasts. Overall, the findings demonstrate that AI-based approaches represent a promising and computationally efficient complementary tool for regional-scale seasonal precipitation forecasting, particularly in climatically heterogeneous regions.

Abstract: To explore the responses of soil aggregate composition and stability to different grazing intensities in alpine meadow of the Qilian Mountains, no grazing (CK) was set as the control, with four treatments including light grazing (LG), moderate grazing (MG), heavy grazing (HG) and extreme grazing (EG) established. The characteristics of soil aggregates in the 0–10 cm and 10–20 cm soil layers were determined by the dry sieving method and wet sieving method, and three stability parameters including the mean weight diameter (MWD), geometric mean diameter (GMD) and fractal dimension (D) were analyzed. Combined with environmental and biological factors, the mechanisms underlying the effects of grazing on soil aggregates structure and stability were elucidated. The results showed that: (1) Soil aggregates with the particle size of 5–10 mm were the dominant fraction in the soil structure of the alpine meadow, and this fraction changed drastically with grazing intensity. CK maintained relatively high aggregate mechanical stability but exhibited weaker resistance to water erosion compared to grazed plots. Under the CK condition, the content of water-stable aggregates with the 5–10 mm particle size decreased significantly compared with mechanical-stable aggregates (by 60.07% in the topsoil and 70.66% in the subsoil). Light and moderate grazing maintained a dynamic balance and high stability of soil structure. Heavy and extreme grazing intensified soil structure fragmentation and overall stability declined. (2) Soil aggregate stability was correlated with environmental factors. Altitude and soil bulk density were significantly positively correlated with aggregate stability (P<0.001).Root biomass exerted a significant effect on the stability indices of mechanically stable aggregates in the topsoil (P<0.05); high root biomass destroyed soil macroaggregates but enhanced the resistance to water erosion. Soil microbial biomass carbon (SMBC), nitrogen (SMBN), phosphorus (SMBP) were significantly positively correlated with GMD of water-stable aggregates, but negatively correlated with GMD, MWD and D of mechanical-stable aggregates, also MWD and D of water-stable aggregates. Nitrate nitrogen had a positive effect on aggregate stability, while ammonium nitrogen had a negative effect. (3) The stability of aggregate in different soil layer varied under different grazing intensity. Under LG and MG conditions, the subsoil exhibited higher aggregate stability than the topsoil, whereas the opposite pattern was observed under HG, EG and CK conditions. Therefore, from the perspective of soil structural stability and sustainable utilization, light and moderate grazing are the optimal utilization patterns for alpine meadow in the Qilian Mountains. It not only maintains the structural stability of subsoil aggregates but also balances biological cementation and physical disturbance, avoiding aggregate water stability insufficiency under no grazing and the risk of structural fragmentation under heavy or extreme grazing. The findings provide a scientific basis for rational grazing management and soil conservation in alpine meadow of the Qilian Mountains.

Abstract: Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. However, since the 1970s, the lake has experienced a series of problems, including declining water levels and water pollution. In recent years, the water quality of Lake Erhai has continued to deteriorate, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measure (IEPM) were considered as influencing factors for a comprehensive and systematic analysis of Lake Erhai's water quality. The results indicate that air temperature primarily affects total phosphorus (TP) concentration and exhibits a positive correlation. Rainfall predominantly influences TP and total nitrogen (TN) concentrations, also showing positive correlations. Wind speed affects chemical oxygen demand (CODMn), TP, and TN concentrations, exhibiting negative correlations with each. Socio-economic development mainly affects CODMn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. This study systematically analyzed the anthropogenic and natural factors affecting Lake Erhai's water quality, identified the dominant influencing factors, and provides technical support for the subsequent enhancement of Lake Erhai protection measures.

Abstract: This research examines the relationship between energy generation, electricity produc-11 tion, energy consumption, and economic welfare across countries classified by income 12 level: high-income, upper-middle-income, lower-middle-income, and low-income. It uses 13 annual panel data from 2000 to 2023 sourced from the World Bank’s World Development 14 Indicators (WDI). Based on the World Bank’s income classifications and data availability 15 during the study period, 152 countries were selected, including 49 high-income, 35 upper-16 middle-income, 43 lower-middle-income, and 26 low-income economies. Pedroni cointe-17 gration tests indicate a long-term equilibrium relationship among energy generation, elec-18 tricity production, energy consumption, and economic welfare across all income groups, 19 with Kao cointegration tests confirming these results as robustness checks. The study uti-20 lizes panel dynamic differenced and system Generalized Method of Moments (GMM) to 21 estimate the model. Results reveal a significant positive long-term relationship among the 22 main energy and welfare variables across all income categories. However, when broken 23 down by income class, high-income and upper-middle-income countries show positive 24 associations between energy metrics and economic welfare. In contrast, lower-middle-in-25 come and low-income nations exhibit negative associations. The study recommends poli-26 cies focused on improving living standards and overall economic welfare, especially 27 through providing consistent, affordable, and clean energy.

Abstract: The livestock sector underpins food security, employment, and rural livelihoods across the Southern African Development Community (SADC), contributing up to 50 % of agricultural GDP and supporting more than 60 % of rural households. Yet, climate change poses escalating threats through heat stress, declining pasture productivity, water scarcity, and vector-borne diseases that compromise productivity and economic resilience. This review identifies and locates effective climate change mitigation strategies along the livestock value chain, spanning production, processing, transport, and consumption, to promote sustainable, low-emission, and inclusive growth in the SADC region. A broad review of 46 peer-reviewed and institutional sources (2000 – 2024) was undertaken, focusing on livestock-related mitigation within SADC and comparable agro-ecological systems. Strategies were thematically categorized by value-chain stage and assessed for their emission-reduction and livelihood-enhancement potential. Located strategies include genetic improvement for low-methane and heat-tolerant breeds, adaptive rangeland and feed management, renewable-energy adoption in processing, climate-resilient transport infrastructure, and consumer awareness of low-emission products. Evidence suggests potential GHG-emission reductions of 18–30 %, coupled with productivity gains and improved smallholder incomes. Coordinated implementation through the SADC Regional Agricultural Investment Plan (2021–2030) and national policies can transform the livestock sector into a climate-resilient driver of inclusive growth. Further research should quantify the socio-economic feasibility and scaling potential of these strategies across production systems. Successful integration of climate change mitigation imperatives must be tailored to local biophysical conditions (e.g., rainfall, soil type) and socio-economic contexts (e.g., market access, cultural practices).

Abstract: Water supplies contaminated by heavy metals pose a serious threat to human health, especially in areas without access to centralized testing facilities. While copper is a necessary heavy metal in trace levels, high concentrations can have detrimental effects on health, such as oxidative stress, cognitive impairment, and liver damage. Due to their expense, complexity, and reliance on laboratories, conventional detection tech-niques are accurate but unsuitable for real-time, dispersed deployment. Machine learning offers a potent solution to these constraints by facilitating the automatic, pre-cise, and quick interpretation of complicated sensor data. It makes it possible to make decisions in real time without requiring a large laboratory infrastructure. In this work a dual-mode optical sensor was developed using the colorimetry and fluorometry images of carbon dots embedded in hydrogels with the Cu2+ concentration of 0, 20, 50, 100, 200, and 500 μM. Data augmentation was used to expand the RGB picture dataset for each modality, and these data were interpolated to provide re-sponses at 1 µM intervals (0–500 µM). We trained a comprehensive set of supervised machine learning models including Logistic Regression, Support Vector Machines, Random Forest, and XGBoost to categorize water samples into five risk-informed quality levels. The system achieved classification accuracies exceeding 96%. Further-more, we built a simple user interface to make the system practically deployable in mobile phone. Together, these results demonstrate a scalable, interpretable, cost-effective, and quick solution for real-time water quality monitoring in re-source-constrained environments.

Abstract: The modeling study investigated the impact of the 2023 Canadian wildfire aerosols (primarily black carbon and organic aerosol) on weather forecasts, concluding that incorporating real-time aerosol forcing improved model skill over using a climatology. Experiments without real-time data severely underestimated the Aerosol Optical Depth (AOD), an error mitigated by including the forcing or by using the coupled atmosphere-chemistry model. The aerosols exerted a strong direct radiative effect, reducing the surface downward shortwave (SW) flux and causing a corresponding surface cooling over the wildfire region. Furthermore, including aerosol-cloud interactions amplified this cooling and led to an increase in overall cloud fraction and precipitation, illustrating complex indirect effects. While these physical improvements enhanced the representation of the atmosphere, the positive impact on overall medium-range forecast skill (5–10 days) was modest, suggesting that the benefits of accurately representing wildfire feedback on the coupled Earth system are achieved through relatively slow processes, such as radiation feedback.

Abstract: Microplastics (MPs) are routinely present throughout wastewater treatment plants (WWTPs) due to their widespread occurrence, while current treatment technologies achieve only partial removal. Therefore, WWTP effluents can still discharge a substantial fraction of MPs to receiving water bodies leading to environmental contamination. Most previous studies reported MP concentrations at specific time points, precluding a long-term monitoring and may result in over- or underestimation. The aim of this study is to examine the concentration, size, and polymer composition of MPs at inlet and outflow waters over a six-month period, from July to December, to assess the temporal variability of MPs across seven conventional urban WWTPs located in the Andalusia region, southern Spain. MPs were found in all sampling campaigns. In influent samples, concentrations were found to reach 6 – 78 MP/L, while the WWTP effluents contained a range of 12 – 65 MP/L. Fibers were the most abundant shape across all the WWTPs. The average size in the influent was 848 ± 1427 μm and effluent 918 ± 1221 μm. Polymers such as PA, PP, PVC and LDPE were the most abundant, reflecting the domestic origin of water samples.

Abstract: This study examines the carbonation behavior and CO₂ storage potential of a Ca-rich alkali-activated binder produced entirely from industrial residues, namely ladle furnace slag (LFS), coal ash (CA), and cement kiln dust (CKD). The system was designed as a one-part alkali-activated material (AAM), with CKD acting as an internal activator, and subjected to ambient curing, water curing, and accelerated CO₂ curing at ambient pressure. Phase evolution, microstructural development, and pore-structure characteristics were investigated using X-ray diffraction, FTIR spectroscopy, DSC–TG analysis, scanning electron microscopy, and X-ray micro-computed tomography, together with measurements of density, water absorption, and compressive strength. CO₂ curing fundamentally altered the reaction pathway of the binder, shifting it from hydration-dominated to carbonation-controlled phase evolution, leading to the decomposition of calcium-bearing hydrates and complete carbonation of non-hydraulic γ-belite with the formation of vaterite, aragonite, and calcite. These transformations induced pronounced microstructural densification, reflected in a near-doubling of compressive strength (>48 MPa), increased apparent density, reduced water absorption, and simplified pore-network topology. The results demonstrate that controlled carbonation is an effective post-treatment strategy for waste-derived alkali-activated binders, enabling simultaneous performance enhancement and permanent CO₂ sequestration.

Abstract: To investigate the impacts of combining controlled-release urea (CRU) with controlled-release potassium chloride (CRK) on nutrient leaching and use efficiency in wheat fields, we carried out experiments spanning three consecutive years from 2022 to 2024, utilizing a split-plot design. In this study, the control plot received neither nitrogen nor potassium applications (Control). The main plots were designated based on nitrogen fertilizer types: controlled-release urea (CRU) and conventional urea (Urea). The sub-plots were assigned potassium fertilizer rates using CRK, specifically 50 kg ha-1 (LCRK), 75 kg ha-1 (MCRK), and 100 kg ha-1 (HCRK). The findings revealed that the nutrient release pattern of CRU combined with CRK aligned well with wheat's nutrient uptake requirements. Notably, the wheat yields in CRU treatments witnessed a significant average increase of 2.2% from 2022 to 2024 compared to ordinary urea treatments. In the final season, nitrogen recovery efficiency augmented by 10.9%. Furthermore, CRU treatments significantly boosted the number of effective wheat spikes and grains per spike but had no notable influence on wheat's thousand-grain weight (TGW). Consequently, the yield enhancement observed in CRU treatments was primarily attributed to an increase in wheat's effective tiller count. CRU also markedly elevated inorganic nitrogen levels in the plow layer soil during wheat's mid to late growth stages, effectively mitigating nitrate nitrogen leaching into deeper soil layers. The application of CRU×MCRK notably and significantly improved wheat leaf photosynthesis during its mid to late growth stages, yielding substantial economic benefits and theoretical significance.

Abstract:

Wind energy stands as one of the most technologically mature renewable sources, playing a pivotal role in the mitigation of greenhouse gas emissions. However, wind farms and associated infrastructures increase collision risk for flying organisms. Implementing higher cut-in speeds is a proven mitigation strategy to significantly decrease wildlife mortality rates, particularly for bat species, by preventing turbine operation during low-wind periods of high activity. The suggested, non-standard, increased cut-in speed for wind turbines is generally 5.0 m/s. To test the effectiveness of cut-in speed increase, bat activity was monitored at three wind farms in northern Portugal (Gevancas, Azinheira and Dom João e Feirão), using ultrasonic acoustic detection, to characterize spatial and temporal activity patterns and assess the potential risk associated. Monitoring was carried out at fixed stations, at heights of 55m above ground level during seven consecutive nights per month, from march to October. Wind speed data were recorded concurrently using anemometers mounted on meteorological towers. Contradicting cut-in speed recommendations, the results show that 90% of bat activity occurred at wind speeds above the current mitigation thresholds (5.0 m/s.). Since turbine operation coincides with peak bat activity, it is imperative to implement site-specific mitigation strategies, such as optimized cut-in speeds, to minimize mortality risk.

Abstract: Snow water equivalent (SWE), the amount of water that will be liberated when a given snowpack melts, is considered an essential climate variable. Snowmelt drives annual run-off in snow-dominant basins. However, detecting daily SWE changes in lake-effect snowfall regions such as the Great Lakes Basin (GLB), is challenging for classical methods. We developed a Siamese U-Net (Si-UNet) model to detect and characterize daily changes and trends in SWE. Our Si-UNet detected daily changes in SWE over the GLB with an F1-score of 98.73%. To characterize the basin-wide extent of anomalies in SWE distribution, we compared SWE trends to a 35-year median (35-YB) baseline and identified decadal trends in SWE. We found that the period from 1989 to 2008 was the temporal window with minimal anomalies, compared to the 35-YB of ~0.5108. Positive deviations from the 35-YB were prevalent over these 20 years, indicating less significant daily changes. A significant shift to daily SWE similarity below the 35-YB occurred after 2009, especially in January and February. Daily changes in SWE were high in April, beginning in the second week. The strongest positive trend, likely associated with lake-effect snowfall, was observed in April 2000 (R² = 0.47).

Abstract: Gully erosion is a significant threat to the sustainability of soil in Mediterranean basins. Despite its impact, there is a lack of research providing accurate regional-scale cartography of complete gully network. This study aims to automatically map the gully network in the olive-growing landscapes of the Guadalquivir basin (Spain) using Machine Learning (ML) algorithms: Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR). We integrated these models with 17 predictive variables (including hydrotopographic, climatic, and edaphic factors) and the Gully Head Initiation (GHI) index. RF was the most suitable model, achieving an Area Under the Curve (AUC) of 0.91 and an F1-score of 0.83 and enabled the delineation of a gully network totalling 8439.05 km. Variable importance analysis revealed that flow accumulation (17.33 %) and the GHI index (nearly 30%) were the primary predictors, with the Rainy Day Normal (RDN)-based formulation outperforming the maximum daily precipitation (Pmax)-based one. Spatially, countryside hills landscapes exhibited the highest gully densities (42.50 m/ha). The results demonstrate the effectiveness of combining ML with physically-based indices to generate high-resolution gully cartography for soil conservation planning in Mediterranean olive groves.

Abstract: Terrestrial Gross Primary Productivity (GPP) is pivotal to the global carbon cycle, and its response to climate change is strongly regulated by topographic conditions through complex, non-linear mechanisms that remain poorly quantified at macro scales. Integrating multi-source remote sensing data with structural equation modeling (SEM), geographical detectors, and generalized additive models (GAM), this study investigated the spatiotemporal dynamics of GPP and its non-linear responses to coupled climate-topography gradients across China from 2001 to 2020. Results revealed a significant increasing trend in GPP across nearly 80% of China, with precipitation identified as the dominant driver, surpassing temperature and radiation. Topography significantly modulated climate sensitivity by redistributing hydrothermal resources. A distinct transition in dominant limiting factors was observed along the altitudinal gradient, shifting from water-limited (<2000 m) to energy-limited (>3000 m) regimes. Notably, mid-altitude regions (1000–2000 m) exhibited the highest sensitivity to precipitation, representing an ecological "sweet spot". Furthermore, we quantified critical ecological thresholds for climatic drivers, identified saturation points for temperature (~17.4°C) and precipitation (~1974 mm), and an inhibition threshold for solar radiation (>101 W/m²). These findings elucidate the transition mechanisms of climatic constraints and non-linear thresholds in complex terrain, providing robust scientific evidence for region-specific ecosystem and carbon management.

Abstract: Subalpine grasslands represent highly sensitive ecosystems that are increasingly exposed to climate extremes, yet their long-term disturbance dynamics remain poorly documented. This study investigates climate-driven dieback of subalpine grasslands in Central Europe using a harmonized, multi-decadal satellite time series. We analyzed Landsat (TM, ETM+, OLI, OLI-2) and Sentinel-2 imagery spanning 1984–2024 to detect changes in grassland condition, supported by field-based validation, climatic indices, and geomorphological analysis. Several spectral indices related to non-photosynthetic vegetation were evaluated, with the Normalized Burn Ratio (NBR) showing the highest ability to discriminate dead grassland biomass from live vegetation. Retrospective mapping revealed four distinct dieback events since 2000, comprising two short-term episodes with rapid within-season recovery and two long-term events characterized by persistent degradation and slow regeneration. Dieback timing corresponded closely with climatic extremes, particularly droughts of varying duration, while winter frost under shallow soil conditions likely contributed to long-term damage in some cases. Geomorphological analysis indicated that wind exposure, elevation, and terrain convexity strongly modulate dieback susceptibility, highlighting the importance of fine-scale environmental controls. Our results demonstrate the value of long-term, multi-sensor satellite observations for detecting and interpreting climate-driven disturbances in subalpine grasslands and provide a transferable framework to support monitoring and conservation of mountain ecosystems under ongoing climate change.

Abstract: This study examined the knowledge of journalists in the newsroom to determine what they know, what they do, and how they could improve their reporting of climate change in Nigeria. The study population included 15,000 registered members of the Nigeria Union of Journalists (NUJ) from 37 councils (chapters). It was a mixed method that employed both quantitative and qualitative data. The sample size was 403. A purposive sampling procedure was applied while gathering data from the respondents via the Internet. It was found that journalists in Nigeria do not have sufficient knowledge of critical issues around climate change, such as mitigation and adaptation strategies. The study also found that only 14% of journalists regularly report climate change issues. This study concludes that professional deficiency was the reason why the majority of journalists do not report on climate change. It recommended training, retraining, and exposure to awards and other opportunities to encourage journalists to report climate change frequently.

Abstract: Urban green space (UGS) is a critical component of sustainable cities and a modifiable determinant of mental health (MH). This review synthesizes 113 empirical studies and 929 bibliometric records to map theoretical advances, methodological evolution, and governance implications in the UGS–MH field. We integrate six validated pathways into a unified socio-ecological framework, including attention restoration, stress recovery, behavioral activation, physiological regulation, social cohesion, and environmental buffering. Methodological trends indicate a shift from static greenness proxies to street view and multimodal exposure measures, and from cross sectional correlations to models that address spatial heterogeneity, causal identification, and AI enabled prediction. Bibliometric mapping shows increasing interdisciplinarity, geographic diversification, and a growing focus on dynamic exposure science. Persistent challenges include exposure outcome spatial and temporal misalignment, reliance on single modality indicators, limited causal inference, and constrained cross cultural generalizability. Building on these insights, we propose a governance oriented framework to support sustainable and healthy cities through equitable green access, behavior informed planning, nature based interventions, and data driven decision support. Overall, this review strengthens the evidence to action bridge at the interface of urban sustainability and population mental health.

Abstract: This study investigates ice crystal sedimentation calculation errors arising from three-moment bulk cloud scheme. Both offline tests and one-dimensional cloud model simulations indicate that sedimentation calculation errors are most pronounced at both the cloud bottom and cloud top. At the cloud bottom, the error stems from how the bulk method treats ice crystal sedimentation. Specifically, the method uses three weighted fall velocities (corresponding to the three moments) to represent instantaneous fluxes through a fixed altitude, which inherently assumes that falling ice crystals can only affect the adjacent model layer below. This assumption artificially constrains the falling distance of larger ice crystals. At the cloud top, the differences among these three weighted fall velocities can give rise to physical inconsistencies. This issue is handled by artificial adjustment, which leads to a spurious narrow size distribution shape of ice crystals, especially under model configurations with coarse temporal resolution (large dT) and fine vertical resolution (small dH). If only the sedimentation process is considered, the above calculation errors can be effectively minimized by lowering the dT/dH ratio.

Abstract: Residential buildings in tropical regions contribute significantly to extreme indoor heat, low air quality, and excessive cooling energy demand, yet the widespread adoption of passive climate‑responsive retrofit measures remains limited. In Nigeria, it is not clear to what extent there is an awareness of such policies, nor is it clear to what extent different passive retrofit measures are well understood and preferred. This study examined policy awareness and preference for passive retrofit measures by using quantitative data from 118 property managers and 163 homeowners in Lagos State, Nigeria, and semi-structured interviews of officials from the various building regulatory and control agencies. From the results of the Mann-Whitney U-test, there was no significant awareness of a national environmental policy (NCCP; p-value > 0.05), although there was a significant lack of awareness of policies on building efficiency (BEEC, BEEG, and EDGE; p-value < 0.001). The emphasis on passive retrofits is evident in the mean scores of 4.45 for “planting trees and vegetation around buildings to provide natural shade and reduce cooling loads”, 4.38 for “enhancing the building's ability to prevent moisture from entering or escaping”, and 4.24 for “integrating openings in building envelopes”, thus establishing that these are the most preferred solutions. However, from the fuzzy TOPSIS analysis, the highest value of CCᵢ was 0.974 for “enhancing the building's ability to prevent moisture from entering or escaping” and the lowest value of 0.000 for “increasing the thickness of wall insulation layers to reduce heat absorption.” Based on these findings, technical retrofit solutions are less preferred than nature-inspired and easy solutions. The qualitative study revealed that passive retrofits are embedded in the national building code, rather than being included as a retrofit policy. It is therefore necessary to first identify solutions and programs that resonate well with property managers and owners, using this as the foundation to slowly build up to more technical solutions.

Abstract: Urban air quality assessment increasingly relies on spatial interpolation to complement fixed monitoring networks; however, the reliability of geostatistical methods depends strongly on temporal conditions and pollutant characteristics. Despite extensive appli-cation, limited attention has been given to how kriging performance varies across dif-ferent hours of the day and months of the year, particularly when contrasting primary pollutants driven by local emissions and secondary pollutants formed through atmos-pheric chemistry. This study evaluates the temporal sensitivity of Ordinary Kriging (OK) for urban air pollutant mapping in the Mexico City Metropolitan Area. Using hourly observations from the official air quality monitoring network (2021), we analyze ozone (O₃), a secondary pollutant, and sulfur dioxide (SO₂), a primary pollutant, under representative diurnal and monthly scenarios. Variogram model selection and predictive performance are assessed through leave-one-out cross-validation and external holdout validation across multiple temporal blocks and months. Results indicate that kriging performance is highly sensitive to both hour of day and month. For O₃, smoother Gaussian variogram structures perform best during peak photochemical conditions, producing coherent regional concentration fields with gradual spatial gradients. In contrast, SO₂ exhibits stronger local variability and sharper spatial gradients, favoring exponential variogram models, particularly under stable morning atmospheric conditions associated with primary emission accumulation. Sensitivity analyses further reveal that no single variogram model is universally optimal and that interpolation accuracy depends more on temporal stratification and pollutant behavior than on variogram form alone. These findings demonstrate that geostatistical interpolation is a valuable tool for urban air quality assessment only when temporal sensitivity and pollutant-specific dynamics are explicitly incorporated. The proposed framework provides practical guidance for the responsible use of interpolated air quality maps, supports sustainable urban monitoring strategies, and contributes to more reliable exposure assessment in megacities with limited sensor coverage.