Abstract: Continuous monitoring and nowcasting of tornadic near-storm environments remain challenging, particularly in regions with limited ground-based weather radar coverage. High-spatiotemporal-resolution geostationary satellite remote sensing offers a valuable approach to track the evolution of severe convective storms. Combining 10-minute cloud-top brightness temperature (TBB) data from the Himawari-8 satellite and ERA5 reanalysis, this study investigates the atmospheric environments of 177 documented tornadoes in China from 2016 to 2023. Tracking storm convective centers using TBB minima reveals clear regional differences in tornadogenesis paradigms. Southern China tornadoes exhibit a "dynamically driven" pattern within quasi-steady, warm, and moist environments. These environments feature low Lifted Condensation Levels (LCL; ~790 m) and weak Convective Inhibition (CIN). Intense low-level wind shear and storm-relative helicity (SRH) dominate the convective triggering. Northern China tornadoes follow a "coupled thermodynamic-kinematic" paradigm under relatively drier and cooler backgrounds. Their initiation relies on the rapid, synchronized accumulation of Mixed-Layer convective available potential energy (MLCAPE) and deep-layer SRH. Furthermore, intensity-based comparative analysis indicates that significant tornadoes (Enhanced Fujita [EF] scale, EF ≥ 2) are favored by higher MLCAPE, deep-layer shear, and lower LCLs compared to weak ones (EF ≤ 1). Himawari-8 TBB data capture a more rapid pre-storm convective cloud-top cooling for strong tornadoes, with medians reaching -73 °C. This study demonstrates that combining high-frequency satellite observations with reanalysis data provides quantitative precursor signals for regional severe tornado nowcasting.

Abstract: Identifying the main drivers of soil CO₂ emissions in tropical agroecosystems is essential for balancing productivity and climate mitigation. This study evaluated the effects of crop type, irrigation, phenological stage, and fertilization on soil respiration in a humid marshland system in Rwanda using a two-season field experiment. Five crops (maize, soybean, common bean, Irish potato, and Brachiaria) were grown under irrigated and rainfed conditions, and soil CO₂ emissions were measured across 19 sampling campaigns in both crop-covered and adjacent bare soil conditions in all plots. Crop type and growth stage were the dominant drivers of soil CO₂ emissions (p < 0.001), while irrigation had no significant direct effect despite increasing yields (p < 0.001). As a result, irrigation reduced yield-scaled CO₂ emissions for several crops (p < 0.05–0.01). Brachiaria showed higher emissions, particularly during the development stage, but its high bio-mass led to lower emissions per unit yield. Fertilization significantly increased soil respiration (p < 0.001), and emissions were higher under crop-covered soils than bare soils (p < 0.001). These findings indicate that crop traits and nutrient inputs primarily control soil respiration under moisture-sufficient tropical conditions.

Abstract: For 24 of the 69 chemicals measured in U.S. National Health and Nutrition Examination Survey (NHANES) urine biomonitoring with data for both children aged 3–5 and adults aged 66 and older — including di-2-ethylhexyl phthalate (DEHP) and inorganic tin — no single regulatory exposure standard can be simultaneously epistemically grounded for both populations. This finding, which we term severe regulatory incommensurability, cannot be obtained from Bayesian inference or any significance test: it requires a geometric measure of the overlap between population-specific feasibility regions that has no probabilistic analog.We derive this result by applying the Theory of Epistemic Abductive Geometry (TEAG) — a possibilistic, constraint-based inference framework grounded in possibility theory and tropical mathematics — to the complete 179-chemical, 11-demographic-group dataset of Stanfield et al. (2022), the gold-standard Bayesian biomonitoring pipeline. TEAG recovers Bayesian median intake rate estimates with near-perfect agreement (r = 0.9965, RMSE = 0.15 log₁₀), establishing that the two frameworks agree on point estimates while diverging fundamentally on the geometric structure of the inference.The primary findings are: (i) the κ pairwise overlap coefficient is below 0.5 for 24 chemicals, meaning no intake rate achieves simultaneous epistemic feasibility above 50% for both age groups, with child-to-elder fold differences up to 8.6×; (ii) the TEAG admissible epistemic basin is on average 20.3× narrower than the Bayesian 95% credible interval, reflecting the geometric separation of measurement censoring, metabolite ambiguity, and demographic variability rather than false precision; (iii) demographic groups can be ordered by falsification priority — children aged 3–5 rank first at 1.89× mean distance from the committed population estimate; and (iv) 70% of 138 chemicals with longitudinal NHANES data (1999–2016, 9 cohorts) undergo epistemic phase transitions across cohorts, with atrazine mercapturate showing a 1.21 log₁₀ commitment reversal and arsenous acid — a severely incommensurate chemical — undergoing a persistent PCRB status change beginning in 2011–2012. A formal proof establishes that the κ incommensurability coefficient cannot be reproduced from any function of Bayesian posterior summary statistics, even given identical posterior means, variances, and credible interval widths.We call explicitly for population-differentiated reference doses for the 24 severely incommensurate chemicals and propose that κ < 0.5 between children and elderly adults in NHANES biomonitoring data be adopted as a standing geometric criterion for triggering age-stratified regulatory review.

Abstract: Nigeria faces a staggering housing deficit currently estimated at between 22 million and 28 million units, a crisis that has evolved from a simple shortage of units into a broader failure of habitability. This study assesses the residential dynamics of Oyo State, with specific focus on urban pressures in Ibadan and Ogbomoso. Using a review of current literature and recent case studies from the University of Ibadan's Department of Architecture, the research examines structural, economic, and legislative barriers to adequate housing. The methodology involves an analysis of historical urban morphology, sustainable material science, including the use of sawdust, Bamboo Leaf Ash (BLA), and Palm Kernel Shell Ash (PKSA), and the impact of the 1978 Land Use Act. Results indicate that while rapid urbanization, with Ibadan exceeding 4 million residents, has outpaced formal housing delivery, innovative solutions including the Millard Fuller Foundation's incremental housing model and Construction 5.0 technologies offer clear pathways to affordability. The study concludes that resolving the crisis requires decentralized land governance, the adoption of locally sourced sustainable materials, and a focus on community-centered design to ensure long-term urban resilience.

Abstract: Urban water systems are increasingly challenged by climate change, population growth, and resource scarcity, requiring a shift from centralised, supply-oriented models to decentralised, resilience-based approaches. While energy transition policies have successfully promoted Nearly Zero-Energy Buildings (NZEB) and Renewable Energy Communities (REC), similar concepts for water management remain underdeveloped. This study proposes adapting these energy-based frameworks to the water sector through the concepts of Nearly Zero-Water Buildings (NZWB) and Urban Water Communities (UWC). A structured literature review is combined with a quantitative water balance analysis to evaluated the potential for reducing potable water demand through efficiency measures, greywater reuse, rainwater harvesting, and alternative local renewable sources. Results indicate that potable water consumption in residential buildings can be reduced by 53–100% depending on system configurations and local resources availability. Extending these strategies from building-scale solutions to district scale through water communities enhances system redundancy, flexibility, and adaptive capacity. The study further discusses the integration of decentralised water systems with smart city frameworks, highlighting the role of hybrid infrastructures in improving urban resilience. The findings demonstrate that decentralised and circular water strategies can play a key role in enabling sustainable, climate-adaptive, and smart urban environments, while also identifying regulatory and governance challenges for large-scale implementation.

Abstract: UK forestry faces increasing drought risk under climate change, raising concerns about the resilience of Sitka spruce, the UK’s dominant commercial conifer. This study assessed whether hyperspectral vegetation indices can detect intraspecific drought responses to support resilience screening. An eight-week controlled drought experiment was con-ducted on six clonal groups, using needle-level hyperspectral reflectance to derive indices of chlorophyll status, photoprotective pigments, and water content, alongside chlorophyll fluorescence (Fv/Fm). Drought responses were detected across multiple indices, with pigment-based and red-edge indices showing the earliest and strongest sensitivity, while water-related indices captured later-stage hydraulic decline. Significant clonal variation was observed in the timing and magnitude of pigment regulation, water loss, and photosynthetic impairment, indicating contrasting drought response strategies. These results demonstrate that hyperspectral approaches enable rapid, non-destructive detec-tion of physiologically meaningful drought responses and can support the identification of drought-resilient genotypes for climate-adaptive forest management.

Abstract: Marine-terminating glaciers are major contributors to sea-level rise, yet their frontal ablation—the combined loss from ice discharge and terminus retreat—remains poorly constrained. This study presents a monthly time series of ice discharge for 40 marine-terminating glaciers in Alaska from 2015 to 2021, derived from Sentinel-1 velocity data, and reconstructed ice thickness information. Frontal ablation was calculated as the sum of ice discharge and terminus mass loss, from manually delineated terminus positions. The mean annual ice discharge was 11.81 ± 5.35 Gt a⁻¹, dominated by Hubbard, Columbia and Yahtse glaciers, which together accounted for ~70% of Alaska’s total ice discharge. Terminus retreat contributed an additional 1.30 ± 0.07 Gt a⁻¹, resulting in a total frontal ablation of 13.11 ± 5.35 Gt a⁻¹. Most glaciers exhibited late-summer velocity minima likely indicating seasonal changes in subglacial drainage efficiency, while interannual variability corresponded with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) phases. These findings confirm that Alaska’s marine-terminating glaciers currently lose relatively little mass through frontal retreat compared to their regional mass balance, suggesting that most glaciers have passed their phase of rapid retreat. The presented analysis also provides fundamental information for refining sea-level rise projections.

Abstract: Rapid urbanization intensified spatial variability in air pollution across India, while monitoring networks are limited in capturing local exposure conditions. This study develops and evaluates an exposure assessment framework, with pilot findings from the Air Pollution Exposure on Adolescents’ Lungs (APEAL), a multi-centre prospective cohort study conducted in Delhi, Mumbai, Bengaluru, and Mysuru, which represent diverse air pollution levels. Residential measurements of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were conducted using standardized protocols. Results from pilot-phase showed that mean outdoor PM2.5 concentrations were highest in Delhi (90.4 ± 12.0 µg/m³), followed by Mumbai (57.2 ± 12.8 µg/m³), and Bengaluru (53.0 ± 9.0 µg/m³), with Mysuru having the lowest at 32.3 ± 9.3 µg/m³, indicating a north-south gradient attributed to anthropogenic activities. Optical properties of PM2.5, including absorption coefficients (Babs370 and Babs880), and calculated Absorption Angstrom Exponent (AAE), show significant variations and are primarily influenced by combustion sources. Further, this approach will include seasonal monitoring, chemical characterization, toxicity analysis, land-use regression (LUR) modelling, and time activity pattern to generate high-resolution exposure estimates. This methodology provides a robust, scalable framework for epidemiological studies and urban air pollution assessment in resource-limited settings, with relevance for urban planning and policy making.

Abstract: Agriculture faces compounding pressures from food insecurity, climate change, and resource scarcity, creating urgent demand for scalable analytical tools. This PRISMA 2020-compliant systematic review synthesises 582 peer-reviewed studies on machine learning (ML) and deep learning (DL) applications in agriculture, drawn from Scopus for the period January 2019 to March 2026. The 2026 data cover only the first quarter (January–March) and are therefore not directly comparable to full-year counts. Publication volume grew exponentially — from 6 papers in 2019 to 251 in 2025 — driven by the adoption of convolutional neural networks (CNNs), Vision Transformers (ViT), and YOLO-based object detectors. Plant disease detection (27.0%) and crop yield prediction (13.7%) dominated the application landscape. South Asia and East Asia together contributed 59.3% of the corpus, while Sub-Saharan Africa and Latin America each accounted for only 1.4%, revealing a profound mismatch between research output and global food insecurity burden. Median reported classification accuracy was 98.1% for disease detection, largely reflecting controlled laboratory datasets rather than field conditions. Median R² was 0.823 for yield prediction, based on 22 of 80 yield studies reporting this metric. Unit heterogeneity, dataset artefacts, and inconsistent evaluation practices limit cross-study comparability and the real-world interpretability of these figures. Open science practices remain critically low: only 7.7% of papers shared code and 14.1% shared data openly. Explainable AI, federated learning, and physics-informed modelling represent emerging frontiers. The review identifies benchmark standardisation, smallholder-relevant design, and geographic equity as the field's most pressing unresolved challenges.

Abstract: Electric discharge cavitation is an effective method for water treatment that combines physical and chemical effects within a single process. It enables water disinfection, extraction acceleration, dispersion of solid particles, and enhancement of porous material permeability. Compared to conventional chemical treatment, it reduces the demand for reagents and minimizes secondary pollution. This new and developing technology significantly contributes to the preservation of natural aquatic ecosystems by providing a sustainable alternative to traditional decontamination methods, thereby reducing the overall anthropogenic pressure on the environment. This study focuses on developing a reliable method for assessing electric discharge cavitation intensity and controlling water purification processes. The proposed approach is based on the oxidation of iodide ions to molecular iodine by reactive species generated during electric discharge cavitation. The adapted iodometric method is sensitive, reproducible, and does not require complex optical or acoustic equipment. Experimental results confirmed that iodometry provides accurate evaluation of cavitation intensity, allowing control of specific energy consumption and optimization of treatment parameters. Optimal operating conditions were established to control the water processing by electric discharge cavitation: stainless-steel electrodes, specific input energy not exceeding 280 kJ·L-1, the presence of a free liquid surface in the working chamber, and a discharge pulse frequency below 10 Hz. The proposed method supports the development of energy-efficient, low-waste technologies for wastewater and natural water treatment and facilitates the integration of electric discharge systems into existing water treatment infrastructure, particularly under resource-limited conditions.

Abstract: Fracture networks strongly control fluid flow, reservoir connectivity, and production performance in carbonate systems, yet their multiscale architecture of complexity remains difficult to characterize from heterogeneous geological and geophysical datasets. Here, we introduce the Digital Transformer (DiT), a physics-informed computational framework that automatically analyzes and classifies fracture systems using spatially encoded visuonumerical primitives derived directly from physical measurements. Instead of relying on textual tokenization, the approach performs attention primitives tokenization of multiscale geophysical data. Clusters of absolute integer values act as computational tokens while preserving spatial topology and scale-invariant structure of the original system. The framework integrates two complementary environments: Muuk'il Kaab (MIK) for multidimensional metadata fusion and visualization, and SYM-Fractron, a hybrid binary-symbolic transformer for two-dimensional image analysis. Within this architecture, Digital Twins provide coupled visual and statistical representations of geological systems and their computational counterparts, enabling an interpretable taxonomy of natural fracture patterns while supporting well-trajectory optimization in the exploration of dolomitized carbonate reservoirs. In this view, fracture architectures become visionumerical primitives whose physics-informed tokenization opens a pathway from the architecture of natural complexity to its computational realization through Digital Twins.

Abstract: Water and land are critical natural resources that require effective management, particularly in rapidly urbanizing areas such as Cagayan de Oro City, Philippines. This study aims to assess erosion susceptibility and prioritize conservation needs across nine watersheds using GIS-based morphometric analysis. The entire extent of each watershed was analyzed beyond political boundaries to ensure comprehensive evaluation of geomorphological characteristics. Key morphometric parameters, including drainage density, stream frequency, slope, and basin shape, were computed to determine watershed behavior and erosion risk. Results indicate that the Cagayan de Oro River Basin is the most erosion-prone, followed by the Umalag, Iponan, and Cugman watersheds, while the Bugo–Alae watershed exhibits the lowest susceptibility. Higher slope gradients and elongated basin shapes were associated with increased erosion risk, whereas higher drainage density and stream frequency corresponded to lower susceptibility. These findings provide a scientific basis for prioritizing watershed management and conservation strategies, supporting sustainable land use planning and erosion mitigation in the study area.

Abstract: Cosmic-Ray Neutron Sensing (CRNS) has become a standard method for non-invasive soil moisture monitoring at the field scale. With most CRNS sensors being derivatives from scientific nuclear equipment, the development of instruments based on alternative neutron detection technologies is a major development goal for CRNS. We present a modular instrument family based on boron-10-lined proportional counters, specifically designed for long-term autonomous field operation. The system is controlled by a data logger supporting various telemetry options and external SDI-12 environmental sensors and the frontend electronics with its pulse shape analysis effectively separates neutron signals from background and electronic noise. Our results show high energy efficiency, with the latest generation close to 50 mW, allowing solar-powered operation even in challenging environments. The performance of the instruments has been validated within long-term field deployments in different settings, showing that boron-10-based systems provide a scalable, cost-effective and reliable alternative for the next generation of CRNS monitoring networks.

Abstract: The Junggar Basin (NW China) is a polycyclic intracontinental basin formed within the Central Asian Orogenic Belt and characterized by multi-stage tectonic reactivation and composite petroleum systems. This study integrates tectonic evolution, source rock geochemistry, and basin modeling to clarify the spatial–temporal controls on hydro-carbon generation and accumulation. The basin evolved from Late Paleozoic rifting to Carboniferous–Permian collision, followed by Mesozoic thermal subsidence and Ce-nozoic inversion related to the uplift of the Tianshan. Major source rocks include Car-boniferous marine shales (total organic carbon 1.5–5%), Permian lacustrine deposits (up to 10–12% total organic carbon; hydrogen index up to 700 mg HC/g TOC), and Ju-rassic coal-bearing strata. Thermal maturity ranges from 0.6% to >2.0% vitrinite re-flectance, indicating multi-phase oil and gas generation and secondary cracking in deeply buried depocenters. Hydrocarbon accumulation differs across structural zones. Central depressions are dominated by deep gas generation and composite traps, whereas northwestern segments reflect lateral migration from Permian source kitch-ens. Cenozoic inversion significantly reactivated faults and controlled vertical migra-tion pathways. The results highlight that hydrocarbon distribution in the Junggar Ba-sin is governed by the synchronization of tectonic evolution and generation phases, providing predictive insights for exploration in polycyclic inversion basins.

Abstract: This study presents a comprehensive Life Cycle Assessment (LCA) of seven peanut-derived products processed in central Argentina, aiming to quantify their environmental impacts from agricultural production to end-of-life. The research is framed within the development of Environmental Product Declarations (EPD) in accordance with ISO 14025, 14067 and 14040 standards, using primary data from three farms and one industrial facility representative of the sector. IPCC Tier 2 methodology was applied, with emission factors specific for Argentina, enabling a precise and context-sensitive environmental evaluation. Results show that the agricultural stage is the main source of greenhouse gas emissions (40–66%), particularly due to soil and crop residue management. International distribution, mainly maritime, also represents a significant burden (16–24%). Compared to equivalent products from Brazil and the USA, Argentine peanut products show environmental advantages in terms of carbon footprint, which was 67% lower for peanut butter than in the USA, and 21%lower for blanched peanuts than those from Brazil. The assessment identified opportunities to improve precision agriculture, renewable energy use, and estimation of soil carbon changes, and to optimize packaging. This work provides novel data for the region, strengthens the international competitiveness of Argentina’s peanut sector, and offers valuable inputs for public policy making and business strategies focused on sustainability.

Abstract: Background. The food industry contributes significantly to global greenhouse gas emissions, water consumption, and waste generation. Although environmental impact assessment tools have rapidly diversified, methodological fragmentation continues to limit comparability across studies and the formulation of coherent sustainability strategies. Objective. This study conducted a systematic review to synthesize, critically appraise, and map the evidence on methods, technologies, and applications used in assessing the sustainability of food industry processes, with a view to identifying the most effective approaches and the main research gaps. Data sources and eligibility. The Web of Science Core Collection was queried on November 27, 2025 using a structured strategy based on Boolean operators and Topic fields. Original articles and reviews in English, published between 2020 and 2025, that reported quantitative or qualitative indicators of environmental impact, according to the PICO framework, were included. Results. From an initial 1000 records, 225 studies were included and narratively synthesized into seven major themes. LCA predominated as the standard method, but with significant heterogeneity in system boundaries and functional units. Emerging technologies indicated potential for reducing resource consumption, dependent on subsector and scale. Conclusions: Harmonization of assessment frameworks, industrial validation of circular technologies, and robust comparative studies are essential for the transition to a sustainable food system.

Abstract: Recent offshore hydrocarbon discoveries along the Senegalese–Mauritanian margin increase the need to quantify oil-spill risk under the highly dynamic circulation of the southern Canary Current upwelling system. We investigate seasonal pollutant dispersion along the Senegalese Grande Côte using Lagrangian particle-tracking experiments forced by CROCO ocean model outputs. The analysis focuses on the role of wind-driven circulation, Ekman transport, and upwelling variability in controlling cross-shore and alongshore transport pathways. Results show a strong seasonal contrast. During the cold season (January–May), intensified northerly winds drive coastal upwelling and offshore Ekman transport, enhancing surface divergence and promoting the export of particles away from the coast. This regime limits nearshore accumulation but favors broader offshore dispersion over the continental shelf. In contrast, during the warm season (June–September), weakened upwelling-favorable winds and the establishment of anticyclonic circulation north of the Cape induce onshore transport and coastal retention. Particle-release experiments reveal enhanced trapping and accumulation along the Grande Côte during this period. The Kayar region and the Cape Verde Peninsula exhibit relatively higher exposure during the cold season, whereas the inner shelf along the Grande Côte becomes particularly vulnerable during the warm season. These findings demonstrate that seasonal wind forcing and associated Ekman dynamics exert first-order control on oil-spill pathways. Incorporating this variability into contingency planning is essential, as the inner continental shelf of the Senegalese Grande Côte is a dynamically sensitive, high-risk zone under the warm-season circulation regime.

Abstract: Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of in-dividual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterra-nean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield varia-bility (up to R² ≈ 0.82) under controlled analytical conditions. In contrast, cotton yield var-iability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.76. Recurrence analysis confirmed the stability of these indicator families across analytical stages. Overall, the results indicate that parsimonious, physiologically interpretable indi-cator combinations can account for a substantial proportion of yield variability without reliance on black-box modelling, supporting crop-aware indicator selection for precision agriculture applications.

Abstract: Heatwaves are intensifying across the southern United States, particularly in Texas, placing unprecedented stress on electric distribution networks and increasing power outage risk. Yet the relationship between heatwave characteristics and observed outages remains poorly quantified at multi-year, statewide scales. This study develops an event-based, spatiotemporal framework to quantify heatwave-induced outage risk across 254 Texas counties from 2014–2021 by integrating county-level EAGLE-I outage records with reanalysis-derived heat index measurements. An adaptive percentile-based threshold identifies heatwave days and constructs multi-day events, from which event-level metrics duration, mean heat index, and maximum customers affected are derived. Across 3,048 identified heatwave events, 51% involved at least one outage, revealing widespread heat-related reliability challenges. Spatial indicators show substantial heterogeneity: some counties experience frequent minor outages, while major population exposure is concentrated in large urban load centers. Outage severity and duration exhibit heavy-tailed distributions, with a small number of extreme events disproportionately affecting customers. Logistic regression models under three severity definitions (P90, P95, and ≥500 customers) demonstrate that heat intensity is a significant probabilistic driver of major outages, with each +1 °F increase in mean event heat index raising the odds by approximately 43–52%. These findings offer a scalable methodology for climate-related reliability assessment, supporting grid hardening, resource planning, and public-health preparedness.

Abstract: In this research we analyzed land cover/use processes and their impact on biodiversity in the Megalopolis of Mexico City. We used land cover/use databases from 1976 and 2018, both validated, improved and adapted for conducting landscape dynamic analysis. We also included records of 159 threatened species of fungi, vascular plants and vertebrates to construct spatially explicit biodiversity richness models based upon niche ecological algorithms. The results showed that human settlement encroachment was the main factor driving land cover/use changes, significantly affecting rural and natural landscapes. The extent and location of the dramatic shrinking of agricultural lands was clearly demonstrated. Afforestation was the second most important land cover/use process occurring mainly on native grasslands and shrublands. Biodiversity richness was depleted substantially, affecting about 35 % of the most important biodiversity hot spots and rendering the remainder more vulnerable due to extensive fragmentation of native ecosystems. The results are discussed in the light of the implications of the value of interdisciplinary methodological approaches, potential water recharge, governance of territorial disputes, loss of cultural heritage and poorly implemented environmental policies. Furthermore, the study highlights the urgent need to generate an innovative model for development which gives equal importance to the conservation of natural and rural landscapes as a fundamental form of subsistence for human settlements. Protecting biocultural heritage is of paramount importance. The region's genetic resources and cultural diversity are unique and have played a fundamental role in providing various benefits from nature to urban and rural inhabitants. These findings can serve as a guide for other similar megacities around the world.