Pressure–Resilience Dynamics of Land Systems under Intensified Anthropogenic Load: A Spatial Assessment Framework for Sustainable Land Management

Pressure–Resilience Dynamics of Land Systems under Intensified Anthropogenic Load: A Spatial Assessment Framework for Sustainable Land Management | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pressure–Resilience Dynamics of Land Systems under Intensified Anthropogenic Load: A Spatial Assessment Framework for Sustainable Land Management Olha Biedunkova, Pavlo Kuznietsov, Liudmyla Klymenko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9027181/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Intensified anthropogenic pressure and industrial disturbance increasingly alter land systems by modifying land-use structure, degrading soil–water interactions, and reducing ecosystem service capacity. These processes challenge conventional environmental assessment approaches, particularly where monitoring systems are fragmented or incomplete. This study develops a spatial land-system assessment framework based on pressure–resilience dynamics to evaluate regional differentiation of environmental risks. Technogenic pressure is conceptualized as a composite indicator reflecting land-use intensity, industrial load, and contamination affecting soil, water, and atmospheric components. Ecosystem resilience represents the integrated buffering capacity of atmosphere–hydrosphere–pedosphere–biota subsystems within regional land systems. Anthropo-ecological risk is formalized as an interaction term, enabling differentiation of territories with comparable land-use pressure but contrasting adaptive capacity. A radiological dimension is included to capture spatial variability associated with legacy contamination and nuclear infrastructure. Using open-access national geospatial datasets, the framework integrates normalized environmental indicators within a unified spatial workflow. Principal component and cluster analyses identify four land-system typologies: high-pressure low-resilience industrial systems; transitional systems with moderate buffering capacity; radiological–mixed risk systems; and stable–resilient systems with low pressure and strong ecological buffering potential. The dominant spatial gradient is governed by pressure–resilience interaction, while radiological risk represents a partially independent dimension. The findings demonstrate that land-system vulnerability cannot be inferred from land-use intensity alone but emerges from structured interactions between anthropogenic pressure and ecosystem resilience. The proposed framework advances land system science by formalizing pressure–resilience dynamics as a core analytical principle and provides a transparent screening-level tool for spatial planning, sustainable land management, and resilience-oriented regional development under conditions of environmental stress and data uncertainty. land systems anthropogenic pressure ecosystem resilience spatial planning soil–water interactions land management geospatial analysis environmental indicators sustainable development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Extreme environmental stress has become a defining feature of contemporary socio-ecological systems, particularly in regions affected by armed conflict, large-scale industrial disruption, and compounded anthropogenic pressures. Armed conflicts alter environmental conditions through the destruction of industrial and energy infrastructure, degradation of ecosystems, release of hazardous substances, and disruption of environmental monitoring systems [ 1 – 3 ]. These processes intensify ecosystem degradation and increase risks to human well-being by elevating exposure to pollution and radiological hazards, while simultaneously reducing the provision of ecosystem services essential for health, safety, and livelihoods [ 4 – 6 ]. Under such conditions, environmental security becomes inseparable from regional stability and long-term development. The full-scale war in Ukraine represents one of the most extensive contemporary cases of environmentally intensive armed conflict. Since 2022, military operations have caused widespread damage to industrial facilities, energy infrastructure, hydraulic structures, and transport networks, accompanied by significant contamination of air, soil, and water resources [ 5 , 7 ]. Official estimates indicate that environmental damage amounts to tens of billions of US dollars, with the most severe impacts recorded in highly industrialized eastern and central regions [ 1 , 8 – 10 ]. Beyond direct pollution, occupation of territories and active hostilities have resulted in extensive “monitoring blind zones”, where environmental observations are unavailable or substantially limited [ 9 ]. These conditions challenge conventional environmental assessment approaches, which typically rely on stable monitoring networks and complete datasets [ 2 , 3 , 5 , 10 , 12 ]. Several studies demonstrate that war-related environmental effects are spatially heterogeneous and closely linked to pre-existing patterns of industrial concentration and land use [ 13 , 14 ]. At the same time, broader ecological assessments emphasize the role of ecosystem resilience in shaping recovery trajectories and moderating environmental impacts [ 6 , 15 ]. Despite these advances, most existing research remains sector-specific and does not integrate technogenic pressure, ecological buffering capacity, and radiological risk within a unified spatial framework applicable at the national scale under conditions of disrupted monitoring. A central methodological challenge concerns the relationship between anthropogenic pressure and ecosystem resilience. Pressure-oriented assessments effectively identify hotspots of contamination and infrastructure damage but often treat ecosystems as passive recipients of disturbance. Conversely, resilience-based approaches highlight adaptive and recovery capacities but are typically developed for contexts characterized by stable data availability. There remains limited consensus on how to operationalize environmental risk assessment in conflict-affected regions where monitoring systems are fragmented or partially inaccessible [ 9 , 17 ]. Consequently, there is a need for analytical frameworks capable of integrating heterogeneous environmental indicators while remaining robust under conditions of spatial data gaps. Ukraine’s position within the global conflict landscape further underscores the scale and intensity of current environmental stressors. According to the Armed Conflict Location & Event Data (ACLED) Conflict Index [ 11 ], Ukraine ranks among the most conflict-affected countries worldwide ( Appendix A: Fig. A1 ). This convergence of stressors produces systemic environmental consequences that extend beyond localized damage. Іn this context, spatially explicit environmental risk assessment provides a promising basis for resilient regional development. Spatial indicators enable identification of regional asymmetries, priority intervention zones, and areas where ecosystem resilience may mitigate or amplify environmental pressure [ 18 – 20 ]. Within this study, extreme environmental stress is defined as a persistent condition arising from the interaction of three interrelated drivers: (i) large-scale military activities directly damaging ecosystems and infrastructure, (ii) elevated technogenic pressure associated with industrial legacy and conflict-related releases of hazardous substances, and (iii) degradation or disruption of environmental monitoring systems resulting in spatial uncertainty. Building on this conceptualization, the study advances the hypothesis that environmental risks across Ukraine exhibit pronounced spatial heterogeneity and that anthropo-ecological and radiological risks cannot be explained solely by technogenic pressure, but are significantly moderated by ecosystem resilience. Regions with comparable industrial loads may therefore demonstrate substantially different risk profiles depending on their ecological buffering capacity. The main aim of this study is to assess the spatial distribution of environmental risks across Ukraine and to develop a screening-level framework that integrates environmental monitoring outputs with resilience-oriented regional development under wartime conditions. By combining indicators of technogenic pressure (T), anthropo-ecological risk (Rae), radiological risk (Rrad), and ecosystem resilience (S) within a unified geospatial workflow, the study proposes a transparent and reproducible decision-support tool for spatial prioritization of recovery measures. The methodological contribution lies in the formal integration of pressure and resilience within a spatial modelling structure designed for application under conditions of incomplete or disrupted environmental data. Rather than introducing new indicators, the framework systematically organizes existing monitoring outputs into a pressure–resilience interaction model, enabling differentiation of regions with similar levels of industrial exposure according to their recovery capacity and risk translation potential. By linking environmental assessment with regional development planning, the study contributes to the advancement of applied environmental sciences in conflict-affected contexts. 2. Materials and Methods 2.1. Data sources and spatial preprocessing This study applies an integrated spatial analytical framework designed to evaluate regional differentiation of T, Rae, Rrad, and S under conditions of incomplete and spatially heterogeneous environmental data. The framework is based exclusively on open-access datasets obtained from the national geospatial portal of Ukraine [ 10 ]. The analysis relies on standardized thematic layers representing socio-economic development, environmental contamination, radiological indicators, and resilience-related environmental characteristics ( Appendix B: Fig. B1, Table B1 ). The datasets correspond to the most recent officially available observations and model-derived indicators provided through the portal. In territories temporarily occupied or affected by active hostilities, where direct measurements are unavailable, the most recent pre-war values or officially provided proxy layers were used. These territories are treated as areas of elevated uncertainty in subsequent interpretation. No artificial reconstruction of missing measurements was performed in order to maintain consistency across the national dataset. All spatial layers were harmonized to a common administrative unit (oblast and district levels, depending on data availability). When necessary, area-weighted aggregation was applied. Continuous variables were normalized using min–max scaling to transform indicators into dimensionless values suitable for index construction. This normalization approach preserves relative spatial contrasts and ensures comparability among heterogeneous indicators. 2.2. Data sources and spatial preprocessing The analytical workflow integrates four interrelated components within a unified spatial modelling structure (Appendix B: Fig. B1). The composite indicators were calculated according to the following equations (1–4). The indicator T integrates socio-economic development and total environmental contamination. The latter includes atmospheric pollution, surface water quality status, soil contamination, and a fire-density proxy reflecting wartime disturbances. The aggregated contamination index was derived through additive integration of normalized component layers (Appendix A). S was calculated as the arithmetic mean of four normalized environmental resilience indicators. Each component was processed independently prior to aggregation to avoid scale effects. The equal-weight approach reflects the absence of a priori evidence supporting differential weighting under national-scale screening conditions. Rae represents the relative interaction between pressure and resilience, allowing differentiation of territories with comparable T but varying buffering capacity. Rrad was calculated as a relative index normalized to national averages to capture spatial variability in radiological exposure potential. Rae = T / S (1) T = O + Z (2) Rrad = (Z r · A r · N r ) / (Z u · A u · N u ) (3) S = (ΣP i ) / n (4) where: Z r , Z u – the normalised total density of radionuclide contamination for the specific district and for Ukraine on average; A r , A u – the coefficients of radionuclide transfer into food chains for the district and for Ukraine on average; N r , N u – the population density in the administrative district and the national average; P i – the normalised resilience indicators for the individual environmental components: atmosphere, hydrosphere, pedosphere, biota; n – the number of components (n = 4). 2.3. Expert-based assessment of infrastructure-related environmental incidents To complement the spatial indices, reported disruptions and emergency incidents at critical infrastructure facilities were assessed using an expert judgment approach. The evaluation incorporated facility type, incident characteristics, geographic location, and potential environmental pathways of impact. Risks to ambient air, surface and groundwater, soils, waste generation, and public health were considered separately for each reported event. The probability of release of hazardous substances was estimated in accordance with the UNEP/OCHA Flash Environmental Assessment Tool (FEAT) methodology [ 21 ], with selected indicators adapted to reflect national operational conditions. This assessment was used to support interpretation of spatial patterns rather than to modify the calculated composite indices. 2.4. Multivariate statistical analysis To examine the joint structure of T, S, Rae, and Rrad at the regional level, multivariate statistical analyses were performed. Prior to analysis, all variables were standardized using z-score normalization to eliminate scale effects and ensure comparability. Principal component analysis (PCA) was conducted on the correlation matrix, which is appropriate for dimensionless indices. Components with eigenvalues greater than 1 were retained according to the Kaiser criterion. The cumulative explained variance was used to evaluate the adequacy of dimensionality reduction. Cluster analysis was subsequently performed in the reduced PCA space using retained component scores. This approach minimizes multicollinearity and enhances the stability of typological grouping. The clustering procedure was applied to derive a regional typology reflecting distinct combinations of pressure and resilience characteristics. Pearson correlation coefficients (r) were calculated to quantify pairwise linear relationships among the indicators. The strength of correlations was interpreted using standard thresholds commonly applied in environmental and geospatial studies: |r| ≥ 0.70 — strong correlation; 0.40 ≤ |r| < 0.70 — moderate correlation; 0.20 ≤ |r| < 0.40 — weak correlation; |r| < 0.20 — negligible or no correlation. 2.5. Multivariate statistical analysis Regions were classified into relative categories (admissible, moderate, elevated, high) based on the distribution of index values across the national territory. The classification approach is comparative and screening-oriented, supporting identification of spatial priority zones rather than precise site-level risk evaluation. Particular attention was given to the interaction between T and S, as territories with similar pressure levels may exhibit substantially different Rae values depending on resilience capacity. The resulting spatial typology provides the analytical basis for resilience-informed regional interpretation under conditions of environmental stress and data uncertainty. 3. Results and Discussion 3.1. Spatial patterns of critical infrastructure disruptions as drivers of environmental pressure Under conditions of armed conflict in Ukraine, disruptions of critical infrastructure represent one of the principal mechanisms through which military activity translates into environmental pressure ( Appendix C: Table. C1 ). Damage to industrial facilities, energy systems, transport corridors, hydraulic structures, and waste management infrastructure generates cascading environmental effects, including accidental releases of hazardous substances, uncontrolled combustion, soil contamination, and secondary pollution of surface and groundwater. In the context of Ukraine, these processes contribute directly to the spatial configuration of technogenic pressure and provide an empirical basis for interpreting regional differentiation of environmental risks. The spatial distribution of reported incidents ( Appendix C: Fig. C1 ) reveals pronounced clustering in eastern and central regions characterized by historically high industrial density and proximity to active combat zones. These territories exhibit a convergence of pre-existing industrial load and conflict-induced disturbances, resulting in cumulative environmental stress. In contrast, western regions show comparatively lower incident densities, consistent with lower concentrations of heavy industry and reduced direct exposure to hostilities. Nevertheless, the presence of disruptions in transport, energy, and other infrastructure sectors across multiple regions indicates that environmental pressure propagates through interconnected national systems rather than remaining confined to frontline areas. 3.2. Spatial differentiation of technogenic pressure, ecosystem resilience, and associated risk patterns The spatial configuration of environmental risks across Ukraine reflects the interaction between T, S, Rae, and Rrad. Rather than representing independent phenomena, these components form an interrelated spatial structure in which pressure gradients, buffering capacity, and legacy contamination jointly shape regional risk outcomes. Elevated and high Rae values are concentrated in eastern regions where high T coincides with low S, forming zones of compounded environmental stress (Fig. 1 a). In these areas, ecosystem degradation more directly translates into risks to human well-being through reduced buffering capacity, increased pollutant exposure, and impaired ecosystem services. Central regions illustrate the moderating role of resilience. Although several districts exhibit relatively high T values, moderate resilience capacity reduces Rae relative to eastern industrial territories. Western regions generally display admissible Rae levels, reflecting a more favorable balance between environmental pressure and ecological buffering potential. This pattern supports the central premise of the study: anthropo-ecological risk cannot be inferred directly from technogenic pressure. Regions with comparable industrial loads may exhibit substantially different risk profiles depending on their resilience characteristics, confirming that pressure–resilience interaction governs spatial differentiation of risk [ 6 , 11 – 13 ]. The distribution of T demonstrates pronounced regional asymmetry rooted in long-term industrial development patterns and intensified by war-related disturbances (Fig. 1 b). Elevated values are concentrated in central and eastern regions historically associated with metallurgy, energy production, chemical industries, mining, and dense transport infrastructure. These areas were characterized by substantial industrial loads prior to 2022 and have subsequently experienced additional stress due to infrastructure damage, accidental releases, fires, and destruction of industrial facilities [ 11 – 15 ]. Southern regions display heterogeneous patterns, combining moderate pressure in predominantly agricultural districts with elevated values in coastal and port-related territories affected by military activity. In contrast, western and parts of northern Ukraine exhibit comparatively low T values, reflecting lower industrial density and a landscape structure dominated by agriculture, forestry, and mountainous ecosystems. The clustering of high T values around industrial belts and energy hubs (Fig. 1 b) suggests that wartime environmental pressure amplifies pre-existing structural asymmetries rather than creating entirely new spatial gradients. These findings are consistent with previous assessments indicating that war-related environmental impacts are superimposed upon historical industrial legacies [ 13 , 14 ]. In contrast to the broader gradients of T and Rae, Rrad exhibits a localized spatial structure (Fig. 2 a). Most of Ukraine is characterized by minimal or permissible radiological levels; however, significantly elevated values occur in northern regions adjacent to the Chornobyl Exclusion Zone, where historical radionuclide contamination intersects with population exposure and monitoring limitations. Additional areas of concern are associated with regions hosting nuclear power facilities, particularly where military activity has heightened concerns regarding nuclear safety [ 16 , 17 ]. The occupation of the Chornobyl Exclusion Zone during the initial phase of the invasion resulted in disturbance of contaminated soils and temporary resuspension of radioactive particles, increasing exposure risks [ 16 ]. Ongoing international concern regarding the Zaporizhzhia Nuclear Power Plant further highlights the strategic significance of radiological safety under conflict conditions [ 17 ]. A methodological limitation relevant to Fig. 2 a concerns the presence of “monitoring blind zones” in temporarily occupied territories, where direct radiological measurements remain unavailable and baseline values or proxy indicators are used [ 18 ]. Although this introduces spatial uncertainty, the identified pattern remains consistent with documented legacy contamination and known nuclear infrastructure distribution. The spatial pattern of S (Fig. 2 b) reveals a contrasting gradient. High resilience values are observed in the Carpathian region, Polissia, and other forested or less transformed territories characterized by relatively intact hydrological systems, lower population density, and higher biodiversity potential. These areas demonstrate stronger ecological buffering capacity, supporting pollutant attenuation, soil self-recovery, and hydrological stability. Conversely, southern and southeastern regions, particularly steppe and intensively cultivated landscapes, exhibit lower S values. These territories are characterized by limited water availability, high soil transformation, fragmented biotic structures, and reduced ecological redundancy, factors that constrain adaptive capacity under both chronic technogenic pressure and acute military disturbances. Similar observations regarding ecosystem vulnerability under combined anthropogenic and conflict-related stress have been reported in broader environmental assessments [ 6 , 15 , 19 ]. The non-uniform spatial distribution of resilience underscores its role as a structural property of regional socio-ecological systems rather than a secondary descriptor. Regions with higher S demonstrate greater capacity to buffer pressure, whereas territories with low resilience are more prone to translating environmental load into persistent ecological degradation. Overall, the combined interpretation of Figs. 1 , 2 demonstrates that environmental risk in Ukraine is spatially structured along interacting gradients of pressure and resilience, with radiological factors introducing an additional, spatially selective dimension. High-risk territories emerge where elevated T coincides with low S, whereas regions characterized by strong resilience moderate the translation of pressure into risk. 3.3. Regional differentiation and cluster-based typology of environmental risk profiles The joint regional analysis of T, S, Rae, and Rrad reveals a structured spatial differentiation of environmental stress profiles across Ukraine (Fig. 3 ). Unlike single-indicator interpretations, the regional aggregation highlights systematic combinations of pressure and resilience that define distinct environmental risk regimes at the oblast level. As shown in Fig. 3 a–d, eastern and southeastern regions are characterized by consistently elevated T and Rae values combined with depressed S, indicating a convergence of intensive technogenic load and reduced ecosystem buffering capacity. In contrast, western regions display a markedly different configuration, with low T, high S, and correspondingly low Rae, reflecting a structurally more favorable balance between anthropogenic pressure and natural recovery potential. Rrad exhibits a more spatially localized pattern, with elevated regional values concentrated in northern regions affected by legacy contamination and nuclear-related factors, while remaining relatively low across most western and southern regions. The multivariate structure of these regional patterns is further clarified by PCA and cluster analysis (Fig. 4 a). The PCA space demonstrates a clear separation of regions along gradients dominated by technogenic pressure–resilience interaction and radiological influence. The first principal component (PC1), explaining 62.05% of the total variance, is primarily associated with the contrast between high T and low S versus low T and high S, reflecting the core pressure–resilience axis that governs Rae variability. The second principal component (PC2), accounting for 21.03% of the variance, is strongly influenced by Rrad, separating radiologically affected northern regions from other typological groups. This confirms that radiological risk represents a partially independent dimension of environmental stress, superimposed on the general technogenic–resilience structure. The PCA space demonstrates a clear separation of regions along gradients dominated by technogenic pressure–resilience interaction and radiological influence. The Pearson correlation matrix (Fig. 4 b) quantitatively characterizes the relationships among T, S, Rae, and Rrad. A strong positive correlation is observed between T and Rae (r = 0.86), confirming that increasing technogenic pressure is closely associated with higher anthropo-ecological risk. In contrast, S exhibits a strong negative correlation with Rae (r = − 0.81), demonstrating the buffering role of ecosystem resilience in moderating the translation of environmental pressure into risk outcomes. A moderate negative correlation between T and S (r = − 0.75) indicates that regions with higher technogenic loads tend to exhibit reduced resilience capacity, reflecting long-term ecosystem degradation in heavily industrialized landscapes. Rrad shows only weak to moderate correlations with the other indicators, including a weak positive correlation with Rae (r = 0.15), a weak positive correlation with T (r = 019), and a negligible to weak negative correlation with S (r = − 0.25). This pattern highlights the partially independent and legacy-driven nature of radiological risk, which is not fully explained by contemporary technogenic pressure–resilience interactions. Based on the cluster analysis in PCA space and the regional indicator profiles, four distinct regional types are identified (Table 1 ), representing coherent environmental risk regimes with direct implications for resilient regional development. Cluster 1 (Critical Industrial Conflict Zone) comprises Kharkiv, Luhansk, Donetsk, Dnipro, and Zaporizhzhia regions. This cluster is characterized by very high technogenic pressure combined with very low ecosystem resilience. These regions represent the most critical environmental risk category, where intense industrial legacy, direct war-related infrastructure damage, and degraded ecosystem capacity jointly amplify anthropo-ecological risk. The convergence of high T and low S results in the highest Rae values nationally, indicating limited buffering capacity and a high likelihood that environmental stress will translate into persistent risks to human well-being and environmental security. Cluster 2 (Transitional High Pressure) includes Kyiv city, Poltava, Odesa, Mykolaiv, Kherson, Kirovohrad, and Cherkasy regions. These areas are characterized by high technogenic pressure but moderate ecosystem resilience. Although T remains elevated, the presence of moderate S reduces Rae relative to Cluster 1, indicating a transitional risk regime. These regions retain a partial capacity for ecological buffering and recovery, suggesting that targeted management and remediation measures could significantly reduce long-term environmental risk if implemented during the recovery phase. Cluster 3 (Radiological / Mixed Risk) consists of Kyiv region, Zhytomyr, Chernihiv, and Sumy. This cluster is distinguished by moderate technogenic pressure combined with elevated radiological risk. The defining feature of this group is the disproportionate influence of Rrad, reflecting proximity to the Chornobyl Exclusion Zone and other nuclear-related factors. Although T is not extreme, the presence of elevated radiological exposure shifts the overall risk profile toward a mixed regime, where conventional technogenic pressure indicators alone would underestimate total environmental and health-related risk. Cluster 4 (Stable–Resilient) includes Volyn, Rivne, Lviv, Ternopil, Zakarpattia, Ivano-Frankivsk, Chernivtsi, Khmelnytskyi, and Vinnytsia. These regions are characterized by low technogenic pressure and high ecosystem resilience. As a result, Rae values are consistently low, indicating a structurally favorable environmental balance. These regions function as relative stability zones and ecological anchors within the national system, with strong buffering capacity and higher potential for sustainable recovery, biodiversity conservation, and nature-based development pathways. Table 1 Typology of Ukrainian regions based on T, S, Rae, Rrad. Cluster Characteristics Type Regions 1 Critical industrial conflict zone Very high T + very low S Kharkiv, Luhansk, Donetsk, Dnipro, Zaporizhzhia 2 Transitional high pressure High T + moderate S Kyiv city, Poltava, Odesa, Mykolaiv, Kherson, Kirovohrad, Cherkasy 3 Radiological / Mixed risk Moderate T + elevated Rrad Kyiv region, Zhytomyr, Chernihiv, Sumy 4 Stable-resilient Low T + high S Volyn, Rivne, Lviv, Ternopil, Zakarpattia, Ivano-Frankivsk, Chernivtsi, Khmelnytskyi, Vinnytsia Overall, the regional cluster typology demonstrates that environmental risk under extreme environmental stress is not uniformly distributed but organized into distinct spatial regimes defined by the interaction of technogenic pressure, ecosystem resilience, and, in selected areas, radiological factors. 3.4. Integrated spatial risk typology for resilient regional development The joint interpretation of technogenic pressure, anthropo-ecological risk, radiological risk, and ecosystem resilience enables the identification of integrated spatial risk typologies relevant to resilient regional development (Fig. 5 a). Rather than analysing individual indicators in isolation, their combined assessment reveals how specific configurations of pressure and resilience generate differentiated regional risk profiles under conditions of extreme environmental stress. Four generalized typological situations can be distinguished. The first includes regions characterized by high pressure and low resilience. These territories are primarily concentrated in eastern and southeastern Ukraine and represent the most critical zones from the perspective of sustainable recovery. In such areas, cumulative technogenic loads, ecosystem degradation, and limited buffering capacity reinforce one another, amplifying risks to environmental security and human well-being. The second typology encompasses regions with high pressure but comparatively higher resilience, mainly observed in parts of central Ukraine. Although technogenic load remains significant, stronger ecosystem structure moderates anthropo-ecological risk, indicating a comparatively greater recovery potential provided that appropriate environmental management measures are implemented. The third category includes regions characterized by low pressure and high resilience, predominantly located in western and northern Ukraine. These territories function as relative stability zones within the national environmental system and may serve as ecological and socio-economic anchors in recovery strategies. The fourth group comprises transitional regions with moderate levels of both pressure and resilience. Their future trajectory depends strongly on policy choices and investment priorities, as they may shift toward either stabilization or increased vulnerability depending on management effectiveness. This typological differentiation supports spatial prioritization and aligns with resilience-based approaches to regional planning under uncertainty [ 22 ]. Building on the identified typologies, differentiated management and recovery pathways are proposed to support resilient regional development (Fig. 5 b–e). In regions with elevated anthropo-ecological risk, priority should be given to reducing technogenic pressure through remediation of contaminated soils, reclamation of degraded industrial sites, and the establishment of buffer zones around high-risk facilities. These measures are essential to limit pollutant migration and restore core ecosystem functions [ 23 ]. The principle of environmental safety further requires minimizing the negative impacts of reconstruction activities themselves by prioritizing environmentally clean technologies, nature-based solutions, rational resource use, and systematic reclamation of contaminated areas [ 24 ]. For territories characterized by elevated radiological risk, a precautionary management approach is necessary. This includes restoration and modernization of monitoring systems, updating emergency preparedness plans, and strict adherence to international nuclear safety standards [ 16 , 17 ]. In regions with low ecosystem resilience, particularly in southern and southeastern Ukraine, recovery strategies should emphasize strengthening adaptive capacity through soil restoration, improved water resource management, and biodiversity-supporting measures such as the creation of ecological corridors [ 25 – 27 ]. Conversely, areas with high resilience potential offer opportunities for sustainable land use, ecological tourism, and broader application of nature-based solutions that simultaneously contribute to environmental protection and regional economic development. Importantly, these pathways are not prescriptive but represent a flexible, evidence-based framework adaptable to evolving environmental and socio-economic conditions. 3.5. Policy implications, uncertainty, and alignment with sustainable development goals The spatial differentiation of environmental risks identified in this study has direct implications for sustainable and resilient regional development in Ukraine (Fig. 6 a). The results demonstrate that uniform reconstruction approaches are unlikely to be effective under conditions of extreme environmental stress. Instead, recovery strategies must be spatially differentiated and grounded in regional risk profiles and resilience capacities. Integrating environmental monitoring outputs into regional planning processes enhances the ability of decision-makers to allocate resources efficiently, prioritize interventions, and avoid reproducing pre-war patterns of environmental degradation. By explicitly linking environmental risk assessment with development planning, the proposed framework bridges the gap between spatial analysis and policy implementation. From a conceptual perspective, the emphasis on resilience reflects contemporary sustainability paradigms that treat ecosystems as active components of socio-ecological systems rather than passive recipients of pressure. The proposed approach therefore contributes to long-term environmental security, social stability, and economic recovery under persistent uncertainty (Fig. 6 b). The results are subject to several sources of uncertainty inherent to environmental assessment under wartime conditions. The most significant limitation arises from restricted access to monitoring data in temporarily occupied territories and active combat zones, resulting in “monitoring blind zones,” where risk estimates rely on pre-war baselines or proxy indicators [ 9 , 18 ]. Additional uncertainty is associated with the use of aggregated administrative units, normalization procedures, and equal weighting of indicators. While these methodological choices are appropriate for screening-level analysis, they may obscure local-scale variability. Nevertheless, the proposed framework differs conceptually and methodologically from conventional approaches by explicitly treating ecosystem resilience as a moderating system property rather than an auxiliary descriptor. This enables systematic differentiation between regions with comparable technogenic pressure but varying capacity to buffer, absorb, and recover from extreme environmental stress, even under fragmented monitoring conditions. As shown in Table 2 , traditional cumulative risk mapping approaches emphasize additive pressures without explicit consideration of resilience, limiting their applicability under disrupted monitoring conditions. Pressure-only indices neglect ecosystem recovery capacity, while resilience-weighted indices often treat resilience as a secondary adjustment factor and require stable data streams. In contrast, the proposed framework conceptualizes pressure–resilience interaction as the core analytical principle, enhancing applicability under data uncertainty while maintaining transparency. Table 2 Comparison of control framework characteristics. Approach Main focus Treatment of resilience Applicability under data disruption Limitation Cumulative risk mapping Additive pressures and hazards Not explicitly considered Limited Overemphasizes pressure accumulation Pressure-only indices Industrial and anthropogenic load Absent Moderate Ignores ecosystem recovery capacity Resilience-weighted indices Pressure adjusted by ecological factors Secondary weighting factor Low Requires stable monitoring conditions Proposed framework Pressure–resilience interaction Explicit moderating property High Screening-level, relative assessment By addressing environmental dimensions of infrastructure damage, urban-industrial concentration, and population exposure, the framework contributes to selected Sustainable Development Goals (SDGs) (Fig. 7 ). Moreover, supports SDG 11 (Sustainable Cities and Communities) by emphasizing spatially differentiated recovery pathways instead of uniform reconstruction strategies. The explicit integration of ecosystem resilience aligns with SDG 13 (Climate Action), as resilient ecosystems enhance adaptive capacity to both climate-related and conflict-induced stressors. Furthermore, the focus on biodiversity, soil self-recovery, and ecosystem stability directly contributes to SDG 15 (Life on Land) by identifying regions where degraded resilience may undermine ecosystem services and long-term ecological restoration. Despite the outlined limitations (Table 2 ), the robustness of the identified spatial patterns is supported by consistency across multiple indicators and alignment with independent studies documenting war-related environmental impacts in Ukraine [ 2 , 5 , 11 , 28 , 29 ]. The indices should therefore be interpreted as relative measures intended for regional comparison and strategic prioritization rather than precise quantification of site-specific risks. As monitoring capacity is progressively restored, future research should refine the framework by incorporating temporal dynamics, alternative weighting schemes, and scenario-based modelling. Under current conditions of extreme environmental stress, however, the proposed approach provides a scientifically grounded and transparent basis for informed decision-making in support of resilient regional development. 4. Conclusions This study develops and applies a spatial environmental risk framework structured around the interaction between technogenic pressure and ecosystem resilience, complemented by a distinct radiological dimension. The results confirm that environmental risk patterns in Ukraine are organized along a dominant pressure–resilience gradient, while radiological factors introduce a spatially selective and partially independent risk component. Four coherent regional typologies were identified: critical industrial conflict zones, transitional high-pressure regions, radiological/mixed-risk regions, and stable–resilient territories. The study formalizes pressure–resilience interaction within a reproducible spatial modelling structure applicable under fragmented or incomplete monitoring conditions. By operationalizing Rae as a ratio-based interaction term and integrating it with radiological exposure indicators, the framework moves beyond additive cumulative risk mapping. The research advances environmental risk theory by explicitly positioning ecosystem resilience as a moderating system property rather than a secondary adjustment factor. This dual-axis structure (pressure–resilience gradient + radiological dimension) provides a coherent conceptual basis for analysing environmental stress in conflict-affected socio-ecological systems. The framework is designed as a comparative, screening-level tool for spatial prioritization. It enables identification of structurally differentiated regional regimes, supports evidence-based allocation of recovery resources, and remains operational under conditions of monitoring blind zones and data uncertainty. Overall, the findings demonstrate that resilient regional development under extreme environmental stress requires spatially differentiated assessment grounded in pressure–resilience dynamics rather than uniform reconstruction logic. The proposed approach offers a transparent and adaptable analytical basis for strategic environmental governance in conflict-affected contexts. Declarations Conflicts of Interest: The authors declare no conflicts of interest. Clinical trial number not applicable. Ethics, Consent to Participate, and Consent to Publish declarations not applicable. Funding: This research received no external funding. Author Contribution Conceptualization, O.B. and P.K.; methodology, O.B.; software, P.K.; validation, O.B. and P.K. and L.K.; formal analysis, P.K.; investigation, O.B.; resources, O.B. and P.K. and L.K.; data curation, P.K.; writing—original draft preparation, O.B. and P.K. and L.K.; writing—review and editing, , O.B. and P.K. and L.K.; visualization, P.K.; supervision, O.B.; project administration, O.B.; funding acquisition, L.K. All authors have read and agreed to the published version of the manuscript. Data Availability The data presented in this study are available on request from the corresponding author. References Hryhorczuk D, Levy BS, Prodanchuk M. The environmental health impacts of Russia’s war on Ukraine. J Occup Med Toxicol. 2024;19(1):1. https://doi.org/10.1186/s12995-023-00398-y . Leal Filho W, Eustachio JHPP, Fedoruk M, Lisovska T. War in Ukraine: an overview of environmental impacts and consequences for human health. Front Sustainable Resource Manage. 2024;3:1423444. https://doi.org/10.3389/fsrma.2024.1423444 . Hanoshenko O, Halaktionov M, Huber-Humer M. Exploratory study on the impact of military actions on the environment and infrastructure in the current Ukraine war with a specific focus on waste management. Waste Manag Res. 2025;43(8):1245–59. https://doi.org/10.1177/0734242X241305909 . Shevchuk O, Rochshyna N, Lazarenko I, Stets O. 2023, Towards a sustainable future: overcoming the challenges of post-war ecosystem reconstruction in Ukraine. IOP Conference Series: Earth and Environmental Science 1269(1), 012018. https://doi.org/10.1088/1755-1315/1269/1/012018 Pereira P, Bašić F, Bogunovic I, Barcelo D. Russian-Ukrainian war impacts the total environment. Sci Total Environ. 2022;837:155865. https://doi.org/10.1016/j.scitotenv.2022.155865 . UNEP. 2022, Environmental Impact of the Conflict in Ukraine: A Preliminary Review. United Nations Environment Programme. https://www.unep.org IAEA, Nuclear Safety and Security During Armed Conflict. 2022,. International Atomic Energy Agency, Vienna. https://www.iaea.org/topics/response/nuclear-safety-security-and-safeguards-in-ukraine Cynk KW. The war in Ukraine and environmental security in Central European ministerial discourse. Policy Stud. 2024;46(3):363–90. https://doi.org/10.1080/01442872.2024.2361696 . IPBES. 2019, Global Assessment Report on Biodiversity and Ecosystem Services. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn. https://www.ipbes.net/global-assessment Open Ecological Data Portal of Ukraine. Available online: https://geomap.land.kiev.ua (accessed on 2 January 2026). (In Ukrainian). Armed Conflict Location. & Event Data (open data portal). Available online: https://acleddata.com/ (accessed on 2 January 2026). Rawtani D, Gupta G, Khatri N, Rao PK, Hussain CM. Environmental damages due to war in Ukraine: A perspective. Sci Total Environ. 2022;850:157932. https://doi.org/10.1016/j.scitotenv.2022.157932 . Hook K, Marcantonio R. Environmental dimensions of conflict and paralyzed responses: the ongoing case of Ukraine and future implications for urban warfare. Small Wars Insurgencies. 2023;34(8):1400–28. https://doi.org/10.1080/09592318.2022.2035098 . Saxena V, Quality W, Pollution A, Change C. Investigating the Environmental Impacts of Industrialization and Urbanization. Water Air Soil Pollut 236, 73. https://doi.org/10.1007/s11270-024-07702-4 . Symochko L, Pereira P, Demyanyuk O, Pinheiro MC, Barcelo D. Resistome in a changing environment: Hotspots and vectors of spreading with a focus on the Russian-Ukrainian War. Heliyon. 2024;10(12):e32716. https://doi.org/10.1016/j.heliyon.2024.e32716 . Lincoln E, Noori A. Phytoremediation potential for radionuclide removal following the Chernobyl Nuclear Power Plant disaster. Int J Phytoremediation. 2025;1–13. https://doi.org/10.1080/15226514.2025.2542559 . Rezmer J, Szpak A. Legal boundaries: Ensuring protection amidst threats to the Zaporizhzhya Nuclear Power Plant in the Ukraine. Energy Res Social Sci. 2024;116:103700. https://doi.org/10.1016/j.erss.2024.103700 . Snizhko S, Didovets I, Bronstert A. Ukraine’s water security under pressure: Climate change and wartime. Water Secur. 2024;23:100182. https://doi.org/10.1016/j.wasec.2024.100182 . Yutilova K, Shved E, Rozantsev G, et al. Russia–Ukraine war impacts on environment: warfare chemical pollution and recovery prospects. Environ Sci Pollut Res. 2025;32:5685–702. https://doi.org/10.1007/s11356-025-36098-9 . Biedunkova O, Kuznietsov P, Mandryk O. Study of the dominant modes of formation and variability of potentially toxic element concentrations and their impact on environmental quality. Chemosphere. 2025;388:144688. https://doi.org/10.1016/j.chemosphere.2025.144688 . Ecodozor environmental consequences. and risks of the war in Ukraine (open data portal). Available online: https://ecodozor.org/ (accessed on 2 January 2026). Clark JN. 2025, Exploring the environmental impacts of war through sound and listening: a study of the Russia-Ukraine war. Environmental Sociology 1–15. https://doi.org/10.1080/23251042.2025.2510416 Hallioui A, Pedroni N Enhancing the Resilience and Sustainability of Integrated Energy Systems Exposed to Extreme Natural Hazards by Means of Artificial Intelligence, Advanced Simulation, and, Methods O. Within an Integrative Systems Framework: A Critical Review of Literature. Energies 2026, 19, 957. https://doi.org/10.3390/en19040957 Han S, Peng D, Guo Y, Aslam MU, Xu R. Harnessing technological innovation and renewable energy and their impact on environmental pollution in G-20 countries. Sci Rep. 2025;15(1):2236. https://doi.org/10.1038/s41598-025-85182-0 . Biedunkova O, Kuznietsov P, Korbutiak V, Petruk A, Gabrielyan B, Andreji J, Grokhovska Y, Konontsev S. Dominant meristic traits of fish and their association with habitat water quality parameters: A case study. Fishes. 2025;10(11):561. https://doi.org/10.3390/fishes10110561 . Gatti RC. Ecological Peace Corridors: A new conservation strategy to protect human and biological diversity. Biol Conserv. 2025;302:110947. https://doi.org/10.1016/j.biocon.2024.110947 . Zhou G, Gao J, Zhang R, et al. A new perspective for enhancing social and ecological systems coordination in ecological restoration. Ecol Processes. 2025;14:20. https://doi.org/10.1186/s13717-025-00588-y . Biedunkova O, Kuznietsov P, Tsos O, Boiaryn M, Karaim O. Sustainable Hydrochemical Reference Conditions in the Headwaters of Western Ukraine. Sustainability 2026, 18, 821. https://doi.org/10.3390/su18020821 Biedunkova O, Kuznietsov P, Kucherova A, Taipan Y. Monitoring of soil chemical properties in Ukraine using digital soil mapping. Discov Environ. 2026;4:78. https://doi.org/10.1007/s44274-026-00545-2 . Additional Declarations No competing interests reported. Supplementary Files supplementary.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 07 Mar, 2026 Submission checks completed at journal 07 Mar, 2026 First submitted to journal 04 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9027181","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607044356,"identity":"a3ef6239-897c-47a4-ab94-15d16ce4ff58","order_by":0,"name":"Olha Biedunkova","email":"","orcid":"","institution":"National University of Water and Environmental Engineering","correspondingAuthor":false,"prefix":"","firstName":"Olha","middleName":"","lastName":"Biedunkova","suffix":""},{"id":607044357,"identity":"25b3af17-074c-43c1-854b-79b09783c449","order_by":1,"name":"Pavlo Kuznietsov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYJACxgYgwQZh20DF2IjSwgxip5GghQGi5TBhLeYM3AmMM9vq8vjY+w9/+Nh2Ps/gRnYCw4eywwwGxw9g1WLZwLuBcWMbWzEbz2E2yZltt4sNbuRuYJxxDqjlTAJWLQYHgFoetvEktkkkszHznLmduOF27gZm3jaglgN4tUgktsk/Zv7Mc+YcRMtfkJbzD3Br2dhmALSFmUGap+IARAsjSMsN7LZYNvNuODjjXEJiG0+ymeSMiuTEmfffbjjYcy6dR/IGdlvM2Xs3Puwpq0uc337w8YcPBnaJfWfObnzwo8xaju88Dr8AY+MAhihIhAerepAWXBKjYBSMglEwCuAAALFRZAMzfAuhAAAAAElFTkSuQmCC","orcid":"","institution":"National University of Water and Environmental Engineering","correspondingAuthor":true,"prefix":"","firstName":"Pavlo","middleName":"","lastName":"Kuznietsov","suffix":""},{"id":607044358,"identity":"8f4ae4df-0778-4699-aaa5-dc7c96c4d979","order_by":2,"name":"Liudmyla Klymenko","email":"","orcid":"","institution":"National University of Water and Environmental Engineering","correspondingAuthor":false,"prefix":"","firstName":"Liudmyla","middleName":"","lastName":"Klymenko","suffix":""}],"badges":[],"createdAt":"2026-03-04 07:39:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9027181/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9027181/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105007711,"identity":"3a1629b5-0a60-4437-9b72-b7eba92f3076","added_by":"auto","created_at":"2026-03-19 19:01:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45495,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the Rae (\u003cem\u003ea\u003c/em\u003e), T (\u003cem\u003eb\u003c/em\u003e) across the territory of Ukraine.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/34c9de22c857a9bb527d476e.jpg"},{"id":105007717,"identity":"11f3dbda-7155-4d18-9626-b991413a6de5","added_by":"auto","created_at":"2026-03-19 19:01:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52831,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the Rrad (\u003cem\u003ea\u003c/em\u003e), and the S (\u003cem\u003eb\u003c/em\u003e) across the territory of Ukraine.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/009d83b22b09c0554085f6a4.jpg"},{"id":105007713,"identity":"7e2587f2-7468-44d5-8821-d0584a9fb8d1","added_by":"auto","created_at":"2026-03-19 19:01:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64567,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of T (\u003cem\u003ea\u003c/em\u003e), S (\u003cem\u003eb\u003c/em\u003e), Rae (\u003cem\u003ec\u003c/em\u003e), Rrad (\u003cem\u003ed\u003c/em\u003e) for the regions of Ukraine.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/995094070fc38940dd1cc8fb.jpg"},{"id":105035963,"identity":"1700fc2d-fd2c-458d-99cd-6d3307e0bfe3","added_by":"auto","created_at":"2026-03-20 07:27:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25810,"visible":true,"origin":"","legend":"\u003cp\u003eCluster map on PCA space (\u003cem\u003ea\u003c/em\u003e) and Pearson correlation matrix (\u003cem\u003eb\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/535b2e7243ec2b5765a5c866.jpg"},{"id":105007718,"identity":"fc2161b6-105d-48e2-bf91-6eda44905430","added_by":"auto","created_at":"2026-03-19 19:01:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89170,"visible":true,"origin":"","legend":"\u003cp\u003ePrinciples of sustainable-recovery strategies (\u003cem\u003ea\u003c/em\u003e) and management (\u003cem\u003eb-e\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/4e4bf6b75d98e9784653ef77.jpg"},{"id":105007715,"identity":"f9491ee3-8266-4585-bd49-43082bf79001","added_by":"auto","created_at":"2026-03-19 19:01:17","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32694,"visible":true,"origin":"","legend":"\u003cp\u003eApproaches (\u003cem\u003ea\u003c/em\u003e) and effects (\u003cem\u003eb\u003c/em\u003e) of integrated sustainable recovery strategies.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/a6b720444e8fdc830c6ebb14.jpg"},{"id":105035064,"identity":"9f62111a-10d3-4897-a565-0e31ae4fed8d","added_by":"auto","created_at":"2026-03-20 07:25:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60567,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual linkage between the sustainable recovery strategies and selected SDGs.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/d9ff25de4d148b3a1f89495c.jpg"},{"id":105036906,"identity":"dbdc3b6d-ef82-46ee-a29b-83c24d65151b","added_by":"auto","created_at":"2026-03-20 07:36:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1282277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/cd88575d-c3df-4d1c-b2db-70e899709443.pdf"},{"id":105007716,"identity":"c7009c89-3d39-43f5-a521-36c7bfaa894d","added_by":"auto","created_at":"2026-03-19 19:01:17","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1471385,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.zip","url":"https://assets-eu.researchsquare.com/files/rs-9027181/v1/8196c7377bf0cae1466a232c.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pressure–Resilience Dynamics of Land Systems under Intensified Anthropogenic Load: A Spatial Assessment Framework for Sustainable Land Management","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eExtreme environmental stress has become a defining feature of contemporary socio-ecological systems, particularly in regions affected by armed conflict, large-scale industrial disruption, and compounded anthropogenic pressures. Armed conflicts alter environmental conditions through the destruction of industrial and energy infrastructure, degradation of ecosystems, release of hazardous substances, and disruption of environmental monitoring systems [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These processes intensify ecosystem degradation and increase risks to human well-being by elevating exposure to pollution and radiological hazards, while simultaneously reducing the provision of ecosystem services essential for health, safety, and livelihoods [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Under such conditions, environmental security becomes inseparable from regional stability and long-term development.\u003c/p\u003e \u003cp\u003eThe full-scale war in Ukraine represents one of the most extensive contemporary cases of environmentally intensive armed conflict. Since 2022, military operations have caused widespread damage to industrial facilities, energy infrastructure, hydraulic structures, and transport networks, accompanied by significant contamination of air, soil, and water resources [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Official estimates indicate that environmental damage amounts to tens of billions of US dollars, with the most severe impacts recorded in highly industrialized eastern and central regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Beyond direct pollution, occupation of territories and active hostilities have resulted in extensive \u0026ldquo;monitoring blind zones\u0026rdquo;, where environmental observations are unavailable or substantially limited [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These conditions challenge conventional environmental assessment approaches, which typically rely on stable monitoring networks and complete datasets [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Several studies demonstrate that war-related environmental effects are spatially heterogeneous and closely linked to pre-existing patterns of industrial concentration and land use [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. At the same time, broader ecological assessments emphasize the role of ecosystem resilience in shaping recovery trajectories and moderating environmental impacts [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Despite these advances, most existing research remains sector-specific and does not integrate technogenic pressure, ecological buffering capacity, and radiological risk within a unified spatial framework applicable at the national scale under conditions of disrupted monitoring. A central methodological challenge concerns the relationship between anthropogenic pressure and ecosystem resilience. Pressure-oriented assessments effectively identify hotspots of contamination and infrastructure damage but often treat ecosystems as passive recipients of disturbance. Conversely, resilience-based approaches highlight adaptive and recovery capacities but are typically developed for contexts characterized by stable data availability. There remains limited consensus on how to operationalize environmental risk assessment in conflict-affected regions where monitoring systems are fragmented or partially inaccessible [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, there is a need for analytical frameworks capable of integrating heterogeneous environmental indicators while remaining robust under conditions of spatial data gaps.\u003c/p\u003e \u003cp\u003eUkraine\u0026rsquo;s position within the global conflict landscape further underscores the scale and intensity of current environmental stressors. According to the Armed Conflict Location \u0026amp; Event Data (ACLED) Conflict Index [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], Ukraine ranks among the most conflict-affected countries worldwide (\u003cem\u003eAppendix A: Fig. A1\u003c/em\u003e). This convergence of stressors produces systemic environmental consequences that extend beyond localized damage. Іn this context, spatially explicit environmental risk assessment provides a promising basis for resilient regional development. Spatial indicators enable identification of regional asymmetries, priority intervention zones, and areas where ecosystem resilience may mitigate or amplify environmental pressure [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Within this study, extreme environmental stress is defined as a persistent condition arising from the interaction of three interrelated drivers: (i) large-scale military activities directly damaging ecosystems and infrastructure, (ii) elevated technogenic pressure associated with industrial legacy and conflict-related releases of hazardous substances, and (iii) degradation or disruption of environmental monitoring systems resulting in spatial uncertainty. Building on this conceptualization, the study advances the hypothesis that environmental risks across Ukraine exhibit pronounced spatial heterogeneity and that anthropo-ecological and radiological risks cannot be explained solely by technogenic pressure, but are significantly moderated by ecosystem resilience. Regions with comparable industrial loads may therefore demonstrate substantially different risk profiles depending on their ecological buffering capacity.\u003c/p\u003e \u003cp\u003eThe main aim of this study is to assess the spatial distribution of environmental risks across Ukraine and to develop a screening-level framework that integrates environmental monitoring outputs with resilience-oriented regional development under wartime conditions. By combining indicators of technogenic pressure (T), anthropo-ecological risk (Rae), radiological risk (Rrad), and ecosystem resilience (S) within a unified geospatial workflow, the study proposes a transparent and reproducible decision-support tool for spatial prioritization of recovery measures. The methodological contribution lies in the formal integration of pressure and resilience within a spatial modelling structure designed for application under conditions of incomplete or disrupted environmental data. Rather than introducing new indicators, the framework systematically organizes existing monitoring outputs into a pressure\u0026ndash;resilience interaction model, enabling differentiation of regions with similar levels of industrial exposure according to their recovery capacity and risk translation potential. By linking environmental assessment with regional development planning, the study contributes to the advancement of applied environmental sciences in conflict-affected contexts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources and spatial preprocessing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study applies an integrated spatial analytical framework designed to evaluate regional differentiation of T, Rae, Rrad, and S under conditions of incomplete and spatially heterogeneous environmental data. The framework is based exclusively on open-access datasets obtained from the national geospatial portal of Ukraine [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The analysis relies on standardized thematic layers representing socio-economic development, environmental contamination, radiological indicators, and resilience-related environmental characteristics (\u003cem\u003eAppendix B: Fig. B1, Table B1\u003c/em\u003e). The datasets correspond to the most recent officially available observations and model-derived indicators provided through the portal. In territories temporarily occupied or affected by active hostilities, where direct measurements are unavailable, the most recent pre-war values or officially provided proxy layers were used. These territories are treated as areas of elevated uncertainty in subsequent interpretation. No artificial reconstruction of missing measurements was performed in order to maintain consistency across the national dataset. All spatial layers were harmonized to a common administrative unit (oblast and district levels, depending on data availability). When necessary, area-weighted aggregation was applied. Continuous variables were normalized using min\u0026ndash;max scaling to transform indicators into dimensionless values suitable for index construction. This normalization approach preserves relative spatial contrasts and ensures comparability among heterogeneous indicators.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data sources and spatial preprocessing\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe analytical workflow integrates four interrelated components within a unified spatial modelling structure (Appendix B: Fig. B1). The composite indicators were calculated according to the following equations (1\u0026ndash;4). The indicator T integrates socio-economic development and total environmental contamination. The latter includes atmospheric pollution, surface water quality status, soil contamination, and a fire-density proxy reflecting wartime disturbances. The aggregated contamination index was derived through additive integration of normalized component layers (Appendix A). S was calculated as the arithmetic mean of four normalized environmental resilience indicators. Each component was processed independently prior to aggregation to avoid scale effects. The equal-weight approach reflects the absence of a priori evidence supporting differential weighting under national-scale screening conditions. Rae represents the relative interaction between pressure and resilience, allowing differentiation of territories with comparable T but varying buffering capacity. Rrad was calculated as a relative index normalized to national averages to capture spatial variability in radiological exposure potential.\u003c/p\u003e \u003cp\u003eRae\u0026thinsp;=\u0026thinsp;T / S (1)\u003c/p\u003e \u003cp\u003eT\u0026thinsp;=\u0026thinsp;O + Z (2)\u003c/p\u003e \u003cp\u003eRrad = (Z\u003csub\u003er\u003c/sub\u003e \u0026middot; A\u003csub\u003er\u003c/sub\u003e \u0026middot; N\u003csub\u003er\u003c/sub\u003e) / (Z\u003csub\u003eu\u003c/sub\u003e \u0026middot; A\u003csub\u003eu\u003c/sub\u003e \u0026middot; N\u003csub\u003eu\u003c/sub\u003e) (3)\u003c/p\u003e \u003cp\u003eS = (ΣP\u003csub\u003ei\u003c/sub\u003e) / n (4)\u003c/p\u003e \u003cp\u003ewhere: Z\u003csub\u003er\u003c/sub\u003e, Z\u003csub\u003eu\u003c/sub\u003e \u0026ndash; the normalised total density of radionuclide contamination for the specific district and for Ukraine on average; A\u003csub\u003er\u003c/sub\u003e, A\u003csub\u003eu\u003c/sub\u003e \u0026ndash; the coefficients of radionuclide transfer into food chains for the district and for Ukraine on average; N\u003csub\u003er\u003c/sub\u003e, N\u003csub\u003eu\u003c/sub\u003e \u0026ndash; the population density in the administrative district and the national average; P\u003csub\u003ei\u003c/sub\u003e \u0026ndash; the normalised resilience indicators for the individual environmental components: atmosphere, hydrosphere, pedosphere, biota; n \u0026ndash; the number of components (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Expert-based assessment of infrastructure-related environmental incidents\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo complement the spatial indices, reported disruptions and emergency incidents at critical infrastructure facilities were assessed using an expert judgment approach. The evaluation incorporated facility type, incident characteristics, geographic location, and potential environmental pathways of impact. Risks to ambient air, surface and groundwater, soils, waste generation, and public health were considered separately for each reported event. The probability of release of hazardous substances was estimated in accordance with the UNEP/OCHA Flash Environmental Assessment Tool (FEAT) methodology [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], with selected indicators adapted to reflect national operational conditions. This assessment was used to support interpretation of spatial patterns rather than to modify the calculated composite indices.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Multivariate statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo examine the joint structure of T, S, Rae, and Rrad at the regional level, multivariate statistical analyses were performed. Prior to analysis, all variables were standardized using z-score normalization to eliminate scale effects and ensure comparability. Principal component analysis (PCA) was conducted on the correlation matrix, which is appropriate for dimensionless indices. Components with eigenvalues greater than 1 were retained according to the Kaiser criterion. The cumulative explained variance was used to evaluate the adequacy of dimensionality reduction. Cluster analysis was subsequently performed in the reduced PCA space using retained component scores. This approach minimizes multicollinearity and enhances the stability of typological grouping. The clustering procedure was applied to derive a regional typology reflecting distinct combinations of pressure and resilience characteristics. Pearson correlation coefficients (r) were calculated to quantify pairwise linear relationships among the indicators. The strength of correlations was interpreted using standard thresholds commonly applied in environmental and geospatial studies: |r| \u0026ge; 0.70 \u0026mdash; strong correlation; 0.40 \u0026le; |r| \u0026lt; 0.70 \u0026mdash; moderate correlation; 0.20 \u0026le; |r| \u0026lt; 0.40 \u0026mdash; weak correlation; |r| \u0026lt; 0.20 \u0026mdash; negligible or no correlation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Multivariate statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRegions were classified into relative categories (admissible, moderate, elevated, high) based on the distribution of index values across the national territory. The classification approach is comparative and screening-oriented, supporting identification of spatial priority zones rather than precise site-level risk evaluation. Particular attention was given to the interaction between T and S, as territories with similar pressure levels may exhibit substantially different Rae values depending on resilience capacity. The resulting spatial typology provides the analytical basis for resilience-informed regional interpretation under conditions of environmental stress and data uncertainty.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Spatial patterns of critical infrastructure disruptions as drivers of environmental pressure\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUnder conditions of armed conflict in Ukraine, disruptions of critical infrastructure represent one of the principal mechanisms through which military activity translates into environmental pressure (\u003cem\u003eAppendix C: Table. C1\u003c/em\u003e). Damage to industrial facilities, energy systems, transport corridors, hydraulic structures, and waste management infrastructure generates cascading environmental effects, including accidental releases of hazardous substances, uncontrolled combustion, soil contamination, and secondary pollution of surface and groundwater. In the context of Ukraine, these processes contribute directly to the spatial configuration of technogenic pressure and provide an empirical basis for interpreting regional differentiation of environmental risks. The spatial distribution of reported incidents (\u003cem\u003eAppendix C: Fig. C1\u003c/em\u003e) reveals pronounced clustering in eastern and central regions characterized by historically high industrial density and proximity to active combat zones. These territories exhibit a convergence of pre-existing industrial load and conflict-induced disturbances, resulting in cumulative environmental stress. In contrast, western regions show comparatively lower incident densities, consistent with lower concentrations of heavy industry and reduced direct exposure to hostilities. Nevertheless, the presence of disruptions in transport, energy, and other infrastructure sectors across multiple regions indicates that environmental pressure propagates through interconnected national systems rather than remaining confined to frontline areas.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Spatial differentiation of technogenic pressure, ecosystem resilience, and associated risk patterns\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatial configuration of environmental risks across Ukraine reflects the interaction between T, S, Rae, and Rrad. Rather than representing independent phenomena, these components form an interrelated spatial structure in which pressure gradients, buffering capacity, and legacy contamination jointly shape regional risk outcomes. Elevated and high Rae values are concentrated in eastern regions where high T coincides with low S, forming zones of compounded environmental stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In these areas, ecosystem degradation more directly translates into risks to human well-being through reduced buffering capacity, increased pollutant exposure, and impaired ecosystem services. Central regions illustrate the moderating role of resilience. Although several districts exhibit relatively high T values, moderate resilience capacity reduces Rae relative to eastern industrial territories. Western regions generally display admissible Rae levels, reflecting a more favorable balance between environmental pressure and ecological buffering potential. This pattern supports the central premise of the study: anthropo-ecological risk cannot be inferred directly from technogenic pressure. Regions with comparable industrial loads may exhibit substantially different risk profiles depending on their resilience characteristics, confirming that pressure\u0026ndash;resilience interaction governs spatial differentiation of risk [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe distribution of T demonstrates pronounced regional asymmetry rooted in long-term industrial development patterns and intensified by war-related disturbances (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Elevated values are concentrated in central and eastern regions historically associated with metallurgy, energy production, chemical industries, mining, and dense transport infrastructure. These areas were characterized by substantial industrial loads prior to 2022 and have subsequently experienced additional stress due to infrastructure damage, accidental releases, fires, and destruction of industrial facilities [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Southern regions display heterogeneous patterns, combining moderate pressure in predominantly agricultural districts with elevated values in coastal and port-related territories affected by military activity. In contrast, western and parts of northern Ukraine exhibit comparatively low T values, reflecting lower industrial density and a landscape structure dominated by agriculture, forestry, and mountainous ecosystems. The clustering of high T values around industrial belts and energy hubs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) suggests that wartime environmental pressure amplifies pre-existing structural asymmetries rather than creating entirely new spatial gradients. These findings are consistent with previous assessments indicating that war-related environmental impacts are superimposed upon historical industrial legacies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn contrast to the broader gradients of T and Rae, Rrad exhibits a localized spatial structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Most of Ukraine is characterized by minimal or permissible radiological levels; however, significantly elevated values occur in northern regions adjacent to the Chornobyl Exclusion Zone, where historical radionuclide contamination intersects with population exposure and monitoring limitations. Additional areas of concern are associated with regions hosting nuclear power facilities, particularly where military activity has heightened concerns regarding nuclear safety [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The occupation of the Chornobyl Exclusion Zone during the initial phase of the invasion resulted in disturbance of contaminated soils and temporary resuspension of radioactive particles, increasing exposure risks [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ongoing international concern regarding the Zaporizhzhia Nuclear Power Plant further highlights the strategic significance of radiological safety under conflict conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A methodological limitation relevant to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea concerns the presence of \u0026ldquo;monitoring blind zones\u0026rdquo; in temporarily occupied territories, where direct radiological measurements remain unavailable and baseline values or proxy indicators are used [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although this introduces spatial uncertainty, the identified pattern remains consistent with documented legacy contamination and known nuclear infrastructure distribution.\u003c/p\u003e \u003cp\u003eThe spatial pattern of S (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) reveals a contrasting gradient. High resilience values are observed in the Carpathian region, Polissia, and other forested or less transformed territories characterized by relatively intact hydrological systems, lower population density, and higher biodiversity potential. These areas demonstrate stronger ecological buffering capacity, supporting pollutant attenuation, soil self-recovery, and hydrological stability. Conversely, southern and southeastern regions, particularly steppe and intensively cultivated landscapes, exhibit lower S values. These territories are characterized by limited water availability, high soil transformation, fragmented biotic structures, and reduced ecological redundancy, factors that constrain adaptive capacity under both chronic technogenic pressure and acute military disturbances. Similar observations regarding ecosystem vulnerability under combined anthropogenic and conflict-related stress have been reported in broader environmental assessments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The non-uniform spatial distribution of resilience underscores its role as a structural property of regional socio-ecological systems rather than a secondary descriptor. Regions with higher S demonstrate greater capacity to buffer pressure, whereas territories with low resilience are more prone to translating environmental load into persistent ecological degradation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverall, the combined interpretation of Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates that environmental risk in Ukraine is spatially structured along interacting gradients of pressure and resilience, with radiological factors introducing an additional, spatially selective dimension. High-risk territories emerge where elevated T coincides with low S, whereas regions characterized by strong resilience moderate the translation of pressure into risk.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Regional differentiation and cluster-based typology of environmental risk profiles\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe joint regional analysis of T, S, Rae, and Rrad reveals a structured spatial differentiation of environmental stress profiles across Ukraine (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Unlike single-indicator interpretations, the regional aggregation highlights systematic combinations of pressure and resilience that define distinct environmental risk regimes at the oblast level. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;d, eastern and southeastern regions are characterized by consistently elevated T and Rae values combined with depressed S, indicating a convergence of intensive technogenic load and reduced ecosystem buffering capacity. In contrast, western regions display a markedly different configuration, with low T, high S, and correspondingly low Rae, reflecting a structurally more favorable balance between anthropogenic pressure and natural recovery potential. Rrad exhibits a more spatially localized pattern, with elevated regional values concentrated in northern regions affected by legacy contamination and nuclear-related factors, while remaining relatively low across most western and southern regions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe multivariate structure of these regional patterns is further clarified by PCA and cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The PCA space demonstrates a clear separation of regions along gradients dominated by technogenic pressure\u0026ndash;resilience interaction and radiological influence. The first principal component (PC1), explaining 62.05% of the total variance, is primarily associated with the contrast between high T and low S versus low T and high S, reflecting the core pressure\u0026ndash;resilience axis that governs Rae variability. The second principal component (PC2), accounting for 21.03% of the variance, is strongly influenced by Rrad, separating radiologically affected northern regions from other typological groups. This confirms that radiological risk represents a partially independent dimension of environmental stress, superimposed on the general technogenic\u0026ndash;resilience structure. The PCA space demonstrates a clear separation of regions along gradients dominated by technogenic pressure\u0026ndash;resilience interaction and radiological influence. The Pearson correlation matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) quantitatively characterizes the relationships among T, S, Rae, and Rrad. A strong positive correlation is observed between T and Rae (r\u0026thinsp;=\u0026thinsp;0.86), confirming that increasing technogenic pressure is closely associated with higher anthropo-ecological risk. In contrast, S exhibits a strong negative correlation with Rae (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.81), demonstrating the buffering role of ecosystem resilience in moderating the translation of environmental pressure into risk outcomes. A moderate negative correlation between T and S (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.75) indicates that regions with higher technogenic loads tend to exhibit reduced resilience capacity, reflecting long-term ecosystem degradation in heavily industrialized landscapes. Rrad shows only weak to moderate correlations with the other indicators, including a weak positive correlation with Rae (r\u0026thinsp;=\u0026thinsp;0.15), a weak positive correlation with T (r\u0026thinsp;=\u0026thinsp;019), and a negligible to weak negative correlation with S (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25). This pattern highlights the partially independent and legacy-driven nature of radiological risk, which is not fully explained by contemporary technogenic pressure\u0026ndash;resilience interactions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBased on the cluster analysis in PCA space and the regional indicator profiles, four distinct regional types are identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), representing coherent environmental risk regimes with direct implications for resilient regional development. Cluster 1 (Critical Industrial Conflict Zone) comprises Kharkiv, Luhansk, Donetsk, Dnipro, and Zaporizhzhia regions. This cluster is characterized by very high technogenic pressure combined with very low ecosystem resilience. These regions represent the most critical environmental risk category, where intense industrial legacy, direct war-related infrastructure damage, and degraded ecosystem capacity jointly amplify anthropo-ecological risk. The convergence of high T and low S results in the highest Rae values nationally, indicating limited buffering capacity and a high likelihood that environmental stress will translate into persistent risks to human well-being and environmental security. Cluster 2 (Transitional High Pressure) includes Kyiv city, Poltava, Odesa, Mykolaiv, Kherson, Kirovohrad, and Cherkasy regions. These areas are characterized by high technogenic pressure but moderate ecosystem resilience. Although T remains elevated, the presence of moderate S reduces Rae relative to Cluster 1, indicating a transitional risk regime. These regions retain a partial capacity for ecological buffering and recovery, suggesting that targeted management and remediation measures could significantly reduce long-term environmental risk if implemented during the recovery phase. Cluster 3 (Radiological / Mixed Risk) consists of Kyiv region, Zhytomyr, Chernihiv, and Sumy. This cluster is distinguished by moderate technogenic pressure combined with elevated radiological risk. The defining feature of this group is the disproportionate influence of Rrad, reflecting proximity to the Chornobyl Exclusion Zone and other nuclear-related factors. Although T is not extreme, the presence of elevated radiological exposure shifts the overall risk profile toward a mixed regime, where conventional technogenic pressure indicators alone would underestimate total environmental and health-related risk. Cluster 4 (Stable\u0026ndash;Resilient) includes Volyn, Rivne, Lviv, Ternopil, Zakarpattia, Ivano-Frankivsk, Chernivtsi, Khmelnytskyi, and Vinnytsia. These regions are characterized by low technogenic pressure and high ecosystem resilience. As a result, Rae values are consistently low, indicating a structurally favorable environmental balance. These regions function as relative stability zones and ecological anchors within the national system, with strong buffering capacity and higher potential for sustainable recovery, biodiversity conservation, and nature-based development pathways.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypology of Ukrainian regions based on T, S, Rae, Rrad.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical industrial conflict zone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery high T\u0026thinsp;+\u0026thinsp;very low S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKharkiv, Luhansk, Donetsk, Dnipro, Zaporizhzhia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransitional high pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh T\u0026thinsp;+\u0026thinsp;moderate S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKyiv city, Poltava, Odesa, Mykolaiv, Kherson, Kirovohrad, Cherkasy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiological / Mixed risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate T\u0026thinsp;+\u0026thinsp;elevated Rrad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKyiv region, Zhytomyr, Chernihiv, Sumy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable-resilient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow T\u0026thinsp;+\u0026thinsp;high S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVolyn, Rivne, Lviv, Ternopil, Zakarpattia, Ivano-Frankivsk, Chernivtsi, Khmelnytskyi, Vinnytsia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOverall, the regional cluster typology demonstrates that environmental risk under extreme environmental stress is not uniformly distributed but organized into distinct spatial regimes defined by the interaction of technogenic pressure, ecosystem resilience, and, in selected areas, radiological factors.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Integrated spatial risk typology for resilient regional development\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe joint interpretation of technogenic pressure, anthropo-ecological risk, radiological risk, and ecosystem resilience enables the identification of integrated spatial risk typologies relevant to resilient regional development (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Rather than analysing individual indicators in isolation, their combined assessment reveals how specific configurations of pressure and resilience generate differentiated regional risk profiles under conditions of extreme environmental stress. Four generalized typological situations can be distinguished. The first includes regions characterized by high pressure and low resilience. These territories are primarily concentrated in eastern and southeastern Ukraine and represent the most critical zones from the perspective of sustainable recovery. In such areas, cumulative technogenic loads, ecosystem degradation, and limited buffering capacity reinforce one another, amplifying risks to environmental security and human well-being. The second typology encompasses regions with high pressure but comparatively higher resilience, mainly observed in parts of central Ukraine. Although technogenic load remains significant, stronger ecosystem structure moderates anthropo-ecological risk, indicating a comparatively greater recovery potential provided that appropriate environmental management measures are implemented. The third category includes regions characterized by low pressure and high resilience, predominantly located in western and northern Ukraine. These territories function as relative stability zones within the national environmental system and may serve as ecological and socio-economic anchors in recovery strategies. The fourth group comprises transitional regions with moderate levels of both pressure and resilience. Their future trajectory depends strongly on policy choices and investment priorities, as they may shift toward either stabilization or increased vulnerability depending on management effectiveness. This typological differentiation supports spatial prioritization and aligns with resilience-based approaches to regional planning under uncertainty [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBuilding on the identified typologies, differentiated management and recovery pathways are proposed to support resilient regional development (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u0026ndash;e). In regions with elevated anthropo-ecological risk, priority should be given to reducing technogenic pressure through remediation of contaminated soils, reclamation of degraded industrial sites, and the establishment of buffer zones around high-risk facilities. These measures are essential to limit pollutant migration and restore core ecosystem functions [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The principle of environmental safety further requires minimizing the negative impacts of reconstruction activities themselves by prioritizing environmentally clean technologies, nature-based solutions, rational resource use, and systematic reclamation of contaminated areas [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For territories characterized by elevated radiological risk, a precautionary management approach is necessary. This includes restoration and modernization of monitoring systems, updating emergency preparedness plans, and strict adherence to international nuclear safety standards [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In regions with low ecosystem resilience, particularly in southern and southeastern Ukraine, recovery strategies should emphasize strengthening adaptive capacity through soil restoration, improved water resource management, and biodiversity-supporting measures such as the creation of ecological corridors [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Conversely, areas with high resilience potential offer opportunities for sustainable land use, ecological tourism, and broader application of nature-based solutions that simultaneously contribute to environmental protection and regional economic development. Importantly, these pathways are not prescriptive but represent a flexible, evidence-based framework adaptable to evolving environmental and socio-economic conditions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Policy implications, uncertainty, and alignment with sustainable development goals\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe spatial differentiation of environmental risks identified in this study has direct implications for sustainable and resilient regional development in Ukraine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The results demonstrate that uniform reconstruction approaches are unlikely to be effective under conditions of extreme environmental stress. Instead, recovery strategies must be spatially differentiated and grounded in regional risk profiles and resilience capacities. Integrating environmental monitoring outputs into regional planning processes enhances the ability of decision-makers to allocate resources efficiently, prioritize interventions, and avoid reproducing pre-war patterns of environmental degradation. By explicitly linking environmental risk assessment with development planning, the proposed framework bridges the gap between spatial analysis and policy implementation. From a conceptual perspective, the emphasis on resilience reflects contemporary sustainability paradigms that treat ecosystems as active components of socio-ecological systems rather than passive recipients of pressure. The proposed approach therefore contributes to long-term environmental security, social stability, and economic recovery under persistent uncertainty (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The results are subject to several sources of uncertainty inherent to environmental assessment under wartime conditions. The most significant limitation arises from restricted access to monitoring data in temporarily occupied territories and active combat zones, resulting in \u0026ldquo;monitoring blind zones,\u0026rdquo; where risk estimates rely on pre-war baselines or proxy indicators [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additional uncertainty is associated with the use of aggregated administrative units, normalization procedures, and equal weighting of indicators. While these methodological choices are appropriate for screening-level analysis, they may obscure local-scale variability.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNevertheless, the proposed framework differs conceptually and methodologically from conventional approaches by explicitly treating ecosystem resilience as a moderating system property rather than an auxiliary descriptor. This enables systematic differentiation between regions with comparable technogenic pressure but varying capacity to buffer, absorb, and recover from extreme environmental stress, even under fragmented monitoring conditions. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, traditional cumulative risk mapping approaches emphasize additive pressures without explicit consideration of resilience, limiting their applicability under disrupted monitoring conditions. Pressure-only indices neglect ecosystem recovery capacity, while resilience-weighted indices often treat resilience as a secondary adjustment factor and require stable data streams. In contrast, the proposed framework conceptualizes pressure\u0026ndash;resilience interaction as the core analytical principle, enhancing applicability under data uncertainty while maintaining transparency.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of control framework characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApproach\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMain focus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreatment of resilience\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApplicability under data disruption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative risk mapping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdditive pressures and hazards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot explicitly considered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLimited\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOveremphasizes pressure accumulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure-only indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial and anthropogenic load\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIgnores ecosystem recovery capacity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience-weighted indices\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePressure adjusted by ecological factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary weighting factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRequires stable monitoring conditions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProposed framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePressure\u0026ndash;resilience interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExplicit moderating property\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScreening-level, relative assessment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy addressing environmental dimensions of infrastructure damage, urban-industrial concentration, and population exposure, the framework contributes to selected Sustainable Development Goals (SDGs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Moreover, supports SDG 11 (Sustainable Cities and Communities) by emphasizing spatially differentiated recovery pathways instead of uniform reconstruction strategies. The explicit integration of ecosystem resilience aligns with SDG 13 (Climate Action), as resilient ecosystems enhance adaptive capacity to both climate-related and conflict-induced stressors. Furthermore, the focus on biodiversity, soil self-recovery, and ecosystem stability directly contributes to SDG 15 (Life on Land) by identifying regions where degraded resilience may undermine ecosystem services and long-term ecological restoration.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDespite the outlined limitations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the robustness of the identified spatial patterns is supported by consistency across multiple indicators and alignment with independent studies documenting war-related environmental impacts in Ukraine [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The indices should therefore be interpreted as relative measures intended for regional comparison and strategic prioritization rather than precise quantification of site-specific risks. As monitoring capacity is progressively restored, future research should refine the framework by incorporating temporal dynamics, alternative weighting schemes, and scenario-based modelling. Under current conditions of extreme environmental stress, however, the proposed approach provides a scientifically grounded and transparent basis for informed decision-making in support of resilient regional development.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study develops and applies a spatial environmental risk framework structured around the interaction between technogenic pressure and ecosystem resilience, complemented by a distinct radiological dimension. The results confirm that environmental risk patterns in Ukraine are organized along a dominant pressure\u0026ndash;resilience gradient, while radiological factors introduce a spatially selective and partially independent risk component. Four coherent regional typologies were identified: critical industrial conflict zones, transitional high-pressure regions, radiological/mixed-risk regions, and stable\u0026ndash;resilient territories. The study formalizes pressure\u0026ndash;resilience interaction within a reproducible spatial modelling structure applicable under fragmented or incomplete monitoring conditions. By operationalizing Rae as a ratio-based interaction term and integrating it with radiological exposure indicators, the framework moves beyond additive cumulative risk mapping. The research advances environmental risk theory by explicitly positioning ecosystem resilience as a moderating system property rather than a secondary adjustment factor. This dual-axis structure (pressure\u0026ndash;resilience gradient\u0026thinsp;+\u0026thinsp;radiological dimension) provides a coherent conceptual basis for analysing environmental stress in conflict-affected socio-ecological systems. The framework is designed as a comparative, screening-level tool for spatial prioritization. It enables identification of structurally differentiated regional regimes, supports evidence-based allocation of recovery resources, and remains operational under conditions of monitoring blind zones and data uncertainty. Overall, the findings demonstrate that resilient regional development under extreme environmental stress requires spatially differentiated assessment grounded in pressure\u0026ndash;resilience dynamics rather than uniform reconstruction logic. The proposed approach offers a transparent and adaptable analytical basis for strategic environmental governance in conflict-affected contexts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, O.B. and P.K.; methodology, O.B.; software, P.K.; validation, O.B. and P.K. and L.K.; formal analysis, P.K.; investigation, O.B.; resources, O.B. and P.K. and L.K.; data curation, P.K.; writing\u0026mdash;original draft preparation, O.B. and P.K. and L.K.; writing\u0026mdash;review and editing, , O.B. and P.K. and L.K.; visualization, P.K.; supervision, O.B.; project administration, O.B.; funding acquisition, L.K. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHryhorczuk D, Levy BS, Prodanchuk M. The environmental health impacts of Russia\u0026rsquo;s war on Ukraine. J Occup Med Toxicol. 2024;19(1):1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12995-023-00398-y\u003c/span\u003e\u003cspan address=\"10.1186/s12995-023-00398-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeal Filho W, Eustachio JHPP, Fedoruk M, Lisovska T. War in Ukraine: an overview of environmental impacts and consequences for human health. 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Monitoring of soil chemical properties in Ukraine using digital soil mapping. Discov Environ. 2026;4:78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44274-026-00545-2\u003c/span\u003e\u003cspan address=\"10.1007/s44274-026-00545-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"land systems, anthropogenic pressure, ecosystem resilience, spatial planning, soil–water interactions, land management, geospatial analysis, environmental indicators, sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-9027181/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9027181/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntensified anthropogenic pressure and industrial disturbance increasingly alter land systems by modifying land-use structure, degrading soil\u0026ndash;water interactions, and reducing ecosystem service capacity. These processes challenge conventional environmental assessment approaches, particularly where monitoring systems are fragmented or incomplete. This study develops a spatial land-system assessment framework based on pressure\u0026ndash;resilience dynamics to evaluate regional differentiation of environmental risks. Technogenic pressure is conceptualized as a composite indicator reflecting land-use intensity, industrial load, and contamination affecting soil, water, and atmospheric components. Ecosystem resilience represents the integrated buffering capacity of atmosphere\u0026ndash;hydrosphere\u0026ndash;pedosphere\u0026ndash;biota subsystems within regional land systems. Anthropo-ecological risk is formalized as an interaction term, enabling differentiation of territories with comparable land-use pressure but contrasting adaptive capacity. A radiological dimension is included to capture spatial variability associated with legacy contamination and nuclear infrastructure. Using open-access national geospatial datasets, the framework integrates normalized environmental indicators within a unified spatial workflow. Principal component and cluster analyses identify four land-system typologies: high-pressure low-resilience industrial systems; transitional systems with moderate buffering capacity; radiological\u0026ndash;mixed risk systems; and stable\u0026ndash;resilient systems with low pressure and strong ecological buffering potential. The dominant spatial gradient is governed by pressure\u0026ndash;resilience interaction, while radiological risk represents a partially independent dimension. The findings demonstrate that land-system vulnerability cannot be inferred from land-use intensity alone but emerges from structured interactions between anthropogenic pressure and ecosystem resilience. The proposed framework advances land system science by formalizing pressure\u0026ndash;resilience dynamics as a core analytical principle and provides a transparent screening-level tool for spatial planning, sustainable land management, and resilience-oriented regional development under conditions of environmental stress and data uncertainty.\u003c/p\u003e","manuscriptTitle":"Pressure–Resilience Dynamics of Land Systems under Intensified Anthropogenic Load: A Spatial Assessment Framework for Sustainable Land Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 19:01:13","doi":"10.21203/rs.3.rs-9027181/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-14T10:27:49+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"197730785420311436970294147793160445246","date":"2026-05-12T09:07:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T06:20:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223358208699216105156860759004313387601","date":"2026-05-12T06:11:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282240827196600308335062450081464756363","date":"2026-05-02T11:31:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172290982320442173059673911271517658637","date":"2026-04-28T06:22:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T10:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214642399988302809498410361280955935149","date":"2026-04-06T18:30:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T10:41:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-08T01:53:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-08T01:53:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2026-03-04T07:23:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"230c64d3-739d-43fd-95d6-54ba52f1118f","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-14T10:27:49+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"197730785420311436970294147793160445246","date":"2026-05-12T09:07:48+00:00","index":78,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T06:20:38+00:00","index":73,"fulltext":""},{"type":"reviewerAgreed","content":"223358208699216105156860759004313387601","date":"2026-05-12T06:11:00+00:00","index":72,"fulltext":""},{"type":"reviewerAgreed","content":"282240827196600308335062450081464756363","date":"2026-05-02T11:31:09+00:00","index":49,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T04:39:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 19:01:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9027181","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9027181","identity":"rs-9027181","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}