Search Results (256)

Water-retaining roller-compacted concrete (RCC) dams suffer severe deterioration under coupled moisture ingress and freeze–thaw (F-T) cycles. To elucidate the damage mechanisms, this study employed industrial X-ray computed tomography (CT) synchronized with uniaxial compression and acoustic emission (AE) monitoring. The cross-scale damage evolution of RCC was investigated under dry, water-saturated, 25, and 50 F-T cycle conditions. The results indicate the following: (1) Macroscopically, F-T damage causes linear peak stress attenuation, shifting the failure mode from brittle axial splitting to ductile oblique shear. (2) Mesoscopically, frost-heaving stress expands native mesopores (500–2500 μm), increasing their volume fraction from 8.45% to 14.86% and remodeling isolated voids into a 3D interconnected defect network. (3) Microscopically, GMM-based AE clustering reveals a fracture transition. Driven by moisture lubrication and defect propagation, global shear cracks surpass the 50% threshold at 25 cycles (53.5%), reaching 68.6% at 50 cycles. (4) For cross-scale mapping, calibrating the AE b-value via Aki’s method decouples pore-water signal attenuation. Its pre-peak characteristic (an initial decrease followed by a rebound) accurately maps microcracks unstably coalescing along interconnected pores to form macroscopic shear planes. This cross-scale mechanism provides a scientific paradigm for condition monitoring of massive concrete in cold regions. Full article

The central challenge of the energy transition lies in whether an economy possesses the institutional capacity to systematically regulate its own energy inputs and carbon emissions. Drawing upon social metabolism theory, this study constructs an indicator of carbon–energy metabolic control capacity (CMCC). Building on business ecosystem theory, it conceptualizes the intelligent economic ecosystem (IEE) and incorporates industrial chain intellectual property empowerment (IP) into a causal framework of institutional provision → ecosystem development → enhancement of metabolic control capacity. Using panel data from 30 provincial-level administrative regions in China covering the period 2010–2022, this study employs a spatial Durbin difference-in-differences (SDID) model and a double machine learning (DML) framework for empirical analysis. The results indicate that industrial chain intellectual property empowerment significantly enhances carbon–energy metabolic control capacity and generates positive spatial spillover effects on neighboring regions through the public diffusion of patent information. Furthermore, intelligent economic ecological development serves as a significant partial mediator between intellectual property empowerment and carbon–energy metabolic control capacity, with the indirect effect accounting for more than one-third of the total effect. This mediating mechanism remains robust after replacing machine learning algorithms, altering sample-splitting ratios, controlling for concurrent innovation policies, and excluding the impact of the COVID-19 pandemic. Path-specific mediation analysis further reveals that computing power acquisition and value transformation together with digital substrate robustness constitute the dominant transmission channels, whereas innovation metabolic flux contributes a relatively smaller mediating effect due to the long gestation period required for translating fundamental research into practical applications. Heterogeneity analysis further demonstrates that the transmission mechanism exhibits full mediation in the dimension of metabolic structure, indicating that the contribution of industrial chain intellectual property empowerment to the clean substitution of energy structures depends almost entirely on the mediating role of the intelligent economic ecosystem. These findings provide clear actionable guidelines for three specific policy-making domains to advance low-carbon transitions. First, intellectual property authorities should transition from quantity-driven patent creation to establishing cross-regional patent navigation and industrial chain IP pooling. Second, digital economy and industry regulators need to prioritize computing power value conversion (CCV) over raw infrastructure expansion to mitigate energy rebound effects. Third, energy and environmental agencies ought to integrate real-time algorithmic dispatching with green finance incentives. Ultimately, this study demonstrates that achieving deep low-carbon transformation requires leveraging institutional public goods to catalyze digital ecosystems, which in turn enable precise, dynamic carbon–energy metabolic control. Full article

Accurate prediction of excavation-induced soil rebound is critical for intelligent foundation pit construction. The existence of engineering piles in the foundation pit will affect soil rebound at the bottom of the pit caused by excavation. Based on the layer-wise summation method, the Mindlin solution and the existing calculation methods of pile-soil displacement, the rebound deformation of the soil at the bottom of the pit caused by the excavation unloading of the foundation pit was studied and an automated computation algorithm for calculating soil rebound under the existence of an engineering pile was proposed. Finally, the calculation results of the proposed method and the finite element simulation results were compared and analyzed with an example of engineering. The results show that for the rebound curve of the foundation pit bottom, when there are no engineering piles, it presents a smooth ‘convex’ shape; when engineering piles exist, it presents a fluctuant ‘wave’ shape. The rebound of the soil around the engineering piles is significantly lower than that of the neighboring soil, with the smallest drop in the edge area of the foundation pit and the largest drop in the central area, and the maximum reduced rebound can reach about 20% at most. The ability of piles to limit soil rebound is greatly related to the pile spacing and pile diameter. It is necessary to correctly consider the existence of engineering piles in the rebound calculation of the soil at the bottom of the foundation pit, and the results obtained by the proposed method in the paper are in good agreement with the simulated rebound values. The method proposed in this paper can effectively predict the rebound deformation of the foundation pit. Full article

This study employs panel data from 11 provinces and municipalities along the Yangtze River Economic Belt spanning 2010–2021. It measures green transport efficiency (GTE) using principal component analysis (PCA) and the undesirable Super-SBM model while constructing an economic high-quality development index (HQEDI) through an entropy-weighted CRITIC approach. The study combines coupling coordination degree modeling with spatial autocorrelation analysis (Global Moran’s I, LISA, hotspot/coldspot detection) to empirically investigate their synergistic evolution mechanism. The findings indicate the following: (1) Multidimensional policy combinations exhibit a nonlinear threshold effect on enhancing green transport efficiency, with efficiency significantly rebounding post-2015 as low-carbon policies deepened. (2) High-quality economic development displays a dual-stage ‘convergence-divergence’ pattern, where downstream regions lead in HQEDI but mid- and upstream regions show faster growth in coordination and green dimensions. (3) The coupling coordination degree exhibits pronounced spatial spillover effects, with the global Moran’s I mean reaching 0.485. High-value clusters form in downstream regions, while upstream areas predominantly exhibit low-value clusters, revealing an ‘east-high, west-low’ regional differentiation pattern. (4) The gradient divergence mechanism stems from heterogeneity in infrastructure density, industrial structure, and policy responsiveness elasticity. Accordingly, it is recommended to establish a multi-level governance mechanism to dismantle administrative barriers and to construct a tripartite policy package integrating ‘digital transport, ecological compensation, and industrial radiation’ to advance coordinated basin development. Full article

Engineered nanomaterials have been increasingly proposed for the treatment of contaminated soils. Nevertheless, most available evidence has been obtained in water, artificial substrates or short-term laboratory experiments, and performance in real soil is substantially more variable. This review examines nanoscale zero-valent iron, photocatalytic metal oxides, carbon-based nanomaterials, and supported or hybrid formulations, with particular attention to the soil properties and contaminant characteristics that control their mobility, transformation, reactivity, and persistence. Nano-enabled treatments can decrease the mobility of arsenic, chromium, lead, and other potentially toxic elements and can promote the degradation of selected pesticides and hydrocarbons. However, opposite responses have also been reported, including mobilisation of non-target elements, nanoparticle aggregation and passivation, effects on microbial communities and plants, contaminant rebound, and potential transport beyond the treated zone. Environmental assessment should therefore consider both the target contaminant and the applied or transformed nanomaterial, together with ecological and occupational exposure pathways. Current evidence does not support nanoremediation as a general replacement for conventional technologies. Its main value lies in its use as a site-specific component of integrated remediation strategies selected according to soil properties, contaminant behaviour, treatment scale, cost, life-cycle impacts, and future land use. European field experience remains limited, particularly in unsaturated soils, and no harmonised EU-wide authorisation procedure specifically for soil nanoremediation currently exists. Wider implementation will require realistic field trials, long-term monitoring, safer and recoverable formulations, transparent regulatory assessment, and evaluation of soil functions and ecosystem-service recovery. A site-specific decision framework is proposed to support material selection, risk–benefit evaluation, and responsible implementation. Full article

Physical clogging limits managed aquifer recharge (MAR), yet the threshold-crossing time alone may not predict subsequent performance. Constant-head columns packed with borosilicate glass beads or quartz sand received a 50 mg L⁻¹ silica suspension (0.9–2.7 μm) for 240 h. Hydraulic heads and discharge yielded relative apparent hydraulic-conductivity trajectories ($K_r$); X-ray computed tomography supported box-counting and block-network analyses. Sustained crossings below $K_r=0.60$, 0.50, and 0.40 occurred at 44/46, 61/59, and 88/78 h for the glass-bead/quartz-sand columns. Despite similar 0.60 and 0.50 crossing times, $K_r$ values at 240 h were 0.475 and 0.043, respectively. The glass-bead trajectory rebounded after its minimum and remained above 0.40. Interface box-counting slopes depended on imaging branch and segmentation threshold, but the regional $D_2$ and $D_3$ ranks remained positively associated within each medium. Removing the highest-flow 5% of flow-carrying edges caused conductance-proxy losses of 0.521–0.692 across three capacity laws, greater than under random removal. For the two tested cases, threshold persistence, subsequent direction, and terminal state provided complementary evidence of hydraulic stability; broader application requires replicated tests across particle and loading conditions while retaining the distinction between obstruction probability and flow-path consequence. Full article

Background/Objectives: Croatia is among the European countries with moderately high antibiotic consumption and correspondingly elevated antimicrobial resistance (AMR) rates. National-level surveillance, however, masks substantial county-level heterogeneity that is relevant for targeted stewardship. This study aimed to (i) quantify and statistically test temporal changes in total antibacterial consumption (J01), beta-lactam antibiotics (J01C), and macrolides/lincosamides/streptogramins (J01F) across all 21 Croatian counties between 2017 and 2024, spanning the pre-pandemic, pandemic, and post-pandemic periods, and (ii) characterize geographic disparities that could inform region-specific stewardship interventions. J01C and J01F were selected because they are, respectively, the most heavily consumed and the most steadily rising antibiotic subgroups in Croatia, and both carry direct relevance to the WHO Access, Watch, Reserve (AWaRe) stewardship framework. Methods: A retrospective, registry-based analysis of antibiotic consumption in all 21 Croatian counties was conducted using annual distribution data from the Croatian Agency for Medicinal Products and Medical Devices (HALMED), covering 1 January 2017 to 31 December 2024. Consumption was expressed as defined daily doses per 1000 inhabitants per day (DDD/TID). County-level values for 2017, 2020, 2021, and 2024 were compared using paired Student’s t-tests (confirmed with Wilcoxon signed-rank tests) and a Friedman test across all four time points; national annual means (2017–2024) were compared descriptively against European Union/European Economic Area (EU/EEA) surveillance benchmarks. Results: National J01 consumption declined from 21.16 to 16.96 DDD/TID between 2017 and 2021 (−19.8%) and rebounded to 22.75 DDD/TID by 2024 (+34.1%); the county-level decline and rebound were both statistically significant (paired t-tests, p < 0.001 for each comparison; Friedman χ² = 57.97, p < 0.001). J01C followed the same U-shaped pattern, temporally coincident with the COVID-19 pandemic period (12.59 → 5.13 → 9.05 DDD/TID; all comparisons p < 0.001), whereas J01F rose continuously throughout the study period, without a corresponding dip during that period (2.81 → 3.49 → 4.43 DDD/TID; p < 0.001 for every interval). Croatia’s 2024 mean county-level J01 consumption was significantly above the 2024 EU/EEA population-weighted mean of 18.8 DDD/TID (one-sample t-test, p = 0.016). The sharpest pandemic-era declines and post-pandemic rebounds were concentrated in Adriatic coastal counties (Primorje-Gorski Kotar, Zadar, Šibenik-Knin, and Istria). Conclusions: Total and beta-lactam antibiotic consumption in Croatia followed a U-shaped trajectory temporally coincident with the COVID-19 pandemic period, while macrolide, lincosamide, and streptogramin use rose steadily throughout 2017–2024, a pattern of stewardship concern given that this subgroup is predominantly classified as “Watch” under the WHO AWaRe framework. Coastal counties displayed disproportionate fluctuations, plausibly reflecting seasonal population dynamics not captured by resident-population denominators. AWaRe-stratified, tourism-adjusted, county-level surveillance is recommended to guide targeted stewardship. Full article

Emission inventories for high-altitude Andean cities rely on sea-level certification cycles that misrepresent real-world combustion conditions. This study derives altitude-resolved emission factors (EFs) for light-duty gasoline vehicles across 0–4000 m a.s.l. in Ecuador using the purpose-built Andean Ecuador Driving Cycle (aedc), validated against naturalistic data via the Percentile Speed Vector metric (PSV_m10; IGS $=1.89$ vs. IGS $=2.30$ for the WLTC). Ten vehicles (Euro III–V) were instrumented with OBD-II and portable analysers recording $\mathrm{CO}$, $\mathrm{NO}$, $\mathrm{HC}$, and ${\mathrm{CO}}_2$ at 1 Hz over a four-year campaign (2021–2025; ≈2000 h). K-Means clustering on PSV_m10 identified five operating regimes (silhouette ≈0.384). Under dynamically equivalent aedc conditions, $\mathrm{NO}$, $\mathrm{CO}$, and $\mathrm{HC}$ all peaked in the 1000–2000 m band ($\mathrm{NO}$: $0.188\mathrm{g}{\mathrm{km}}^{-1}$, $6.7\times$ the sea-level value; $\mathrm{CO}$: $4.47\mathrm{g}{\mathrm{km}}^{-1}$, $+50\%$; $\mathrm{HC}$: $0.047\mathrm{g}{\mathrm{km}}^{-1}$, $+292\%$), fell in the 2000–3000 m band, and partially rebounded above 3000 m ($\mathrm{NO}$: $0.186\mathrm{g}{\mathrm{km}}^{-1}$); ${\mathrm{CO}}_2$ instead declined monotonically with altitude (182 to $119\mathrm{g}{\mathrm{km}}^{-1}$, $-35\%$), tracking a near-stable-to-slightly-declining fuel consumption (8.56 to $8.11\mathrm{L}/100\mathrm{km}$) consistent with reduced aerodynamic drag at altitude partially offsetting the density penalty. These results show that altitude affects pollutants through distinct, non-monotonic mechanisms rather than a uniform trend, so that single-coefficient altitude corrections introduce systematic bias in Andean emission inventories. Full article

The COVID-19 pandemic created a unique natural experiment to evaluate the impact of reduced human activity on urban air quality. This study assessed the effects of lockdown measures on major air pollutants across three Georgian cities with contrasting emission profiles: traffic-dominated Tbilisi, coastal Batumi, and industrial Rustavi. Daily concentrations of NO₂, SO₂, PM_2.5, PM₁₀, and O₃ were obtained from the National Environmental Agency’s automated monitoring network for March–June 2019–2022. Following systematic missing value imputation, meteorologically adjusted interrupted time-series (segmented regression) models, incorporating daily temperature and wind speed as covariates, were used as the primary analysis; Mann–Kendall trend analysis was retained as a secondary, descriptive measure. Results were evaluated against WHO 2021 Air Quality Guidelines. Tbilisi showed the strongest lockdown response: NO₂ declined by 47.9% in 2020 (vs. 2019), before rebounding fully in 2022. PM_2.5 and PM₁₀ showed attenuated reductions, reflecting continued heating combustion and dust sources. Ground-level O₃ increased in Tbilisi in 2020 (+25.0% vs. 2019). Rustavi exhibited no NO₂ response and an O₃ decrease of 43.2% in 2020, with partial recovery in 2021–2022. Batumi showed intermediate responses, with PM_2.5 and PM₁₀ increases in 2020. Meteorologically adjusted interrupted time-series models confirmed 11 of 30 city–pollutant effect estimates after false discovery rate correction, most robustly for NO₂; several borderline ozone and SO₂ findings did not survive correction and are reported as suggestive only. Lockdown measures were associated with source-specific and temporary air quality improvements, after accounting for meteorological conditions. Sustained reductions require structural interventions targeting traffic, residential combustion, and industrial emissions simultaneously. Full article

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms. Full article

For many countries, the reclamation of coastal saline–alkali land is of great significance for ensuring food security. However, systematic research on the effects of leaching irrigation and biochar application on the “soil–crop” system in coastal areas remains scarce. This study investigated the effects of three irrigation quotas (8, 16, and 24 mm per application, applied once every 10 days) and four biochar rates (0, 3, 5, 7 t·ha⁻¹, from composite biomass) on soil salinity, available nutrients, and yield and quality of sunflower in coastal saline–alkali soils of Jiangsu, China. The experiment used a completely randomized block design with three replications. The results showed that soil salinity under all treatments exhibited a consistent decline–rebound–decline pattern over the growing season, with the lowest values occurring around 55 days after sowing and peaking around 95 days. The combined application of water and biochar enhanced salt reduction, with the highest irrigation and biochar levels achieving the greatest desalination effect. Biochar application increased soil available phosphorus and potassium contents, with average increases of 19.6% and 13.3% under the highest biochar rate compared with the control, while available nitrogen showed no clear response to any treatment. Crop yield responded nonlinearly to water and biochar inputs: under low irrigation, yield increased continuously with biochar addition, whereas under medium or high irrigation, yield plateaued at the moderate biochar rate (5 t·ha⁻¹), with no further gains from additional biochar or water. Regarding seed quality, linoleic acid content generally increased with higher water and biochar levels, oleic acid decreased, and crude fat remained unaffected. Based on a balanced consideration of yield, quality, salt reduction, and water conservation, the combination of 16 mm irrigation per application and 5 t·ha⁻¹ biochar is recommended as the optimal amelioration strategy for coastal saline–alkali land. Full article

Understory mosses modify near-surface soil conditions, but how elevation regulates their influence on active and mineral-associated soil organic carbon (SOC) remains unclear. We compared independently selected moss-covered and non-moss-covered soils across a 3200–3500 m elevational gradient and integrated soil physicochemical measurements, microbial biomass (MB), dissolved organic matter (DOM), microbial necromass carbon (MNC), particulate organic carbon (POC), mineral-associated organic carbon (MAOC), metagenomic profiling, and piecewise structural equation modeling. Moss-covered soils consistently contained higher SOC and MAOC, but lower DOM, MB, and generally lower POC, than non-moss-covered soils. MNC showed an elevation-dependent reversal, with higher values under moss cover at 3200 m but lower values under moss cover at 3300–3500 m. Elevation was not a significant uniform driver of MB, DOM, MNC, POC, or MAOC; instead, its influence was mainly reflected in interactions with surface cover and in elevation-related changes in moss-layer structure, diversity, and hydrothermal conditions. Core carbon-fixation and degradation functions remained broadly stable, whereas specific functional modules shifted within moss-covered soils: acetate and acetyl-CoA metabolism genes (ackA and abfD) were relatively abundant at 3300–3400 m, while the polysaccharide-reprocessing gene SGA1 and oxidative-transformation gene katG increased toward higher elevations, and pmoC/amoC rebounded at 3500 m. Structural equation models linked the microbial functional gene system more strongly to POC, whereas MNC was positively associated with MAOC, and the direct POC-to-MAOC pathway was not significant. These findings indicate that moss cover is associated with contrasting SOC allocation patterns and stronger microbial necromass–MAOC coupling, while elevation modulates these relationships indirectly through changes in moss communities, soil microenvironment, and microbial functional potential. Full article

Promoting the green development of the tourism industry is a crucial pathway for achieving coordinated progress in ecological civilization development and industrial transformation and upgrading. Based on panel data for 16 prefecture-level cities in Anhui Province from 2011 to 2022, this study constructs an “inputs–desirable outputs–undesirable outputs” indicator system, measures city-level tourism eco-efficiency (TEE) using a super-efficiency SBM model incorporating undesirable outputs, decomposes provincial disparities and their sources using the Theil index and its decomposition, and further identifies city-specific heterogeneity in influencing factors by employing a panel variable-coefficient fixed-effects model. The results show that: (1) Anhui’s TEE exhibited an overall fluctuating upward trend during 2011–2022, with provincial efficiency values ranging from 1.465 (2016) to 1.500 (2022), and a more pronounced rebound after 2017; (2) spatially, TEE displays a pattern of “higher in the south, lower in the north, with a central uplift,” with southern Anhui cities such as Huangshan and Xuancheng performing relatively well, while many northern Anhui cities lag behind; (3) Theil decomposition indicates that overall disparities are driven mainly by within-region differences, whereas between-region differences contribute relatively little; and (4) influencing factors are markedly heterogeneous: scale- and affluence-related variables promote TEE in core cities such as Hefei, but tend to inhibit it in cities such as Bozhou, Anqing, Chuzhou, and Wuhu. The mechanisms associated with technology and structural variables are more complex; in particular, the expansion of energy consumption exerts a significantly negative effect on TEE in most cities and constitutes a common constraint on efficiency improvement, while the effects of R&D investment, digitalization, and the share of the tertiary sector vary across cities. Accordingly, policy efforts should prioritize energy-efficiency improvement and low-carbon substitution at the provincial level while implementing differentiated, city-specific pathways at the municipal level to jointly advance the low-carbon transition and high-quality development of the tourism industry. Full article

This study examines how energy-security shocks shape macroeconomic resilience in import-dependent economies and which energy-management strategies remain robust under alternative shock conditions. Using a balanced panel of 18 mainly European energy-importing economies and Türkiye for 2000–2024, the study constructs a Macroeconomic Resilience Index (MRI) from five dimensions: GDP growth, inflation, unemployment, current account balance, and industrial production growth. Inflation and unemployment are treated as inverse resilience indicators, and a Principal Component Analysis (PCA)-based alternative index is used as a robustness check. Methodologically, the study develops a hybrid framework that first applies a Shock-Augmented Cross-Sectionally Dependent Panel Quantile ARDL model to estimate heterogeneous shock effects across resilience levels, and then translates the econometric evidence into a Shock-Conditioned Bayesian Network–Regret MCDM model for strategy prioritization. The findings show that exchange-rate pressure is the most consistent long-run vulnerability channel, while energy intensity weakens resilience across short-run, benchmark, and quantile robustness results. The renewable energy share supports resilience under some conditions, but its effect depends on complementary investments in storage, grid flexibility, and demand-side capacity. Scenario results indicate that exchange-rate pressure produces the weakest resilience profile. The positive MRI value observed during combined-crisis years should be interpreted cautiously, as additional sensitivity evidence indicates that it mainly reflects the 2021–2022 post-pandemic rebound rather than a beneficial effect of shocks. Bayesian Network results identify macro-financial stabilization, import-dependency reduction, energy efficiency, and grid reliability as key resilience drivers. The regret-based MCDM results rank energy efficiency improvement as the most robust strategy, followed by energy import diversification. The study contributes by linking dynamic macroeconometric shock analysis with probabilistic scenario modeling and regret-sensitive decision support, offering an evidence-informed framework for prioritizing energy-security strategies in the sampled import-dependent economies. Full article

In recent years, with the significant decline in fine particulate matter (PM_2.5) concentrations, ozone (O₃) has emerged as a major composite air pollutant during the warm season in China, attracting increasing attention due to its associated health burden and economic costs. This study focuses on Tianjin, using ozone monitoring data from 2017 to 2023 combined with health statistics to assess the health impacts and economic losses attributable to ozone pollution. First, ozone exposure indicators and compliance criteria were constructed based on national air quality standards, and the interannual variation and spatial differences of O₃ levels were analyzed at both citywide and district scales. Second, multiple machine learning classification models, including logistic regression, decision tree, k-nearest neighbors, and gradient boosting, were developed using ozone and meteorological variables to predict the occurrence risks of five diseases: cardiovascular diseases, respiratory diseases, hand-foot-and-mouth disease (HFMD), influenza, and dengue fever. Finally, excess cases were estimated using health impact functions, and the associated economic losses were quantified by combining the value of a statistical life (VSL) with cost-of-illness and willingness-to-pay (WTP) approaches. The results showed that the annual evaluation value of ozone in Tianjin, defined as the 90th percentile of the daily maximum 8 h average O₃ concentration, exhibited a pattern of initially increasing, then decreasing, and subsequently rebounding. It peaked at 201 µg/m³ in 2018, declined to a minimum of 164 µg/m³ in 2021, and rebounded to 188 µg/m³ in 2023. Machine-learning results indicated that the logistic regression model showed relatively stable overall performance across predictions of different diseases, while the gradient boosting tree model also achieved high accuracy in predicting certain infectious diseases. Overall, ozone pollution exhibits significant heterogeneous effects across different disease types, and the associated health-related economic losses show stage-wise fluctuations in response to pollution levels. Based on these findings, it is recommended to implement refined control measures during periods of high ozone exceedance and in key regions, while strengthening protection for vulnerable populations such as the elderly, children, and patients with respiratory diseases, in order to achieve synergistic improvements in air quality management and public health outcomes. Full article