Cerebration Science Publishing CO., LIMITED
Solar Photothermal CO2 Conversion: Harnessing Light and Heat for Carbon Neutrality: A Mini Review
Photothermal CO2 conversion is a promising strategy for sustainable solar fuel production, which integrates photocatalytic and thermocatalytic processes to enhance solar energy utilization, reaction kinetics, and product selectivity. By coupling photon-induced charge excitation with localized thermal effects, photothermal systems enable the efficient activation of thermodynamically stable CO2 molecules under relatively mild conditions. This review provides a comprehensive overview of the fundamental principles underlying photocatalysis and thermocatalysis, and their synergistic effects in photothermal catalysis. It discusses photonic–thermal coupling, plasmonic and non-plasmonic pathways, and major CO2 conversion routes such as hydrogenation and artificial photosynthesis. Advances in the design of efficient nanostructured catalysts, focusing on light absorption, heat management, charge dynamics, and optofluidic reactors, are also highlighted. Despite significant progress, challenges remain, such as the unclear thermal and nonthermal contributions, which limit mechanistic understanding and catalyst design. Insufficient insight into energy transfer, deactivation, and local reaction environments also hinders the efficiency. Practical issues, such as stability, light penetration, heat control, and scalability, limit industrial use. Future research should integrate operando characterization, modeling, and machine learning to understand the structure–performance relationships and develop efficient catalysts. Photothermal CO2 conversion is advancing rapidly, with the potential for carbon-neutral energy production through efficient solar-driven catalytic processes.
Copyright (c) 2026 Mirza Abdullah Rehan (Author) https://creativecommons.org/licenses/by/4.0
Battery Passports for Sustainable Battery Systems: A Review of Roles, Data Requirements, and Implementation Challenges Across Value Chain Stages
Battery passports are emerging as important digital governance tools for improving sustainability, transparency, and circularity in battery value chains. This review examines the role of battery passports in advancing traceability, transparency, compliance, and circularity across the battery lifecycle, while also identifying the main challenges associated with their implementation. A systematic review approach was used to synthesize academic, regulatory, and technical sources related to battery passports, lifecycle data systems, and circular battery management. The findings show that traceability is the most emphasized role of battery passports, as it enables battery identity, provenance, composition, and lifecycle history to be connected across multiple actors and stages. Transparency is also strongly emphasized because passport systems can make sustainability and technical information more visible, comparable, and useful for decision-making. Circularity is supported by preserving data needed for reuse, second-life applications, remanufacturing, and recycling, while compliance is increasingly driven by regulatory requirements, particularly under the European Union Batteries Regulation. The review further shows that implementation is constrained mainly by interoperability, data quality and availability, governance and access rights, standards harmonization, and confidentiality concerns. A lifecycle-stage mapping demonstrates that battery passports create different forms of value across raw material sourcing, manufacturing, use, second-life assessment, recycling, and cross-cutting governance. Overall, the study shows that battery passport effectiveness depends not only on data collection, but also on reliable data updating, verification, sharing, and governance across lifecycle stages. The review provides a structured basis for future research, policy development, and industry implementation.
Copyright (c) 2026 Zeshan Alam, Yossapong Laoonual, Shabbir H. Gheewala (Author) https://creativecommons.org/licenses/by/4.0
AI-Augmented Clear-Sky Algorithms for High-Granularity Solar Irradiance Modelling
Accurate short-term solar irradiance forecasting is important for reliable photovoltaic operation in regions affected by rapid cloud movement and atmospheric variability. However, clear-sky models cannot adequately respond to cloud-driven irradiance fluctuations, while purely data-driven models may suffer from weak physical consistency and reduced generalisability. The present study developed a physics-informed hybrid framework in which clear-sky irradiance was used as the physical baseline, while machine learning was employed to learn the residual atmospheric deviations caused by clouds, aerosols, moisture and near-surface meteorology. The proposed model was evaluated against persistence, clear-sky, AI-only and standalone Gated Recurrent Unit benchmark models using 10-min-ahead global horizontal irradiance prediction. During the Roodepoort summer test period, the hybrid model achieved an RMSE of 79 W/m2, MAE of 59 W/m2, MBE of −8 W/m2 and R2 of 0.91, corresponding to RMSE reductions of 32%, 44%, 27% and 18% when compared with the persistence, clear-sky, AI-only and Gated Recurrent Unit models, respectively. Furthermore, the model maintained seasonal RMSE values between 79 and 96 W/m2 without retraining, retained useful transferability at external Southern African Universities Radiometric Network stations with a mean external RMSE of 95.5 W/m2 and R2 of 0.86, and produced calibrated 95% prediction intervals with 93% empirical coverage. These findings showed that clear-sky constrained residual learning can improve deterministic accuracy, uncertainty reliability and seasonal robustness, thus supporting more reliable photovoltaic forecasting, grid integration and operational energy management under variable sky conditions.
Copyright (c) 2026 Dr. Kudzanayi Chiteka, Prof Christopher Enweremadu (Author) https://creativecommons.org/licenses/by/4.0
Composition-Resolved Spectral Absorption and Thermal Heating Effects of Dust Layers on Crystalline Silicon Photovoltaic Modules
Dust deposition on photovoltaic modules induces losses through wavelength-selective attenuation and dust-driven heating, but most soiling models quantify only optical transmission. The present study developed a composition-resolved optical-thermal framework that links measured dust mineralogy to coupled spectral and temperature-dependent power degradation in crystalline silicon modules. Dust was collected from operating PV modules in Harare (Zimbabwe) and Roodepoort (South Africa) and characterized for particle size, mass loading, and oxide composition. Oxide fractions were converted to effective complex refractive indices using an effective-medium formulation, and Mie-based calculations were used to compute spectral absorptance in the range 400-900 nm. The absorbed radiative flux was then introduced as a heat-input term in a surface energy-balance model to predict dust-induced cell-temperature rise, and the modified spectrum and temperature were propagated to electrical metrics. At a representative loading of 2.0 g/m2, the model predicted a temperature rise of 4.8 °C for Roodepoort dust and 4.6 °C for Harare dust. The corresponding total power losses were 14.2% and 13.8%, respectively. Solar simulator validation showed measured temperature rises of 4.52 °C and 4.29 °C for Roodepoort and Harare dust, giving deviations of 5.73% and 6.81% from the model predictions. Using the measured temperature rises and a crystalline silicon temperature coefficient of 0.45%/°C, the temperature-aligned total power losses were estimated as 14.08% and 13.66% for Roodepoort and Harare dust, respectively. These results confirm that dust induced heating amplifies optical soiling losses and should be included when forecasting yield and defining cleaning triggers in dusty, high irradiance environments.
Techno-economic assessment of low-cost adsorbents for CO₂ capture and mineralization in the UK aluminium industry
Decarbonising heavy industry demands cost-effective, scalable carbon capture pathways that go beyond simple storage. This study investigates the technical and economic viability of capturing CO₂ emissions from an aluminium smelter and converting them into stable magnesium carbonate materials through ex-situ mineral carbonation. Two distinct process routes are compared: one based on serpentinite rock (ultramafic feedstock) from the Ballantrae region of Scotland, which co-produces magnesium carbonate, nesquehonite, and silica sand; and one based on seawater brine, which yields only nesquehonite. Following a systematic evaluation of adsorbent materials, the serpentinite route was selected as the primary case for full techno-economic modelling. Applied to the ALVANCE British Aluminium smelter at Lochaber—which emits approximately 187,200 tonnes of CO₂ per year—a bottom-up capital expenditure (CAPEX) estimate of £152 million was developed, with annual operating expenditures (OPEX) of approximately £62.4 million. Revenue projections incorporating UK Emissions Trading System (ETS) carbon credits at £75 per tonne, magnesium carbonate sales at £260 per tonne, and silica by-product revenue yield a simple payback period of approximately 11 years and an after-tax internal rate of return (IRR) of 16%. The seawater route does not achieve financial break-even within a 20-year project horizon under the same assumptions. The findings demonstrate that on-site mineral carbonation at industrial cluster facilities can offer commercially viable CO₂ abatement while simultaneously producing construction materials with a negative carbon footprint.
Copyright (c) 2026 Ali Sheibani, Wenbin Zhang (Author) https://creativecommons.org/licenses/by/4.0
A Multimodel Ensemble Approach for Spatiotemporal Variability and Trend Analysis of Extreme Precipitation Indices Using CMIP6 Climate Model Projections in Mono River Basin
Rainfall extremes in the Mono River Basin (MRB) exhibit marked spatial and temporal heterogeneity, and their accurate characterization is essential for sustainable water governance and natural hazard mitigation. This study evaluated the capacity of a multimodel ensemble built from CMIP6 General Circulation Models (GCMs) to capture and project the spatiotemporal evolution of extreme precipitation indices across the MRB. The methodological framework integrates three successive components: a performance-based selection of GCMs using a multicriteria ranking score, a systematic bias adjustment using the Quantile Delta Mapping (QDM) technique, and the construction of a weighted multi-model ensemble (MME) using inverse RMSE weighting. The ranking procedure, applied to 10 CMIP6 GCMs against CHIRPS satellite-derived as reference data, led to the selection of five top-performing models. Trend analyses of four extreme precipitation indices (Rx1day, SDII, PRCPTOT, and R20) were subsequently conducted over the projection period of 2030-2060 under SSP2-4.5 and SSP5-8.5 scenarios, using both the Modified Mann-Kendall (MMK) test and Innovative Trend Analysis (ITA). The results indicate that the ensemble configuration systematically outperformed the individual GCMs in reproducing the observed hydroclimatic variability, as evidenced by the superior RMSE, R², NSE, and KGE scores. For the spatial aspect, the results reveal clear spatial variability in extreme precipitation over the MRB, with stronger positive trends mainly in the central and southern regions. Extreme precipitation indices indicate the intensification of extreme rainfall, particularly in downstream areas. The magnitude of these changes amplified considerably under SSP5-8.5, indicating the sensitivity of MRB hydrology to emission pathway assumptions. The obtained results enable us to provide valuable insights for improving the understanding of the extreme precipitation indices distribution for informing flood risk assessment, agricultural planning, and integrated water resource management in the basin under future climate conditions.
Copyright (c) 2026 Joselyne Lisbeth Godonou, Jerome Enagnon Ahouandjinou, Edia Flavien Dovonou (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Injection Phasing and Methanol Blend Optimization for Low- Carbon Engine Performance: A Meta- Analysis and Machine-Learning Framework with CCS Integration
The transition to net-zero mobility, aligned with global decarbonization targets, has intensified interest in methanol as a clean combustion fuel and carbon-neutral energy carrier. When optimally blended, methanol’s high-octane rating, strong charge-cooling effect, and inherent oxygen content enhance brake thermal efficiency (BTE), improve premixed combustion, and suppress knock in both spark- and compression-ignition engines. This study integrates meta-analysis, physics-informed modelling, and machine learning to establish predictive relationships between methanol blend ratio, start-of-injection (SOI) phasing, and engine performance. Meta-analysis revealed a significant positive correlation between SOI advancement and BTE (r = 0.74, 95% CI: 0.60–0.85), with moderate heterogeneity (I² = 38–45%). Optimal performance was achieved at methanol blend ratios of 25–30% with SOI between −30° and −35° CA aTDC, balancing efficiency gains with emission constraints. Quantitative model evaluation demonstrated high predictive accuracy (R² ≈ 0.99, RMSE < 0.30), exceeding typical machine learning combustion models (R² ≈ 0.91–0.97). SHAP-based sensitivity analysis confirmed that methanol blend ratio (0.125 ± 0.015) and SOI (0.082 ± 0.010) are dominant parameters controlling system behaviour. Model comparison showed that XGBoost achieved the highest accuracy (MAE = 0.01 °CA, RMSE = 0.02 °CA, R² = 0.985), outperforming Random Forest (R² = 0.90) and the physics-informed correlation model (R² = 0.93), while linear regression exhibited inferior performance (R² = 0.90) due to its inability to capture nonlinear interactions. Residual analysis (±0.4 BTE) and validation with 95% confidence bands (±0.42 BTE) confirmed strong predictive reliability and generalisation. The integration of carbon capture and utilization (CCU) pathways with methanol synthesis further supports closed-loop CO₂ utilisation, reinforcing methanol’s potential in carbon-neutral and carbon-negative applications. Overall, the findings highlight the importance of adaptive, AI-driven SOI optimisation and integrated CCU strategies for advancing next-generation low-emission engine systems.
Copyright (c) 2026 Luke Ajuka, Christopher Enweremadu (Author) https://creativecommons.org/licenses/by/4.0
Sequential Valorization of Musa balbisiana var elavazhai peel into Biogenic Metallic nanoparticles and Starch-based biocomposite
Fruit-derived nanofactories are futuristic industries for sustainable development with a promising greener outlook. The forward thinking to embrace them as valuable nexus for nanoparticle phytosyntheis by intertwining circular economy and zero waste culture; in turn integrates green chemistry and sustainability principles via transforming fruit waste into cutting-edge nanomaterials. This study adopts an integrated peel valorization approach for total biomass valorization of fruit peels of Musa balbisiana var elavazhai by waste to value conversion pathway. Its aqueous extract is utilized as a universal bioreductant for green synthesis of Ag and Au nanoparticle, while its post extraction solid residue is repurposed into sustainable biocomposite films. The bio-agumented AgNPs (AgNP-MB) were spherical with a particle size of 22.42 nm (FE-SEM), monodisperse with a PDI of 0.407 (DLS), crystalline with an average crystal size of 20.74 nm (XRD) and showcased SPR peak at 432 nm (UV-Vis). FTIR analysis depicted the potential phytochemicals that acted as capping and reducing agents during AgNP biosynthesis. They were effective in degrading water-soluble organic dyes (MO & MB) with an efficiency of 97% and 85% via pseudo first-order kinetics. This integrated zero waste model advocates the generic value of fruit peels, by linking high performance nanocatalysis with sustainable material fabrication for environmental remediation.
Copyright (c) 2026 Hiba Habeeb, John E Thoppil (Author) https://creativecommons.org/licenses/by/4.0
Optimization of HVAC Energy Efficiency through Minimum Diffuser Airflow Control in High-Ambient Regions
This study addresses the persistent issue of energy inefficiency in Variable Air Volume (VAV) HVAC systems operating in hot-arid climates, specifically focusing on the impact of reducing minimum diffuser airflow setpoints in commercial office buildings in Kuwait. To evaluate the feasibility of airflow rescheduling as a cost-effective energy-saving measure, a field intervention was implemented across six Air Handling Units (AHUs) serving over 200 VAV terminals in two government buildings. Using real-time energy metering, Building Automation System (BAS) control logic, and occupant thermal comfort surveys, the study measured system performance before and after the adjustment of minimum airflow rates to 30% and 25% of design capacity. Results showed energy savings of 18–25% across all monitored AHUs, with average chilled water and fan power consumption reduced by 21.4%. Indoor environmental conditions remained within ASHRAE comfort and air quality standards, with over 87% of occupants reporting satisfaction. Minimum diffuser airflow rescheduling offers a significant, low-investment opportunity for HVAC energy savings in commercial buildings located in hot climates. The measured energy reductions confirm the effectiveness of this strategy without major modifications to existing systems. Broader implementation across similar facilities could contribute substantially to energy conservation goals. This study demonstrates that significant HVAC energy savings, ranging from 15–20%, can be achieved through minimum diffuser airflow rescheduling, even in extreme high-ambient environments like Kuwait.
Copyright (c) 2026 K J Sreekanth, Fotouh Al-Ragom, Sara Al-Osaimi, Ruba Al-Foraih, Aarthi Elumalai (Author) https://creativecommons.org/licenses/by/4.0
Ultra-short-term power prediction of photovoltaic power systems based on multi-climatic environments
Ultra-short-term power prediction of photovoltaic (PV) power generation system is an important basis for grid scheduling and energy management. In order to adapt to the complex and changing climatic conditions, this paper proposes an MHSA-LSTM ultra-short-term power prediction model for PV power generation system that integrally considers all kinds of climatic factors. In this study, the raw data of a PV power plant is processed by multiple interpolation, and four key climate environment variables, namely, temperature, irradiance, relative humidity and atmospheric pressure, which affect the power generation, are extracted from them. The time series data containing the influencing variables are used as samples, and the MHSA module is utilized to filter the importance of the data at different historical moments, and the strong and weak weights of each environmental variable are adaptively assigned to the LSTM network to obtain the prediction results. The results show that climatic factors such as solar irradiance, temperature, relative humidity and atmospheric pressure can affect the PV power, with irradiance having the most significant effect.The MHSA-LSTM model is oriented to the climatic conditions of different seasons and weather, and the predicted ultrashort-term power is closer to the actual power value. Taking spring conditions as an example, the MAPE\RMSE of the MHSA-LSTM coupled model is reduced by 10.33%\2.771kW, 23.12%\6.296kW, 5.34%\1.234kW, compared to BP-LSTM, LSTM and LSTNet, respectively.The fluctuation of the ultra-short-term power prediction model based on deep learning is basically the same as that of the actual fluctuation. It is more adapted to the subsequent grid power scheduling and operation requirements.
photovoltaic power generation; ultra-short-term power prediction; deep learning; MHSA-LSTM; multi-climatic environment
Copyright (c) 2026 Zhengqi Fu (Author) https://creativecommons.org/licenses/by/4.0
Community Driven Approaches To Sustainable Settlement Planning
The housing and settlement sector encounters considerable technical and social obstacles, especially in coastal and rural regions where irregular construction patterns, excessive population, insufficient infrastructure, and minimal community involvement result in unsuitable living circumstances. These concerns are intricately linked to poverty and the constrained ability of local communities to fulfil basic housing standards. This study proposes an integrated, community-based settlement planning model that combines participatory SWOT analysis, spatial zoning, and tourism-oriented development to upgrade traditional Bajo over-water settlements while preserving the existing settlement footprint. Following this research, a sustainable settlement planning strategy was developed without modifying the current settlement footprint. Two primary development models were proposed: (1) a shift towards a tourism-centric village model and (2) spatial zoning comprising a seaside recreation/tourism zone, a transition zone, and a residential zone. The proposed plan amalgamates environmental sustainability, socio-cultural preservation, and economic development via marine and culinary tourism. The results indicate that participatory settlement planning can improve living circumstances, preserve coastal environmental integrity, and bolster local economic resilience.
Copyright (c) 2026 Ishak Kadir, Jenny Delly, Uniadi Mangidi, Try Sugiyarto Soeprayanto, Januar Saleh Kaimuddin (Author) https://creativecommons.org/licenses/by/4.0
Unveiling Climate Complexity: A Multiscale Geological–Molecular Perspective
This narrative review examines the conceptual foundations of the term “climate” and argues for its rigorous redefinition within a geopaleontological framework. Geological and paleoclimatic archives demonstrate that climate, in the Earth-system sense, represents a long-term emergent state governed primarily by orbital dynamics, tectonic boundary conditions, heliophysical variability, ocean circulation reorganizations, and large-scale biogeochemical feedbacks operating across multi-millennial to multimillion-year timescales. In contrast, contemporary usage often applies the term “climate change” to centennial-scale atmospheric trends derived from instrumental records spanning little more than a century. This scale compression generates conceptual ambiguity by conflating meteorological variability, sub-climatic oscillations, and geological climate states. Drawing upon paleoclimate phase relationships, stratigraphic evidence, Milanković orbital theory, molecular greenhouse-gas physics, and modeling constraints, this review proposes a hierarchical temporal framework distinguishing meteorological (decadal), sub-climatic (centennial), and climatic (millennial and longer) domains. Within this framework, greenhouse gases are interpreted as radiative agents operating within the shorter temporal strata of the Earth system, while long-term climatic regimes remain structured primarily by astronomical and geophysical boundary conditions. This distinction does not deny measurable radiative perturbations but clarifies their position within a multiscale system. By restoring climate to its geological context, the study aims to resolve definitional inconsistencies and promote greater epistemological coherence in climate science and its applications.
Copyright (c) 2026 Paul Mazza (Author) https://creativecommons.org/licenses/by/4.0
Intraseasonal Variability and Synergistic Effects on Extreme Temperature Variations in Eastern China during the 2023–2024 Winter
In the context of global warming, abrupt transitions between extreme temperature states (extreme temperature variability events) pose severe threats to both ecosystems and socioeconomic systems. However, previous studies have primarily focused on the regulatory effects of atmospheric intraseasonal oscillation (ISO) on extreme temperatures from the perspective of a single scale, leaving the synergistic driving mechanisms of multi-scale ISO on extreme temperature variability poorly understood. This study investigates the synergistic influence mechanisms of 10–30-day and 30–60-day ISO on extreme temperature variability events in eastern China. Utilizing ERA5 reanalysis data from 1979 to 2024, the record-breaking extreme winter of 2023–2024 is employed as a representative case study to systematically elucidate these mechanisms. The results indicate that the winter of 2023–2024 was marked by a record-high surface air temperature variance, accompanied by two prominent extreme temperature variation processes. Wavelet and empirical orthogonal function (EOF) analyses reveal that both ISOs were anomalously strong during this winter and exhibited significant positive correlations with temperature variance, with the 10–30-day ISO playing a more dominant role. Phase evolution analysis demonstrates that phase-locking between the leading 10–30-day modes precedes extreme temperature events by approximately 5 days, whereas the 30–60-day ISO modulates the monthly-scale persistent warm or cold backgrounds. Dynamic diagnosis shows that the ISO-related wave activity flux drives the evolution of key circulation systems, such as the Ural blocking high and the East Asian trough, thereby governing the abrupt temperature reversals. Thermodynamic budget analysis highlights the dominant role of diabatic heating, particularly in the northern key region, where its 10–30-day component is crucial for rapid temperature reversals. Critically, a synergistic effect significantly amplifies the intensity and frequency of extreme temperature variations when both ISOs are in positive phases concurrently. This study advances the understanding of the multi-scale dynamical mechanisms underlying extreme temperature variability and provides a scientific foundation for the extended-range forecasting of such events. Moreover, the findings offer critical insights for enhancing regional climate resilience and informing risk mitigation strategies for major urban clusters in eastern China.
Copyright (c) 2026 Guangxin He, Junru Li, Boqi Liu, Jingjing Duan, Jingjia Luo (Author) https://creativecommons.org/licenses/by/4.0
Aluminum-Based Thermal Management of Lithium-Iron Energy Storage for Sustainable Cooling
Climate change represents one of the most pressing challenges of our time, necessitating urgent action across various sectors, including transportation. The transition to electric vehicles is a critical component of this response, offering a pathway to reduce greenhouse gas emissions and promote sustainable mobility. However, the impact of electric vehicles on climate change is closely linked to the lifespan and efficiency of their batteries, which act as energy storage media. As these batteries operate under varying thermal conditions, effective cooling management is essential to optimise their efficiency and ensure safety. Immersion cooling combined with an aluminium casing has emerged as a feasible solution due to its potential for enhanced heat dissipation. Further research is required to understand how aluminium case thickness and cell spacing affect cooling performance with different types of coolants. Therefore, the thermal behaviour of lithium iron phosphate (LiFePO₄) battery packs under immersion cooling conditions was analysed in this study using computational fluid dynamics with ANSYS Fluent. The battery cells were encased in aluminium sheets of varying thicknesses and positioned at different intervals. Two types of coolants (pure water and a 40% ethylene glycol (EG)–water mixture) were tested for their effect on temperature regulation and cooling efficiency. The simulation results showed that increasing the thickness of the aluminium casing slightly improved heat dissipation, although the impact was limited at a certain point. Furthermore, larger spacing between cells improved fluid circulation, resulting in a more consistent temperature distribution. The EG–water mixture provided better heat management than pure water, especially at larger casing thicknesses. The optimal configuration for efficient heat management included using a 40% EG–water mixture, a casing thickness of 1.5 mm, and an inter-cell spacing of 40 mm. As a result of these setups, the highest temperature was below 308.00 K and the temperature difference was less than 0.12 K. The results of this research provide useful design suggestions for improving the safety and dependability of LiFePO₄ battery systems, in addition to contributing to the current understanding of active thermal management strategies.
Copyright (c) 2026 Awangku Muhammad Dzul Hisyam, Fauziah Jerai, Nor Afifah Yahaya, Norhayati Mat Wajid (Author) https://creativecommons.org/licenses/by/4.0
AI-Based Modeling of Indoor Air Quality and Thermal Comfort: A Systematic Review of Human-Centric and Adaptive Approaches
Indoor Air Quality (IAQ) and thermal comfort are fundamental determinants of occupant health, cognitive performance, and building energy efficiency. Integrating Artificial Intelligence (AI) and soft computing methods—such as machine learning, fuzzy logic, and hybrid architectures—has enabled more intelligent, adaptive, and predictive control of indoor environments. Since the COVID-19 pandemic, interest in data-driven environmental quality management has intensified, particularly in health-sensitive and energy-constrained settings. This systematic literature review (SLR) analyzes 72 peer-reviewed studies published between 2020 and 2024, synthesizing emerging trends in AI-based modeling of IAQ and thermal comfort. The review is organized across four dimensions: (i) environmental sensing and feature engineering; (ii) AI and soft computing-based modeling strategies; (iii) multi-objective integration of IAQ, thermal comfort, and energy metrics; and (iv) human-in-the-loop, interpretable, and adaptive AI frameworks. Using thematic synthesis and advanced visual analytics—including streamgraphs, bubble plots, Sankey diagrams, UpSet plots, and research gap maps—the review identifies four persistent challenges: (i) the lack of unified, multi-objective models that balance comfort, air quality, and energy use; (ii) underutilization of interpretable and hybrid AI approaches that foster transparency and trust; (iii) limited incorporation of real-time occupant feedback, personalization, and behavioral adaptation; and (iv) scarcity of real-world validation across diverse building types and climatic conditions. The findings highlight the need to develop inclusive, explainable, and field-tested AI systems that integrate subjective and objective data for responsive indoor environmental management. This review offers a structured foundation and roadmap for advancing deployable, human-aware, and sustainable AI solutions in the built environment.
Copyright (c) 2025 Nurul Athirah Aziz, Tajul Rosli Razak, Sharifalillah Nordin, Hasila Jarimi, Yuehong Su, Saffa Riffat (Author) https://creativecommons.org/licenses/by/4.0
Multi-Climate Transient Seasonal Analysis of Integrated Solar Assisted Desiccant Indirect Evaporative Cooling System with Dynamic Building Loads
Thermal comfort demand in the building sector is increasing remarkably due to population growth, urbanization, and global warming, particularly in hot and humid climates like Pakistan. However, the performance of such systems is strongly influenced by dynamic building loads and transient climate conditions, such as solar irradiance, ambient air temperature, and humidity. This study investigated the adequacy of the solar-assisted desiccant cooler with the Maisotsenko cycle cooler (SADC-Mc) system by performing a transient seasonal analysis for multiple climate zones of Pakistan. The climate zones include humid subtropical (Cwa), Warm semi-arid (BSh), and Warm desert climate (BWh), as exemplified by various cities such as Peshawar, Taxila, Lahore, Multan, and Karachi. Furthermore, the performance of SADC-Mc is also compared with a traditional solar-assisted desiccant cooling system with a direct evaporative cooler (SAD-DEC) in each climate. A detailed solar-assisted desiccant cooling system model was developed in TRNSYS, and a Maisotsenko cycle cooler (M-cycle) EES component model was also incorporated to perform the seasonal (April-September) transient simulation analysis under dynamic building loads for the selected climates. The system model validation findings showed good agreement with published experimental data, with a mean percentage error of 2.7%. The SADC-Mc system was found to be 40-60% more efficient than the traditional desiccant cooling system in terms of COPth for the less humid climates of Taxila, Peshawar, and Lahore. In terms of cooling capacity (CC), the integrated system performed effectively well in all cities, achieving its peak monthly performance in Multan at 5407 kWh. Furthermore, the maximum thermal energy requirement of the SADC-Mc system was reported for Karachi (6603 kWh) in June. In contrast, the SAD-DEC system's thermal energy requirement is almost double (17072 kWh) as compared to the SADC-Mc system. Similarly, the monthly average variation in COP thermal was recorded as ranging from 0.8 to 1.2, along with a total seasonal solar energy gain of 40 MWh for the Taxila climate.
Copyright (c) 2025 Muzaffar Ali, Suoying HE, Fouzia Begum, Mohammad Usman, Ghulam Qadir Chaudhary, Mirza Abdullah Rehan, Guiqiang Li (Author) https://creativecommons.org/licenses/by/4.0
Towards Climate-Resilient Data Center Cooling: Experimental Study of Water Conservation Technologies
The rapid expansion of data centers, driven by the proliferation of 5G and cloud technologies, has intensified environmental concerns, particularly related to water consumption and energy use. Data centers consume billions of liters of water daily for cooling, with wet cooling towers accounting for the majority of this usage. Despite various efforts, effective strategies to reduce water consumption in these systems remain limited. This study proposes the use of hygroscopic solutions as alternative cooling media in wet cooling towers to address this challenge. An experimental system was developed to evaluate this technology's performance. The results demonstrate a significant reduction in water consumption of up to 84.72% while maintaining comparable thermal performance to traditional systems. For data center operators, especially those in water-stressed regions, this technology offers a viable path to drastically lower operational water costs and reduce environmental impact, supporting compliance with increasingly stringent sustainability regulations. The water-saving mechanisms are further explored through an analysis of the interactions between the hygroscopic solution and moist air, utilizing physical properties and enthalpy-humidity charts. This research contributes to sustainable cooling strategies essential for adapting to water stress and environmental demands in an era of accelerating technological growth.
water saving; wet cooling tower; hygroscopic solutions; data center; climate resilience
Copyright (c) 2025 Lu Wang, Liang Chen, Zhen Li (Author) https://creativecommons.org/licenses/by/4.0
Study the Temporal Variability of Climate Change in Urban City Karachi, Pakistan
Climate change particularly changes in temperature and rainfall patterns have serious long-term influences, mainly caused by human actions. It can leads to severe events like heat-waves, floods, increasing seas, and more extreme weather, which in turn affects across various aspects of socioeconomic factors, ecosystems, hydrology, health, and global warming. Temperature and rainfall are essential climatological variables that are being comprehensively studied across the Karachi city to understand and manage their dynamic nature. The objective of this study is to analyze the trend, magnitude, and change points in climatic variables to investigate their variability in the urban city Karachi. Rainfall and temperature datasets for the Karachi station from 1980 to 2020 was used. Monthly and annual precipitation as well as temperature (maximum, and minimum) was analyzed for possible trends using nonparametric Mann-Kendall (MK) test, while the Sen’s slope (SS) estimator was used for magnitude of a trend. The Pettitt’s test was applied to detect the abrupt change point in climatic variables. The results revealed increasing trend in annual and monthly maximum temperatures while for minimum temperature all the months illustrated significant increasing trend in the study period. The SS estimator revealed that the annual temperature increased with rate of 0.025 °C per year for the certain time interval. However, in contrast to variation in the temperature trend, the mean annual rainfall of the study period was observed 180.4 mm with no significant increasing trend while 366.8 mm rainfall was observed in the month of the August 2020. The only month of the February has statistically significant decreasing trend with rate of change of 0.097mm/year and no significant trends was present for all other months. This study provides evidence that can aid policy adaptation, and addressing climate change by responding extreme events like floods and heat-waves.
Copyright (c) 2025 Mrs. Zehra A. Naveed, Bulbul Jan, Syeda Ismat Zehra, Syed Inayatullah (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Temperature Trends and Variability in Eastern Himalaya: Evidence from Sikkim
The study examines temperature patterns in Sikkim for an extended period, using 72 years (1951-2023) of IMD gridded data. Several statistical methods, including Coefficient of Variation, Analysis of Variance, Moving Average, Standardized Anomaly Index, and Regression analysis, were applied to explore the trends and variability across mean, minimum, and maximum seasonal temperatures. Results reveal a significant warming tendency in monsoon and post monsoon seasons, with minimum temperatures showing the highest variability. Extreme hot anomalies are rising in recent years which are more pronounced in the cases of monsoon and post monsoon seasons. Maximum winter temperature shows a declining trend, where mean and minimum temperatures are rising but not statistically significant. On the other hand, summer temperatures have been relatively stable over the decades. These temperatures shifts are critical for the livelihoods of rural population who heavily depend on natural resources. These outcomes emphasize the urgent need of climate adaptation strategies and enhanced monitoring and early warning systems to support sustainable development in this vulnerable mountain region.
Copyright (c) 2025 Jyotirmayee Sarkar, Yash Nayan, Dr. Amrita Dwivedi (Author) https://creativecommons.org/licenses/by/4.0
Analysis of Rainfall Trends and Variability in Eastern Himalaya: Evidence from Sikkim
Since last century, Sikkim has witnessed substantial climatic fluctuations that can affect the local water availability, agricultural productivity, and livelihood security. Despite this growing concern, a limited study incorporated century-long datasets to explore the underlying climatic pattern of the state and often focuses only on trend analysis without applying a diverse set of statistical and spatial techniques. Hence, a comprehensive analysis has been employed to explore the long-term trend and variability of Sikkim’s yearly and seasonal precipitation patterns from 1923 to 2023.Several statistical matrices have been applied viz. Coefficient of Variation (CV), Analysis of Variance (ANOVA) and Tukey’s Honest Significant Difference (HSD) Test to analyze the intra-seasonal and inter-seasonal rainfall variability, Standardized Precipitation Index (SPI) has been used to find out wet and dry years, Precipitation Concentration Index (PCI) shows annual and seasonal rainfall distribution, Simple Moving Average method has been applied to detect long-term trend, Inverse Distance Weighted interpolation technique visualizes the spatial rainfall variation in the state. This research offers new insights into the precipitation behavior and shifting rainfall dynamics such as summer rainfall demonstrates the most significant inter-cluster difference, wet and dry events are significantly increasing in summer season, the highest variability in precipitation distribution can be observed in monsoon rain, the summer and winter rainfall is increasing, where monsoon rainfall is decreasing significantly over the region, monsoon rainfall shows significant increasing trend in Gyalshing and Mangan, where summer and winter rain is increasing at Gyalshing and Mangan. These findings can help in regional adaptation strategies, water resource management, agricultural planning, and minimize climate-related livelihood vulnerabilities, as most of the farmers of this state rely on rainfall for their irrigation purposes.
Copyright (c) 2025 Jyotirmayee Sarkar, Yash Nayan, Amrita Dwivedi (Author) https://creativecommons.org/licenses/by/4.0
Assessment of Different Machinery Packages for Identification of Climate Smart Wheat Cultivation in Central India
Wheat (Triticum aestivum) is a major food grain crop cultivated in India (31.4 million hectares in 2022-2023). In view of rising concerns of emissions from agriculture, it was considered to assess the global warming potential (GWP) of various wheat cultivation practices being followed in central India. Wheat sowing by five different machines along with associated cultivation practices were evaluated and compared with traditional wheat cultivation with seed cum fertilizer drill. The five wheat cultivation machinery packages evaluated were i) Zero till seed cum fertilizer drill; ii) Zero till drill with straw handler; iii) Happy seeder; iv) Broad bed former seeder; and v) Rotary tiller assisted broad bed former seeder–over a period of three years from 2021-2022 to 2023-2024. The GWP was found minimum with Happy seeder at 555.0 kg CO2eq./ha, followed by Zero till drill with straw handler (648.3 kg CO2 eq./ha) and Zero till drill (651.8 kg CO2 eq./ha). Wheat cultivation with conventional Seed cum fertilizer drill had highest GWP of 775.4 kg CO2 eq./ha due to higher fuel usage in the whole cultivation process. The grain yield was highest on wheat cultivation with Rotary tiller assisted broad bed former seeder(5401.3 kg/ha)and this cultivation practice also had minimum Greenhouse Gas(GHG) intensity of 0.126 kg CO2 eq./kg of grain output. Wheat cultivation with Rotary tiller assisted broad bed former seeder was considered climate resilient due to its higher yield, lower GHG intensity, and maximum economic return (Rs.23,530 per hectare) over conventional method.
Copyright (c) 2025 Chandrashekhar Sahay, Niveta Jain, Uday Rangnath Badegaonkar, Krishna Pratap Singh, Manoj Kumar, Satish Kumar Singh, Purvi Tiwari, Ved Prakash Chaudhary, Chetan Sawant (Author) https://creativecommons.org/licenses/by/4.0
Climate Change Influences the Livelihood Vulnerability of Small-Scale Fishermen Communities in Kaptai Lake
Climate change has a significant impact on the livelihood of the fishermen community of Kaptai Lake. Climate change related disasters alter community resilience. The households which depend particularly on fisheries are prone to the changes. There is limited research on the livelihood vulnerability of fishermen in the Kaptai Lake region. In this study, multi-methods approach uses to evaluate the impact of environmental change on livelihood of the fisherman in Kaptai Lake region. The study includes questionnaire surveys, interview and focus group discussion to livelihood assessment of the fishermen community. This research explores the sensitivity, exposure, and adaptive capacity affecting the livelihood of fishermen in various way as a result of climate-induced effects on Kaptai Lake. The information was gathered from five regions including Puran Jelepara, Notun Jelepara, Soriyatpur, 16 no Tila, and Islampur. A questionnaire was used to collect data by interviewing 100 fishermen. The data was analyzed and categorized using the IPCC approach to the Livelihood Vulnerability Index (LVI). A total LVI score of 0.40 suggests the community had medium coping ability with respect to climate fluctuations. Moderate exposure, low sensitivity and moderate adaptive capacity indicated potential vulnerabilities in livelihoods. Working with governments and local communities can create new perspectives of reducing climate risks, implementing more cost-effective adaptation measures, and social inclusion and empowerment to promote livelihoods.
Copyright (c) 2025 Nayem Mahmud Akash, Md. Sarwar Hossian, Md Istiaque Haidar, Helena Khatoon, Tashrif Mahmud Minhaz (Author) https://creativecommons.org/licenses/by/4.0
Sustainable energy from the sea: A Comprehensive review of Hybrid Offshore Solar, Wind, and Wave Technologies
The increasing global demand for clean and sustainable energy has intensified research focus on offshore renewable energy systems, particularly those integrating wind, solar, and wave resources. Offshore hybrid renewable energy systems represent a transformative opportunity to harness diverse marine energy sources, aiming to improve energy yield, capacity factor, and reliability compared to single-technology solutions. Evaluation of past literature reveals a critical gap in comprehensive evaluations of fully integrated hybrid offshore platforms that simultaneously deploy floating solar photovoltaic (FPV), offshore wind turbines, and wave energy converters (WECs), including their techno-economic performance and environmental impacts. This study addresses this gap by systematically reviewing the current state-of-the-art offshore floating solar, wind, and wave energy technologies and analyzing key commercial pilot hybrid projects such as Hollandse Kust Noord, W2POWER, and the Hybrid Floating POSEIDON system. A mixed-methods approach combining qualitative literature review, multi-criteria technical, economic, and environmental evaluation, and case study analysis was employed to assess the design innovations, integration strategies, and deployment challenges. Results demonstrate that hybrid offshore systems leveraging synergies between solar, wind, and wave resources can achieve up to five times higher energy output than single-source systems, with floating wind currently leading in maturity and energy production scale, complemented effectively by floating solar and wave converters to enhance seasonal and operational stability. Novel modular floating platforms and advanced mooring systems enable scalable, durable solutions capable of withstanding harsh marine environments. Environmental considerations, including biofouling, corrosion, and ecosystem impacts, can be addressed via mitigation strategies and adaptive site selection.
Copyright (c) 2025 Nisha Kaur, K. Sudhakar, M.R. Mohamed, Erdem Cuce, Dan Barbulescu (Author) https://creativecommons.org/licenses/by/4.0
Spatio-Temporal Variability of Extreme Precipitation and Flow Indices in the Lower and Middle Ouémé Valley in Benin
The urgent need to study rainfall variability and its link to river flows is well established, given its direct impact on the socio-economic development of nations. This study analyzed the variability of extreme precipitation and flow indices at several stations in the lower and middle Ouémé valley in Benin. Five extreme precipitation indices (RX1DAY, RX3DAY, RX10DAY, SPI, PRCPTOT), as well as two extreme river flow indices (Q50 and Q95) were used. To conduct this analysis, we employed the non-parametric Mann-Kendall test to examine trends and the Pettitt test to identify stationarity breaks in the data series. The results indicate strong spatio-temporal variability in the indices studied. The break years are generally similar among indices but vary from one station to another. Three main breaks were identified over the study period (1960-2022): the first in 1970, the second in 1990, and the third after the 2000s. Regarding trends and the distribution of water surplus and deficit years, a cyclical pattern emerges. There is an overall downward trend in precipitation indices, with three distinct periods: a first and third water surplus period, separated by a deficit period, which is the longest. Concerning flow indices, two main breaks have been identified: one in 1970 and another in 1990. Trend analysis reveals a significant decrease in the Q95, while the Q50 shows an upward trend. Finally, an examination of the relationships between precipitation and flow indices shows that only the Q95 is significantly influenced by precipitation indices, especially the SPI and PRCPTOT. However, the relationship between Q95 and PRCPTOT is inversely proportional.
Copyright (c) 2025 Freddy Zinsè HOUNNONDAHO, Ezéchiel Obada, Casimir Y. Da-Allada, Adéchina Eric Alamou, Marius Bocco Koubè, Josué Zandagba (Author) https://creativecommons.org/licenses/by/4.0
Parametric Analysis of Thermal and Electrical Performance of Photovoltaic Thermal Systems Using Nano-Graphene Coolants and Enhanced Fin Design
This study presents a comprehensive three-dimensional numerical simulation to assess the performance of a photovoltaic thermal (PVT) system utilizing nanofluids (NFs) and enhanced fin configurations. The system integrates a copper flat plate collector, where nanofluids circulate within heat absorber tubes, and rectangular fins are strategically arrayed to augment heat dissipation. The effects of fin dimensions, count, nanofluid concentration, flow rate, and solar intensity on the PVT's thermal and electrical performance are examined. Key findings illustrate that fin configuration, particularly the number of fins, significantly dictates the system’s thermal management, with minimal impact from fin thickness. Optimal thermal behavior was observed with 0.5 wt% graphene nanoplatelet nanofluid at a mass flow rate of 0.1 kg/s, employing 400 fins. Analysis reveals that increasing the number of fins from 10 to 400 could enhance the thermal efficiency by approximately 18%, while reducing the overall system temperature by up to 7°C under peak solar conditions. Furthermore, the study indicates that an increase in solar radiation intensity from 800 to 1000 W/m2 reduces the electrical efficiency by about 2%, even though the electrical power output increases by 5%.
Copyright (c) 2025 Ehsan Baniasadi, Farzin Bahadori, Zahra Taheri, Saffa Riffat (Author) https://creativecommons.org/licenses/by/4.0
Comparative Thermal and Electrical Performance Analysis of PV/T Collectors with Different Finned Absorber Configurations
This study investigates and compares the thermal and electrical performance of three photovoltaic/thermal (PV/T) solar collectors with different absorber plate geometries. A standard flat-plate PV/T panel serves as the baseline, while two enhanced designs incorporate aluminium finned absorbers (with 112 and 229 fins) beneath the PV cells to increase surface area for heat dissipation via water cooling. Performance was evaluated through both computational fluid dynamics (CFD) simulations and outdoor experimental testing. Both simulated and measured results confirmed that absorber plate designs affect the efficiency of PV/T systems. The prototype featuring 229 fins delivered better thermal performance and lower PV cell operating temperatures, resulting in improved electrical output. The 112-fin variant demonstrated intermediate performance in both aspects, balancing manufacturability and effectiveness. Sustainability implications were also evaluated, with the 229-fin configuration showing a 24.48% increase in CO₂ reduction potential compared to the standard design, offering a substantial contribution to climate change mitigation. These findings underscore the potential of finned PV/T systems in enhancing both energy efficiency and environmental benefits, making them a promising option for large-scale renewable energy applications.
Copyright (c) 2025 Mahmut Sami Buker, Ahmet Emre Onay, Halil Ibrahim Dag (Author) https://creativecommons.org/licenses/by/4.0
A High-Sensitivity Gas Sensor for Simultaneous Detection of Methane and Ethane in Urban Gas Networks
To address the challenge of precise discrimination of multi-component trace gases for urban natural gas leakage monitoring, a dual-component gas sensor based on mid-infrared laser absorption spectroscopy was developed for simultaneous detection of methane (CH4) and ethane (C2H6). The sensor integrates an interband cascade laser, a 13-m optical path length multi-pass gas cell and a high-sensitivity detector. The optimal absorption lines of CH4 and C2H6 were identified through analysis of gas absorption spectra, and the negative-pressure (0.24 atm) operation was applied to effectively suppress water vapor interference, which significantly improved the anticross-interference capability. Experimental results demonstrate minimum detection limits of 17.77 parts-per-billion in volume (ppbv) for CH4 and 12.95 ppbv for C2H6 at a 1-second averaging time, and the dynamic response time is approximately 6 s. During the In-vehicle mobile monitoring experiment, the changes of CH4 and C2H6 concentrations near the leakage source show a high degree of correlation (Pearson correlation coefficient of 0.9991), which effectively distinguishes natural gas leakage from biogenic and anthropogenic interference sources such as landfills, automobile exhaust. This research provides a high-precision and low-false-alarm solution for the safety inspection of urban gas pipeline networks, and the application scenarios can be further expanded through miniaturization and intelligent optimization in the future.
Copyright (c) 2025 Zhao Rao, Bin Zhou, Denghao Zhu, Xiao Chen, Jingjing Liu, Yuan Lv (Author) https://creativecommons.org/licenses/by/4.0
Factors of Vulnerability Forming Considerations For Informed Legal-Policy Formulation In Addressing Climate Change-Induced Internal Displacement: Case Study on Munroe Island
Globally, the effects of climate change are felt increasingly than ever before with significant implications for human mobility. At one end, extreme climatic conditions raise displacement risks and migration, on the other, mobility constraints may prevent people from leaving, thereby trapping them in places exhibiting high levels of climatic stress. In situ adaptations are becoming difficult entailing risks for livelihoods and health of populations; and migration destinations are also becoming affected by adverse climate impacts. The vulnerability of the migrating populations further creates novel challenges to climate resilience and human security. This premise is evaluated in the paper by employing a case study involving Munroe Island in the State of Kerala in India. The paper is premised on the proposition that effective redressal of issues caused by climate change require legal-policy interfaces. Now, the issue revolves around identification of determinants which should form the basis of such interfaces. The case study undertaken reveals vulnerability as the primary criterion to be considered relevant in formulation of legal-policy interfaces. The study concludes that when different sections of a geographically homogenous community are subjected to climate change adversities, the ill-effects though expected to be similar across the board, vary owing to differing socio-economic vulnerabilities of the units of the study population. Since climate change effects remain constant, such adverse effects can be pacified through modifications directed towards socio-economic indicators through targeted interventions.
Copyright (c) 2025 Abhijith S Kumar (Author) https://creativecommons.org/licenses/by/4.0
Exposure to Climatic Variability and Associated Hydro-Meteorological Hazards in Beas River Basin of Western Himalaya, India
The factors contributing to vulnerability can significantly differ across different locations and time periods, as certain communities, age demographics, areas, landscapes, and countries are more at risk from climate change. As a result, comprehending the spatial and temporal trends of current climates and their dependable projections is essential for better preparing to tackle the consequences of climate change. Another critical element of climate change mitigation and adaptation involves creating strategies that are relevant to specific local contexts. Exposure was defined as the cumulative impact of various climate and hydro-meteorological hazards. This approach allowed us to establish a climate change exposure index that takes into account both historical and current conditions. The exposure index can amalgamate numerous factors representing climate variability and related hydro-meteorological threats to inform proactive decision-making. Consequently, several indicators have been consolidated into a unified index to assess the level of exposure to climate variability. A positive correlation has been identified between climate change, hydro-meteorological hazards, and exposure. Our analysis revealed that, out of fifteen districts, ten exhibit a high degree of exposure to rainfall variability, residents from eight districts might face the risks associated with landslides, six districts are vulnerable to wind damage, five are at risk of temperature variability, and five are exposed to flood events.
Copyright (c) 2025 Shekhar Kumar, K Nageswara Rao, Harjinder Kumar (Author) https://creativecommons.org/licenses/by/4.0
Spatiotemporal Variability of Rainfall in Central Guizhou: Multi-Time Scale Analysis and Implications for Climate Change Adaptation
Precipitation plays a critical role in the global water cycle, with its spatial distribution and variations significantly impacting both the environment and socio-economic systems, making it central to climate change research. This study investigates the spatiotemporal evolution of rainfall intensity and frequency in central Guizhou, China, using monthly data from 56 rain gauge stations (1965–2016). The analysis employs the Precipitation Anomaly Index (PAI) and P-III distribution to examine rainfall patterns across time scales of 3 to 12 months. Key findings include: (1) Rainfall intensity shows an "N-shaped" trend, with an increase from the 1960s to 1980s, followed by a decline and gradual recovery through the 2010s. The proportion of wet years peaked in the 1970s (47.55%), rising to 69.39% by the 2010s. (2) Rainfall frequency was highest during normal years (62.8%), with medium-frequency events becoming more dominant over time. (3) Significant differences in rainfall intensity were most pronounced at 9-month and 12-month scales, with rainfall frequency exhibiting significant variability across all time scales. This research highlights the multi-scale nature of rainfall patterns in central Guizhou, offering valuable insights for hydrological management and climate adaptation in karst regions.
spatiotemporal evolution of precipitation; Precipitation Anomaly Index (PAI); P-III distribution; multi-scale analysis; central Guizhou; climate adaptation.
Copyright (c) 2025 Zhonghua He, Xiaolin Gu, Feng Qiu, Maoqiang Wang, Mingjin Xu, Siyu Chen (Author) https://creativecommons.org/licenses/by/4.0