The hospitality sector is a significant contributor to global greenhouse gas (GHG) emissions due to its energy-intensive operations. This study quantifies the carbon footprint of a small-scale hotel in Sri Lanka, identifies major emission sources, and proposes mitigation pathways toward sustainable hospitality. It was conducted over 12 months from January 2025 to December 2025. A comprehensive assessment was conducted in accordance with the GHG Protocol and ISO 14064 guidelines, encompassing direct (Scope 1), indirect (Scope 2), and value chain-related (Scope 3) emissions. The total carbon footprint was estimated at 649,979 kg CO₂-eq, with Scope 1 emissions, primarily from refrigerant gases in air-conditioning systems (R-32 and R-22), constituting 77.22% of total emissions. Scope 3 emissions, including transportation of staff and food waste management, accounted for 12.55%, while Scope 2 emissions from grid electricity contributed 10.57%. Mitigation strategies, including transitioning to low-Global Warming Potential (GWP) refrigerants, enhancing energy efficiency through green roofs and cool roofs, adopting solar photovoltaic systems under Net Metering, optimizing LPG consumption, and improving food waste management, were identified as effective measures to reduce the hotel’s carbon footprint. This study provides a critical benchmark for operational sustainability, offering actionable insights for hotel managers, policymakers, and the tourism sector to align hospitality operations with climate action and Sustainable Development Goals.
The Kelani River basin (KRB) is the highest flooding conditions appeared river basin in Sri Lanka. Based on this, the study investigates the impact of climate resilience on rainfall patterns, temperature, wind speed, and streamflow dynamics of the Kelani River basin in Sri Lanka to enhance flood risk prediction and floodplain restoration planning. Rainfall, temperature, and wind speed data for the 2013 to 2024 time period were obtained from five stations: Angoda, Colombo, Hanwella, Maliboda, and Rathnapura. Long-term trends were detected using statistical approaches such as the Mann-Kendall test. Actual versus predicted streamflow data were used to determine the effectiveness of the hydrological model, along with error distribution analysis. There was some variation in the rainfall; Rathnapura peaked at 6000 mm in 2019, and Maliboda was almost constant throughout. Statistical tests indicated an increase of 3.12 mm per year in rainfall. Within the temperature trends, there was no long-term variation at the Colombo and Rathnapura stations, while in wind speed, a moderate decline without statistical significance was recorded. The hydrological model showed shortcomings in the streamflow forecast, mainly due to underestimating the high flows and overestimating the low flows. The residual analysis indicated a deficiency in the model's representation of extreme events, and the Q-Q plot indicated poor performance in predicting runoff extremes. These limitations highlight the need for model refinement using nonlinear-learning-based approaches to improve flood risk assessment and inform adaptive restoration strategies under changing climate conditions.
The construction sector accounts for a substantial share of global greenhouse gas emissions, yet many low-carbon building approaches remain inaccessible in resource-constrained settings because they depend on capital-intensive technologies and formal supply chains. This study examines how community-led construction can contribute to climate-responsive infrastructure through the integration of material reuse, passive environmental design and collective action. Using a qualitative case study of the Ulwazi Educare Centre in Delft, Cape Town, the research draws on 45 semi-structured interviews, field observations, project documents and photographs to explore the environmental, social and institutional dimensions of an alternative construction approach. The findings show that discarded tyres and other locally available materials were reconfigured into durable community infrastructure through participatory construction. Participants consistently associated the building with improved thermal comfort, reduced reliance on mechanical heating and cooling, opportunities for skills development, employment and collective ownership. The study interprets these findings through the lens of post-natural building, an analytical framework that explains how climate-responsive infrastructure can emerge from the interaction of material reuse, passive environmental design, and collective labour in resource-constrained contexts. The study also identifies important constraints to the wider adoption of community-led climate-responsive infrastructure. While participants perceived environmental and social benefits, scaling such approaches depends on organisational capacity, professional expertise and institutional legitimacy. The findings are based on qualitative evidence and do not quantify thermal performance or embodied carbon, highlighting the need for complementary engineering assessments. By integrating environmental, social and institutional dimensions, the study contributes a socio-technical perspective on low-carbon construction and expands debates on climate-responsive infrastructure in the Global South.
As buildings face growing exposure to climate-induced stressors such as heatwaves, energy demand spikes, and humidity fluctuations, rethinking insulation strategies become crucial to achieving thermal resilience and energy efficiency. Conventional insulation materials often rely on non-renewable resources and exhibit poor environmental performance, highlighting the urgent need for sustainable alternatives in building applications. This study aims to develop an eco-efficient thermal insulation material derived from agricultural residues to support climate-adaptive and low-carbon building practices. A bio-based thermal insulation panel composed of rice husk fiber was fabricated using polyvinyl alcohol (PVOH) as a binder, with formulations containing 0%, 5%, 10%, and 15% PVOH by weight. The prepared panels were evaluated for their mechanical strength, dimensional stability, and thermal conductivity. Experimental results indicate that the thermal insulation panel formulation containing 10% PVOH is optimal, exhibiting superior mechanical properties with a flexural strength of 9.822 MPa, a tensile strength of 4.369 MPa, and an internal bond strength of 0.038 MPa. Water absorption and thickness swelling were recorded at 92.13% and 33.61%, respectively, confirming good moisture stability. The best-performing sample achieved the lowest thermal conductivity of 0.245 W/m·K, indicating strong insulation potential. The findings highlight the viability of agricultural waste as a raw material for bio-insulation panels, offering both environmental and performance benefits. By reducing dependency on synthetic materials and enhancing indoor thermal comfort, this bio-based thermal insulation panel contributes to the design of resilient, low-carbon buildings capable of adapting to future climate conditions.
Thermochemical Energy Storage (TCES) offers high-density thermal storage for building applications, but the thermal delivery limitations of conventional subcritical heat pumps usually restrict system performance. In this study, a new integration of a low-GWP transcritical R1234yf heat pump for charging a 50gram Vermiculite-Calcium Chloride (CaCl₂) composite bed is numerically studied. A transient lumped parameter model with linear driving force (LDF) reaction kinetics was developed and extensively validated against experimental subcritical R134a baseline data. A detailed sensitivity analysis shows that the thermodynamic results are robust against realistic hardware degradation. The results indicate that the intrinsic limitation of isothermal condensation in the baseline subcritical R134a cycle restricts the maximum bed temperature to 52.0°C, which traps residual moisture and limits the material energy storage density to 658.0 kJ/kg. In contrast, the phase-change plateau in the gas cooler is replaced by a sensible temperature glide when operating the R1234yf cycle at a transcritical discharge pressure of 3.8 MPa. This steep thermal gradient drives the composite bed to 54.8°C, forcing a significantly deeper moisture desorption. It is found that the transcritical system attains the energy storage density of 923.6 kJ/kg, which is a significant 40.4% increase over the baseline. The mechanical charging efficiency (COP = 2.70) is inevitably lower than the subcritical cycle (COP = 3.58) because of the extreme transcritical compression. However, this loss in mechanical energy is fundamentally compensated for by a disproportionate gain in latent chemical storage. A reasonable thermal penetration approach, rather than instantaneous compressor efficiency, best realises the ultimate objective of maximising the TCES capacity. In conclusion, this work provides a mathematical proof that the combination of low-GWP R1234yf heat pumps and vermiculite-CaCl₂ composites is a very efficient, high-capacity and structurally resilient architecture for sustainable building decarbonisation.
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.
Climatic station data is crucial for understanding the meteorological characteristics of the Indian Sundarbans, a World Heritage site, where over 70% of the population is engaged in agriculture. However, due to the exiguity of rain gauge stations and insufficient data from the operational stations, quantifying the climate change information and prediction at the ground level is challenging. Moreover, studies on imputing missing rainfall values, particularly in Indian Sundarbans, are limited. The present study experimented various imputation algorithms such as hot deck, k-Nearest Neighbour, Linear Regression and Inverse Distance Weighted. These were applied to the nearest IMD rain gauge stations in the study area. We have also considered the k-NN technique, as applied to IMD gridded data (0.25◦ × 0.25◦). In our analysis, we artificially excluded 6%, 16%, and 25% of the data points at random. The results obtained from the various imputation methods were then compared with the actual observed data, using agreement indices such as R2, MAE, RMSE, and MAPE. The findings reveal that the k-NN applied to IMD gridded data is the most effective approach, achieving an R2 value exceeding 0.9.
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.
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.
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.
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.
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.
Deciphering the El Niño/Southern Oscillation (ENSO) influences on hydroclimatic factors in both tropical and extratropical regions is crucial. This study employed empirical methods to identify areas with consistent hydroclimatic signals in relation to extreme ENSO phases. We examined the climatic linkages between ENSO's warm and cold phases and local temperature patterns across Southeastern US. Spatial coherence values were calculated using monthly temperature composites over a 2-year ENSO cycle, and candidate regions were identified using the first harmonic fit. Temporal consistency rates were determined through aggregate composites and index time series (ITS) to pinpoint core regions. This study identified two core regions: Western Inland Region (WIR) and Easter Coastal Region (ECR), with the WIR showing the more significant response to both warm and cold ENSO forcings. During ENSO warm (cold) years, temperature composites showed below (above) normal levels in these regions from winter to spring. Spatial coherence rates for El Niño (La Niña) in WIR and ECR were 0.97 to 0.98 (0.97 to 0.99), and temporal consistency rates ranged from 0.72 to 0.76 (0.82 to 0.86). Composite-harmonic analysis revealed that temperature anomalies tend to reverse signs between opposite ENSO phases, with positive anomalies in warm years showing more coherence and stronger responses compared to negative anomalies in cold years. The findings indicate that Southeastern US temperature patterns are significantly influenced by ENSO, highlighting a climatic teleconnection between El Niño and La Niña events and local middle latitude temperature.
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.
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.
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.
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.
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, Milankovi & cacute; 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.
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.
Ensuring sustainable indoor environments in school buildings is essential for safeguarding student health and well-being. Indoor air pollution from particulate matter (PM) and total volatile organic compounds (TVOCs) has emerged as a critical issue exacerbated by modern building materials and occupant behaviors. The rising prevalence of respiratory illnesses among students highlights the urgent need for improved indoor air quality (IAQ) standards, particularly as climate change influences air pollution dynamics. This review explores the sources of PM and TVOCs in elementary and primary school buildings and their health implications through a systematic review of literature published between 2010 and 2024. It also considers studies conducted during the COVID-19 pandemic when indoor air conditions shifted due to online learning periods. Out of 325 identified articles, the predominant sources of TVOCs were found to be cleaning activities, chemicals, furniture, and occupant behavior. PM sources encompassed classroom activities, building attributes, instructional materials, and external factors. The findings reveal that average PM2.5 and PM10 concentrations frequently exceed recommended health thresholds, posing risks such as reduced lung function, respiratory distress, and a higher prevalence of asthma among students. This review underscores the necessity of integrating IAQ management into sustainable building practices and climate adaptation strategies. By emphasizing long-term health monitoring, air dynamics analysis, and pollutant exposure assessments, it advocates for proactive policies to enhance school environments to ensure resilience against future climate-related air quality challenges.
