General circulation models (GCMs) frequently display systematic parameter-ization flaws that distort localized land-atmosphere interactions, limiting their reliability for regional climate risk assessments. This study introduces a pro-cess-based evaluation framework to diagnose monsoonal convective triggers and thermodynamic coupling fidelity in the CMCC-CM2-SR5 model against high-resolution Princeton terrestrial reanalysis data (1981-2014) over the high-variability region of East Coast Peninsular Malaysia (Kelantan and Ter-engganu). Traditional and physics-informed diagnostics reveal that the GCM systematically decouples precipitation from localized boundary layer specif-ic humidity during the wet winter Northeast Monsoon (NEM), culminating in a profound accumulation of convective "Miss" days under highly saturated, low-pressure conditions. Furthermore, the GCM imposes an unphysically rig-id negative coupling sensitivity to horizontal moisture transport (MF = ws × shum) during active monsoonal surges, while erroneously converting real-world dry-season venting mechanics into false positive coupling biases (+0.3180 in the south-central interior) during the Inter-monsoon and South-west Monsoon (Inter/SWM) periods. By revealing these grid-scale atmos-pheric mass-balance and threshold failures, this study provides an essential suite of historical emergent constraints directly operationalized from atmos-pheric water budget dynamics (∂q/∂t + ∇ ∙ (uq) = E - P). This empirical framework establishes a verifiable physical baseline necessary to bound re-gional monsoonal mechanics, filter future hydroclimatic scenarios, and dras-tically minimize predictive uncertainty for localized extreme precipitation projections.

Understanding long-term changes in drought persistence is essential for evaluating climate risk across humid tropical regions. This study focuses exclusively on the spatiotemporal trends of consecutive dry days (CDD) in Sabah, Borneo, during 1981-2024 using bias-corrected ERA5-CHIRPS rainfall data and ETCCDI-compliant CDD computation. Temporal trends were quantified using the non-parametric Mann-Kendall test and Sen's slope estimator to identify statistically robust changes in dry-spell duration. Results reveal a pronounced and spatially coherent east-west hydroclimatic dipole. Eastern and southeastern coastal districts exhibit increasing CDD, with pixel-level trends indicating statistically significant lengthening of dry-spell duration and Sen's slope values locally reaching +3.0 days per decade, indicating longer and more persistent dry-spells. In contrast, the southwestern interior and west coast regions display decreasing CDD, with Sen's slope values reaching −5.5 days per decade, reflecting shorter, less persistent dry periods and enhanced rainfall persistence. Transitional zones show weak or near-neutral trends, highlighting spatial heterogeneity associated with topographic influences and localised hydroclimatic variability. The observed reorganisation of dry-spell dynamics indicates that drought risk across Sabah is becoming increasingly spatially uneven. These findings establish the first high-resolution, four-decade assessment of long-term CDD trends across Sabah and provide a robust climatological baseline for future drought monitoring, hydroclimatic research, and regional adaptation planning.

A polarization-independent graphene-based metamaterial absorber is proposed for highly sensitive terahertz oxidizing gas sensing, including ozone (O₃), nitrogen dioxide (NO₂), and chlorine (Cl₂). The design is formed by a rotated dual-octagonal structure enclosed within a square frame. The design achieves near-unity absorption of 99.999% at 6.37 THz. The absorber exhibits stable performance under both transverse electric (TE) and transverse magnetic (TM) modes, as well as wide-angle polarization independence from 0° to 89°. Gas sensing performance is analyzed by monitoring resonance frequency shifts induced by changes in the surrounding refractive index caused by oxidizing gas adsorption. Significant resonance shifts are observed for O₃, NO₂, and Cl₂ environments, indicating strong sensitivity to dielectric variations. The corresponding sensitivities are calculated as 0.4 THz/RIU, 1.0 THz/RIU, and 1.4 THz/RIU, respectively. The proposed structure demonstrates high sensing performance of oxidizing gas detection, where oxidizing gases can cause surface oxidation, and early detection of corrosive and toxic atmospheric conditions.

Volcanic eruptions provide valuable observational analogues for understanding the climatic effects of enhanced atmospheric aerosol loading. Major eruptions introduce large quantities of aerosols that may alter radiative balance. The resulting perturbations can lead to measurable changes in key hydroclimatic variables, including temperature, precipitation, and cloud dynamics. This study examines the regional climate response to the 1991 eruption of Mount Pinatubo over Southeast Asia using total column aerosol optical thickness (AOT) as a proxy for aerosol perturbations. AOT data from the National Oceanic and Atmospheric Administration Climate Data Record were analyzed with ERA5 reanalysis variables, including precipitation, temperature, solar radiation, relative humidity, cloud cover, and wind speed. A pre-eruption baseline (Jan 1986-May 1991) was used to establish climatological conditions, and anomalies are computed for the post-eruption period (June 1991-December 1992). Correlation analysis is performed to evaluate the association between aerosol anomalies and climate responses. Domain-averaged time series indicate weak overall correlations (|r|<0.3) between AOT and all climate variables, suggesting limited uniform regional signals. Positive correlations are identified with wind speed and solar radiation, while negative correlations are observed for temperature, relative humidity, cloud cover, and precipitation. In contrast, spatial correlation analysis reveals localized regions of statistically significant relationships, particularly over 0-10°S and 15-20°N. These regions exhibit contrasting correlation patterns, indicating heterogeneous atmospheric responses to increased aerosol loading. The results reveal spatially heterogeneous, region-specific climate responses to aerosol perturbations that are not fully captured by domain-averaged analyses. This study provides observational evidence of climate responses to volcanic aerosol perturbations in Southeast Asia, addressing a key gap in regional assessments. Future work should focus on finer spatial scales or targeted subregions to better resolve microclimate responses.

This study examines the correlation between outdoor thermal perception in parking lots and its effect on indoor air conditioning and fan use as people enter classrooms or offices. To analyse these trends, the study employed Spearman's correlation and Fisher's exact tests for crosstabs. Results show a non-significant association between thermal sensation and dependence on air-conditioning (p = 0.501), but significant associations for dependence on a fan (p = 0.03) and time taken to cool down (p = 0.00). Many of the campus communities set the temperature below 21 °C (n=222, 76.6%), followed by 22 to 25 °C (n=53, 18.3%), and 26 °C and above (n=15, 5.2%). Similarly, most campus communities tend to set the highest fan speed (n=174, 60%), followed by moderate (n=95, 32.8%), and low (n=21, 7.2%). Even in neutral thermal conditions, a substantial portion of the community still maximises fan speeds (71.2%, n = 37). They also set air-conditioning below 21°C (78.8%, n = 41) upon entering buildings, behaviours that increase energy consumption and greenhouse gas emissions. Surprisingly, the result also shows that as perceived thermal sensation shifts from neutral to hot, the reported duration required to cool down decreases. The study helps to in-form campus management by providing a foundation for implementing targeted strategies to improve institutional thermal conditions and reduce reliance on energy-intensive cooling systems.

Flood disasters have become one of the most frequent environmental hazards affecting local communities in Malaysia, causing disruptions to livelihood, housing security, and social well-being. Despite numerous flood mitigation initiatives introduced by relevant agencies, many communities remain vulnerable due to limited adaptive capacity and insufficient preparedness. This study aims to identify the factors influencing community resilience towards flood disasters, assess the level of resilience among flood-affected residents, and propose suitable improvement measures to strengthen future community preparedness in Sungai Isap Damai, Pahang.A quantitative research design was employed using a structured questionnaire distributed to 124 residents who had experienced flood incidents in the study area. Respondents were selected through purposive sampling, while the collected data were analysed using descriptive statistics through the Statistical Package for Social Sciences (SPSS). The analysis focused on several resilience dimensions including financial, social, physical, and knowledge-related capacity.The findings indicate that financial capacity emerged as the most critical factor constraining resilience, as many affected households lacked sufficient resources to undertake preventive and recovery measures. Overall, the level of community resilience in Sungai Isap Damai was found to be moderate, suggesting that existing preparedness and adaptation efforts remain inadequate to fully withstand recurring flood events. The study further highlights the importance of enhancing disaster knowledge, strengthening local support systems, and improving household resource readiness.This study contributes practical insights for local authorities and disaster management agencies in formulating more sustainable community-based strategies to improve resilience and reduce the long-term social impacts of flood disasters.

Understanding how tropical river basins respond to abrupt climate perturbations is essential for strengthening water security under future climate change. Large volcanic eruptions provide useful natural analogues for short-term radiative forcing because volcanic aerosols can temporarily alter atmospheric circulation, rainfall patterns, and surface hydrology. This study investigates the hydrological sensitivity of major river basins in Peninsular Malaysia to volcanic-induced climate forcing, with emphasis on implications for climate change adaptation and water resource management. Monthly streamflow records from six major river basins representing Kedah, Perak, Kelantan, Pahang, Selangor, and Johor were analysed in relation to three major volcanic events: Mount Pinatubo in 1991, Mount Sinabung in 2010, and Mount Taal in 2020. To reduce the influence of internal climate variability, the El Niño-Southern Oscillation signal was statistically removed prior to analysis. Streamflow anomalies were then standardised using long-term climatology, and Superposed Epoch Analysis was applied over a ±24-month window relative to each eruption onset. This approach enabled the identification of common post-eruption hydrological responses while preserving basin-specific variability. The results indicate that streamflow reductions generally occurred following major eruptions, with the strongest negative anomalies observed within the first 6-12 months after eruption onset. Among the selected events, the 1991 Mount Pinatubo eruption produced the most distinct and widespread hydrological signal, suggesting stronger basin-scale sensitivity to major aerosol forcing. However, the magnitude and timing of streamflow response varied across basins, highlighting the importance of local hydroclimatic conditions, catchment characteristics, and monsoonal controls. These findings demonstrate the value of volcanic analogue analysis for assessing tropical hydrological sensitivity to external climate forcing. The study contributes to climate change impact assessment by providing evidence on how Malaysian river systems may respond to short-term climate disturbances, supporting more climate-resilient planning for water resources, drought preparedness, and basin management.

The study investigates the temporal variation characteristics of extreme high temperatures in Chongqing, China, under the background of ongoing climate warming. Using ERA5 reanalysis data (2000-2024) and surface meteorological observation data (2008-2024), this study analyzed the temporal variations of mean temperature, the 95th percentile of maximum temperature (TX95p), and multi-level high-temperature days (≥35 °C, ≥37 °C, and ≥40 °C). Linear trend analysis, Mann-Kendall trend testing, Sen's slope estimation and regression analysis were applied to characterize temporal variations and the relationship between background temperature changes and extreme heat occurrence. Ground-based observations were used as independent references to evaluate the consistency of ERA5-derived extreme heat variability. The results indicated increasing tendencies in mean temperature and extreme high-temperature indicators during the study period, with several extreme heat peaks occurring in 2006, 2013, and 2022. Higher temperature thresholds had greater interannual variability and stronger extremes. Regression showed strong statistical association between mean temperature and extreme high temperature days (R2 = 0.8555), suggesting that background temperature variation was closely related to extreme heat. ERA5 validation results were moderately consistent with ground observations (R = 0.621, p = 0.010), although ERA5 tends to underestimate extreme heat frequency. Other factors, such as basin topography, atmospheric circulation variability and urbanization-related thermal effects, may also contribute to extreme heat events in Chongqing. However, the quantitative correlation effects of these factors are more likely to be more quantitatively related. The frequency and intensity of extreme high temperatures continue to rise, increasing the risk of public health, urban construction, ecology and energy supply. The findings provide important scientific evidence for improving urban climate adaptation, heat-risk management, and resilience planning in mountainous cities under future climate change.

The squid cast net fishery which utilizes surface lamps metal halide (MH) has long been practiced coastal fishermen in Langkawi Island, Kedah. Due to rapid advancements in fishing technology, fishermen are increasingly utilizing light-emitting diode (LED) underwater lamps for capturing squid and fish. A study was conducted to assess the effects of surface MH lamps and underwater LED lamps on the coastal squid cast net fishery in Langkawi. A total of 10 fishing trips using MH surface lamps, underwater LED lamps (UWLED) and a combination of surface and underwater lamps (UWLEDMH) were conducted from November 2023 to January 2024. The study showed that 100% of the species caught using MH, UWLED, and UWLEDMH lamps were commercially valuable species. The subsamples were compared according to their length at first maturity for each species, resulting in UWLED lamps recording the highest percentage of immature species at 77.01%, followed by MH surface lamps at 69.26%, and the combined lamps, UWLEDMH, at 60.59%. This demonstrates that the use of underwater lighting in coastal areas can attract and capture immature-sized squid and fish, thus affecting the overall coastal fisheries resources.

COVID-19 pandemic forced global movement shutdown for at least 2 years between 2020 and 2021, which nearly halted anthropogenic activities and pressure on natural ecosystem. Globally, there were consent of ecosystem recovery during that period, but little study was conducted to observe the effect of coral reefs of Malaysia: pre-, during and post- Movement Control Order (MCO). This study aims to examine the benthic cover composition of Malaysia between 2018 and 2024, using the validated long-term coral reef monitoring program of Reef Check Malaysia. There were in total 114 sites that were annually surveyed across 16 islands in three Marine Ecoregions: Straits of Malacca (n = 4 sites), Sunda Shelf (n = 71 sites), and North Borneo (n = 39 sites). 10 general benthic cover categories were examined as dependent variables with independent variables (treatment) of Years (2018-2024, n = 7) nested in three Periods (pre-MCO, MCO, Post-MCO). The dominant benthic covers within Malaysian reefs were Hard Coral (41.67%), Rock (24.36%) and Rubble (11.92%). Hard Coral cover recovered from 41.41% in 2018 to 46.80% in 2022, but decreased to 40.22% in 2024. Contrastingly, Rubbles reported reverse pattern relative to Hard Coral, with 10.94% in 2018, decreased to 9.84% in 2022, and increased to 14.68% in 2024. Overall, the coral reef health based on Hard Coral cover experienced recovery during MCO but declined after the order was lifted. This suggests the MCO unintentionally facilitated coral reef recovery in Malaysia, which could be used in future disaster action plan.

Marine shrimp yields in Malaysia's coastal fisheries are approaching Maximum Sustainable Yield. To support science-based harvest limits, this study assessed the species composition and growth parameters of shrimp in Perak and Perlis along the Northern West Coast of Peninsular Malaysia. Length-frequency data from 17,690 individuals were collected monthly from March to December 2025. Growth parameters (𝐿∞, K, Ø') for dominant species were estimated using ELEFAN I in FiSAT II. Perak supported a diverse community (11,556 individuals) dominated by Mierspenaeopsis sculptilis (21%). Perlis (6,134 individuals) exhibited a simpler structure dominated by Metapenaeus affinis (21.6%), indicating stronger environmental filtering. Growth analyses of M. affinis and Metapenaeus brevicornis showed clear site and sex variations. In Perak, M. affinis exhibited superior growth performance (Ø' = 3.117-3.255) compared to Perlis (Ø' = 2.959-3.046). Across both species and sites, females attained larger asymptotic lengths and higher growth performance than males. These findings highlight the need to integrate spatial habitat variability and sex-specific growth into regional fisheries management.

Understanding the water balance component is essential for optimizing the allocation of water resources. The water resources are affected by climate change and an increase in population. This study assesses the water balance component and examines the incoming, outgoing, and storage variation within the Ping River Basin (PRB), utilizing QGIS and QSWAT (a specialized plugin of the Soil and Water Assessment Tool). First, the input data are collected from different resources (DEM from NASA/Earth Data, land use/land cover from the Land Development Department Thailand, soil properties from FAO, and climatic data from the Thai Meteorological Department TMD). The QGIS interface was used to prepare and reclassify the maps of the input data for running the QSWAT model setup. Next, the QSWAT model was used to create the stream networks and sub-basins of the watersheds. In the second step, prepared the LULC and soil lookup tables according to map classifications and QSWAT codes for making the hydrological response units. Third, preparing and arranging the 10 years' daily climatic data (2016-25) that were collected from the different locations' climatic gauge stations within the PRB. After running the QSWAT model, the QSWAT model simulation (2016-25) for the Ping River Basin revealed mean annual precipitation was 1,794.5 mm and actual evapotranspiration (ET) totaled 553.4 mm, accounting for 31% of precipitation, with plant transpiration 311.8 mm exceeding soil evaporation 202.1 mm. Total streamflow represented 67% of precipitation, indicating a runoff-dominated regime. Surface runoff comprised 72% of total flow, while base flow contributed only 28%, reflecting limited groundwater contribution to streams. Percolation to the shallow aquifer was 15% of precipitation, but deep recharge was negligible, 1% of precipitation. The average curve number of 74.5 suggests moderately high runoff potential. Calibration and validation will be applied on QSWAT simulations for model efficiency.

The increasing accumulation of microplastics in marine environments has raised concerns regarding their impacts on reef-associated fish. This study investigated the effects of waterborne microplastic exposure on the growth performance of Chrysiptera parasema under controlled laboratory conditions. Five treatment groups were established based on different microplastic concentrations: control (0 mg/L), low (0.025 mg/L), medium (0.055 mg/L), high (0.083 mg/L), and very high (0.100 mg/L). Fish growth was monitored over a 49-day experimental period using weekly measurements of body weight and total length. Fish in the control group exhibited consistent growth throughout the study, while fish exposed to low microplastic concentrations showed growth performance comparable to the control group, suggesting minimal short-term effects at lower exposure levels. In contrast, fish exposed to medium, high, and very high microplastic concentrations demonstrated are associated with growth performance, particularly in weight gain. The very high treatment recorded the lowest final weight, while fish in the higher concentration treatments showed signs of impaired growth efficiency despite moderate increases in body length. Overall, the findings indicate that prolonged exposure to elevated microplastic concentrations may negatively affect the growth of C. parasema, potentially due to physiological stress and reduced nutrient assimilation. This study highlights the ecological risks associated with increasing microplastic pollution in marine ecosystems.

Mangrove forests are among the most important coastal ecosystems in Malaysia, functioning not only as major blue carbon sinks but also as critical ecological lifelines for fisheries. Through high primary productivity, biomass accumulation and long-term sediment carbon burial, mangroves contribute substantially to climate change mitigation. At the same time, they provide nursery, feeding and shelter habitats for fish, crustaceans and other aquatic resources that support coastal fisheries, biodiversity and the livelihoods of fishing communities. However, accelerating sea-level rise has become a growing climate-related threat to these interconnected ecosystem functions. Rising sea level, coastal erosion, saltwater intrusion, altered sediment supply, changing salinity regimes, increasing temperature and extreme weather events may reduce mangrove carbon storage capacity, increase greenhouse gas emissions and weaken habitat quality for fisheries resources. This paper provides a synthesis of recent evidence on mangrove blue carbon, sea-level rise impacts and fisheries-linked ecosystem functions, with specific relevance to climate adaptation planning. A systematic review of 78 Scopus-indexed studies published between 2021 and 2026 showed that research on mangrove blue carbon under climate stress has expanded globally, with sea-level rise emerging as the most frequently studied stressor at 29.5%, followed by temperature at 23.1% and salinity at 19.2%. However, only 15% of studies examined multiple interacting climate stressors, despite mangrove ecosystems being exposed to compound pressures that directly affect carbon dynamics. Meta-analysis further indicated that rapid sea-level rise exceeding 6 mm yr⁻¹ may cause significant carbon loss, while warming may increase methane (CH₄) emissions by approximately 21% per 1°C, potentially offsetting 13-27% of blue carbon sequestration benefits. The findings highlight several critical research gaps, including limited field-based measurements of carbon stocks under sea-level rise conditions, insufficient CH₄ and nitrous oxide flux data, weak integration between carbon assessment and fisheries habitat functions, limited application of remote sensing and machine learning for monitoring coastal vulnerability, and inadequate evaluation of carbon additionality in rehabilitated mangroves. Addressing these gaps is essential for positioning mangrove blue carbon as a nature-based solution that supports climate mitigation, fisheries resilience, biodiversity conservation and coastal adaptation. In the Malaysian context, this synthesis provides a science-based foundation for integrating mangrove conservation, sea-level rise risk assessment and fisheries adaptation strategies into national planning and decision-making, particularly for vulnerable coastal communities and fisheries-dependent areas.

This study investigates rice yield variability and climate-yield associations in Malaysia's double rice-cropping system, based on data from the eight major granary areas of Peninsular Malaysia from 1985 to 2023. A descriptive and exploratory framework was used to examine rice yield changes, detrended yield anomalies and climate-yield associations through Spearman correlation analysis and season-specific fixed-effects regression. The analysis focused on the core rice-growing window and used ten climate indicators: mean maximum temperature (Avg Tmax), mean minimum temperature (Avg Tmin), diurnal temperature range (DTR), mean temperature (TM), warm days (TX90p), warmest night (TNx), accumulated solar radiation (Rad sum), number of heavy precipitation days (R10mm), simple daily intensity index (SDII) and consecutive dry days (CDD). Raw rice yield generally increased across the eight granary areas, although the magnitude varied by region and season. By contrast, detrended yield anomalies fluctuated mainly around zero, indicating short-term interannual variability after removing long-term yield trends. Spearman correlation analysis showed that bivariate associations between detrended yield anomalies and individual climate indicators were generally weak. However, season-specific fixed-effects regression revealed clearer off-season patterns. Avg Tmin and R10mm were significantly and negatively associated with off-season yield anomalies, while TM showed a marginal negative association. In the main season, most climate indicators were not significant, except for a weak positive association with R10mm. These findings suggest that off-season yield variability may be more sensitive to night-time thermal conditions and heavy-rainfall exposure, although individual climate indicators explain only a limited part of interannual yield anomalies.

Aquifer sustainability relies on recharge mechanisms that are increasingly threatened by climate change. Despite these threats, groundwater recharge mechanisms, especially the impacts of direct rainfall and seasonal monsoons, remain poorly understood. This study applies stable isotopes (δ2H, δ18O) together with hydrochemical and physicochemical data (pH, EC, TDS, salinity) from 15 groundwater wells and 8 surface-water stations in the Langat River Basin. The objectives of this study are to characterize groundwater recharge sources, pathways, and their sensitivity to climatic variability. Groundwater δ2H and δ18O values were narrowly clustered close to the Global and Malaysian Meteoric Water Lines (GMWL, MMWL; δ2H = -40.8 to -48.2‰, δ18O = −6.3 to −7.34‰) with minimal seasonal scatter, indicating a well-buffered reservoir recharged predominantly by modern meteoric water that is, at present, relatively resilient to short-term seasonal rainfall variability. Surface water, in contrast, showed a much wider isotopic range (δ2H = -11.6 to -63.8‰) and an evaporative offset below the meteoric water lines, consistent with the markedly elevated EC, TDS and salinity recorded at downstream stations (S7, S8) a vulnerability likely to intensify under projected warming and more erratic rainfall. These findings provide an isotopic and spatial baseline for assessing groundwater recharge resilience under climate change and for guiding climate-adaptive groundwater management in tropical, monsoon-influenced basins.

Low-lying coastal regions in Peninsular Malaysia are vulnerable to coastal inundation especially from extreme meteorological events. This study investigates the nonlinear interactions between storm surge and local tide induced by Cyclonic Storm Senyar. To explore the hydrodynamic interactions and their impact on coastal inundation in Lumut, Malaysia, high-resolution Finite Volume Community Ocean Model (FVCOM) is utilised. A series of numerical experiments is conducted using European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) and tidal constituents from Oregon State University TOPEX/Poseidon Global Inverse Solution (TPXO10-atlas-v2). The experiments are performed under three different simulation scenarios namely combined tide-surge, astronomical tide only, and storm surge only to analyse their nonlinear interaction. An analysis of practical and modelled surge shows positive differences peaking at approximately 20 cm in the southern part of study area. Further assessment of the nonlinear tide-surge interaction through differences of combined modelled tide-surge with ‘virtual' storm tide (linear superposition) showed negative differences except for the northern open ocean. Analysis of the tide-surge interaction indicated that the study area is strongly dominated with tide where the nonlinear interaction is negligible relative to the tidal signal (relative intensity = 0.004) but comparatively significant relative to storm surge component (relative intensity = 1.25) across most of the domain. Consequently, the inundated areas from the modelled combined storm tide reduce at approximately 6% compared to the virtual storm tide. These findings highlight that for tide-dominated systems such as Lumut, site-specific hydrodynamic modelling is crucial in the assessment of coastal inundation hazard risks.

The electrification of city buses in Malaysia is accelerating to reduce greenhouse gas (GHG) emissions from the transport sector. However, using generic driving cycles to evaluate energy use and life-cycle emissions does not reflect local traffic conditions, leading to biased results. This study develops a representative bus driving cycle for Putrajaya city, and applies it to compare the energy use and well-to-wheel (WTW) GHG emissions of two electric bus technologies: battery electric buses (BEB) and hydrogen fuel cell electric buses (FCEB). On-road speed-time data were collected along the operating city bus route using smartphone GPS. A microtrip selection method was used to construct a driving cycle that matches key parameters of the full dataset within 10% error. Then, the driving cycle was implemented in MATLAB/Simulink to simulate energy demand, producing electricity consumption for BEB and hydrogen consumption for FCEB. These results were used as foreground activity data in estimating WTW GHG emissions under Malaysia's current energy mix. Under the developed Putrajaya driving pattern, the simulated BEB electricity consumption was 90.06 kWh/100 km, while the FCEB hydrogen consumption was 11.88 kg H2/100 km. The WTW results show that the FCEB option yields 68% lower GHG emissions than the BEB under Malaysia's present electricity grid. Overall, the proposed Putrajaya City bus driving cycle improves the robustness of energy and emissions assessments and supports informed policy, such as National Energy Transition Roadmap, for low-carbon public bus systems in Malaysia.

Lithium-ion batteries (LIBs) are increasingly important in supporting electric vehicles, consumer electronics, and renewable energy systems. However, their rapid growth has created significant end-of-life management challenges, particularly in developing countries such as Malaysia where recycling infrastructure is still limited. This review aims to evaluate the environmental performance of different LIB end-of-life management pathways and identify sustainable strategies applicable to Malaysia. This systematic review applies the PRISMA 2020 approach to evaluate existing Life Cycle Assessment (LCA) studies on LIB end-of-life management and their environmental implications. From 605 identified records, 24 studies were selected for qualitative synthesis. The review shows that recycling methods consistently result in lower environmental burdens than disposal options such as landfilling and incineration. Among the technologies examined, hydrometallurgical and direct recycling demonstrated the greatest environmental benefits, particularly in reducing greenhouse gas emissions, conserving critical materials, and lowering overall energy demand. Direct recycling generally recorded the lowest carbon footprint, while hydrometallurgical processes achieved substantial emission reductions through material recovery and reuse in battery production. Commonly assessed environmental indicators included carbon emissions, resource depletion, acidification, eutrophication, and human toxicity. The findings also indicate that recycling efficiency varies according to battery chemistry, with nickel-cobalt-manganese batteries showing greater recovery potential than lithium iron phosphate batteries. Despite the growing body of international research, no comprehensive Malaysia-specific LCA studies were identified, highlighting a significant regional knowledge gap. Existing findings may not fully reflect regional conditions such as electricity generation, waste management infrastructure, and policy frameworks. Therefore, further region-specific LCA studies are needed to support evidence-based strategies for sustainable LIB waste management. Strengthening recycling systems and circular economy practices will be essential for reducing environmental impacts and improving resource sustainability in Malaysia.

Near-surface wind fields in tropical regions are strongly influenced by monsoon dynamics and land-sea interactions, yet their multi-decadal spatial evolution and implications for potential wind-energy resources remain poorly constrained. This study quantifies spatial and temporal changes in 10 m wind speed across Malaysia from 1981 to 2025 using ERA5-Land reanalysis data and assesses their implications for preliminary onshore wind-resource screening. The spatially explicit framework integrates pixel-level Mann-Kendall trend testing, Sen's slope estimation, false discovery rate correction, three 15-year climatological periods (1981-1995, 1996-2010, and 2011-2025), and a coastal-inland analysis using a 0-100 km inland buffer. The results reveal a spatially heterogeneous trend pattern, with localised increases in northern Borneo and decreases across parts of Peninsular Malaysia; trend magnitudes range from −0.13 to +0.02 m s⁻¹ decade⁻¹. Although the broad spatial structure of the wind field remains stable, with persistent coastal maxima along the east coast of Peninsular Malaysia, its overall intensity exhibits a progressive multi-decadal decline. The regional maximum of mean wind speed decreased from 3.69 m s⁻¹ during 1981-1995 to 3.27 m s⁻¹ during 2011-2025, representing a reduction of approximately 11%. These findings indicate that the detected multi-decadal changes are expressed principally through modulation of wind-field intensity rather than broad spatial reorganisation. The detected decline has important implications for potential onshore wind resources in low-wind tropical environments.

This study introduces the Met-Percentile Hybrid Index to resolve critical monitoring omissions in the current single-variable threshold-based heatwave frameworks in Malaysia. Utilizing high-resolution ERA5 hourly data span-ning nearly nine decades (1940-2025), the study evaluates thermal vulnera-bilities across Malaysia. The findings reveal a profound macro-climatological regime shift where contemporary tropical heatwaves are increasingly gov-erned by nocturnal cooling failures, such as urban heat islands and coastal moisture trapping, rather than isolated afternoon maximums. While the threshold-based frameworks perform reliably in naturally high-radiance inte-rior plains (e.g., Kedah and Perlis), they generate massive tracking deficien-cies along humid coastal and maritime divisions (e.g., Sarawak, Sabah, and Trengganu), where relative percentage errors exceed 2,500% because ambi-ent daytime peaks rarely breach the rigid national 35.0°C threshold despite severe overnight heat retention. Furthermore, multi-panel diagnostic metrics illustrate a substantial spatiotemporal acceleration velocity, with the hybrid framework capturing an accumulated grid-averaged exposure of 8.17 days by 2025, compared to just 3.20 days recorded under the threshold-based baseline. Ultimately, this widening detection gap highlights that static opera-tional indicator leave densely populated coastal and urban zones structurally unprotected, providing compelling empirical justification for environmental and public health agencies to incorporate adaptive, percentile-based com-pound monitoring index.

Kuala Lumpur equatorial tropical megacity climate profiles, characterized by a consistent high temperature baseline with minimal fluctuation and high concentrations of anthropogenic and natural precursors, specifically biogenic volatile organic compounds (BVOCs) from tropical evergreen vegetation, establish a critical need to understand compound environmental hazards. This study investigates the historical trends (1980-2025) and future projections (2030-2080) of concurrent heatwave and tropospheric ozone (O3) pollution events in Kuala Lumpur to quantify the frequency, intensity, and duration of this concurrent extreme. The methodology integrates ground-based observations from METMalaysia and the Department of Environment with ERA5 reanalysis data to construct a 45-year historical baseline. A Bias-Corrected ERA5 (BC-ERA5) dataset was developed to ensure local accuracy across the study period. Heatwaves were identified using a relative 95th-percentile threshold of daily maximum temperature (Tmax) for ≥ 3 consecutive days, while ozone pollution was defined by the Malaysian MDA8 standard of ≥ 51 ppb. Future projections were simulated under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) using downscaled CMIP6 outputs. Statistical analysis shows a warming trend (p < 0.01) in daily maximum temperatures, providing a rising baseline for compound hazard analysis. Historical heatwave frequency is heavily modulated by ENSO cycles, with major peaks identified in 1983, 1998, and 2016. 2005 recorded the highest event frequency due to a unique interaction between a moderate El Niño and regional biomass burning, which created a "blanketing effect" that trapped thermal radiation and exacerbated urban heat. These findings demonstrate that background warming acts as a persistent catalyst, lowering the threshold for dangerous compound events. This research provides essential localized evidence for policymakers to formulate climate adaptation strategies and safeguard public health against escalating synergistic risks in equatorial megacities.

Prolonged tropical eruptions occur in complex aerosol environments where volcanic emissions interact with biomass burning, regional haze, and atmospheric transport. This study examined Sinabung-period aerosol variability over northern Sumatra using monthly MERRA-2 total and sulfate aerosol optical thickness (AOT) together with MODIS active-fire detections. Stage-wise contrasts relative to the August 2005-July 2010 reference period were evaluated over the Southeast Asian region of interest (ROI), a near-source domain, and a downwind-sensitive sector. Later-stage total AOT enhancements were weak over the ROI but much larger near the source and downwind. The spatial maximum shifted from the near-source domain in 2014-2015 to the downwind sector in 2015-2017, where the 3-month contrast peaked at 0.558. Sulfate AOT contrasts remained substantially smaller, and active-fire detections increased during the later stages. These results indicate localized, stage-dependent mixed-aerosol disturbances rather than a region-wide sulfate-dominated signal. They also show that spatial scale, aerosol component, atmospheric transport, and concurrent non-volcanic sources must be considered when interpreting aerosol variability during prolonged tropical volcanic activity.

This study quantifies the structural uncertainty and operational decision risks introduced by utilizing alternative potential evapotranspiration (PET) equations within the Standardized Precipitation Evapotranspiration Index (SPEI-3) framework across Malaysia from 1948 to 2016. Benchmarked against the full-physics Penman-Monteith FAO-56 reference standard, a 49-member alternative ensemble stratified into four physical categories was evaluated using non-stationary trend analytics, informational entropy, wavelet coherence teleconnections, machine learning diagnostics, and threshold calibration verification. Results indicate an ensemble mean Area Under the Curve (AUC) of 0.9758 and a mean Brier Score of 0.0315. Combination and radiation-based categories exhibit the highest operational fidelity, led by the McGuinness Bordne and Guangxi formulations (Mean AUC = 0.9998; Mean Brier Score = 0.0050). Conversely, substantial structural degradation occurs within mass-transfer and temperature-based categories, culminating in critical threshold misclassifications for the Rohwer (Mean AUC = 0.6067, Mean Brier Score = 0.2251) and Romanenko (Mean AUC = 0.7829, Mean Brier Score = 0.1924) equations. High-dimensional Random Forest models reveal that these spatial error distributions are structurally constrained by non-linear interactions with regional temperature and relative humidity anomalies. Omitting aerodynamic or radiative parameters induces severe model drift and attenuates climate teleconnection signals, highlighting the operational necessity of comprehensive energy-balance parameterization for robust drought monitoring in humid tropical environments.

Many tropical peatlands in Peninsular Malaysia were historically disturbed through logging and associated drainage before being converted into managed agricultural landscapes, including oil palm plantations. Within these previously altered peat environments, oil palm cultivation involves structured water management, fertilisation, and long-term plantation practices that may further shape peat physicochemical conditions, microbial community composition, and biogeochemical processes. This study examined the relationships between environmental variables, prokaryotic community structure, and greenhouse gas (GHG)-related microbial functions in a 13-year-old oil palm plantation established on previously logged tropical peatland in Peninsular Malaysia. Soil samples were collected during wet and dry seasons across five soil depths (L1: 0-15 cm; L2: 15-30 cm; L3: 30-45 cm; L4: 45-60 cm; L5: 80-100 cm) and various management zones within the plantation. Microbial communities were investigated using DNA-based approach, specifically Illumina 16S rRNA V4-V5 amplicon sequencing, coupled with advanced PICRUSt2-SC functional prediction. Proteobacteria, Actinobacteriota, and Acidobacteriota were the dominant bacterial phyla (22% to 32% reads classified), while Streptosporangiaceae, Acidobacteriaceae, Xanthobacteraceae, and Burkholderiaceae were among the most abundant families (10% to 29% reads classified). Distance-based redundancy analysis (db-RDA) revealed that wet-season microbial communities were strongly associated with moisture-related and cation-linked variables, including K, Cu, and pH. Thermoplasmatota showed positive associations with high resistivity in deeper layers during the dry season, suggesting adaptation to low-moisture and anoxic environments. Proteobacteria were positively correlated with nutrient-rich conditions, including total N, available P, and Ca, consistent with their copiotrophic condition. Interestingly, during the dry seasons, the retention of frond pile biomass within designated field zones appeared to create microbial hotspot enriched with microorganisms carrying predicted greenhouse gas regulatory functions, including pmoA and mmoX-mediated CH₄ oxidation, together with nosZ-associated N₂O reduction. In conclusion, these findings establish an important baseline for advancing climate-resilient and sustainable management strategies in Malaysia, where molecular-scale studies of such environments remain limited.

This study assessed planted mangrove development in Bagan Nakhoda Omar (BNO), Selangor, using multi-temporal vegetation indices (VIs) and Land Surface Temperature (LST) derived from five Landsat observation epochs spanning 2006-2024. The Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Green NDVI (GNDVI), and LST were analysed at the plot level, while field measurements from 333 trees across five 20 m × 20 m plots provided independent structural context. Results showed progressive vegetation development and canopy recovery following planting activities initiated in 2008, with all VIs increasing substantially from 2011 onwards. NDVI increased from 0.323 ± 0.058 in 2011 to 0.757 ± 0.021 in 2024, with comparable increases in SAVI and GNDVI. Field measurements indicated established but spatially variable stand structure among the sampled plots. LST exhibited a non-linear temporal pattern but was lower in 2024 than in 2006, coinciding with the highest VI values. VI-LST relationships were consistently negative, with the strongest correlation recorded for NDVI in 2024 (r = −0.915, p = 0.030). These findings provide converging spectral, thermal, and field-based evidence of planted mangrove development and associated surface thermal moderation. The integrated approach offers a practical basis for long-term planted mangrove monitoring and coastal ecosystem management.

Genting Highlands (GH) is located at the peak of Gunung Ulu Kali in Pahang, within a crucial region of Peninsular Malaysia (PM), which is Banjaran Titiwangsa famously known as the backbone of PM. GH was a virgin forest near the Selangor border until 1965 when it started to develop and open for business in 1971. This research aims to analyse the Surface Urban Heat Island (SUHI) and changes in Land Surface Temperature (LST) based on the land use in GH. This research utilizes Landsat-8 datasets for both LST and land cover classification, while MODIS (MOD11A2) data were used to fill missing LST values for the years 2014 and 2024. The research area is divided into Upper GH (elevation > 1200 m) and Lower GH (elevation < 1200 m) to account for elevation-related variations. The classification of land cover was conducted by the supervised Support Vector Machine (SVM) classifier. The SUHI intensity in Lower GH increased by 0.87°C between 2014 and 2024, rising from 3.27°C to 4.14°C. In contrast, Upper GH exhibited a weakening of SUHI effect over the same period, with the intensity decreasing by 0.71°C from 5.54°C in 2014 to 4.38°C in 2024. Land conversion is predominantly concentrated in urban areas for both Upper and Lower GH, with the most significant transitions occurring from forest to developed land. The conversion from forest to barren land resulted in a higher increase in LST compared to the conversion from forest to developed land. The greatest LST changes were observed in rural areas, associated with the conversion of forest to barren land. Vegetation and built-up conditions were assessed using the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). NDVI values were consistently lower in urban areas and showed a decrease from 2014 to 2024 in Lower GH. NDBI values increased in Lower GH over the same period, but decreased in Upper GH. These NDVI and NDBI results are consistent with the observed land use changes and variations in SUHI intensity. Overall, the findings are beneficial for the future development planning and sustainability initiatives in the GH region.

This paper examines which environmental indicators explain differences in the Environmental Performance Index (EPI) among eleven Southeast Asian countries. Using the same country-level indicator set from Our World in Data for 2005-2025, the analysis shifts from country grouping to driver attribution. A gradient-boosting model was trained to predict EPI 2022 from indicators representing emissions, energy structure, air pollution, water and sanitation, land, tree-cover loss, waste, and water stress. Permutation importance identified air-pollution mortality as the strongest negative driver, followed by safe drinking water as the strongest positive driver, tree-cover loss as a negative ecosystem driver, and carbon intensity of GDP as a negative efficiency driver. Partial-dependence analysis showed correctly signed monotonic relationships: predicted EPI declined as air-pollution mortality, tree-cover loss, and carbon intensity increased, while it improved with safe drinking water. Leave-one-out validation produced R2 = 0.51 and RMSE = 6.4 EPI points, suggesting moderate exploratory performance despite the small eleven-country sample. Residual analysis indicated that Myanmar and Vietnam were over-predicted, while Laos and the Philippines were under-predicted, pointing to country-specific factors not captured by environmental indicators alone. The findings support a policy-lever framework centered on air quality, drinking water, forest protection, and carbon efficiency.

Southeast Asian countries experience diverse environmental pressures stemming from rapid urbanization, economic development, the energy transition, pollution, water access, land-use change, and waste generation. This study develops an initial unsupervised machine-learning typology of eleven Southeast Asian countries using environmental indicators from Our World in Data covering climate, energy, air pollution, water, land, and waste dimensions from 2005 to 2025. Correlation analysis was first used to examine redundancy and identify dominant indicator relationships. Principal component analysis was then applied to reduce the multidimensional indicator set into interpretable latent gradients, followed by K-means clustering to identify country groupings. A nonlinear t-SNE projection was used as a supplementary neighborhood-preserving check. Results show that Southeast Asian environmental sustainability is primarily structured by a development-affluence gradient, in which energy use, municipal waste, carbon emissions, sanitation, and access to safe drinking water move together. Four initial country types emerged: Brunei as a fossil-intensive petro-economy outlier; Singapore, Malaysia, and Thailand as advanced service-economy countries; Laos, Vietnam, Indonesia, and Myanmar as lower-income, combustion-affected countries; and Cambodia, the Philippines, and Timor-Leste as transitional countries. The findings indicate that environmental sustainability in Southeast Asia is not defined by a single uniform regional pathway but by differentiated environmental-development profiles that require type-specific strategies.

Seagrass meadows are vital blue carbon ecosystems that support biodiversity, mitigate climate change and provide essential coastal protection services. Within the European Union (EU), their conservation aligns with major policy frameworks such as the EU Biodiversity Strategy for 2030, Nature Restoration Regulation and the European Green Deal. Although several public and hybrid financing mechanisms have emerged to support seagrass restoration, including blue carbon credits, Payments for Ecosystem Services (PES) and EU funding programs, their scalability is often hindered by challenges in cost-effective site selection, monitoring, management, and verification. Current approaches largely rely on field-based data collection, which has numerous logistical limitations regarding scale and environmental conditions while satellite imagery faces technical limitations related to spatial resolution. In this article, we argue that low-cost drones represent an underutilized tool that can complement existing technologies, by enhancing the efficiency, transparency, adoptability and scalability of seagrass conservation and financing. We also note that in Europe, drones and other remote sensing tools are increasingly viewed as dual-use (civilian/military) technologies, a development that could shape future access and governance. We outline how drones can offer assistance and discuss key implementation challenges and future directions. Our aim is to spark dialogue and pilot projects to bridge the gap between drone technology and conservation finance, ultimately accelerating scalable seagrass restoration efforts.

As the world moves increasingly beyond the 1.5°C warming threshold, solar radiation modification (SRM) has emerged as a potential climate intervention capable of rapidly reducing global temperatures and partially offsetting climate extremes. However, scientific assessments and governance discussions surrounding SRM remain dominated by perspectives from the Global North, despite Asia being highly vulnerable to its potential climatic and socio-economic consequences. Dense populations, complex monsoon systems, strong aerosol-cloud interactions, and heavy dependence on climate-sensitive water and agricultural resources may amplify regional risks, inequities, and trade-offs associated with SRM deployment. This review synthesises 52 Asia-focused studies identified from the Web of Science to evaluate how SRM research has addressed physical climate responses, sectoral impacts, and governance dimensions across the region. Existing studies are concentrated mainly in East and South Asia and are largely dominated by coordinated climate model experiments, with comparatively limited attention given to hydrology, agriculture, public health, ecosystems, and governance frameworks. Across modelling studies, SRM consistently reduces regional mean temperatures and partially offsets projected warming. However, projected changes in precipitation, monsoon circulation, and hydrological systems remain highly heterogeneous and strongly dependent on deployment strategy, regional climate variability, and background aerosol conditions. Several studies further suggest that while SRM may reduce certain climate hazards, it may also introduce new environmental and socio-political risks, including uneven regional impacts, transboundary governance tensions, and uncertainties associated with abrupt termination effects. These findings reveal substantial uncertainties, uneven distributions of risks and benefits, and critical knowledge gaps regarding SRM interactions with Asia's coupled climate-human systems. The review argues that Asia represents a critical testbed for evaluating the feasibility, risks, and ethical implications of SRM in a warming world. Greater leadership by Asian researchers is therefore essential for strengthening regional assessments and informing internationally coordinated SRM governance and decision-making.

Climate-driven environmental stress, characterized by severe precipitation fluctuations and sea-level encroachment, induces profound physical and geochemical modifications in tropical coastal ecosystems. This study establishes a novel non-destructive sensing framework using MCNPX Monte Carlo simulations to map gamma-ray transport perturbations within porous estuarine sediment matrices under four distinct climate stress scenarios: a dry baseline, freshwater saturation, standard seawater saturation (35 g/kg), and advanced hypersaline stress (50 g/kg). Gamma-ray transport kinetics were evaluated across the 137Cs (0.662 MeV) and 60Co (1.33 MeV) energy lines. The effective atomic number (Zeff) analysis showed a sharp drop from 10.560 to 9.651 during freshwater intrusion due to hydrogenic mass dilution, followed by a monotonic recovery to 9.715 under peak hypersaline stress driven by halogen loading. Attenuation coefficients derived from a fully collimated pencil-beam geometry with matched-energy surface-flux tallies achieved strict Beer-Lambert linearity (R2 = 1.0000). They agreed with NIST-equivalent elemental mixture-rule reference values within 2-3% across all scenarios and energies. Analysis of the energy-resolved Compton-scattered flux fraction (fscat) at the 5 cm reference depth revealed a statistically significant enhancement upon the transition from freshwater to standard seawater saturation (6.1σ), identifying this fraction as a robust, physically grounded salinity-sensitive metric. Void-normalised Exposure Buildup Factors (EBF) at the same depth exhibited consistent layer-dependent escalation, accurately characterizing multi-scattered photon accumulation. These findings, benchmarked against standard NIST reference data, provide a validated physical baseline for the future design and calibration of in-situ gamma-based coastal salinity sensors.

Climate change is an essential factor that regulates phytoplankton dynamics and ecosystem processes in Climate change is an essential factor that regulates phytoplankton dynamics and ecosystem processes in freshwater lakes. However, phytoplankton monitoring still relies mainly on field sampling and laboratory analysis. This study examines the reliability of satellite data for assessing the distribution of phytoplankton biomass, represented by chlorophyll-a (Chl-a) concentration, in Beris Lake, Kedah. From July to November 2025, Sentinel-2 satellite imagery was utilised to extract Chl-a using the Google Earth Engine (GEE) platform and compared with in-situ observations collected from five sites. The Red and Red-Edge bands were used to calculate the Normalized Difference Chlorophyll Index (NDCI), which was subsequently applied to estimate Chl-a concentration from satellite data. Results showed a correlation between NDCI and Chl-a in August 2025, with a correlation coefficient of 0.80 indicating good model performance. However, the remaining months (July, September, October, and November) showed lower model performance, with weak to moderate correlations ranging from R² = 0.34 (July) to R² = 0.45 (September). In some cases, particularly during September and October, inverse relationships were observed between NDCI values and measured Chl-a concentrations. These variations are likely attributed to the optical complexity of Beris Lake, including possible interference from coloured dissolved organic matter (CDOM) and suspended solids. Overall, the findings confirm that the NDCI algorithm has potential to estimate Chl-a in Beris Lake, but emphasize the critical need for calibration and validation to account for environmental changes. Continuous monitoring of phytoplankton biomass and Chl-a remains essential for improving the understanding of ecosystem dynamics and supporting the sustainable management of the Beris Lake ecosystem.

Climate conditions play an increasingly important role in shaping tourism demand and supporting sustainable destination management. Understanding how climate variability influences international travel patterns is essential for developing climate-adaptive tourism strategies and evidence-based policy. This study investigates the relationship between climate conditions and inbound tourism demand in Japan through the development of an AI-assisted interactive tourism analytics dashboard. The study utilizes monthly inbound tourism statistics published by the Japan National Tourism Organization (JNTO) and climate data collected from 2015 to 2025. The data were integrated into a web-based dashboard developed using artificial intelligence-assisted tools. Several analytical modules were implemented, including seasonality analysis, market concentration analysis, demand stability analysis, trend analysis, and future demand forecasting. Popular tourism periods were identified by detecting consecutive months with visitor numbers exceeding the annual monthly average. Market concentration was evaluated using market share indicators and the Herfindahl-Hirschman Index (HHI), while demand stability was assessed through monthly fluctuations, peak-to-bottom ratios, and variability rankings among source markets. The results reveal clear seasonal patterns in inbound tourism demand, with significant increases during spring and autumn, indicating the influence of favorable climate conditions on international travel behavior. The analysis also demonstrates differences in demand stability among source countries and highlights the concentration of visitor arrivals from a limited number of major markets. Furthermore, trend analysis indicates a strong recovery in inbound tourism following the COVID-19 pandemic, with continued growth expected in the coming years. The dashboard provides an effective framework for integrating tourism and climate data, enabling climate-aware tourism planning, sustainable destination management, and evidence-based decision-making under changing climate conditions. These findings contribute to a better understanding of climate-sensitive tourism demand dynamics and offer practical insights for future tourism policy and planning in Japan.

This paper details the design and development of an Internet of Things (IoT)-enabled smart waste bin system aimed at optimizing the sustainability and efficiency of urban waste management. The proposed system integrates three primary sensors: the HC-SR04 ultrasonic sensor for monitoring fill levels, the DHT22 sensor for tracking ambient temperature and humidity, and the MQ-135 sensor for detecting hazardous gases, such as methane and ammonia. These sensors are interfaced with a NodeMCU ESP8266 microcontroller, which transmits data via a LoRa SX1278 module to the Blynk cloud platform for real-time visualization and alert generation. The system is powered by a 12V 30W solar panel and a 24V 20,000mAh LiPo battery, providing up to 1.6 days of autonomous operation during periods of limited sunlight. Experimental results demonstrate high sensor accuracy and system reliability. The system successfully triggers alerts when gas concentrations exceed the safety threshold of 150 analog units. Although the prototype shows significant potential for scalable smart city applications, challenges regarding solar energy supply and gas sensor calibration persist. Overall, this system offers a promising, data-driven approach to enhancing energy-efficient waste management in urban environments.

This study investigates the intrinsic radiation shielding capabilities of Palm Oil Fuel Ash (POFA) as a sustainable, eco-friendly green matrix candidate under the STACLIM 2026 framework, benchmarking its performance against conventional commercial Barite. The physical and atomic stopping-power parameters were meticulously evaluated across standard nuclear diagnostic and industrial energy windows (0.1-10 MeV). The effective atomic number (Zeff), derived via Mayneord's power law, was established at 12.37 for the POFA matrix compared to 41.50 for Barite, fundamentally justifying the expected photoelectric dominance of Barite at lower energy bounds. To reconcile the differing spatial and energy definitions of the two Monte Carlo tallies without introducing micro-stochastic noise, an advanced Dual-Normalization protocol was executed to map the Exposure Buildup Factors (EBF). The calculated EBF profiles strictly adhered to physical boundaries (B≥1.0), exhibiting a smooth, monotonic upward trend tracking the progressive accumulation of Compton-scattered secondary photons. At 0.662 MeV, the Barite matrix asserted a consistently higher EBF profile (reaching ~4.60 at the deepest evaluated thickness of 15 cm) than the lighter POFA framework (~4.24) due to enhanced intermediate macroscopic interaction cross-sections per unit volume. Although structural thickness compensation margins (HVL) are required to offset bulk density variations, these findings conclusively validate that the agricultural POFA by-product possesses a reliable atomic capacity for gamma radiation mitigation, presenting a potent pathway for carbon footprint reduction in non-space-limited shielding applications.

The ionosphere plays a critical role in Global Navigation Satellite System (GNSS) signal propagation, where variations in electron density can degrade positioning accuracy and signal reliability. This study investigates the ionospheric responses observed at the geomagnetically conjugate GNSS stations LASA (4.9238° N, 101.068° E, 4.40° S geomagnetic latitude) and CUSV (13.736° N, 100.534° E, 4.40° N geomagnetic latitude) during three significant disturbance events: the Hunga Tonga-Hunga Ha'apai volcanic eruption on 15 January 2022, the Mother's Day geomagnetic storm (10-13 May 2024), and the October 2024 geomagnetic storm. GNSS observations were processed using Total Electron Content (TEC), Detrended TEC (DTEC), and the Rate of TEC Index (ROTI), while geomagnetic activity was assessed using the Disturbance Storm Time (Dst) and Kp indices. The results indicate that TEC exhibited largely symmetrical behaviour between the conjugate stations, reflecting similar large-scale ionospheric responses during all three events. In contrast, DTEC and ROTI showed more pronounced asymmetrical variations during geomagnetic storms, particularly during the October 2024 event, indicating enhanced small-scale ionospheric irregularities under disturbed conditions. These findings improve the understanding of EPB-related ionospheric depletion and geomagnetic conjugate behaviour, providing valuable insights into plasma irregularity development during disturbed conditions over Southeast Asia.

Concerns about environmental impacts and resource scarcity are driving a transition from fossil fuels to a more sustainable and green economy. Malaysia is committed to achieving net-zero emissions by 2050, driven by the National Energy Transition Roadmap (NETR) and New Industrial Master Plan 2030 (NIMP). The initiative focuses on accelerating renewable energy to 70% of capacity by 2050. In this context, renewable hydrogen and fuel cell technologies are expected to play key roles in the energy mix and emerging clean technology of the coming years in Malaysia. However, unlike the fossil-based alternatives, many emerging fuel cell technologies are still at the early lab or pilot scale and are not representative of optimised industrial conditions. This makes a robust comparison of their environmental performances through life cycle assessment (LCA) challenging. Thus, a case study to analyse the environmental burdens of scaling-up projections of early-stage technologies (ex-ante LCA) with the influence of future socio-economic scenarios (prospective LCA) of hydrogen production and fuel cell technology deployment was performed. The findings call for future research to further explore the integration of ex-ante and prospective LCAs in analysing environmental impacts of emerging hydrogen production and fuel cell technology in Malaysia. A consistent consideration of ex-ante and prospective LCAs is instrumental to prioritise research and investments for upscaling the early-stage technologies that are most promising from a sustainability perspective, and ultimately guide a sustainable transition towards a sustainable economy in Malaysia.

Photolysis rate coefficients (J-values) are critical parameters driving photochemical reactions in the atmosphere. However, Taiwan's lack of long-term photolysis monitoring has limited our understanding of the temporal evolution of photochemical environments. To address this gap, we utilized spectroradiometer measurements of eight specific photolysis rate coefficients and meteorological parameters collected by the National Yunlin University of Science and Technology from 2022-2025, combined with the Tropospheric Ultraviolet and Visible (TUV) Radiation Model, to reconstruct long-term J-values for Taiwan. We employed 2022-2023 observational data as a baseline to establish a cloud correction formula using TUV model simulations. Subsequently, we reconstructed J-values for Taiwan during 2018-2023 by integrating aerosol optical properties from AERONET stations (including aerosol optical depth, AOD; Angstrom exponent, AE; and single scattering albedo, SSA) and meteorological parameters from agricultural weather stations (temperature, pressure, and global horizontal irradiance). Long-term trends in the reconstructed J-values were compared with those in historical surface ozone observations. Trend analysis revealed that ozone concentrations at Qiaotou and Zuoying stations in high-ozone southern Taiwan declined significantly during 2020-2022, with reductions of 6.20 ppb and 6.23 ppb, respectively. Further analysis attributed 4.13 ppb and 3.85 ppb of these declines to ozone precursor emission reductions, while meteorological changes contributed 2.07 ppb and 2.38 ppb, respectively. Notably, reconstructed J(NO2) and J(O¹D) exhibited synchronized downward trends during this period, corroborating the impact of meteorological changes on photochemical processes. These results demonstrate that our corrected reconstructed J-values effectively enhance our capacity to analyze temporal variations in Taiwan's photochemical environment and their impacts on air quality.

Black pomfret (Parastromateus niger, Bloch, 1795) is an economically important species in Malaysia, but intensive trawl fishing has raised concerns about stock sustainability. A total of 9,250 individuals were sampled from commercial trawl landings in Perak and Perlis waters, northern Strait of Malacca, between April 2024 and July 2025. This study applied length-based stock assessment methods to evaluate the biological parameters, exploitation status, and reproductive capacity of the species. Length-frequency analysis indicated dominance of juvenile size classes, while the length-weight relationship showed negative allometric growth (b = 2.831). The estimated growth parameters were asymptotic length (L∞) of 440.48 mm and growth coefficient (K) of 0.59 year⁻¹. Mortality estimates revealed high fishing pressure, with total mortality (Z) = 2.55 year⁻¹, natural mortality (M) = 0.61 year⁻¹, fishing mortality (F) = 1.94 year⁻¹, and exploitation rate (E) = 0.76. The estimated maximum exploitation rate (Emax = 0.496) further indicated that the stock is overexploited. Recruitment occurred throughout the year with two major peaks in April and October. Overall, the findings suggest that the stock is biologically overexploited and may be experiencing recruitment impairment, highlighting the urgent need for management measures to support stock recovery and sustainable fisheries management.

Drought stress can significantly affect plant growth, and its effects may become permanent if plants are not managed promptly. Arbuscular mycorrhizal fungi (AMF) can be used to reduce the negative effects of water stress on plants, in-cluding oil palm seedlings. This study aimed to determine the most effective type of AMF in enhancing oil palm seedling growth and mitigating the effects of wa-ter stress. The experiment was conducted in a greenhouse for seven months. Treatments were arranged in a factorial (3 × 4) completely randomized block de-sign with four replications. The first factor was the type of AMF, consisting of no AMF, Glomus sp., and Entrophospora sp. The second factor was the duration of water stress, namely daily watering, no watering for 7 days, 14 days, and 21 days. The results showed that the application of Glomus sp. resulted in better seedling growth than the control, as indicated by greater seedling height and higher fresh and dry weights of shoots and roots. Water stress treatment without watering for 21 days significantly inhibited the growth of oil palm seedlings in both AMF-treated and non-AMF-treated plants. However, the effects were more severe in non-AMF-treated seedlings.

Despite rapid industrial and urban development, the state of Penang still maintains diverse forest ecosystems, including mangrove forests, which play a critical role in balancing coastal ecosystems and the national economy. These mangrove forests, mostly located along coastlines and estuaries, are dominated by species of Rhizophoraceae and Avicenniaceae families, with stands of Nypa fruticans along tidal riverbanks towards the land. However, the total mangrove coverage area has now significantly decreased, necessitating a comprehensive assessment of the primary threats driving these changes. This study identifies and assesses ecosystem threats in two main areas: Penang Island and the mainland. The research methodology integrates Landsat satellite imagery with digitised historical maps from the L7010 series topographic maps (published in 1962 at a scale of 1:63,360) to measure spatial-temporal changes in mangrove forests. Google Earth Engine was used to classify land-use types, which were then used to compare current coverage with historical maps and measure changes in ecosystem cover. Results from the overlay of historical and current data show that threats across the state are driven by anthropogenic activities, particularly land conversion to urban areas. Geospatial analysis reveals distinct regional patterns: on Penang Island, the main threats are urban development (30.0%), aquaculture (29.7%), and coastal erosion (24.1%). Conversely, the mainland is more affected by urban development (56.7%), followed by agriculture (18%) and aquaculture (8.7%). This study has identified threats to mangrove forests and accurately mapped the distribution of degraded mangrove ecosystems. This information can be used for coastal ecosystem conservation planning and formulating mitigation measures to ensure the long-term sustainability of these ecosystems, such as the restoration of mangrove forests in degraded areas. Furthermore, this study assists decision-makers in balancing the needs of urban expansion and environmental conservation, as well as forest management planning and the determination of local conservation priorities. Additionally, this study provides information on the reduction in ecosystem distribution required under Criterion A of the IUCN Red List of Ecosystems risk assessment.

Seedling quality is essential for supporting cocoa growth and productivity under field conditions. This study evaluated the effects of shade and biocompost on cocoa seedling growth in the nursery up to three months after application. The experiment used a 2x2 factorial Randomised Complete Block Design with eight complete blocks. The first factor was shade, with shaded and unshaded conditions, and the second factor was biocompost, with and without biocompost. The data were analysed using analysis of variance for a factorial block design followed by Fisher's least significant difference test at the 5% level. Biocompost increased leaf number at 3 MAA, stem diameter at 1 and 3 MAA, leaf greenness, and shoot fresh and dry weights, but reduced root fresh and dry weights. Unshaded seedlings produced more leaves and greater root fresh and dry weights than shaded seedlings. A significant interaction between shade and biocompost occurred only for root length. The shaded biocompost treatment showed the most complex soil microbial community, with the highest protozoan population and the presence of nematodes. These findings indicate that biocompost improves selected vegetative traits of cocoa seedlings, while its effect on root elongation depends on shade conditions.

Rapid urbanization in Malaysia over the last decade and a half has highlighted significant inefficiencies in traditional waste management systems, characterized by irregular collection schedules, overflowing bins, and operational lapses. These challenges are exacerbated by insufficient infrastructure, ineffective route planning, and a lack of real-time monitoring, all of which contribute to environmental pollution and rising greenhouse gas emissions. This study proposes an IoT-based Smart Waste Management System (SWMS) integrated with machine learning to optimize waste collection and reduce operational costs. The research addresses key questions regarding how IoT and machine learning can improve waste collection while mitigating environmental impact. Specifically, this study focuses on: (1) investigating machine learning models for smart waste management, (2) training algorithms using datasets from IoT sensors, and (3) fusing sensor data for real-time monitoring and alerting. The study was conducted at waste dumping sites near Universiti Kebangsaan Malaysia (UKM), where frequent bin overflows were observed due to unscheduled collections. A Random Forest model was employed for predictive analytics, utilizing data from DHT11 (temperature and humidity) and MQ4 (methane gas) sensors to predict bin fill levels and trigger collection reminders. The model achieved 99.78% accuracy, with methane levels identified as the most significant predictor (64% weightage). The system features real-time data visualization via the ThingsSentral platform and automated notifications via Telegram for anomalies. This research demonstrates the potential of IoT and machine learning to enable optimized, cost-effective, and environmentally sustainable waste management.

This paper presents the development and evaluation of a low-cost High Frequency (HF) radio monitoring system designed for long-term ionospheric observation in the equatorial region. The system integrates the RTL-SDR dongle, a compact and affordable Software Defined Radio (SDR) receiver based on the RTL2832U chipset with an R820T2 tuner, with a Ham It Up v1.3 upconverter to extend the operational frequency range from 3 MHz to 30 MHz. Since the RTL-SDR operates natively above 24 MHz, the upconverter shifts the target HF band upward using a 125 MHz local oscillator, enabling seamless full-band acquisition. Custom Python software developed using the pyrtlsdr library controls the hardware, performs Fast Fourier Transform (FFT)-based spectral analysis, and continuously logs signal parameters - including frequency, signal strength, noise floor, and signal-to-noise ratio (SNR) - to CSV format with automatic timestamping. The system was deployed at Research Complex, Universiti Kebangsaan Malaysia (2.92°N, 101.78°E), where a passive field monitoring campaign was conducted using commercial and amateur radio transmissions across Southeast Asia as propagation signal sources. A 24-hour HF spectrum heatmap recorded on 24 December 2025 demonstrates clear diurnal propagation patterns characteristic of equatorial ionospheric conditions, with peak SNR values exceeding 50 dB observed between 10:00 and 18:00 local time in the 10-20 MHz frequency range. These results confirm the system's sensitivity and reliability for detecting HF signal variations across the full monitoring band. The proposed low-cost RTL-SDR approach offers a viable and scalable solution for HF spectrum monitoring and ionospheric research, particularly in tropical regions where conventional instrumentation remains limited and costly.

Malaysia has updated its Nationally Determined Contribution (NDC 3.0) to the UNFCCC and committed to an absolute reduction of 15-30 MtCO2eq by 2035 from peak levels. This commitment involves a "whole-of-nation" approach, requiring collaboration across all levels of government, the private sector, and civil society, as well as international support to achieve these targets. Sabah, as Malaysia's largest carbon sink is well-positioned to play its part in reducing emissions while ensuring that it is ready for the growth of the low-carbon economy and climate resilience. This review aims to assess the policy alignment, coherence and readiness of Sabah's State-level policies, institutions and strategies with the National framework for low-carbon and climate resilient development. The review was done based on a desktop assessment of more than 10 existing state and national documents and policies, complemented by an initial mapping of the governance framework, a synthesis assessment of the alignment level in energy transition, climate governance, biodiversity protection, low-carbon urban initiatives and carbon market readiness. The findings indicate that there is a strong strategic alignment between the national directions such as National Energy Policy 2040, National Energy Transition Roadmap, Low Carbon Cities initiative and Climate Change governance that will be implemented with Sabah instruments including Sabah Energy Master Plan and Roadmap 2040 (SE-RAMP 2040), Climate Change and Carbon Governance Enactment (2025), Renewable Energy and OTEC Enactment (2024), as well as biodiversity and forestry strategies. Key gaps exist in the integration of carbon pricing with national market mechanisms, circular economy detailing, monitoring, reporting and verification (MRV) systems, and the scale of climate finance at state level. In conclusion, the findings provide targeted policy recommendations to further strengthen institutional policy alignment, accelerate sectoral decarbonization and enhance climate resilience outcomes across the socio-ecological context of Sabah State, Malaysia

Dengue remains a significant public health concern in rapidly urbanizing regions of Malaysia, particularly in high-density urban areas such as Klang, Selangor, where socio-environmental conditions shape mosquito proliferation and disease transmission dynamics. This study investigates the influence of socio-environmental determinants on mosquito density and dengue transmission risk in selected endemic urban communities in Klang. A cross-sectional study design was employed, incorporating structured household questionnaires, environmental risk assessments, ovitrap surveillance for mosquito density monitoring, and secondary dengue case data obtained from the Klang District Health Office. Descriptive and inferential statistical analyses, including chi-square tests, were conducted to examine associations between socio-environmental variables, mosquito density, and dengue transmission risk. The findings demonstrate that high population density, socioeconomic vulnerability, limited community participation in vector control activities, and environmental risk factors-particularly the presence of unmanaged outdoor breeding sites-are significantly associated with increased mosquito density and elevated dengue transmission risk. Ovitrap surveillance indicated higher positivity rates in densely populated residential zones, especially in outdoor settings, highlighting the role of urban spatial and environmental conditions in facilitating vector breeding. Overall, the study reveals that dengue transmission risk in Klang is driven by the complex interaction between environmental exposure and sociodemographic determinants rather than isolated factors. These findings underscore the need for integrated vector management approaches that incorporate urban planning, community engagement, and targeted public health interventions to strengthen dengue prevention efforts in high-density urban settings.

Malaysia is prone to extreme rainfall events that frequently cause flooding, infrastructure damage, and substantial socioeconomic losses. Rainfall variability across Peninsular Malaysia is strongly influenced by the Northeast Monsoon (NEM), Southwest Monsoon (SWM), and large-scale atmospheric circulation such as the Madden-Julian Oscillation (MJO). This study investigates the influence of MJO on extreme rainfall patterns using daily rainfall data from 13 stations across Peninsular Malaysia over approximately 20 years. Extreme rainfall was analyzed using the block maxima approach under the Generalized Extreme Value Distribution (GEV) framework. Both stationary and non-stationary GEV models were fitted, where the non-stationary models incorporated MJO as a covariate in the location and scale parameters to account for intraseasonal climate variability. Parameters were estimated using Maximum Likelihood Estimation (MLE), while model performance was evaluated using the Likelihood Ratio Test (LRT), Akaike In-formation Criterion (AIC), and Bayesian Information Criterion (BIC). The results demonstrate that MJO effects on extreme rainfall are spatially and seasonally heterogeneous across Malaysia. Several stations, particularly in Kelantan, Pahang, and Terengganu, show significant improvements under non-stationary GEV models, indicating that MJO influences both rainfall in-tensity and variability. In contrast, stationary models remain adequate for several stations where monsoonal circulation and local climatic conditions dominate rainfall behavior. Return level analysis further indicates that extreme rainfall risks are substantially higher during the Northeast Monsoon, with Kelantan recording the highest return levels under active MJO conditions. Overall, the findings highlight the importance of incorporating intraseasonal climate variability into extreme rainfall modeling to improve flood risk assessment, early warning systems, and climate-resilient planning in tropical regions.

The building sector contributes substantially to final energy consumption and energy-related emissions, making the identification of energy-use drivers a critical foundation for efficiency strategies. This study presents a Systematic Literature Review aimed at classifying the determinants of building energy consumption through three analytical lenses: building type or size, environmental factors, and occupants, while also comparing the magnitude and direction of their influence across different building contexts. The search process was conducted in Scopus, Wiley Online Library, and Google Scholar following PRISMA principles. From the screening stage, 25 eligible articles were included in the final synthesis. The publication distribution indicates a peak in 2024 (28%) and continued growth in 2025 (16%). Research designs were evenly dominated by review studies and modeling/simulation or machine-learning-based control approaches, each accounting for 36%. The integrative multi-lens category constituted the largest share (40%), followed by studies dominated by the occupant lens (32%). Within the environmental lens, climate or meteorological variables were most frequently modeled (44%), whereas within the occupant lens, occupancy levels and occupant-system interactions each accounted for 32%. Cross-study findings indicate that occupancy and behavior tend to increase energy consumption when unmanaged, yet can reduce it when integrated into adaptive control systems. This study highlights the importance of a multi-lens approach to improve modeling accuracy and the effectiveness of energy-efficiency interventions.

This study characterizes the scientific productivity, intellectual impact, and evolving research landscape of Southeast Asian (SEA) atmospheric science between 2015 and 2025 through a Scopus-based Scientometric analysis combined with unsupervised machine learning. Using a Boolean query on title, abstract, and keyword fields, 11,738 records were retrieved and filtered for affiliations from ASEAN member and observer states, producing 244 unique papers, of which 210 were suitable for Latent Dirichlet Allocation (LDA) topic modeling. The analytical pipeline integrated bibliometric profiling, LDA with collapsed Gibbs sampling (k = 5, perplexity = 343.5), keyword co-occurrence, citation impact assessment, and a topic-by-methodology coverage matrix. Results show a fivefold expansion of annual output, from 7 documents in 2015 to 45 by mid-2025, with Malaysia, Singapore, Indonesia, and Thailand accounting for approximately 87% of total publications. Conference proceedings dominate the publication ecosystem (26.6% of documents; 71% of top outlets). LDA recovered five thematically coherent clusters: Climate & weather (27%), Remote sensing (21%), Materials & energy (19%), SEA haze & monsoon (17%), and Air quality & health (16%), indicating thematic diversification rather than narrow specialization. Citation analysis revealed an inversion between publication share and impact: Air quality & health attracted the highest citations despite its smaller corpus share, while the regionally distinctive SEA haze & monsoon cluster remained under-cited. Intra-SEA co-authorship was sparse, accounting for less than 10% of the corpus. The coverage matrix exposed pronounced gaps in machine learning/AI methods, policy linkage, and health-impact integration. These findings define a clear research agenda for ASEAN atmospheric science focused on capacity equity, methodological innovation, and policy translation.

Machine-learning typologies are useful for comparing environmental sustainability profiles, but their interpretability depends on whether the resulting clusters are stable, distinct, and meaningful. This study validates and interprets a four-cluster environmental sustainability typology of eleven Southeast Asian countries using Ward hierarchical clustering, silhouette analysis, and mean z-score cluster signatures. Environmental indicators from Our World in Data covering climate, energy, air pollution, water, land, and waste domains were standardized and analyzed. Ward hierarchical clustering confirmed that Southeast Asian countries can be understood through a broad developed-versus-developing structure and through a finer four-type resolution. The dendrogram reproduced the major country memberships identified in the initial typology, including Brunei as a fossil-intensive outlier, Singapore-Malaysia-Thailand as an advanced service-economy group, Laos-Indonesia-Myanmar-Vietnam as a lower-income combustion-affected group, and Cambodia-Philippines-Timor-Leste as a transitional group. However, silhouette analysis produced a weak average coefficient of 0.19, indicating that the clusters are soft and overlapping rather than sharply separated. Cluster signature analysis showed that the groups represent interpretable environmental archetypes. The study concludes that Southeast Asian sustainability clusters are meaningful but should be treated as an organizing lens rather than a rigid taxonomy.

This study investigated the spatial distribution of atmospheric carbon dioxide (CO₂) concentrations across Najaf City, Iraq. Measurements were conducted at sampling sites distributed among residential neighbourhoods and residential complexes. Marked spatial variability in atmospheric CO₂ concentrations was observed throughout the study area, with measured values. Higher concentrations were predominantly observed in densely populated neighbourhoods characterized by heavy traffic, intensive diesel generator use, commercial activities, and industrial workshops, while lower concentrations occurred in less urbanized areas with lower population density and fewer anthropogenic emission sources. For spatial interpretation, CO₂ concentrations were classified using the commonly adopted indoor ventilation reference categories; these categories were used solely as comparative reference values and not as regulatory standards for outdoor air quality. The observed spatial variability was primarily associated with anthropogenic emission sources, although natural factors, including vegetation cover and local meteorological conditions, may also have contributed to the measured patterns. Geographic Information System (GIS)-based spatial analysis was employed to identify CO₂ hotspots and visualize their distribution, providing valuable information for environmental monitoring, urban planning, and the development of strategies aimed at reducing urban CO₂ emissions.

Coastal ecosystems are increasingly threatened by the combined impacts of climate change, sea-level rise, tidal amplification, and anthropogenic pressures, necessitating integrated approaches to assess long-term coastal vulnerability and resilience. This study investigated the influence of the 18.6-year Lunar Nodal Cycle on tidal variability, sea-level fluctuation, mangrove resilience, and coastal vulnerability across selected bays in Luzon, Philippines, namely Legazpi Bay, Manila Bay, and Subic Bay. Comparative analyses were conducted using tidal height, sea-level variability, mangrove vegetation index (MVI), tidal range assessment, coastal vulnerability mapping, and forecasting techniques to evaluate environmental responses to long-term astronomical forcing. Radar analysis revealed distinct environmental profiles among the bays, with Manila Bay exhibiting the highest relative tidal and sea-level variability, while Legazpi Bay demonstrated greater mangrove resilience. Tidal range comparison further indicated enhanced hydrodynamic exposure in Manila Bay, suggesting increased susceptibility to coastal inundation and shoreline instability. Coastal vulnerability assessment identified Manila Bay as the most vulnerable system due to elevated sea-level stress, tidal amplification, and reduced ecological buffering capacity. Forecasting analysis projected continued increases in tidal variability and sea-level conditions over the next decade, indicating heightened risks of flooding, saline intrusion, ecosystem degradation, and infrastructure exposure. The findings suggest that the interaction between long-period lunar tidal modulation and climate-driven sea-level rise may intensify coastal hazards and influence mangrove ecosystem dynamics. Furthermore, healthier mangrove systems were associated with lower relative vulnerability, emphasizing the importance of ecosystem-based adaptation strategies. This study highlights the significance of incorporating astronomical tidal forcing into coastal climate assessments and provides valuable insights for coastal planning, mangrove conservation, disaster risk reduction, and climate adaptation initiatives in vulnerable Philippine coastal systems.

Urban culturally themed culinary spaces are increasingly positioned as every-day leisure environments within sustainable urban development and urban placemaking agendas, especially in dense cities where public dining places can support social interaction, cultural representation, and place experience. How-ever, current evidence still focuses more on tourism and heritage sites than everyday public dining spaces. This research focuses on the effect of heritage motivation and object-based authenticity on revisit intention among young urban visitors. The research evaluates the effect of heritage motivation and object-based authenticity on revisit intention towards Old Shanghai, a Chinese-themed public dining venue in Kelapa Gading, Jakarta. Quantitative surveys were conducted on 110 generation Z visitors aged 20 years and above who visited Old Shanghai. The data were analyzed using PLS-SEM with SmartPLS, including outer model assessment, inner model evaluation and hypothesis testing. Heritage motivation did not significantly influence revisit intention, but significantly increased object-based authenticity. Object-based authenticity did not have a significant effect on revisit intention and it did not mediate the link of heritage motivation and revisit intention. The outcomes indicate that cultural interest can affect perceptions of authenticity, whereas physical authenticity alone does not result in revisit intention. This research contributes to the literature on cities and urban development by situating cultural-themed culinary spaces in a sustainable urban placemaking context. It suggests that managers should enhance the quality of experience, interpretation, atmosphere and engagement of visitors.

Space weather within the Solar System is mainly driven by Coronal Mass Ejections (CMEs), and they depend on the magnetic complexity of their originating active regions (ARs). Evaluating the statistical relationships between sunspot classifications (McIntosh Zpc and Hale magnetic classes) and select CME parameters (linear speed, mass, and angular width), the Kruskal-Wallis H-test revealed significant differences across categories only for linear speed (p < 0.05). Conversely, variations in mass and angular width remained statistically insignificant across Z-, c-, and Hale classes, while the p-type classification showed no significant relationship with any parameter. Post-hoc Dunn's tests adjusted via Benjamini-Hochberg False Discovery Rate (FDR) control isolated specific pairing dynamics for linear speed. Post-FDR, only the E-D pair (Z-type), the c-o and i-o pairs (c-type), and the α and β-γ pair (Hale class) retained statistical significance.

Recurrent monsoon floods in Kelantan are commonly addressed through structural measures, forecasting and emergency response, yet community safety also depends on whether warnings, mobility, social networks, infra-structure and institutional support function together at the required time. This exploratory study examined community flood resilience in Tumpat, Pasir Puteh, Pasir Mas and Salor in Kota Bharu. A multi component design inte-grated administrative flood and temporary evacuation-centre records for sev-en northeast monsoon seasons (2019/2020-2025/2026), 16 structured inter-view records compiled across the four locations, and a targeted theme led re-view of Scopus-indexed evidence. The interview file represented 15 named individuals because one individual appeared in two role-based records. All available responses in were examined; the 13 extracts in the preliminary cod-ing matrix were illustrative quotations rather than 13 separate interviews. Overlapping or near identical summaries were not treated as independent corroboration. The administrative dataset recorded 26 flood event waves and 268 cumulative active event days. Four connected themes emerged: cascad-ing and socially differentiated consequences; warning to action and evacua-tion constraints; community led preparedness and solidarity; and institutional and infrastructural conditions. Accounts described linked disruptions to in-come, agriculture, schooling, markets and mobility; rapid night time inunda-tion, warning failure and limited rescue assets; reliance on family networks, private boats and collective shelter practices; and spatially uneven experi-ences of assistance and accessibility. The study conceptualizes resilience as a relational and time sensitive warning to recovery pathway. The proposed framework is preliminary and provides candidate domains not a calculated or validated index for future measurement, testing and Flood Resilience In-dex development.