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  Long-Term Ambient PM2.5 Exposure and Premature Mortality Across of Türkiye

  (Multidisciplinary Digital Publishing Institute (MDPI), 2026) Özmen, Nebile; Duran, Volkan; Şencan, Fatma; Paşa, Yasin; Çelik, Mehmet Ali

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  Long-term exposure to ambient fine particulate matter (PM2.5) is the leading environmental risk factor for premature mortality worldwide, yet comprehensive province-level evidence quantifying its health burden across Türkiye remains limited. This study investigated the spatial relationship between long-term PM2.5 exposure and all-cause attributable mortality across all 81 Turkish provinces in 2022 using province-level annual mean PM2.5 concentrations and World Health Organisation (WHO) AirQ+ estimates of PM2.5-attributable deaths among adults aged ≥30 years, assuming a counterfactual concentration of 5 µg/m3 . The association between PM2.5 exposure and mortality was evaluated using Pearson and Spearman correlation analyses, ordinary least squares (OLS) regression, a log–log elasticity model, and population-weighted regional and exposure-quartile comparisons, while national temporal indicators for 2010–2023 were reported solely as supplementary context for the primary single-year 2022 cross-sectional analysis. The population-weighted annual mean PM2.5 concentration was 27.0 µg/m3 , exceeding the WHO Air Quality Guideline by a factor of 5.4, and all 81 provinces exceeded the recommended threshold. The bivariate OLS model accounted for 41% of the between-province variation in attributable mortality rates (OLS slope = 3.23 additional deaths per 100,000 population for each 1 µg/m3 increase in PM2.5; 95% CI: 2.37–4.10; R2 = 0.41; p < 0.001), while the log–log elasticity model indicated that a 1% increase in PM2.5 concentration was associated with a 0.80% increase in the attributable mortality rate (95% CI: 0.65–0.95). The attributable fraction of natural-cause mortality increased progressively from 8.8% in the lowest exposure quartile to 24.6% in the highest. Nationwide, an estimated 68,440 premature deaths, representing 14.2% of all natural-cause deaths among adults aged ≥30 years, were attributable to PM2.5 exposure. These findings quantify a steep, spatially graded PM2.5-attributable mortality burden across Türkiye. As the attributable estimates derive from the WHO AirQ+ concentration–response function, the gradient describes the magnitude and spatial distribution of the modelled burden rather than an independently estimated exposure–response relationship, and on that basis the results support the adoption of WHO-aligned air-quality standards and accelerated decarbonization strategies to reduce the national health burden attributable to ambient air pollution.

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  Öğe Türü: Öğe ,

  Precipitation-Driven Land Cover Dynamics in Türkiye: A Multi-Dataset Assessment Using CHIRPS, TerraClimate, and TRMM

  (Multidisciplinary Digital Publishing Institute (MDPI), 2026) Çelik, Mehmet Ali; Bilik, Adile; Akpınar, Figen; Paşa, Yasin

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  This study investigates the spatiotemporal dynamics of Land Use/Land Cover (LULC) along precipitation gradients across Türkiye by integrating high-resolution satellite-based precipitation datasets (CHIRPS, TerraClimate, and TRMM) with the European Space Agency (ESA) WorldCover (10 m) product and multi-sensor Normalized Difference Vegetation Index (NDVI) composites (Landsat, MODIS, Sentinel-2). Türkiye’s heterogeneous climate, characterized by a sharp contrast between humid coastal belts and semi-arid interiors, serves as a natural laboratory to assess ecosystem responses to moisture availability. The results reveal a systematic and non-linear transformation of LULC classes as precipitation increases. In low-rainfall zones (200–400 mm), agricultural activities and bare surfaces predominate, reflecting human-induced land management in water-constrained environments. A critical ecological threshold was identified between 400 mm and 700 mm, where grassland areas expand rapidly, becoming the dominant class. Beyond the 900 mm isohyet, forest cover exhibits a sharp increase, approaching nearly 100% dominance in regions exceeding 1200 mm, effectively displacing other LULC categories. Comparative analysis of precipitation products shows that while all datasets capture the “coastal-wet/inland-dry” pattern, TRMM tends to overestimate winter precipitation (exceeding 100 mm), whereas CHIRPS and TerraClimate provide more conservative estimates (75–80 mm). Overlay analyses between seasonal NDVI and precipitation confirm a pronounced “time-lag effect” in vegetation phenology. Despite peak precipitation occurring in winter (~75 mm), NDVI reaches its minimum (~0.03) due to thermal limitations and dormancy. Conversely, vegetation greenness peaks during the dry summer months (NDVI ~0.14 to 0.40), utilizing antecedent soil moisture stored during the spring recharge. High-resolution Sentinel-2 data proved superior in delineating micro-topographic vegetation responses compared to Landsat and MODIS. These findings provide a scientific baseline for sustainable land management and climate adaptation strategies, highlighting that precipitation thresholds are the primary determinants of Türkiye’s ecological boundaries.

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  Understanding Long-Term Groundwater Storage Variability Using GRACE Data and Explainable Machine Learning

  (Multidisciplinary Digital Publishing Institute (MDPI), 2026) Çelik, Mehmet Ali; Bilik, Adile; Paşa, Yasin

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  The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor I˘gdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R2 of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions.

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  Design of Thermal Management System Based on Phase Change Material for Fuel Cell Hybrid Electric Vehicles: A Numerical Study

  (John Wiley and Sons Ltd, 2026) Yağcı, Damla; Genceli, Hadi; Turgut, Oğuz Emrah

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  In this study, a numerical analysis of the thermal management system for a fuel cell hybrid electric vehicle (FCHEV), based onthe second-generation Toyota Mirai, has been conducted. Within the scope of the model, the fuel cell stack, electric motor, high-voltage battery, cabin heating/cooling system, and phase-change material (PCM) based thermal storage unit have been consideredas a single integrated system. Hydrogen consumption, component temperatures, battery state of charge, the effect of regenerativebraking, cabin thermal behavior, and waste heat recovery have been analyzed for summer and winter operating conditionswithin the WLTC Class 3 driving cycle. In the proposed model, while the fuel cell is regarded as the primary energy source, thebattery functions as a secondary energy storage system, used to meet sudden power demands and to store energy recovered fromregenerative braking. The control strategy is designed to recover waste heat energy generated by the fuel cell and electric motorfor cabin heating and PCM charging. The model also considers dynamic battery SOC behavior, fuel-cell load-following operation,cabin thermal behavior, and PCM charge/discharge behavior. The results obtained demonstrate that the proposed integratedthermal management approach can utilize fuel cell and electric motor waste heat to support cabin heating, particularly in winterconditions. The simulated hydrogen consumption is 0.944 kg/100 km under WLTP-like conditions, within 6% of the certified valueof 0.89 kg/100 km, rising to 0.989 and 1.007 kg/100 km under summer and winter HVAC loads, respectively. In winter, 29.2% ofthe powertrain waste heat is recovered; the pre-charged PCM unit delivers 1204 Wh of cabin heat, covers 73.9% of the deliveredheating during the first 10 min of the cold start, and reduces the PTC consumption by 58.4% and the hydrogen consumption by 5.8%compared with the no-PCM baseline In conclusion, the PCM-assisted integrated thermal management system offers a measurableimprovement in energy efficiency and cabin heating performance in fuel cell vehicles, but its effectiveness depends strongly onthe control strategy, the initial thermal state of the storage, and the sizing of the PCM unit.

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  Short- and Long-Term Electrochemical Response Prediction of Ni-Al-Powder-Coated Steel with Machine Learning

  (Multidisciplinary Digital Publishing Institute (MDPI), 2026) Ocak, Ayla; Işıkdağ, Ümit; Nigdeli, Sinan Melih; Bekdaş, Gebrail

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  Steel is the most fundamental material used in structural system elements in the construction industry. It needs to be coated with materials that provide resistance to high temperatures, wear, and corrosion. Ni-Al powder is preferred in coatings because nickel increases corrosion resistance and aluminium forms an oxide layer to reduce oxidation. In the long term, the protective effect of coatings decreases, and corrosion resistance declines. In this study, a random forest model was evaluated using experimental data on the corrosion performance of A36 steel coated with Ni-Al powder for corrosion prevention, after exposure to a 3.5% NaCl solution for 1 h and 30 days for short- and long-term electrochemical response prediction. The impedance and phase angle characteristics, which represent the electrochemical response of coated and uncoated steel, have been predicted. In addition, the model’s reproducibility was investigated using the multi-seed (30 seeds) method to analyse the stability and consistency of the random forest model. The aim of this study was to develop a machine learning model that learns the frequency-dependent electrochemical impedance (Bode) response of graphene oxide-enriched Ni–Al coatings on steel, which reflects the corrosion-related electrochemical behaviour of the coating system, and to evaluate the model for predicting the impedance magnitude and phase angle of reference coatings over the investigated frequency range. The developed artificial intelligence model predicted the Bode response (impedance magnitude and phase angle) of coated and uncoated steel to NaCl solution after 1 h and 30 days as a function of frequency and coating type. The predicted impedance spectra reflected the deterioration of the corrosion protection performance of the Ni–Al coatings with increasing exposure time. The predicted EIS responses were subsequently used to assess changes in the corrosion-related electrochemical behaviour of the coatings over short- and long-term exposure. According to the findings, the random forest models can predict the frequency-dependent electrochemical response (impedance magnitude and phase angle) with high accuracy.

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