Climatic Zones Research Articles

Habitat Loss in the IUCN Extent: Climate Change-Induced Threat on the Red Goral (Naemorhedus baileyi) in the Temperate Mountains of South Asia.

Climate change has severely impacted many species, causing rapid declines or extinctions within their essential ecological niches. This deterioration is expected to worsen, particularly in remote high-altitude regions like the Himalayas, which are home to diverse flora and fauna, including many mountainous ungulates. Unfortunately, many of these species lack adaptive strategies to cope with novel climatic conditions. The Red Goral (Naemorhedus baileyi) is a cliff-dwelling species classified as "Vulnerable" by the IUCN due to its small population and restricted range extent. This species has the most restricted range of all goral species, residing in the temperate mountains of northeastern India, northern Myanmar, and China. Given its restricted range and small population, this species is highly threatened by climate change and habitat disruptions, making habitat mapping and modeling crucial for effective conservation. This study employs an ensemble approach (BRT, GLM, MARS, and MaxEnt) in species distribution modeling to assess the distribution, habitat suitability, and connectivity of this species, addressing critical gaps in its understanding. The findings reveal deeply concerning trends, as the model identified only 21,363 km2 (13.01%) of the total IUCN extent as suitable habitat under current conditions. This limited extent is alarming, as it leaves the species with very little refuge to thrive. Furthermore, this situation is compounded by the fact that only around 22.29% of this identified suitable habitat falls within protected areas (PAs), further constraining the species' ability to survive in a protected landscape. The future projections paint even degraded scenarios, with a predicted decline of over 34% and excessive fragmentation in suitable habitat extent. In addition, the present study identifies precipitation seasonality and elevation as the primary contributing predictors to the distribution of this species. Furthermore, the study identifies nine designated transboundary PAs within the IUCN extent of the Red Goral and the connectivity among them to highlight the crucial role in supporting the species' survival over time. Moreover, the Dibang Wildlife Sanctuary (DWLS) and Hkakaborazi National Park are revealed as the PAs with the largest extent of suitable habitat in the present scenario. Furthermore, the highest mean connectivity was found between DWLS and Mehao Wildlife Sanctuary (0.0583), while the lowest connectivity was observed between Kamlang Wildlife Sanctuary and Namdapha National Park (0.0172). The study also suggests strategic management planning that is a vital foundation for future research and conservation initiatives, aiming to ensure the long-term survival of the species in its natural habitat.

Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds

The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

Harnessing Natural Pozzolan for Sustainable Heating and Cooling: Thermal Performance and Building Efficiency in Moroccan Climates

The need to construct environmentally friendly buildings to meet current environmental and ecological standards is urgent. This study introduces a new multi-layer construction material with two outer layers of ordinary mortar and an inner layer of a pozzolane-limes composite to meet this need. The thermal efficiency of this material in building construction is investigated using TRNSYS18 simulations for two distinct climatic zones in Morocco, with a particular focus on its impact on heating dynamics. The primary objective is to evaluate the thermal performance of multi-layered pozzolanic materials, for which mortar samples are meticulously prepared as a reference in the two different climatic zones (Azilal and Errachidia). Using the asymmetric hot plate method under both stable and transient conditions, the authors conduct thermal characterization experiments. The results underscore the improvement in thermal performance made possible by the incorporation of pozzolan as an aggregate in the multi-layer material compared to ordinary mortar. Specifically, thermal conductivity improves significantly, from 0.735 W m−1 K−1 for ordinary mortar to 0.4 W m−1 K−1 for multi-layered pozzolanic materials, representing a 46% mass gain. Additionally, effusivity decreases from 730 to 604 J m−2 K−1 s−1/2, while diffusivity decreases from 3.78 to 2.23 × 10−7 m2 s−1, further attesting to the material’s thermal efficacy. TRNSYS18 simulations corroborate the viability of using multi-layered materials as building envelopes, revealing potential annual heating gains of 25% in Azilal and 5% in Errachidia. These findings underscore the promising prospects of integrating these materials into sustainable construction practices.

Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.

Path to Net Zero: Understanding the Building Energy Efficiency in Different Climates across Various Building Types

This study analyses the determinants of building energy efficiency in different climate zones and user types. The energy consumption of buildings in different climate zones can be affected by well-known determinants in different ways. So do the buildings with different user types. The primary aim of this study is to investigate how building energy efficiency is determined in five major climate zones and four main property types. This study uses the global building data (Points Achieved dataset) from the Leadership in Energy and Environmental Design (LEED) rating system 2010 to conduct three cross-sectional tests with logit regression models. The results confirm that the determinants of building energy efficiency are the location of the building, adoption of Building Energy Codes (BECs), climate zones, building types, regional economic development level (namely Gross National Income—GNI, Purchasing Power Parity—PPP) and population density. However, the impact of the determinants varies considerably in different climate zones or for different building types. This is the first empirical study exploring building energy efficiency and how it is determined in different climate conditions and user types. The findings are helpful for the stakeholders, such as policymakers, developers, and local authorities, when they hope to implement measures to improve building energy efficiency and the policy/regulation to boost it. Each building requires specific measures that suit its different climate zones or building types to enhance energy efficiency.

Exploring the driving factors of urban flood at the catchment Scale: A case study of multitype megacities in China

With the increasing frequency of extreme rainfall events worldwide, urban flood in densely populated and economically vibrant urban areas has become a pressing issue drawing global attention. Catchments are fundamental units for understanding urban hydrological processes and managing flood. Current research often lacks an investigation into the importance and spatial differences of driving factors for urban flood at the catchment scale, particularly for different types of cities. This study uses 15 megacities in China as case studies and establishes a comprehensive system of driving factors. It categorizes cities based on population levels, climate zones, and spatial locations. The study begins with hotspot analysis to identify areas with high occurrences of urban inundation. Subsequently, it employs Geographic Weighted Regression (GWR), a spatial analysis technique, to unveil the mechanisms driving the spatial distribution of inundation points in different types of cities and the spatial heterogeneity of driving factors at the urban catchment scale. The research findings, based on multiple cities, demonstrate that urban inundation points exhibit clear spatial clustering characteristics. While impervious surfaces and urban buildings are generally more important than factors such as landscape patterns and population density, the importance and explanatory power of driving factors for the spatial distribution of urban inundation vary across different cities and even within different catchments of the same city. The categorization of cities based on population levels and spatial locations demonstrates stronger regularities in driving mechanisms compared to categorization based on climate zones. The study’s results further underscore the complex driving mechanisms behind the spatial distribution of urban inundation. This highlights the need for flood management strategies that consider both universal patterns of inundation occurrence and the specific characteristics of local driving factors to develop targeted and effective mitigation measures.

Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.

The marginal effect of landscapes on urban land surface temperature within local climate zones based on optimal landscape scale

The rising urban land surface temperature (LST) has undoubtedly caused an imminent threat to human habitat. Landscape patterns are among the most significant factors related to LST. However, the marginal effects and thresholds of landscape at optimal grid scale on LST within diverse local climate zones (LCZs) are poorly understood. This study employed the boosted regression trees (BRT) model to evaluate the relative influences and marginal effects of built-up, water, and green landscape metrics on LST at the optimal grid scale across various LCZs. The findings reveal that: (1) The spatial spread trend of LST closely matches the expansion trend of built-up landscapes. (2) Significant variations in average summer LST were observed among LCZs, with LCZ8 (large low-rise) exhibiting the highest temperatures and LCZ11 (dense trees) the lowest. (3) At the optimal scale of 1000 m, the DIVISION metric for water landscapes significantly influenced LCZ6 (open low-rise), LCZ8, and LCZ14 (low plants). (4) In LCZ6 and LCZ8, effective heat mitigation requires increasing water area to 50% and optimizing the water body separation index to 0.88. These findings suggest that heterogeneous landscape heat mitigation strategies should be considered across diverse LCZs in urban areas.

EVALUATING REMOTE SENSING-BASED DROUGHT INDICES: STRENGTHS, LIMITATIONS, AND APPLICABILITY ACROSS SUB-SAHARAN AFRICA'S AGRO-ECOLOGICAL ZONES: A REVIEW

This study reviews the application and effectiveness of various remote sensing (RS) indices for drought monitoring in Sub-Saharan Africa (SSA). Given the region’s diverse climatic zones and frequent drought occurrences, accurate and timely assessment tools are crucial. The study examines indices from different spectral regions, including optical, thermal infrared, and microwave bands, focusing on their spatial and temporal resolutions, data availability, strengths, and limitations. Optical indices such as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI) are effective in semi-arid and sub-humid zones where vegetation density varies. Thermal infrared indices, including the Temperature Condition Index (TCI), the Vegetation Health Index (VHI), and the Temperature Vegetation Dryness Index (TVDI), provide insights into thermal anomalies and vegetation health, with TCI particularly suited for semi-arid zones and TVDI useful in both semi-arid and sub-humid zones. Microwave indices, such as the Normalized Backscatter Moisture Index (NBMI), Vegetation Optical Depth (VOD), and the Microwave Polarization Difference Index (MPDI), excel in capturing soil moisture and vegetation water content, proving useful in humid forest and semi-arid zones. The integration of these indices with other meteorological and hydrological data enhances drought monitoring and management strategies. Recommendations are made for the optimal use of these indices across different SSA agroecological zones.

Arbuscular Mycorrhizal Fungi and Soil Enzyme Activities in Different Fonio Millet (Digitaria exilis Stapf.) Agroecosystems in Senegal

In plant roots, arbuscular mycorrhizal fungi (AMF) are the most prevalent microsymbionts, and thereby provide many key ecosystem services to natural and agricultural ecosystems. Despite AMF’s significance for the environment and the economy, little is known about the mycorrhizal inoculum potential and diversity of AMF associated with orphan African cereal crops, specially fonio millet (Digitaria exilis stapf.) under field conditions. We hypothesized that the type of fonio millet agroecosystem influences the AMF density and distribution in soils. We therefore, assessed the inoculum potential, density and diversity of AMF spores and soil enzyme activities in five fonio millet agroecosystems belonging to three climatic zones (Sudanian, Sudano-Sahelian and Sudano-Guinean). By combining AMF spore identification from field-collected soils and trap culture, 20 species belonging to 8 genera (Acaulospora, Ambispora, Dendiscutata, Gigaspora, Glomus, Racocetra, Sclerocystis and Scutellospora) were identified. Glomus was the most represented genus with 8 species, followed by Gigaspora (5 species) and Acaulospora (2 species); the remaining genera were each represented by one species. Except for Ambispora which was not found in the Sudanian area, all genera occurred in the three climatic zones. The abundance and diversity of AMF species and FDA-hydrolytic and phosphatase activities varied between fonio millet agroecosystems as well as between climatic zones. Soil pH and soil texture were the variables that best explained the density and distribution of AMF spores. Our results contribute to paving the way towards the development of microbial engineering approaches for agronomic improvement of fonio millet.

Spatial dynamics of pCO2 and CO2 emissions from eutrophic lakes

Eutrophication of lakes exhibits a more serious trend in a global scale under the climate change. Eutrophic lakes also exhibit a unique role in global carbon, yet, data on carbon dioxide (CO2) emissions from these eutrophic lakes are too scarce to accurately upscale CO2 emissions for large scale or global estimation. This study presented the results of a partial pressure of carbon dioxide (pCO2) survey covering 43 eutrophic lakes in China in the same season across diverse ecosystem types and climate zones. The first estimation of CO2 flux from Chinese eutrophic lakes was reported as − 0.7 to 1.0 Tg C a−1 based on the surveyed pCO2 values, indicating some lakes act as CO2 sinks while others are sources. The results showed that pCO2 in Chinese eutrophic lakes ranged from 0.9 to 1366.4 μatm with the mean of 356.9 ± 310.3 μatm, almost 69.3 % of the sampling sites in eutrophic lakes were undersaturated with CO2 to the atmosphere. The mean pCO2 variations in eutrophic lakes were consistent with the amount of precipitation across different climate zones, with the trend of semiarid > semihumid > humid regions, and all lower than that for atmosphere. Meanwhile, both the mean and median values of pCO2 went down with the decrease of the lake mean depth and the increasing lake area, and the highest value in deep lakes (mean depth > 10 m, 548.3 ± 353.7 μatm) and small lakes (376.6 ± 329.8 μatm). In all the studied eutrophic lakes, pCO2 values showed significant seasonal variations, and in summer and autumn showed an under-saturated seasonal mean value. This study proved that CO2 undersaturation was prevalent in the eutrophic conditions. Due to the CO2 source-sink conversion role of eutrophic lakes, it is recommended to focus on CO2 fluxs from eutrophic lakes in the first place during the research of inland waters carbon emissions.

Thermal Performance and Building Energy Simulation of Precast Insulation Walls in Two Climate Zones

Traditional concrete buildings exhibit low energy consumption and high heat loss, which results in a larger environmental problem. Precast insulation walls are proposed for strengthening thermal insulation efficiency and mitigating heat loss. Numerous studies have investigated the thermal performance of insulation walls over the past decades. However, gaps remain in practical engineering applications. This study aims to bridge these gaps by providing practical design recommendations based on experimental research. Nine different types of precast insulation walls were tested to examine the thermal performance, and the parameters of the insulation material, insulation form, insulation layer thickness, and concrete rib width were investigated. Then, numerical models of these walls were developed for simulating the thermal performance of the tested specimens. Finally, a six-story student apartment model using designed walls was developed to assess energy consumption in two distinct climate zones: the hot summer and cold winter zone of Changsha City, and the cold zone of Harbin City. The results indicate that the precast insulation wall with external insulation form shows better thermal performance than the sandwich insulation form. It is recommended to use precast insulation walls with 50 mm extruded polystyrene (XPS) external thermal insulation form in Changsha City and 80 mm XPS external thermal insulation form in Harbin City. Furthermore, buildings using precast insulation walls can significantly reduce energy consumption by 49.25% in Changsha and 49.38% in Harbin compared to traditional concrete wall buildings. Based on these findings, suitable design suggestions for this precast concrete wall panel building composed of insulation walls are given.

Soil organic carbon pools as influenced by 21 years of conservation agriculture management practices in Saskatchewan

Soil organic carbon (SOC) content is a key metric of soil quality and limited work has been done to examine the effect of long-term conservation agriculture management practices (CAMP) on SOC pools within western Canadian soils. We assessed the nature and permanence of sequestered SOC within 90 diverse Saskatchewan surface (0–10 cm) agricultural soils before and after 21 years of CAMP. Comparisons were made of total SOC, labile and dynamic SOC fractions (light fraction, water-extractable, and microbial biomass), respirable CO2–C during a 6-week incubation, along with spectroscopic characterization using 13C/12C stable isotope ratio and ATR-FTIR. Among soil climatic zones, the SOC content increased in the semi-arid Brown and Dark Brown soils, ranging from 2.4 to 3.7 Mg C ha−1 (111.4–187.7 kg C ha−1 year−1), but did not change in the subhumid Black, Dark Gray, and Gray soils. Overall, soils having the smallest initial SOC level were most responsive to CAMP and accumulated more SOC. According to the δ13C data, CAMP appeared to reduce annual crop moisture stress, especially within the Brown soil zone. Decreased light fraction and water-extractable SOC contents in Black, Dark Gray, and Gray soils could reflect more intense decomposition and greater surface stratification of crop residues. Brown soils experienced the largest increase in microbial biomass-C content. The CO2–C emissions from the Brown, Dark Brown, and Gray soils under CAMP suggest greater SOC stability in 2018 compared with 1996. The ATR-FTIR data pointed to enhanced SOC persistence, via more stabilized SOC forms and mineral-associated organic C fractions.

Assessing Energy Savings: A Comparative Study of Reflective Roof Coatings in Four US Climate Zones

Assessing Energy Savings: A Comparative Study of Reflective Roof Coatings in Four US Climate Zones

High-resolution greenspace dynamic data cube from Sentinel-2 satellites over 1028 global major cities

Greenspace, offering multifaceted ecological and socioeconomic benefits to the nature system and human society, is integral to the 11th Sustainable Development Goal pertaining to cities and communities. Spatially and temporally explicit information on greenspace is a premise to gauge the balance between its supply and demand. However, existing efforts on urban greenspace mapping primarily focus on specific time points or baseline years without well considering seasonal fluctuations, which obscures our knowledge of greenspace’s spatiotemporal dynamics in urban settings. Here, we combined spectral unmixing approach, time-series phenology modeling, and Sentinel-2 satellite images with a 10-m resolution and nearly 5-day revisit cycle to generate a four-year (2019–2022) 10-m and 10-day resolution greenspace dynamic data cube over 1028 global major cities (with an urbanized area >100 km2). This data cube can effectively capture greenspace seasonal dynamics across greenspace types, cities, and climate zones. It also can reflect the spatiotemporal dynamics of the cooling effect of greenspace with Landsat land surface temperature data. The developed data cube provides informative data support to investigate the spatiotemporal interactions between greenspace and human society.

Atmospheric deposition patterns in bulk open field precipitation and throughfall in Aleppo pine forest and black pine forest on the eastern Adriatic coast

The Mediterranean region, with its unique ecological characteristics, is particularly sensitive to global environmental changes, including climate change and impact of air pollution.Although Aleppo pine and black pine forests are the most abundant on the eastern Adriatic coast, atmospheric deposition in these forests is poorly studied. Changes in the chemical composition of precipitation as it passes through the tree canopy can lead to soil and groundwater eutrophication, and soil acidification, which affects plant vitality. In this study, the dynamics of ion deposition in Aleppo pine forest (Pinus halepensis Mill.) and black pine forest (Pinus nigra Arnold) on the eastern Adriatic coast are investigated, focusing on throughfall and bulk open field depositions.The aim of our research was to fill the gaps in understanding the influence of tree canopies on deposition fluxes in two different Mediterranean pine stands and to compare total inorganic nitrogen loads with critical loads. Over a period of two years, bulk open field precipitation and throughfall were sampled, measured and analysed using the ICP Forest methodology.The results indicate significant differences in ion deposition between bulk open field and throughfall, with throughfall showing higher values for almost all ions. The highest enrichment ratio was determined for K+. The comparison of the actual inorganic nitrogen load with the critical nitrogen load for Mediterranean pine forests revealed that the inorganic nitrogen load exceeded the critical load in the Aleppo pine forest. Ion deposition increased in the throughfall compared to bulk precipitation, which can be attributed to the seasonality of precipitation, including leaching and long dry periods. These findings enhance our understanding of ion deposition fluxes in vulnerable Mediterranean pine ecosystems and emphasize the need for long-term research on this topic in the actual changing environmental conditions.

The potential burden from urbanisation on heat-related mortality in São Paulo, Brazil

Heatwave events are associated with increased hospital admissions and mortality rates worldwide. Heat exposure is often exacerbated by the Urban Heat Island (UHI) effect, especially in large cities. However, an accurate quantification of the additional burden related to heat from urbanisation is understudied in Latin American cities. Therefore, we used advanced meteorological modelling to simulate 2 m air temperature at 1 km spatial resolution across São Paulo, estimating UHI intensity attributing of all-cause mortality to heat and UHI during the 2014 heatwave. The Local Climate Zone classification system provided input land use parameters. Our model was validated against 31 weather stations and bias-corrected using linear regression. We calculated heat-related all-cause mortality, stratified by age, using the modelled temperature data, estimating 394 heat-related deaths. A counterfactual experiment, replacing urban areas with natural land, quantified the additional UHI burden. We found that the UHI may contribute to 69–70% of heat-related mortality (modelled temperature scenario). This work motivates the use of appropriate urban climate data, including careful model validation and bias correction, when assessing heat exposure and health risk assessment. It also highlights the health impacts from heatwaves in cities such as São Paulo, which are likely to increase due to climate change.

Mapping Local Climate Zones (LCZ) Change in the 5 Largest Cities of Switzerland

Mapping Local Climate Zones (LCZ) Change in the 5 Largest Cities of Switzerland

Assessing microplastic pollution in a river basin: A multidisciplinary study on circularity, sustainability, and socio-economic impacts

Plastic pollution has emerged as a significant environmental challenge worldwide, posing serious threats to ecosystems and human health. This study seeks to explore the interplay among circularity, sustainability, and the release of microplastics within the freshwater ecosystems situated along the western Black Sea coast- Düzce, Türkiye. Employing a multidisciplinary approach that integrates environmental science, economics, and policy analysis, the research examines the current state of plastic pollution in the region, considering diverse land uses and socio-economic lifestyles. Conducted over four different seasons, the current study identifies the prevailing types of microplastics in the region. Fibers dominate, comprising 86.7% in each season, followed by film and fragments at 7.7% and 7.0%, respectively. Notably, polyethylene (PE) and polypropylene (PP) emerges as the primary polymer types. The distribution of polymer types varies across different land uses within the region, emphasizing the influential role of land use in shaping the abundance polymer composition. The comprehensive assessment of pollution, as reflected in the overall pollution load index (PLI) of the Melen River indicating a concerning level of pollution (PLI>1). Finally, the study unveiled the relationship between socio-economic activities as well as the seasonal precipitation patterns, and microplastic contamination in the region. This underscored the importance of site-specific mitigation measures on reducing the amount of microplastics. Lastly, incorporating sustainable practices within the circular economy framework fosters a harmonious balance between economic development and environmental protection in Türkiye.

Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning.