Annual Temperature Research Articles

Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models

Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vittata in China and pinpointing the key drivers that influence its distribution can facilitate the identification of optimal areas for using P. vittata as a remediation tool for As-polluted soils. In this study, a comparative analysis was conducted using ten machine learning models to assess the habitat suitability of P. vittata based on 744 specimen records and 20 environmental factors. The key drivers affecting the distribution of P. vittata were also investigated based on the optimal model. The results indicate that the XGBOOST model was the most reliable and stable, achieving a coefficient of determination of 0.95. Approximately 24.47 % of China's land area was identified as suitable for P. vittata. Particularly, it was predominantly found in Hainan (45.9 %), Guangxi (92.96 %), Guangdong (91.68 %), Hunan (91.26 %), Guizhou (90.83 %), Chongqing (88.17 %), Fujian (85.70 %), Yunnan (77.44 %), Jiangxi (73.99 %) and Zhejiang (57.05 %). Furthermore, this study pinpointed the lowest temperature, annual temperature range, and mining density as key drivers, contributing 45.9 %, 31.9 %, and 7.2 %, respectively. Spatial correlation analysis revealed a significant correlation between mining density and the habitat distribution of P. vittata (Moran' I = 0.519). This study confirmed that both natural factors and anthropogenic activities affect the distribution of P. vittata and provided valuable insights for optimizing the application of P. vittata in soil phytoremediation and reclamation.

Middle to late Miocene cooling and drying in the northern Tibetan Plateau based on evidence from plant-insect interactions

The Qaidam Basin in the northern Tibetan Plateau is currently arid, but may have had a semi-humid to semi-arid climate in Miocene times, as suggested by pollen and isotopic data; however, the lack of macroscopic fossils hinders more precise paleoclimatic calibration. In this paper, we report fossil leaves with insect damage from a late Miocene layer (HT-5) in the Huaitoutala section dated ∼11.4 Ma, and compare them with records from a middle Miocene layer (HT-1) dated ∼12.7 Ma. Results show that damage diversity dropped from 36 types in HT-1 to 24 types in HT-5, suggesting a fall in mean annual temperature. Damage frequency decreased from 70 % in HT-1 to 32 % in HT-5, pointing to a drop in the coldest month temperature. Moreover, a slight fall in the diversity of mining damage, from 5 types in HT-1 to 4 types in HT-5, suggests a shift towards a slightly more arid climate from the middle to late Miocene. The flora from HT-5 is composed mainly of Betulaceae, Populus, Ulmus, and shrubs. Based on the distribution of these taxa in modern vegetation, the climate was probably semi-humid, not entirely arid during the late Miocene. These results are corroborated by fossil mammal data from the same section. Therefore, despite a cooling and drying trend from the middle to late Miocene, the climate of the Qaidam Basin was not as extremely arid as today.

Distribution and diversity of ticks determined by environmental factors in Ningxia, China

Ticks are important vectors of zoonotic pathogens, and represent an increasing threat for human and animal health. Considering the complex natural environments of Ningxia Hui Autonomous Region, China, we expect the diverse tick species in this region. Here, we conduct a field survey on parasitic and host-seeking ticks. A total of 10,419 ticks were collected, which belonged to nine species of four genera. There were significant differences in terms of vegetation index, altitude, and seven climatic factors among the four tick genera —Hyalomma, Dermacentor, Haemaphysalis, and Ixodes, except between Haemaphysalis and Ixodes, where no significant differences were observed in these factors. The ecological niche modelling revealed that the suitable habitats for Hyalomma asiaticum was in the northwest Ningxia, with annual ground surface temperature as the most important factor. The suitable area for Dermacentor nuttalli was in the southwest and eastern regions of Ningxia with elevation as the highest contribution. D. silvarum was best suited to the southern Ningxia also with elevation as the most important factor. The four tick species including Haemaphysalis longicornis, Hae. qinghaiensis, Hae. japonica, and Ixodes persulcatus were best suited to the southernmost Ningxia with annual precipitation as the main factors for Hae. longicornis and elevation for the other three ticks. The results of predicted potential distribution of different tick species provide a scientific basis for the prevention and control of ticks and tick-borne diseases in the region. Furthermore, the subsequent impacts of the Greening Program to regain forests and grasslands from former agricultural lands in Ningxia on tick population dynamics deserve further investigation.

Delineating the climate change impacts on urban environment along with heat stress in the Indian tropical city

India experiences significant levels of temperature variance as a result of urbanization, deforestation, and industrial growth, all of which contribute to a progressive increase in heat island effects. Significant landform change has occurred in Bhubaneswar City, Odisha, in recent decades as a result of major plant loss and temperature unpredictability. Thus, during the summer and winter months, an examination of the long-term heat island impacts is essential for Bhubaneswar. The evolution of landforms and heat island impacts in Bhubaneswar are evaluated using Landsat images from 1991 to 2021 datasets and correlation analysis of heat islands and geospatial variables. Between 1991 and 2021 with a 10-year gap, the landforms studied showed an increase in built-up land (38.009 km2), a decrease in vegetation (13.875 km2), bare ground (16.306 km2), and water bodies (7.828 km2). The annual temperature variance in Bhubaneswar was 0.124 °C (summer), and 0.293 °C (winter). Surface temperatures varied by about 3.71 °C (summer) and 8.80 °C (winter). During the study years, heat islands increased from 2.89 to 4.27 (summer) and 2.30 to 3.87 (winter). In Bhubaneswar, there was an increase in ecological variation of 0.092 (summer), and 0.071 (winter). There is a statistically significant negative correlation between the R2 values and the temperature and the built-up indices. Using this dataset, administrative and development authorities can choose which measures to take to counteract the effects of variations in thermal variation. Finding out how man-made land use structures, industrial zones, and other areas contribute to the built-up area a direct consequence of heat islands was the aim of this study.

High and variable hydraulic resistance to embolism among three Asian juniper species

Over a background of a warming planet, with increasing occurrence of extreme climate events, junipers stand out as tree species thriving in inhospitable environments, enduring the cold, hot, and dry. Being part of the drought-adapted Cupressaceae Gray, some juniper species have been identified having among the most negative P50 values (vulnerability to xylem embolism) in the plant kingdom. Still, previous analyses skipped many Asian juniper species, some of which growing in arid conditions. Here we measured xylem vulnerability to embolism in three, yet unexplored, Asian juniper species: Juniperus drupacea growing in Israel, and J. pseudosabina and J. seravschanica growing in Uzbekistan. Branches were sampled from five trees per species and vulnerability curves were constructed using the Cavitron method, from which P50 was calculated. Xylem anatomy measurements were used to define torus overlap and maximum xylem specific hydraulic conductivity. These hydraulic traits were tested against species and site climate and in the context of species phylogeny. P50 of the Eastern Mediterranean J. drupacea was -11.8±0.3 MPa, much more negative than that of the two Asian junipers measured here, but within the same range of values measured in European and North American junipers. P50 was more negative with mean annual temperature and with aridity index of the driest quarter at both species (climatic conditions at the entire distribution range) and site levels. We did not find the relationships generally found in conifers between the properties of xylem pit membrane and vulnerability to embolism, nor with the overall aridity of the environment. To the best of our knowledge, our observations identify J. drupacea as the most drought-tolerant tree species in Asia.

Humid, Warm and Treed Ecosystems Show Longer Time‐Lag of Vegetation Response to Climate

AbstractClimate‐vegetation interaction assessments often focus on vegetation response to concurrent climatic perturbations, seldom on the time‐lag effect of climate. Here we employ global satellite observations, climate data records and CO2 flux measurements to calculate the time‐lag of vegetation response to climate. We analyze the time‐lags of various climate variables under distinct environmental conditions to gain insight into how the long‐term climatic regimes and tree cover influence the time‐lag effects. Our findings reveal that terrestrial ecosystems characterized by arid and cold climates show more concurrent climate‐vegetation interactions than other ecosystems. Whereas areas with higher tree cover and humid ecosystems with both high mean annual temperature and precipitation show substantial time‐lag response of vegetation to climate by up to 6 months. Since the global climate‐vegetation interaction is dominated by time‐lag effects, incorporating these effects is paramount to improve our understanding of vegetation dynamics under a changing climate.

Agricultural land suitability of Manimutha Nadhi watershed using AHP and GIS techniques

A precise land suitability assessment plays a vital role in modern agriculture to sustain production against population explosions worldwide without affecting natural resources or environmental factors. Further, recent urbanization has led to a reduction in arable land and forced the conversion of non-arable land to arable land. Therefore, developing land suitability with its potential and limitations is essential through advanced techniques like multi-criteria Decision Analysis for the particular area. The current research aimed to assess the land suitability of the Manimutha Nadhi watershed, Tamil Nadu, India, using geographical information system (GIS) and Analytic Hierarchy Process (AHP) methods. This study area, Manimutha Nadhi, covers a total area of 263.89 km2. Using a random sampling technique, 57 soil samples were obtained. The parameters for the AHP study were soil pH, EC, Soil organic carbon (SOC), available NPK, soil texture, and soil erosion. The climatic parameters, Annaula average rainfall (AAR) and Annual average temperature (AAT) and topographic parameter slope of the study area are also used as the criteria for generating final agricultural land suitability. The final suitability map through GIS and AHP was created using weighted overlay analysis. The land suitability class on the basis of FAO classification is highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2) for agriculture. The results of the final land suitability map by AHP and GIS revealed that 150.58 km2 (57.09%) of the research region is highly suitable for agriculture (S1). The 29.60 km2 (10.08%) area was moderately suitable (S2) for irrigated agriculture. The 53.37 km2 (20.20%) area was marginally suitable (S3) for irrigated agriculture. The 10.56 km2 (7.53%) area was currently unsuitable (N1) for agricultural uses. The area of about 13.45 km2 (5.10%) was permanently unsuitable (N2) for agriculture. The outcomes can be used as baseline information on the land that could strengthen agriculture production in the Manimutha Nadhi watershed.

Sparse Trend Estimation

Abstract The low-frequency movements of economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes which is guided by a judicious choice of priors and is characterized by sparsity. We present novel stylized facts from longer-run survey expectations that inform the structure of the estimation procedure. The general version of the proposed Bayesian estimator with a spike-and-slab prior accounts explicitly for cyclical dynamics. We show that it performs well in simulations against relevant benchmarks and report empirical estimates of trend growth for U.S. output and annual mean temperature.

An Improved Air Health Index Based on Short-Term Cardiovascular Effects in Tianjin, China.

To construct an improved air health index (AHI) based on cardiovascular years of life lost (YLL) in Tianjin and assess its utility. We derived the exposure-response coefficients from time-series models and calculated the excess YLL (EYLL) for simultaneous exposure to air pollution and non-optimum temperature. The AHI was developed using the EYLL at the WHO 2021 Air Quality Guideline annual mean values and optimum temperature as a reference. We assessed the validity of AHI by comparing the correlations and model fit between the AHI, air quality health index (AQHI), and air quality index (AQI) with cause-specific YLLs. Each inter quartile range (IQR) increase in AHI was associated with 256.31 (95%CI: 183.05, 329.57), 150.34 (95%CI: 108.23, 192.46), 90.41 (95%CI: 64.80, 116.02) and 60.80 (95%CI:33.41, 88.18) person-year increments for non-accidental, cardiovascular, ischaemic, and cerebrovascular YLL, respectively. The AHI, in contrast to the AQHI and AQI, showed the strongest correlations with the risks of cause-specific YLLs, both in the total population and subpopulations. The AHI based on cardiovascular YLL has a greater predictive ability for health risks.

Morphological evolution and niche conservatism across a continental radiation of Australian blindsnakes.

Understanding how continental radiations are assembled across space and time is a major question in macroevolutionary biology. Here, we use a phylogenomic-scale phylogeny, a comprehensive morphological dataset and environmental niche models to evaluate the relationship between trait and environment, and assess the role of geography and niche conservatism in the continental radiation of Australian blindsnakes. This fossorial snake group comprises 47 described species and is widespread across various biomes on continental Australia. Although we expected blindsnakes to be morphologically conserved, we found considerable interspecific variation in all morphological traits we measured. Absolute body length is negatively correlated with mean annual temperature and body shape ratios are negatively correlated with soil compactness. We found that morphologically similar species are likely not a result of ecological convergence. Age-overlap correlation tests revealed niche similarity decreased with relative age of speciation events. We also found low geographical overlap across the phylogeny suggesting speciation is largely allopatric with low rates of secondary range overlap. Our study offers insights into the eco-morphological evolution of blindsnakes, and the potential for phylogenetic niche conservatism to influence continental scale radiations.

Changes in the air temperature regime across the Dniester River basin at the beginning of the 21st century

The relevance of the chosen topic is based on the goals and objectives of the Water Strategy of Ukraine until 2050 that provides for promotion of studies on the impact of climate change on the water content of Ukrainian rivers. The Dniester River is a transboundary river. It supplies water to Moldova and several regions of Ukraine. Predicting possible changes in the Dniester River's runoff due to global warming will help our society adapt to new climate conditions and take preventive measures. The research of the mountainous part of the watershed is of particular importance, as it is the area of runoff formation. Warming can change contribution of the snow component to the river's feed pattern and affect the total river flow. The object of the study: changes in air temperature due to global warming. The subject of the study: assessment of changes in the temperature regime within the Dniester watershed and impact of such changes on formation of its mountainous part's runoff. The study is aimed at assessing the changes in the air temperature regime within the Dniester River watershed at the beginning of the 21st century and assessing the impact of warming during winter season on formation of mountain rivers' spring floods. The main research methods include the method of difference integral curves and the method of regression analysis. The research materials include average monthly and annual air temperatures at 13 meteorological stations located within the Dniester watershed for the period of 1947-2021. The research indicated that the Dniester watershed is subject to warming. Statistically significant changes in air temperature began in 1988. Fluctuations in average annual air temperatures occur synchronously. Positive statistically significant trends over the entire observation period (1947-2021) were found for average annual air temperatures, average monthly temperatures of both warm and cold periods, and for the winter season. When considering two measurement periods (before 1989 and after), it was found that no statistically significant trends were observed before 1989. They were formed after 1989. It was also discovered that mountainous watersheds of the Ukrainian Carpathians respond to warming by forming negative trends in the fluctuations of average monthly runoff of spring floods.

Study on the Factors Affecting the Humus Horizon Thickness in the Black Soil Region of Liaoning Province, China

Understanding the spatial variability and driving mechanisms of humus horizon thickness (HHT) degradation is crucial for effective soil degradation prevention in black soil regions. The study compared ordinary kriging interpolation (OK), inverse distance weighted interpolation (IDW), and regression kriging interpolation (RK) using mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and relative RMSE to select the most accurate model. Environmental variables were then integrated to predict HHT characteristics. Results indicate that: (1) RK was superior to OK and IDW in characterizing HHT with the smallest ME (11.45), RMSE (14.98), MAE (11.45), and RRMSE (0.44). (2) The average annual temperature (0.29), precipitation (0.27), and digital elevation model (DEM) (0.21) were the primary factors influencing the spatial variability of HHT. (3) The HHT exhibited notable variability, with an increasing trend from the southeast towards the central and northern directions, being the thinnest in the southeast. It was thicker in the northeast and southwest regions, thicker but less dense along the southern Bohai coast, thicker yet sporadically distributed in the northwest (especially Chaoyang and Fuxin), and thick with aggregated distribution over a smaller area in the northeastern direction (e.g., Tieling). These findings provide a scientific basis for accurate soil management in Liaoning Province.

СТРУКТУРЫ ИСПОЛЬЗОВАНИЯ ПАХОТНЫХ ЗЕМЕЛЬ В ЛЕСОСТЕПНОЙ ЗОНЕ БАШКИРСКОГО ЗАУРАЛЬЯ СО ВТОРОЙ ПОЛОВИНЫ 20 ВЕКА

We analyzed modern use of arable lands of 1985 in forest-steppe zone of Post-Ural of the Republic of Bashkortostan. Climate is temperate continental. An average annual temperature is 1.6℃ and an average annual precipitation of 427.9 mm. The hottest month is July with an average temperature of 17.5 and the coldest month is January with an average temperature of -14.8℃. Modern agricultural lands usually are used as a hay (43%). Use can be caused with local cattle breeding as traditional horse breeding. Overgrowth captures by 25% of Pinus silvestris. Scot’s pine is growing better on dry lands because it captures more area of overgrowth.

Climate resilience in aquaculture: A cluster analysis of Chinese coastal regional production and farming practices in the face of climatic shifts 2017–2021

This study conducts a comprehensive analysis of the aquaculture industry across 11 coastal regions in eastern China from 2017 to 2021 to assess their adaptability and resilience in the face of climate change. Cluster analysis was employed to examine regional variations in aquaculture adaptation by analyzing data on annual average temperatures, annual extreme high/low temperatures, annual average relative humidity, annual sunshine duration, and total yearly precipitation alongside various aquaculture practices. The findings reveal that southern regions, such as Fujian and Guangdong, demonstrate higher adaptability and resilience due to their stable subtropical climates and advanced aquaculture technologies. In contrast, northern regions like Liaoning and Shandong, characterized by more significant climatic fluctuations, exhibit varying degrees of cluster changes, indicating a continuous need to adjust aquaculture strategies to cope with climatic challenges. Additionally, the study explores the specific impacts of climate change on species selection, disease management, and water resource utilization in aquaculture, emphasizing the importance of developing region-specific strategies. Based on these insights, several strategic recommendations are proposed, including promoting species diversification, enhancing disease monitoring and control, improving water quality management techniques, and urging governmental support for policies and technical guidance to enhance the climate resilience and sustainability of the aquaculture sector. These strategies and recommendations aim to assist the aquaculture industry in addressing future climate challenges and fostering long-term sustainable development.

How can agricultural water use efficiency be promoted in the North China Plain?

Water resources are reliable guarantees for high quality development of agriculture in the North China Plain. Improving agricultural water use efficiency is one of the essential ways to solve agricultural water resources shortage. This study measured agricultural water use efficiency (AWUE) using a stochastic frontier analysis (SFA) model and estimated agricultural water-saving potential (AWP) for 74 prefecture-level cities in the North China Plain. The main factors that influenced AWUE were further identified by developing spatial econometric models. The results showed that: (1) From 2000 to 2020, the AWUE of the study area ranged from 0.701 to 0.755 and first rose then fell, and then leveled off. (2) Spatially, the best AWUE was in the central part, followed by the north, and the worst was in the south, and nearly 65% of prefectural cities had annual average AWUE below 0.8. (3) Significant gaps in AWUE and AWP were observed in different regions. Shijiazhuang, Anyang, Hebi and Xinxiang should be considered as key targets to improve AWUE. (4) The effective irrigation degree, per sown area annual precipitation, and annual average temperature had positive effect on AWUE. Increasing the corn sowing area appropriately and reducing the input of per sown area machinery power could also improve AWUE. Therefore, adopting deep plowing measures, developing semi-arid agriculture, and advancing water-saving irrigation technology were feasible ways to improve AWUE and contribute to sustainable agriculture in the North China Plain. These findings can provide a reference for the formulation of high-water efficiency agricultural management strategies in other similar major grain-producing areas.

Forecast of innovative activity in key areas of energy transition technologies based on analysis of patent activity

Energy transition technologies aimed at increasing energy efficiency are a tool for achieving the goals of the Paris Agreement to limit the increase in average annual temperature. To increase the efficiency of implementation of these technologies, it is necessary to forecast innovative activity in the areas of hydrogen energy, CCUS and information technology based on taking into account key influencing factors, the contribution of sectors of the classical fuel and energy complex and the level of mutual influence of countries. Based on the analysis, a forecast was made to increase the share of the sector of international vertically integrated oil companies (VIOCs) in the total number of patents in the field of hydrogen energy by 2.5% points. and an increase in the share of the national vertically integrated oil company sector in the area of CCUS technologies by 10% points. by 2027. It has been determined that the innovative activity of the electricity and coal industry sectors in energy transition technologies is most resistant to the influence of external geopolitical and infrastructural crises. The market for CCUS technologies is currently more susceptible to the effects of international cooperation than the market for hydrogen energy technologies. The authors show that a decrease in the level of international integration by 25% points. over the past 5 years has led to a decrease in innovative activity in energy transition technologies by 15% points. In order to identify international barriers to the introduction of technologies, the authors provide a comparative analysis of the level of mutual influence of countries within the framework of international economic associations using the examples of the SCO, BRICS, and MERCOSUR. The highest level of mutual influence was identified within the framework of SCO interaction. According to the analysis, the entry of new countries into BRICS in 2024 will lead to a decrease in the integral level of innovation activity by 30% points. from the current value and strength of mutual influence of countries by 50% points. Taking into account the identified positive relationship between revenue and innovative activity in the area of information technology, it is recommended to direct investments primarily in favor of Industry 4.0 solutions.

Spatial heterogeneity in the potential distribution of Aedes mosquitoes in India under current and future climatic scenarios

Aedes is the most globally distributed mosquito genus in the 21st century and transmits various arboviral diseases. The rapid expansion of Ae. Aegypti and Ae. albopictus breeding habitats is a significant threat to global public health, driven by temperature and precipitation changes. In this study, bioclimatic variables were employed to predict the spatial distribution of Ae. aegypti and Ae. albopictus in India. The reference coordinate points of (n = 583) Aedes occurrences at a scale of ∼1 km and nineteen bioclimatic factors were retrieved to train SDM (Species Distribution Models) for both species. Maximum entropy modelling was used to predict the species’ fundamental climatic niche distributions. Future projections were made using global climate models for 2021–2040 and 2081–2100 separately. The models performed reasonably well (AUC > 0.77). Both species thrived in reduced diurnal temperature and higher annual mean temperatures, with suitability increasing alongside precipitation. Ae. aegypti’s projected present and future distribution was broader than that of Ae. Albopictus. The expansion of Aedes suitability varied under different future climatic scenarios. Suitability for Ae. aegypti could expand from between 17.6 and 41.1 % in 2100 under SSP (shared socioeconomic pathways) scenarios 1 and 3, respectively, whereas for Ae. albopictus suitability increased from between 10.2 and 25 % under SSP scenarios 1 and 3 respectively. Preparing for future epidemics and outbreaks requires robust vector distribution models to identify high-risk areas, allocate resources for surveillance and control, and implement prevention strategies.

Summer heat wave in 2022 led to rapid warming of permafrost in the central Qinghai-Tibet Plateau

Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost.

Statistical and machine learning models for predicting spalling in CRCP

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP’s functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.

Experimental study on the thermal performance of non-white near-infrared solar reflective coatings in a permafrost region

Solar reflective coating (SRC) offers a method to reduce surface temperature without the need for additional energy consumption, making it highly applicable in permafrost regions. However, many SRCs with high solar reflectance are white, which can cause glare and not meet the aesthetic pavement requirements of high-grade highways. Herein, we developed two non-white SRCs (black and gray) designed to achieve low reflectance in the visible spectrum while maintaining high reflectance in the near-infrared region. We then evaluated the thermal performance of a white SRC, two non-white SRCs and asphalt (as a control) by analyzing surface temperature, ground temperature, and the heat flux between the soil and pavement in a permafrost region of the Qinghai-Tibet Plateau. The results showed that three SRCs reduced the annual mean surface temperature and surface temperature fluctuation compared to the asphalt. The white SRC exhibited annual heat release, whereas the non-white SRCs (black and gray) reduced heat absorption by 34.53% and 69.44%, respectively, compared to the asphalt. By directly reducing the heat absorption of asphalt, the non-white SRCs can potentially slow permafrost degradation beneath asphalt pavements. This study provides a theoretical foundation and technical support for applying non-white near-infrared SRC on high-grade highways in permafrost regions.