Changes In Minimum Temperatures Research Articles

Epidemiological Study of Downy Mildew Disease of Opium Poppy

The downy mildew caused by Peronosporaspp is a major disastrous disease of many plant species. The symptoms of downy mildew disease incidence on opium poppy (Papaver somniferum L.) were collected in three years from 2019-20 to 2021-22 for prediction of weather parameters on the progression of downy mildew disease. The downy mildew disease initiation was recorded in the 52nd SMW (10.0%DMI) when the maximum temperature was 19.430C. At 3rd SMW, the maximum temperature dropped 17.330C and DMI reached up to 20.00 percent depicting the progression and spread of downy mildew disease in opium poppy with the decrease of maximum temperature. The maximum disease incidence was recorded 92.53 percent in 10th SMW when maximum and minimum temperature were 29.030C and 13.530C. Regression analysis between dependent variable downy mildew disease incidence Vs. independent variables (viz., rainfall, maximum and minimum temperature and relative humidity) showed that all the weather parameters contributed more than 85 percent variation (R2 = 0.869, 0.957, 0.859) in the downy mildew incidence of opium poppy. One unit change of maximum temperature, minimum temperature (1.00c), maximum and minimum relative humidity (1.0%) might cause to change 0.128, 0.70, 0.117 0.130 units in downy mildew incidence, respectively.

Temporal change in minimum mortality temperature under changing climate: A multicountry multicommunity observational study spanning 1986–2015

Background: The minimum mortality temperature (MMT) or MMT percentile (MMTP) is an indicator of population susceptibility to nonoptimum temperatures. MMT and MMTP change over time; however, the changing directions show region-wide heterogeneity. We examined the heterogeneity of temporal changes in MMT and MMTP across multiple communities and in multiple countries. Methods: Daily time-series data for mortality and ambient mean temperature for 699 communities in 34 countries spanning 1986–2015 were analyzed using a two-stage meta-analysis. First, a quasi-Poisson regression was employed to estimate MMT and MMTP for each community during the designated subperiods. Second, we pooled the community-specific temporally varying estimates using mixed-effects meta-regressions to examine temporal changes in MMT and MMTP in the entire study population, as well as by climate zone, geographical region, and country. Results: Temporal increases in MMT and MMTP from 19.5 °C (17.9, 21.1) to 20.3 °C (18.5, 22.0) and from the 74.5 (68.3, 80.6) to 75.0 (71.0, 78.9) percentiles in the entire population were found, respectively. Temporal change was significantly heterogeneous across geographical regions (P < 0.001). Temporal increases in MMT were observed in East Asia (linear slope [LS] = 0.91, P = 0.02) and South-East Asia (LS = 0.62, P = 0.05), whereas a temporal decrease in MMT was observed in South Europe (LS = −0.46, P = 0.05). MMTP decreased temporally in North Europe (LS = −3.45, P = 0.02) and South Europe (LS = −2.86, P = 0.05). Conclusions: The temporal change in MMT or MMTP was largely heterogeneous. Population susceptibility in terms of optimum temperature may have changed under a warming climate, albeit with large region-dependent variations.

Climate Extremes Related with Temperature Change Point Detection in Pambar Kottakaraiyar Basin

Global climate change has changed the trend of climate variables. Also there is change in intensity, frequency and cyclic pattern of climate extremes such as flood, drought, heatwaves and tropical cyclones in the southern part of India. The high temperatures and further increase pose serious socio-economic and environmental impacts. There is an urgent necessity to know about the regional level climate extremes and their adaptation strategies especially for the drought prone and rain fed basins. This paper discusses about the temperature trend and change point over a long term historical time period of 30 years (1992-2021). Based on the Man-Kendall method and Sen’s test, the temperature over the basin is showing a significant rising trend with positive slopes in almost all the months, seasons and annual statistics. The change points of mean temperature are observed in 2008 for both Kundrakudi and Pudukottai climate stations. The minimum temperature change point was identified in 1998 and 2009 years for the Kundrakudi climate station. The results of this paper will be helpful in capacity building of specialised team on extremes risk, vulnerability and sector specific indices for temperature for the water resources and agricultural sectors under changing climate.

An In-Depth Look at Rising Temperatures: Forecasting with Advanced Time Series Models in Major US Regions

With growing concerns over climate change, accurately predicting temperature trends is crucial for informed decision-making and policy development. In this study, we perform a comprehensive comparative analysis of four advanced time series forecasting models—Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Multilayer Perceptron (MLP), and Gaussian Processes (GP)—to assess changes in minimum and maximum temperatures across four key regions in the United States. Our analysis includes hyperparameter optimization for each model to ensure peak performance. The results indicate that the MLP model outperforms the other models in terms of accuracy for temperature forecasting. Utilizing this best-performing model, we conduct temperature projections to evaluate the hypothesis that the rates of change in temperatures are greater than zero. Our findings confirm a positive rate of change in both maximum and minimum temperatures, suggesting a consistent upward trend over time. This research underscores the critical importance of refining time series forecasting models to address the challenges posed by climate change and supporting the development of effective strategies to mitigate the impacts of rising temperatures. The insights gained from this work emphasize the need for continuous advancement in predictive modeling techniques to better understand and respond to the dynamics of climate change.

Spatiotemporal evolution of dry and wet and quantitative analysis of the influence of meteorological factors based on MI and the FAO P–M model

The frequent occurrence of extreme climate events disrupts the regional water budget balance and leads to changes in the dry and wet conditions of the surface, making the water surplus and deficit more complex and variable. To explore the quantitative relationship between the spatiotemporal evolution of dry and wet conditions and meteorological factors in the Hexi Corridor under changing environmental conditions, the relative moisture index (MI) and FAO Penman–Monteith (FAO P–M) model were combined to construct a partial differential quantitative attribution model for dry and wet variations affected by climate factors in the Hexi Corridor. The results show that: (1) MI in the Hexi Corridor increased significantly (Z = 2.341) during 1960–2019, showing a wet-trend change, and the degree of drought increased from southeast to northwest in the Hexi Corridor. (2) The order of drought degree in four seasons is as follows: winter (− 0.95), spring (− 0.93), autumn (− 0.89) and summer (− 0.83). (3) The frequency of extreme drought, severe drought, moderate drought, and mild drought within 60 years of 21 meteorological stations accounted for 28.38%, 50.48%, 8.85%, and 7.38%, respectively, and the frequency above severe drought was the highest. (4) The sensitivity of meteorological factors gradually increased from northwest to southeast, and MI was the most sensitive to the change of precipitation (P), followed by net radiation (Rn), wind speed (u2), mean temperature (Tmean), relative humidity (RH) and maximum temperature (Tmax). MI was the least sensitive to the change of minimum temperature (Tmin). P is the most important meteorological variable that contributes to the increase of MI, followed by u2, Tmean, and Tmin. Rn, Tmax, and RH have the least influence, and the total contribution of the seven meteorological factors is 85.59%. Compared with the reference evapotranspiration, P is the main factor affecting the dry and wet variations in Hexi Corridor.

Impacts of Climate Change on Streamflow on Dawa Sub-watershed, Genale-Dawa River Basin, Southern Ethiopia

<i>Climate change is statistical variations over an extended period in the features of the climate system, such as variations in global temperatures and precipitation, caused by human and natural sources.</i> In this study aimed to measure and examine how streamflow in the Dawa sub-basin, Genale Dawa River basin was affected by climate change. It used the average of five regional climate models from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa, under two different scenarios of Representative Concentration Pathways: RCP4.5 and RCP8.5. The baseline scenario was based on the data from 1975 to 2005, while the future scenarios were based on the data from 2020s (2025–2054) and 2050s (2055–2084). The HBV hydrological model used to assess the impact on streamflow. The HBV model showed good statistical performance in simulating the impact of climate change on streamflow, with a coefficient of determination (R<sup>2</sup>) of 0.88 and Nash-Sutcliffe Efficiency (NSE) of 0.77 for monthly calibration, and R<sup>2</sup> of 0.86 and NSE of 0.83 for monthly validation. The impacts quantified using the mean monthly changes in precipitation, maximum and minimum temperatures. The bias-corrected precipitation and temperature showed a reasonable increase in both future periods for both RCP 4.5 and RCP 8.5 scenarios. These changes in climate variables resulted in a decrease in mean annual streamflow by 1.6 and 3.5% for RCP 4.5 and by 4.6 and 4.9% for RCP 8.5 scenarios of the 2020s and 2050s, respectively. Based on the analysis that predicted a drop in precipitation during the months, and seasons and an increase in precipitation during the <i>Belg</i> season, with a corresponding decrease and rise in stream flow throughout the watershed. So to offset the variation in the watershed, community should adopt various; Soil and water conservation technologies, Using drought tolerant crops, Implementing various trees and appropriate design and applying a water harvesting structure like in-situ, internal or micro catchment, external or macro catchment water harvesting and Surface runoff harvesting. This result offers useful information for current and future water resource management in the basin and similar other watershed in the country.

Assessment of the effect of climate change on the yields and water footprint of crops in arid area

ABSTRACT Investigating the actual amount of water used by agricultural crop yield is very important. This research evaluates the impact of climate change on water demand, yields, and green and blue water footprint of barberry and jujube plants under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The results show that the amount of precipitation in the future under three scenarios varies from −13.08 to 5.13 mm during the future period. It shows maximum temperature changes from 1.07 to 1.25 °C and minimum temperature changes from 1.15 to 1.54 °C. The maximum and minimum temperatures under all three scenarios have an increasing trend compared to the base period. The results show that the values of water requirement, blue water footprint, green water footprint, total water footprint, and finally the relative values of both crop yields increase in all three scenarios in the future period (2022–2038) compared to the base period (2021–2005). Furthermore, according to the three proposed scenarios, the water footprint of the barberry crop in the future is estimated to be 98.74% blue water and 1.24% green water. The jujube crop's water footprint consists of 98.81% blue water and 1.20% green water.

Impacts of future climate change on rice yield based on crop model simulation—A meta-analysis

Rice is one of the world's major food crops. Changes in major climatic factors such as temperature, rainfall, solar radiation and carbon dioxide (CO2) concentration have an important impact on rice growth and yield. However, many of the current studies that predict the impact of future climate change on rice yield are affected by uncertainties such as climate models, climate scenarios, model parameters and structure, and showing great differences. This study was based on the assessment results of the impact of climate change on rice in the future of 111 published literature, and comprehensively analyzed the impact and uncertainty of climate change on rice yield. This study utilized local polynomial (Loess) regression analysis to investigate the impact of changes in mean temperature, minimum temperature, maximum temperature, solar radiation, and precipitation on relative rice yield variations within a complete dataset. A linear mixed-effects model was used to quantitatively analyze the relationships between the restricted datasets. The qualitative analysis based on the entire dataset revealed that rice yields decreased with increasing average temperature. The precipitation changed between 0 and 25 %, it was conducive to the stable production of rice, and when the precipitation changed >25 %, it would cause rice yield reduction. The change of solar radiation was less than −1.15 %, the rice yield increases with the increase of solar radiation, and when the change of solar radiation exceeds −1.15 %, the rice yield decreases. Elevated CO2 concentrations and management practices could mitigate the negative effects of climate change. The results of a quantitative analysis utilizing the mixed effects model revealed that average temperature, precipitation, CO2 concentration, and adaptation methods all had a substantial impact on rice production, and elevated CO2 concentrations and management practices could exert positive influences on rice production. For every 1 °C and 1 % increase in average temperature and precipitation, rice yield decreased by 3.85 % and 0.56 %, respectively. For every 100 ppm increase in CO2 concentration, rice yield increased by 7.1 %. The variation of rice yield under different climate models, study sites and climate scenarios had significant variability. Elevated CO2 concentrations and management practices could compensate for the negative effects of climate change, benefiting rice production. This study comprehensively collected and analyzed a wide range of literature and research, which provides an in-depth understanding of the impacts of climate change on rice production and informs future research and policy development.

Temporal variation in river ice phenology of the Heilongjiang River in response to climate change

Study regionUpstream of the Heilongjiang River, located in Northeast Asia, is the transboundary river between China and Russia. Study focusUsing long-term hydrological records provided by the Mohe station in combination with multiple statistical approaches, we investigate how river ice phenology of the Heilongjiang River has varied during the period 1957–2016, and quantitatively assess the individual contribution of changes in different climatic factors to such variation in river ice phenology. New insights for the regionWe show the later occurrence for newly formed ice date, ice-on date, and freeze-up date and the earlier occurrence for beak-up date and ice-off date, ultimately leading to a decrease in persistence of river ice in the Heilongjiang River. River ice phenology exhibits a general lag response to climate change, with ranging from 1 to 5 months. Further attribution analysis that explicitly considers time-lag effect suggests that climate change plays a dominant role in regulating river ice phenology. More specifically, compared to other climatic factors, air pressure and maximum and minimum temperature changes have larger contributions to variation in river ice phenology, particularly for the delay of formation time. These findings provide technical basis and beneficial reference to the development of regional river management strategies involved in climate change adaptation framework.

Climatic Challenges in the Growth Cycle of Winter Wheat in the Huang-Huai-Hai Plain: New Perspectives on High-Temperature–Drought and Low-Temperature–Drought Compound Events

Global climate change increasingly impacts agroecosystems, particularly through high-temperature–drought and low-temperature–drought compound events. This study uses ground meteorological and remote sensing data and employs geostatistics, random forest models, and copula methods to analyze the spatial and temporal distribution of these events and their impact on winter wheat in the Huang-Huai-Hai Plain from 1982 to 2020. High-temperature–drought events increased in frequency and expanded from north to south, with about 40% of observation stations recording such events from 2001 to 2020. In contrast, low-temperature–drought events decreased in frequency, affecting up to 80% of stations, but with lower frequency than high-temperature–drought events. Sensitivity analyses show winter wheat is most responsive to maximum and minimum temperature changes, with significant correlations to drought and temperature extremes. Copula analysis indicates temperature extremes and drought severity are crucial in determining compound event probability and return periods. High-temperature–drought events are likely under high temperatures and mild drought, while low-temperature–drought events are more common under low temperatures and mild drought. These findings highlight the need for effective agricultural adaptation strategies to mitigate future climate change impacts.

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202–2050) and FP2 (2051–2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

English

The present study is to understand how climatic variables such as precipitation and temperature vary over time and how those changes affect stream flow in the Jhelum River basin in Pakistan under different emission scenarios A2 and B2. The simulation results of HadCM3 were employed to create potential climate change scenarios with the Statistically Downscale Model (SDSM). The calibrated model Soil and Water Assessment Tool (SWAT) was used to forecast imminent stream flow to develop a proposed future climate change scenario. Results indicated that cooling patterns were identified in the north portion of the study area whereas warming patterns were detected in the south portion. The projected mean annual maximum temperature (Tmax) of 2020’s 2050’s and 2080’s would be 0.3 oC, 0.8 oC, and 0.99 oC, respectively, under the A2 scenario. The changes in mean annual minimum temperature (Tmin) were also observed as it would be 0.4 oC, 0.7 oC, and 1.4 oC during 2020’s (2021-2040), 2050’s (2041-2070) and 2080’s (2071-2100), respectively. Similarly, it was observed that average annual rainfall would rise by 14%, 10%, and 20% during the 2020s, 2050s, and 2080s, respectively, in the Mangla basin. The results showed an increase in annual stream flows of 100% (1545 m3/sec), with increases in the winter and autumn seasons of up to 409% and 211%, respectively, and a drop in the spring and summer seasons of up to 29% and 25%, respectively, in the 2080’s compared to baseline. Water managers should consider the current trends and variability brought on by climate change to improve water management where water is scarce.

Anomalous Warm Temperatures Recorded using Tree Rings in the Headwater of the Jinsha River during the Little Ice Age

As a feature of global warming, climate change has been a severe issue in the 21st century. A more comprehensive reconstruction is necessary in the climate assessment process, considering the heterogeneity of climate change scenarios across various meteorological elements and seasons. To better comprehend the change in minimum temperature in winter in the Jinsha River Basin (China), we built a standard tree-ring chronology from Picea likiangensis var. balfouri and reconstructed the regional mean minimum temperature of the winter half-years from 1606 to 2016. This reconstruction provides a comprehensive overview of the changes in winter temperature over multiple centuries. During the last 411 years, the regional climate has undergone seven warm periods and six cold periods. The reconstructed temperature sensitively captures the climate warming that emerged at the end of the 20th century. Surprisingly, during 1650–1750, the lowest winter temperature within the research area was about 0.44 °C higher than that in the 20th century, which differs significantly from the concept of the “cooler” Little Ice Age during this period. This result is validated by the temperature results reconstructed from other tree-ring data from nearby areas, confirming the credibility of the reconstruction. The Ensemble Empirical Mode Decomposition method (EEMD) was adopted to decompose the reconstructed sequence into oscillations of different frequency domains. The decomposition results indicate that the temperature variations in this region exhibit significant periodic changes with quasi-3a, quasi-7a, 15.5-16.8a, 29.4-32.9a, and quasi-82a cycles. Factors like El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and solar activity, along with Atlantic Multidecadal Oscillation (AMO), may be important driving forces. To reconstruct this climate, this study integrates the results of three machine learning algorithms and traditional linear regression methods. This novel reconstruction method can provide valuable insights for related research endeavors. Furthermore, other global climate change scenarios can be explored through additional proxy reconstructions.

Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region.

Variations in Maximum and Minimum Temperature in Mount Qomolangma during 1971–2020

Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.

Climate change and hydrological response of Megech catchment, Upper Blue Nile River Basin, Ethiopia

Climate change scenarios of precipitation and temperature were divided into three time windows of 30 years each from 2011 to 2099. The new version of Soil and Water Assessment Tool (QSWAT v2.6.1) was used to simulate the hydrological response and it was first calibrated and validated using observed data as an input for assessing the hydrological responses. The results showed that the QSWAT calibration and validation reveals a good agreement (R2=0.77) during calibration and validation. Whereas nash and Sutcliffe simulation efficiency (NSE) was found to be 0.76 during calibration and 0.73 during validation. Based on changes of precipitation, maximum and minimum temperature, the monthly flow volume did not show systematic trends i.e. increases non-rainy months and decreases in the wet-months. Thus, the hydrology of Megech River is highly vulnerable to climate change which causes water stress for domestic use and environmental flow.

The evolutionary characteristics of temperature change in China during 1961–2020

AbstractThe changes of annual average, minimum and maximum temperature (Tavg, Tmin and Tmax) in China over 1961–2020 were examined based on the Chinese gridded observation dataset (CN05.1), and the relative contributions of Tmin and Tmax to the tendency of Tavg were addressed. The results show that the nationwide mean warming rate reached 0.29°C·decade−1 for Tavg, 0.39°C·decade−1 for Tmin and 0.24°C·decade−1 for Tmax, respectively, from 1961 to 2020, which depended on temporal windows. The relative contributions of Tmin and Tmax to the trends of Tavg over China during 1961–2020 are 56% and 44%, respectively. On the spatial scale, the majority of China exhibits a greater relative contribution of Tmin than Tmax, except for some areas along the Hu Huanyong Line. As in the case of the warming rate, the temporal patterns of the magnitude of the relative contribution of Tmin and Tmax are also identified. For the first 30 years (1961–1990), Tmin is the primary contributor to changes in the trend of Tavg over a broad area excluding the Qinling Mountain‐Huai River to the Nanling Mountain. The national average magnitudes of the relative contribution of Tmax is slightly larger than Tmin in the latter 30 years (1991–2020). The areas with larger contributions of Tmax are mainly scattered along Hu Huanyong Line, the southern part of the Qinghai‐Tibet Plateau and the patchy parts of Northwest China. Changes in downward longwave radiation, solar radiation, cloud cover, land use, soil moisture, atmospheric circulation and urbanization likely accounted for much of the trend difference of Tmin and Tmax, thereby leading to various relative contributions across different regions during different periods. The warming mechanisms in China have been subjected to novel changes. The study could provide information for decision making on scientific response to warming.

An Assessment of the Impact of Climate Change on Asphalt Binder Selection in East China Based on the ARIMA Model

Temperature is a key factor considered in the selection of asphalt binders for asphalt pavement construction. Currently, the asphalt binders used in some regions’ asphalt pavements are no longer suitable for anticipated climate conditions. The reasonable selection of asphalt binder is an important measure for asphalt pavement to adapt to climate change. This paper focuses on the potential impact of climate change on asphalt binder selection in East China in the future. This study is based on the performance grade (PG) system with SUPERPAVE specifications. It involved collecting meteorological data from 109 meteorological stations in East China from 1960 to 2019 and used the ARIMA prediction model to calculate the maximum and minimum design temperatures for road surfaces over the next 20 years. Based on the forecasted road surface temperature data, the impact of climate change on the choice of asphalt binder in East China was discussed. The research findings indicate that, validated by historical data, using the ARIMA model for future temperature prediction has proven reliability. There are some differences in different regions regarding the change in maximum and minimum pavement design temperatures. In 2019 and 2039, there are three and four high temperature grades in East China; these are PG52, PG58, and PG64 and PG52, PG58, and PG64, PG70 respectively. The dominant high temperature grade in East China will remain PG64, and a total of 23.80% of the regions in East China will experience a one-grade upward shift in high temperature grades. PG-28, PG-22, PG-16, and PG-10 are the four low temperature grades distributed in East China in both 2019 and 2039. Compared with 2019, the proportion of areas with grade PG-16 will increase from 33.86% to 34.89%, and the dominant low temperature grade in East China will remain PG-10 in 2039. In the next 20 years, low-temperature cracking issues related to asphalt pavement in some areas of East China will intensify, but the primary challenge will still be problems caused by high temperatures.

Low-cost, 3D printed irradiation system for invitro photodynamic therapy experiments.

The development of a suitable irradiation setup is essential for invitro experiments in photodynamic therapy (PDT). While various irradiation systems have been developed for PDT, only a few offer practical and high-quality setups for precise and reproducible results in cell culture experiments. This report introduces a cost-effective illumination setup designed for invitro photodynamic treatments. The setup consists of a commercially available light-emitting diode (LED) lamp, a cooling unit, and a specially designed 3D-printed enclosure to accommodate a multiwell plate insert. The LED lamp is versatile, supporting various irradiation wavelengths and adjustable illumination fields, ensuring consistent and reliable performance. The study evaluates the setup through various parameters, including photon flux density, illumination uniformity, photon distribution across the multiwell plate, and temperature changes during irradiation. In addition, the effectiveness of the LED-based illumination system is tested by treating mouse mammary breast carcinoma cells (4T1) with Rose Bengal and LED irradiation at around 525 nm. The resulting IC50 of 5.2 ± 0.9 μM and a minimum media temperature change of ca. 1.2°C indicate a highly promising LED-based setup that offers a cost-effective and technically feasible solution for achieving consistent, reproducible, and uniform irradiation, enhancing research capabilities and potential applications.

Weathergami

Abstract Adapted from the sports concept of scorigami, the weathergami chart is introduced. Weathergami charts depict the frequency of occurrence of the full range of daily maximum and minimum temperature combinations observed at a location. These charts highlight essential features of climate not evident in traditional representations. A variation of the weathergami chart displays transition frequencies, which describe the likelihood of particular day-to-day changes in maximum and minimum temperatures. Likewise, weathergami anomaly charts reveal characteristics of changing climate not evident in standard time series representations. Several examples are provided, with comparisons to climate descriptions found in popular textbooks.