Diurnal Temperature Research Articles

Multifactor-coupled study on freeze-thaw forces of rocks in cold regions

This study delves into the mechanical properties of steep and rocky slopes subjected to long-term freeze-thaw actions. Considering the unique climatic conditions in cold regions, especially the significant impact of seasonal and diurnal temperature variations on slope excavation, the research focuses on a high-cold region iron ore mine. Four types of rocks commonly found in the mining area are thoroughly examined, taking into account the hydrogeological conditions of the mining area. The study systematically analyzes the mechanisms of various factors such as weathering, freeze-thaw cycles, and ice-water phase changes on the stability of cold region fractured rock masses. The research reveals that under prolonged freeze-thaw actions, crack water within the rock continuously undergoes ice-water phase changes, generating substantial freeze expansion forces that result in structural damage to the rock mass. This damage is evident not only in the development of existing microcracks but also leads to the generation of new fractures, ultimately causing deterioration in the rock mass structure. The study of the evolution patterns of freeze-thaw forces contributes to a better understanding of slope stability issues in cold region mineral resource extraction, offering crucial insights for the design, construction, and operation of related engineering projects.

Can urban shrinkage contribute to mitigating surface air temperature warming?

In recent years, urban areas are increasingly experiencing intense warming. Although urbanization is an important driver of warming in urban environments, it remains unclear whether depopulation trends in shrinking cities mitigate this warming effect. Here, we explored the relationship between shrinking cities and observed warming in China. Of the 356 Chinese cities, 95 were identified as shrinking between 2000 and 2010. We categorized 2419 observation stations into three groups—rural, shrinking, and non-shrinking—and generated a surface air temperature (SAT) anomaly series for each group from 1961 to 2014. Temperature differences between the shrinking and non-shrinking urban stations were investigated. Using segmented generalized least squares regression, the spatiotemporal temperature patterns across the mean (Tmean), maximum (Tmax), and minimum temperatures (Tmin), diurnal temperature range (DTR) indicators, and across seasons—spring, summer, autumn, and winter—were explored. Results revealed a cooling effect in shrinking cities, with decadal decreases of 0.042 °C (–0.078 to –0.005 °C), 0.083 °C (–0.126 to –0.039 °C), and 0.029 °C (–0.062 to –0.005 °C) in regional Tmean, Tmax, and Tmin anomalies, respectively. Moreover, pronounced seasonality was identified in this phenomenon—the cooling effect was most notable for Tmean in spring and Tmax in autumn, less significant in summer, and negligible in winter. These results suggested that the population decline in shrinking cities could alleviate regional warming, having implications that could influence urban planning and climate mitigation policies.

Wintertime column modeling in Jezero crater, Mars: Period of near-fog and a dust event

The diurnal water cycle was studied at a Jezero crater base site using M2020 observations and adsorptive column modeling in a cool midwinter period just before a dust peak and at the peak. During low-dust sols 566–571 the observed air relative humidity (RH) at 1.45 m height was high, 40–90% near dawn. For the typical nocturnal wind speed of 2 m/s at 1.45 m and column water of 9.7 μm, the model's diurnal air temperatures and RH were within observations. For weaker winds model-RH increased as in some of the observed sols. In a nearly calm experiment fog formed from 1.45 m upward. Ground frost appeared independently of wind, but only if adsorption was disabled. The dust peak at around sol 577 was also well simulated with the same wind and absolute humidity as before the event, but relative humidities dropped dramatically due to the dust-enhanced higher nocturnal temperatures.

Testing triple oxygen isotope preservation in the new OPEnS totalizer against conventional monthly rainfall collectors

Understanding the mechanisms that drive spatial and temporal triple oxygen isotope (Δ′17O) variations in modern precipitation is the first step to expanding the utility of these measurements as an environmental tracer jointly with traditional stable isotope parameters δD, δ18O, and d-excess. However, “totalizers” designed to collect a single precipitation sample pooled over a calendar month and minimize evaporation and associated isotopic fractionation of the sample during that time have not been tested for Δ′17O. We conducted a 30-day laboratory experiment comparing mass losses and isotopic shifts in four totalizers: 1) the OPEnS (Openly Published Environmental Sensing) totalizer, 2) the classic oil-based totalizer, 3) the commercial tube dip-in/pressure equilibration totalizer (Palmex Ltd. RS1), and 4) a reference totalizer (the control, lacking any evaporation reduction mechanism). The OPEnS totalizer was designed as being readily user built with parts costs of about $10, oil-free to facilitate quick and easy sample preparation and risk-free sample analysis, and its collection device expands as it fills to maintain a small gas/water ratio and minimize internal evaporative losses. All totalizers were filled to 12 % of their 3-L volume and placed in a modified laboratory oven with a diurnal temperature change of 23 to 40 °C and an average relative humidity of 9.1 % to simulate extreme evaporative conditions. The OPEnS totalizer experienced the smallest mass loss of water (0.21 %) and smallest isotopic shifts (p < 0.05 for δ18O and d-excess), which were all within measurement error. The oil, tube, and reference totalizers showed larger mass losses (0.41, 1.37, and 1.61 %, respectively) and evaporative enrichment with respect to δD (+0.3, +0.8, and + 2.1 ‰), δ18O (+0.16, +0.23, and + 0.83 ‰), and d-excess (−0.9, −1.0, and − 4.5 ‰). The Δ′17O variations for all totalizers were within measurement error, so we suggest that in less harsh climates their triple oxygen isotope changes during secondary evaporation would be more acceptable. To test the OPEnS totalizer in field settings, we installed it alongside oil totalizers to collect monthly precipitation over three years in the towns of Jolly and San Antonio, Texas, with mean annual precipitation, temperature, and windspeed values of 556 and 563 mm, 18.7 and 21.9 °C, and 5.0 and 3.5 m/s, respectively. Results indicate that the OPEnS and oil totalizers can produce similar isotopic data in the field, but modifications to OPEnS have been implemented to minimize under-catch and stabilize the collection component where high winds are present and additional testing under a variety of environmental conditions is ongoing. OPEnS is scalable according to expected monthly precipitation amounts, providing a cost-effective, high-performance device for quantification of total rainfall and its isotopic composition without oil contamination risks.

Temporal Variations of 222Rn Density Distributions in the Lunar Exosphere

Because of radiogenic processes, the lunar interior is a source of rare gases like helium (4He), argon (40Ar), and radon (222Rn) that might be released continuously, or impulsively during moonquakes. The detection of radon is therefore important in the sense that it can help trace the crustal dynamics on the Moon. In this study, we will introduce a Monte Carlo–based model designed to investigate the time-dependent transient dynamics of the lunar 222Rn exosphere. Our model accounts for the background emission and transient ejection of gas molecules from the lunar surface, encompassing loss processes such as radioactive decay, photoionization, and the cold trapping in permanently shadowed regions near the poles. Additionally, it incorporates the diurnal temperature fluctuations of the lunar surface, which significantly influence the condensation duration of the radon atoms and their subsequent release near the sunrise. This model also can support future observations in missions such as Chang’E 6 or other lunar explorations.

Assessing the impacts of temperature extremes on agriculture yield and projecting future extremes using machine learning and deep learning approaches with CMIP6 data

Climate change, particularly extreme weather events, has significantly affected various sectors, including agriculture, human health, water resources, sea levels, and ecosystems. It is anticipated that the intensity, duration, and frequency of these extremes will escalate in the future. This study aims to discover the association between temperature extremes and agricultural yield and to project these extremes using machine learning (ML) and deep learning (DL) models with CMIP6 (Coupled Model Intercomparison Project Phase 6) data under two SSPs (Shared Socioeconomic Pathways). A bi-wavelet coherence technique is employed to investigate the association, providing detailed information in both the frequency and time domains for the period of 1980–2014. Various ML and DL models are trained and tested for the periods of 1985–2004 and 2005–2014, respectively, with gradient boosting machine chosen for projecting temperature extremes based on its superior performance. Mann-Kendall test is used for trend analysis in the projected temperature extremes. The results indicate strong negative and positive associations between TN10p (Cold nights) and TN90p (Warm nights), respectively, with wheat production. Additionally, there is a long-term negative association of CSDI (Cold Spell Duration Indicator) and strong positive association of WSDI (Warm Spell Duration Indicator) with rice yield. Projected results show an increase and decrease under SSP2-4.5 and SSP5-8.5, respectively, in DTR (Diurnal Temperature Range) at most stations. TN10p will increase in the future at most stations, with exceptions such as Muree station where it decreases during 2025–2049 and then increases under both SSPs. Projections show that TXn (annual or monthly minimum value of daily maximum temp) will increase in the future, with Muree station exhibiting the lowest value close to zero, while the average maximum value is around 20 °C at Khanpur station. Trend analysis reveals significantly increasing trend in TR20 (Tropical nights) and decreasing trend in CSDI in future durations under both SSPs. These findings hold implications for policymakers and stakeholders in various departments, including agriculture, health, and water resources management.

The spatial distribution of heat related hospitalizations and classification of the most dangerous heat events in California at a small-scale level

Many studies have explored the impact of extreme heat on health, but few have investigated localized heat-health outcomes across a wide area. We examined fine-scale variability in vulnerable areas, considering population distribution, local weather, and landscape characteristics. Using 36 different heat event definitions, we identified the most dangerous types of heat events based on minimum, maximum, and diurnal temperatures with varying thresholds and durations. Focusing on California's diverse climate, elevation, and population distribution, we analyzed hospital admissions for various causes of admission (2004–2013). Our matching approach identified vulnerable zip codes, even with small populations, on absolute and relative scales. Bayesian Hierarchical models leveraged spatial correlation. We ranked the 36 heat event types by attributable hospital admissions per zip code and provided code, simulated data, and an interactive web app for reproducibility. Our findings showed high variation in heat-related hospitalizations in coastal cities and substantial heat burdens in the Central Valley. Diurnal heat events had the greatest impact in the Central Valley, while nighttime extreme heat events drove burdens in the southeastern desert. This spatially informed approach guides local policies, prioritizing dangerous heat events to reduce the heat-health burden. The methodology is applicable to other regions, informing early warning systems and characterizing extreme heat impacts.

Enhanced fusiform rust hazard maps for loblolly pine: Incorporating genotype and climate to predict disease

Hazard maps are a critical component in determining the regional disease pressure for fusiform rust disease [caused by the fungal pathogen Cronartium quercuum (Berk.) Miyabe ex Shirai f. sp. fusiforme], which is considered the most important disease of loblolly pine (Pinus taeda L.) in the southeastern United States. Genetically improved fusiform rust resistant stock should be deployed in areas that are estimated to be at a high hazard for the disease, while stocks with lower resistance to the disease, but perhaps other favorable traits, can be deployed in areas with a lower estimated hazard. Current hazard maps are based on the historic presence of the disease but do not consider the effect of host genotype or climate which can influence the presence and distribution of fungal pathogens. Here, we aim to improve current hazard maps for fusiform rust by identifying, modeling, and predicting the relationship between the disease and yearly climate indicators. Different loblolly pine genotypes were tested: one family improved for resistance and non-improved checklots for each location. We found that, for the non-improved genotypes, location, potential evapotranspiration, consecutive cloud cover, diurnal temperature range, and a yearly available water indicator can explain 89.2 % of the deviance for disease occurrence. For the improved family, we found that longitude, yearly water deficit, yearly consecutive frost days, yearly consecutive cloud cover, and yearly diurnal temperature range explain 93.1 % deviance for disease incidence. We produce two rust hazard maps based on each model, illustrating the probability of the incidence of the disease. With this project, we pave the way for a better-informed determination of where and what loblolly pine resistant stock should be deployed to limit the damage and costs associated with the management of fusiform rust disease.

Comparison of three reanalysis‐driven regional climate models over New Zealand: Climatology and extreme events

AbstractThree ~12‐km reanalysis‐driven regional climate models (RCMs) are evaluated in terms of capturing climatologies and extremes of precipitation, temperature and surface wind over Aotearoa/New Zealand (NZ). NZ provides an excellent case study for evaluating high‐resolution RCMs due to its coastal and complex terrain and isolated geographical position in the midlatitudes, exposed to both tropical and polar influences. Overall, we find that the RCMs faithfully reproduce the observed climate, with precipitation and temperature climatologies particularly well‐captured. However, the RCMs display significant differences from observations in capturing the surface wind climatology, highlighting a remaining key challenge. The excess “drizzle problem” is apparent to varying degrees, leading to a weaker representation in the length of meteorological drought in some regions. The RCMs also diverge in reproducing diurnal temperature range which appears partly related to cloud cover. Finally, we discuss the important role of observational uncertainty in the context of model evaluation.

Modeling and Assessment of Temperature and Thermal Stress Field of Asphalt Pavement on the Tibetan Plateau

The Qinghai–Tibet Plateau (QTP) is the highest altitude plateau in the world, characterized by strong solar radiation and large diurnal temperature differences and so on, which brings a great negative impact on the temperature and thermal stress field of asphalt pavement. The purpose of this study is to analyze the temperature field and thermal stress status of asphalt pavement in the QTP to provide a reference for pavement design and maintenance in high-altitude areas. The finite element method was applied to establish the temperature field model to study the distribution and variation of pavement temperature. On this basis, the influence of cooling amplitude on pavement thermal stress was studied during cold waves. In addition to this, the key internal factors affecting the thermal stress of pavement, such as surface thickness, surface temperature shrinkage coefficient, surface modulus, and base modulus, were analyzed by an orthogonal test. It was found that temperature and solar radiation have a significant effect on the pavement temperature field. When the cold wave came, the cooling rate had a considerable impact on the thermal stress of the pavement, that is, every 5 °C increase in cooling rate would increase the thermal stress by more than 50%. The temperature shrinkage coefficient and surface modulus of the surface layer material had the greatest influence on the pavement thermal stress. The thermal stress could be reduced by more than 0.4 Mpa for every 5 × 10−6/°C reduction in the surface temperature shrinkage coefficient or every 1000 Mpa reduction in the surface modulus. This study can provide a reference for improving the temperature field and thermal stress field of asphalt pavement in the plateau area.

A primitive cell model involving Vesicles, microtubules and asters

HypothesisSimple single-chain amphiphiles (sodium monododecyl phosphate, SDP) and organic small molecules (isopentenol, IPN), both of primitive relevance, are proved to have been the building blocks of protocells on the early Earth. How do SDP-based membrane and coexisting IPN come together in specific ways to produce more complex chemical entities? What kind of cell-like behavior can be endowed with this protocell model? These are important questions in the pre-life chemical origin scenario that have not been answered to date. ExperimentsThe phase behavior and formation mechanism of the aggregates for SDP/IPN/H2O ternary system were characterized and studied by different electron microscopy, fluorescent probe technology, DLS, IR, ESI-MS, SAXS, etc. The stability (freeze–thaw and wet-dry treatments) and cell-like behavior (chemical signaling communication) were tested via simulating particular scenarios. FindingsVesicles, microtubules and asters phases resembling the morphology and structure of modern cells/organelles were obtained. The intermolecular hydrogen bonding is the main driving force for the emergence of the aggregates. The protocell models not only display remarkable stabilities by simulating the primordial Earth’s diurnal temperature differences and ocean tides but also are able to exhibit cell-like behavior of chemical signaling transition.

Diurnal High Temperatures Affect the Physiological Performance and Fruit Quality of Highbush Blueberry (Vaccinium corymbosum L.) cv. Legacy.

In this study, the physiological performance and fruit quality responses of the highbush blueberry (Vaccinium corymbosum) cultivar Legacy to high temperatures (HTs) were evaluated in a field experiment. Three-year-old V. corymbosum plants were exposed to two temperature treatments between fruit load set and harvest during the 2022/2023 season: (i) ambient temperature (AT) and (ii) high temperature (HT) (5 °C ± 1 °C above ambient temperature). A chamber covered with transparent polyethylene (100 µm thick) was used to apply the HT treatment. In our study, the diurnal temperature was maintained with a difference of 5.03 °C ± 0.12 °C between the AT and HT treatments. Our findings indicated that HT significantly decreased CO2 assimilation (Pn) by 45% and stomatal conductance (gs) by 35.2% compared to the AT treatment. By contrast, the intercellular CO2 concentration (Ci) showed higher levels (about 6%) in HT plants than in AT plants. Fruit quality analyses revealed that the fruit weight and equatorial diameter decreased by 39% and 13%, respectively, in the HT treatment compared to the AT treatment. By contrast, the firmness and total soluble solids (TSS) were higher in the HT treatment than in the AT treatment. Meanwhile, the titratable acidity showed no changes between temperature treatments. In our study, Pn reduction could be associated with stomatal and non-stomatal limitations under HT treatment. Although these findings improve our understanding of the impact of HTs on fruit growth and quality in V. corymbosum, further biochemical and molecular studies are need.

Spatio-temporal trends and variability in extreme temperature and precipitation indices in the Kashmir Valley, North Western Himalayas

ABSTRACT Earth's average air temperature is warming at a substantial rate leading to an increase in the frequency and severity of extremes with major environmental and socio-economic impacts. The present study discusses temperature and precipitation extremes in Kashmir Valley using observational data from six meteorological stations. An Expert Team on Climate Change Detection and Indices (ETCCDI) (http://etccdi.pacificclimate.org/) provides 25 extreme climate indices (15 for temperature and 10 for precipitation) to be used. The absolute extreme temperature indices (TXx, TXn, TNx, and TNn) exhibit increasing tendencies, according to the findings. The number of changes witnessed in daily maximum temperature was greater than the daily minimum temperature which was manifested by increasing diurnal temperature range (DTR; 0.012 °C/year). These changes in extremes have impacts that pose a threat to agriculture, snow day and cover, glaciers, water resources, ecosystem services, etc. of the study region. The region is undergoing significant urban and land system changes making it further vulnerable to natural hazards. The findings are expected to further augment the hazard and risk analysis and the necessary disaster risk reduction measures for climate-related disasters in the region. These analyses will be helpful for the development of strategies for climate risk management in Kashmir.

Spatiotemporal variability identification and analysis for non-stationary climatic trends for a tropical river basin of India

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Descriptive, Hospital-Based, 10-Year Study of Malaria Transmission in Goa, a Southwest Indian State in the Malaria Elimination Phase.

The South Asia International Center of Excellence for Malaria Research, an NIH-funded collaborative program, investigated the epidemiology of malaria in the Indian state of Goa through health facility-based data collected from the Goa Medical College and Hospital (GMC), the state's largest tertiary healthcare facility, between 2012 and 2021. Our study investigated region-specific spatial and temporal patterns of malaria transmission in Goa and the factors driving such patterns. Over the past decade, the number of malaria cases, inpatients, and deaths at the GMC decreased significantly after a peak in 2014-2015. However, the proportion of severe malaria cases increased over the study period. Also, a trend of decreasing average parasitemia and increasing average gametocyte density suggests a shift toward submicroscopic infections and an increase in transmission commitment characteristic of low-transmission regions. Although transmission occurred throughout the year, 75% of the cases occurred between June and December, overlapping with the monsoon (June-October), which featured rainfall above yearly average, minimal diurnal temperature variation, and high relative humidity. Sociodemographic factors also had a significant association with malaria cases, with cases being more frequent in the 15-50-year-old age group, men, construction workers, and people living in urban areas within the GMC catchment region. Our environmental model of malaria transmission projects almost negligible transmission at the beginning of 2025 (annual parasitic index: 0.0095, 95% CI: 0.0075-0.0114) if the current control measures continue undisrupted.

Analysis of Thermal and Dielectric Loss Features of Lunar Regolith Considering Real‐Time Effect Solar Irradiance

AbstractSolar irradiance received at the lunar surface is crucial for interpreting brightness temperatures detected by orbiters and for understanding the thermal, physical, and dielectric properties of the lunar regolith. We developed a real‐time effect solar irradiance (ESI) model that accounts for the influence of surface relief and terrain shading. This model was integrated with a standard thermal model to examine ESI fluctuations and their impacts on the diurnal physical temperature variations. To assess the effects of spatial resolution, we selected four locations with significant ESI disparities for simulation, then compared lunar surface temperatures at various spatial scales, ranging from 20 m to 25 km. Utilizing brightness temperature data obtained from the Chang'E‐2 (CE‐2) microwave radiometer (MRM), we integrated the shallow physical temperature profiles with the radiative transfer equation to simulate brightness temperatures and determine dielectric loss at different frequencies. In the Von Kármán crater, the received ESI exhibits a cyclical pattern of approximately 18 years and areas with rugged topography may exhibit larger ESI variations (∼7%). We found that the spatial resolution of ESI has a minimal effect on the physical and brightness temperatures at resolutions of 10 km or coarser. At the shallow layer, the average dielectric loss values are 0.0128–0.0170, 0.0083–0.0110, 0.0055–0.0073, and 0.0061–0.0081 for the CE‐2 frequencies of 3, 7.8, 19.35, and 37 GHz, respectively. The integration of real‐time ESI modeling, thermal dynamics, radiative transfer equations, and observational data enhances our comprehension of the physical temperature profile and thermal characteristics of shallow regolith.

Testing the Sensitivity of a WRF-Based Great Lakes Regional Climate Model to Cumulus Parameterization and Spectral Nudging

Abstract The Weather Research and Forecasting (WRF) Model is used to dynamically downscale ERA-Interim global reanalysis data to test its performance as a regional climate model (RCM) for the Great Lakes region (GLR). Four cumulus parameterizations and three spectral nudging techniques applied to moisture are evaluated based on 2-m temperature and precipitation accumulation in the Great Lakes drainage basin (GLDB). Results are compared to a control simulation without spectral nudging, and additional analysis is presented showing the contribution of each nudged variable to temperature, moisture, and precipitation. All but one of the RCM test simulations have a dry precipitation bias in the warm months, and the only simulation with a wet bias also has the least precipitation error. It is found that the inclusion of spectral nudging of temperature dramatically improves a cold-season cold bias, and while the nudging of moisture improves simulated annual and diurnal temperature ranges, its impact on precipitation is complicated. Significance Statement Global climate models are vital to understanding our changing climate. While many include a coarse representation of the Great Lakes, they lack the resolution to represent effects like lake effect precipitation, lake breeze, and surface air temperature modification. Therefore, using a regional climate model to downscale global data is imperative to correctly simulate the land–lake–atmosphere feedbacks that contribute to regional climate. Modeling precipitation is particularly important because it plays a direct role in the Great Lakes’ water cycle. The purpose of this study is to identify the configuration of the Weather Research and Forecasting Model that best simulates precipitation and temperature in the Great Lakes region by testing cumulus parameterizations and methods of nudging the regional model toward the global model.

Current and lagged associations of meteorological variables and Aedes mosquito indices with dengue incidence in the Philippines.

Dengue is an increasing health burden that has spread throughout the tropics and sub-tropics. There is currently no effective vaccine and control is only possible through integrated vector management. Early warning systems (EWS) to alert potential dengue outbreaks are currently being explored but despite showing promise are yet to come to fruition. This study addresses the association of meteorological variables with both mosquito indices and dengue incidences and assesses the added value of additionally using mosquito indices for predicting dengue incidences. Entomological surveys were carried out monthly for 14 months in six sites spread across three environmentally different cities of the Philippines. Meteorological and dengue data were acquired. Non-linear generalized additive models were fitted to test associations of the meteorological variables with both mosquito indices and dengue cases. Rain and the diurnal temperature range (DTR) contributed most to explaining the variation in both mosquito indices and number of dengue cases. DTR and minimum temperature also explained variation in dengue cases occurring one and two months later and may offer potentially useful variables for an EWS. The number of adult mosquitoes did associate with the number of dengue cases, but contributed no additional value to meteorological variables for explaining variation in dengue cases. The use of meteorological variables to predict future risk of dengue holds promise. The lack of added value of using mosquito indices confirms several previous studies and given the onerous nature of obtaining such information, more effort should be placed on improving meteorological information at a finer scale to evaluate efficacy in early warning of dengue outbreaks.

Drivers of associations between daytime-nighttime compound temperature extremes and mortality in China

BackgroundTemperature extremes are anticipated to become more frequent and more intense under the context of climate change. While current evidence on health effects of compound extreme temperature event is scarce.MethodsThis nationwide cross-sectional study collected daily data on weather and mortality for 161 Chinese districts/counties during 2007-2013. A quasi-Poisson generalized linear model was first applied to assess effects of daytime-only, nighttime-only and compound daytime-nighttime heat wave (and cold spell) on cause-specific mortality. Then a random-effect meta-analysis was used to produce pooled estimates at national level. Stratification analyses were performed by relative humidity, individual and regional characteristics.ResultsHere we show that mortality risks of compound daytime-nighttime temperature extremes are much higher than those occurring only in the daytime or nighttime. Humid weather further exaggerates the mortality risk during heat waves, while dry air enhances the risk during cold weather. People who are elderly, illiterate, and those with ischemic heart disease and respiratory disease are particularly vulnerable to extreme temperature. At the community-level, population size, urbanization rate, proportion of elderly and PM2.5 are positively associated with increased risks associated with heat waves. Temperature, humidity and normalized difference vegetation index are positively associated with the effects of cold weather, with an opposite trend for latitude and diurnal temperature range.ConclusionsThis nationwide study highlights the importance of incorporating compound daytime-nighttime extreme temperature events and humid conditions into early warning systems and urban design/planning.

Impact of urban greenspace on the urban thermal environment: A case study of Shenzhen, China

Rapid urban expansion has significantly altered the urban surface energy balance, consequently impacting the urban thermal environment. Urban greenspace (UG) plays a crucial role in regulating urban climate. This study utilizes a seamless land surface temperature (LST) dataset to elucidate the spatio-temporal patterns of LST and diurnal temperature range (DTR) in Shenzhen, a rapidly urbanizing city in China, and the influence of UG on urban thermal environment. The UG showed a notable decrease of 16.3 % from 2000 to 2020. Over the past 21 years, both daytime LST (LSTD) and nighttime LST (LSTN) exhibited an upward trend, while DTR showed a significant downward trend, especially in autumn. Significant spatial changes in LSTD, LSTN, and DTR were observed in winter across the entire city. Moreover, our results emphasize that the impact of UG on LSTD displays non-uniform cooling and varies significantly in terms of season, greenspace quality, and geographical location. Our assessment of changes in the urban thermal environment in Shenzhen and the role of UG on LST can offer theoretical support for policymakers in developing effective urban cooling measures and healthy urban development.