Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States.

BACKGROUND: Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important. METHODS: We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season. RESULTS: For asthma, increases of 1 µg/m3 in PM2.5 exposure 7-13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6-12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4-10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods. CONCLUSIONS: Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

Assessing the impact of temperature on acute exacerbation of chronic obstructive pulmonary disease hospitalizations in residents of Panzhihua City: a multi-districts study using a distributed lag non-linear model.

BACKGROUND: Temperature fluctuations can impact the occurrence and progression of respiratory system diseases. However, the current understanding of the impact of temperature on acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remains limited. Therefore, our study aims to investigate the relationship between daily mean temperature (DMT) and the risk of AECOPD hospitalizations within Panzhihua City. METHODS: We systematically collected data on AECOPD hospitalizations at Panzhihua Central Hospital from 2015 to 2020 and meteorological factors across Panzhihua City's districts. A two-stage analysis method was used to establish a distributed lag non-linear model to elucidate the influence of DMT on the frequency of admissions for AECOPD. Subgroup analyses were conducted by gender and age to identify populations potentially susceptible to the impact of DMT. RESULTS: A total of 5299 AECOPD hospitalizations cases were included. The DMT and the risk of AECOPD hospitalization showed a non-linear exposure-response pattern, with low temperatures exacerbating the risk of hospitalizations. The lag effects of low temperature and relatively low temperature peaked at 2th day, with the lag effects disappearing at 16-17 days. Females and elders aged ≥ 65 years were more sensitive to effects of low and relatively low temperature at lag 0-4 days, while male AECOPD patients exhibited longer lasting lag effects. CONCLUSIONS: Low temperatures are associated with an increased risk of AECOPD hospitalizations. Females or elders aged ≥ 65 years with chronic obstructive pulmonary disease should pay more attention to taking protective measures in cold environments. These findings are crucial for the formulation of public health policies, as they will help significantly alleviate the burden of AECOPD and improve respiratory health in the face of climate challenges.

The association between air pollutants, meteorological factors and tuberculosis cases in Beijing, China: A seven-year time series study.

BACKGROUND: Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing. OBJECTIVE: The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB. METHODS: Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM10, PM2.5, SO2, NO2, CO and O3, were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing. RESULTS: In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 µg/m3 increase in NO2 in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 µg/m3 increase in O3 in lag 1 week (ER: -1.2%; 95% CI: -1.8%, -0.5%), every 5 °C increase in average temperature (ER: -1.7%; 95% CI: -2.9%, -0.4%) and every 10% increase in average relative humidity (ER: -0.4%; 95% CI: -0.8%, -0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO2, O3, average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM2.5, SO2, sunshine duration and TB cases. CONCLUSION: Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

Life-history traits predict responses of wild bees to climate variation.

Life-history traits, which are physical traits or behaviours that affect growth, survivorship and reproduction, could play an important role in how well organisms respond to environmental change. By looking for trait-based responses within groups, we can gain a mechanistic understanding of why environmental change might favour or penalize certain species over others. We monitored the abundance of at least 154 bee species for 8 consecutive years in a subalpine region of the Rocky Mountains to ask whether bees respond differently to changes in abiotic conditions based on their life-history traits. We found that comb-building cavity nesters and larger bodied bees declined in relative abundance with increasing temperatures, while smaller, soil-nesting bees increased. Further, bees with narrower diet breadths increased in relative abundance with decreased rainfall. Finally, reduced snowpack was associated with reduced relative abundance of bees that overwintered as prepupae whereas bees that overwintered as adults increased in relative abundance, suggesting that overwintering conditions might affect body size, lipid content and overwintering survival. Taken together, our results show how climate change may reshape bee pollinator communities, with bees with certain traits increasing in abundance and others declining, potentially leading to novel plant-pollinator interactions and changes in plant reproduction.

Unravelling insect declines: can space replace time?

Temporal trends in insect numbers vary across studies and habitats, but drivers are poorly understood. Suitable long-term data are scant and biased, and interpretations of trends remain controversial. By contrast, there is substantial quantitative evidence for drivers of spatial variation. From observational and experimental studies, we have gained a profound understanding of where insect abundance and diversity is higher-and identified underlying environmental conditions, resource change and disturbances. We thus propose an increased consideration of spatial evidence in studying the causes of insect decline. This is because for most time series available today, the number of sites and thus statistical power strongly exceed the number of years studied. Comparisons across sites allow quantifying insect population risks, impacts of land use, habitat destruction, restoration or management, and stressors such as chemical and light pollution, pesticides, mowing or harvesting, climatic extremes or biological invasions. Notably, drivers may not have to change in intensity to have long-term effects on populations, e.g. annually repeated disturbances or mortality risks such as those arising from agricultural practices. Space-for-time substitution has been controversially debated. However, evidence from well-replicated spatial data can inform on urgent actions required to halt or reverse declines-to be implemented in space.

Short-term effects of ambient temperature on the risk of preeclampsia in Nanjing, China: a time-series analysis.

OBJECTIVES: Previous studies on the association between temperature and preeclampsia mainly considered temperature on a monthly or seasonal time scale. The objective of this study was to assess the preeclampsia risk associated with short-term temperature exposure using daily data. STUDY DESIGN: Daily preeclampsia hospitalization data, daily meteorological data and daily air pollutant data from Nanjing were collected from 2016 to 2017. Both the T test and distributed lag nonlinear model (DLNM) were applied to assess the short-term effect of temperature on preeclampsia risk. Three kinds of daily temperature, including the daily mean temperature, daily minimum temperature and daily maximum temperature, were analysed. RESULTS: When the daily number of preeclampsia hospital admissions was divided into two subgroups based on temperature, it was significantly larger on cold days than on hot days. Regarding the mean temperature, a very low level of mean temperature (4.5 °C, lag = 0-20) and a low level of mean temperature (9.1 °C, lag = 0-20) increased the cumulative relative risk of preeclampsia by more than 60%. At the same time, a very high level of mean temperature (28.7 °C, lags = 0-10, 0-15, 0-20) and a high level of mean temperature (24.1 °C, lags = 0-10, 0-15) decreased the cumulative relative risk of preeclampsia by more than 35%. At a minimum temperature, a very low level of minimum temperature (0.9 °C, lag 0-5) and a low level of minimum temperature (5.6 °C, lag 0-5) increased the cumulative relative risk of preeclampsia by more than 55%. At the same time, a high level of mean temperature (20.9 °C, lags = 0, 0-5) decreased the cumulative relative risk of preeclampsia by more than 20%. The maximum temperature result was similar to the mean temperature result. CONCLUSIONS: Both direct and lag effects of low temperature on preeclampsia were demonstrated to be significant risk factors. These results could be used to help pregnant women and the government reduce preeclampsia risk.

A Markov Mixed-Effect Multinomial Logistic Regression Model for Nominal Repeated Measures with an Application to Syntactic Self-Priming Effects.

Syntactic priming effects have been investigated for several decades in psycholinguistics and the cognitive sciences to understand the cognitive mechanisms that support language production and comprehension. The question of whether speakers prime themselves is central to adjudicating between two theories of syntactic priming, activation-based theories and expectation-based theories. However, there is a lack of a statistical model to investigate the two different theories when nominal repeated measures are obtained from multiple participants and items. This paper presents a Markov mixed-effect multinomial logistic regression model in which there are fixed and random effects for own-category lags and cross-category lags in a multivariate structure and there are category-specific crossed random effects (random person and item effects). The model is illustrated with experimental data that investigates the average and participant-specific deviations in syntactic self-priming effects. Results of the model suggest that evidence of self-priming is consistent with the predictions of activation-based theories. Accuracy of parameter estimates and precision is evaluated via a simulation study using Bayesian analysis.

Ecological memory of daily carbon exchange across the globe and its importance in drylands.

How do antecedent (past) conditions influence land-carbon dynamics after those conditions no longer persist? In particular, quantifying such memory effects associated with the influence of past environmental (exogenous) and biological (endogenous) conditions is crucial for understanding and predicting the carbon cycle. Here we show, using data from 42 eddy covariance sites across six major biomes, that ecological memory-decomposed into environmental and biological memory components-of daily net carbon exchange (NEE) is critical for understanding the land-carbon metabolism, especially in drylands for which memory explains ~ 32% of the variation in NEE. The strong environmental memory in drylands was primarily driven by short- and long-term moisture status. Moreover, the strength of environmental memory scales with increasing water stress. This universal scaling relationship, emerging within and among major biomes, suggests a potential adaptive response to water limitation. Our findings underscore the necessity of considering ecological memory in experiments, observations and modelling.

Effects of extreme temperatures on cerebrovascular mortality in Lisbon: a distributed lag non-linear model.

Cerebrovascular diseases are the leading cause of mortality in Portugal, especially when related with extreme temperatures. This study highlights the impacts of the exposure-response relationship or lagged effect of low and high temperatures on cerebrovascular mortality, which can be important to reduce the health burden from cerebrovascular diseases. The purpose of this study was to assess the effects of weather on cerebrovascular mortality, measured by ambient temperature in the District of Lisbon, Portugal. A quasi-Poisson generalized additive model combined with a distributed lag non-linear model was applied to estimate the delayed effects of temperature on cerebrovascular mortality up to 30 days. With reference to minimum mortality temperature threshold of 22 °C, there was a severe risk (RR = 2.09, 95% CI 1.74, 2.51) of mortality for a 30-day-cumulative exposure to extreme cold temperatures of 7.3 °C (1st percentile). Similarly, the cumulative effect of a 30-day exposure to an extreme hot temperature of 30 °C (99th percentile) was 52% (RR = 1.65, 95% CI 1.37, 1.98) higher than same-day exposure. Over the 13 years of study, non-linear effects of temperature on mortality were identified, and the probability of dying from cerebrovascular disease in Lisbon was 7% higher in the winter than in the summer. The findings of this study provide a baseline for future public health prevention programs on weather-related mortality.

Anthropogenic nitrogen deposition ameliorates the decline in tree growth caused by a drier climate.

Most forest ecosystems are simultaneously affected by concurrent global change drivers. However, when assessing these effects, studies have mainly focused on the responses to single factors and have rarely evaluated the joined effects of the multiple aspects of environmental change. Here, we analyzed the combined effects of anthropogenic nitrogen (N) deposition and climatic conditions on the radial growth of Acer saccharum, a dominant tree species in eastern North American forests. We capitalized on a long-term N deposition study, replicated along a latitudinal gradient, that has been taking place for more than 20 yr. We analyzed tree radial growth as a function of anthropogenic N deposition (ambient and experimental addition) and of summer temperature and soil water conditions. Our results reveal that experimental N deposition enhances radial growth of this species, an effect that was accentuated as temperature increased and soil water became more limiting. The spatial and temporal extent of our data also allowed us to assert that the positive effects of growing under the experimental N deposition are likely due to changes in the physiological performance of this species, and not due to the positive correlation between soil N and soil water holding capacity, as has been previously speculated in other studies. Our simulations of tree growth under forecasted climate scenarios specific for this region also revealed that although anthropogenic N deposition may enhance tree growth under a large array of environmental conditions, it will not mitigate the expected effects of growing under the considerably drier conditions characteristic of our most extreme climatic scenario.

[Research on the relationship between the daily mean temperature and the daily cases of varicella during 2008-2016 in Lanzhou, China].

Objective: To explore the effects of temperature on the daily cases of varicella. Methods: The data of daily cases of varicella was collected during 2008 to 2016 in Lanzhou from National Notifiable Disease Report System, and the meteorological data at the same period was integrated from Gansu Meteorological Administration. Distributed lag nonlinear model was fitted to reveal the relationship between the daily mean temperature and the daily cases of varicella and susceptible population. The minimum morbidity temperature was defined as the reference for the estimation of RRs in different temperature level (-5.2 ℃, 1.7 ℃, 20.1 ℃ and 25.4 ℃). Results: The total of 21 254 cases were reported from 2008 to 2016, of which the ratio of male to female was 1.28 (11 951/9 303) and people aged 6-14 years accounted for 52.87%. The relationship between the daily mean temperature and the daily cases of varicella was M type. For all subjects, the accumulative effects of temperature had statistical significance from lag 0-14 d when temperatures was at -5.2 ℃, 1.7 ℃ and 20.1 ℃,while the RRs (95%CI) were 1.87 (1.64-2.12) , 1.33 (1.10-1.62) ,1.60 (1.38-1.86) ,while from lag 0-7 d when temperatures was at 25.4 ℃,and the RR (95%CI) was 2.51 (1.93-3.27) . The RR value of accumulative effects was 6.23(95%CI: 4.38-8.86) on lag 7 d when temperatures was at -5.2 ℃, which was the highest value at different temperature during lag days. The cumulative effects trends of different temperatures were similar for different gender population or different age subjects. However, the cumulative effects of was highest for children aged 6-14 years among all subjects, and the value of RR was 6.12 (95%CI:3.71-10.10) on lag 5d when temperatures was at -5.2 ℃. Conclusion: We conclude that the increasing risk of varicella is associative with low and high temperature in Lanzhou. The effects of low temperature are stronger than those of high temperature. The children aged 6-14 years belong to the high-risk population of varicella.

Does a lag-structure of temperature confound air pollution-lag-response relation? Simulation and application in 7 major cities, Korea (1998-2013).

BACKGROUND: Temperature must be controlled when estimating the associations of short-term exposure to air pollution and mortality. Given that multi-country studies have implied temperature has lagged effects, we aim to explore confounding by temperature-lag-response and investigate PM10-lag-mortality relation in 7 cities, Korea. METHODS: In a simulation study, we compared the performance of different methods to control for: the same day temperature, a lagged temperature and distributed lags of temperature. In a real data study, we explored PM10-lag-mortality relation in 7 cities using these different methods. RESULTS: We confirmed that a model with insufficient control of temperature offers a biased estimate of PM10 risk. The degree of bias was from -82% to 95% in simulation settings. A real data study shows estimates among different models by temperature adjustments and PM10 lag variables ranging from -0.3% to 0.4% increase in the risk of all-cause mortality, with a 10µg/m3 increase in PM10. Controlling for temperature as distributed lags for 21 days provided 0.25% (95% CI: 0.1, 0.4) increase in the risk of all-cause mortality. CONCLUSIONS: A lag structure of temperature can confound the air pollution-lag-response relation. Temperature-lag-response relation should be evaluated when estimating air pollution-lag-response relation. As a corollary, air pollution and temperature risk in mortality can be estimated using the same regression model.

What drives cold-related excess mortality in a south Asian tropical monsoon climate-season vs. temperatures and diurnal temperature changes.

Despite the tropical climate which is characterized by generally high temperatures and persistent mild temperatures during the winter season, Bangladesh, along with many other tropical countries, experiences strong winter and cold-related excess mortality. The objective of this paper was to analyse the nature of these cold effects and understand the role of season vs. temperature and diurnal changes in temperature. For approaching these questions, we applied different Poisson regression models. Temperature as well as diurnal temperature range (DTR) were considered as predictor variables. Different approaches to seasonality adjustment were evaluated and special consideration was given to seasonal differences in atmospheric effects. Our findings show that while seasonality adjustment affected the magnitude of cold effects, cold-related mortality persisted regardless the adjustment approach. Strongest effects of low temperatures were observed at the same day (lag 1) with an increase of 1.7% (95% CI = 0.86-2.54%) per 1 °C decrease in temperature during the winter season. Diurnal temperature affected mortality with increasing levels at higher ranges. Mortality increased with 0.97% (95% CI = 0.17-1.75%) when looking at the entire season, but effects of DTR were not significant during winter when running a seasonal model. Different from effects observed in the mid-latitudes, cold effects in Bangladesh occurred on a very short time scale highlighting the role of temperature versus season. Insufficient adaptation with regard to housing and clothing might lead to such cold-related increases in mortality despite rather moderate temperature values. Although the study did not demonstrate an effect of DTR during the cold season, the strong correlation with (minimum) temperature might cause a multicollinearity problem and effects are difficult to attribute to one driver.

Quantifying ecological memory in plant and ecosystem processes.

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research.

Time-lag effects of global vegetation responses to climate change.

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time-lag effects, which are the most important mechanism of climate-vegetation interactive effects. Extensive studies focused on large-scale vegetation-climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time-lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time-lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time-lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate-driving factors for different vegetation types were determined. The results showed that (i) both the time-lag effects of the vegetation responses and the major climate-driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time-lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time-lag effects; (iii) for the area with a significant change trend (for the period 1982-2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time-lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.

Quantifying the timescales over which exogenous and endogenous conditions affect soil respiration.

Understanding how exogenous and endogenous factors and above-ground-below-ground linkages modulate carbon dynamics is difficult because of the influences of antecedent conditions. For example, there are variable lags between above-ground assimilation and below-ground efflux, and the duration of antecedent periods are often arbitrarily assigned. Nonetheless, developing models linking above- and below-ground processes is crucial for estimating current and future carbon dynamics. We collected data on leaf-level photosynthesis (Asat ) and soil respiration (Rsoil ) in different microhabitats (under shrubs vs under bunchgrasses) in the Sonoran Desert. We evaluated timescales over which endogenous and exogenous factors control Rsoil by analyzing data in the context of a semimechanistic temperature-response model of Rsoil that incorporated effects of antecedent exogenous (soil water) and endogenous (Asat ) conditions. For both microhabitats, antecedent soil water and Asat significantly affected Rsoil , but Rsoil under shrubs was more sensitive to Asat than that under bunchgrasses. Photosynthetic rates 1 and 3 d before the Rsoil measurement were most important in determining current-day Rsoil under bunchgrasses and shrubs, respectively, indicating a significant lag effect. Endogenous and exogenous controls are critical drivers of Rsoil , but the relative importance and the timescale over which each factor affects Rsoil depends on above-ground vegetation and ecosystem structure characteristics.

Influence of Time-Lag Effects between Winter-Wheat Canopy Temperature and Atmospheric Temperature on the Accuracy of CWSI Inversion of Photosynthetic Parameters.

When calculating the CWSI, previous researchers usually used canopy temperature and atmospheric temperature at the same time. However, it takes some time for the canopy temperature (Tc) to respond to atmospheric temperature (Ta), suggesting the time-lag effects between Ta and Tc. In order to investigate time-lag effects between Ta and Tc on the accuracy of the CWSI inversion of photosynthetic parameters in winter wheat, we conducted an experiment. In this study, four moisture treatments were set up: T1 (95% of field water holding capacity), T2 (80% of field water holding capacity), T3 (65% of field water holding capacity), and T4 (50% of field water holding capacity). We quantified the time-lag parameter in winter wheat using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis. Based on the time-lag parameter, we modified the CWSI theoretical and empirical models and assessed the impact of time-lag effects on the accuracy of the CWSI inversion of photosynthesis parameters. Finally, we applied several machine learning algorithms to predict the daily variation in the CWSI after time-lag correction. The results show that: (1) The time-lag parameter calculated using time-lag peak-seeking, time-lag cross-correlation, time-lag mutual information, and gray time-lag correlation analysis are 44-70, 32-44, 42-58, and 76-97 min, respectively. (2) The CWSI empirical model corrected by the time-lag mutual information method has the highest correlation with photosynthetic parameters. (3) GA-SVM has the highest prediction accuracy for the CWSI empirical model corrected by the time-lag mutual information method. Considering time lag effects between Ta and Tc effectively enhanced the correlation between CWSI and photosynthetic parameters, which can provide theoretical support for thermal infrared remote sensing to diagnose crop water stress conditions.

Investigating the cumulative lag effects of environmental exposure under urban differences on COVID-19.

Although all walks of life are paying less attention to COVID-19, the spread of COVID-19 has never stopped. As an infectious disease, its transmission speed is closely related to the atmosphere environment, particularly the temperature (T) and PM2.5 concentrations. However, How T and PM2.5 concentrations are related to the spread of SARS-CoV-2 and how much their cumulative lag effect differ across cities is unclear. To identify the characteristics of cumulative lag effects of environmental exposure under city differences, this study used a generalized additive model to investigate the associations between T/PM2.5 concentrations and the daily number of new confirmed COVID-19 cases (NNCC) during the outbreak period in the second half of 2021 in Shaoxing, Shijiazhuang, and Dalian. The results showed that except for PM2.5 concentrations in Shaoxing, the NNCC in the three cities generally increased with the unit increase of T and PM2.5 concentrations. In addition, the cumulative lag effects of T/PM2.5 concentrations on NNCC in the three cities reached a peak at lag 26/25, lag 10/26, and lag 18/13 days, respectively, indicating that the response of NNCC to T and PM2.5 concentrations varies among different regions. Therefore, combining local meteorological and air quality conditions to adopt responsive measures is an important way to prevent and control the spread of SARS-CoV-2.

Increased stress from compound drought and heat events on vegetation.

Compound drought and heat events (CDHEs), which are frequently occurring compound extreme climate events, have garnered considerable attention because of their detrimental effects on ecosystems. However, the intricacies of the spatial and temporal distributions of different durations of compound events, along with the variability in vegetation responses remain unclear. Here, we delineated the CDHEs based on meteorological observation data and investigated the spatial and temporal characteristics of CDHEs from 1993 to 2020 using the Theil-Sen trend test and Mann-Kendall nonparametric test. Furthermore, we utilized sliding correlation analysis to evaluate the impacts of CDHEs on vegetation among different climatic regions and ecosystems. Our findings indicate significant increasing trends in both the frequency and persistence of CDHEs from 1993 to 2020. The average trend of CDHEs frequency across different duration periods amounted to 13.80 %/decade. The fractional contribution of CDHEs lasting more than three days exhibited a significant increase, with an average trend of 2.00 %/decade. We also observed that vegetation is most significantly affected by compound events lasting 5-9 days. During the study period, the geographical extent of vegetation significantly impacted by CDHEs expanded by 0.89 %, correlation strength increased by 0.02, and lag time decreased by 0.25 months. These insights highlight the growing impact of CDHEs on vegetation under climate change, improving our understanding of vegetation responses to these compound events.

Age-Related Hearing Loss, Cognitive Performance, and Metabolic Risk in Healthy Adults: A Seven-Year Longitudinal Study.

OBJECTIVES: Sensorineural hearing loss (presbycusis) affects up to half of the adults, is associated with cognitive decline. Whether this association reflects the cause, the consequence, or parallel processes driven by other factors remains unclear. Both presbycusis and cognition are linked to elevated metabolic risk, which in turn raises with age. METHOD: In a multioccasion longitudinal design, we used latent change score models with strong factorial invariance to assess the change in pure-tone threshold auditory function, fluid intelligence, metabolic risk, variability therein, and the dynamic relationships among the 3 domains. We examined, up to 4 times over more than 7 years, a sample of relatively healthy 687 adults (aged 18.17-83.25 years). RESULTS: We found that levels of auditory and cognitive functioning at time t-1 influence each other's subsequent change between times t-1 and t, even when controlling for the reciprocal effects of metabolic risk on both. Thus, auditory and cognitive functioning do not only decline in parallel in healthy adults, but also affect each other's trajectories. To the best of our knowledge, this is the first long-term study with such evidence. DISCUSSION: Our results are in accord with extant hypotheses about auditory-cognitive associations in old age (e.g., social isolation, cognitive load, increased inflammation, reduced gene expression, and other microvascular or neuropathological factors). They also echo previous reports underscoring the need for improving access to hearing aids and other rehabilitative services aimed at reducing hearing loss. If applied early in the aging process, such interventions may mitigate cognitive decline.