Effects of ambient temperature on pediatric incident seizure: A case-crossover analysis using distributed lag non-linear models.

OBJECTIVE: Emerging evidence supports that brain dysfunction may be attributable to environmental factors. This study aims to examine associations of ambient temperature and temperature variability (TV) with seizure incidence in children, which has not been explored. MATERIAL AND METHODS: Data on 2718 outpatient visits due to seizure were collected in Shanghai, China, from 2018 to 2023. Exposure to ambient temperature was estimated at children's residential addresses using spatial-temporal models. A time-stratified case-crossover design with a distributed lag non-linear model (DLNM) was conducted to assess the association between seizure incidence and daily average of ambient temperature over a period of 21 days prior to a case date of disease onset. For a given case date, we selected all dates falling on the same day of the week within the same month as control dates. We calculated a composite index of intra-day and inter-day TV, which was the standard deviation of the daily minimum and maximum temperatures, respectively, over 7 days preceding a case date. We then assessed the association between TV and seizure incidence. Stratified analyses were conducted by age (73.51% < 5 years old and 26.49 % ≥ 5 years old), sex (41.83% female), presence of fever (69.72%), and diagnosis of epilepsy (27.63%). RESULTS: We observed inversed J-shaped temperature-response curves. Lower temperatures had a significant and prolonged effect than higher temperatures. Using 20 °C (with the minimum effect) as the reference, the cumulative odds ratios (ORs) for over 0-21 days preceding the onset at the 5th percentile of the temperature (3 °C) and at the 95th percentile (29 °C) were 3.17 (95% CI: 1.77, 5.68) and 1.54 (95% CI: 0.97, 2.44), respectively. In addition, per 1 °C increases in TV0-7 was associated with OR of 1.08 (95% CI: 1.01, 1.15). Older children and those experiencing seizure with fever exhibited a higher risk of seizure onset at both lower and higher ambient temperatures. CONCLUSION: Both low and high temperatures can contribute to the morbidity related to pediatric seizure. Lower temperatures, however, exerted a longer period of effect prior to seizure onset than higher temperatures. An increased risk for incident seizure was significantly associated with temperature variability during preceding 7 days.

Neighborhood-level socioeconomic disparities in Radon testing in North Carolina from 2010 to 2020.

Radon is a naturally occurring radioactive gas that poses significant health risks to humans, including increased risk of lung cancer. This study investigates the association of neighborhood-level socioeconomic variables with radon testing and radon exposure levels in North Carolina between 2010 and 2020. Our analysis of the two largest commercial household radon tests reveals that 67% of census tracts had testing rates below 10 tests per 1000 population, indicating low testing prevalence. Low radon levels (<2 pCi/L) were detected in 74.1% of the tracts (n = 1626), while medium levels of 2-4 pCi/L and ≥4 pCi/L were observed in 17.2% (n = 378) and 1.6% (n = 36) of the tracts. A generalized spatial regression model was employed to analyze the association between neighborhood-level socioeconomic variables and radon testing rates (per 1000 households), controlling for median radon testing results. The results show a positive correlation (P-value <0.001) of testing rate with various indicators of neighborhood affluence including education level, income, and occupation. In contrast, neighborhood disadvantage, including poverty, unemployment, and public assistance, was associated with a lower radon-testing rate (P-value <0.001). These findings highlight the need for targeted interventions to address socioeconomic disparities in radon testing and promote awareness and access to testing resources in lower socio-economic neighborhoods. Improving testing rates can effectively address radon-related health risks in North Carolina and across the U.S.

Critical windows of greenness exposure during preconception and gestational periods in association with birthweight outcomes.

Few studies have examined the association between greenness exposure and birth outcomes. This study aims to identify critical exposure time windows during preconception and pregnancy for the association between greenness exposure and birth weight. A cohort of 13 890 pregnant women and newborns in Shanghai, China from 2016-2019 were included in the study. We assessed greenness exposure using Normalized Difference Vegetation Index (NDVI) during the preconception and gestational periods, and evaluated the association with term birthweight, birthweight z-score, small-for-gestational age, and large-for-gestational age using linear and logistic regressions adjusting for key maternal and newborn covariates. Ambient temperature, relative humidity, ambient levels of fine particles (PM2.5) and nitrogen dioxide (NO2) assessed during the same period were adjusted for as sensitivity analyses. Furthermore, we explored the potential different effects by urbanicity and park accessibility through stratified analysis. We found that higher greenness exposure at the second trimester of pregnancy and averaged exposure during the entire pregnancy were associated with higher birthweight and birthweight Z-score. Specifically, a 0.1 unit increase in second trimester averaged NDVI value was associated with an increase in birthweight of 10.2 g (95% CI: 1.8-18.5 g) and in birthweight Z-score of 0.024 (0.003-0.045). A 0.1 unit increase in an averaged NDVI during the entire pregnancy was associated with 10.1 g (95% CI: 1.0-19.2 g) increase in birthweight and 0.025 (0.001-0.048) increase in birthweight Z-score. Moreover, the associations were larger in effect size among urban residents than suburban residents and among residents without park accessibility within 500 m compared to those with park accessibility within 500 m. Our findings suggest that increased greenness exposure, particularly during the second trimester, may be beneficial to birth weight in a metropolitan area.

Identifying critical windows of air pollution exposure during preconception and gestational period on birthweight: a prospective cohort study.

BACKGROUND: Few studies have assessed air pollution exposure association with birthweight during both preconception and gestational periods. METHODS: Leveraging a preconception cohort consisting of 14220 pregnant women and newborn children in Shanghai, China during 2016-2018, we aim to assess associations of NO2 and PM2.5 exposure, derived from high-resolution spatial-temporal models, during preconception and gestational periods with outcomes including term birthweight, birthweight Z-score, small-for-gestational age (SGA) and large-for-gestational age (LGA). Linear and logistic regressions were used to estimate 3-month preconception and trimester-averaged air pollution exposure associations; and distributed lag models (DLM) were used to identify critical exposure windows at the weekly resolution from preconception to delivery. Two-pollutant models and children's sex-specific associations were explored. RESULTS: After controlling for covariates, one standard deviation (SD) (11.5 µg/m3, equivalent to 6.1 ppb) increase in NO2 exposure during the second and the third trimester was associated with 13% (95% confidence interval: 2 - 26%) and 14% (95% CI: 1 - 29%) increase in SGA, respectively; and one SD (9.6 µg/m3) increase in PM2.5 exposure during the third trimester was associated with 15% (95% CI: 1 - 31%) increase in SGA. No association have been found for outcomes of birthweight, birthweight Z-score and LGA. DLM found that gestational weeks 22-32 were a critical window, when NO2 exposure had strongest associations with SGA. The associations of air pollution exposure tended to be stronger in female newborns than in male newborns. However, no significant associations of air pollution exposure during preconception period on birthweight outcomes were found. CONCLUSION: Consistent with previous studies, we found that air pollution exposure during mid-to-late pregnancy was associated with adverse birthweight outcomes.

Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data.

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 µg/m3 to 2.96 µg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 µg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

Urinary 1-hydroxypyrene in pregnant women in a Northeastern U.S. city: socioeconomic disparity and contributions from air pollution sources.

BACKGROUND: Maternal exposure to polycyclic aromatic hydrocarbons (PAHs), ubiquitous constituents of air pollution, has been associated with adverse birth outcomes. Yet it remains unclear whether and how socioeconomic status (SES) affects gestational PAH exposure. OBJECTIVE: To examine whether there are socioeconomic disparities in PAHs exposure among pregnant women from Rochester, NY, and if so, to what extent disproportionate proximity to air pollution sources, measured by residential distance to transportation-related sources, contributed to the exposure disparity. METHODS: We measured 1-hydroxypyrene concentrations in 726 urine samples collected from 305 pregnant women up to three samples throughout pregnancy. Residential distances to transportation-related sources were calculated based on participants' home addresses. We used linear mixed-effects models with random intercepts of participants to examine associations between 1-hydroxypyrene, SES indicators, and distance to transportation-related sources. We used structural equation modelling to assess to what extent distance to transportation-related sources contributes to the socioeconomic disparity in 1-hydroxypyrene concentrations. RESULTS: Reduced household income and maternal education level were both significant SES predictors of 1-hydroxypyrene concentrations, after the adjustment for other maternal demographic characteristics. Each interquartile range (IQR) increases in residential proximity to the airport (from 14.3 to 6.0 km), the railroad yard (from 22.3 to 6.0 km), and annual average daily traffic within 300 m (from 3796 to 99,933 vehicles/year) were associated with 15.0% (95%CI: 7.0-22.2%), 15.4% (95%CI: 6.5-23.5%), and 13.6% (95%CI: 4.7-23.3%) increases in 1-hydroxypyrene concentrations, respectively. Proximity to these sources jointly explained 10% (95%CI: 1.6-18.4%) of the 1-hydroxypyrene concentration change associated with decreases in SES as a latent variable defined by both household income and education level. IMPACT STATEMENT: Our findings suggest that efforts to address disproportionate residential proximity to transportation-related sources may reduce the socioeconomic disparity in PAH exposure.

Nanoarchitecture Manipulation by Polycondensation on KCl Crystals toward Crystalline Lamellar Carbon Nitride for Efficient H2O2 Photoproduction.

A lamellar carbon nitride (CN) framework is one of the most promising materials for solar-driven hydrogen peroxide production. The low dielectric constant of the organic CN framework leads to severe recombination of the excitons, and the photon-to-chemical conversion efficiency is thus unsatisfactory. In this work, by polycondensation of the small molecules on the KCl crystal surface, K+-incorporated crystalline CN (CNK) frameworks show significantly extended periodicity of the stacking layers and in-plane orderly organized heptazine/triazine units. The crystalline CNK frameworks exhibit a series of favorable photophysical properties, such as enhanced photon absorption, negatively shifted LUMO potentials, and attenuated emissive decay of the excitons. The CNK frameworks thus present remarkable performance in the photocatalytic selective oxygen reduction reaction for hydrogen peroxide production, e.g., CNK framework from the polycondensation of NH4SCN on the KCl surface could produce hydrogen peroxide at a remarkable reaction rate of 26.7 mmol h-1 g-1 with a high apparent quantum yield of 25.0%, which is 23.5 times that on its counterpart synthesized in the absence of KCl. This method is generally applicable to all of the precursors for CN synthesis.

Nanoarchitectonics in the Ionothermal Synthesis for Nucleation of Crystalline Potassium Poly (heptazine imide) Towards an Enhanced Solar-Driven H2 O2 Production.

Solar-driven selective oxygen reduction reaction on polymeric carbon nitride framework is one of the most promising approaches toward sustainable H2 O2 production. Potassium poly(heptazine imide) (PHI), with regular metal sites in the framework and favorable crystalline structure, is highly active for photocatalytic selective 2e oxygen reduction to produce H2 O2 . By introducing NH4 Cl into the eutectic KCl-LiCl salt mixture, the PHI framework exhibits a remarkable performance for photocatalytic production of H2 O2 , for example, a record high H2 O2 photo-production rate of 29.5 µmol h-1  mg-1 . The efficient photocatalytic performance is attributed to the favorable properties of the new PHI framework, such as improved porosity, negatively shifted LUMO position, enhanced exciton dissociation and charges migration properties. A mechanistic investigation by quenching and electron spin resonance technique reveals the critical role of superoxide radicals for the formation singlet oxygen, and the singlet oxygen is one of the critical intermediates towards the formation of the H2 O2 by proton extraction from the ethanol.

Association between outdoor artificial light at night and sleep duration among older adults in China: A cross-sectional study.

BACKGROUND: Light after dusk disrupts the circadian rhythms and shifts the timing of sleep later; but it is unknown whether outdoor artificial light at night (ALAN) affects sleep quality. This study aimed to explore the association between residential outdoor ALAN and sleep duration in a nationally representative sample of Chinese older adults. METHODS: We examined the cross-sectional associations of outdoor ALAN with self-reported sleep duration in 13,474 older adults participating in the 2017-2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Outdoor ALAN exposure was estimated at the residence level using satellite images. We applied generalized linear mixed models to investigate the association between ALAN exposure and sleep duration. We performed stratified analyses by age, sex, education, and household income levels. Moreover, we used multi-level logistic regression models to investigate the effects of ALAN on the short sleep duration (≤6 h) and the long sleep duration (>8 h), respectively, in reference to sleep for >6-8 h per day. RESULTS: We found a significant association between outdoor ALAN intensity and sleep duration. The highest quartile of ALAN was associated with 17.04 (95% CI: 9.42-24.78) fewer minutes of sleep as compared to the lowest quartile. The reductions in sleep duration per quartile change in ALAN were greater in the young old (≥65-85 years) and in those with higher levels of education, and those with higher household income, respectively. We did not detect a sex difference. In addition, those in the highest quartile of ALAN were more likely to report a 25% (95% CI: 10%-42%) increase in short sleep (<6 h), and a 21% (95% CI: 9%-31%) decrease in long sleep (>8 h). CONCLUSIONS: Increasing outdoor nighttime light intensity surrounding residences was associated with shorter sleep duration in older residents in China. This finding implies the importance of urban outdoor artificial light management as a potential means to lower the public health burden of sleep disorders.

A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China.

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 µm (PM2.5 and PM10) and nitrogen dioxide (NO2) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land-use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM2.5 and PM10 were consistently reliable, but the EV for NO2 had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM2.5 and PM10 model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the "Growth trajectories and air pollution" (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring.

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM2.5) in high-density cities, few studies have revealed the vertical differences in PM2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM2.5 exposure that combines a high-resolution LUR model with a vertical PM2.5 variation model to investigate the results of horizontal and vertical mobile PM2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM2.5 variation, respectively. Vertical measurements of PM2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM2.5 changes at a certain height relative to the ground. The vertical variation model for PM2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R2 of 0.91 and explained 92.8% of the variance. PM2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

High spatial resolution land-use regression model for urban ultrafine particle exposure assessment in Shanghai, China.

BACKGROUND: Little is currently known about long-term health effects of ambient ultrafine particles (UFPs) due to the lack of exposure assessment metrics suitable for use in large population-based studies. Land use regression (LUR) models have been used increasingly for modeling small-scale spatial variation in UFPs concentrations in European and American, but have never been applied in developing countries with heavy air pollution. OBJECTIVE: This study developed a land-use regression (LUR) model for UFP exposure assessment in Shanghai, a typic mega city of China, where dense population resides. METHOD: A 30-minute measurement of particle number concentrations of UFPs was collected at each visit at 144 fixed sites, and each was visited three times in each season of winter, spring, and summer. The annual adjusted average was calculated and regressed against pre-selected geographic information system-derived predictor variables using a stepwise variable selection method. RESULT: The final LUR model explained 69% of the spatial variability in UFP with a root mean square error of 6008 particles cm-3. The 10-fold cross validation R2 reached 0.68, revealing the robustness of the model. The final predictors included traffic-related NOx emissions, number of restaurants, building footprint area, and distance to the nearest national road. These predictors were within a relatively small buffer size, ranging from 50 m to 100 m, indicating great spatial variations of UFP particle number concentration and the need of high-resolution models for UFP exposure assessment in urban areas. CONCLUSION: We concluded that based on a purpose-designed short-term monitoring network, LUR model can be applied to predict UFPs spatial surface in a mega city of China. Majority of the spatial variability in the annual mean of ambient UFP was explained in the model comprised primarily of traffic-, building-, and restaurant-related predictors.

Different hypertension thresholds and cognitive decline: a pooled analysis of three ageing cohorts.

BACKGROUND: The 2017 American College of Cardiology (ACC)/American Heart Association (AHA) guidelines for high blood pressure (BP) in adults came up with a new definition of hypertension with a threshold BP level of 130/80 mmHg. But the 2018 European Society of Cardiology (ESC)/European Society of Hypertension (ESH) guidelines adhered to a conventional hypertension definition as BP ≥ 140/90 mmHg. We aimed to compare the trajectories of cognitive decline between participants with BP < 130/80 mmHg in all BP measurement waves and others with all BP < 140/90 mmHg. METHODS: This pooled analysis involved middle-aged and older participants from three nationally representative ageing cohorts, including the Health and Retirement Study (HRS), the English Longitudinal Study of Ageing (ELSA), and the China Health Retirement Longitudinal Study (CHARLS). Participants were divided into the Normal (BP < 130/80 mmHg on all occasions throughout the study), the Borderline (BP < 140/90 mmHg on all occasions throughout the study but not in the Normal group), and the High (the rest of participants) BP groups. Global cognitive Z score was calculated from tests on memory, executive function, and orientation. RESULTS: A total of 17,590 participants (HRS 6964, median follow-ups 12 years; ELSA 5334, median follow-ups 16 years; CHARLS 5292, median follow-ups 7 years) were included. No significant difference in global cognitive decline rate was detected between the Normal and the borderline groups (men, pooled ß = - 0.006 standard deviation [SD]/year; 95% confidence interval [CI], - 0.020 to 0.008; P = 0.377; women, pooled ß = 0.006 SD/year; 95% CI - 0.005 to 0.018; P = 0.269). Participants in the High group had a significantly faster cognitive decline (men, pooled ß = - 0.011 SD/year; 95% CI - 0.020 to - 0.002; P = 0.013; women, pooled ß = - 0.017 SD/year; 95% CI - 0.026 to - 0.008; P < 0.001) than that in the Borderline group. CONCLUSIONS: Individuals in the Borderline group did not experience significantly faster cognitive decline compared with those in the Normal group. It might not be necessary for individuals with borderline BP (between 130/80 and 140/90 mmHg) to initiate antihypertension therapy in consideration of cognitive decline.

Urinary Amino-Polycyclic Aromatic Hydrocarbons in Urban Residents: Finding a Biomarker for Residential Exposure to Diesel Traffic.

Despite substantial evidence of marked exposure to and ill-health effects from diesel exhaust (DE) emissions among occupational population (e.g., miners, truck drivers, and taxi drivers), it is less understood to what extent non-occupational population was exposed to DE among various combustion sources, largely due to the lack of biomarkers that would indicate specific exposure to DE. We evaluated whether urinary amino-polycyclic aromatic hydrocarbons (APAHs), such as major metabolites of DE-specific nitrated PAHs, can be used as DE exposure biomarkers in residential settings. We measured five urinary APAHs in 177 urine samples from 98 UK residents, 89 (91%) of them were London residents, and estimated their residential proximity to various traffic indicators (e.g., the road type, road length, traffic flow, and traffic volume). Participants living within 100 m of major roads exhibited increased levels of all five APAHs, among which 2-amino-fluorene (2-AFLU) reached statistical significance (p < 0.05). We estimated that a 10 m increase in the length of nearby major roads (<100 m) was associated with a 4.4% (95% CI of 1.1 to 7.6%) increase in 2-AFLU levels. Levels of 2-AFLU were significantly associated with the traffic flow of nearby buses and heavy-duty vehicles but not motorbikes, taxis, or coaches. We did not observe a significant association between distance to major roads or the sum of the major road length within 100 m with the other four biomarker concentrations. These results suggest the use of urinary 2-AFLU as a biomarker of DE exposure in urban residents.

Development of season-dependent land use regression models to estimate BC and PM1 exposure.

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM1) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM1 based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM1. The mean concentrations of pollutants were higher in winter (BC: 4.72 µg/m3; PM1: 56.97 µg/m3) than in fall (BC: 3.74 µg/m3; PM1: 33.29 µg/m3) and summer (BC: 2.77 µg/m3; PM1: 27.04 µg/m3). For both BC and PM1, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R2 of the LUR models for BC and PM1 ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM1 models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM1 was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM1 in typical inner-city environments, and support pollution management.

Dose-Response Relationship Between Long-Term Blood Pressure Variability and Cognitive Decline.

Background and Purpose: We aimed to test whether higher long-term blood pressure variability was associated with accelerated rate of cognitive decline and evaluate potential dose-response relationship. Methods: Original survey data from the Health and Retirement Study and the English Longitudinal Study of Ageing were used. Standardized Z score of cognitive function was the main outcome measure. Visit-to-visit blood pressure SD, coefficient of variation, and variation independent of mean were used. Linear mixed model and restricted spline were applied to assess association and explore dose-response pattern. Segmented regression was used to analyze dose-response relationship and estimate turning point. Meta-analysis using random-effects model was conducted to pool results, with I2 used to test heterogeneity. Results: A total of 12 298 dementia-free participants were included (mean age: 64.6±8.6 years). Significant association was observed between blood pressure variability and cognitive decline. Each 10% increment in coefficient of variation of systolic and diastolic blood pressure was associated with accelerated global cognitive decline of 0.026 SD/y (95% CI, 0.016­0.036, P<0.001) and 0.022 SD/y (95% CI, 0.017­0.027, P<0.001), respectively. Nonlinear dose-response relationship was found (P<0.001 for nonlinearity), with clear turning point observed (P<0.001 for change in slopes). Conclusions: Higher long-term blood pressure variability was associated with accelerated cognitive decline among general adults aged ≥50 years, with nonlinear dose-response relationship. Further randomized controlled trials are warranted to evaluate potential benefits of blood pressure variability-lowering strategies from a cognitive health perspective.

Mechanistic analysis of multiple processes controlling solar-driven H2O2 synthesis using engineered polymeric carbon nitride.

Solar-driven hydrogen peroxide (H2O2) production presents unique merits of sustainability and environmental friendliness. Herein, efficient solar-driven H2O2 production through dioxygen reduction is achieved by employing polymeric carbon nitride framework with sodium cyanaminate moiety, affording a H2O2 production rate of 18.7 µmol h -1 mg-1 and an apparent quantum yield of 27.6% at 380 nm. The overall photocatalytic transformation process is systematically analyzed, and some previously unknown structural features and interactions are substantiated via experimental and theoretical methods. The structural features of cyanamino group and pyridinic nitrogen-coordinated soidum in the framework promote photon absorption, alter the energy landscape of the framework and improve charge separation efficiency, enhance surface adsorption of dioxygen, and create selective 2e- oxygen reduction reaction surface-active sites. Particularly, an electronic coupling interaction between O2 and surface, which boosts the population and prolongs the lifetime of the active shallow-trapped electrons, is experimentally substantiated.

Persistent moderate to severe pain and long-term cognitive decline.

BACKGROUND: The longitudinal association between persistent moderate to severe pain and subsequent long-term cognitive decline remains inconclusive. METHODS: Study population came from the English Longitudinal Study of Ageing, an ongoing prospective and nationally representative cohort of community-dwelling adults aged ≥50 years in England. At waves 1 (2002/2003) and 2 (2004/2005) of the study, pain severity was measured based on pain intensity scores ranged from 0 to 10. We defined moderate to severe pain as pain intensity scores ≥5 points. Persistent moderate to severe pain was defined as participants reported moderate to severe pain at both waves 1 and 2. Standardized global cognitive Z scores derived from verbal memory, temporal orientation and semantic fluency were used as the primary outcome. RESULTS: A total of 6,869 individuals (3,896 women; mean age: 63.9 ± 9.5 years) who have accepted twice measurements of pain at waves 1 and 2 (baseline), and at least one reassessment of cognitive function at waves 3 (2006/2007) to 8 (2016/2017), were included in this study. Each 5-point increase in the sum of pain intensity scores was associated with a faster rate of -0.009 (95% CI: -0.013 to -0.006, p < .001) in global cognitive Z scores. Compared with no pain group, persistent moderate to severe pain group was associated with a significantly faster decline rate of -0.031 SD/year (95% CI: -0.043 to -0.018), in global cognitive Z scores. The relationships of persistent moderate to severe pain with verbal memory, temporal orientation and semantic fluency were similar. CONCLUSION: Cognitive function should be monitored in individuals with persistent moderate to severe pain. SIGNIFICANCE: Persistent moderate to severe pain in adults age 50 and older was associated with accelerated cognitive decline over a median follow-up of 12 years. More severe pain was associated with faster cognitive decline in a dose-response pattern, and the relationship was demonstrated throughout multiple cognitive domains. While the overall effect was subtle, clinicians should be aware that older adults with persistent pain are at risk of faster cognitive decline.

Potential for developing independent daytime/nighttime LUR models based on short-term mobile monitoring to improve model performance.

Land use regression model (LUR) is a widespread method for predicting air pollution exposure. Few studies have explored the performance of independently developed daytime/nighttime LUR models. In this study, fine particulate matter (PM2.5), inhalable particulate matter (PM10), and nitrogen dioxide (NO2) concentrations were measured by mobile monitoring during non-heating and heating seasons in Taiyuan. Pollutant concentrations were higher in the nighttime than the daytime, and higher in the heating season than the non-heating season. Daytime/nighttime and full-day LUR models were developed and validated for each pollutant to examine variations in model performance. Adjusted coefficients of determination (adjusted R2) for the LUR models ranged from 0.53-0.87 (PM2.5), 0.53-0.85 (PM10), and 0.33-0.67 (NO2). The performance of the daytime/nighttime LUR models for PM2.5 and PM10 was better than that of the full-day models according to the results of model adjusted R2 and validation R2. Consistent results were confirmed in the non-heating and heating seasons. Effectiveness of developing independent daytime/nighttime models for NO2 to improve performance was limited. Surfaces based on the daytime/nighttime models revealed variations in concentrations and spatial distribution. In conclusion, the independent development of daytime/nighttime LUR models for PM2.5/PM10 has the potential to replace full-day models for better model performance. The modeling strategy is consistent with the residential activity patterns and contributes to achieving reliable exposure predictions for PM2.5 and PM10. Nighttime could be a critical exposure period, due to high pollutant concentrations.

Development and transferability of ultrafine particle land use regression models in London.

Due to a lack of routine monitoring, bespoke measurements are required to develop ultrafine particle (UFP) land use regression (LUR) models, which is especially challenging in megacities due to their large area. As an alternative, for London, we developed separate models for three urban residential areas, models combining two areas, and models using all three areas. Models were developed against annual mean ultrafine particle count cm-3 estimated from repeated 30-min fixed-site measurements, in different seasons (2016-2018), at forty sites per area, that were subsequently temporally adjusted using continuous measurements from a single reference site within or close to each area. A single model and 10 models were developed for each individual area and combination of areas. Within each area, sites were split into 10 groups using stratified random sampling. Each of the 10 models were developed using 90% of sites. Hold-out validation was performed by pooling the 10% of sites held-out each time. The transferability of models was tested by applying individual and two-area models to external area(s). In model evaluation, within-area mean squared error (MSE) R2 ranged from 14% to 48%. Transferring individual- and combined-area models to external areas without calibration yielded MSE-R2 ranging from -18 to 0. MSE-R2 was in the range 21% to 41% when using particle number count (PNC) measurements in external areas to calibrate models. Our results suggest that the UFP models could be transferred to other areas without calibration in London to assess relative ranking in exposures but not for estimating absolute values of PNC.