Winter Peak Research Articles

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study.

Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Dominant flood types in mountains catchments: Identification and change analysis for the landscape planning

The classification of floods may be a supporting tool for decision-makers in regard to water management, including flood protection. The main objective of this work is the classification of flood generation mechanisms in 28 catchments of the upper Vistula basin. A significant innovation in this study lies in the utilization of decision trees for flood classification. The methodology has so far been applied in the Alpine region. The analysis reveals that peak daily precipitation in the catchments mainly occurs in summer, particularly from June to August. Maximal daily snowmelt typically happens at the end of winter (March to April) and occasionally in November. Winter peaks are observed in March to April and, in some areas, in November to December, while summer peaks occur in May and, in specific catchments, in October. Higher peak flows for annual floods are noted in March to April and June to August. Most annual floods in the Upper Vistula basin are classified as Rain-on-Snow Floods (RoSFs) or Lowland River Floods (LRFs). LRFs contribute from 19% to almost 72%, while RoSFs range from 18% to 75%. In Season 1 (summer), most seasonal floods are identified as LRFs (51%–100%), with very few as RoSFs (0%–46.9%). In Season 2 (winter), the opposite pattern is observed, with most RoSFs (48.4%–97.9%) and fewer LRFs (0%–20.6%). While there are changes in flood patterns, they are not statistically significant. Conducted studies and obtained results can be useful for the preparation of flood prevention documentation and for flood management in general.

Post-midnight irregularities in equator and its adjacent regions of topside ionosphere obtained by the CSES satellite

The post-midnight irregularities in equator and its adjcent regions, which have remained least explored so far, are investigated using the in situ electron density (Ne) measurements from 2019 to 2021 obtained by the China Seismo-Electromagnetic Satellite (CSES) in the topside ionosphere. The followings are salient points of our findings: 1) Equatorial and low latitude post-midnight irregularities are distributed along the dip equator, exhibiting a wavenumber 4 longitude variation pattern and a seasonal variation pattern characterized by peaks in summer and winter and valleys in spring and autumn during low solar activity (LSA) period in the topside ionosphere, 2) The longitude variation pattern of post-midnight irregularities aligns with that of the background Ne, with irregularities concentrated at the peaks of background Ne, 3) The occurrence rate of post-midnight irregularities increases significantly with higher solar flux, which can be attributed to the rapid increase in the background Ne under LSA conditions. Additionally, the growth rate of occurrence rate varies across different longitudes, with the Pacific and the Atlantic longitudes standing out as prominent regions, and 4) Post-midnight irregularities in the topside ionosphere may have two possible origins. One is irregularities generated at the lower altitudes that drift upward into the topside ionosphere, while the other is the irregularities generated directly in the topside ionosphere. The latter is supported by the presence of conditions conducive to irregularity generation in the topside ionosphere during midnight hours in LSA period. Given the scarcity of research on post-midnight irregularity compared to post-sunset irregularity, further investigations are essential to fully comprehend their generation mechanisms. The abundance of post-midnight Ne measurements from the CSES satellite offers a valuable opportunity for this research, which can enhance our understanding of post-midnight ionospheric dynamics, and their variations with solar activity.

Forecasting of neonatal mortality trend at a special new-born care unit in Odisha, India: A time-series analysis

New born mortality is a public health problem in the state of Odisha. Newborn mortality is a dynamic process and variations in mortality are observed temporally and seasonally, and also across health facilities. Prior knowledge of mortality burden can enable health system’s readiness in terms of resources allocation and timely intervention, thereby improving the chances of survival of sick newborns admitted in the hospitals. Hence, this study aimed to examine temporal trends of newborn mortality in a Special Newborn Care Unit of Saheed Laxman Nayak Medical College and Hospital (SLNMCH) in Odisha and forecast a short-term monthly projection.The Box-Jenkins approach was used to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded mortality among the hospitalized new borns in the SNCU during 2016-2020. The best-fit model for forecasting was found based on the Akaike Information Criterion.The time-series analysis revealed a modest upward trend in newborn mortality rate among SNCU admitted newborns, with peaks in the late winter and late summer months. The seasonal ARIMA (0,1,1)(1,1,1)12 model offered the best fit for time-series data. This model predicted the monthly percentage of mortality in SNCU admitted newborns in the range of 9% to 35% with respective 95% confidence interval for two years period (2021-2022).SARIMA models are useful for monitoring newborn mortality and provide an estimate of temporal trends and seasonality. The models are helpful for predicting occurrence of mortality in the SNCU of SLNMCH and could be useful for developing early warning systems. It may help in early detection, timely treatment, and prevention of serious complications in admitted sick newborns.

Impact of thermal zone modeling on a small office building with all-electric HVAC systems in hot-humid and cold-humid climates

In code-compliant simulations, the impact of thermal zoning method is significant on building energy use of building systems. However, in practice, thermal zone modeling is often determined by general rules without careful consideration of the specific characteristics of different HVAC systems. Therefore, this study evaluated the impact of thermal zone modeling on all-electric HVAC Systems in hot-humid and cold-humid climates using different levels of usage profiles. The results of this study identify the significant influence of thermal zoning methods on the annual energy use in all-electric HVAC systems depending on usage rates and climate. When different thermal zoning models were used, the amount of energy used differed depending on HVAC system type and zone usage (i.e., PSZ system varied from 4.6% to 8.8% in Houston, TX, and from 4.6% to 20.1% in Chicago, IL). In addition, a significant difference can be found in peak heating demands for selected HVAC systems in buildings located in cold climates (i.e., for winter peak days, the daily load differences of 21.8% to 24.5% in Chicago, IL). The results of this study can be used to improve the accuracy of thermal zone modeling for code compliance that considers different HVAC system types.

Ambient temperature and seasonal variation in inpatient mortality from respiratory diseases: a retrospective observational study.

The seasonal fluctuation in mortality and hospital admissions from respiratory diseases, with a winter peak and a summer trough, is widely recognized in extratropical countries. However, little is known about the seasonality of inpatient mortality and the role of ambient temperature remains uncertain. We aimed to analyse the association between ambient temperature and in-hospital mortality from respiratory diseases in the provinces of Madrid and Barcelona, Spain. We used data on daily hospitalisations, weather (ie, temperature and relative humidity) and air pollutants (ie, PM2.5, PM10, NO2and O3) for the Spanish provinces of Madrid and Barcelona during 2006-2019. We applied a daily time-series quasi-Poisson regression in combination with distributed lag non-linear models (DLNM) to assess, on the one hand, the seasonal variation in fatal hospitalisations and the contribution of ambient temperature, and on the other hand, the day-to-day association between temperature and fatal hospital admissions. The analyses were stratified by sex, age and primary diagnostic of hospitalisation. The study analysed 1710012 emergency hospital admissions for respiratory diseases (mean [SD] age, 60.4 [31.0] years; 44.2% women), from which 103845 resulted in in-hospital death (81.4 [12.3] years; 45.1%). We found a strong seasonal fluctuation in in-hospital mortality from respiratory diseases. While hospital admissions were higher during the cold season, the maximum incidence of inpatient mortality was during the summer and was strongly related to high temperatures. When analysing the day-to-day association between temperature and in-hospital mortality, we only found an effect for high temperatures. The relative risk (RR) of fatal hospitalisation at the 99th percentile of the distribution of daily temperatures vs the minimum mortality temperature (MMT) was 1.395 (95% eCI: 1.211-1.606) in Madrid and 1.612 (1.379-1.885) in Barcelona. In terms of attributable burden, summer temperatures (June-September) were responsible for 16.2% (8.8-23.3) and 22.3% (15.4-29.2) of overall fatal hospitalisations from respiratory diseases in Madrid and Barcelona, respectively. Women were more vulnerable to heat than men, whereas the results by diagnostic of admission showed heat effects for acute bronchitis and bronchiolitis, pneumonia and respiratory failure. Unless effective adaptation measures are taken in hospital facilities, climate warming could exacerbate the burden of inpatient mortality from respiratory diseases during the warm season. European Research Council Consolidator Grant EARLY-ADAPT, European Research Council Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Energetic Complementarity Solar PV and Wind Power Based on Satellite Data

With the current global energy crisis renewable energy integration is expected to grow drastically in the near future. However, integrating high amount of renewables to the grid is subjected to various operational challenges. One solution for this challenge is looking for complementing intermittent renewable resources. In this paper solar PV and wind power complementarity analysis was carried out over the three topographic regions of Eritrea based on monthly satellite-based power generation data. Three different approaches (Pearson correlation coefficient, graphical and dimensionless index) were employed to investigate the complementarity of PV and wind power in the chosen sites. The analysis results showed that the southern coastal and central highlands sites have low complementarity behaviour. The solar PV and wind power synergy was however, found to be the maximum in the northern highlands and western lowlands sites of the country. It is interesting to find out that the high wind potential sites in southern coastal region are positively correlated to solar availability with peaks in winter and low in summer with no complementarity pattern.

1681. Persistent Increased Invasive Pediatric Streptococcus pyogenes (Group A Streptococcus) Infections with Correlated Increase in Superficial Infection and Pharyngitis

Abstract Background Children's Hospital of Philadelphia (CHOP) clinicians observed an increase in invasive group A Streptococcus infections (iGAS) in November 2022. In December 2022, the Centers for Disease Control and Prevention published an investigation of increased iGAS. At CHOP iGAS remain increased, even after peak respiratory viral season. Our objective was to compare pre-pandemic and current case counts and to investigate if the increase in iGAS correlates with an increase in non-invasive GAS infections (nGAS). Methods Data for positive Streptococcus pyogenes results at CHOP from January 2018 to April 2023 was collected from the electronic medical record (n=466). Results were classified by source and specimen type as pharyngitis, superficial, and invasive, then further classified as non-invasive (pharyngitis and superficial) or invasive. For infection events with multiple results only one result was included, giving preference to the most aggressive classification (invasive >pharyngitis >superficial). Fisher's exact test was used to assess association between the increase in nGAS and iGAS prior to and after the start of the COVID-19 pandemic (2018-2019 vs. 2021-2023). Fisher's exact test, with Monte Carlo simulation, was used to assess association between the increase in nGAS and iGAS infections by year. Results There was a statistically significant association in the increase in pharyngitis, superficial, and invasive group A Streptococcus infections (GAS) (p-value < 0.0001) and in nGAS and iGAS (p-value = 0.0001), when comparing pre- and post-2020. We found statistically significant associations in the increase of pharyngitis, superficial, and invasive GAS cases (p-value = 0.0005) and of nGAS and iGAS cases (p-value = 0.0005). Conclusion Our data shows a significant increase in cases from pre-pandemic years for nGAS and iGAS infections. iGAS is correlated with nGAS, even with further stratification of nGAS into pharyngitis and superficial. Notably, increases in nGAS were significantly more than the increase in iGAS, which has unexpectedly persisted beyond the typical winter viral infection peak. Continued monitoring or GAS, particularly iGAS, is justified. Disclosures All Authors: No reported disclosures

Wavelet Analysis of Ozone Driving Factors Based on ~20 Years of Ozonesonde Measurements in Beijing

A long-term vertical ozone observational dataset has been provided during 2001–2019 by ozonesonde measurements in Beijing on the North China Plain. Previous studies using this dataset primarily focused on the vertical characteristics of climatological ozone and its variation; however, the driving factors of ozone variation have not been well discussed. In this study, by applying the wavelet analysis method (including continuous wavelet transform and cross wavelet) and sliding correlation coefficients to ~20 years of ozonesonde measurements collected in Beijing, we analyzed the dominant modes of ozone column variability within three height ranges over Beijing (total column ozone: TOT; stratospheric column ozone: SCO; and tropospheric column ozone: TCO). Moreover, we also preliminarily discussed the relationship between these three ozone columns and the El Niño Southern Oscillation (ENSO), Quasi-biennial Oscillation (QBO), and 11-year solar activity cycle. The results revealed that the ozone columns within the three height ranges predominantly adhered to interannual variability patterns, and the short-term variabilities in TOT and SCO may have been related to eruptive volcanic activity. In comparison to the TOT and SCO, the TCO was more susceptible to the forcing influences of high-frequency factors such as pollutant transport. Similar to the results in other mid-latitude regions, strong ENSO and QBO signals were revealed in the interannual ozone column variability over Beijing. The TOT and SCO showed positive anomalous responses to ENSO warm-phase events, and the peak of the ENSO warm phase led the winter peaks of the TOT and SCO by approximately 3–6 months. During the strong cold–warm transition phase in 2009–2012, the TOT and SCO showed a significant positive correlation with the ENSO index. The strong seasonality of the meridional circulation process driven by the QBO led to a significant positive correlation between the QBO index and the TOT and SCO in the interannual cycle, except for two periods of abnormal QBO fluctuations in 2010–2012 and 2015–2017, whereas the TCO showed a time-lagged correlation of approximately 3 months in the annual cycle relative to the QBO due to the influence of the thermodynamic tropopause. In addition, analysis of the F10.7 index and the ozone columns revealed that the ozone columns over Beijing exhibited lagged responses to the peaks of sunspot activity, and there was no obvious correlation between ozone columns and 11-year solar activity cycle. Given the complex driving mechanism of the climatic factors on local ozone variability, the preliminary results obtained in this study still require further validation using longer time series of observational data and the combination of chemical models and more auxiliary data.

Detection rates of pesticide residues in Saudi Arabian produce as influenced by season

Pesticide residues and persistent organic pollutants (POPs) are known hazardous chemicals that exhibits bioaccumulation in organisms and the ecosystem in general. There is limited evidence on the levels of these contaminants among common Saudi crops grown as well as the effects of season on their levels. Hence, the present investigated the levels of pesticide residues in major fruit and vegetable crops collected during the winter and summer seasons in Saudi Arabia (SA). A total of 392 samples taken from 28 locally produced vegetables and fruits were purchased from local markets in Riyadh, SA during peak summer (N = 263) (June-August 2022) and peak winter (N = 129) (December-February 2022). Food samples were extracted and cleaned up using the modified Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) technique. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) and gas chromatography–tandem mass spectrometry (GC–MS/MS) instruments were used to determine pesticide residues. Crops that had the highest percentage exceeding the maximum residue limit (MRL) include pepper (7.9 %) as well as apricot, fig and pomegranates (3.1 %). Cypermethrin was the most prevalent pesticide residue in both winter and summer samples with detection rates of 13.2 % and 14.1 % respectively (p = 0.33). Thiamethoxam detection rates was significantly higher in winter (7.8 %) than summer (3.0 %) (p = 0.04). There is a high detection rate of pesticide residues in SA independent of season, with cypermethrin being the most common. Thiamethoxam is more commonly detected in winter than summer crops. The study should be extended to include other potential sources of pesticide residues such as fishes grown in aqua farms and other poultry products.

Seasonal dynamics of soil microbial growth, respiration, biomass, and carbon use efficiency in temperate soils

Soil microbial growth, respiration, and carbon (C) use efficiency (CUE) are essential parameters to understand, describe and model the soil carbon cycle. While seasonal dynamics of microbial respiration are well studied, little is known about how microbial growth and CUE change over the course of a year, especially outside the plant growing season. In this study, we measured soil microbial respiration, gross growth via 18O incorporation into DNA, and biomass in an agricultural field and a deciduous forest 16 times over the course of two years. We sampled soils to a depth of 5 cm from plots at which harvest residues or leaf litter remained on the plot or was removed. We observed strong seasonal variations of microbial respiration, growth, and biomass. All these microbial parameters were significantly higher at the forest site, which contained 4.3 % organic C compared to the agricultural site with 0.9 % organic C. CUE also varied strongly (0.1 to 0.7) but was overall significantly higher at the agricultural site compared to the forest site. We found that microbial respiration and to a lesser extent microbial growth followed the seasonal dynamics of soil temperature. Microbial growth was further affected by the presence of plants in the agricultural system or foliage in the forest. At low temperatures in winter, both microbial respiration and gross growth showed the lowest rates, whereas CUE (calculated from both respiration and growth) showed amongst the highest values determined during the two years, due to the higher temperature sensitivity of microbial respiration. Microbial biomass C strongly increased in winter. Surprisingly, this winter peak was not connected to high microbial growth or an increase in DNA content. This suggests that microorganisms accumulated C and N, potentially in the form of osmo- or cryoprotectants or increased in cell size but did not divide. This microbial winter bloom and following decline, where C is released from microbial biomass and freely available, might constitute a highly dynamic time in the annual C cycle in temperate soil systems. Highly variable CUE, which was observed in our study, and the fact that CUE is calculated from independently controlled microbial respiration and microbial growth, ask for great caution when CUE is used to describe soil microbial physiology, soil C dynamics or C sequestration. Instead, microbial respiration, microbial growth, and microbial biomass C should be investigated individually in combination to better understand the soil C cycle.

Seasonal Characteristics of Long-Range Transport and Potential Associated Sources of Particulate Matter (Pm<sub>10</sub>) Pollution at the Station Elk, Poland, on 2021-2022 Data

The current study aimed to determine the potential sources of distant emissions of PM10 particles that significantly affect PM10 levels at a given site in southeastern Baltic. The EEA Air Quality Monitoring Station in Elk City, northeastern Poland, was selected for this study. This station is located approximately 50 km from the border of the Russian exclave (Kaliningrad Region). In this study, the NOAA HYSPLIT_4 trajectory model, potential source contribution function (PSCF), and concentration-weight trajectory (CWT) were employed to investigate the origin of the measured PM10 mass at a receptor site. PSCF and CWT utilize back-trajectory analysis and Lagrangian particle dispersion simulations to reconstruct the advection pathways of air masses arriving at the site. These reconstructed retroplumes provide detailed information regarding the geographic locations traversed by polluted air masses on their way to the receptor. By integrating trajectory information with concurrent pollutant concentration data, the PSCF and CWT enable the identification of potential source regions and quantification of their impact on the observed atmospheric levels. From January 1, 2021, to December 31, 2022, at 200 m the 72h backward trajectories of air masses entering the receptor point were calculated and categorized by clustering them into 5-4-4-5 clusters. Subsequently, the PM10 levels at the Elk site associated with each air mass cluster were examined during the observation period. The seasonal variation in PM10 was generally characterized by a peak in winter and minimum values in summer. PM10 was lower during warmer periods, particularly during summer, and significantly, higher concentrations were observed during colder periods. Cluster analyses showed that airflow followed a seasonal pattern, with different results obtained in different seasons. According to the PSCF and CWT results, in winter and spring, the receptor site was influenced more by long-range PM10 pollution, particularly from heavily industrialized areas in Central-Eastern Europe. In contrast, in summer and autumn, the receptor site was less influenced by long-range pollution. The findings demonstrate that the seasonal distributions of PM10 source areas obtained using these two methods generally share similar characteristics, suggesting the credibility and accuracy of the analytical results.

The seasonal Antarctic sea ice concentration anomalies related to the Atlantic Niño index

Antarctic sea ice concentration anomalies (ASICA) have been found to be linked to sea surface temperature anomalies in tropical oceans. However, it is not clear whether and how ASICA is linked to the Atlantic Niño mode (ANM). This study demonstrates a significant relationship between ASICA and ANM. The relationships vary by season, with a peak in austral winter and a secondary one in spring. Significant sea ice anomalies associated with a positive phase of ANM are mostly negative in austral winter and spring, and mostly positive in austral summer and autumn. This teleconnection is established by atmospheric wavetrains that are excited over the tropical southwestern Pacific and Indian Oceans and the southern Atlantic Oceans and propagate over the Southern Ocean. These wavetrains induce anomalous near-surface circulations, which generate dynamic and thermodynamic forcing on sea ice, resulting in the observed ASICA patterns. The absence of El Niño Southern Oscillation weakens the connection.

Seasonality and risk factor analysis of respiratory syncytial virus infection in children in Taiwan-A retrospective study from 1995 to 2005.

Respiratory syncytial virus (RSV) is one of the important pathogens leading to acute respiratory tract infection in infants and young children. We aimed to investigate the seasonality of RSV infection in different parts of Taiwan and to delineate the risk factors for severe RSV infections. We collected RSV-infected patients' data by retrospective chart review. A total of 1740 RSV-infected children aged under 18 years were enrolled. The infection was acquired during hospitalization in 103 (7.1%) children, while none of them required ventilator support or needed intensive care before RSV infection. The need for intensive care or ventilator support was significantly associated with congenital heart disease (CHD), chronic lung disease, and neuromuscular diseases. Age <1 year and nosocomially acquired infection are also significant predictors for the need of intensive care. Only the presence of CHD, especially acyanotic CHD, was significantly associated with a fatal outcome. RSV infection occurs all year round. Monthly distribution of RSV infections in Northern Taiwan showed a bimodal pattern, with one peak from March to May, and another from August to October. The distribution in Southern Taiwan showed a single peak from April to July. The occurrence of RSV correlates positively with temperature and rain. The bimodal seasonal distribution pattern in Northern Taiwan may be a transitional pattern shifting from a single high winter peak in temperate areas to a wider summer peak in tropical areas. Continuous surveillance is needed to explore the possible effect of global warming on the seasonality of RSV infection.

Machine learning forecasts for seasonal epidemic peaks: Lessons learnt from an atypical respiratory syncytial virus season.

Seasonal peaks in infectious disease incidence put pressures on health services. Therefore, early warning of the timing and magnitude of peak activity during seasonal epidemics can provide information for public health practitioners to take appropriate action. Whilst many infectious diseases have predictable seasonality, newly emerging diseases and the impact of public health interventions can result in unprecedented seasonal activity. We propose a Machine Learning process for generating short-term forecasts, where models are selected based on their ability to correctly forecast peaks in activity, and can be useful during atypical seasons. We have validated our forecasts using typical and atypical seasonal activity, using respiratory syncytial virus (RSV) activity during 2019-2021 as an example. During the winter of 2020/21 the usual winter peak in RSV activity in England did not occur but was 'deferred' until the Spring of 2021. We compare a range of Machine Learning regression models, with alternate models including different independent variables, e.g. with or without seasonality or trend variables. We show that the best-fitting model which minimises daily forecast errors is not the best model for forecasting peaks when the selection criterion is based on peak timing and magnitude. Furthermore, we show that best-fitting models for typical seasons contain different variables to those for atypical seasons. Specifically, including seasonality in models improves performance during typical seasons but worsens it for the atypical seasons.

Influence of Urban-Road Green Space Plant Configurations on NO2 Concentrations in Nanjing City during Winter

The rapid urbanization and growing number of motor vehicles in China have led to a significant increase in NO2 emissions, posing a severe threat to the air quality in cities. Road traffic pollution has emerged as a significant environmental issue in China. Exploring the utilization of landscape plants for air pollutant mitigation and assessing the influence of various plant concentrations on reducing air pollution holds great significance in urban ecological environment protection and urban development. Through field surveys and data collection in January 2022, the objectives of this study are to explore the relationship between road meteorological factors and the reduction in air pollutant concentrations in Nanjing city’s road green spaces and to investigate the influence of plant configuration in road green spaces on pollutant concentration. The findings demonstrate a distinct positive correlation between road traffic volume during winter peak hours and the concentration of NO2 pollution gas. Furthermore, meteorological factors, including temperature and light intensity, strongly correlate with air pollutant concentrations. Open green spaces with ventilated structures and high tree planting density (deciduous trees are preferred) exhibit optimal purification effects. Excessive or insufficient planting density hinders the purifying function of green belts. In conclusion, our research on plant configurations and air pollutant concentrations in Nanjing City during the winter suggests that the recommended road green space plant configuration in Nanjing is a combination of arbor (deciduous tree), shrub, and grass.

Roles of Dynamic and Thermodynamic Processes in Regulating the Decay Paces of El Niño Events

Abstract Most El Niño events decay after a peak in boreal winter, but some persist and strengthen again in the following year. Several mechanisms for regulating its decay pace have been proposed; however, their relative contributions have not been thoroughly examined yet. By analyzing the fast-decaying and persistent types of the events in a 1200-yr coupled simulation, we quantify the key dynamic and thermodynamic processes in the decaying spring that are critical to determining the decay pace of El Niño. The zonal advection due to upwelling Kelvin wave accounts for twice as much the cooling difference as evaporation or meridional advection does. The upwelling Kelvin wave is much stronger in the fast-decaying events than the others, and its strength is equally attributed to the reflected equatorial Rossby wave and the equatorial easterly wind forcing over the western Pacific in the preceding 2–3 months. Relative to the fast-decaying events, the evaporative cooling is weaker but the meridional warm advection is stronger in the persistent events. The former is due to more meridionally asymmetric wind and sea surface temperature anomalies (SSTA) signaling positive Pacific meridional mode. The latter results from the advection of equatorial warm SSTA by climatological divergent flow, and the warmer SSTA persists from the mature stage subject to weaker cloud-radiative cooling in response to the central-Pacific-type SSTA distribution in the persistent events relative to the fast-decaying events. Our result consolidates the existing knowledge and provides a more comprehensive and physical pathway for the causality of El Niño’s diverse duration.

Summer diarrhea in children: a monocentric French epidemiological observational study

Pediatric diarrhea is a major public health problem worldwide. In France, continuous surveillance shows a winter epidemic peak and a more modest summer recrudescence. Few studies describe the infectious agents responsible for pediatric summer diarrhea in France. The objectives were to estimate the prevalence of infectious diarrhea and describe the pathogens responsible for summer diarrhea in children; and to describe common factors that can be used as guidance on the etiology of these diarrheas. A cross-sectional, single-center, epidemiological observational study was conducted in the pediatric emergency department of a French hospital between June and September in 2019 and 2020. Multiplex gastrointestinal pathogen panels were used for diagnostics. A multiple correspondence analysis was used to determine profiles of patients. A total of 95 children were included, of whom 82.1% (78/95) were under five years old. The prevalence of infectious summer diarrhea was 81.1% (77/95, 95%CI 71.7–88.4%). A total of 126 infectious agents were detected (50.0% bacteria, 38.1% viruses, 11.9% parasites). The main enteric pathogens were enteropathogen Escherichia coli (24/126), rotavirus (17/126) and Salmonella (16/126). A co-detection was found in 51.9% (40/77) of cases. Four patient profiles, considering the severity and the pathogen involved, were highlighted.

The mechanism of boreal summer SSTA phase-locking in the far eastern Pacific

In observations, the boreal winter El Niño—Southern Oscillation (ENSO) phase-locking phenomenon is evident in the central-eastern Pacific. In the far eastern equatorial Pacific (FEP) and South American coastal regions, however, the peak of sea surface temperature anomalies (SSTA) tends to occur in the boreal summer, with fewer winter peak events. By separating the direct ENSO forcing from the FEP SSTA, we found that the summer peak preference is contributed by the residual SSTA component, while the ENSO forcing provides only a small probability of winter peak. The dynamics of FEP SSTA phase-locking in observations and its biases in the climate models are investigated by adopting a linear stochastic-dynamical model. In observations, the summer phase-locking of FEP SSTA is controlled by the seasonal modulation of the SSTA damping process. In contrast, in the climate models the strength of FEP SSTA phase-locking is much smaller than observed due to the overly negative SSTA damping rate.

Optimization of Heat Interaction Between Borehole Heat Exchanger and Ground Using Taguchi Method During Space Cooling and Heating Operation of GSHP System

Abstract In this research work, optimization of heat exchange between borehole heat exchanger (BHE) and the ground soil for space cooling and heating applications, incorporating the optimum thermal effectiveness of BHE has been reported. Initially, Taguchi technique is employed to optimize the effectiveness of borehole heat exchanger. Later, the experimental data of 24 h are coupled with the theoretically optimized parameters to compute the optimum heat exchange during peak summer and peak winter seasons. In the Taguchi optimization approach, six control variables at three levels are employed and a standard, L27 (36) orthogonal array is selected for the analysis. Among the six control variables, thermal conductivity of the grouting material is observed to be the most influential parameter and tube radius of BHE as the least parameter in the optimized thermal effectiveness of the BHE. Both the experiments for space heating and cooling were conducted on a 17.5 kW cooling capacity ground source heat pump system (GSHP), connected with five parallelly connected double U-tube BHE and one single U-tube BHE. To compute the optimum heat transfer to/ from the BHE, time-dependent borehole temperature was incorporated to include the dynamic thermal load of the GSHP system. After incorporating the Taguchi-optimized thermal effectiveness in the experimental data, there is an enhancement of 30% to 48% of heat rejection into the ground during the summer season, whereas in the winter season, there is an enhancement of 35– 52% of heat extraction from the ground.