Nondestructive Detection and Early Warning of Pavement Surface Icing Based on Meteorological Information

Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.

Seasonal and diurnal measurement of ambient benzene at a high traffic inflation site in Delhi: Health risk assessment and its possible role in ozone formation pathways

Benzene is the most toxic and hazardous pollutant among volatile organic compounds (VOCs), as it comes under group 1 carcinogens recognized by the International Agency for Research on Cancer (IARC). It also plays a significant role in forming secondary pollutants like ozone. The benzene concentration was measured using a charcoal sorbent tube by active sampling at a traffic junction and analysis was done using GC-FID. The maximum average concentration of benzene in ambient air was found to be 33 μg/m3. A diurnal study of benzene measurement shows higher benzene concentrations in the evening compared to the morning. Seasonal variation of benzene is found to be winter > spring > summer > autumn > monsoon and OFP was found to be 21, 19, 14, 13, and 10 respectively. Cancer (ILCR) and non-cancer (HQ) health risk assessment was done to determine the impact of ambient benzene on the residents of urban areas. The yearly average value of ILCR was found to be 2×10-6 ± 1×10-6 which ranges from acceptable value to three times the WHO acceptable value i.e 1×10-6. The correlation of ozone and its precursor, benzene with meteorological parameters is also evaluated. The correlation of benzene and ozone with solar radiation shows the influence of photochemical reactions on the levels of benzene and ozone at the study site, although it is low.

Effects of Season of Birth and Meteorological Parameters on Serum Bilirubin Levels during the Early Neonatal Period: A Retrospective Chart Review

To establish whether serum bilirubin levels vary in healthy term neonates according to seasonal variations and meteorological factors, we retrospectively studied 3344 healthy term neonates born between 2013 and 2018. Total serum bilirubin (TSB) levels were measured on the fourth day after birth. The monthly and seasonal variations in TSB levels and clinical and meteorological effects on TSB levels were assessed. In the enrolled neonates, the median TSB level was 195 µmol/L. The TSB level peaked in December and was the lowest in July, but the variation was not statistically significant. The TSB level was significantly higher in the cold (October to March) than in the warm season (April to September; p = 0.01). The comparison between seasonal differences according to sex showed TSB levels were significantly higher in the cold than in the warm season in male infants (p = 0.001), whereas no significant difference was observed in female infants. A weakly negative but significant association existed between TSB levels and the mean daily air temperature (r = -0.07, p = 0.007) in only the male population; the female population showed no significant correlation between TSB levels and meteorological parameters. The season of birth is an etiological factor in neonatal jaundice, with an additional influence from sex.

Seasonal Evolution of Size-Segregated Particulate Mercury in the Atmospheric Aerosol Over Terra Nova Bay, Antarctica

Size-fractionated particulate mercury (PHg) measurements were performed from November 2017 to January 2018 at Terra Nova Bay (Antarctica) for the first time. Samples were collected every 10 days by a six-stage high-volume cascade impactor with size classes between 10 μm and 0.49 μm. Total PHg concentrations were maxima (87 ± 8 pg m⁻³) in November, then decreased to values ~40% lower and remained almost constant until the end of the sampling period (~30 pg m⁻³). The trimodal aerosol mass distribution reveals that from 30% to 90% of the total PHg came in the size > 1.0 μm. Hg in the two coarse fractions was probably produced by the adsorption of oxidized Hg species transported by air masses from the Antarctic plateau or produced locally by sea ice edges. PHg in accumulation mode seemed to be related to gas–particle partitioning with sea salt aerosol. Finally, average dry deposition fluxes of PHg were calculated to be 0.36 ± 0.21 ng m⁻² d⁻¹ in the accumulation mode, 47 ± 44 ng m⁻² d⁻¹ in the first coarse mode, and 37 ± 31 ng m⁻² d⁻¹ in the second coarse mode. The present work contributed to the comprehension of the Hg biogeochemical cycle, but further research studies are needed.

Environmental Factors and Kawasaki Disease Onset in Emilia-Romagna, Italy

Kawasaki disease (KD)is an idiopathic acute febrile illness that primarily occurs in children <5 years of age and can lead to artery lesions if not promptly treated. Recent studies have shown possible correlations between KD onset and currents and air pollutants.The present study describes results on the correlation between environmental conditions, frequency, and variability ofKD onset in children under five years of age in Emilia-Romagna, a region of Northern Italy, over the period from 2000 to 2017. Since there are substantial climatic differences between the western-central (Emilia) and the eastern area (Romagna) of the region, the data for these areas are analyzed separately. The environmental factors considered are observed local surface daily temperature, daily precipitation, upper air wind regimes, and local air pollution. The results indicate that in Emilia-Romagna, KD onset occurs mainly during late autumn and early spring, which is in agreement with the literature. The frequency of KD onset in Emilia is significantly higher in months characterized by a high frequency of southerly flow, which is associated with milder than average night-time temperature, and in years with a prevailing south-westerly mean flow. These results are consistent with other studies, suggesting that certain wind conditions are more favorable for disease onset, which are possibly associated with one or more airborne agents.

Corrigendum: Species Richness, rRNA Gene Abundance, and Seasonal Dynamics of Airborne Plant-Pathogenic Oomycetes

[This corrects the article DOI: 10.3389/fmicb.2018.02673.].

Species Richness, rRNA Gene Abundance, and Seasonal Dynamics of Airborne Plant-Pathogenic Oomycetes

Oomycetes, also named Peronosporomycetes, are one of the most important and widespread groups of plant pathogens, leading to significant losses in the global agricultural productivity. They have been studied extensively in ground water, soil, and host plants, but their atmospheric transport vector is not well characterized. In this study, the occurrence of airborne Oomycetes was investigated by Sanger sequencing and quantitative PCR of coarse and fine aerosol particle samples (57 filter pairs) collected over a 1-year period (2006-2007) and full seasonal cycle in Mainz, Germany. In coarse particulate matter, we found 55 different hypothetical species (OTUs), of which 54 were plant pathogens and 29 belonged to the genus Peronospora (downy mildews). In fine particulate matter (<3 μm), only one species of Hyaloperonospora was found in one sample. Principal coordinate analysis of the species composition revealed three community clusters with a dependence on ambient temperature. The abundance of Oomycetes rRNA genes was low in winter and enhanced during spring, summer, and fall, with a dominance of Phytophthora, reaching a maximum concentration of ∼1.6 × 10⁶ rRNA genes per cubic meter of sampled air in summer. The presence and high concentration of rRNA genes in air suggests that atmospheric transport, which can lead to secondary infection, may be more important than currently estimated. This illustrates the need for more current and detailed datasets, as potential seasonal shifts due to changing meteorological conditions may influence the composition of airborne Oomycetes. An insight into the dynamics of airborne plant pathogens and their major drivers should be useful for improved forecasting and management of related plant diseases.

Skin sensitivity and intolerance in Shanghai: cumulative influence of different meteorological parameters

BACKGROUND

Although Sensitive skin (SS) conditions are reported to be affected by climate changes, the correlations with meteorological parameters remain mostly unclear.

OBJECTIVE

To investigate the changes of facial skin conditions in different seasons in Shanghai, including the speed and severity of the influences imposed by different meteorological parameters.

METHODS

Totally eighty-one healthy female volunteers completed the study in both spring (May) and summer (August). Evaluations of facial skin conditions included stimulating sensory test, clinical grading and biophysical measurements. Correlations between the meteorological parameters and ΔTEWL as well as clinical parameters were analyzed.

RESULTS

There was no significant difference between two seasons in lactic acid sting test. The stimulation sensations felt by Vitamin B3 (VB3) containing cream was significantly higher in May than that in August. In accordance, baseline visual scores for erythema and scaliness in May were also significantly higher than those scores in August. The baseline transepidermal water loss (TEWL) value was significantly higher in May than that in August. Positive correlations were detected between ΔTEWL and atmospheric pressure on the test day, adjust 3-day and 7-day mean of daily solar radiation respectively. There were negative correlations between ΔTEWL and temperature outside on the test day, adjust 3-day mean as well as adjust 7-day mean of relative humidity.

CONCLUSIONS

Facial skin conditions in summer are better than that in spring in Shanghai. The influences from atmospheric pressure and temperature are relatively quick in speed, while the influences from relative humidity and solar radiation are slow but cumulative.

[Occurrence of acute cardiovascular diseases under different atmospheric parameters]

INTRODUCTION

Research on the effects of meteorological parameters on cardiovascular diseases may allow the development of novel prevention strategies.

AIM

The aim of the authors was to examine the correlation between meteorological parameters and the occurrence of acute cardiovascular diseases.

METHOD

A retrospective analysis was performed in 343 patients diagnosed with acute cardiovascular disease and treated at the Department of Vascular Surgery, Semmelweis University in 2010.

RESULTS

Acute cardiovascular diseases showed a seasonal variation with the highest occurrence in winter months (p = 0.0001). The daily increase of the events (n ≥ 3) were associated with front movements days (in 62.5% of cases). A significant correlation was found between the intraday temperature difference (p<0.0001), the intraday atmospheric pressure difference (p = 0.0034), the lowest maximum daily temperature (p<0.0001) and the occurrence of acute cardiovascular diseases. During the days with front movements 64% of the patients were older than 66 years of age. Among risk factors, hypertension showed front sensitivity.

CONCLUSIONS

Meteorological parameters are minor risk factors in the occurrence of acute cardiovascular diseases.

Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland)

Birch pollen is one of the main causes of allergy during spring and early summer in northern and central Europe. The aim of this study was to create a forecast model that can accurately predict daily average concentrations of Betula sp. pollen grains in the atmosphere of Szczecin, Poland. In order to achieve this, a novel data analysis technique--artificial neural networks (ANN)--was used. Sampling was carried out using a volumetric spore trap of the Hirst design in Szczecin during 2003-2009. Spearman's rank correlation analysis revealed that humidity had a strong negative correlation with Betula pollen concentrations. Significant positive correlations were observed for maximum temperature, average temperature, minimum temperature and precipitation. The ANN resulted in multilayer perceptrons 366 8: 2928-7-1:1, time series prediction was of quite high accuracy (SD Ratio between 0.3 and 0.5, R > 0.85). Direct comparison of the observed and calculated values confirmed good performance of the model and its ability to recreate most of the variation.

The effects of meteorological factors on the occurrence of Ganoderma sp. spores in the air

Ganoderma sp. is an airborne fungal spore type known to trigger respiratory allergy symptoms in sensitive patients. Aiming to reduce the risk for allergic individuals, we analysed fungal spore circulation in Szczecin, Poland, and its dependence on meteorological conditions. Statistical models for the airborne spore concentrations of Ganoderma sp.-one of the most abundant fungal taxa in the area-were developed. Aerobiological sampling was conducted over 2004-2008 using a volumetric Lanzoni trap. Simultaneously, the following meteorological parameters were recorded: daily level of precipitation, maximum and average wind speed, relative humidity and maximum, minimum, average and dew point temperatures. These data were used as the explaining variables. Due to the non-linearity and non-normality of the data set, the applied modelling techniques were artificial neural networks (ANN) and mutlivariate regression trees (MRT). The obtained classification and MRT models predicted threshold conditions above which Ganoderma sp. appeared in the air. It turned out that dew point temperature was the main factor influencing the presence or absence of Ganoderma sp. spores. Further analysis of spore seasons revealed that the airborne fungal spore concentration depended only slightly on meteorological factors.