Beyond lung cancer: air pollution and bladder, breast and prostate cancer incidence.

BACKGROUND: The carcinogenicity of air pollution and its impact on the risk of lung cancer is well known; however, there are still knowledge gaps and mixed results for other sites of cancer. METHODS: The current study aimed to evaluate the associations between ambient air pollution [fine particulate matter (PM2.5) and nitrogen oxides (NOx)] and cancer incidence. Exposure assessment was based on historical addresses of >900 000 participants. Cancer incidence included primary cancer cases diagnosed from 2007 to 2015 (n = 30 979). Cox regression was used to evaluate the associations between ambient air pollution and cancer incidence [hazard ratio (HR), 95% CI]. RESULTS: In the single-pollutant models, an increase of one interquartile range (IQR) (2.11 µg/m3) of PM2.5 was associated with an increased risk of all cancer sites (HR = 1.51, 95% CI: 1.47-1.54), lung cancer (HR = 1.73, 95% CI: 1.60-1.87), bladder cancer (HR = 1.50, 95% CI: 1.37-1.65), breast cancer (HR = 1.50, 95% CI: 1.42-1.58) and prostate cancer (HR = 1.41, 95% CI: 1.31-1.52). In the single-pollutant and the co-pollutant models, the estimates for PM2.5 were stronger compared with NOx for all the investigated cancer sites. CONCLUSIONS: Our findings confirm the carcinogenicity of ambient air pollution on lung cancer and provide additional evidence for bladder, breast and prostate cancers. Further studies are needed to confirm our observation regarding prostate cancer. However, the need for more research should not be a barrier to implementing policies to limit the population's exposure to air pollution.

Association of ambient temperature on acute ischemic stroke in Yancheng, China.

BACKGROUND: Acute ischemic stroke (AIS) is a major global public health issue. There is limited research on the relationship between ambient temperature and AIS hospital admissions, and the results are controversial. Our objective is to assess the short-term impact of ambient temperature on the risk of AIS hospital admissions in Yancheng, China. METHODS: We collected data on daily AIS hospital admissions, meteorological factors, and air quality in Yancheng from 2014 to 2019. We used Poisson regression to fit generalized linear models and distributed lag non-linear models to explore the association between ambient temperature and AIS hospital admissions. The effects of these associations were evaluated by stratified analysis by sex and age. RESULTS: From 2014 to 2019, we identified a total of 13,391 AIS hospital admissions. We observed that the influence of extreme cold and heat on admissions for AIS manifests immediately on the day of exposure and continues for a duration of 3-5 days. Compared to the optimal temperature (24.4 °C), the cumulative relative risk under extreme cold temperature (-1.3 °C) conditions with a lag of 0-5 days was 1.88 (95%CI: 1.28, 2.78), and under extreme heat temperature (30.5 °C) conditions with a lag of 0-5 days was 1.48 (95%CI: 1.26, 1.73). CONCLUSIONS: There is a non-linear association between ambient temperature and AIS hospital admission risk in Yancheng, China. Women and older patients are more vulnerable to non-optimal temperatures. Our findings may reveal the potential impact of climate change on the risk of AIS hospital admissions.

A comprehensive perspective on potentially hazardous elements (PHEs) bound with particulate matters in ambient air of landfill sites: a systematic review and probabilistic risk assessment.

Air pollution from Potentially Hazardous Elements bound with particulate matter (PHEs bound PM) in landfill air is a significant concern for human health. To date, no comprehensive research has focused on the health risks of PHEs bound to PM in landfill air. This systematic review aimed to examine PHEs, including Lead (Pb), Cadmium (Cd), Chromium (Cr), Arsenic (As), Nickel (Ni), and Zinc (Zn) bound with PM in landfill air and assess the health risk for workers and waste management personnel. The systematically search was made in different electronic databases. After the screening, 18 most relevant studied focused on PHEs bound PM in landfill air were selected. The data extraction analysis indicated that mean concentrations of Pb, Cd, Cr, As, Ni, and Zn in landfill air were 0.3037, 0.0941, 0.4093, 0.0221, 0.2768, and 0.7622 µg/m3, respectively. Except for Pb, the concentrations of other PHEs bound to PM exceeded USEPA air quality standards. In addition, Non-carcinogenic risks from Cd, Cr, and Ni exposure exceeded permissible limits (HQ > 1), while As had an HQ value of 0.401. Carcinogenic risks from As, Cd, Ni, and Pb exposure were 1.31 × 10-5, 5.10 × 10-4, 3.51 × 10-5, and 2.03 × 10-6, respectively. Notably, the lifetime carcinogenic risk for workers exposed to Cr in polluted air (6.17 × 10-3) exceeded permissible limits (10-4 to 10-6). In conclusion, given the high carcinogenic and non-carcinogenic risks of some PHEs bound PM in landfills, it is necessary to conduct more research on the health effects of interaction with these PHEs bound PM on communities and the environment in different countries. Also, it is necessary to evaluate the role of different landfill operational activities on atmospheric dispersion of PHEs bound PM) in landfill air.

Moho Imaging with Fiber Borehole Strainmeters Based on Ambient Noise Autocorrelation.

Moho tomography is important for studying the deep Earth structure and geodynamics, and fiber borehole strainmeters are broadband, low-noise, and attractive tools for seismic observation. Recently, many studies have shown that fiber optic seismic sensors can be used for subsurface structure imaging based on ambient noise cross-correlation, similar to conventional geophones. However, this array-dependent cross-correlation method is not suitable for fiber borehole strainmeters. Here, we developed a Moho imaging scheme for the characteristics of fiber borehole strainmeters based on ambient noise autocorrelation. S-wave reflection signals were extracted from the ambient noise through a series of processing steps, including phase autocorrelation (PAC), phase-weighted stacking (PWS), etc. Subsequently, the time-to-depth conversion crustal thickness beneath the station was calculated. We applied our scheme to continuous four-component recordings from four fiber borehole strainmeters in Lu'an, Anhui Province, China. The obtained Moho depth was consistent with the previous research results. Our work shows that this method is suitable for Moho imaging with fiber borehole strainmeters without relying on the number of stations.

A Robust Deep Feature Extraction Method for Human Activity Recognition Using a Wavelet Based Spectral Visualisation Technique.

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of 'scalograms', derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.

Taste Preferences at Different Ambient Temperatures and Associated Changes in Gut Microbiota and Body Weight in Mice.

Taste, dietary choices, and gut microbiota are often analyzed as major factors of metabolic health. Populations living in cold or hot regions have different dietary habits. This study aims to investigate the potential association among ambient temperature, food taste preferences, and cecal microbiota community profiles in mice. By exposing mice to mixed diets containing sweet, sour, salty, and bitter flavors at low (4 °C) and high (37 °C) ambient temperatures, the taste preferences of mice at both ambient temperatures were in the order of saltiness > sweetness > bitterness > sourness. Exposing mice to sweet, sour, salty, and bitter diets, respectively, revealed that in a low-temperature environment, mice consuming salty (5.00 ± 1.49 g), sweet (4.99 ± 0.35 g), and sour (3.90 ± 0.61 g) diets had significantly higher weight gain compared to those consuming normal feeds (2.34 ± 0.43 g, p < 0.05). Conversely, in a high-temperature environment, no significant changes in body weight were observed among mice consuming different flavored diets (p > 0.05). In a low-temperature environment, mice fed sour and sweet diets showed a significant difference in the gut microbiota composition when compared to those fed a normal diet. A higher abundance of Lachnospiraceae, UBA1819, and Clostridiales was identified as the most significant taxa in the sour group, and a higher abundance of Ruminiclostridium was identified in the sweet group. These differences were associated with microbial pathways involved in carbohydrate metabolism, amino acid metabolism, and energy metabolism. A high-temperature environment exhibited only minor effects on the gut microbiota profile. Overall, our findings provide evidence for temperature-modulated responses to the taste, gut microbiota functions, and body weight changes in mice.

Gut microbiota mediates ambient PM2.5 exposure-induced abnormal glucose metabolism via short-chain fatty acids.

PM2.5 exposure has been found to cause gut dysbiosis and impair glucose homeostasis in human and animals, yet their underlying biological connection remain unclear. In the present study, we aim to investigate the biological significance of gut microbiota in PM2.5-induced glucose metabolic abnormalities. Our results showed that microbiota depletion by antibiotics treatment significantly alleviated PM2.5-induced glucose intolerance and insulin resistance, as indicated by the intraperitoneal glucose tolerance test, glucose-induced insulin secretion, insulin tolerance test, insulin-induced phosphorylation levels of Akt and GSK-3ß in insulin sensitive tissues. In addition, faecal microbiota transplantation (FMT) from PM2.5-exposed donor mice successfully remodeled the glucose metabolism abnormalities in recipient mice, while the transplantation of autoclaved faecal materials did not. Faecal microbiota analysis demonstrated that the composition and alpha diversity of the gut bacterial community were altered by PM2.5 exposure and in FMT recipient mice. Furthermore, short-chain fatty acids levels analysis showed that the circulating acetate was significantly decreased in PM2.5-exposed donor and FMT recipient mice, and supplementation of sodium acetate for 3 months successfully improved the glucose metabolism abnormalities induced by PM2.5 exposure. These results indicate that manipulating gut microbiota or its metabolites could be a potential strategy for preventing the adverse health effects of ambient PM2.5.

Metal-Organic Framework-Induced Rh Monocoodination on Diphosphine Ligand Enables Catalytic Hydroformylation of Aliphatic Olefins at Room Temperature and Pressure.

Persistent challenges in hydroformylation of olefins include controlling regioselectivity, particularly for short aliphatic olefins and conducting reactions under ambient conditions.  We report here the synthesis of monophosphine-Rh complexes on a typical chelated diphosphine ligand mediated by a Zr-MOF through isolating a pair of phosphorus atoms. We demonstrate that single-crystal X-ray diffraction can elucidate the structural transformation of the Rh catalyst during olefin hydroformylation, providing valuable information on active site reconstruction during catalysis. The Rh-MOF catalyst demonstrates excellent catalytic and recyclable performance in the hydroformylation of short aliphatic olefins with linear to branched ratios of up to 99:1. Due to the framework's capacity to adsorb and concentrate gases, the catalytic reactions occur under room temperature and pressure, eliminating the need for the high temperature and pressures typically required in homogeneous systems. This study show that Zr-MOF can be a unique platform for synthesizing unusual catalytic species that cannot exist in solutions for meaningful chemical transformations and elucidate valuable structural information pertaining to metal-based catalysis.

Study of CO2 Adsorption Properties on the SrTiO3(001) Surface with Ambient Pressure XPS.

The adsorption properties of CO2 on the SrTiO3(001) surface were investigated using ambient pressure X-ray photoelectron spectroscopy under elevated pressure and temperature conditions. On the Nb-doped TiO2-enriched (1 × 1) SrTiO3 surface, CO2 adsorption, i.e., the formation of CO3 surface species, occurs first at the oxygen lattice site under 10-6 mbar CO2 at room temperature. The interaction of CO2 molecules with oxygen vacancies begins when the CO2 pressure increases to 0.25 mbar. The adsorbed CO3 species on the Nb-doped SrTiO3 surface increases continuously as the pressure increases but starts to leave the surface as the surface temperature increases, which occurs at approximately 373 K on the defect-free surface. On the undoped TiO2-enriched (1 × 1) SrTiO3 surface, CO2 adsorption also occurs first at the lattice oxygen sites. Both the doped and undoped SrTiO3 surfaces exhibit an enhancement of the CO3 species with the presence of oxygen vacancies, thus indicating the important role of oxygen vacancies in CO2 dissociation. When OH species are removed from the undoped SrTiO3 surface, the CO3 species begin to form under 10-6 mbar at 573 K, thus indicating the critical role of OH in preventing CO2 adsorption. The observed CO2 adsorption properties of the various SrTiO3 surfaces provide valuable information for designing SrTiO3-based CO2 catalysts.

Cardiovascular disease mortality and air pollution in countries with different socioeconomic status.

Background: Cardiovascular diseases (CVDs) account for 17.9 million deaths annually. Behavioral risk factors increase the risk of dying from CVD. Air pollution is not included in this risk calculation since the appreciation of air pollution as a modifiable risk factor is still limited. The purpose of this study was to analyze CVD mortality attributed to air pollution in all World Health Organization WHO member states and demonstrate the association of CVD mortality with air pollution depending on countries' income level. Methods: The CVD death rate was calculated by dividing the number of deaths by the total population. The proportion of the population with primary reliance on clean fuels and technologies for cooking was calculated as an indicator of household air pollution. The annual mean concentration of fine particulate matter ≤2.5 µg/m3 and ≤10.0 µg/m3 to which the population is exposed was used as an indicator of ambient air pollution. Results: There is a gradual increase in CVD mortality attributed to air pollution from high-income countries (HICs) to low-income countries (LICs). Household air pollution is the major cause of CVD mortality in LICs. Ischemic heart disease mortality attributed to ambient air pollution in all countries is higher than stroke mortality attributed to ambient air pollution. In LIC, mortality from stroke is attributed to household air pollution of 39.27 ± 14.47, which is more than twice the stroke mortality attributed to ambient air pollution at 18.60 ± 5.64, t = 7.17, p < 0.01. Conclusion: Air pollution control should be an essential component of the CVD preventive strategy, along with lifestyle modifications and effective disease management.

Assessing the risks associated with indoor and outdoor air quality in relation to the geographic placement of nursing home.

Indoor air quality significantly impacts the well-being and health of elderly residents in nursing homes. This study was conducted to explore the connection between indoor and outdoor PM (Particulate Matter) concentrations in nursing homes and their association with the facilities' location and construction characteristics. The findings revealed that indoor PM2.5 and PM10 concentrations ranged from 0.2 to 124 µg/m3 and 2-188.4 µg/m3, respectively, which were approximately 12.67 and 1.25 times higher than their outdoor counterparts. A strong correlation (P < 0.05) was identified between indoor PM levels and various factors, including proximity to parks, passenger terminals, and gas stations, as well as building attributes such as single-glazed windows, ceramic floor coverings, and the use of radiators. The risk assessment indicated that carcinogenic risk factors were well within acceptable limits for all nursing homes. However, it's important to note that certain PM components, particularly polycyclic aromatic hydrocarbons (PAH), may have long-term adverse effects on the health of nursing home residents. Even though indoor PM levels met the standards established by the U.S. Environmental Protection Agency (USEPA) for particulate matter risk assessments, the study emphasized that even low levels of indoor air pollutants can affect the health and well-being of older adults, particularly considering the increased vulnerability associated with aging. Consequently, the study underscores the importance of nursing home location selection and the regular monitoring of particulate matter concentrations. These measures are essential for enhancing air quality within nursing homes, ultimately contributing to the improved well-being and health of their residents.

HSFA1 heat shock factors integrate warm temperature and heat signals in plants.

Warm temperatures and heat stress trigger distinct plant responses. Recently, Li et al. and Tan et al. identified HSFA1 heat shock transcription factors (HSFs) as central gatekeepers of high-temperature signaling, integrating warm temperature and heat shock responses (HSRs) in arabidopsis (Arabidopsis thaliana). HSFA1d stabilizes phytochrome-interacting factor 4 (PIF4) and activates HSFA2, establishing a crosstalk between thermomorphogenesis and thermotolerance.

The comparison of decay rates of infectious SARS-CoV-2 and viral RNA in environmental waters and wastewater.

Understanding the decay characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater and ambient waters is important for multiple applications including assessment of risk of exposure associated with handling wastewater samples, public health risk associated with recreation in wastewater polluted ambient waters and better understanding and interpretation of wastewater-based epidemiology (WBE) results. We evaluated the decay rates of infectious SARS-CoV-2 and viral RNA in wastewater and ambient waters under temperature regimes representative of seasonal fluctuations. Infectious virus was seeded in autoclaved primary wastewater effluent, final dechlorinated wastewater effluent, lake water, and marine water at a final concentration of 6.26 ± 0.07 log10 plaque forming units per milliliter. Each suspension was incubated at either 4°, 25°, and 37 °C. Samples were initially collected on an hourly basis, then approximately every other day for 15 days. All samples were analyzed for infectious virus via a plaque assay using the Vero E6 cell line, and viral gene copy levels were quantified with the US CDC's N1 and N2 reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assays. The infectious virus decayed significantly faster (p ≤ 0.0214) compared to viral RNA, which persisted for the duration of the study irrespective of the incubation conditions. The initial loss (within 15 min of seeding) as well as decay of infectious SARS-CoV-2 was significantly faster (p ≤ 0.0387) in primary treated wastewater compared to other water types, but viral RNA did not degrade appreciably in this matrix until day 15. Overall, temperature was the most important driver of decay, and after 24 h, no infectious SARS-CoV-2 was detected at 37 °C in any water type. Moreover, the CDC N2 gene assay target decayed significantly (p ≤ 0.0174) faster at elevated temperatures compared to CDC N1, which has important implications for RT-qPCR assay selection for WBE approach.

Interaction between ambient CO and temperature or relative humidity on the risk of stroke hospitalization.

Although the independent effects of ambient CO, temperature or humidity on stroke have been confirmed, it is still unclear where there is an interaction between these factors and who is sensitive populations for these. The stroke hospitalization and ambient CO, temperature, humidity data were collected in 22 Counties and districts of Ningxia, China in 2014-2019. The lagged effect of ambient CO, temperature or humidity were analyze by the generalized additive model; the interaction were evaluated by the bivariate response surface model and stratified analysis with relative excessive risk (RERI). High temperature and CO levels had synergistic effects on hemorrhagic stroke (RERI = 0.05, 95% CI 0.033-0.086) and ischemic stroke (RERI = 0.035, 95% CI 0.006-0.08). Low relative humidity and CO were synergistic in hemorrhagic stroke (RERI = 0.192, 95% CI 0.184-0.205) and only in ischemic stroke in the elderly group (RERI = 0.056, 95% CI 0.025-0.085). High relative humidity and CO exhibited antagonistic effects on the risk of ischemic stroke hospitalization in both male and female groups (RERI = - 0.088, 95% CI - 0.151to - 0.031; RERI = - 0.144, 95% CI - 0.216 to - 0.197). Exposure to CO increases the risk of hospitalization related to hemorrhagic and ischemic strokes. CO and temperature or humidity interact with risk of stroke hospitalization with sex and age differences.

Sensitive and isotopic interference-free analysis of Sb using hydride generation-microwave plasma torch-mass spectrometry under ambient condition.

A sensitive and isotopic interference-free analysis method for Sb was developed based on hydride generation-microwave plasma torch-mass spectrometry (HG-MPT-MS). Compared to the conventional ICP-MS, MPT coupled to an ion trap mass spectrometer enabled much "softer" ionization of Sb under ambient condition, which provided multi-detection modes and various ion forms, such as Sb+, SbO+, SbO2-, SbO++H2O and so on. These ion formations can be easily regulated by tuning capillary voltage and tube lens voltage, which facilitated elimination of isotopic interference during analysis, for instance the interference of 123Te on 123Sb could be effectively excluded by optimizing parameters of capillary voltage and tube lens voltage. The potential application of HG-MPT-MS for Sb isotope ratio analysis was also demonstrated, which could be determined in different forms, e.g., 123Sb/121Sb or 123Sb16O/121Sb16O. The value of 123Sb/121Sb was determined to be 0.75110 ± 0.00038 (2σ, n > 50). In addition, the detection limit, linearity and spike recovery were also studied. Overall, HG-MPT-MS performed equally well on detection limit (0.05 µg/L) with ICP-MS or HG-AFS. The linearity (R2 = 0.998) was checked in the concentration range of 10-500 µg/L. Spike recovery were evaluated with two soil samples, and the obtained spike recovery ranged 90-100 %. In general, HG-MPT-MS was expected to be a versatile tool for study the biochemical or geochemical behaviors of Sb and other hydride forming elements under ambient condition in a much simpler and more efficient way.

Association of ambient air pollution and pesticide mixtures on respiratory inflammatory markers in agricultural communities.

Air pollution exposure is associated with adverse respiratory health outcomes. Evidence from occupational and community-based studies also suggests agricultural pesticides have negative health impacts on respiratory health. Although populations are exposed to multiple inhalation hazards simultaneously, multidomain mixtures (e.g. environmental and chemical pollutants of different classes) are rarely studied. We investigated the association of ambient air pollution-pesticide exposure mixtures with urinary leukotriene E4 (LTE4), a respiratory inflammation biomarker, for 75 participants in four Central California communities over two seasons. Exposures included three criteria air pollutants estimated via the Community Multiscale Air Quality model (fine particulate matter, ozone, and nitrogen dioxide) and urinary metabolites of organophosphate (OP) pesticides (total dialkyl phosphates (DAPs), total diethyl phosphates (DE), and total dimethyl phosphates (DM)). We implemented multiple linear regression models to examine associations in single pollutant models adjusted for age, sex, asthma status, occupational status, household member occupational status, temperature, and relative humidity, and evaluated whether associations changed seasonally. We then implemented Bayesian kernel machine regression (BKMR) to analyse these criteria air pollutants, DE, and DM as a mixture. Our multiple linear regression models indicated an interquartile range (IQR) increase in total DAPs was associated with an increase in urinary LTE4 in winter (ß: 0.04, 95% CI: [0.01, 0.07]). Similarly, an IQR increase in total DM was associated with an increase in urinary LTE4 in winter (ß:0.03, 95% CI: [0.004, 0.06]). Confidence intervals for all criteria air pollutant effect estimates included the null value. BKMR analysis revealed potential non-linear interactions between exposures in our air pollution-pesticide mixture, but all confidence intervals contained the null value. Our analysis demonstrated a positive association between OP pesticide metabolites and urinary LTE4 in a low asthma prevalence population and adds to the limited research on the joint effects of ambient air pollution and pesticides mixtures on respiratory health.

Emergency room visits (ERVs) among occupational groups associated with ambient conditions in Taiwan.

OBJECTIVE: This population-based study explored emergency room visits (ERVs) from all-causes, circulatory and respiratory diseases among different occupational groups in Taiwan associated with ambient average temperature. METHOD: Daily area-age-sex specific ERVs records were obtained from the Taiwan's Ministry of Health and Welfare from 2009 to 2018. Distributed lag-nonlinear model (DLNM) was used to estimate the exposure-response relationships between daily average temperature and ERVs for all-causes, circulatory and respiratory diseases by occupational groups. Random-effects meta-analysis was used to pool the overall cumulative relative risk (RR) and 95% confidence interval (CI). RESULTS: The exposure-response curves showed ERVs of all-cause and respiratory diseases increased with rising temperature across all occupational groups. These effects were consistently stronger among younger (20-64 years old) and outdoor workers. In contrast, ERVs risk from circulatory diseases increased significantly during cold snaps, with a substantially higher risk for female workers. Interestingly, female workers, regardless of indoor or outdoor work, consistently showed a higher risk of respiratory ERVs during hot weather compared to males. Younger workers (20-64 years old) exhibited a higher risk of ERVs, likely due to job profiles with greater exposure to extreme temperatures. Notably, the highest risk of all-causes ERVs was observed in outdoor male laborers (union members), followed by farmers and private employees, with the lowest risk among indoor workers. Conversely, female indoor workers and female farmers faced the highest risk of respiratory ERVs. Again, female farmers with consistent outdoor exposure had the highest risk of circulatory ERVs during cold conditions. CONCLUSION: Our findings highlighted the complexity of temperature-related health risks associated with different occupational contexts. The population-level insights into vulnerable occupational groups could provide valuable comprehension for policymakers and healthcare practitioners.

The impact of ambient temperature and powertrains of SUVs on the environment in Slovakia during the use phase.

This study compares different powertrains of sport utility vehicles (SUVs) with respect to ambient temperature and energy mix in Slovakia using the well-to-wheel (WTW) Life Cycle Assessment (LCA) method. Battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and petrol and diesel vehicles were assessed and compared. The WTW study was conducted in SimaPro software assessing electricity/petrol/diesel production, transport, and use (energy conversion in the vehicle), with impact categories being climate change, particulates, NOx emissions, ionizing radiation, and fossil resource scarcity depending on the season (summer and winter). The results indicate that for Slovak conditions, BEV generally had the lowest environmental impact in both seasons studied. The only exceptions were ionizing radiation, which is clearly caused by the high share of nuclear power in the Slovak energy mix, and NOx emissions, which are caused by the combustion of biomass for electricity generation. The other impact categories were dominated by vehicles with an internal combustion engine. The results of emissions from fuel production are also given for each impact category. The transportation of fuel did not exceed the value of 1% for any impact category or for any powertrain. The conclusions of the study support the global trend in favour of vehicle electrification as an important way to reduce the negative environmental impacts of internal combustion engine vehicles in Slovakia.

Fluctuations in equine cutaneous pH and transepidermal water loss with time of day and ambient conditions.

Cutaneous pH and transepidermal water loss (TEWL) are commonly utilized measures in dermatological research as they provide information concerning barrier function. The importance of dermal health has become more evident in recent years. Accordingly, the aim of this work was to identify natural fluctuations in the biophysical parameters of healthy equine skin. Cutaneous pH and TEWL was collected on nine mares at 6:00 AM, 12:00 PM, and 6:00 PM daily for five days on the nose, withers, girth area, mid-back, and base of tail. Ambient temperature and humidity were measured at each collection. Statistical analysis was completed using SAS On Demand. Tests included repeated measures, ANOVA, and regression analysis. Mean cutaneous pH significantly differed by day (P = 0.0052) and time (P = 0.0073) but was unaffected by anatomical location (P = 0.2841). Interestingly, cutaneous pH had a significant interaction of day and location (P = 0.0004). Mean TEWL measures significantly differed by day (P < 0.0001), time (P < 0.0001), and anatomical location (P = 0.0231). Interaction of day and time had a significant effect on TEWL (P < 0.0001) and also resulted in a three-way interaction of day, time, and location (P = 0.0167). There were no significant associations of pH with temperature and humidity. All measures of TEWL across all locations were significantly correlated with temperature and humidity (P < 0.0001). Cutaneous pH and TEWL measures are affected by environmental conditions which should be considered in future models and work using dermal characteristics of horses.

Radiological characterization of the tailings of an abandoned copper mine using a neural network and geostatistical analysis through the Co-Kriging method.

The radiological characterization of soil contaminated with natural radionuclides enables the classification of the area under investigation, the optimization of laboratory measurements, and informed decision-making on potential site remediation. Neural networks (NN) are emerging as a new candidate for performing these tasks as an alternative to conventional geostatistical tools such as Co-Kriging. This study demonstrates the implementation of a NN for estimating radiological values such as ambient dose equivalent (H*(10)), surface activity and activity concentrations of natural radionuclides present in a waste dump of a Cu mine with a high level of natural radionuclides. The results obtained using a NN were compared with those estimated by Co-Kriging. Both models reproduced field measurements equivalently as a function of spatial coordinates. Similarly, the deviations from the reference concentration values obtained in the output layer of the NN were smaller than the deviations obtained from the multiple regression analysis (MRA), as indicated by the results of the root mean square error. Finally, the method validation showed that the estimation of radiological parameters based on their spatial coordinates faithfully reproduced the affected area. The estimation of the activity concentrations was less accurate for both the NN and MRA; however, both methods gave statistically comparable results for activity concentrations obtained by gamma spectrometry (Student's t-test and Fisher's F-test).