Winter Period Research Articles

Microclimate Investigation in a Conference Room with Thermal Stratification: An Investigation of Different Air Conditioning Systems

This paper investigates the microclimate in a conference room with thermal stratification, taking as a case study the chapel of Villa San Saverio, now the seat of the “Scuola Superiore” of the University of Catania (Italy). Surveys of the former chapel were conducted to monitor air temperature and relative humidity. Subsequently, the investigation relied on numerical simulations of a simplified computational fluid dynamics (CFD) model built with the DesignBuilder v7.0 software and validated by comparison with measured values. Simulations were then carried out considering three different scenarios: the current state without any HVAC system and two possible HVAC system configurations providing both air conditioning and ventilation. The results show that, from a comfort perspective, a lightweight radiant floor heating system, assisted by an appropriate ventilation system for air renewal placed at the floor level near the occupants, is preferable to floor-level fan coils and high ventilation channels. Furthermore, this was also confirmed by a preliminary energy analysis of the two HVAC options, where the ventilation effectiveness of the winter period, the temperature of the water the emitters are fed, the consequent COP value of the heat pump, and the electricity consumption were taken into consideration.

Estimating the probability of occurrence of African dust outbreaks over regions of the western Mediterranean basin from thermodynamic atmospheric parameters

Desert dust is currently recognized as a health risk factor. Therefore, the World Health Organization (WHO) is actively promoting the establishment of early warning systems for sand and dust storms. This study introduces a methodology to estimate the probability of African dust outbreaks occurring in eight different regions of the Iberian Peninsula and the Balearic Islands. In each region, a multilinear regression model was developed to calculate daily probabilities of dust events using three thermodynamic variables (geopotential thickness in the 1000–500 hPa layer, mean potential temperature between 925 and 700 hPa, and temperature anomalies at 850 hPa) as assessment parameters. All days with African dust transport over each study region were identified in the period 2001–2021 using a proven procedure. This information was then utilized to establish a functional relationship between the values of the thermodynamic parameters and the probability of African dust outbreaks occurring. The validation of this methodology involved comparing the daily probabilities of dust events generated by the models in 2001–2021 with the daily African dust contributions to PM10 regional background levels in each region. On average, daily dust contributions increased proportionally with the increase in daily probabilities, reaching zero for days with low probabilities. Furthermore, a well-defined seasonal evolution of probability values was observed in all regions, with the highest values in the summer months and the lowest in the winter period, ensuring the physical relevance of the models' results. Finally, upward trends were observed in all regions for the three thermodynamic parameters over 1940–2021. Thus, the probability of dust events development also increased in this period. It demonstrates that the aggravation of warm conditions in southern Europe in the last decades, have modified the frequency of North-African dust outbreaks over the western Mediterranean basin.

Basin-analogy for calculating spring flood volumes at insufficiency of meteorological data (Tuba river as a case study)

Relevance. The necessity in provision of insufficient data on streamflow formation factors when predicting floods in the poorly explored and unstudied basins. For this purpose, in hydrological calculations and forecasts the method of hydrological analogy is applicable. Aim. To study probable application of the hydrological analogy method in calculations of spring flood volumes of a mountain river. The analysis was carried out by the example of the basins of the Tuba river and its tributaries (rivers Amyl, Kazyr and Kizir), as well as neighboring catchments (rivers Abakan and Syda). Methods. Integrated geographical-hydrometeorological analysis, correlation and regression analysis, and the hydrological analogy method. Results. We calculated total winter precipitation in the Amyl basin by means of (traditionally used in hydrology) elevation dependences, estimated average annual precipitation in the Tuba basin using modern glaciation data, and built a stochastic predictive model of flood runoff volume on the Amyl river (settl. Kachulka) based on the relationship between precipitation for the previous winter and flood periods. The influence of liquid (flood) precipitation on the forecast quality was analyzed. A comparative analysis of other Tuba tributaries (Kazyr, Kizir), including neighboring basins (rivers Abakan and Syda) in terms of their application as basins-analogies was performed. For instance, a feasible use of the Amyl basin as an analogy for forecasting flood runoff depth of the Tuba river (vill. Bugurtak) was analyzed. Conclusions. The correlation analysis of flood volumes of the Tuba, its main tributaries and neighboring rivers suggests that as an analogy the application of basins with similar conditions for precipitation formation (slope orientation relative to the direction of the prevailing moisture-bearing air masses) and average catchment elevation is currently favored. The Amyl basin use as the Tuba analogy is expedient for a medium-term forecast of melt runoff volumes (excluding liquid (flood) precipitation).

Performance of off-season Okra under Protected Structures in Semi-Arid Region

Okra is a staple vegetable in Indian cuisine which is extensively cultivated year-round due to its rich composition of essential minerals, vitamins, carbohydrates, proteins and dietary fiber. The primary objective of this research was to investigate the impact of mulching and protective structures on okra cultivation during the winter period (off-season). Consequently, okra seeds were planted in three distinct environments: a Net-cum-poly house equipped with a ridge vent, Net-cum-poly house lacking a ridge vent and an open field. These environments were combined with three different mulch conditions: silver-black plastic mulch, black plastic mulch, and no-mulch. The experiment employed a two-factor large-plot design with four replications. The findings of this study revealed the substantial influence of mulching and protective structures on off-season okra cultivation. Key agronomic parameters, including stem diameter (17.20 mm), plant height (164 cm), internode length (18 cm), the number of fruits per plant (35), fruit weight per plant (367.31gm) and overall fruit yield (19755 kg/ha) exhibited statistically significant improvements in the combination of a Net-cum-poly house without a ridge vent and silver-black plastic mulch. In terms of water management, irrigation volumes delivered under mulched and non-mulched conditions were 387 mm and 446 mm respectively for the open field, while the corresponding figures for the protected environment were 319 mm and 366 mm. The highest water use efficiency was observed 61.97 kg/ha-mm in treatment combination with silver black plastic mulch and Net-cum-poly house without ridge vent. Notably, mulching resulted in a 13.34% reduction in water consumption compared to the non-mulched open field. In the protected environment, water savings reached 28.43% under mulch and 17.76% under no-mulch conditions relative to the non-mulched open field.

A study of annual tree-wise LiDAR intensity patterns of boreal species observed using a hyper-temporal laser scanning time series

This study introduces the annual tree-wise intensity patterns of three boreal tree species, silver birch (Betula pendula Roth.), Scots pine (Pinus sylvestris L.), and Norway spruce (P,icea abies H. Karst.), observed from a long-term hyper-temporal point cloud dataset collected with a permanent laser scanning (LiDAR) station. An annual LiDAR intensity pattern refers to the trend of variations of tree-wise calibrated LiDAR intensity values over the course of a year, primarily linked to species-specific phenological characteristics. Such pattern was discovered from hyper-temporal (76 observations between April 2020 and April 2021) high resolution (0.01 m 3D point spacing at 100 m distance) point cloud observations acquired using a single wavelength (1550 nm) static LiDAR system installed on a climate monitoring tower at the Hyytiälä forest research station in central Finland. A set of experiments was designed to explore the interactions among tree-wise calibrated LiDAR intensity, species, data quality, and observation dates. Thus, to provide practical suggestions for expectable accuracies of tree species classification using diverse types of LiDAR systems and data acquisition strategies. It was revealed that by combining high spatial resolution (up to 100,000 points/m2) with bi-weekly observations (two scans per week except winter period), the average tree species classification accuracy reaches 96.8% for the three boreal tree species in the study area, where the few misclassified cases are attributed to inaccurate individual tree segmentation. This suggests that the annual tree-wise LiDAR intensity patterns, which reflects the tree-wise phenological dynamics, are robust and species-specific. When the spatial resolution was reduced to 5 points/ m2, the average accuracy of species classification among the three studied species reached 85% when using bi-temporal data from spring and autumn. Our analysis further indicates that arranging data acquisition with a two-week variability in late autumn and late spring could yield higher accuracy in species classification. Furthermore, our study revealed the importance of subcanopy information for species classification, specifically in distinguishing different coniferous species. When limited to upper canopy data (as in simulated airborne LiDAR scenarios), achieving a tree species classification accuracy of over 80% required either high point density (e.g. 8000 points/m2) combined with bi-temporal collection in a year, or low point density (5 points/ m2) with a dense time series (e.g., 76 time points within a year), which showcased the possibility to compensate low spatial resolution with high temporal resolution, and vice versa, in capturing species-specific phenological characteristics of trees.

The yield of various varieties of lettuce grown in hydroponic conditions

The study is aimed at evaluating the effectiveness of growing various varieties of lettuce using flow hydroponics in protected soil. The following varieties of lettuce were studied: cabbage with an oily leaf (Gauguin, Fairlie, Santoro, Aquino, Casey), semi-rolled with a crispy leaf (Dunstar, Frillis, Extemp, Exact, Skilton), leafy (Lalique, Jokari, Litska, Rosaline, Alzevir, Xerafin). The experiments were conducted in the conditions of the Yelets district of the Lipetsk region in the conditions of the greenhouse of I.A. Bunin YSU. The assessment of product quality helped to identify the main productive varieties recommended for cultivation in protected soil by the method of flow hydroponics. For all-season cultivation: Casey, Gauguin, Dunstar, Alzevir, Jokari, Litska, for the autumn–winter period - Exact, for the spring-summer period – Extemp. Keywords: LETTUCE, VARIETY, YIELD, HYDROPONICS

Estimation of pollutant baseline concentration in Delhi city: An opportunity from coupled effect of rainfall scavenging and lockdown restriction

The accurate measurement of pollutant baseline concentration is a crucial step for robust assessment, prediction and forecasting of air pollution in any region, especially in metro cities that witness extreme high level of pollution. The present study is an attempt to fill this gap and derive the baseline concentrations of two critical air pollutants, i.e., PM2.5 and NOx in Delhi city. The study critically analyzes the data of winter period having consistent rainfall followed by complete weekend lockdown due to COVID-19. The findings indicate that the estimated baseline concentration is in the range of 20–36 μg/m3 and 12–63 μg/m3 for PM2.5 and NOx (NO + NO2), respectively during winter period of year 2022. The estimated baseline levels can be reasonably well compared with other reported values, both in the past studies as well as estimated using other referred methods. The baseline values are found higher in central areas of the city compared to outskirt located stations. The estimated baseline concentrations can be used in models for predicting the actual urban increment (actual increase in the source contribution with respect to the baseline), which will further help in decision making of target-oriented control actions.

Canopy Structural Changes in Black Pine Trees Affected by Pine Processionary Moth Using Drone-Derived Data

Pine species are a key social and economic component in Mediterranean ecosystems, where insect defoliations can have far-reaching consequences. This study aims to quantify the impact of pine processionary moth (PPM) on canopy structures, examining its evolution over time at the individual tree level using high-density drone LiDAR-derived point clouds. Focusing on 33 individuals of black pine (Pinus nigra)—a species highly susceptible to PPM defoliation in the Mediterranean environment—bitemporal LiDAR scans were conducted to capture the onset and end of the major PPM feeding period in winter. Canopy crown delineation performed manually was compared with LiDAR-based methods. Canopy metrics from point clouds were computed for trees exhibiting contrasting levels of defoliation. The structural differences between non-defoliated and defoliated trees were assessed by employing parametric statistical comparisons, including analysis of variance along with post hoc tests. Our analysis aimed to distinguish structural changes resulting from PPM defoliation during the winter feeding period. Outcomes revealed substantive alterations in canopy cover, with an average reduction of 22.92% in the leaf area index for defoliated trees, accompanied by a significant increase in the number of returns in lower tree crown branches. Evident variations in canopy density were observed throughout the feeding period, enabling the identification of two to three change classes using LiDAR-derived canopy density metrics. Manual and LiDAR-based crown delineations exhibited minimal differences in computed canopy LiDAR metrics, showcasing the potential of LiDAR delineations for broader applications. PPM infestations induced noteworthy modifications in canopy morphology, affecting key structural parameters. Drone LiDAR data emerged as a comprehensive tool for quantifying these transformations. This study underscores the significance of remote sensing approaches in monitoring insect disturbances and their impacts on forest ecosystems.

Methods of assessing emissions of contaminants from sties into the air

Provision of competitiveness in the sphere of animal farming in Ukraine requires the introduction of innovative systems of technological support involving modern microprocessor control-measurement systems and devices. The method of continuous automatic record of emissions of contaminants from animal premises reveals patterns in emissions of the main air contaminants and allows their mass concentrations and dynamics of emissions to be tracked over 24 h period. By employing this method, we determined the coefficients of 24 h emissions of carbon dioxide and methane from premises for fattening young swine. The coefficients of 24 h emission of ammonia were found to be significantly lower in the sty with a slatted floor in pens for the winter, spring, and autumn periods compared with the sties with concrete floor. However, coefficients of 24 h emission of methane were significantly higher by 76.1–286.9% in the sty with a slatted floor, which is associated with the peculiarities of technology of the self-cleaning system. Two-times removal of manure from the sty with concrete floor significantly reduced the average annual coefficients of emission of carbon dioxide, ammonia, and methane, by 12.1%, 22.4%, and 13.5%, respectively. Analysis of emission of the main contaminants from the sties with slatted and concrete floor in pens indicated a significant effect of this factor on the amount of emission, structure, and presence of seasonal and daily variability in those parameters. Accumulation of experimental materials regarding emission of contaminants will allow average actual parameters of emissions from small pig farms to be identified, which will allow adequate assessment of the effect their activity has on the environment, particularly in residential areas, and substantiate the minimal allowable distances to sanitary-protective zones.

Risk of winter hospitalisation and death from acute respiratory infections in Scotland: national retrospective cohort study.

We undertook a national analysis to characterise and identify risk factors for acute respiratory infections (ARIs) resulting in hospitalisation during the winter period in Scotland. A population-based retrospective cohort analysis. Scotland. The study involved 5.4 million residents in Scotland. Cox proportional hazard models were used to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for the association between risk factors and ARI hospitalisation. Between 1 September 2022 and 31 January 2023, there were 22,284 (10.9% of 203,549 with any emergency hospitalisation) ARI hospitalisations (1759 in children and 20,525 in adults) in Scotland. Compared with the reference group of children aged 6-17 years, the risk of ARI hospitalisation was higher in children aged 3-5 years (aHR = 4.55; 95% CI: 4.11-5.04). Compared with those aged 25-29 years, the risk of ARI hospitalisation was highest among the oldest adults aged ≥80 years (aHR = 7.86; 95% CI: 7.06-8.76). Adults from more deprived areas (most deprived vs. least deprived, aHR = 1.64; 95% CI: 1.57-1.72), with existing health conditions (≥5 vs. 0 health conditions, aHR = 4.84; 95% CI: 4.53-5.18) or with history of all-cause emergency admissions (≥6 vs. 0 previous emergency admissions, aHR = 7.53; 95% CI: 5.48-10.35) were at a higher risk of ARI hospitalisations. The risk increased by the number of existing health conditions and previous emergency admission. Similar associations were seen in children. Younger children, older adults, those from more deprived backgrounds and individuals with greater numbers of pre-existing conditions and previous emergency admission were at increased risk for winter hospitalisations for ARI.

Seyfe lake seasonal drought analysis for the winter and summer periods between 2017 and 2022

This study focused on the effects of the increasingly prevalent drought phenomena in Central Anatolia every year on Lake Seyfe in Kırşehir. The drought has been investigated through change detection using Multispectral (MSI) PlanetScope remote sensing data between 2017 and 2022. Changes in wetlands, moist regions, swamps, agricultural areas, and drought over the summer and winter were observed using the normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and soil adjusted vegetation index (SAVI) indices applied to mosaic data. The findings of the temporal variation over the summer and winter seasons are displayed as pixel-based spatial variations. The analysis used surface temperature, monthly temperature change tables, and precipitation maps for the years operated in the study. According to the research, the wetlands, which comprised 5% of the total surface area in the summer of 2020, were prevented from drying out entirely by the usual wet winters of 2021 and 2022.

Prevalence, Genetic Diversity, and Quantification of the RNA Genome of the Hepatitis E Virus in Slaughtered Pigs in Serbia.

The goal of this study conducted in Serbia was to detect HEV in pig liver samples from slaughterhouses, retail outlets, and environmental swabs over the course of a year. All positive HEV samples were measured and expressed as HEV gene copy numbers per gram of sample, and a representative number of samples were sequenced using the Sanger approach. A total of 45 HEV-positive samples were re-amplified using nested RT-PCR employing CODEHOP primers targeting ORF2 (493 nucleotides). The average prevalence of the HEV genotype 3 in all pig liver samples from the slaughterhouses was 29%, while HEV prevalence was 44% in liver samples from animals younger than 3 months. HEV RNA was found in thirteen out of sixty (22%) environmental swab samples that were taken from different surfaces along the slaughter line. Our findings confirmed seasonal patterns in HEV prevalence, with two picks (summer and winter periods) during the one-year examination. Among HEV-positive samples, the average viral particles for all positive liver samples was 4.41 ± 1.69 log10 genome copies per gram. Phylogenetic analysis revealed the majority of HEV strains (43/45) from Serbia were grouped in the HEV-3a subtype, while two strains were classified into the HEV-3c subtype, and one strain could not be classified into any of the HEV-3 subtypes.

Simulation and prediction of water temperature in a water transfer channel during winter periods using a new approach based on the wavelet noise reduction-deep learning method

Abstract In winter, the water transfer channel of the Middle Route of South-to-North Water Transfer Project (MR-StNWTP) in China always encounters ice problems. The preciously simulation and prediction of water temperature is essential for analyzing the ice condition, which is important for the safety control of the water transfer channel in winter. Due to the difference of specific heat between water and air, when the air temperature rises and falls dramatically, the range of change of water temperature is relatively small and has a lag, which often affects the accuracy of simulation and prediction of water temperature based on air temperature. In the present study, a new approach for simulating and predicting water temperature in water transfer channels in winter has been proposed. By coupling the neural network theory to equations describing water temperature, a model has been developed for predicting water temperature. The temperature data of prototype observations in winter are preprocessed through the wavelet decomposition and noise reduction. Then, the wavelet soft threshold denoising method is used to eliminate the fluctuation of certain temperature data of prototype observations, and the corresponding water temperature is calculated afterward. Compared to calculation results using both general neural network and multiple regression approaches, the calculation results using the proposed model agree well with those of prototype measurements and can effectively improve the accuracy of prediction of water temperature.

Impact of Ambient Temperature, Passenger Load, and Delay on the Consumption of Battery Electric Buses

The operation of state-of-the-art battery electric buses (BEBs) has started recently, and their consumption model is unknown. Therefore, the service providers are lenient regarding the energy used. Our aim was to reveal the effects of ambient temperature and bus service characteristics, such as passenger load and delay, on the energy consumption. Real-time consumption data of BEBs were analyzed in Nyíregyháza, Hungary, and the functional relationships among the aforementioned variables were revealed. We found that while ambient temperature is negatively correlated with consumption in the cold winter periods, the consumption surplus is positively correlated with passenger load and generally positively correlated with the delay on the trip because of road traffic. In the case of delay, the consumption increase is caused by more frequent acceleration and the greater consumption of auxiliary systems due to longer running times. Accordingly, the energy consumption can be estimated if the characteristics data are available. The results can be used by bus service operators to plan the daily schedule and predict the energy consumption of a BEB.

Full year seasonality of benthos in the nearshore of Lake Superior

Relatively little is known about the full-year dynamics of benthic invertebrates in seasonally-freezing lakes. In this study, we describe the seasonal variability in benthic invertebrate abundance and coarse-level taxonomic composition across five nearshore locations in Lake Superior, with a focus on the winter period. We found that benthos abundances were relatively stable across the year, with similar winter (2294 ± 987 SD ind. m−2) and summer densities (2710 ± 1445 SD ind. m−2) across all stations. Community composition was also relatively stable across the year at our study stations, with Hexagenia sp., chironomids, and oligochaetes dominating our shallowest station (Duluth Harbor) and oligochaetes, Diporeia sp., and clams (Sphaeriidae) dominating deeper locations. Across all stations, diversity was similar across seasons, with the highest number of taxa observed in the fall (5.4 ± 1.8) and lowest number in the summer (4.5 ± 1.4). We found that the winter-spring period was an important time for the reproduction of the Lake Superior keystone amphipod Diporeia sp. Finally, we show that community structure was more variable across sites than across seasons. This is one of very few studies of winter benthos in the Great Lakes and suggests that benthic invertebrate communities show muted seasonal variability compared to planktonic organisms.

Influence of urban environment factors on morphometric parameters and accumulation of secondary metabolites in Cercis canadensis L. and Cercis siliquastrum ‘Alba’

Consideration of the role of green spaces in urban landscaping is extremely important, as they not only decorate the urban landscape, but also effectively reduce air pollution, regulate the microclimate and play an important role in improving the ecology and quality of life of residents. The purpose of the study was to determine the influence of urban environmental factors on the development and secondary metabolism of plants of the genus Cercis L. Plants growing in Kyiv (Ukraine) under conditions with different air humidity, anthropogenic load, and excess or lack of insolation were selected for the study. Plant growth processes were studied by morphometric parameters of annual shoots of Cercis siliquastrum ‘Alba’ and Cercis canadensis L. growing under different conditions. Morphometric methods, fluorescence microscopy, high-performance thin-layer chromatography and regression analysis were used in the study. The research data show that insufficient sunlight has a negative impact on shoot growth. The specificity of the phenolic profiles that can be used to distinguish between the species Cercis canadensis L. and Cercis siliquastrum ‘Alba’ was established. The principal component analysis confirmed that phenolic profiles depend on habitat conditions and affect plant growth and development. The Gaussian model was used to analyse the growth dynamics of annual shoots during the growing season, which allowed us to describe the specifics of internode formation during the growing season quite accurately. This model allowed us to detect the phenomenon of the last internodes falling off at the end of the growing season, which caused the deviation of the morphometric parameters of metameres from the normal distribution along the time axis. Anatomical studies confirmed that the separation of one or more of the last internodes was due to active corking of the internal tissues of the stem, blocking of the xylem and formation of a separating layer, which led to the fall of the shoot tip. This phenomenon shows signs of plant adaptation to low temperatures in winter. The tops of the shoots often do not have time to lignify and accumulate the required amount of cryoprotectants. With the formation of a separating layer, the available energy resources are redistributed to the lateral buds, which receive the necessary resources to prepare for the winter period and provide new growth in the next growing season. The adaptive potential of the two species of the genus Cercis L. confirms the prospects of their use in urban ecosystems, which can positively affect the conservation of biodiversity in general

Different patterns of inter-annual variability in mean vessel area and tree-ring widths of beech from provenance trials in Slovenia and Hungary

Key messageStudied beech provenances showed different patterns of inter-annual variability in mean vessel area and ring widths, indicating influence of intraspecific variability and diverse environment on hydraulic conductivity and carbon storage potential.International provenance trials of ecologically and economically important tree species are crucial to deciphering the influence of environmental factors and intraspecific variability on tree growth and performance under climate change to guide assisted gene flow and assisted migration of tree provenances and species. In this context, we compared inter-annual trends in tree-ring widths (carbon sequestration potential) and vessel characteristics (conductivity optimisation) of four beech provenances in two international provenance trials, one in Slovenia (Kamenski hrib, a core beech growing site) and one in Hungary (Bucsuta, a marginal beech site) in 2009–2019. We found different patterns of inter-annual variability in mean vessel area and tree-ring widths among provenances and sites, pointing to diverse genetic background and environmental influence on these two wood-anatomical traits. The average values of the vessel area varied less between provenances at Kamenski hrib than at Bucsuta. Weather conditions differently affected tree-ring width and mean vessel area. Furthermore, the length of the period of response of vessel area to the analysed weather conditions differed in summer and winter periods. The differences in the mean vessel area within the tree ring were more pronounced in the weather-wise extreme years, regardless of the provenance. Consistent with previous studies, we confirmed that site conditions affect the climate sensitivity of trees, which is more pronounced at marginal sites or in extreme years. The findings on how different environmental conditions affect the radial growth of young beech trees of different origin are very important for future forest management.

Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach

Abstract. Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability in SD in the WH region. Currently, low-frequency passive microwave (PMW) remote-sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, fewer PMW SD estimation studies have been carried out for the WH region to date, which are mainly confined to small subregions of the WH region. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and fewer parameters and therefore cannot be implemented for the entire region. Further, these models have not taken the auxiliary parameters such as location, topography, and snow cover duration (SCD) into consideration and have poor accuracy (particularly in deep snow) and coarse spatial resolution. Considering the high spatiotemporal variability in snow depth characteristics across the WH region, region-wise multifactor models are developed for the first time to estimate SD at a high spatial resolution of 500 m × 500 m for three different WH zones, i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCD data, terrain parameters (i.e., elevation, slope, and ruggedness), and geolocation for the winter period (October to March) during 2012–2013 to 2016–2017 are used for developing the SD models for dry snow conditions. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are used to develop snow depth models, which are evaluated further to find if any of these models can address the heterogeneous association between SD observations and PMW TB. From the results, it is observed from the analysis that the power regression SD model has improved accuracy in all WH zones with the low root mean square error (RMSE) in the MHZ (i.e., 27.21 cm) compared to the LHZ (32.87 cm) and the UHZ (42.81 cm). The spatial distribution of model-derived SD is highly affected by SCD, terrain parameters, and geolocation parameters and has better SD estimates compared to regional and global products in all zones. Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of the WH region compared to previously developed SD models.

Insights into the seasonal characteristics of single particle aerosols in Chengdu based on SPAMS

To investigate the seasonal characteristics in air pollution in Chengdu, a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter, respectively. The results showed that, apart from O3, the concentrations of other pollutants (CO, NO2, SO2, PM2.5 and PM10) were significantly higher in winter than in summer. All single particle aerosols were divided into seven categories: biomass burning (BB), coal combustion (CC), Dust, vehicle emission (VE), K mixed with nitrate (K-NO3), K mixed with sulfate and nitrate (K-SN), and K mixed with sulfate (K-SO4) particles. The highest contributions in both seasons were VE particles (24%). The higher contributions of K-SO4 (16%) and K-NO3 (10%) particles occurred in summer and winter, respectively, as a result of their different formation mechanisms. S-containing (K-SO4 and K-SN), VE, and BB particles caused the evolution of pollution in both seasons, and they can be considered as targets for future pollution reduction. The mixing of primary sources particles (VE, Dust, CC, and BB) with secondary components was stronger in winter than in summer. In summer, as pollution worsens, the mixing of primary sources particles with 62 [NO3]− weakened, but the mixing with 97 [HSO4]− increased. However, in winter, the mixing state of particles did not exhibit an obvious evolution rules. The potential source areas in summer were mainly distributed in the southern region of Sichuan, while in winter, besides the southern region, the contribution of the western region cannot be ignored.

Copper Speciation in Wine Growing-Drain Waters: Mobilization, Transport, and Environmental Diffusion

Copper (Cu) has been used to treat vines for a long time, which has led to its accumulation in vineyard soils. In the present work, the mobilization of copper from these soils and its transport, and diffusion outside the plots by drain water were investigated. For this, the distribution of copper between the dissolved and colloidal phases, and within the colloidal phase, of these waters was determined using an investigation strategy based on the coupling between a size separation technique, asymmetric flow field-flow fractionation, and several detectors. First, the total copper concentrations in water from different drains were monitored over a period of 2 years: Cu was mainly found in the fraction of < 450 nm. Then, the distribution of copper on the size continuum was more closely studied in water from one of the drains, sampled over a winter period. Between 45 and 75% of Cu was found in the 2–450 nm colloidal fraction. The <450 nm colloidal phase of the drain waters was found to be mainly composed of humic acids (~15 to 60 mg L−1) and clay-rich particles (~100 to 650 mg (Al) L−1). These particles also contained (hydr)oxides of iron and manganese. The concentrations of Fe and Mn were approximately 100 to 200 times lower than those of Al. The majority of humic acids had an apparent molar mass of ≤ 10 kDa. They were distributed along the size continuum: (i) in a population with an average size of ~20 nm, probably consisting of supramolecular entities, and (ii) associated with clay-rich particles with a size of ~120–200 nm. Copper was found to be complexed with humic acids and associated with clays via clay-humic complexes. Copper mobilization from the soil to the water and its transport to the drain water appeared governed by the soil humidity level and the rainfall.