Changes In Meteorological Conditions Research Articles

Experimental validation of fuzzy logic controller based on voltage perturbation algorithm in battery storage photovoltaic system

Introduction. Solar photovoltaic (PV) has recently become very important especially in electrical power applications for countries with high luminosity because it is an effectively unlimited available energy resource. Depending on solar radiation and temperature, the PV generator has a non-linear characteristic with a maximum power point (MPP). The novelty is the efficiency improvement of a PV energy module, it is necessary to track the MPP of the PV array regardless of temperature or irradiation circumstances. Purpose. This paper presents the modeling and the digitally simulation under MATLAB/Simulink of a Fuzzy Logic Controller based on Voltage Perturbation Algorithm (FLC-VPA) applied to PV battery charging system, which consists of PV module, DC-DC boost converter, MPP tracking (MPPT) unit and battery storage. Methods. The DSP1104 is then used to experimentally implement this MPPT algorithm for real-time driving. The obtained results show the high precision of the proposed FLC-VPA MPPT around the optimal point compared to the conventional VPA under stable and changing meteorological conditions. Practical value. The experimental results approve the effectiveness and validity of the proposed total control system in the PV system. References 30, tables 3, figures 17.

Assessing emission-driven changes in health risk of source-specific PM2.5-bound heavy metals by adjusting meteorological covariates

Heavy metals (HMs) in PM2.5 gain much attention for their toxicity and carcinogenic risk. This study evaluates the health risks of PM2.5-bound HMs, focusing on how meteorological conditions affect these risks against the backdrop of PM2.5 reduction trends in China. By applying a receptor model with a meteorological normalization technique, followed by health risk assessment, this work reveals emission-driven changes in health risk of source-specific HMs in the outskirt of Tianjin during the implementation of China’ second Clean Air Action (2018–2020). Sources of PM2.5-bound HMs were identified, with significant contributions from vehicular emissions (on average, 33.4 %), coal combustion (26.3 %), biomass burning (14.1 %), dust (11.7 %), industrial boilers (9.7 %), and shipping emission and sea salt (4.7 %). The source-specific emission-driven health risk can be enlarged or dwarfed by the changing meteorological conditions over time, demonstrating that the actual risks from these source emissions for a given time period may be higher or smaller than those estimated by traditional assessments. Meteorology contributed on average 56.1 % to the interannual changes in source-specific carcinogenic risk of HMs from 2018 to 2019, and 5.6 % from 2019 to 2020. For the source-specific noncarcinogenic risk changes, the contributions were 38.3 % and 46.4 % for the respective periods. Meteorology exerts a more profound impact on daily risk (short-term trends) than on annual risk (long-term trends). Such meteorological impacts differ among emission sources in both sign and magnitude. Reduced health risks of HMs were largely from targeted regulatory measures on sources. Therefore, the meteorological covariates should be considered to better evaluate the health benefits attributable to pollution control measures in health risk assessment frameworks.

Verifying turbulence models in the atmospheric boundary layer by acoustical means

The impacts of turbulence on sound propagation in the atmospheric boundary layer (ABL) are important in many practical applications such as acoustic source localization, sonic boom propagation, and auralization of flying aircraft. However, turbulence impacts can vary dramatically in response to changing meteorological conditions. To address this issue, a turbulence spectral model has been introduced that captures the complexities of turbulence generation by wind shear, buoyancy instabilities, and ground blocking in the ABL. An experiment on vertical and slanted sound propagation through the ABL was conducted to verify the model. Various statistical characteristics of sound signals, including variances of the log-amplitude and phase fluctuations, coherences, and signal probability distributions were measured and compared to theoretical predictions. The dependence of the statistics on meteorological conditions was found to be accurately predicted in conditions of well-developed turbulence, as occurs in windy conditions and the daytime. Predictions in conditions of weak and intermittent turbulence, as often occur at night and around sunrise and sunset, remain challenging.

Long-term monitoring (1953–2019) of geomorphologically active sections of Little Ice Age lateral moraines in the context of changing meteorological conditions

Abstract. We show a long-term erosion monitoring of several geomorphologically active gully systems on Little Ice Age lateral moraines in the European Central–Eastern Alps, covering a total time period from 1953 to 2019 and including several survey periods in order to identify corresponding morphodynamic trends. For the implementation, DEM (digital elevation model) of Differences (DoDs) were calculated, based on multitemporal high-resolution digital elevation models from historical aerial images (generated by structure from motion photogrammetry with multi-view stereo) and light detection and ranging from airborne platforms. Two approaches were implemented to achieve the corresponding objectives. First, by calculating linear regression models using the accumulated sediment yield and the corresponding catchment area (on a log–log scale), the range of the variability in the spatial distribution of erosion values within the sites. Second, we use volume calculations to determine the total and the mean sediment yield (as well as erosion rates) of the entire sites. Subsequently, both the sites and the different time periods of both approaches are compared. Based on the slopes of the calculated regression lines, it can be shown that the highest variability in the sediment yield at the sites occurs in the first time period (mainly 1950s to 1970s). This can be attributed to the fact that within some sites the sediment yield per square metre increases clearly more strongly (regression lines with slopes up to 1.5). In contrast, in the later time periods (1970s to mid-2000s and mid-2000s to 2017/2019), there is generally a decrease in 10 out of 12 cases (regression lines with slopes around 1). However, even at sites with an increase in the variability in the sediment yield over time, the earlier high variabilities are no longer reached. This means that the spatial pattern of erosion in the gully heads changes over time as it becomes more uniform. Furthermore, using sediment volume calculations and corresponding erosion rates, we show a generally decreasing trend in geomorphic activity (amount of sediment yield) between the different time periods in 10 out of 12 sites, while 2 sites show an opposite trend, where morphodynamics increase and remain at the same level. Finally, we summarise the results of long-term changes in the morphodynamics of geomorphologically active areas on lateral moraines by presenting the “sediment activity concept”, which, in contrast to theoretical models, is based on actually calculated erosion. The level of geomorphic activity depends strongly on the characteristics of the sites, such as size, slope length, and slope gradient, some of which are associated with deeply incised gullies. It is noticeable that especially areas with influence of dead ice over decades in the lower slope area show high geomorphic activity. Furthermore, we show that system internal factors, as well as the general paraglacial adjustment process, have a greater influence on long-term morphodynamics than changing external weather and climate conditions, which, however, had a slight impact mainly in the last, i.e. most recent, time period (mid-2000s to 2017/2019) and may have led to an increase in erosion at the sites.

Key Stage and Its Optimum Meteorological Conditions Affecting the Nutritional Quality of Maize

The impact of changing meteorological conditions on crop quality has become a trending topic in current agriculture research. In this study, we analyzed the combined effects of both meteorological conditions and key stages on the nutritional quality of maize based on the data of field-staged sowing trials from 2018 to 2022. The results are as follows: (1) The key stage of meteorological conditions affecting the content of major nutritional qualities of maize is from 6 d before to 35 d after flowering. (2) The maximum temperature from 6 d before to 8 d after flowering, average temperature from 5 d before to 20 d after flowering, and minimum temperature from 9–20 d after flowering have significant positive effects on protein, fat, and essential amino acids, respectively; the daily difference in temperature from 9 to 35 d and 24 to 35 d after flowering have a significant negative effect on crude fiber and essential amino acids, respectively, and the daily difference in temperature from 24 to 35 d and the minimum temperature from 5 d before to 35 d after flowering have a significant effect on non-essential amino acids. (3) When the maximum temperature during the key stage of nutritional quality is 31.2 °C, the average temperatures are 24.9 °C and 22.4 °C, the minimum temperature is 18.9 °C, and the daily difference in temperature is 15.0 °C, the contents could reach the optimal values of 9.66% (protein), 4.80% (fat), 4.97% (crude fiber), 40.39 g·kg−1 (essential amino acids), and 58.96 g·kg−1 (non-essential amino acids), respectively. The findings provide a basis for adjusting the sowing period to improve the nutritional quality of maize in the context of climate change.

Prevention of meteoropathic reactions in children with bronchial asthma

Due to the increasing prevalence of bronchial asthma in children and a more severe course of the disease, prevention, treatment, and medical rehabilitation of such children becomes relevant. Reduced resistance of the organism to changing meteorological conditions is accompanied by the development of pathological meteotropic (meteoropathic) reactions. Meteoprophylaxis using the method of interval hypoxic training contributes to reducing the frequency and severity of meteoropathic reactions and improving the functional state of the respiratory system and psychological status. Materials and methods. 80 children with increased weather sensitivity were examined: the main group consisted of 40 children who received normobaric hypoxia therapy; the comparison group consisted of 40 children who did not receive normobaric hypoxia therapy. The study of the influence of normobaric hypoxia therapy in dynamics on the clinical course of bronchial asthma, indicators of the pulmonary function (computer flowmetry), and psychoemotional status was carried out. Results and discussion. The beneficial effect of normobaric hypoxia therapy on the clinical course (reduction of dry paroxysmal cough and dry wheezing) was revealed. A statistically significant increase in the indices of the pulmonary function was obtained (peak expiratory flow rate, MEF75 (p < 0.05)). The data from the results of psychological testing revealed a decrease in irritability, emotional lability, and anxiety, normalization of activity levels, and increased performance in children under the influence of normobaric hypoxia therapy. In most children, the manifestations of weather sensitivity decreased, and the severity structure of weather-related exacerbations changed: reactions of moderate severity decreased, while severe manifestations of meteoropathology practically disappeared. Conclusions. Based on the conducted studies, the types and seasonal patterns in the formation of biotropic weather conditions in the Moscow metropolis, which are a risk factor in the development of weather-related exacerbations and manifestations of increased weather sensitivity in children with bronchial asthma, were identified, and the frequency of formation of increased meteorolability in such patients was determined.The effectiveness of normobaric hypoxia therapy as a method of preventing meteoropathic reactions in children with bronchial asthma has been established.

Features of Seismological Observations in the Arctic Seas

This paper is devoted to the features of seismological observations in the Arctic seas, which are complicated by harsh climatic conditions, the presence of ice cover, stamukhi and icebergs, and limited navigation. Despite the high risk of losing expensive equipment, the deployment of local networks of bottom seismographs or stations installed on ice is still necessary for studying the seismotectonic characteristics and geodynamic processes of the region under consideration, the deep structure of the crust and upper mantle, seismic hazards, and other marine geohazards. Various types of seismic stations used for long-term and short-term deployments in the Russian sector of the Arctic Ocean, as well as various schemes and workflows for their deployment/recovery, are described. The characteristics of seafloor seismic noise and their features are also considered. The results of deployments demonstrate that the characteristics of the stations make it possible to reliably record earthquake signals and seismic noise. Based on the experience gained, it was concluded that the preferred schemes for deploying ocean-bottom seismographs are those in which their subsequent recovery does not depend on their power resources. Usually, such schemes allow for the possibility of dismantling stations via trawling and are suitable for the shelf depths of the sea. The advantages of such schemes include the possibility of installing additional hydrophysical and hydrobiological equipment. When using pop-up ocean-bottom seismographs, special attention should be paid to the careful planning of the recovery because its success depends on the possibility of a passage to the deployment site, which is not always possible due to changing meteorological and ice conditions. Seismic records obtained on the seafloor are characterized by a high noise level, especially during periods of time when there is no ice cover. Therefore, it is recommended to install bottom stations for periods of time when ice cover is present. The frequency range of the prevailing noise significantly overlaps with the frequency range of earthquake signals that must be taken into account when processing bottom seismic records.

Mathematical models to forecast temporal variations of power law shape parameters of a PV module working in real weather conditions: Prediction of maximum power and comparison with single-diode model

In this study, an innovative approach has been proposed to generate real time current–voltage characteristics and forecast peak power of photovoltaic (PV) modules working outdoors beneath real meteorological conditions. Power-Law Model (PLM) including four unknown parameters, two PV metrics (ISC, VOC), and two profile parameters (γ, m), has been used for fast, accurate and reliable modelling of PV modules. Explicit expressions giving γ and m parameters as functions of cardinal point coordinates (IMPP,VMPP, ISC and VOC) have been established. A daily monitoring of PV quantities, throughout one reference day, has been used to derive novel mathematical models of γ and m depending on module temperature and solar irradiance. To describe PV module behavior under changing meteorological conditions during an arbitrary day, mathematical models have been called to reproduce dynamical values of PLM shape parameters γ and m. Reliability of the approach has been assessed by comparing current–voltage characteristics and maximum power coming from PLM and Single-Diode Model (SDM) to experimental data of twenty-two PV modules measured at NREL. Comparison also reveals that I-V characteristics generated and predicted separately by PLM and SDM parameters are very close. In some cases, I-V characteristics generated by PLM parameters are more accurate. Furthermore, NRMSE values for generated and predicted I-V characteristics for all kinds of PV modules evaluated for different working conditions do not exceed 4% for both PLM and SDM, and determination coefficient values for long-term prediction of maximum power are close to one and annual average value is greater than 0,95.

Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant

Malaysia's most plentiful renewable energy (RE) source, solar, only generate power during the day. High penetration of intermittent PV generation, especially from Large-Scale Solar Photovoltaic (LSSPV) plants, may complicate power system planning and operation. Hence, the need to accurately forecast solar generation for sustaining grid stability. This study identified the meteorological variables that significantly impact the power generation of an LSSPV plant using the Pearson Correlation Coefficient (PCC). The LSSPV plant, located in the central area of Malaysia, is used as a case study. To this end, a regression analysis model was developed using Artificial Neural Network (ANN) to predict solar generation with changing meteorological conditions. From August 1, 2020, to July 1, 202,1 and September 1, 2022, to November 1, 2022, a 5-minute dataset is collected. The meteorological variables with the strongest correlation on solar generation were the total global horizontal irradiance and global irradiance on the module plane selected by PCC, followed by PV module temperature, ambient temperature, wind speed, total slope and total horizontal irradiation. The final input to the forecasting model consists of four primary features that indicate a significant correlation with solar PV generation, which are total global horizontal irradiance (W/m²), PV module temperature (°C), ambient temperature (°C) and wind speed (m/s). The simulation findings reveal that the accuracy evaluation metrics are higher, with R² metrics improved by 45% when employing feature selection for prediction. While ANN has been commonly used for solar generation prediction, this study uniquely combines ANN with PCC as a feature selection technique. This method has not been widely explored in the literature and has resulted in significant improvements in the accuracy and robustness of the prediction model. Thus, our study provides a valuable contribution to solar energy prediction and can potentially enhance solar energy utilisation efficiency in various applications.

Responses of sap flow density of two shrub species to rainfall classes on the semiarid Loess Plateau of China.

Rainfall events can determine a cascade of plant physiological and ecological processes, and there is considerable interest in the way that rainfall modifies plant water flux dynamics. The sap flow density (SF) of the planted species of Vitex negundo and Hippophae rhamnoides, on the Loess Plateau of China was monitored using the heat balance method from 2015 to 2017. The results showed that SF responded differently to rainfall classes because of the changing meteorological and soil water content (SWC) conditions. For class 1: 0.2-2 mm, SF increased by 14.36-42.93% for the two species, which were mainly attributable to the effect of solar radiation and vapor pressure deficit after rainfall. For class 2: 2-10 mm, SF remained nearly stable for V. negundo and decreased for H. rhamnoides because of the relative humidity's effect. For class 3: > 10 mm, SF increased significantly because of increased SWC and the increasing response to solar radiation. The increased percentage of SF was relatively higher for V. negundo when rainfall was less than 20 mm, while the value was higher for H. rhamnoides when rainfall was greater than 10 mm. Further, V. negundo's water potential increased at the soil-root interface (ψ0) and ψL, indicating that the plant, which has shallower roots and a coarser of leaf and bark texture, considered as anisohydric species and used precipitation-derived upper soil water to survive. The relatively consistent ψL and ψ0 for H. rhamnoides, which has deep roots and leathery leaves, indicated that this species was considered as isohydric species and insensitive to the slight change in the soil water status. The differed response patter and water use strategies between the two species showed that species as V. negundo are more susceptible to frequent, but small rainfall events, while larger, but less frequent rainfall events benefit such species as H. rhamnoides. This study quantified the effect of environmental factors for SF variation. The results could help formulate a selection process to determine which species are more suitable for sustainable management in the afforestation activities under the context of more frequent and intense rainfall events.

Four decades of aviation visibility at Bhairahawa airport, gateway to Buddha's birthplace Lumbini, Nepal

Over four decades of visibility data at Bhairahawa airport (BWA), a recently upgraded international airport near Lumbini, a UNESCO heritage site in Nepal has been analyzed. In this study, we also investigated one of the important microclimatic behavior of fog i.e., onset and dispersal timings, and its implication on aviation. Temporal variations of poor visibility conditions at BWA are found to be primarily associated with variations in haze and fog. Haze at BWA accounts for the highest percentage (∼27%) of time and its annual occurrence is increasing (0.57% yr−1). There is a significant upward trend of hazy days in all seasons, the highest (1.46% day yr−1) being in post-monsoon. The overall seasonal poor visibility, too, has increased significantly in all seasons, with the highest trend in post-monsoon (1.57% yr−1). Similarly, fog frequency in the winter season has also increased noticeably for fog days (1.05% day yr−1), dense fog days (0.51% day yr−1), general fog hours (0.55% hour yr−1) and dense fog hours (0.20% hour yr−1). We found that fog at BWA is usually formed overnight and dissipates before noon. Daytime onset and late dispersion of fog are more common in the peak winter months of December and January. Further, we investigated the relationship between visibility and aerosol optical depth (AOD) and found a moderate negative correlation (r = − 0.66, p < 0.001) between them in the monsoon season. However, AOD is found to have a weaker correlation with visibility during winter (r = −0.36) and pre-monsoon (r = −0.23) seasons, when there is a more pronounced influence of meteorological conditions on the occurrence of visibility. We have observed a better correlation (r = −0.74) between fine particulate matter concentration (PM2.5) and visibility. Examining the effect of relative humidity (RH) on AOD (or, PM2.5) and visibility revealed that higher RH tends to lower visibility. Visibility at BWA airport is gradually worsening due to local and regional air pollution emissions and changing meteorological conditions. The degraded visibility at BWA airport will negatively impact flight safety and timeliness. Effective implementation of regionally coordinated air pollution mitigation measures can be a sustainable step towards the improvement of visibility in long run. However, the installation of ground equipment like CAT-II/III Instrument Landing System (ILS) for aircraft take-off and landings, and advanced surface movement guidance and control system (A-SMGCS) are highly advisable to lessen the damage potential to aviation.

Mechanism of persistent heavy PM2.5 pollution over the Beijing-Tianjin-Hebei region of China: A combination of aerosol radiative effect and atmospheric quasi-biweekly oscillation

The persistent heavy PM2.5 pollution (PHP) occurs frequently over the Beijing-Tianjin-Hebei (BTH) region in winter, but the formation mechanism is not fully understood. Using a regional climate-chemistry-ecology model RegCM-Chem-YIBs, we investigated the synergetic impacts of aerosol radiative effect (ARE) and atmospheric quasi-biweekly oscillation (QBWO) on a PHP event in December 2013. The event consists of two peak stages and a persistence stage between them. Process analysis shows that chemical reactions and advection are the most important processes in the accumulation of PM2.5. Chemical reactions contribute 30–40 μg m−3 hr−1 to the rapid development of PHP, which is far more than that in clean days. Through sensitivity experiments, the influences of ARE and QBWO on PM2.5 variation via changing meteorological conditions were quantified respectively. During the event, ARE leads to a reduction of 24.0% in solar radiation and −33.8% in surface air temperature. The planetary boundary layer height is lowered by 20.8%. The feedback of ARE to PM2.5 ranges from 20 to 170 μg m−3, with the largest changes at PM2.5 peak stages. In comparison, QBWO exerts dominant influence at the persistence stage, during which the East Asia trough and northerlies are weakened constantly. Besides, QBWO causes a decrease of −39.5% in wind speed and an increase of 14.3% in relative humidity near the surface. The rates of chemical process and advection are increased considerably at the first peak stage and the persistence stage. QBWO also slows down the dissipation of PHP, prolonging the pollution duration. Finally, a range of 60–110 μg m−3 increase in PM2.5 is attributed to QBWO. In all, ARE and QBWO take different roles in the formation of PHP. ARE mainly affects the pollution intensity by accelerating chemical reactions, while QBWO determines the persistence of heavy pollution by changing both advection and chemical processes.

Design and Implementation of a Low-Voltage Photovoltaic System Integrated with Battery Energy Storage

In this paper, the simulation and design of a power converter suitable for a low-voltage photovoltaic (PV) battery energy storage converter was investigated. The converter was suitable for sources and loads with near voltage levels and were aimed at efficiency improvement. The converter was called a series partial power converter (SPPC). A continuous current and a boost SPPC topology based on an isolated Cûk converter was constructed. The operation modes of the converter were analyzed. The efficiency verification test of the SPPC in a laboratory environment was completed using an outdoor start-up test, a photovoltaic full cover test and a steady-state operation test under different power levels. The results show that the SPPC can achieve 95–98% converter efficiency within the 87 W–242 W power range. The incremental conductance method based on the SPPC can realize the dynamic MPPT under constant illumination and changing meteorological conditions.

Future Ozone Levels Responses to Changes in Meteorological Conditions under RCP 4.5 and RCP 8.5 Scenarios over São Paulo, Brazil

Since the implementation of emission control policies in 1983, the Metropolitan Area of São Paulo (MASP) has experienced a significant decrease in the annual mean concentration of air pollutants, except for ozone, which has remained relatively stable. This work analyzes the future impact on surface ozone formation in the MASP caused by changes in atmospheric conditions. The authors performed air quality simulations using the weather research and forecasting with chemistry (WRF-Chem) model under two representative concentration pathway (RCP) atmospheric conditions. A base case simulation from September and October 2018 was compared to scenarios for the same months in 2030, using the same anthropogenic emissions. Results show an average increase in peak ozone concentrations (0.43% for RCP 4.5 and 5.92% for RCP 8.5) with variations depending on the month and location. However, under the RCP 4.5 scenario, peak ozone concentrations in October were higher in urban areas than under the RCP 8.5. These outcomes can assist decision-makers in understanding the potential future impacts of high ozone formation, which has historically occurred in September and October in São Paulo by considering the effects of changing meteorological conditions, such as increased temperatures, higher surface radiation, and reduced cloudiness.

Trend and Interannual Variations of Reactive Nitrogen Deposition in China During 2008–2017 and the Roles of Anthropogenic Emissions and Meteorological Conditions

AbstractAbundant emissions have made China as one of the hotpots of atmospheric reactive nitrogen (Nr) deposition over the world. Here we conducted multi‐year simulation and explored the trend and interannual variations (IAVs) of China's Nr deposition for different forms (wet and dry) and species (oxidized and reduced nitrogen, OXN and RDN) during 2008–2017. Sensitivity analysis scenarios were applied to evaluate the roles of anthropogenic emissions and meteorological conditions on the changing deposition. The annual mean Nr deposition was simulated at 15.7 ± 0.9 kg N ha−1 yr−1 for China. Attributed mainly to the changing OXN, the trend in Nr deposition over the 10‐year period showed an inverted “V” pattern, reflecting the benefit of nation actions on NOX emissions controls. The standard deviation ellipse analysis suggested a clear interannual shift of spatial distribution of Nr deposition to the southwest during 2013–2017. Based on the mean absolute deviation (MAD) and absolute percent departure from the mean obtained in different simulation scenarios, the IAVs of Nr deposition were found to be driven mainly by the changing meteorological conditions, and the correlation coefficient between MADs of Nr deposition and precipitation reached 0.47. Relative to meteorological factor, the changing emissions could explain 61%–83% of 10‐year trend of Nr deposition for the concerned regions, thus were identified as the main driver of the trend. The changing meteorological conditions played a more important role on shaping the IAVs of Nr deposition, with the relative contributions estimated at 60% for the concerned regions.

Variability of yield components in winter wheat varieties and breeding lines under environment of Ukrainian Forest-Steppe

Topicality. The main task of winter wheat breeding is to increase productivity, therefore, the development and introduction of new varieties of winter wheat with high productivity potential being well adapted to changing growing conditions into agricultural production is an urgent area of research. Breeders' efforts are aimed at developing varieties with a successful combination of high parameters of all the main productivity elements in one genotype, which will maximise grain yield. Breeding work is determined by many factors, among which the search and development of new genetic sources with high performance indicators. Therefore, the peculiarities of yield potential realization and the study of the mechanisms of productivity element formation under changing meteorological conditions are important for establishing the response rate and selecting the most stable productive genotypes and their further involvement in breeding programs. Purpose. To identify the best varieties and breeding lines of winter wheat in terms of productivity elements in the environment of the Forest-Steppe of Ukraine for their involvement in crossbreeding programmes. Methods. Visual – phenological observations; laboratory-field – determination of biometric indicators; mathematical and statistical – establishing the reliability of the obtained data. Results. It was found that the maximum realisation of the yield potential of winter wheat plants by structural elements occurred under optimal growing conditions such as in years with higher moisture availability and sowing on 5 October. Yield formation was significantly influenced by the weather conditions of the year, genotype and unaccounted factors. Sowing dates had a significant effect only on the manifestation of the plant height trait. There was a minor variation (3.8 %÷5.5 %) in plant height in all varieties and breeding lines. Different levels of variability were found for the following traits: number of productive stems varied from Cv=16.4 % (second sowing date in the dry 2019/20) to Cv=27.6 % (first sowing date in the favourable 2020/21); main head grain content and thousand grain weight – Cv=10.7÷17.6 % and Cv=8.8÷16.9 %, respectively. Regardless of varietal characteristics, the main spike length had insignificant (Сv=5.6÷8.8 %) phenotypic variability with a range of variation of 0.2–2.0 cm. There were determined the variability levels of traits by the spikelet number per spike (Сv=5.7 %÷8.6 %), by grain weight per spike (Сv=13.1÷20.5 %) and per plant (Сv=20.5÷36.3 %). Conclusions. The varieties MIP Assol, Hratsiia MYR, MIP Dniprianka, MIP Yuvileina and breeding lines LUT 55198, LUT 37519 consistently formed a high level of productivity elements regardless of sowing dates and conditions of the growing year, which indicates their high adaptive capacity. They are recommended for breeding programmes as valuable parental components. Key words: productivity parameters, trait, variation, sowing date, year conditions

METHOD OF THE SENSOR SYSTEMS' SYNTHESIS AND DRONES FOR IDENTIFYING THUNDERSTORM ACTIVITY'S AREAS

A feature of the aviation use in various physical and geographical conditions, in the event of emergency situations, in combat operations is the performance of tasks in the conditions of limited existing navigation and meteorological support systems functioning. Ensuring flight safety , monitoring the state and changing meteorological conditions can be implemented through the use of alternative solutions.It is suggested in the work to choose as a direction of research modern technologies that are introduced in various spheres of life, primarily on issues of national security and defense. These areas include the use of unmanned aerial vehicles (UAVs) to solve various problems, modern infocommunication technologies in which the tasks of wireless data exchange and electronic components with low power supply are considered. The integrated use of such technologies makes it possible to expand the possibilities of monitoring meteorological conditions in mountainous areas, the coastal zone where the capabilities of stationary meteorological support systems are limited. A method for the synthesis of sensor systems and UAVs is presented and that makes it possible to monitor atmospheric parameters and, based on the analysis of the data obtained, to identify zones of thunderstorm activity. The method is based on the use of prognostic models for calculating the indices of the atmospheric convection occurrence. The values of the calculated indices are the basis for the forecast of thunderstorm activity. The conditions for the application of these models is the determination of atmospheric parameters at different heights, the value of which is determined by the level of given isobaric surfaces. Such measurements are proposed to be implemented on the basis of a monitoring sensor network’s creation, the main elements of which are sensor systems, unmanned aerial vehicles and wireless data exchange technologies. The assessment of the possibility of implementing the proposed method was carried out by conducting experimental studies and comparing the results obtained with the data of existing systems for monitoring meteorological conditions. It is confirmed that the results of the study can serve as a basis for creating both local systems for monitoring thunderstorm activity and as part of integrated monitoring systems that ensure the safe and efficient use of airspace by manned and unmanned aerial vehicles.

Seasonal Variability of Air Pollutants and Their Relationships to Meteorological Parameters in an Urban Environment

Air quality in urban areas is deteriorating over time with the increased pollutant distribution levels mainly caused due to anthropogenic activities. In addition, these pollutant distribution levels may relate to changing meteorological conditions. However, the relationships were not researched in-depth in the context of Sri Lanka, a country with a significant impact on climate change. The main objective of this study was to provide a broader perspective on the seasonal variation of tiny particles in air (PM2.5 and PM10), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2) in two urban cities (Colombo and Kandy) in Sri Lanka over 3 years period (2018–2021) and the possible relationships between air pollution and meteorological variables. Results show that all the aforementioned pollutants except O3 consistently depict two peaks during the day, one in the morning (∼07:00–09:00 local time) and the other in the evening (∼18:00–20:00 local time). These peaks coincided with the traffic jams observed in both cities. The results further revealed that the concentration of all pollutants has significant seasonal variations. Compared to two monsoon seasons, the highest daily average PM2.5 (31.2 μg/m3), PM10 (49.5 μg/m3), NO2 (18.9 ppb), CO (717.5 ppb), O3 (18.5 ppb), and SO2 (9.4 ppb) concentrations in Colombo are recorded during northeast monsoon (NEM) seasons while contrast pattern is observed in Kandy. In addition, it was found that wind speed with its direction is the most influencing factor for the pollutant concentration except for SO2 and O3 in two cities, and this is irrespective of the season. This study’s findings contribute to understanding the seasonality of ambient air quality and the relationship between meteorological factors and air pollutants. These findings ultimately lead to designing and implementing season-specific control strategies to achieve air pollution reduction at a regional scale.

Characterizing changes in extreme ozone levels under 2050s climate conditions: An extreme-value analysis in California

Ground-level ozone has a well established climate penalty associated with changing meteorological conditions under future climate change, but most existing research focuses exclusively on ozone-temperature relationships defined at specific locations. Using an extreme-value theory approach, we model relationships between temperature, humidity, and vapor pressure deficit and maximum daily 8 h ozone concentrations (MDA8) across California using a combination of ozone station network data and Community Multiscale Air Quality Modeling System (CMAQ) model output from 2008 to 2017. We use a spatial regression with time varying bias coefficients to fuse station observations and modeled output into a spatially explicit gridded ozone dataset at 12 km resolution, then fit independent Point Process models to every grid cell to estimate 5, 20, and 50 year ozone return-levels across California. We evaluate the impact of climate change on ozone return levels and episode days under RCP4.5 conditions using an ensemble of 18 downscaled CMIP5 climate models, finding changes of -6t to 8 ppb in effective return levels and −8 to 16 episode days, Our results further strengthen the evidence for anozone climate penalty in California and show the advantages of considering multiple climate variables when modeling ground-level ozone. • Extreme ozone levels are affected by multiple meteorological variables, including temperature, relative humidity, and vapor pressure deficit. • Climate change increases extreme ozone levels across most of California. • Extreme value theory model fits are improved with meteorological covariates.

Investigating water quality sensitivity to climate variability and its influencing factors in four Lake Erie watersheds

Climate change alters weather patterns and hydrological cycle, thus potentially aggravating water quality impairment. However, the direct relationships between climate variability and water quality are complicated by a multitude of hydrological and biochemical mechanisms dominate the process. Thus, little is known regarding how water quality responds to climate variability in the context of changing meteorological conditions and human activities. Here, a longitudinal study was conducted using trend, correlation, and redundancy analyses to explore stream water quality sensitivity to temperature, precipitation, streamflow, and how the sensitivity was affected by watershed climate, land cover percentage, landscape configuration, fertilizer application, and tillage types. Specifically, daily pollutant concentration data of suspended solid (SS), total phosphorus (TP), soluble reactive phosphorus (SRP), total Kjeldahl nitrogen (TKN), nitrate and nitrite (NOx), and chloride (Cl) were used as water quality indicators in four Lake Erie watersheds from 1985 to 2017, during which the average temperature has increased 0.5 °C and the total precipitation has increased 9%. Results show that precipitation and flow were positively associated with SRP, NOx, TKN, TP, and SS, except for SRP and NOx in the urban basin. The rising temperatures led to increasing concentrations of SS, TKN, and TP in the urban basin. SRP and NOx sensitivity to precipitation was higher in the years with more precipitation and higher precipitation seasonality, and the basins with more spatially aggregated cropland. No-tillage and reduced tillage management could decrease both precipitation and temperature sensitivity for most pollutants. As one of the first studies leveraging multiple watershed environmental variables with long-term historical climate and water quality data, this study can assist target land use planning and management policy to mitigate future climate change effects on surface water quality.