Suchergebnisse

There are many factors that have an impact on the conditions of people's existence: administrative and political, economic development, cultural and moral level of society, etc. The last century actualized another vital factor. It is the ecological crisis of the planet. Community development is accompanied by an obvious conflict combination progress and regress.The temperature on the Earth planet was constantly changing, but not so fast, like now. Remaining ten years become obvious those, that there is a change to the climate and one of the head global problems. Carbon - in coal, which accumulated in plants and animals, and then - under the land during million years. Burning coal, oil, gas on an industrial scale increases greenhouse gas emissions into the environment. As a result, this leads to tremendous climate changes on the planet. This leads to changes within countries, cities, animals, people.The paper considers the main causes of climate fluctuations in the world and Ukraine, predicted possible economic, environmental, economic and political consequences of changes in natural and climatic conditions and proposed possible solutions to the problem. The article analyzes the average daily concentrations of PM2.5 in Kiev for April 2020, and also makes a comparative description of the Environmental Performance Index – EPI of Ukraine according to the data of 2018 and 2020. Attention is paid to the comprehensive assessment of the EPI and the rank of Ukraine in comparison with other countries of the world in 2018 and 2020.The principles of cities' economic subsystems with sustainable development and human potential are described in the article. The authors consider problems and objectives of social subsystem. The main directions of ecological subsystem development are determined. ; У роботі розглянуті головні причини коливання клімату у світі та Україні, прогнозовані можливі економічні, екологічні, економічні та політичні наслідки від зміни природно-кліматичних умов та запропоновані можливі шляхи вирішення проблеми. У статті проаналізовано середньодобові концентрації РМ2.5 у Києві за квітень 2020 року, а також, зроблено порівняльну характеристику Індексу стану навколишнього середовища (ІСНС) України за даними 2018 та 2020 років. Приділено увагу комплексній оцінці ІСНС та рангу України, порівняно з іншими країнами світу 2018 та 2020 років.

Abstract
This paper aims to indicate the periods within the year that are most and least suited to outdoor recreation along the southern coast of the Baltic Sea. The author indicates these periods based on the analysis of annual variability of several meteorological and bioclimatic parameters from the years 1981-2010 in Greifswald and Świnoujście, and from 1985 to 2010 in Hel and Gdynia. Passive recreation is favoured in July and the first half of August. The whole of the summer season has the most favourable conditions for active recreation. Good conditions frequently occur in May, and to a lesser degree in September. The advantages of May are that it has the highest sunshine duration and the lowest frequency of precipitation.

Abstract
The study analysed long-term changes and the variability of the Icelandic Low and the Azores High in January and their influence on thermal, precipitation and nival conditions in Poland in the period 1901-2000. There were no statistically significant (0.05) trends of analysed centres of action in January in the 20th century. However, in the second half of the century the center of the Icelandic Low in January moved northwards and the sea-level pressure in the center of the Azores High increased significantly. The latter change was the reason for the significant increasing difference between the Azores High and the Icelandic Low. This change caused the intensification of western advection over Europe. Changes in sea-level pressure in the Icelandic Low and the longitude of the Azores High in January explain about 40% of temperature and snow cover variability in Poland. The variability of some features of the centres of action describes the changes of precipitation and snow cover in Poland better than the NAO index.

AbstractToday's many giant sectors including energy, industry, tourism, and agriculture should closely track the variation trends of solar radiation to take more benefit from the sun. However, the scarcity of solar radiation measuring stations represents a significant obstacle. This has prompted research into the estimation of global solar radiation (GSR) for various regions using existing climatic and atmospheric parameters. While prediction methods cannot supplant the precision of direct measurements, they are invaluable for studying and utilizing solar energy on a global scale. From this point of view, this paper has focused on predicting daily GSR data in three provinces (Afyonkarahisar, Rize, and Ağrı) which exhibit disparate solar radiation distributions in Türkiye. In this context, Gradient-Based Optimizer (GBO), Harris Hawks Optimization (HHO), Barnacles Mating Optimizer (BMO), Sine Cosine Algorithm (SCA), and Henry Gas Solubility Optimization (HGSO) have been employed to model the daily GSR data. The algorithms were calibrated with daily historical data of five input variables including sunshine duration, actual pressure, moisture, wind speed, and ambient temperature between 2010 and 2017 years. Then, they were tested with daily data for the 2018 year. In the study, a series of statistical metrics (R2, MABE, RMSE, and MBE) were employed to elucidate the algorithm that predicts solar radiation data with higher accuracy. The prediction results demonstrated that all algorithms achieved the highest R2 value in Rize province. It has been found that SCA (MABE of 0.7023 MJ/m2, RMSE of 0.9121 MJ/m2, and MBE of 0.2430 MJ/m2) for Afyonkarahisar province and GBO (RMSE of 0.8432 MJ/m2, MABE of 0.6703 MJ/m2, and R2 of 0.8810) for Ağrı province are the most effective algorithms for estimating GSR data. The findings indicate that each of the metaheuristic algorithms tested in this paper has the potential to predict daily GSR data within a satisfactory error range. However, the GBO and SCA algorithms provided the most accurate predictions of daily GSR data.