Krkoleva, Aleksandra
Preferred name
Krkoleva, Aleksandra
Official Name
Krkoleva, Aleksandra
Main Affiliation
Email
krkoleva@feit.ukim.edu.mk
13 results
Now showing 1 - 10 of 13
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Item type:Publication, Green Infrastructure Impact on Air Pollution Reduction Considering the Effects of Meteorological and Climate Factors(2019-10); ; ; A major concern in urban areas is the low quality of air with high levels of particulate matter and various pollutants that have significant impacts on human health and global environment. Thus, there is an urgent need to reduce air pollution by implementing various short- and long-term actions. Skopje is also struggling with unprecedented increase in air pollution. This is the major motivation for the research presented in the paper. The objective is to provide an assessment of the influence of green walls on air quality in urban areas and correlate it with meteorological factors. Research has shown that one of the methods for decreasing air pollution in urban areas is by implementation of green walls, as plants absorb the particulate matter through their leaves and growing medium. The paper presents research undertaken to assess the influence of the meteorological factors, such as wind speed and direction, relative humidity, and temperature on air quality and to determine which one has the highest impact on particulate matter reduction. With daily monitoring of temperature, humidity, and heat variations near and within the green wall and a reference case, it is possible to analyse the effect of the green walls on air pollution reduction. The air quality monitoring system used to perform the experiments is a low-cost and energy-efficient solution that uses wireless sensor network technology that can be easily deployed in highly polluted areas. The paper presents results of the data analysis of the effect of meteorological and climatic factors in particulate matter reduction and the influence of the wind conditions, seasonal variations, and plant characteristics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessing the Impact of Air Pollution in North Macedonia: A Meteorological and Green Infrastructure Study(2023-09); ; ; ; Viktor AndonovicThis paper presents a meteorological and green infrastructure study to evaluate the impact of air pollution in North Macedonia. The study focuses on the spatial and temporal patterns of air pollution in the country and it investigates the influence of various factors on air pollution mitigation. Namely, it considers the effects of the pandemic, as well as measures that tackle air pollution directly, or have other broader goals, but still impact air pollution. The paper utilizes a variety of data sources, including meteorological data – temperature, humidity, wind speed and direction; air quality monitoring data – concentrations of particulate matter (PM), nitrogen dioxide (NO2) and carbon monoxide (CO); and existence and influence of green infrastructure. In addition, the study incorporates advanced statistical and geospatial techniques to create an accurate assessment of the impacts of air pollution in North Macedonia. The statistical analyses reveal the correlations between the collected data and their statistical significance. The investigation confirms the negative correlation between PM on one side and higher air temperatures/wind speed on the other. It provides evidence of the higher concentrations of pollutants during night hours. The positive effects of reduced traffic and use of fossil fuels for heating in winter months are also observed with the analyses of the acquired data. The results of the analyses are used to discuss possible directions for air pollution reduction. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning model for air pollution prediction in Skopje, North Macedonia(2020-07); ; ; The low quality of air, especially high concentration of particulate matter that have significant negative effect on human health and environment, is a global problem in urban areas. Thus, early air pollution prediction is an urgent need in Skopje, North Macedonia with highly increased concentration of particulate matter especially during the winter months. The objective of this paper is to develop machine learning model for predicting the air pollution in Skopje. The methods are based on processing the collected data from different measurement locations in Skopje, generating numerous weather and pollution features, and choosing the optimal parameters (hyperparameters) for the model. The information for the various pollutants were provided from the measurement stations located near the Faculty of Electrical Engineering and Information Technologies building. The measured data are gathered from the three sensor nodes that are collecting data for following parameters: particulate matter with 10 or less micrometres (PM10), particulate matter with 2.5 or less micrometres (PM2.5), CO and NO2, and sending these data to a server for online monitoring or off-line analysis. The pollution data, together with the weather information for temperature, humidity, wind speed, and wind direction were combined to train the prediction model. The results show that the weather information is correlated with the air pollution, which allows to train a model that predicts the air pollution based on the weather data and the historical data about the pollution. The experimental evaluation showed that the best performing model, XGBoost, achieves Mean Absolute Error for PM10 values of 6.8, 9.7, and 12.4 for the nodes 3, 2, and 1 respectively, and for PM2.5 values 6.36, 8.81 and 8 for nodes 3, 2 and 1 respectively. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Breathing Easy in North Macedonia: The Effect of Green Infrastructure and Movement Restrictions on The Air Quality(Faculty of Information Studies in Novo Mesto, 2023-11); ; ; Air quality is a significant issue in urban areas, marked by poor air quality characterized by elevated levels of particulate matter (PM). This particulate matter includes black carbon, volatile organic compounds, and assorted pollutants that pose threats to both human health and the overall environment on a global scale. Consequently, there exists an urgent need to reduce air pollution by adopting various strategies. In this paper, we analyze the effect of green areas and movement restrictions on the concentrations of PM. The tests are based on the data collected during the period 2018-2022 at the technical campus of Ss Cyril and Methodius University. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Еnvironmental wireless sensor monitoring and estimation of green infrastructure location impact on particulate matter reduction for improved air quality(International Measurement Confederation, 2022-10); ; ; ; Low quality of the air is becoming a major concern in urban areas. High values of particulate matter (PM) concentrations and various pollutants may be very dangerous for the human health and the global environment. The challenge to overcome the problem with the air quality includes efforts to improve healthy air not only by reducing emissions, but also by modifying the urban morphology to reduce the exposure of the population to air pollution. The aim of this paper is to analyze the influence of the green zones on air quality mitigation through measurements, and to identify the correlation with the meteorological factors. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Quantifying the impact of meteorological factors and green infrastructure location on particulate matter (PM) mitigation in Republic of North Macedonia using sensor collected data(Elsevier BV, 2023-06); ; ; ; Low quality of the air is becoming a major concern in urban areas. High values of particulate matter (PM) concentrations and various pollutants may be very dangerous for human health and the global environment. The challenge to overcome the problem with the air quality includes efforts to improve healthy air not only by reducing emissions, but also by modifying the urban morphology to reduce the exposure of the population to air pollution. The aim of this contribution is to analyse the influence of the green zones on air quality mitigation through sensor measurements, and to identify the correlation with the meteorological factors. Actually, the objective focuses on identifying the most significant correlation between PM2.5 and PM10 concentrations and the wind speed, as well as a negative correlation between the PM concentrations and wind speed across different measurement locations. Additionally, the estimation of slight correlation between the PM concentrations and the real feel temperature is detected, while insignificant correlations are found between the PM concentrations and the actual temperature, pressure, and humidity. In this paper the effect of the pandemic restriction rules COVID-19 lockdowns and the period without restriction are investigated. The sensor data collected before the pandemic (summer months in 2018), during the global pandemic (summer months 2020), and after the period with restriction measures (2022) are analysed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimation of the Effect of COVID-19 Lockdown Impact Measures on Particulate Matter (PM) Concentrations in North Macedonia(MDPI AG, 2023-01-17); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Location Impact on Particulate Matter (PM) Concentration Reduction Dduring COVID-19 Pandemic(University St.Kliment Ohridski, Bitola, Macedonia, 2022-06); ; ; —Significant topic of interest in many European countries is monitoring the air pollution, especially particulate matter (PM) concentrations, mostly because of its harmful effects on the human health. Measurement of the particulate matter concentrations can be done in a different ways, one of the possible solutions is by using low-cost and energy-efficient monitoring system using sensor network. The main goal of this paper is to analyze the influence of the green areas on particulate matter mitigation, analyzing the period of pandemic COVID-19 restrictions. The paper analyze the connection among the impact of the location of the sensor nodes and green areas and other objects to the particulate matter concentrations using various statistical tools and hypothesis testing. The tests are based on the data collected during summer 2020 at the technical campus of the Ss Cyril and Methodius University. This is the period when the World Health Organization (WHO) declared COVID 19 pandemic, and the universities were closed. In this research it can be confirmed that green areas at the Faculty pacio, reduced traffic vehicles and not having presence of the faculty staff in this period have a high impact in the reduction of particulate matter. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Simultaneous Optimization Of Electrical Interconnection Configuration In Onshore Wind Fields(Faculty of Electrical Engineering and Information Technologies, “Ss. Cyril and Methodius” University in Skopje, 2019-01); ; ; ; The electrical interconnection cost of onshore wind fields (WF) is a part that should not be neglected in the design of WF. In order to minimize the investment and operational costs, this paper pro-poses an optimization formulation to find the optimal electrical interconnection configuration of wind turbines (WTs), simultaneously. This simultaneous minimization of total trenching length is done during а WF layout optimization by application of evolutional algorithm. In this paper, an algorithm is considered to comprehensively assess the optimal interconnection layout of the WTs. The interconnections are considerable fraction of the overall design cost of the WF. The optimal electrical interconnection design obtained by an algorithm with implemented Euclidean Minimum Spanning Tree method, corresponds to a lower cost and coupled with the technological developments can help policy makers and WF developers increase the use of onshore wind energy as a feasible unlimited renewable source of electrical energy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The effect of small green walls on reduction of particulate matter concentration in open areas(Elsevier BV, 2021-01); ; ; A major concern in urban areas is the low quality of air, with high levels of particulate matter (PM), consisting of black carbons, volatile organic compounds and various pollutants that are hazardous for the human health and the global environment. Thus, there is an urgent need to decrease air pollution by implementing various short and long-term measures. One of the methods for decreasing air pollution in urban areas is increasing the green infrastructure as plants absorb the particulate matter through their leaves and stems. The initial step in dealing with this problem is raising the public awareness, which is generally low in Skopje and the Balkan region. The aim of the research is to quantify the positive effects on green infrastructure on air pollution and provide research-based inputs that can be used by local governments and decision makers. This paper presents data from continuous measurements on a location in Skopje, provides an assessment of the influence of green zones on air quality in urban areas and correlates it with meteorological factors. This is achieved by using an innovative, low-cost, easy replicable and energy-efficient system, consisted of green wall and stations for monitoring the air quality which are based on wireless sensor network technology. By using statistical tools as Freidman and Mann-Whitney tests, the impact of the relative position of the measurement sensors and the green areas and other objects to the PM concentrations is quantified. The performed analyses confirm that green areas, including green walls, have a high impact in the reduction of PM concentrations in their proximity. The differences in measured values obtained by measurement nodes positioned in relatively small distances are not negligible, thus implying that the relative position of the measurement nodes to the green infrastructure influences the measured PM concentrations. Therefore, the measurement location should be carefully considered for any air quality monitoring system. Measurements with higher spatial granularity should be used for modelling and air quality forecasting purposes. The results in this paper show that the green area mitigates the PM of 2.5 or less micrometers (PM2.5) on average by 25% and PM of 10 or less micrometers (PM10) on average by 37% compared to the neighboring non-green areas. The results show a strong correlation between PM2.5 and PM10. In Skopje, the combination of low temperatures, high humidity and no, or low wind speed lead to high PM concentrations. The presented algorithm compares the statistically obtained data to the reference categories from WHO (from very low to very high, with reference to PM2.5). The described methodology is used to develop a simple decision-making support algorithm for local governments to support their decisions on applying PM mitigation measures.