Faculty of Electrical Engineering and Information Technologies
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Item type:Publication, Smart forecasting: Enhancing virtual power plant performance with analytical frameworks(Elsevier BV, 2025-04-24); ; ; ; - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Indoor–Outdoor Particulate Matter Monitoring in a University Building: A Pilot Study Using Low-Cost Sensors(MDPI AG, 2026-01-30); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modelling and prediction of air pollution using hybrid tree–LASSO approach(SAGE Publications, 2025-09-17); - Some of the metrics are blocked by yourconsent settings
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, 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. - 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, Low-cost energy-efficient air quality monitoring system using sensor network(Inderscience Publishers, 2021); ; ; ; The air pollution has a significant impact on human’s health and global environment. In urban areas the air quality significantly decreased over the past few years. One of the methods for air pollution reduction is by installing green walls, green roofs or by implementing green buildings as plants have capabilities to absorb the particulate matter through their leaves. The main goals of this paper are to present system for air quality monitoring using sensor network technology that can be easily deployed in polluted areas and to examine the influence of the experimental green wall setup to particulate matter more precisely PM10 and PM2.5 concentrations in Skopje, Republic of North Macedonia. Furthermore, the paper presents the preliminary results of the ongoing experiment developed to evaluate the impact of green walls in reduction of air polluting particles' concentrations. The air quality monitoring system can be easily replicated on other locations in the urban areas of Skopje. - 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, 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.