Srbinovska, Mare
Preferred name
Srbinovska, Mare
Official Name
Srbinovska, Mare
Main Affiliation
Email
mares@feit.ukim.edu.mk
23 results
Now showing 1 - 10 of 23
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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, Low-cost energy-efficient air quality monitoring system using sensor network(Inderscience Publishers, 2021); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Location Impact on Particulate Matter (PM) Concentration Reduction Dduring COVID-19 Pandemic(IEEE, 2022-06-16); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Comprehensive Study on Air Pollution Prediction in North Macedonia: Insights from LASSO Modeling(IEEE, 2024-06-03); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Environmental wireless sensor monitoring and estimation of green infrastructure location impact on particulate matter reduction for improved air quality(IMECO, 2022-09); ; - 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, The effect of small green walls on reduction of particulate matter concentration in open areas(Elsevier BV, 2021-01); ; - 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, 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); ; - 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);
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