Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/30074
Title: Quantifying the impact of meteorological factors and green infrastructure location on particulate matter (PM) mitigation in Republic of North Macedonia using sensor collected data
Authors: Srbinovska, Mare 
Andova, Vesna 
Krkoleva, Aleksandra 
Celeska, Maja 
CHundeva-blajer, Marija 
Kutirov, Matej
Majstoroski, Martin
Keywords: air pollution reduction, green wall, particulate matter, meteorological factors, sensor network
Issue Date: Jun-2023
Publisher: Elsevier BV
Journal: Measurement: Sensors
Abstract: 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.
URI: http://hdl.handle.net/20.500.12188/30074
DOI: 10.1016/j.measen.2023.100819
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Journal Articles

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