The effect of small green walls on reduction of particulate matter concentration in open areas

Abstract

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 developing economies. 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 measurement data, provides an assessment of the influence of green zones on air quality in urban areas and correlates it with meteorological factors. Тhe proposed solution can be also implemented on other locations with mid-continental climate conditions. 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. More precisely, 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, while the combination of low temperatures, high humidity and no, or low wind speed lead to high PM concentrations. The measurement results and the described methodology are used to develop a simple decision-making support algorithm for local governments to support their decisions on applying PM mitigation measures.