Analysing type of plants for reducing noise pollution in climate change adaptation

Abstract

Abstract The noisier a region or city is, the faster the rate of global warming. Noise is not a substance. However, most sources of carbon dioxide and other greenhouse gases are also sources of noise; for example, busy highways in cities can occasionally see as many as 100 cars per hour. Planting trees to mitigate noise pollution has been identified as one of the most sustainable methods to employ, since plants may function as a buffer and absorb sounds. Several plant species were chosen to assess noise absorption based on parameters such as leaf thickness, breadth, surface area, and length. The initial goal of this study is to estimate the noise absorption on the selected flora native to the Malaysian environment, where the noise was measured using an in-house developed impedance tube to discover the effective acoustic characteristics of leaves. The investigation was then carried on by assessing the data on the correlation coefficient parameter in order to determine the link between noise absorption and leaf features. Because of the freshness and perishability of the materials, measurements were repeated twice. Only 100% and 50% vegetation quantity size were measured for 10 sample species during the initial data collection. According to the findings, half of the leaves’ features were connected with noise absorption. This might be owing to the freshness state, which cannot be maintained for an extended period of time. To acquire a better value, the experiment was repeated within the permissible freshness time. The findings are as predicted, with the maximum noise absorption and features correlation at 100% plant density including twigs. When the vegetation amount is lowered to 50%, this will progressively diminish. The notion is confirmed by the fact that a tube packed with thicker samples absorbs more noise. The correlation study identifies that each leaf has its unique capacity for noise absorption dependent on its properties and freshness level.