Evaluation of urban noise during the pandemic period in a medium sized city

Abstract This article describes a study on the evolution of urban noise in the city of São Carlos, SP, Brazil based on acoustic measurements carried out during the COVID-19 pandemic period and proposes models to predict the sound pressure level (L Aeq ), in order to verify the influence of the variation of the vehicular traffic composition on the measured noise. A study is presented, considering five measuring points in the centre of the city. Acoustic measurements were collected concurrently with vehicle counts. Based on the collected data, the results were analysed: variation in traffic composition and noise measured during the flexibilisation of the restrictive measures. Multiple regression models were developed to verify the influence of each type of vehicle on the generation of urban noise. The results obtained made it possible to verify a reduction in the noise level in the beginning of the pandemic, in May 2020. In November 2021 the L Aeq levels were higher than those measured before the pandemic period, even with some restriction measures still in place. The proposed regression models showed the increasing contribution of motorcycles to urban noise. The correlation coefficient (R²) of the models were greater than 0.75, validating the generated models.

摘要 本研究针对巴西圣保罗州圣卡洛斯市的城市噪声演化展开探讨，基于新冠疫情期间获取的声学测量数据，提出用于预测声压级（L_Aeq）的模型，旨在验证车辆交通组成变化对实测噪声的影响。本次研究选取该市中心城区的5个监测点位，同步采集声学测量数据与车辆计数信息。基于采集得到的数据集，研究团队对管控限制措施放宽阶段的交通组成变化与实测噪声变化情况开展分析。研究构建了多元回归模型，用以验证各类车辆对城市噪声产生的影响。结果表明，2020年5月疫情初期城市噪声水平出现下降；至2021年11月，尽管仍有部分管控措施存续，但实测L_Aeq水平已高于疫情前的基准值。所提出的回归模型显示，摩托车对城市噪声的贡献度逐步提升。各模型的相关系数（R²）均大于0.75，证实了所构建模型的有效性。