Despite growing attention from researchers and governments, challenges persist in comprehensively assessing urban sound quality by integrating both quietness and naturalness aspects. This study aimed to develop an innovative soundscape quality index that concurrently evaluates quietness and naturalness in urban soundscapes. Our objectives included conducting urban soundscape quality mapping, analyzing influential mechanisms, and identifying priority zones for sound environment management. We collected sound pressure level (SPL) and raw audio data, from which we computed a normalized difference soundscape index (NDSI). With a dataset comprising 28 explanatory variables encompassing land use, built environment, vegetation characteristics, and temporal factors, we employed the random forest (RF) model to predict 10 indicators, including eight SPL-related indices, NDSI, and the QNS (quietness and naturalness soundscape) index. Crucially, we utilized SHAP (SHapley Additive exPlanations) values to interpret the RF model. Spatial variations in quietness and naturalness were evident, closely associated with road networks and vegetation, respectively, with discernible temporal variations. The top three variables influencing QNS were distance to major roads, normalized difference vegetation index (NDVI), and proportion of tree coverage. Moreover, interaction effects highlighted dual negative or synergistic promoting effects on QNS from factors such as road width, human disturbance, vegetation configurations, and land cover. Notably, these mechanisms were successfully applied to six typical tourist attractions in Xiamen city, where five types of management zones were mapped based on priority considerations of population density and soundscape quality. Interestingly, natural soundscape reserves were highly correlated with city parks, high-risk zones predominantly overlapped with road networks, and potential zones comprised inner communities between streets. The framework demonstrated effectiveness in mapping, exploring mechanisms, and guiding management strategies for the urban sound environment.

中文翻译：

---

城市声景质量映射、机制和管理的探索性框架：从安静到自然


尽管研究人员和政府越来越关注，但通过整合安静度和自然度来全面评估城市声音质量仍然面临挑战。本研究旨在开发一种创新的声景质量指数，同时评估城市声景的安静度和自然度。我们的目标包括进行城市声景质量测绘、分析影响机制以及确定声音环境管理的优先区域。我们收集了声压级 (SPL) 和原始音频数据，从中计算出归一化差异声景指数 (NDSI)。我们利用包含土地利用、建筑环境、植被特征和时间因素等 28 个解释变量的数据集，采用随机森林 (RF) 模型来预测 10 个指标，包括 8 个 SPL 相关指数、NDSI 和 QNS（安静度和自然度音景）索引。至关重要的是，我们利用 SHAP（SHapley Additive exPlanations）值来解释 RF 模型。安静度和自然度的空间变化很明显，分别与道路网络和植被密切相关，并具有明显的时间变化。影响QNS的前三个变量是距主要道路的距离、归一化植被指数（NDVI）和树木覆盖率。此外，相互作用效应凸显了道路宽度、人类干扰、植被配置和土地覆盖等因素对 QNS 的双重负向或协同促进作用。值得注意的是，这些机制已成功应用于厦门市六个典型旅游景区，根据人口密度和声景质量的优先考虑，划分出五类管理区。 有趣的是，自然声景保护区与城市公园高度相关，高风险区域主要与道路网络重叠，潜在区域由街道之间的内部社区组成。该框架在城市声环境规划、探索机制和指导管理策略方面展现了有效性。