Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland.
Department of Pediatrics at the Medical School of São Paulo University, São Paulo, Brazil.
Environ Res. 2021 Aug;199:111231. doi: 10.1016/j.envres.2021.111231. Epub 2021 May 7.
Noise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of São Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice for one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (L) and night sound levels (L). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source. Noise exposure levels in the study area were high with a site average L of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average L of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R of 0.56 for L and 0.63 for L in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (L) or 25 m (L) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available.
噪声污染对健康有负面影响，随着城市化的快速发展，这一问题变得越来越重要。在中低收入国家，由于缺乏暴露数据和噪声图，关于噪声对健康影响的研究受到阻碍。在这项研究中，我们开发了基于土地利用的回归（LUR）模型，以评估巴西圣保罗西部地区社区噪声的空间变异性。我们在夏季和冬季，每周在 42 户家庭中进行一次或两次持续测量室外噪声水平。这些测量结果与各种地理信息系统变量相结合，开发了 LUR 模型，用于预测平均 A 加权（dB（A））日-晚-夜等效声级（L）和夜间声级（L）。我们进行了有监督的混合线性回归分析，以测试各种缓冲区大小和家庭与噪声源之间距离的潜在噪声预测因子。研究区域的噪声暴露水平较高，站点平均 L 为 69.3 dB（A），范围为 60.3 至 82.3 dB（A），站点平均 L 为 59.9 dB（A），范围为 50.7 至 76.6 dB（A）。LUR 模型在离开一个站点的交叉验证中具有良好的拟合度，R 为 0.56 用于 L，R 为 0.63 用于 L。噪声的主要预测因子是到中等道路的倒数距离、400 m 缓冲区内在 100 m 缓冲区内的教育设施数量、100 m 缓冲区内归一化差异植被指数（NDVI）的平均值、50 m（L）或 25 m（L）缓冲区内的居民区以及 400 m 缓冲区内的贫民窟。我们的研究表明，使用地理预测因子数据的 LUR 建模是一种很有前途且高效的方法，可用于低中收入国家的噪声暴露评估，这些国家没有噪声图。
