Environmental Research ( IF 8.3 ) Pub Date : 2018-08-17 , DOI: 10.1016/j.envres.2018.08.021 Masoud Fallah-Shorshani , Laura Minet , Rick Liu , Céline Plante , Sophie Goudreau , Tor Oiamo , Audrey Smargiassi , Scott Weichenthal , Marianne Hatzopoulou
Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LAeq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R2 (0.44–0.64), deviance (51−72%) and Akaike information criterion (AIC) (469.2–434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.
中文翻译:
根据短期监测活动,捕获噪声水平的空间变异性,并将噪声表面与通过面板研究收集的个人暴露进行比较
环境噪声会导致重要的心血管影响,压力和睡眠障碍。由于存在各种运输噪声源(公路,铁路和飞机),开发用于估计单个市区内噪声暴露的适当方法仍然是一项艰巨的任务。在这项研究中,我们使用通用加性模型(GAM)针对LA eq开发了土地利用回归(LUR)方法(等效噪声级)以捕获加拿大多伦多噪声级的空间变异性。根据在92个站点上连续20分钟的噪声测量和留一法交叉验证(LOOCV),提出了四种不同的模型公式。那些被认为是噪声源的变量的系数被迫为正的模型,导致了更现实的曝光表面的发展。三种不同的测量方法被用来评估模型。调整后的R 2(0.44-0.64),偏差(51-72%)和赤池信息标准(AIC)(469.2-434.6)。当将来自四种方法的风险暴露与来自小组研究的个人风险暴露进行比较时,我们观察到所有方法的表现都非常相似,其分数均值偏差(FB),归一化均方误差(NMSE)和归一化绝对差(NAD)值)非常接近0。最后,我们将噪声表面与从先前活动收集的数据进行了比较,该数据包括对多伦多200个固定站点进行的1周测量,并发现我们的预测与沿主要道路和公路收藏家。