Weichenthal Scott, Ryswyk Keith Van, Goldstein Alon, Bagg Scott, Shekkarizfard Maryam, Hatzopoulou Marianne
Air Health Science Division, Health Canada, Ottawa, Canada; Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.
Air Health Science Division, Health Canada, Ottawa, Canada.
Environ Res. 2016 Apr;146:65-72. doi: 10.1016/j.envres.2015.12.016. Epub 2015 Dec 22.
Existing evidence suggests that ambient ultrafine particles (UFPs) (<0.1µm) may contribute to acute cardiorespiratory morbidity. However, few studies have examined the long-term health effects of these pollutants owing in part to a need for exposure surfaces that can be applied in large population-based studies. To address this need, we developed a land use regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure.
现有证据表明，环境超细颗粒物（UFPs，直径<0.1µm）可能会导致急性心肺疾病。然而，由于需要适用于大规模人群研究的暴露表面，很少有研究考察这些污染物对健康的长期影响。为满足这一需求，我们利用2011年至2012年夏季和冬季期间从414个路段收集的移动监测数据，开发了加拿大蒙特利尔市UFPs的土地利用回归模型。研究了两种不同的模型开发方法，包括标准多变量线性回归和机器学习方法（基于核的正则化最小二乘法（KRLS）），该方法从数据中学习协变量对环境UFPs浓度影响的函数形式。最终模型包括人口密度、环境温度和风速、土地利用参数（公园面积和开放空间）、当地道路和铁路长度以及交通产生的估计年度平均氮氧化物排放量等参数。最终的多变量线性回归模型解释了环境UFPs浓度空间变异的62%，而KRLS模型解释了79%的变异。使用外部数据集（R(2)=0.58对0.55）或交叉验证程序（R(2)=0.67对0.60）进行评估时，KRLS模型的表现略优于线性回归模型。总体而言，我们的研究结果表明，与用于估计环境UFPs空间变异的标准多变量线性回归模型相比，KRLS方法在预测性能上可能有适度的提升。然而，使用交叉验证程序进行评估时，预测性能的差异在统计学上并不显著。
