Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter
Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter
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DOI：
10.1021/es0606780
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发表时间：
2007-04-01
影响因子：
11.4
通讯作者：
Brauer, Michael
中科院分区：
文献类型：
--
作者：
Henderson, Sarah B.;Beckerman, Bernardo;Brauer, Michael
土地利用回归（LUR）是一种高空间分辨率的环境空气污染物浓度预测技术。我们扩大了以前的工作，在加拿大温哥华，使用两种交通措施的氮氧化物和细颗粒物的建模。对历史数据进行系统回顾，确定了NO和NO2的最佳采样期。2003年春季和秋季，在116个地点用被动采样器测量了14天的综合平均浓度。研究估计的年平均NO和NO2分别为5.4-98.7和4.8-28.0 ppb。监管测量范围为4.8-29.7和9.0-24.1 ppb，空间变异性较小。在另一个活动期间，在25个地点的子集进行颗粒质量浓度（PM2.5）和吸光度（ABS）的测量。在地理信息系统（GIS）中生成了55个描述每个采样点的变量，并用最具预测性的协变量建立了NO，NO2，PM2.5和ABS的线性回归模型。调整后的R（2）值范围为0.39至0.62，并且在流量指标中相似。由此产生的地图显示NO的分布比NO2的分布更不均匀，支持这种方法用于评估交通相关污染的空间格局的有用性。
Land use regression (LUR) is a promising technique for predicting ambient air pollutant concentrations at high spatial resolution. We expand on previous work by modeling oxides of nitrogen and fine particulate matter in Vancouver, Canada, using two measures of traffic. Systematic review of historical data identified optimal sampling periods for NO and NO2. Integrated 14-day mean concentrations were measured with passive samplers at 116 sites in the spring and fall of 2003. Study estimates for annual mean NO and NO2 ranged from 5.4-98.7 and 4.8-28.0 ppb, respectively. Regulatory measurements ranged from 4.8-29.7 and 9.0-24.1 ppb and exhibited less spatial variability. Measurements of particle mass concentration (PM2.5) and light absorbance (ABS) were made at a subset of 25 sites during another campaign. Fifty-five variables describing each sampling site were generated in a Geographic Information System (GIS) and linear regression models for NO, NO2, PM2.5, and ABS were built with the most predictive covariates. Adjusted R (2) values ranged from 0.39 to 0.62 and were similar across traffic metrics. Resulting maps show the distribution of NO to be more heterogeneous than that of NO2, supporting the usefulness of this approach for assessing spatial patterns of traffic-related pollution.