Institute for Resources Environment and Sustainability The University of British Columbia , Rm 411, 2202 Main Mall, Vancouver, BC V6T 4T1, Canada.
Environ Sci Technol. 2013 Nov 19;47(22):12903-11. doi: 10.1021/es401489h. Epub 2013 Oct 28.
Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM2.5, BC, and UFPN data respectively, to develop separate morning (0800-1200) and afternoon (1200-1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC, and UFPN at LUR sites were 133 (96-232) μg m(-3), 11 (6-21) μg m(-3), and 40 (27-72) × 10(3) cm(-3) respectively. In addition (a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R(2) values were higher for morning (for PM2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.
印度新德里的空气污染是一个严重的环境和健康问题。为了评估空气污染物浓度变化的决定因素，我们为细颗粒物（PM2.5）、黑碳（BC）和超细颗粒数浓度（UFPN）开发了土地利用回归（LUR）模型。我们分别使用 136 小时（39 个站点）、112 小时（26 个站点）和 147 小时（39 个站点）的 PM2.5、BC 和 UFPN 数据来开发单独的早晨（0800-1200）和下午（1200-1800）模型。在一个位于高收入居民区的单一固定屋顶站点上，还进行了 PM2.5 和 BC 的连续测量。UFPN 的连续测量数据不可用。除了空间变量外，固定连续监测站点的测量值也被用作 PM2.5 和 BC 模型的独立变量。LUR 站点的 PM2.5、BC 和 UFPN 的中位数浓度（和四分位距）分别为 133（96-232）μg/m3、11（6-21）μg/m3和 40（27-72）×103cm-3。此外，（a）对于 PM2.5 和 BC，时间变异性高于空间变异性；（b）在早晨，污染物浓度的幅度和空间变异性均高于下午。此外，早晨模型的 R2 值更高（对于 PM2.5、BC 和 UFPN，分别为 0.85、0.86 和 0.28），而下午模型的 R2 值更低（0.73、0.69 和 0.23）；（c）城市各地 LUR 站点测量的 PM2.5 和 BC 浓度与固定屋顶站点测量的浓度高度相关；（d）PM2.5 和 BC 的空间模式相似，但 UFPN 的空间模式不同；（e）人口密度和道路变量是污染物浓度的统计学显著预测因子；（f）可用的地理预测因子对测量的 PM2.5、BC 和 UFPN 变异性的解释比例远低于其他 LUR 研究，这表明时间变异性的重要性，并表明存在未被识别的来源。
