Estimation of exposure to air pollution using land use regression (LUR) models often focuses on spatial variation in (annual) average concentration. However, temporal variability is known to be an important factor for exposure. To estimate the short-term street-level exposure to black carbon (BC), we build a spatio-temporal LUR model by including time-dependent variables as predictor variables. We developed and evaluated the model based on data from an opportunistic mobile monitoring campaign in which city employees measured black carbon (BC) during their surveillance tours. Exposure estimates based on the hourly LUR model are more accurate than those based on a fixed site monitoring station or on a spatial LUR model, and can be used to estimate exposure of cyclists or pedestrians to traffic-related pollution based on a GPS track. We demonstrate the potential of building a real-time dynamic pollution map based on unstructured opportunistic measurements to provide personalized exposure information.

使用土地利用回归（LUR）模型估算空气污染暴露程度通常侧重于（年度）平均浓度的空间变化。然而，已知时间变异性是暴露的重要因素。为了估计短期街头对黑碳（BC）的暴露，我们通过将时变变量作为预测变量来建立时空LUR模型。我们基于机会移动监控活动中的数据开发和评估了该模型，在该活动中，城市员工在其监视旅行期间测量了黑碳（BC）。基于小时LUR模型的暴露估计比基于固定站点监视站或空间LUR模型的估计更准确，并且可用于基于GPS轨迹估计骑自行车的人或行人受到交通相关污染的暴露。