Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA; Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA.
Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA; Institute for Environmental Studies, Faculty of Science, Charles University, Prague, Czech Republic.
Environ Res. 2018 Nov;167:7-14. doi: 10.1016/j.envres.2018.06.052. Epub 2018 Jul 4.
Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. However, they require pollutant concentrations at multiple locations along with land-use variables. Often, monitoring is performed over short durations using mobile monitoring with research-grade instruments. Low-cost PM monitors provide an alternative approach that increases the spatial and temporal resolution of the air quality data. LUR models were developed to predict hourly PM concentrations across a metropolitan area using PM concentrations measured simultaneously at multiple locations with low-cost monitors. Monitors were placed at 23 sites during the 2015/16 heating season. Monitors were externally calibrated using co-located measurements including a reference instrument (GRIMM particle spectrometer). LUR models for each hour of the day and weekdays/weekend days were developed using the deletion/substitution/addition algorithm. Coefficients of determination for hourly PM predictions ranged from 0.66 and 0.76 (average 0.7). The hourly-resolved LUR model results will be used in epidemiological studies to examine if and how quickly, increases in ambient PM concentrations trigger adverse health events by reducing the exposure misclassification that arises from using less time resolved exposure estimates.
土地利用回归（LUR）模型提供了空气污染暴露的位置和时间特定估计，从而提高了健康影响模型的灵敏度。然而，它们需要在多个位置以及土地利用变量处进行污染物浓度监测。通常，使用带有研究级仪器的移动监测进行短期监测。低成本 PM 监测器提供了一种替代方法，可以提高空气质量数据的空间和时间分辨率。LUR 模型是为了预测大都市地区的每小时 PM 浓度而开发的，这些浓度是使用低成本监测器在多个位置同时测量的 PM 浓度来预测的。在 2015/16 供暖季节期间，监测器被放置在 23 个地点。监测器使用包括参考仪器（GRIMM 粒子光谱仪）在内的共置测量进行外部校准。使用删除/替代/添加算法为每天的每小时和工作日/周末开发了 LUR 模型。每小时 PM 预测的决定系数范围为 0.66 到 0.76（平均为 0.7）。每小时解析的 LUR 模型结果将用于流行病学研究，以检验环境 PM 浓度增加是否以及如何通过减少因使用时间分辨率较低的暴露估计而导致的暴露分类错误来快速引发不良健康事件。
