Wai Travis Hee, Apte Joshua S, Harris Maria H, Kirchstetter Thomas W, Portier Christopher J, Preble Chelsea V, Roy Ananya, Szpiro Adam A
Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington, Seattle, WA.
Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA.
Atmos Environ (1994). 2022 May 15;277. doi: 10.1016/j.atmosenv.2022.119069. Epub 2022 Mar 23.
Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal =0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.
现有的监管污染物监测网络依赖于少数位于中心位置的测量站点，这些站点特意设置在远离主要排放源的地方。虽然这些网络能提供区域空气质量总体趋势的信息，但它们往往无法完全捕捉社区内空气污染暴露的局部变异性。最近的技术进步降低了传感器成本，使得能够开展具有高空间分辨率的空气质量监测活动。2017年夏天，100×100黑碳（BC）监测网络在加利福尼亚州奥克兰西部15公里的社区内部署了100个低成本BC传感器，为期100天，生成了近乎连续的特定地点BC浓度时间序列，并将其汇总为每小时平均值。利用该数据集，我们采用了分层时空模型来准确预测整个奥克兰西部未设监测器地点的局部时空浓度模式（平均交叉验证每小时时间 =0.60）。通过我们的模型，我们识别出了与小规模地理特征和靠近本地源相关的空间变化的时间污染模式。在一次子采样分析中，我们证明，通过使用100×100 BC网络中约30%的数据，并辅以短期高密度监测活动，我们的建模方法能够获得精度几乎相当的精细尺度预测结果。
