Insights from Application of a Hierarchical Spatio-Temporal Model to an Intensive Urban Black Carbon Monitoring Dataset.
Insights from Application of a Hierarchical Spatio-Temporal Model to an Intensive Urban Black Carbon Monitoring Dataset.
复制标题
分层时空模型在城市黑碳密集监测数据集中应用的见解。
DOI：
10.1016/j.atmosenv.2022.119069
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Szpiro,AdamA
中科院分区：
文献类型：
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作者：
Wai,TravisHee;Apte,JoshuaS;Harris,MariaH;Kirchstetter,ThomasW;Portier,ChristopherJ;Preble,ChelseaV;Roy,Ananya;Szpiro,AdamA
现有的管制污染物监测网络依赖于少数集中的测量点，这些测量点有目的地远离主要排放源。虽然这些网络提供了区域一般空气质量趋势的信息，但往往不能充分捕捉社区内空气污染暴露的局部变化。最近的技术进步降低了传感器的成本，使空气质量监测活动具有高空间分辨率。2017年夏天，100× 100黑碳（BC）监测网络在加利福尼亚州西奥克兰15平方公里的社区部署了100个低成本BC传感器，为期100天，产生了几乎连续的特定地点BC浓度时间序列，我们将其汇总为1小时平均值。利用这个数据集，我们采用了一个分层的时空模型，以准确地预测当地的时空浓度模式在整个西奥克兰，在没有监视器的位置（平均交叉验证每小时的时间R 2= 0.60）。使用我们的模型，我们确定了空间变化的时间污染模式与小规模的地理特征和接近当地的来源。在子采样分析中，我们证明了通过使用100× 100 BC网络的约30%辅以短期高密度活动，可以使用我们的建模方法获得几乎相当精度的精细尺度预测。
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 2 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 1-h 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 R 2= 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.