Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA
Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA
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DOI：
10.1016/j.envres.2019.108810
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发表时间：
2020-01-01
影响因子：
8.3
通讯作者：
Liu, Yang
中科院分区：
文献类型：
--
作者：
Bi, Jianzhao;Stowell, Jennifer;Liu, Yang
监管监测网络往往过于稀疏，无法支持社区规模的PM2.5暴露评估，而新兴的低成本传感器有可能填补这一空白。迄今为止，利用低成本传感器测量来提高基于统计模型的高时空分辨率PM2.5预测的研究（如果有的话）有限。加州的帝国县是一个典型的地区，它拥有稀疏的空气质量系统（AQS）监测仪和一个由社区运营的低成本网络，名为“识别影响社区的违规行为”（IVAN）。本研究旨在评估IVAN测量对PM2.5预测质量的贡献。我们采用随机森林算法，使用3个不同的PM2.5数据集（AQS-only、IVAN-only和AQS/IVAN组合）在1公里空间分辨率下估计PM2.5的日浓度。结果表明，低成本传感器测量的整合是显著提高PM2.5预测质量的有效途径，交叉验证（CV） R-2提高了0.2左右。IVAN测量结果还有助于提高排放源相关协变量的重要性，并使PM2.5的空间格局更加合理。校准后的IVAN测量中剩余的不确定性仍可能导致预测模型出现明显的异常值，因此需要更有效的校准或积分方法来减轻其负面影响。
Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R-2 by similar to 0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.