Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value
Data fusion for air quality mapping using low-cost sensor observations: Feasibility and added-value
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DOI：
10.1016/j.envint.2020.105965
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发表时间：
2020-10-01
影响因子：
11.8
通讯作者：
Scimia, Romain
中科院分区：
文献类型：
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作者：
Gressent, Alicia;Malherbe, Laure;Scimia, Romain
在南特，低成本传感器安装在市中心，并部署在驾校汽车、救护车和服务车辆上，以测量PM 10浓度。这项工作的目的是使用大量的观测提供的传感器在城市规模的空气质量映射，以显示潜在的附加值相对于分散模型（ADMS-Urban）的计算。对原始传感器数据集进行预处理，以去除基于离群值检测技术的不可靠观测，并通过调整参考站的基础背景PM10浓度的估计每日变化来补偿测量漂移。然后，通过结合预处理的固定和移动的低成本传感器观测值以及ADMS-Urban输出的2016年平均值来进行数据融合。在数据融合中，将低成本传感器的测量不确定度和数据的离散性作为测量误差方差（VME）来考虑。空间插值以每小时的分辨率实现，结果将于2018年11月29日上午7点至下午7点呈现。每小时融合的地图显示不同的响应数据融合主要取决于传感器数据的可变性和传感器观测值和漂移之间的相关性。通过比较空气卢瓦尔河网络每个站点的估计浓度、参考观测值和小时模型输出的日平均值，研究了数据融合性能。结果表明，单独考虑模型意味着8%的偏差，而包括LCS观测值将偏差降低到2.5%。然而，与数据融合相关的浓度分布的特征在于比参考观测值和模型估计值具有更低的分散性。因此，融合平滑了PM10峰值。此外，通过将测量不确定度加倍或减小到基准站测量不确定度，研究了测量不确定度的影响。敏感性研究表明，性能是通过减少不确定性增加。这突出了准确估计设备的测量不确定性以确保相关空气质量映射的重要性。该方法的效率还受到传感器观测值与克里金法中用作外部漂移的模型之间的低相关性的限制，这可以通过LCS数据上的剩余偏差来解释。在这个问题上的努力可能会提高空间插值的性能。
In Nantes, low-cost sensors were installed in the city center and deployed on driving school cars, ambulances and service vehicles to measure PM10 concentrations. This work aims to use the large amount of observations provided by the sensors for air quality mapping at the urban scale in order to show the potential added-value with respect to the dispersion model (ADMS-Urban) calculations. A preprocessing is applied to the raw sensor dataset to remove the unreliable observations based on an outlier detection technique and to compensate for the measurement drift by adjusting the estimated daily variation of the underlying background PM10 concentrations with the reference stations. Then, data fusion is performed by combining the preprocessed fixed and mobile lowcost sensor observations and the 2016 annual average of the ADMS-Urban outputs. The measurement uncertainty related to the low-cost sensors and the dispersion of the data are considered in data fusion as the Variance of Measurement Error (VME). The spatial interpolation is achieved at hourly resolution and results are presented for November 29th, 2018 from 7 am to 7 pm. Hourly fused maps show disparate responses to data fusion mainly depending on the variability of the sensor data and the correlation between the sensor observations and the drift. The data fusion performance has been investigated by comparing the daily average of the estimated concentrations, the reference observations and the hourly model outputs at each station of the Air Pays de la Loire network. Results show that considering the model alone implies 8% bias whereas including the LCS observations reduces the bias to 2.5%. However, the concentration distributions related to the data fusion are characterized by a lower dispersion than the reference observations and the model estimation. Thus, the fusion smooths the PM10 peaks. In addition, the effect of the measurement uncertainty has been investigated by doubling it or reducing it to the reference station measurement uncertainty. The sensitivity study demonstrates that the performance is increasing by reducing the uncertainty. This highlights the importance to estimate accurately the measurement uncertainty of the devices to ensure relevant air quality mapping. The method efficiency is also quite limited by the low correlation between the sensor observations and the model used as external drift in the kriging that may be explained by the remaining bias on LCS data. Efforts on this issue might increase the performance of the spatial interpolation.