Faculty of Civil and Environmental Engineering, Technion IIT, Haifa, 32000, Israel.
Faculty of Civil and Environmental Engineering, Technion IIT, Haifa, 32000, Israel.
Environ Pollut. 2021 Feb 15;271:116334. doi: 10.1016/j.envpol.2020.116334. Epub 2020 Dec 16.
Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.
土地利用回归建模是评估环境污染物暴露的常用方法，但它的时间分辨率非常粗糙。无线分布式传感器网络（WDSN）是一种很有前途的技术，可以提供极高的时空污染物模式，但已知存在一些限制，使其数据可靠性受到质疑。本研究探讨了融合这两种方法的数据并获得可用于暴露评估的高时空分辨率产品的优势。我们通过在郊区尺度上估计二氧化氮（NO）浓度来演示这种方法，研究区域受 WDSN 节点部署的限制。具体来说，通过将基于静止交通的土地利用回归（LUR）模型与低成本传感器节点阵列进行的观测（15 分钟采样频率）相结合，获得了小时分辨率的融合数据估计，传感器的读数映射到整个研究区域。通过使用模糊逻辑方法将两个独立的信息产品合并来进行数据融合。将融合产品的性能与位于研究区域内的四个空气质量监测（AQM）站的参考小时观测值进行了比较，AQM 数据未用于任何基础信息层的开发。融合数据产品与 AQM 记录之间的平均每小时 RMSE 为 9.3ppb，小于两个基础产品的 RMSE（LUR：14.87ppb，WDSN：10.45ppb）。融合产品的归一化 Moran's I 表明，与基础产品相比，数据融合产品揭示了更现实的空间模式。与 WDSN 记录和 AQM 站数据的插值相比，融合的 NO 浓度产品显示出相当大的空间变异性，在研究期间的 74%有显著的非随机模式。
