Hassani Amirhossein, Schneider Philipp, Vogt Matthias, Castell Núria
The Climate and Environmental Research Institute NILU, P.O. Box 100, Kjeller 2027, Norway.
Environ Sci Technol. 2023 Oct 10;57(40):15162-15172. doi: 10.1021/acs.est.3c03661. Epub 2023 Sep 27.
Conventional monitoring systems for air quality, such as reference stations, provide reliable pollution data in urban settings but only at relatively low spatial density. This study explores the potential of low-cost sensor systems (LCSs) deployed at homes of residents to enhance the monitoring of urban air pollution caused by residential wood burning. We established a network of 28 Airly (Airly-GSM-1, SP. Z o.o., Poland) LCSs in Kristiansand, Norway, over two winters (2021-2022). To assess performance, a gravimetric Kleinfiltergerät measured the fine particle mass concentration (PM) in the garden of one participant's house for 4 weeks. Results showed a sensor-to-reference correlation equal to 0.86 for raw PM measurements at daily resolution (bias/RMSE: 9.45/11.65 μg m). High-resolution air quality maps at a 100 m resolution were produced by combining the output of an air quality model (uEMEP) using data assimilation techniques with the network data that were corrected and calibrated by using a proposed five-step network data processing scheme. Leave-one-out cross-validation demonstrated that data assimilation reduced the model's RMSE, MAE, and bias by 44-56, 38-48, and 41-52%, respectively.
传统的空气质量监测系统，如参考站，在城市环境中能提供可靠的污染数据，但空间密度相对较低。本研究探讨了部署在居民家中的低成本传感器系统（LCSs）在加强对住宅燃木造成的城市空气污染监测方面的潜力。我们在挪威克里斯蒂安桑的两个冬季（2021 - 2022年）建立了一个由28个Airly（Airly - GSM - 1，波兰SP. Z o.o.公司）LCSs组成的网络。为了评估性能，一台重量法Kleinfiltergerät在一名参与者房屋花园中测量细颗粒物质量浓度（PM），为期4周。结果显示，每日分辨率下原始PM测量值的传感器与参考值相关性为0.86（偏差/均方根误差：9.45/11.65 μg/m）。通过使用数据同化技术将空气质量模型（uEMEP）的输出与采用所提出的五步网络数据处理方案进行校正和校准后的网络数据相结合，生成了分辨率为100米的高分辨率空气质量地图。留一法交叉验证表明，数据同化分别将模型的均方根误差、平均绝对误差和偏差降低了44 - 56%、38 - 48%和41 - 52%。
