Development of data correction model for low-cost particle sensors under ambient conditions

环境条件下低成本颗粒传感器数据校正模型的开发

• 批准号: 521823-2017
• 负责人: Du, Ke
• 金额: $ 1.82万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=640583
• 关键词: Development; data; correction; model; low

The advancement of the Internet of Things (IoT) technology makes it possible for mapping air qualityvariations in large spatial scale regions. Deployment of such technology requires large number low-costsensors. To monitor PM2.5 (suspended aerosol particles with an aerodynamic diameter no more than 2.5micrometers) distribution in a large area such as the urban area of a city, hundreds of low-cost PM2.5 sensorsare required. Such sensors mostly are based on light scattering technology, and the readings were calibratedwith standard aerosols by their manufacture. The calibration curves are only valid for certain aerosols undercertain conditions (e.g., humidity, temperature, etc.), which makes their readings questionable when comparingwith the results from mass or beta-attenuation -based technologies used by environmental monitoring agencies.Therefore, there is need to calibration the low-cost PM2.5 sensors against the certified technologies byenvironmental authorities under the real-world conditions so that the sensor network could provide acceptablemonitoring data for air quality assessment.SensorUp is a leading sensor network provider based on IoT technology that has deployed an air qualitymonitoring network consisting of 200+ PM2.5 sensors to cover 75% of the area of metropolitan Calgary. Thecompany is interested in an in-depth, quantitative study of the impacts of aerosols properties andmeteorological factors on the readings of their sensors and developing a method to calibrate the sensors so thatthey can provide comparable reading with the certified instrument being used by Calgary Regional AirshedZone (CRAZ). The outcome of this study will not only improve SensorUp's capacity in PM2.5 monitoring, butthe method is also transferable to other instrument/pollutant providing environmental monitoring agencies andservice providers a low-cost option for conducting high-resolution monitoring.

物联网（IoT）技术的发展使得在大空间尺度区域绘制空气质量变化成为可能。这种技术的部署需要大量的低成本传感器。为了监测PM2.5（空气动力学直径不超过2.5微米的悬浮气溶胶颗粒）在大面积（如城市的市区）的分布，需要数百个低成本的PM2.5传感器。此类传感器大多基于光散射技术，读数由其制造商用标准气溶胶校准。校准曲线仅对某些条件下的某些气溶胶有效（例如，湿度、温度等），这使得他们的读数与环境监测机构使用的质量或β衰减技术的结果相比时有问题。因此，需要根据真实的环境当局的认证技术校准低成本PM2.5传感器，世界条件，使传感器网络可以提供可接受的监测数据的空气质量评估。SensorUp是一个领先的传感器网络供应商，基于物联网技术部署了一个由200多个PM2.5传感器组成的空气质量监测网络，覆盖卡尔加里大都市75%的区域。该公司感兴趣的是深入的，定量研究气溶胶属性和气象因素对传感器读数的影响，并开发一种方法来校准传感器，使其能够提供与卡尔加里地区AirshedZone（CRAZ）使用的认证仪器相当的阅读。这项研究的结果不仅将提高SensorUp在PM2.5监测方面的能力，而且该方法也可以转移到其他仪器/污染物提供环境监测机构和服务提供商，为进行高分辨率监测提供低成本的选择。