Air quality sensors for mobile air monitoring and real-time air pollution mapping

用于移动空气监测和实时空气污染测绘的空气质量传感器

• 批准号: RTI-2019-00807
• 负责人: Hatzopoulou, Marianne
• 金额: $ 9.89万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653217
• 关键词: Air; quality; sensors; mobile; air

In urban areas, traffic is the primary air pollution source and traffic-related air pollution (TRAP) has been linked with various forms of cancer, cardiovascular, and respiratory health effects, both in Canada and elsewhere. Urban air pollution exhibits substantial seasonal and spatial variability that is important to capture in a) health effects studies, b) studies of the impacts of transformative transportation systems (automated vehicles, electric vehicles, ride hailing services) on urban air quality, and c) the development of exposure management tools geared at vulnerable individuals. The question of air pollution monitoring and its value for the development of exposure surfaces at the level of large urban areas is an active field of research. To date, very few efforts have successfully demonstrated the ability to use real-time monitoring data for the generation of spatial predictions of air quality, also in real-time. These efforts are often limited by the inadequacy of the monitoring equipment used. ******While it is important to acknowledge that the recent development of low-cost air pollution sensors has opened up new ways of collecting dense air pollution data, most sensors are limited in terms of their ability to accurately capture the spatio-temporal variability of urban air pollution. Most commercial gas and particulate sensors have presented researchers with significant challenges for meaningful data collection. In fact, the quality of the sensor itself is as important as the “intelligence” and data processing algorithms built around it.******The proposed sensors will overcome many of the limitations of existing commercial sensors. These units integrate capability for self-calibration and allow the user to access various diagnostics remotely, ensuring reliable measurements are taken while the system is in the field. They can also withstand vibration and are therefore particularly suited for mobile measurements, which are critical for achieving high spatial coverage. If successful, this grant will enable the purchase of 15 units, promoting the development of a range of studies that have not yet been feasible with existing sensors, and allowing my research group to design and implement unique air pollution monitoring campaigns geared towards developing spatio-temporal predictions of urban air quality at unparalleled levels of accuracy. **

在城市地区，交通是主要的空气污染源，与交通有关的空气污染（TRAP）与加拿大和其他地方的各种癌症，心血管和呼吸系统健康影响有关。城市空气污染具有显著的季节性和空间变异性，这对于以下研究非常重要：a）健康影响研究，B）变革性交通系统（自动驾驶汽车、电动汽车、海陵服务）对城市空气质量影响的研究，以及c）开发针对弱势群体的暴露管理工具。空气污染监测问题及其对大城市地区暴露面发展的价值是一个活跃的研究领域。到目前为止，很少有努力成功地证明了使用实时监测数据生成空气质量空间预测的能力，也是实时的。这些努力往往因所用监测设备不足而受到限制。** 虽然必须承认，低成本空气污染传感器的最新发展开辟了收集密集空气污染数据的新途径，但大多数传感器在准确捕捉城市空气污染时空变化方面的能力有限。大多数商用气体和颗粒传感器为研究人员提供了有意义的数据收集的重大挑战。事实上，传感器本身的质量与围绕它构建的“智能”和数据处理算法一样重要。所提出的传感器将克服现有商业传感器的许多局限性。这些单元集成了自校准功能，并允许用户远程访问各种诊断，确保系统在现场时进行可靠的测量。它们还可以承受振动，因此特别适合移动的测量，这对于实现高空间覆盖至关重要。如果成功的话，这笔赠款将使购买15个单位，促进一系列研究的发展，这些研究还没有可行的与现有的传感器，并允许我的研究小组设计和实施独特的空气污染监测活动，面向开发时空预测城市空气质量在无与伦比的准确性水平。**