An ultra-low-cost integrated paper-based sensing and electronics platform

传统的空气质量监测方法涉及固定监测站网络，这需要大量投资。例如，自动城乡网络（AURN）在英国有175个站点。这些监测站通常位于远离路边和主要交通挤塞地区的地方，导致局部地区的污染物浓度很高。这些站点分布在城市周围，可以提供精确的时间序列数据，但空间细节有限。这使得从测量数据中确定空气质量的空间分布非常具有挑战性，因此使用建模方法来计算污染物的分布。来自大型传感器网络的实时连续测量将改变确定污染物分布和空气质量水平的过程，支持这些建模方法，甚至可能使它们变得多余。大量部署低成本传感器将能够探测污染热点，并将为实时污染缓解战略提供信息。黑熊正在开发的技术将使这一切成为可能。

Traditional approaches to air quality monitoring involve networks of fixed measurement stations, which requires significant investment. For example, the Automatic Urban and Rural Network (AURN) has 175 sites across the UK. These monitoring stations are often located away from roadsides and major traffic congestion areas, which result in localised high concentrations of pollutants. Sparsely distributed around a city, these stations can provide accurate time-series data, but with limited spatial detail.This makes it highly challenging to determine the spatial distribution of air quality from the measured data, and so modelling approaches are used to calculate distribution of pollutants. Real-time continuous measurements from a large sensor networks would transform the process of determining distributions of pollutants and air quality levels, supporting these modelling approaches, and potentially even making them redundant.Deployment of low-cost sensors in significant numbers will enable detection of pollution hotspots, and will inform real-time pollution mitigation strategies. The technology that BlakBear is developing will enable precisely this.