Pollution Guardian

“一项可行性研究表明，一种经济实惠的空气质量警报解决方案将在车辆内使用，这将**改变现有呼吸系统疾病的消费者和其他希望保护肺部的人采取行动改善健康状况的能力**。空气污染目前在许多国家都是一个挑战，但对于那些既有呼吸系统疾病的人来说，这是一个紧迫的问题。在今天的英国，每年有540万哮喘患者，其中超过60%的人认为空气污染是哮喘发作的主要诱因，**30%的人需要在医院/非工作时间中心**进行计划外护理**。一些研究表明，汽车是暴露在空气污染中最严重的地方。我们相信，减少车内暴露于空气中会对整体健康结果产生重大影响。我们的目标是在哮喘组内外进行市场研究，寻找合适和可接受的“智能干预措施”，使使用者能够采取行动，避免最严重的空气污染。这项研究的结果将被用于可行性试验，与萨里大学合作开发一个系统，对系统进行表征，并验证其在现场的性能，然后与友好的用户进行试验，以获得现实世界的反馈。解决方案将包括一个可充电的电池供电的传感器，它将安装在车内，测量NO2和颗粒物污染的水平，并与智能手机上运行的蓝牙应用程序进行本地通信。数据将在智能手机和云中的后端系统之间传输，为智能手机应用程序提供处理、存储和“呈现”反馈。当污染水平变得很高，并通过视觉和听觉手段提示用户改变车辆的通风方式以减少他们的空气污染剂量时，将根据机器学习训练的算法发出本地警报。线下指导，如日志和污染地图，将根据来自市场研究的指导进行开发。

"A feasibility study for an affordable, air quality alerting solution for use within vehicles which will **transform the ability** of both consumers with existing respiratory conditions and others wishing to protect their lungs to take actions to improve their health outcomes.Air pollution is a challenge in many countries at the moment, but it is a burning issue for those with existing respiratory conditions. In the UK today, there are 5.4M asthmatics with over 60% identifying air pollution as the main trigger for an asthma attack and **30% requiring unplanned care** at hospital/out-of-hours centres each year.Several studies indicate the car as the place for the highest exposure to air pollution. We believe that mitigating the exposure within the car will have a significant impact on overall health outcomes.We aim to perform market research within and beyond the asthmatic group to identify suitable and acceptable ""smart interventions"" to enable the users to take action to avoid the worst air pollution. The results of the research will be fed into a feasibility exercise, working with the University of Surrey to develop a system, characterise the system and validate its performance in the field, before trialing with friendly users to gain real world feedback.The solution will consist of a re-chargeable, battery powered sensor which will be mounted in-car to measure the levels of NO2 and particulate pollution and communicate locally with a Bluetooth enabled app running on a smartphone. Data will be transferred between the smartphone and back-end system in the cloud to provide processing, storage and ""presentation"" feedback to the smartphone app. Local alerts will be raised according to a 'machine learning' trained algorithm, when pollution levels become high and the user prompted through visual and audible means to make ventilation changes in the vehicle to reduce their air pollution dosage. Offline guidance e.g. logs and pollution maps will be developed according to the guidance seen coming from market research."