MULTISENSORY AND MACHINE LEARNING APPROACH TO IDENTIFY DRIVERS OF INDOOR AIR QUALITY

据估计，空气污染每年可导致全球800万人死亡。因此，应对空气污染及其对人类健康的影响仍然是一项关键的全球挑战。目前，关于室内空气质量及其影响因素还有许多尚未解决的问题。特别是，有证据表明，居住者的行为和活动对室内空气质量有很大的影响。然而，迄今为止，收集可靠的证据一直是一个挑战。随着机器学习和分析方法的进步，小型化、分立和低成本传感器的进步为揭示这些行为提供了新的希望。使用多传感器方法（例如多种空气污染物、温度、湿度、光、声音、能量）和机器学习技术，假设可以从长期、非侵入性监测中成功识别许多关键活动。例如，颗粒物、二氧化氮和湿度的同时峰值可能表明特定的烹饪活动，而噪音水平和能量数据进一步表明抽油烟机的使用。本博士的总体目标是解锁这些特征，以更好地了解居住者活动和行为对室内空气质量的影响。这将为健康和低能耗的建筑设计策略以及改进的运营策略和通风实践提供信息。更具体地说，博士的目标是：描述关键活动（烹饪，清洁等）的基线特征。开发数据收集的多传感器监测方法。识别和测试适当的机器学习技术。验证该方法并部署在更广泛的领域研究中。

Air pollution can be attributed to an estimated 8 million global deaths per year. Tacking air pollution and its impact upon human health therefore remains a key global challenge. At present, there are many unresolved questions regarding indoor air quality and the factors that influence it. In particular, there is emerging evidence that occupant behaviours and activities have a strong influence upon indoor air quality. However, to date it has been challenging to gather robust evidence.The advancement of miniaturised, discrete and low-cost sensors alongside advancements in machine learning and analytical methods offers new promise in uncovering these behaviours. Using a multi-sensor approach (e.g. multiple air pollutants, temperature, humidity, light, sound, energy) and machine learning techniques, it is hypothesized that many key activities may be successfully identified from long-term, non-intrusive monitoring. For example, simultaneous peaks in particulate matter, nitrogen dioxide and humidity might indicate a particular cooking activity, with noise levels and energy data further indicating the use of an extractor hood.The overall aim of this PhD would be to unlock these signatures to better understand the influence of occupant activities and behaviours upon indoor air quality. This would inform strategies for healthy and low-energy building design as well as improved operational strategies and ventilation practices. More specifically the PhD would aim to:Characterise the baseline signatures of key activities (cooking, cleaning, etc.).Develop multisensory monitoring approach for data collection.Identify and test appropriate machine learning techniques.Validate this approach and deploy in wider field studies.