Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH₃, CO, NO₂, CH₄, CO₂, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

中文翻译：

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基于物联网（IoT）的室内空气质量感测和预测分析-COVID-19观点

室内空气质量通常涵盖建筑物和公共设施内可能影响个人心理和呼吸健康的环境条件。在COVID-19爆发之前，室内空气质量监测并不是诸如购物中心，医院，银行，饭店，教育机构等公共设施的重点领域。但是，这种病毒的迅速传播及其带来的不利影响使室内空气质量成为人们关注的焦点。与室外空气相反，室内空气不断循环利用，导致其捕获并积聚污染物，这可能会促进病毒的传播。商业上有几种监测解决方案，典型的系统使用气体和颗粒传感器监测空气质量。将这些传感器读数与众所周知的阈值进行比较，然后在违反阈值时生成警报。但是，这些系统无法预测未来的空气质量，这对于及时采取先发制人的行为至关重要，尤其是对于COVID-19实际和潜在患者以及患有急性肺部疾病和其他健康问题的人。在这方面，我们提出了一种基于最新物联网（IoT）传感器和机器学习功能的室内空气质量监测和预测解决方案，为测量众多室内污染物提供了平台。为此，一个由多个传感器组成的物联网节点可处理8种污染物，包括NH 这些系统无法预测未来情况的空气质量，这对于及时采取先发制人的行为至关重要，特别是对于COVID-19实际和潜在患者以及患有急性肺部疾病和其他健康问题的人。在这方面，我们基于最新的物联网（IoT）传感器和机器学习功能，提出了一种室内空气质量监测和预测解决方案，为测量众多室内污染物提供了一个平台。为此，一个由数个传感器组成的物联网节点可处理8种污染物，包括NH 这些系统无法预测未来情况的空气质量，这对于及时采取先发制人的行为至关重要，特别是对于COVID-19实际和潜在患者以及患有急性肺部疾病和其他健康问题的人。在这方面，我们基于最新的物联网（IoT）传感器和机器学习功能，提出了一种室内空气质量监测和预测解决方案，为测量众多室内污染物提供了一个平台。为此，一个由数个传感器组成的物联网节点可处理8种污染物，包括NH 特别是对于COVID-19实际和潜在患者以及患有急性肺部疾病和其他健康问题的人。在这方面，我们提出了一种基于最新的物联网（IoT）传感器和机器学习功能的室内空气质量监测和预测解决方案，为测量众多室内污染物提供了一个平台。为此，一个由多个传感器组成的物联网节点可处理8种污染物，包括NH 特别是对于COVID-19实际和潜在患者以及患有急性肺部疾病和其他健康问题的人。在这方面，我们提出了一种基于最新的物联网（IoT）传感器和机器学习功能的室内空气质量监测和预测解决方案，为测量众多室内污染物提供了一个平台。为此，一个由多个传感器组成的物联网节点可处理8种污染物，包括NH₃，CO，NO ₂，CH ₄，CO ₂，开发了PM 2.5以及环境温度和空气湿度。为了进行概念验证和研究目的，将IoT节点部署在研究实验室内部以获取室内空气数据。拟议的系统具有通过GSM / WiFi技术向网络门户和移动应用实时报告空气状况的能力，并在检测到空气质量异常后生成警报。为了对室内空气质量进行分类，已将几种机器学习算法应用于记录的数据，其中神经网络（NN）模型以99.1％的准确率胜过所有其他算法。为了预测每种空气污染物的浓度，然后预测室内环境的整体质量，应用了长期和短期记忆（LSTM）模型。该模型在预测空气污染物的浓度以及整体空气质量方面显示出令人鼓舞的结果，其准确度为99.37％，精确度为99％，召回率为98％，F1得分为99％。所提出的解决方案具有几个优点，包括远程监视，可伸缩性的简化，周围环境的实时状态以及便携式硬件等等。