低成本物联网室内空气质量监测系统:系统综述

João Peixe, Gonçalo Marques
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摘要

与室外空气质量相比,室内空气质量(IAQ)是一个难以控制的严峻挑战。糟糕的室内空气质量与严重的健康并发症有关,如呼吸系统问题、心脏病和癌症。许多人每天大部分时间都在建筑物内度过,却没有空气质量监测系统。因此,当空间中的污染物含量高于建议值时,居住者并不知情,从而导致环境饱和,损害人们的健康。要解决这个问题,可以利用物联网(IoT)技术开发监测系统,使最终用户在存储和处理数据以及获取信息方面有更多的可能性,从而协助有关室内空气污染问题的决策过程。实时数据可以与默认值进行比较,提醒用户注意这种情况,并建议采取降低空气污染物浓度的行动。目前已经有多种基于物联网技术的解决方案,其中许多使用了低成本传感器。本系统综述将对这些方案进行分析。此外,COVID-19 大流行还指出了 IAQ 监测对评估污染风险的重要性。本系统分析了监测系统中使用的微控制器、室内空气质量参数、传感器、数据存储和可视化方法。结果显示,大多数研究将数据存储在云系统中,并使用网络平台进行数据咨询。然而,传感器校准和高效能源消耗仍然是存在的挑战。
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Low-cost IoT-enabled indoor air quality monitoring systems: A systematic review
Indoor air quality (IAQ) is a critical challenge much less controlled in comparison with outdoor air quality. Bad IAQ is related to significant health complications such as respiratory problems, heart disease, and cancer. Many people spend most of their days inside buildings and don’t have air quality monitoring systems. Therefore, the occupants don’t know when the space has a higher quantity of pollutants than recommended, saturating the environment, and compromising people’s health. This is a problem that can be addressed by using Internet of Things (IoT) technologies to develop monitoring systems that allow a greater number of possibilities regarding the storage and processing of data and access to information by the end user, assisting the decision-making process regarding the indoor air pollution problem. Real-time data can be compared to default values, alerting the user of that situation, and suggesting an action to decrease the air pollutants concentration. There already are multiple solutions involving IoT-based technologies, many of them using low-cost sensors. Those are analyzed in this systematic review. Furthermore, the COVID-19 pandemic pointed out the importance of IAQ monitoring to evaluate the risk of contamination. The microcontrollers, IAQ parameters, sensors, data storage and visualization methods used in monitoring systems have been analyzed. The results show that most of the studies store data in Cloud systems and use Web platforms for data consulting. However, sensor calibration and efficient energy consumption are challenges that still exist.
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