Air pollution is a major concern worldwide due to its significant impacts on the global environment and human health. The conventional instruments used by the air quality monitoring stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. Although energy-efficient, wireless sensor network with the high spatio-temporal resolution is one of the potential solutions, real-time remote monitoring of all significant air quality parameters with low power consumption is challenging. To address this challenge, we propose internet of things-enabled low power environment monitoring system for real-time monitoring of ten significant air quality parameters. Moreover, the proposed system enables remote monitoring and storage of data for future analysis. Unlike earlier research work, further expansion of the proposed system is easily possible, as the proposed Wireless Sensor Node (WSN) can interface a higher number of sensors with the same number of interfacing pins. We did an in-depth analysis through calibration, experiments, and deployment which confirms the power efficiency, flexibility, reliability and accuracy of the proposed system. Results illustrate the low power consumption of 25.67mW, data transmission reliability of 97.4%, and battery life of approximately 31 months for a sampling time of 60 min. The study of the correlation between Particulate Matter 2.5 (PM2.5) and other pollutants is performed using Central Pollution Control Board data of 41 months. The initial study related to correlation is performed for the future work of developing a prediction model of PM2.5 using highly correlated pollutants. The future approach for developing a prediction model in the form of analytical equations with the help of artificial neural network is demonstrated. This approach can be implemented using the proposed WSN or low-cost processing tool for evaluating PM2.5 from precursor gases. Therefore, this approach can be one of the promising approaches in the future for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.

空气污染因其对全球环境和人类健康的重大影响而成为全球主要关注的问题。空气质量监测站使用的常规仪器昂贵，笨重，费时且耗电。此外，由于有限的数据可用性和不可扩展性，这些站点无法实时提供高空间和时间分辨率。尽管具有高时空分辨率的高能效无线传感器网络是潜在的解决方案之一，但是以低功耗实时监控所有重要的空气质量参数仍然是一项挑战。为了应对这一挑战，我们建议启用物联网的低功耗环境监测系统，以实时监测十个重要的空气质量参数。此外，所建议的系统可以实现远程监视和数据存储以供将来分析。与早期的研究工作不同，提议的系统可以轻松扩展，因为提议的无线传感器节点（WSN）可以使用相同数量的接口引脚连接更多传感器。我们通过校准，实验和部署进行了深入分析，确认了所提议系统的电源效率，灵活性，可靠性和准确性。结果说明低功耗25.67 部署可以确认所提议系统的电源效率，灵活性，可靠性和准确性。结果说明低功耗25.67 部署可以确认所提议系统的电源效率，灵活性，可靠性和准确性。结果说明低功耗25.67mW，数据传输可靠性为97.4％，电池寿命约为31个月，采样时间为60分钟。使用中央污染控制委员会41个月的数据对颗粒物2.5（PM2.5）与其他污染物之间的相关性进行了研究。进行了与相关性相关的初步研究，以供将来使用高度相关的污染物开发PM2.5预测模型时使用。演示了在人工神经网络的帮助下以解析方程形式开发预测模型的未来方法。可以使用提议的WSN或低成本处理工具来评估前体气体中的PM2.5来实施此方法。因此，这种方法可以成为将来在无需耗​​电的气体传感器和笨重的分析仪的情况下监测PM2.5的有前途的方法之一。