The air quality monitoring system is the most prominent tool for monitoring air pollution levels, especially in areas where forest fires often occur. The South Sumatra Province of Indonesia is one of the greatest contributors to haze events in Indonesia due to peatlands fires. It does not sufficiently possess a ground monitoring system to cover rural areas, and thus, delayed actions can result in severe air pollution within this region. Therefore, the aim of this current study is to analyze the distribution and classification of PM2.5 observed from 2019 to 2021 within the South Sumatra Province, Indonesia. The acquisition of PM2.5 data was from the Merra-2 Satellite with a spatial resolution of 0.5˚ × 0.625˚ and an hourly interval. The hierarchical cluster analysis (HCA) was applied in this study for the clustering method. The result of the study revealed that the daily mean of PM2.5 levels varied from 5.9±0.01 to 21.3±0.03 μg/m3. The study area was classified into three classes: high pollution areas (HPA), moderate pollution areas (MPA) and low pollution areas (LPA), based on the HCA method. The average level of PM2.5 observed in HPA was notably higher, at 16.8±0.02 μg/m3, followed by MPA and LPA. Furthermore, this study indicated that the highest level of PM2.5 was found during 2019, with a severe haze event in the study area due to the intensive burning of forests, bush and peatlands. As a whole, the output of this study can be used by authorities for air quality management due to forest fire events in a certain area.
The emergence of the Internet of Things (IoT) has brought a revolution in global communication network technology. It has acquired many day-to-day applications in healthcare, education, agriculture, etc. In addition, IoT has also had a significant impact in the field of environmental monitoring.The significant factors in a healthy environment are air quality, water pollution, and waste management, where the world's population can live securely. Monitoring is necessary for us to achieve global sustainability. As monitoring technology has advanced in recent years, environmental monitoring systems have evolved from essential remote monitoring to an advanced environment monitoring (AEM) system, incorporating Internet of Things (IoT) technology and sophisticated sensor modules.The present manuscript aims to accomplish a critical review of noteworthy contributions and research studies about environmental monitoring systems, which involve monitoring air quality, water quality, and waste management.The rapid growth of the world's population and the exhaustion of natural resources, coupled with the increasing unpredictability of environmental conditions, lead to significant concerns about worldwide food security, global warming, water pollution, and waste overflowing. Automating tasks in the building environment, based on the Internet of Things (IoT) application, is meant to eliminate problems with the traditional approach. This study aims to examine and evaluate numerous studies involving monitoring air, water, waste, and overall environmental pollution, as well as their effect on the environment. This article categorizes studies based on their research purposes, techniques, and findings. This paper examines advanced environmental monitoring systems through sensor technology, IoT, and machine learning.