# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 1 Introduction  
Systems for environmental monitoring play a crucial role in assessing and understanding the state of our natural surroundings. These systems employ a combination of sensors, data collection instruments, and advanced technologies to monitor various environmentalparameters such as air quality, water quality, noise levels, and biodiversity [1]. By continuously gathering and analyzing data, these systems provide valuable insights into the health of ecosystems, the impact of human activities, and the effectiveness of conservation efforts. Environmental monitoring systems enable scientists, policymakers, and stakeholders to make informed decisions, develop mitigation strategies, and track changes over time. With the advancements in technology, including remote sensing, Internet of Things (IoT), and machine learning, these systems are becoming increasingly sophisticated, allowing for more accurate and timely environmental assessments. Air pollutants, such as 2.5 and 10 (\(\mu\)m) particulate matter (PM10, PM2.5), sulphur dioxide (SO\({}_{2}\)), carbon monoxide (CO), ground-level ozone (O\({}_{3}\)), hydrocarbons (HC), volatile organic compounds (VOC), and nitrogen oxides (NOx) [2], contribute to the presence of particulate matter (PM) in the air. Various methods are employed to monitor PM levels, including ground measurements, such as comprehensive emission inventory modeling to determine emission source distribution [3] or the gravimetric method that calculates daily particulate pollutant levels based on handheld devices measuring PM10 and PM2.5 along main roads in urban areas [4]. In 1975, [PERSON] discovered that satellite data can be used to determine atmospheric aerosol optical depth (AOD) values by analyzing the scattering and absorption of shortwave radiation in the atmosphere [5]. As a result, optical satellite imagery in the visible, near-infrared, and short-wave infrared portions of the electromagnetic spectrum has emerged as a promising data source for estimating the mass concentration of air pollutants. To estimate PM10 and PM2.5 levels, several methods have been employed based on the relationship between measurements at ground stations by specialized equipment and AOD values extracted by complex computational models from images captured by satellites. The used computation models include regression with two or more variables, machine learning, and the surface aerosol concentration approach. Up to now, regression models have shown high efficiency in estimating PM concentrations from daily AOD at a 1000 \(\times\) 1000 m spatial resolution at local and regional levels. [PERSON] et al., in 2004, determined the regional sources of air pollution events, the intensity of the events, and their motion using imagery and statistical analysis [6]. AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite were used in combination with ground PM measurements from 1 April to 30 September 2002 in the United States to determine their correlation. As a result, correlations of MODIS AOD with ground PM were better in the eastern and Midwest portion of the United States, but poorer in the western. In 2014, [PERSON] et al. analyzed seasonal variations in aerosol concentration in Bangladesh during the period 2002-2011, using MODIS AOD data products [7]. Linear regression models were used to determine seasonal trends in AOD. The results indicated that seasonal variations show maximum AOD values during the pre-monsoon season while minimum AOD during post-monsoon, and a decadal decreasing trend in AOD is found during monsoon season while all other seasons show increasing trends. The authors used a hybrid single particle Lagrangian integrated trajectory (HYSPLIT) model, a computer model that is used to compute air parcel trajectories to determine how far and in what direction a parcel of air will travel, to generate a backward trajectory in order to identify the origins of air pollutants.