# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 1 Introduction  
Man-made emissions are the main cause of global climate change and exert myriad impacts on environmental ecosystems and human well-being, including greater frequency and magnitude of disruptive climate events, such as exceptional drought, hurricanes, and deadly flash flooding. Action must be taken to stop the trends in the next decades to slow down the increase of global temperature which has already increased by 1.5 Celsius, on average, above pre-industrial levels, a rate which has not been seen in the past 2000 years. Many groups and world governments are taking steps to reduce emissions through precautionary short-term and long-term goals to fight the concerning change. U.S. EPA (Environmental Protection Agency) reports that over a quarter of the total emissions of both pollutants and greenhouse gases in the United States come from the energy sector and the majority is from burning coal, fossil fuels, and natural gas. Power demand varies closely with the weather (especially extreme warmth and cold), which changes significantly over daily timescales. An essential step to control emissions is to accurately monitor them on a timely basis to prevent further severe emission releases to the atmosphere. Currently, EPA requires coal-fired power plant facilities to install real-time sensors to their emission outlets, such as chimneys, and to send the data back regularly. The data is accurate and continuous and serves as the sole source of information for policymaking and emissions control. However, ground observation is expensive to install and maintain and only power plants in the United States and some western countries allow open access to their high-resolution dataso far. To manage global emissions as a whole, we should have the ability to monitor all the power plants around the world at low cost and in an environment-friendly manner. EPA currently regulates nitrogen dioxide (NO\({}_{2}\)), the most important member in the NOx family in the troposphere. NO\({}_{2}\) can react and create ozone (O\({}_{3}\)). EPA defined national ambient air quality standards (NAAQS) to regulate tropospheric NO\({}_{2}\) levels for the sake of public health. The recommended safe threshold of annual arithmetic mean concentration is 0.053 parts per million (ppm) (100 micrograms per cubic meter) [1]. Power plants are one of the main sources of human-induced NO\({}_{2}\) due to their coal/gas combustion. One of three chemical mechanisms results in the formation of NOx: (1) \"thermal\" NOx, which accounts for 75% of NOx, is produced by oxidation of molecular nitrogen by combustion in air; (2) \"fuel\" NOx, which accounts for 20% of NOx, is produced by oxidation of chemically bound nitrogen in fuel, and (3) \"prompt\" NOx, which accounts for 5% of NOx, is produced by the reaction between molecular nitrogen and hydrocarbon radicals [2]. NO\({}_{2}\) is a trace gas generated by both anthropogenic production and the environment. Sunlight can cause chain chemistry reactions to produce nitric oxide and ozone in the troposphere. Thus, NO\({}_{2}\) is often used to indicate the concentration of the larger family of nitrogen oxides (NOOx), other members of which are acidic and harmful (e.g., NO). NOx emissions lead to poor air quality events, such as smog, haze, and even acid rain, and cause severe respiratory problems. The emissions can also result in overpopulation of algae and contamination of water bodies and soil. Although many power plants in the U.S. have adopted techniques to absorb and reduce NOx and CO from their emissions, they are still a major concern on a large spatial scale with significant accumulation after long-term operation. Furthermore, even after 30 years of policies and regulations to improve air quality by reducing emissions, some U.S. power plants still do not control emissions of pollutants, even though control technology is widely available [3]. Satellite remote sensing has been developing rapidly in the past few decades.