Machine learning-based country-level annual air pollutants exploration using Sentinel-5P and Google Earth Engine

Abstract

Climatic condition is triggering human health emergencies and earth’s surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth’s health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human’s health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50–60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.

Unveiling Air Pollution in Crimean Mountain Rivers: Analysis of Sentinel-5 Satellite Images Using Google Earth Engine (GEE)

“…As emphasized by many researchers, this observed phenomenon is also typical of other regions of the world [64][65][66][67]. However, within the river basins under consideration, which have a relatively small area, the drop in emissions is not as pronounced as in many other countries [64][65][66][68][69][70][71]. When comparing the values obtained in our study for the year 2019 with the results obtained in [26] for the territory of North Macedonia, it can be stated that the NO 2 concentration values for the largest cities in North Macedonia (Skopje, Bitola, Prilep) exceed the values in the largest inhabited areas within the investigated territory by 2-3 times.…”

Modelling of Air Pollution Dispersion in the Utilization of Used Oil as a Fuel

“…Based on the emission distribution map above, the wind tends to blow from east to west, as seen from the red color on the map, most of which are in the western part. The red color indicates the area with the largest concentration of emissions [39]. Meanwhile, when the color turns to purple, it means that the area has the lowest emission concentration [40].…”

Large-Scale Flood Hazard Monitoring and Impact Assessment on Landscape: Representative Case Study in India

“…The images were then classified using supervised classification techniques in the GEE platform, using the spectral signatures of different land cover features. These features could be identified through a remote sensing-based (RS-based) dataset [36]. Land use and land cover (LULC) classification resulted in the desired number of classes [37].…”