Estimating Daily NO2 Ground Level Concentrations Using Sentinel-5P and Ground Sensor Meteorological Measurements

Abstract

Environmental and health deterioration due to the increasing presence of air pollutants is a pressing topic for governments and organizations. Institutions such as the European Environment Agency have determined that more than 350,000 premature deaths can be attributed to atmospheric pollutants. The measurement of trace gas atmospheric concentrations is key for environmental agencies to fight against the decreased deterioration of air quality. NO2, which is one of the most harmful pollutants, has the potential to cause diseases such as Chronic Obstructive Pulmonary Disease (COPD). Unfortunately, not all countries have local atmospheric pollutant monitoring networks to perform ground measurements (especially Low- and Middle-Income Countries). Although some alternatives, such as satellite technologies, provide a good approximation for tropospheric NO2, these do not measure concentrations at the ground level. In this work, we aim to provide an alternative to ground sensor measurements. We used a combination of ground meteorological measurements with satellite Sentinel-5P observations to estimate ground NO2. For this task, we used state-of-the-art Machine Learning models, linear regression models, and feature selection algorithms. From the results obtained, we found that a Multi-layer Perceptron Regressor and Kriging in combination with a Random Forest feature selection algorithm achieved the lowest RMSE (2.89 µg/m3). This result, in comparison with the real data standard deviation and the models using only satellite data, represented an RMSE decrease of 55%. Future work will focus on replacing the use of meteorological ground sensors with only satellite-based data.

Estimation of Daily Ground Level Air Pollution in Italian Municipalities with Machine Learning Models Using Sentinel-5P and ERA5 Data

“…It is mentioning that the spatial resolution of their estimates is 1 km, which is higher than our model. Cedeno et al [33] reported a RMSE value of 6.37 µg/m 3 predicting the daily concentration of NO 2 in the area of Milan and using ARPA's control units as ground truth and Machine Learning models.…”

NO2 Concentration Estimation at Urban Ground Level by Integrating Sentinel 5P Data and ERA5 Using Machine Learning: The Milan (Italy) Case Study

“…This time period included the years 2019 and 2021 as a pre-and post-lockdown baseline. We also considered the dates from 1 January 2022 to 27 September 2022 to assess the performance of the ML model against previous works [15].…”

“…The ERA5 project was funded by the European Union and the ESA [25,26]. The main objective of replacing ARPA meteorological data that were used in previous works [15] is to provide a model that relies on datasets that can be used worldwide, especially in LMICs.…”

“…Previously, this algorithm was evaluated using satellite data (Sentinel-5P) and ground-based meteorological sensors. It was demonstrated that by using ground meteorological data, a Root Mean Square Error of 2.89 µg/m 3 was achieved [15]. Although meteorological ground stations are virtually available worldwide, some regions have lower station densities, predominantly in low-income countries.…”

Qgis and Open Data Cube Applications for Local Climate Zones Analysis Leveraging Prisma Hyperspectral Satellite Data

“…This area is particularly significant to be used as a first approach to the LCZ-ODC project, due to its extensive urbanization and industrialisation. Furthermore, the Metropolitan City of Milan is located in the upper Po River valley where territorial characteristics (including low wind circulation) are additional potential drivers for heat wave events (Cedeno Jimenez et al, 2023).…”