The estimation of ground-level nitrogen dioxide (NO2) and ozone (O3) concentrations using Real-Time Learning (RTL)-based machine learning approach

Department of Urban and Environmental Engineering (Environmental Science and Engineering)Nitrogen dioxide (NO2) and ozone (O3) are the significant components of gaseous air pollutants that have harmful effects on human health. The monitoring and analysis of air pollutant exposure and persistence, and short-term forecasts are necessary for efficient public health management. In this study, the estimation model for the ground-level O3 and NO2 concentrations was developed which are spatially continuous over the land and ocean. The ground-level estimation was developed using the RTL-based machine learning technique with various satellite data and numerical model data as input variables. Three models were tested to build an accurate model using the most available data. 1) the ocean model using only ocean variables that have values for all regions2) the land model using all available data with assigning constant values to ocean variables3) the combined model that combines the results of the ocean model for sea area and the results of the land model for land area. Since NO2 and O3 have a relatively short lifespan, the real-time learning model is effective in estimating accurate ground-level concentrations.ope

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.open0

?????? ????????? ???????????? ????????? ????????? ????????? ????????? ?????? SO2 ???????????? ??????

Sulfur dioxide (SO2) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO2 can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO2 crucial. In South Korea, SO2 monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO2 concentrations. Thus, this research estimated spatially continuous ground-level SO2 concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO2 estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO2 concentrations based on the proposed model agreed with the general seasonality of SO2 and the temporal patterns of emission sources

Estimation of Ground-level Nitrogen Dioxide and Ozone Concentrations Using Satellite Data and Numerical Model Output

Long exposure to high concentrations of nitrogen dioxide (NO2) and ozone (O3) at ground level could be harmful to human health. Air pollutant concentrations including NO2 and O3 have been measured at monitoring stations, which has a major limitation that it is difficult to provide spatially continuous air quality information. In this study, machine learning based models were developed to estimate ground-level NO2 and O3 concentrations using satellite-based remote sensing data and numerical model output over East Asia to overcome such a limitation. NO2 and O3 vertical column density products from the Aura Ozone Monitoring Instrument (OMI) play an important role in monitoring of the spatial and temporal patterns of the gases, although one third to one half of the OMI products have been missing due to row anomalies. In this study, missing pixels of OMI products were filled using an interpolation approach to generate spatio-temporally continuous distribution of NO2 and O3 concentrations. In addition to satellite-derived data, model-based meteorological parameters and emission information during 2015-2016 were used to estimate surface air quality concentrations over East Asia. Random forest (RF) was used to develop the estimation models for NO2 and O3 concentrations. Over South Korea, the RF-based models showed good performance resulting in R2 values of 0.78 and 0.73, and RMSEs of 8.88 ppb and 10.50 ppb for NO2 and O3, respectively. The NO2 vertical column density was identified most important variable in both models. The model-based meteorological variables such as max wind speed, planetary boundary layer height (PBLH), frictional velocity, and solar radiation were also considered significant for estimation. Spatial distribution of ground-level NO2 and O3 concentrations were also examined over South Korea. Relatively high concentrations were shown around large cities including Seoul metropolitan area