Nitrate formation mechanisms causing high concentration of PM2.5 in a residential city with low anthropogenic emissions during cold season.

During the cold season in South Korea, NO3- concentrations are known to significantly increase, often causing PM2.5 to exceed air quality standards. This study investigated the formation mechanisms of NO3- in a suburban area with low anthropogenic emissions. The average PM2.5 was 25.3 µg m-3, with NO3- identified as the largest contributor. Ammonium-rich conditions prevailed throughout the study period, coupled with low atmospheric temperature facilitating the transfer of gaseous HNO3 into the particulate phase. This result indicates that the formation of HNO3 played a crucial role in determining particulate NO3- concentration. Nocturnal increases in NO3- were observed alongside increasing ozone (O3) and relative humidity (RH), emphasizing the significance of heterogeneous reactions involving N2O5. NO3- concentrations at the study site were notably higher than in Seoul, the upwind metropolitan area, during a high concentration episode. This difference could potentially attributed to lower local NO concentrations, which enhanced the reaction between O3 and NO2, to produce NO3 radicals. High concentrations of Cl- and dust were also identified as contributors to the elevated NO3- concentrations.

Is replacing missing values of PM2.5 constituents with estimates using machine learning better for source apportionment than exclusion or median replacement?

East Asian countries have been conducting source apportionment of fine particulate matter (PM2.5) by applying positive matrix factorization (PMF) to hourly constituent concentrations. However, some of the constituent data from the supersites in South Korea was missing due to instrument maintenance and calibration. Conventional preprocessing of missing values, such as exclusion or median replacement, causes biases in the estimated source contributions by changing the PMF input. Machine learning (ML) can estimate the missing values by training on constituent data, meteorological data, and gaseous pollutants. Complete data from the Seoul Supersite in 2018 was taken, and a random 20% was set as missing. PMF was performed by replacing missing values with estimates. Percent errors of the source contributions were calculated compared to those estimated from complete data. Missing values were estimated using a random forest analysis. Estimation accuracy (r2) was as high as 0.874 for missing carbon species and low at 0.631 when ionic species and trace elements were missing. For the seven highest contributing sources, replacing the missing values of carbon species with estimates minimized the percent errors to 2.0% on average. However, replacing the missing values of the other chemical species with estimates increased the percent errors to more than 9.7% on average. Percent errors were maximal at 37% on average when missing values of ionic species and trace elements were replaced with estimates. Missing values, except for carbon species, need to be excluded. This approach reduced the percent errors to 7.4% on average, which was lower than those due to median replacement. Our results show that reducing the biases in source apportionment is possible by replacing the missing values of carbon species with estimates. To improve the biases due to missing values of the other chemical species, the estimation accuracy of the ML needs to be improved.