Inter- and intra-city comparisons of PM2.5 concentration changes under COVID-19 social distancing in seven major cities of South Korea

Impacts of the COVID-19 lockdown in China on new particle formation and particle number size distribution in three regional background sites in Asian continental outflow

Despite the curtailment of atmospheric condensing precursor gases during the Coronavirus disease 2019 (COVID-19) lockdown (LD) period, unexpected haze events via the formation of new particles and their subsequent growth have been identified. This study investigated the impact of emission reduction during the Chinese LD period on the new particle formation (NPF) frequency and corresponding particle number size distribution (PNSD) at three regional background atmospheric monitoring sites in the western coastal areas of the Korean Peninsula. During this duration, the number concentrations of the nucleation- (<25 nm) and accumulation-mode (>90 nm) particles significantly decreased in Baengryeong (BRY), showing decreases of 34% and 29%, respectively. Unlike BRY, the PNSD in Anmyeon (AMY), which is influenced by nearby industrial emissions, remained nearly unchanged during the LD period, possibly because the reduction in industrial emissions was not significant during the social distancing period enforced by Korea. Bongseong (BOS) showed a similar variation to that of BRY; however, the magnitude of the reduction was weaker because of its higher altitude compared to other sites. The cyclostationary empirical orthogonal function technique was applied to the measured PNSDs at the three sites to objectively classify NPF events. Because mode 1 of cyclostationary loading vectors commonly represented the typical diurnal variation of PNSD during regional NPF events at three sites, mode 1 of the corresponding principal component time series was used for NPF classification. The NPF frequency decreased by 7%, 1%, and 7% in BRY, AMY, and BOS, respectively, despite favorable meteorological conditions, such as increased temperature and insolation during the LD period. The diurnal variation in the sulfuric acid (H2SO4) proxy implied that the H2SO4 proxy acted as a determining factor for NPF events during the NPF occurrence time (8-12 local hours) in AMY and BOS; however, NPF occurrence in BRY was not connected to the H2SO4 proxy level. This suggests that BRY was more likely to be influenced by the reduction in organic species in the continental upwind regions, while the occurrence of NPF events in AMY and BOS can be suppressed in association with the distinct reduction in inorganic compounds represented by the H2SO4 proxy during the LD period.

PM2.5 Forecast in Korea using the Long Short-Term Memory (LSTM) Model

The National Institute of Environmental Research, under the Ministry of Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of particulate matter with diameters of ≤ 2.5 μm (PM2.5) in terms of four grades (low, moderate, high, and very high) over 19 districts nationwide. Particulate grades are subjectively designated by human forecasters based on forecast results from the Community Multiscale Air Quality (CMAQ) and artificial intelligence (AI) models in conjunction with weather patterns. This study evaluates forecasts from the long short-term memory (LSTM) algorithm relative to those from CMAQ-solely and AirKorea using observations from 2019. The skills of the one-day PM2.5 forecasts over the 19 districts were 39-70% for CMAQ, 72-79% for LSTM, and 73-80% for AirKorea; the AI forecasts showed comparable skills to the human forecasters at AirKorea. The one-day forecast skill levels of high and very high PM2.5 pollution grades are 31-98%, 31-74%, and 39-81% for the CMAQ-solely, the LSTM, and the AirKorea forecasts, respectively. Despite good skills for forecasting the high and very high events, CMAQ-solely forecasts also generate substantially higher false alarm rates (up to 86%) than the LSTM and AirKorea forecasts (up to 58%). Hence, applying only the LSTM model to the CMAQ forecasts can yield reasonable forecast skill levels comparable to the operational AirKorea forecasts that elaborately combine the CMAQ model, AI models, and human forecasters. The present results suggest that applications of appropriate AI models can greatly enhance PM2.5 forecast skills for Korea in a more objective way.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13143-022-00293-2.

Spatiotemporal variations and sources of PM2.5 in the Central Plains Urban Agglomeration, China

The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM_2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM_2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM_2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m³, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM_2.5 pollution mainly occurred in winter (100.8 μg/m³, 4-year average). The diurnal change in PM_2.5 concentrations varied slightly over the season. The centroid of the PM_2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM_2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM_2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM_2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.

PM2.5, NO2, wildfires, and other environmental exposures are linked to higher Covid 19 incidence, severity, and death rates

Numerous studies have linked outdoor levels of PM_2.5, PM₁₀, NO₂, O₃, SO₂, and other air pollutants to significantly higher rates of Covid 19 morbidity and mortality, although the rate in which specific concentrations of pollutants increase Covid 19 morbidity and mortality varies widely by specific country and study. As little as a 1-μg/m³ increase in outdoor PM_2.5 is estimated to increase rates of Covid 19 by as much as 0.22 to 8%. Two California studies have strongly linked heavy wildfire burning periods with significantly higher outdoor levels of PM_2.5 and CO as well as significantly higher rates of Covid 19 cases and deaths. Active smoking has also been strongly linked significantly increased risk of Covid 19 severity and death. Other exposures possibly related to greater risk of Covid 19 morbidity and mortality include incense, pesticides, heavy metals, dust/sand, toxic waste sites, and volcanic emissions. The exact mechanisms in which air pollutants increase Covid 19 infections are not fully understood, but are probably related to pollutant-related oxidation and inflammation of the lungs and other tissues and to the pollutant-driven alternation of the angiotensin-converting enzyme 2 in respiratory and other cells.