Air-pollutant mass concentration changes during COVID-19 pandemic in Shanghai, China.

To curb the spread of the coronavirus, China implemented lockdown policies on January 23, 2020. The resulting extreme changes in human behavior may have influenced the air pollutants concentration. However, despite these changes, hazy weather persisted in Shanghai and became a public issue. This study aims to investigate air pollutant mass concentration changes during the lockdown in Shanghai. Air pollutant mass concentration data and meteorological data during the pre-lockdown period and the level I response lockdown period were analyzed by statistical analysis and a Lagrangian particle diffusion model. The data was classified in three periods: P1 (pre-lockdown: 10 days before the Spring Festival), P2 (the first 10 days after lockdown: during the Spring Festival celebration), and P3 (the second 10 days after lockdown: after the Spring Festival). Data for the same period in 2019 were used as a reference. The results indicate that the Spring Festival holiday in 2019 resulted in a reduction in energy consumption, which led to a decrease in PM2.5 (26.4%) and NO2 (43.41%) mass concentration, but an increase in ozone mass concentration (31.39%) in P2 compared with P1. The integrated effect of the Spring Festival holiday and lockdown in 2020 resulted in a decrease in PM2.5 (36.5%) and NO2 (51.9%) mass concentrations, but an increase in ozone mass concentration (43.8%) in P2 compared with P1. After the Spring Festival, the mass concentrations of PM2.5, SO2, and NO2 increased by 74.41%, 5.52%, and 53.28%, respectively in P3 compared with P2 in 2019. However, PM2.5 and SO2 concentrations in 2020 continued to decrease, by 14.74% and 4.61%, respectively, while NO2 mass concentration increased by 7.82% in P3 compared with P2. We also found that PM2.5 mass concentration is susceptible to regional transmission from the surrounding cities. PM2.5 and other gaseous pollutants show different correlations in different periods, while NO2 and O3 always show a strong negative correlation. The principal components before the Spring Festival in 2019 were O3 and NO2, and after the Spring Festival, they were PM2.5 and CO, while the principal components before the lockdown in 2020 were PM2.5 and CO, and during lockdown they were O3 and NO2.

Emergency preparedness for the accidental release of radionuclides from the Uljin Nuclear Power Plant in Korea.

Site specific radionuclide dispersion databases were archived for the emergency response to the hypothetical releases of 137Cs from the Uljin nuclear power plant in Korea. These databases were obtained with the horizontal resolution of 1.5 km in the local domain centered the power plant site by simulations of the Lagrangian Particle Dispersion Model (LPDM) with the Unified Model (UM)-Local Data Assimilation Prediction System (LDAPS). The Eulerian Dispersion Model-East Asia (EDM-EA) with the UM-Global Data Assimilation Prediction System (UM-GDAPS) meteorological models was used to get dispersion databases in the regional domain. The LPDM model was performed for a year with a 5-day interval yielding 72 synoptic time-scale cases in a year. For each case hourly mean near surface concentrations, hourly mean column integrated concentrations, hourly total depositions for 5 consecutive days were archived by the LPDM model in the local domain and by the EDM-EA model in the regional domain of Asia. Among 72 synoptic cases in a year the worst synoptic case that showed the highest mean surface concentration averaged for 5 days in the LPDM model domain was chosen to illustrate the emergency preparedness to the hypothetical accident at the site. The simulated results by the LPDM model with the 137Cs emission rate of the Fukushima nuclear power plant accident for the first 5-day period were found to be able to provide prerequisite information for the emergency response to the early phase of the accident whereas those of the EDM-EA model could provide information required for the environmental impact assessment of the accident in the regional domain. The archived site-specific database of 72 synoptic cases in a year could have a great potential to be used as a prognostic information on the emergency preparedness for the early phase of accident.

Estimation of radionuclide (137Cs) emission rates from a nuclear power plant accident using the Lagrangian Particle Dispersion Model (LPDM).

A methodology for the estimation of the emission rate of 137Cs by the Lagrangian Particle Dispersion Model (LPDM) with the use of monitored 137Cs concentrations around a nuclear power plant has been developed. This method has been employed with the MM5 meteorological model in the 600 km × 600 km model domain with the horizontal grid scale of 3 km × 3 km centered at the Fukushima nuclear power plant to estimate 137Cs emission rate for the accidental period from 00 UTC 12 March to 00 UTC 6 April 2011. The Lagrangian Particles are released continuously with the rate of one particle per minute at the first level modelled, about 15 m above the power plant site. The presently developed method was able to simulate quite reasonably the estimated 137Cs emission rate compared with other studies, suggesting the potential usefulness of the present method for the estimation of the emission rate from the accidental power plant without detailed inventories of reactors and fuel assemblies and spent fuels. The advantage of this method is not so complicated but can be applied only based on one-time forward LPDM simulation with monitored concentrations around the power plant, in contrast to other inverse models. It was also found that continuously monitored radionuclides concentrations from possibly many sites located in all directions around the power plant are required to get accurate continuous emission rates from the accident power plant. The current methodology can also be used to verify the previous version of radionuclides emissions used among other modeling groups for the cases of intermittent or discontinuous samplings.