Improving PM2.5 predictions during COVID-19 lockdown by assimilating multi-source observations and adjusting emissions.

The Coronavirus Disease 2019 (COVID-19) outbreak caused a suspension of almost all non-essential human activities, leading to a significant reduction of anthropogenic emissions. However, the emission inventory of the chemistry transport model cannot be updated in time, resulting in large uncertainty in PM2.5 predictions. This study adopted a three-dimensional variational approach to assimilate multi-source PM2.5 data from satellite and ground observations and jointly adjusted emissions to improve PM2.5 predictions of the WRF-Chem model. Experiments were conducted to verify the method over Hubei Province, China, during the COVID-19 epidemic from Jan 21st to Mar 20th, 2020. The results showed that PM2.5 predictions were improved at almost all the validation sites, and the benefit of data assimilation (DA) can last for 48 h. However, the benefits of DA diminished quickly with the increase of the forecast time. By adjusting emissions, the PM2.5 predictions showed a much slower error accumulation along forecast time. At 48Z, the RMSE still has an 8.85 µg/m3 (19.49%) improvement, suggesting the effectiveness of emissions adjustment based on the improved initial conditions via DA.

Short-term effects of ambient PM1 and PM2.5 air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China.

BACKGROUND: Ambient PM1 (particulate matter with aerodynamic diameter ≤1 µm) is an important contribution of PM2.5 mass. However, little is known worldwide regarding the PM1-associated health effects due to a wide lack of ground-based PM1 measurements from air monitoring stations. METHODS: We collected daily records of hospital admission for respiratory diseases and station-based measurements of air pollution and weather conditions in Shenzhen, China, 2015-2016. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate hospitalization risks associated with short-term exposures to PM1 and PM2.5. RESULTS: PM1 and PM2.5 showed significant adverse effects on respiratory disease hospitalizations, while no evident associations with PM1-2.5 were identified. Admission risks for total respiratory diseases were 1.09 (95% confidence interval: 1.04 to 1.14) and 1.06 (1.02 to 1.10), corresponding to per 10 µg/m3 rise in exposure to PM1 and PM2.5 at lag 0-2 days, respectively. Both PM1 and PM2.5 were strongly associated with increased admission for pneumonia and chronic obstructive pulmonary diseases, but exhibited no effects on asthma and upper respiratory tract infection. Largely comparable risk estimates were observed between male and female patients. Groups aged 0-14 years and 45-74 years were significantly affected by PM1- and PM2.5-associated risks. PM-hospitalization associations exhibited a clear seasonal pattern, with significantly larger risks in cold season than those in warm season among some subgroups. CONCLUSIONS: Our study suggested that PM1 rather than PM1-2.5 contributed to PM2.5-induced risks of hospitalization for respiratory diseases and effects of PM1 and PM2.5 mainly occurred in cold season.