THREE-DIMENSIONAL ANALYTICAL SOLUTION OF THE ADVECTION-DIFFUSION EQUATION FOR AIR POLLUTION DISPERSION

We develop a new analytical solution of a three-dimensional atmospheric pollutant dispersion. The main idea is to subdivide vertically the planetary boundary layer into sub-layers, where the wind speed and eddy diffusivity assume average values for each sub-layer. Basically, the model is assessed and validated using data obtained from the Copenhagen diffusion and Prairie Grass experiments. Our findings show that there is a good agreement between the predicted and observed crosswind-integrated concentrations. Moreover, the calculated statistical indices are within the range of acceptable model performance.

Satellite-based estimates of daily NO2 exposure in urban agglomerations of China and application to spatio-temporal characteristics of hotspots

The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd

Derivation of Emissions From Satellite‐Observed Column Amounts and Its Application to TROPOMI NO2 and CO Observations

A unified framework that connects emissions with satellite‐observed column amounts is derived from first principles. The emission information originates from the inner product of the horizontal wind and the gradient of column amount, which is more accurate than the horizontal flux divergence as used in previous studies. Additionally, the topographical and chemical effects are accounted for through fitted scale height and chemical lifetime. This framework is applied to derive NOx and CO emissions over the CONUS from TROPOspheric Monitoring Instrument NO2 and CO observations. High‐resolution (0.04°) emission mapping over the CONUS reveals unprecedented details, including CO emissions in major cities and NOx emissions from large cities, power plants, and major roadways. Monthly resolved NOx emissions show decrease and rebound after the COVID‐19 pandemic. This framework is integrated with the physical oversampling algorithm and can be readily applied to other products from the new‐generation satellite instruments.Alternate :Plain Language SummarySatellites usually measure the vertically integrated column amount of atmospheric species from space. For short‐lived species like nitrogen oxides, the observed column amount indicates location and strength of emission sources. However, atmospheric dispersion smears the relationship between emission and column amount as the lifetime of species gets longer. This study directly maps emission based on the principle of mass balance. Namely, the spatial gradient of column amount should align with horizontal wind if there is an emission. Additionally, topography and chemical reaction may cause spatial gradients of column amount that are unrelated to emissions and are accounted for. Unprecedented details in the emission of air pollutants are unveiled by applying this approach to the TROPOspheric Monitoring Instrument products.

Local atmospheric factors that enhance air-borne dispersion of coronavirus - High-fidelity numerical simulation of COVID19 case study in real-time.

The spatial patterns of the spreading of the COVID19 indicate the possibility of airborne transmission of the coronavirus. As the cough-jet of an infected person is ejected as a plume of infected viral aerosols into the atmosphere, the conditions in the local atmospheric boundary layer together dictate the fate of the infected plume. For the first time - a high-fidelity numerical simulation study - using Weather-Research-Forecast model coupled with the Lagrangian Hybrid Single-Particle Lagrangian Integrated Trajectory model (WRF-HYSPLIT) model has been conducted to track the infected aerosol plume in real-time during March 9-April 6, 2020, in New York City, the epicenter of the coronavirus in the USA for comparing the morning, afternoon and evening release. Atmospheric stability regimes that result in low wind speeds, low level turbulence and cool moist ground conditions favor the transmission of the disease through turbulence energy-containing large-scale horizontal "rolls" and vertical thermal "updrafts" and "downdrafts". Further, the wind direction is an important factor that dictates the direction of the transport. From the initial time of release, the virus can spread up to 30 min in the air, covering a 200-m radius at a time, moving 1-2 km from the original source.