Spatial and seasonal patterns of atmospheric nitrogen deposition in North China

As one of the regions with intensive agriculture and rapid economic development in China,North China also has a high nitrogen(N)deposition.This study characterized the spatial pattern of N deposition in North China,combining the tropospheric columns from satellite measurements and the simulated profiles from an atmospheric chemistry transport model.The total N deposition fluxes ranged from 16.3 to 106.5 kg N ha−1 yr−1,with an average of 54.5±17.2 kg N ha−1 yr−1.The high values were concentrated in urban and farmland areas,while low values were found in forests and grasslands with less human activities.Of the total N deposition,36%was deposited via precipitation,12%was deposited through dry particulate deposition,and the remaining 52%was comprised of dry gaseous deposition.For the seasonal variation of dry deposition,gaseous HNO3 and particulate NO3−were higher in winter and autumn,but lower in spring and summer.In contrast,gaseous NH3 and particulate NH4+were higher in spring and summer,but lower in winter and autumn.This is possibly caused by the seasonal differences in emission intensity between NOx and NH3 emission sources.The gaseous NO2 deposition did not show strong seasonal variation.The wet deposition was mainly affected by precipitation,with high values in summer and low values in winter.This research provides an objective spatial perspective and insight into the total N deposition in North China.

Using Longwave Infrared Hyperspectral Imaging for a Quantitative Atmospheric Tracer Monitoring in Outdoor Environments

Quantitative real-time retrieval of concentration-lengths (CL) through gaseous plumes is an important tool for environmental monitoring, enabling remote monitoring of emissions from industrial facilities and risk assessment in scenarios of toxic gas releases. The adoption of LWIR (Long Wave InfraRed) hyperspectral imaging as a leading technique for remote gas plume detection paved the way for an introduction of a precise CL estimation and two-dimensional (2D) mapping. A novel methodology for evaluating and characterizing the performance of a retrieval algorithm is presented. The algorithm utilizes state-of-the-art retrieved hyperspectral 2D mapping and is applied on a series of localized atmospheric tracer gas (SF6) releases in monitored environmental conditions. The retrieved CL distributions are compared to a numeric atmospheric T&D (Transport and Diffusion) model. Satisfactory agreement between retrieved and simulated CL prediction is manifested, and the uncertainty involved is quantified. Possible sources for the remained discrepancies between retrieved and simulated CL values are characterized, and methods to minimize them are discussed.

Simulation and Validation of the Aerosol Optical Thickness over China in 2006

The Model of Atmospheric Transport and Chemistry （MATCH） developed by the US National Center for Atmospheric Research （NCAR） was used to calculate the aerosol optical thickness （AOT） over China in 2006, with emission source data of the Intercontinental Chemical Transport Experiment Phase B （INTEX-B） and NCEP/NCAR reanalysis data as inputs. The simulation results of AOT were then validated with obser- vational data from the Moderate Resolution Imaging Spectroradiometer （MODIS）, Chinese Sun Hazemeter Network （CSHNET）, Aerosol Robotics Network （AERONET）, and China Aerosol Remote Sensing Network （CARSNET） at more than 30 stations over China. The comparison results indicated that the high values of AOT in the areas such as the Sichuan basin and East and South China and the low values of AOT over the Tibetan Plateau and Northwest and Northeast China were reasonably simulated by the MATCH. This model tended to underestimate the AOT values in high-aerosol-loading areas but overestimate the AOT val- ues in less polluted areas because there are still large uncertainties in the expression of emission sources, the description of the optical properties of aerosols, the treatment of cloud and precipitation, and the selection of grid resolution. The modeling results were consistent with the CSHNET, CARSNET, AERONET, and MODIS data in most parts of China, and the correlation coefficient of the monthly mean AOT between the model and the observation was 0.79 with CSHNET data at 23 stations, 0.51 with MODIS data, and 0.88 with data at 3 CARSNET stations and 2 other stations. All of them passed the significance test with c 〈 0.0001. The results demonstrated that the MATCH has the ability to simulate the characteristics of the AOT distribution and its seasonal variation over China.

A new implementation of FLEXPART with Enviro-HIRLAM meteorological input,and a case study during a heavy air pollution event

We integrated Enviro-HIRLAM(Environment-High Resolution Limited Area Model)meteorological output into FLEXPART(FLEXible PARTicle dispersion model).A FLEXPART simulation requires meteorological input from a numerical weather prediction(NWP)model.The publicly available version of FLEXPART can utilize either ECMWF(European Centre for Medium-range Weather Forecasts)Integrated Forecast System(IFS)forecast or reanalysis NWP data,or NCEP(U.S.National Center for Environmental Prediction)Global Forecast System(GFS)forecast or reanalysis NWP data.The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields.We compared backward trajectories gener-ated with FLEXPART using Enviro-HIRLAM(both with and without aerosol effects)to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs,for a case study of a heavy haze event which occurred in Beijing,China in November 2018.We found that results from FLEXPART were considerably different when using different meteorological inputs.When aerosol effects were included in the NWP,there was a small but noticeable differ-ence in calculated trajectories.Moreover,when looking at potential emission sensitivity instead of simply expressing trajectories as lines,additional information,which may have been missed when looking only at trajectories as lines,can be inferred.