[i] An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P experiment funded by the National Aeronautics and Space Administration (NASA) and the ACE-Asia experiment funded by the National Science Foundation (NSF) and the National Oceanic and Atmospheric Administration (NOAA). Emissions are estimated for all major anthropogenic sources, including biomass burning, in 64 regions of Asia. We estimate total Asian emissions as follows: 34.3 Tg SO 2 , 26.8 Tg NO x , 9870 Tg CO 2 , 279 Tg CO, 107 Tg CH 4 , 52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH 3 . In addition, NMVOC are speciated into 19 subcategories according to functional groups and reactivity. Thus we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements. Emissions in China dominate the signature of pollutant concentrations in this region, so special emphasis has been placed on the development of emission estimates for China. China's emissions are determined to be as follows: 20.4 Tg SO 2 , 11.4 Tg NO x , 3820 Tg CO 2 , 116 Tg CO, 38.4 Tg CH 4 , 17.4 Tg NMVOC, 1.05 Tg BC, 3.4 Tg OC, and 13.6 Tg NH 3 . Emissions are gridded at a variety of spatial resolutions from 1° × 1° to 30 s x 30 s, using the exact locations of large point sources and surrogate GIS distributions of urban and rural population, road networks, landcover, ship lanes, etc. The gridded emission estimates have been used as inputs to atmospheric simulation models and have proven to be generally robust in comparison with field observations, though there is reason to think that emissions of CO and possibly BC may be underestimated. Monthly emission estimates for China are developed for each species to aid TRACE-P and ACE-Asia data interpretation. During the observation period of March/ April, emissions are roughly at their average values (one twelfth of annual). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of ±16% for SO 2 to a high of ±450% for OC.

An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 : NASA global tropospheric experiment transport and chemical evolution over the pacific (TRACE-P): Measurements and analysis (TRACEP1)

. In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2. 5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the Beijing–Tianjin–Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.

/pdf/improving-pm-2-5-forecast-over-china-by-the-joint-adjustment-1xp4tihq2y.pdf

Improving PM 2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

. An ensemble Kalman filter data assimilation (DA) system has been
developed to improve air quality forecasts using surface measurements of
PM 10 , PM 2.5 , SO2 , NO2 , O3 , and CO together
with an online regional chemical transport model, WRF-Chem (Weather Research
and Forecasting with Chemistry). This DA system was applied to simultaneously
adjust the chemical initial conditions (ICs) and emission inputs of the
species affecting PM 10 , PM 2.5 , SO2 , NO2 ,
 O3 , and CO concentrations during an extreme haze episode that occurred in early
October 2014 over East Asia. Numerical experimental results indicate that
ICs played key roles in PM 2.5 , PM 10 and CO forecasts during the
severe haze episode over the North China Plain. The 72 h verification
forecasts with the optimized ICs and emissions performed very similarly to
the verification forecasts with only optimized ICs and the prescribed
emissions. For the first-day forecast, near-perfect verification forecasts
results were achieved. However, with longer-range forecasts, the DA impacts
decayed quickly. For the SO2 verification forecasts, it was efficient
to improve the SO2 forecast via the joint adjustment of SO2 
ICs and emissions. Large improvements were achieved for SO2 forecasts
with both the optimized ICs and emissions for the whole 72 h forecast range.
Similar improvements were achieved for SO2 forecasts with optimized
ICs only for the first 3 h, and then the impact of the ICs decayed
quickly. For the NO2 verification forecasts, both forecasts performed
much worse than the control run without DA. Plus, the 72 h O3 
verification forecasts performed worse than the control run during the
daytime, due to the worse performance of the NO2 forecasts, even
though they performed better at night. However, relatively favorable
 NO2 and O3 forecast results were achieved for the Yangtze
River delta and Pearl River delta regions.

/pdf/the-impact-of-multi-species-surface-chemical-observation-1080zun1hp.pdf

The impact of multi-species surface chemical observation assimilation on air quality forecasts in China

A comprehensive investigation using the air quality network and meteorological data of China in 2015 showed that PM2.5 driven by cold surges from the ground level could travel up to 2000 km from northern to southern China within two days. Air pollution is more severe and prominent during the winter in north China due to seasonal variations in energy usage, trade wind movements, and industrial emissions. In February 2015, two cold surges traveling from north China caused a temporary increase in the concentration of PM2.5 in Shanghai. Subsequently, the concentration of PM2.5 in Xiamen increased to a high of 80 µg/m3
 , which is double the average PM2.5 concentration in Xiamen during the winter. This finding is a new long-range transport mechanism comparing to the well-established mechanism, with long-range transport more likely to occur in the upper troposphere than at lower levels. These observations were validated by results from the back trajectory analysis and the RAMS- CMAQ model. While wind speed was found to be a major facilitator in transporting PM2.5 from Beijing to Xiamen, more investigation is required to understand the complex relationship between wind speed and PM2.5 and how it moderates air quality in Beijing, Shanghai, and Xiamen.

/pdf/large-scale-transport-of-pm-2-5-in-the-lower-troposphere-3l3bnxditf.pdf

Large-scale transport of PM 2.5 in the lower troposphere during winter cold surges in China

Effects of atmospheric aerosols on terrestrial carbon fluxes and CO2 concentrations in China

. In order to optimize surface CO2 fluxes at grid scales, a regional surface CO2 flux inversion system (Carbon Flux Inversion system and Community Multi-scale Air Quality, CFI-CMAQ) has been developed by applying the ensemble Kalman filter (EnKF) to constrain the CO2 concentrations and applying the ensemble Kalman smoother (EnKS) to optimize the surface CO2 fluxes. The smoothing operator is associated with the atmospheric transport model to constitute a persistence dynamical model to forecast the surface CO2 flux scaling factors. In this implementation, the "signal-to-noise" problem can be avoided; plus, any useful observed information achieved by the current assimilation cycle can be transferred into the next assimilation cycle. Thus, the surface CO2 fluxes can be optimized as a whole at the grid scale in CFI-CMAQ. The performance of CFI-CMAQ was quantitatively evaluated through a set of Observing System Simulation Experiments (OSSEs) by assimilating CO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The results showed that the CO2 concentration assimilation using EnKF could constrain the CO2 concentration effectively, illustrating that the simultaneous assimilation of CO2 concentrations can provide convincing CO2 initial analysis fields for CO2 flux inversion. In addition, the CO2 flux optimization using EnKS demonstrated that CFI-CMAQ could, in general, reproduce true fluxes at grid scales with acceptable bias. Two further sets of numerical experiments were conducted to investigate the sensitivities of the inflation factor of scaling factors and the smoother window. The results showed that the ability of CFI-CMAQ to optimize CO2 fluxes greatly relied on the choice of the inflation factor. However, the smoother window had a slight influence on the optimized results. CFI-CMAQ performed very well even with a short lag-window (e.g. 3 days).

/pdf/a-regional-carbon-data-assimilation-system-and-its-13heoraj74.pdf

A regional carbon data assimilation system and its preliminary evaluation in East Asia

A regional chemical transport model, RAMS-CMAQ, was employed to assess the impacts of biosphere-atmosphere C2 exchange on seasonal variations in atmospheric C2 concentrations over East Asia. Simulated C2 concentrations were compared with observations at 12 surface stations and the comparison showed they were generally in good agreement. Both observations and simulations suggested that surface C2 over East Asia features a summertime trough due to biospheric absorption, while in some urban areas surface C2 has a distinct summer peak, which could be attributed to the strong impact from anthropogenic emissions. Analysis of the model results indicated that biospheric fluxes and fossil-fuel emissions are comparably important in shaping spatial distributions of C2 near the surface over East Asia. Biospheric flux plays an important role in the prevailing spatial pattern of C2 enhancement and reduction on the synoptic scale due to the strong seasonality of biospheric C2 flux. The elevation of C2 levels by the biosphere during winter was found to be larger than 5 ppm in North China and Southeast China, and during summertime a significant depletion (⩾ 7 ppm) occurred in most areas, except for the Indo-China Peninsula where positive bioflux values were found.

Assessment of the Biospheric Contribution to Surface Atmospheric CO 2 Concentrations over East Asia with a Regional Chemical Transport Model

A regional surface carbon dioxide (CO2) flux inversion system, the Tan-Tracker-Region, was developed by incorporating an assimilation scheme into the Community Multiscale Air Quality (CMAQ) regional chemical transport model to resolve fine-scale CO2 variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach (POD-4DVar) is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO2 concentrations and surface CO2 fluxes are applied to help reduce the uncertainty in initial CO2 concentrations. A persistence dynamical model was developed to describe the evolution of the surface CO2 fluxes and help avoid the “signal-to-noise” problem; thus, CO2 fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments (OSSEs). The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the performance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation network in different CO2 flux situations. The results indicate that more observation sites would be useful to systematically improve the estimation of CO2 concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO2 flux variability over East Asia could be performed with the regional inversion system.

Xingxia Kou

Papers

A regional carbon data assimilation system and its preliminary evaluation in East Asia

Assessment of the Biospheric Contribution to Surface Atmospheric CO 2 Concentrations over East Asia with a Regional Chemical Transport Model

Accounting for CO 2 variability over East Asia with a regional joint inversion system and its preliminary evaluation