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h i g h l i g h t sDerived multi-year urban NOx trend from satellite (OMI) and ground observations (AQS). Revealed NOx responses to the 2008 Economic Recession by OMI and AQS. The trend not well captured by emissions used for national air quality forecasting. Demonstrated how to use space and ground observations to evaluate emission updates. Emission Trend Air quality forecast Recession OMI NO2 Ozone AQS NAQFC a b s t r a c t National emission inventories (NEIs) take years to assemble, but they can become outdated quickly, especially for time-sensitive applications such as air quality forecasting. This study compares multi-year NO x trends derived from satellite and ground observations and uses these data to evaluate the updates of NO x emission data by the US National Air Quality Forecast Capability (NAQFC) for next-day ozone prediction during the 2008 Global Economic Recession. Over the eight large US cities examined here, both the Ozone Monitoring Instrument (OMI) and the Air Quality System (AQS) detect substantial downward trends from 2005 to 2012, with a seven-year total of À35% according to OMI and À38% according to AQS. The NO x emission projection adopted by NAQFC tends to be in the right direction, but at a slower reduction rate (À25% from 2005 to 2012), due likely to the unaccounted effects of the 2008 economic recession. Both OMI and AQS datasets display distinct emission reduction rates before, during, and after the 2008 global recession in some cities, but the detailed changing rates are not consistent across the OMI and AQS data. Our findings demonstrate the feasibility of using space and ground observations to evaluate major updates of emission inventories objectively. The combination of satellite, ground observations, and in-situ measurements (such as emission monitoring in power plants) is likely to provide more reliable estimates of NO x emission and its trend, which is an issue of increasing importance as many urban areas in the US are transitioning to NO x -sensitive chemical regimes by continuous emission reductions.

We report on mercury in the atmosphere of east Asia (Japan, Korea, and China) as measured from sea level to ∼7000 m during 16 research flights in the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) C‐130 aircraft during the Asian Pacific Regional Aerosol Characterization Experiment (ACE‐Asia) campaign (http://saga.pmel.noaa.gov/aceasia/). The air at all altitudes contained concentrations of atmospheric mercury above the global background. The atmosphere was highly stratified with plumes originating from massive dust storms carried out of China, from local industrial pollution, from volcanoes, and, less well defined, from biomass burning. Most often the air masses were mixtures, e.g., dust layers contained anthropogenic emissions or volcanic plumes were embedded in anthropogenic pollution; thus the total data set showed no significant correlations of gaseous mercury with the most common anthropogenic pollutants in the area (CO and SO2), but good correlations were observed for identifiable plumes. Highest mixing ratios for gaseous elemental mercury (GEM) were found in industrial plumes exiting China (∼6.3 ng/m3), Korea (∼3 ng/m3), and Japan (∼3 ng/m3). The core of the plume from Miyake Jima volcano contained ∼3.7 ng/m3 of GEM. Crustal mercury was also present, emitted and subsequently deposited during the outbreak of the spring dust storms. Some of the nondust aerosols contained soluble mercury in highly variable ratios with the gas‐phase mercury contained in the same plume. Preliminary estimates for the export from China are 5–15 t of crustal mercury during the dust storms, ∼150 t/yr of gas‐phase mercury from biomass/biofuel combustion, and ∼600 t/yr from industrial sources, mostly from coal combustion. Gaseous mercury is a useful tracer for industrial, volcanic, and biomass‐burning sources, but in most cases, robust plume identification required one or more cotracers. Because of the inertness of GEM and the ease of its measurement it is well suited for the tracking of long‐range transport.

Abstract. East Asia contributes to nearly 50% of the global anthropogenic mercury emissions into the atmosphere. Recently, there have been concerns about the long-range transport of mercury from East Asia, which may lead to enhanced dry and wet depositions in other regions. In this study, we performed four monthly simulations (January, April, July and October in 2005) using CMAQ-Hg v4.6 for a number of emission inventory scenarios in an East Asian model domain. Coupled with mass balance analyses, the chemical transport of mercury in East Asia and the resulted mercury emission outflow were investigated. The total annual mercury deposition in the region was estimated to be 821 Mg, with 396 Mg contributed by wet deposition and 425 Mg by dry deposition. Anthropogenic emissions were responsible for most of the estimated deposition (75%). The deposition caused by emissions from natural sources was less important (25%). Regional mercury transport budgets showed strong seasonal variability, with a net removal of RGM (7-15 Mg month −1 ) and PHg (13-21 Mg month −1 ) in the domain, and a net export of GEM (60-130 Mg month −1 ) from the domain. The outflow caused by East Asian emissions (anthropogenic plus natural) was estimated to be in the range of 1369-1671 Mg yr −1 , of which 50-60% was caused by emissions from natural sources. The emission outflow repCorrespondence to: C.-J. Lin (jerry.lin@lamar.edu) resented about 75% of the total mercury emissions in the region, and would contribute to 20-30% of mercury deposition in remote receptors.

2019) The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?, Tellus B: Chemical and Physical Meteorology, 71:1, 1550324, ABSTRACT Dry deposition is an important loss process for atmospheric particles and can be a significant part of total deposition estimates calculated for critical loads analyses. However, algorithms used in large-scale air quality and atmospheric chemistry models to predict particle deposition velocity as a function of particle size are highly uncertain. Many of these algorithms, although derived from a common heritage, predict vastly different particle deposition velocities for a given particle diameter even under identical environmental conditions for major land use classes. Even more problematic, for vegetated landscapes (forests, in particular) the algorithms do not agree very well with available measurements. In this work, we perform a sensitivity study to estimate how significant the uncertainties in particle deposition algorithms may be in an air quality model's predictions of ground-level fine particle concentrations, particle deposition and overall total deposition of nitrogen and sulfur. Our results suggest that fine particle concentration predictions at the surface may vary by 5-15% depending on the choice of particle deposition velocity algorithm, while particle dry deposition is affected to a much greater extent with differences among algorithms >200%. Moreover, if accumulation mode particle dry deposition measurements over forests are correct, then dry particle deposition and total elemental deposition to these landscapes may be much larger than is typically simulated by current air quality and atmospheric chemistry models, calling into question commonly available estimates of total deposition and their use in critical loads analyses. Since accurate predictions of atmospheric particle concentrations and deposition are critically important for future air quality, weather and climate models and management of pollutant deposition to sensitive ecosystems, an investment in new dry deposition measurements in conjunction with integrated modelling efforts seems not only justified but vitally necessary to advance and improve the treatment of particle dry deposition processes in atmospheric models.

The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 μm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers. There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow- and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseous-phase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U.S. Environmental Protection Agency’s (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005. Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA’s AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 μg m−3 against a monthly average of 9.4 μg m−3 for the north-central United States.

[1] Measurements from the Transport and Chemical Evolution over the Pacific (TRACE-P) and Asian Pacific Regional Aerosol Characterization Experiment (ACE-Asia) field experiments obtained during the period of March-April 2001 are used to evaluate the impact of megacity emissions on regional air quality in east Asia. A classification method built upon back trajectory analysis and sensitivity runs using the Sulfur Transport and Emissions Model 2001 (STEM-2K1) regional chemical transport model are used to identify the aircraft observations that were influenced by megacity emissions. More than 30% of measurement points are classified as urban points, with a significant number of plumes found to have originated from Shanghai, Qingdao, Beijing, Taiyuan, Tianjin and Guiyang, Seoul, and Pusan. These data are then analyzed, and chemical characteristics of these megacities are compared. Emission estimates for the megacities are also presented and discussed in the context of expected similarities and differences in the chemical signals in the ambient air impacted by these cities. Comparisons of the observation-based ratios with emission-based estimates are presented and provide a means to test for the consistency of the emission estimates. The observation-based ratios are shown to be generally consistent with the emissions ratios. The megacity emissions are used in the STEM-2K1 model to study the effects of these emissions on criteria and photochemical species in the region. Over large portions of the Japan Sea, Yellow Sea, western Pacific Ocean, and the Bay of Bengal, megacity emissions contribute in excess of 10% of the near-surface ambient levels of O 3 , CO, SO 2 , H 2 SO 4 , HCHO, and NO z . The megacity emissions are also used to study ozone levels in Asia under a scenario where all cities evolve their emissions in a manner such that they end up with the same VOC/NO x emission ratio as that for Tokyo. Monthly mean ozone levels are found to increase by at least 5%.

Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM 2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM 2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM 2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM 2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM 2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM 2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.