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Huabei, located between 32° N and 42° N, is part of eastern China and includes administratively the Beijing and Tianjin Municipalities, Hebei and Shanxi Provinces, and Inner-Mongolia Autonomous Region. Over the past decades, the region has experienced dramatic changes in air quality and climate, and has become a major focus of environmental research in China. Here we present a new inventory of air pollutant emissions in Huabei for the year 2003 developed as part of the project Influence of Pollution on Aerosols and Cloud Microphysics in North China (IPAC-NC). <br><br> Our estimates are based on data from the statistical yearbooks of the state, provinces and local districts, including major sectors and activities of power generation, industrial energy consumption, industrial processing, civil energy consumption, crop straw burning, oil and solvent evaporation, manure, and motor vehicles. The emission factors are selected from a variety of literature and those from local measurements in China are used whenever available. The estimated total emissions in the Huabei administrative region in 2003 are 4.73 Tg SO₂, 2.72 Tg NO_x (in equivalent NO₂), 1.77 Tg VOC, 24.14 Tg CO, 2.03 Tg NH₃, 4.57 Tg PM₁₀, 2.42 Tg PM_2.5, 0.21 Tg EC, and 0.46 Tg OC. <br><br> For model convenience, we consider a larger Huabei region with Shandong, Henan and Liaoning Provinces included in our inventory. The estimated total emissions in the larger Huabei region in 2003 are: 9.55 Tg SO₂, 5.27 Tg NO_x (in equivalent NO₂), 3.82 Tg VOC, 46.59 Tg CO, 5.36 Tg NH₃, 10.74 Tg PM₁₀, 5.62 Tg PM_2.5, 0.41 Tg EC, and 0.99 Tg OC. The estimated emission rates are projected into grid cells at a horizontal resolution of 0.1° latitude by 0.1° longitude. Our gridded emission inventory consists of area sources, which are classified into industrial, civil, traffic, and straw burning sectors, and large industrial point sources, which include 345 sets of power plants, iron and steel plants, cement plants, and chemical plants. <br><br> The estimated regional NO₂ emissions are about 2–3% (administrative Huabei region) or 5% (larger Huabei region) of the global anthropogenic NO₂ emissions. We compare our inventory (IPAC-NC) with the global emission inventory EDGAR-CIRCE and the Asian emission inventory INTEX-B. Except for a factor of 3 lower EC emission rate in comparison with INTEX-B, the biases of the total emissions of most primary air pollutants in Huabei estimated in our inventory, with respect to EDGAR-CIRCE and INTEX-B, generally range from −30% to +40%. Large differences up to a factor of 2–3 for local emissions in some areas (e.g. Beijing and Tianjin) are found. It is recommended that the inventories based on the a...

Intermediate volatility organic compound (IVOC) emissions from a large cargo vessel were characterized under realworld operating conditions using an on-board measurement system. Test ship fuel-based emission factors (EFs) of total IVOCs were determined for two fuel types and seven operating conditions. The average total IVOC EF was 1003 ± 581 mg•kg-fuel −1 , approximately 0.76 and 0.29 times the EFs of primary organic aerosol (POA) emissions from low-sulfur fuel (LSF, 0.38 wt % S) and high-sulfur fuel (HSF, 1.12 wt % S), respectively. The average total IVOC EF from LSF was 2.4 times that from HSF. The average IVOC EF under low engine load (15%) was 0.5−1.6 times higher than those under 36%−74% loads. An unresolved complex mixture (UCM) contributed 86.1 ± 1.9% of the total IVOC emissions. Ship secondary organic aerosol (SOA) production was estimated to be 546.5 ± 284.1 mg•kg-fuel −1 ; IVOCs contributed 98.9 ± 0.9% of the produced SOA on average. Fuel type was the dominant determinant of ship IVOC emissions, IVOC volatility distributions, and SOA production. The ship emitted more IVOC mass, produced higher proportions of volatile organic components, and produced more SOA mass when fueled with LSF than when fueled with HSF. When reducing ship POA emissions, more attention should be paid to commensurate control of ship SOA formation potential.

Abstract. A system for source attribution of tropospheric ozone produced from both NOx and volatile organic compound (VOC) precursors is described, along with its implementation in the Community Earth System Model (CESM) version 1.2.2 using CAM4. The user can specify an arbitrary number of tag identities for each NOx or VOC species in the model, and the tagging system rewrites the model chemical mechanism and source code to incorporate tagged tracers and reactions representing these tagged species, as well as ozone produced in the stratosphere. If the user supplies emission files for the corresponding tagged tracers, the model will produce tagged ozone tracers which represent the contribution of each of the tag identities to the modelled total tropospheric ozone. Our tagged tracers preserve Ox. The size of the tagged chemical mechanism scales linearly with the number of specified tag identities. Separate simulations are required for NOx and VOC tagging, which avoids the sharing of tag identities between NOx and VOC species. Results are presented and evaluated for both NOx and VOC source attribution. We show that northern hemispheric surface ozone is dominated year-round by anthropogenic emissions of NOx, but that the mix of corresponding VOC precursors changes over the course of the year; anthropogenic VOC emissions contribute significantly to surface ozone in winter–spring, while biogenic VOCs are more important in summer. The system described here can provide important diagnostic information about modelled ozone production, and could be used to construct source–receptor relationships for tropospheric ozone.

No abstract

Molecular markers in ambient organic aerosol (OA) provide highly specific source information. Their traditional quantification is based on offline analysis of filter samples, and the coarse time resolution and labor-intensive nature hugely limit the utility of the tracer data. In this study, hourly organic molecular markers in fine particulate matter were measured using a recently commercialized thermal desorption aerosol gas chromatography− mass spectrometry (TAG) technique at an urban location in Shanghai, China during a three-week campaign from 9 November to 3 December, 2018. Selected primary OA molecular markers, including anhydrosugars, fatty acids, aromatic acids, and polycyclic aromatic hydrocarbons (PAHs), were examined in detail. Their diurnal variations showed characteristic features representing the corresponding emission source activities. For example, stearic acid showed a clear peak around 7 pm, in accordance with the enhanced cooking activities during mealtime. Diagnostic ratios of related makers of different reactivities provided unique information in uncovering the source information and tracking evolution of the OA in the atmosphere, for example, ratios of levoglucosan to its isomers and K + identified crop residue burning as the major form of biomass burning (BB). Ratios of unsaturated and saturated fatty acids gave unambiguous indication of atmospheric degradation of unsaturated fatty acids after emissions. Oleic acid to stearic acid ratios in ambient data (0.83 ± 0.54) were lower than those in the source profiles (1.2−6.5). Furthermore, the oleic acid to stearic acid ratio was found to be highly correlated with O/C ratios (R p : −0.66), suggesting the possible utility of oleic acid as a model compound to examine the heterogeneous reaction of cooking-related OA. PAH ratio−ratio plots helped identify varying influences of major combustion sources associated with air masses of different origins, revealing that BB and coal combustion were dominant under the influence of long-range transport air mass, while vehicle emissions were dominant under local/median-range air mass influence. This study demonstrated the utility of high time-resolution organic markers in capturing the dynamic change of source emissions and atmospheric aging, providing observational evidence to support their use in source apportionment.

An offline-coupled model (WRF/Polyphemus) and an online-coupled model (WRF/Chem-MADRID) are applied to simulate air quality in July 2001 at horizontal grid resolutions of 0.5° and 0.125° over Western Europe. The model performance is evaluated against available surface and satellite observations. The two models simulate different concentrations in terms of domainwide performance statistics, spatial distribution, temporal variations, and column abundance. WRF/Chem-MADRID at 0.5° gives higher values than WRF/Polyphemus for the domainwide mean and over polluted regions in Central and southern Europe for all surface concentrations and column variables except for the tropospheric ozone residual (TOR). Compared with observations, WRF/Polyphemus gives better statistical performance for daily HNO₃, SO₂, and NO₂ at the European Monitoring and Evaluation Programme (EMEP) sites, maximum 1 h O₃ at the AirBase sites, PM_2.5 at the AirBase sites, maximum 8 h O₃ and PM₁₀ composition at all sites, column abundance of CO, NO₂, TOR, and aerosol optical depth (AOD), whereas WRF/Chem-MADRID gives better statistical performance for NH₃, hourly SO₂, NO₂, and O₃ at the AirBase and BDQA (Base de données de la qualité de l'air) sites, maximum 1 h O₃ at the BDQA and EMEP sites, and PM₁₀ at all sites. WRF/Chem-MADRID generally reproduces well the observed high hourly concentrations of SO₂ and NO₂ at most sites except for extremely high episodes at a few sites, and WRF/Polyphemus performs well for hourly SO₂ concentrations at most rural or background sites where pollutant levels are relatively low, but it underpredicts the observed hourly NO₂ concentrations at most sites. Both models generally capture well the daytime maximum 8 h O₃ concentrations and diurnal variations of O₃ with more accurate peak daytime and minimal nighttime values by WRF/Chem-MADRID, but neither model reproduces extremely low nighttime O₃ concentrations at several urban and suburban sites due to underpredictions of NO_x and thus insufficient titration of O₃ at night. WRF/Polyphemus gives more accurate concentrations of PM_2.5, and WRF/Chem-MADRID reproduces better the observations of PM₁₀ concentrations at all sites. The differences between model predictions and observations are mostly caused by inaccurate representations of emissions of gaseous precursors and primary PM species, as well as biases in the meteorological predictions. The differences in model predictions are caused by differences in the heights of the first model layers and thickness of each layer that affect vertical distributions of emissions, model treatments such as dry/wet deposition, heterogeneous chemistry, and aerosol and cloud, as well as model inputs such as emissions of soil dust and sea salt and chemical boundary conditions of ...

We demonstrate with field data the benefit of using high‐time‐resolution chemical speciation data in achieving more robust source apportionment of fine particulate matter (PM2.5) using positive matrix factorization (PMF). Hourly composition data were collected over a month in Shanghai, including four inorganic ions, 13 elements, organic, and elemental carbon. PMF analysis of the hourly data set (PMF1h) resolves eight factors: secondary nitrate/sulfate, vehicular/industrial emissions, coal combustion, secondary sulfate, tire wear, Cr and Ni point source, residual oil combustion, and dust, with the first three being the major ones and each contributing to >20% of PM2.5 mass. To characterize the benefit gained from time resolution, we carried out separate PMF analyses of 4‐ and 6‐hr averaged data of the same data set (PMF6h and PMF4h). PMF6h and PMF4h produce an eight‐factor solution sharing similar factors to those by PMF1h but show less stability and more mixing in source profiles. Profile mixing was especially noticeable for tire wear, coal combustion, and Cr and Ni point source in PMF6h, as the 6‐hr averaging significantly decreased between‐sample variability and increased rotational ambiguity. While the three sets of PMF solutions were similar in contributions for factors with major species as source markers (e.g., secondary nitrate/sulfate), larger variations existed for factors with trace species as markers due to mixing of major species in the profiles and higher rotational uncertainties in PMF4h and PMF6h. Our results indicate that hourly time series of elements and major components could achieve more robust source apportionment through better capturing of diurnal‐scale dynamics in source activities.