Most primary organic-particulate emissions are semivolatile; thus, they partially evaporate with atmospheric dilution, creating substantial amounts of low-volatility gas-phase material. Laboratory experiments show that photo-oxidation of diesel emissions rapidly generates organic aerosol, greatly exceeding the contribution from known secondary organic-aerosol precursors. We attribute this unexplained secondary organic-aerosol production to the oxidation of low-volatility gas-phase species. Accounting for partitioning and photochemical processing of primary emissions creates a more regionally distributed aerosol and brings model predictions into better agreement with observations. Controlling organic particulate-matter concentrations will require substantial changes in the approaches that are currently used to measure and regulate emissions.

Organic aerosols (OA) are a highly dynamic system dominated by both variable gas particle partitioning and chemical evolution; however, these phenomena are poorly represented in current air quality models. The chemical transport model Comprehensive Air‐Quality Model with extensions dealing with particulate matter (PMCAMx) was extended to investigate the effects of partitioning and photochemical aging of primary emissions on OA concentrations in the eastern United States during July 2001 and January 2002. In both the summer and the winter, much of the traditionally defined primary OA (POA) emissions evaporate, creating a large pool of low‐volatility organic vapors. During the summertime, photochemical aging of these vapors creates substantial oxygenated OA that is regionally distributed. Little production of oxygenated OA is predicted in the winter because oxidant levels are low. OA formed from the oxidation of low‐volatility vapors is most important in and around urban areas located in the northeast and midwest. In rural locations and throughout the southeast, traditional secondary OA (SOA) formed from biogenic precursors is predicted to be the dominant class of oxidized OA. PMCAMx can only reproduce the large fractional contributions of oxidized OA observed in the atmosphere if some of the POA in the model evaporates. Sensitivity analysis illustrates that the volatility distribution of the existing POA emissions and the amount of intermediate volatility compounds not accounted for in current inventories are key uncertainties. At an upper bound, better accounting for emissions of low‐volatility organics has the potential to increase summertime OA concentrations in northeastern and midwestern cities by as much as 50%.

The Energy Exascale Earth System Model Atmosphere Model version 1, the atmospheric component of the Department of Energy's Energy Exascale Earth System Model is described. The model began as a fork of the well‐known Community Atmosphere Model, but it has evolved in new ways, and coding, performance, resolution, physical processes (primarily cloud and aerosols formulations), testing and development procedures now differ significantly. Vertical resolution was increased (from 30 to 72 layers), and the model top extended to 60 km (~0.1 hPa). A simple ozone photochemistry predicts stratospheric ozone, and the model now supports increased and more realistic variability in the upper troposphere and stratosphere. An optional improved treatment of light‐absorbing particle deposition to snowpack and ice is available, and stronger connections with Earth system biogeochemistry can be used for some science problems. Satellite and ground‐based cloud and aerosol simulators were implemented to facilitate evaluation of clouds, aerosols, and aerosol‐cloud interactions. Higher horizontal and vertical resolution, increased complexity, and more predicted and transported variables have increased the model computational cost and changed the simulations considerably. These changes required development of alternate strategies for tuning and evaluation as it was not feasible to “brute force” tune the high‐resolution configurations, so short‐term hindcasts, perturbed parameter ensemble simulations, and regionally refined simulations provided guidance on tuning and parameterization sensitivity to higher resolution. A brief overview of the model and model climate is provided. Model fidelity has generally improved compared to its predecessors and the CMIP5 generation of climate models.

Secondary organic aerosols (SOA) are large contributors to fine-particle loadings and radiative forcing but are often represented crudely in global models. We have implemented three new detailed SOA treatments within the Community Atmosphere Model version 5 (CAM5) that allow us to compare the semivolatile versus nonvolatile SOA treatments (based on some of the latest experimental findings) and to investigate the effects of gas-phase fragmentation reactions. The new treatments also track SOA from biomass burning and biofuel, fossil fuel, and biogenic sources. For semivolatile SOA treatments, fragmentation reactions decrease the simulated annual global SOA burden from 7.5 Tg to 1.8 Tg. For the nonvolatile SOA treatment with fragmentation, the burden is 3.1 Tg. Larger differences between nonvolatile and semivolatile SOA (up to a factor of 5) exist in areas of continental outflow over the oceans. According to comparisons with observations from global surface Aerosol Mass Spectrometer measurements and the U.S. Interagency Monitoring of Protected Visual Environments (IMPROVE) network measurements, the FragNVSOA treatment, which treats SOA as nonvolatile and includes gas-phase fragmentation reactions, agrees best at rural locations. Urban SOA is underpredicted, but this may be due to the coarse model resolution. All three revised treatments show much better agreement with aircraft measurements of organic aerosols (OA) over the North American Arctic and sub-Arctic in spring and summer, compared to the standard CAM5 formulation. This is mainly due to the oxidation of SOA precursor gases from biomass burning, not included in standard CAM5, and long-range transport of biomass burning OA at high altitudes. The revised model configurations that include fragmentation (both semivolatile and nonvolatile SOA) show much better agreement with MODerate resolution Imaging Spectrometers (MODIS) aerosol optical depth data over regions dominated by biomass burning during the summer compared to standard CAM5, and predict biomass burning and biofuel as the largest global source of OA, followed by biogenic and fossil fuel sources. The large contribution of biomass burning OA in the revised treatments is supported by these measurements, but the emissions and aging of SOA precursors and POA are uncertain, and need further investigation. The nonvolatile and semivolatile configurations with fragmentation predict the direct radiative forcing of SOA as À0.5 W m À2 and À0.26 W m À2 respectively, at top of the atmosphere, which are higher than previously estimated by most models, but in reasonable agreement with a recent constrained modeling study. This study highlights the importance of improving process-level representation of SOA in global models.

[1] Climatological mean estimates of forest burning and crop waste burning based on broad assumptions of the amounts burned have so far been used for India in global inventories.Here we estimate open biomass burning representative of 1995-2000 from forests using burned area and biomass density specific for Indian ecosystems and crop waste burning as a balance between generation and known uses as fuel and fodder. High-resolution satellite data of active fires and land cover classification from MODIS, both on a scale of 1 km  1 km, were used to capture the seasonal variability of forest and crop waste burning and in conjunction with field reporting. Correspondence in satellite-detected fire cycles with harvest season was used to identify types crop waste burned in different regions. The fire season in forest areas was from February to May, and that in croplands varied with geographical location, with peaks in April and October, corresponding to the two major harvest seasons. Spatial variability in amount of forest biomass burned differed from corresponding forest fire counts with biomass burned being largest in central India but fire frequency being highest in the east-northeast. Unutilized crop waste and MODIS cropland fires were predominant in the western IndoGangetic plain. However, the amounts of unutilized crop waste in the four regions were not strictly proportional to the fire counts. Fraction crop waste burned in fields ranged from 18 to 30% on an all-India basis and had a strong regional variation. Open burning contributes importantly (about 25%) to black carbon, organic matter, and carbon monoxide emissions, a smaller amount (9-13%) to PM 2.5 (particulate mass in particles smaller than 2.5 micron diameter) and CO 2 emissions, and negligibly to SO 2 emissions (1%). However, it cannot explain a large ''missing source'' of BC or CO from India.

Experimental measurements of gas-particle partitioning and organic aerosol mass in diluted diesel and wood combustion exhaust are interpreted using a two-component absorptive-partitioning model. The model parameters are determined by fitting the experimental data. The changes in partitioning with dilution of both wood smoke and diesel exhaust can be described by two lumped compounds in roughly equal abundance with effective saturation concentrations of approximately 1600 microg m(-3) and approximately 20 microg m(-3). The model is used to investigate gas-particle partitioning of emissions across a wide range of atmospheric conditions. Under the highly dilute conditions found in the atmosphere, the partitioning of the emissions is strongly influenced by the ambient temperature and the background organic aerosol concentration. The model predicts large changes in primary organic aerosol mass with varying atmospheric conditions, indicating that it is not possible to specify a single value for the organic aerosol emissions. Since atmospheric conditions vary in both space and time, air quality models need to treat primary organic aerosol emissions as semivolatile. Dilution samplers provide useful information about organic aerosol emissions; however, the measurements can be biased relative to atmospheric conditions and constraining predictions of absorptive-partitioning models requires emissions data across the entire range of atmospherically relevant concentrations.

No abstract

Hate speech detection in social media texts is an important Natural language Processing task, which has several crucial applications like sentiment analysis, investigating cyber bullying and examining socio-political controversies. While relevant research has been done independently on code-mixed social media texts and hate speech detection, our work is the first attempt in detecting hate speech in HindiEnglish code-mixed social media text. In this paper, we analyze the problem of hate speech detection in code-mixed texts and present a Hindi-English code-mixed dataset consisting of tweets posted online on Twitter. The tweets are annotated with the language at word level and the class they belong to (Hate Speech or Normal Speech). We also propose a supervised classification system for detecting hate speech in the text using various character level, word level, and lexicon based features.