Modeling the formation and aging of secondary organic aerosols during CalNex 2010

Four different literature parameterizations for the formation and evolution of urban secondary organic aerosol (SOA) frequently used in 3-D models are evaluated using a 0-D box model representing the Los Angeles metropolitan region during the California Research at the Nexus of Air Quality and Climate Change (CalNex) 2010 campaign. We constrain the model predictions with measurements from several platforms and compare predictions with particle- and gas-phase observations from the CalNex Pasadena ground site. That site provides a unique opportunity to study aerosol formation close to anthropogenic emission sources with limited recirculation. The model SOA that formed only from the oxidation of VOCs (V-SOA) is insufficient to explain the observed SOA concentrations, even when using SOA parameterizations with multi-generation oxidation that produce much higher yields than have been observed in chamber experiments, or when increasing yields to their upper limit estimates accounting for recently reported losses of vapors to chamber walls. The Community Multiscale Air Quality (WRF-CMAQ) model (version 5.0.1) provides excellent predictions of secondary inorganic particle species but underestimates the observed SOA mass by a factor of 25 when an older VOC-only parameterization is used, which is consistent with many previous model–measurement comparisons for pre-2007 anthropogenic SOA modules in urban areas. Including SOA from primary semi-volatile and intermediate-volatility organic compounds (P-S/IVOCs) following the parameterizations of Robinson et al. (2007), Grieshop et al. (2009), or Pye and Seinfeld (2010) improves model–measurement agreement for mass concentration. The results from the three parameterizations show large differences (e.g., a factor of 3 in SOA mass) and are not well constrained, underscoring the current uncertainties in this area. Our results strongly suggest that other precursors besides VOCs, such as P-S/IVOCs, are needed to explain the observed SOA concentrations in Pasadena. All the recent parameterizations overpredict urban SOA formation at long photochemical ages (3 days) compared to observations from multiple sites, which can lead to problems in regional and especially global modeling. However, reducing IVOC emissions by one-half in the model to better match recent IVOC measurements improves SOA predictions at these long photochemical ages. Among the explicitly modeled VOCs, the precursor compounds that contribute the greatest SOA mass are methylbenzenes. Measured polycyclic aromatic hydrocarbons (naphthalenes) contribute 0.7% of the modeled SOA mass. The amounts of SOA mass from diesel vehicles, gasoline vehicles, and cooking emissions are estimated to be 16–27, 35–61, and 19–35 %, respectively, depending on the parameterization used, which is consistent with the observed fossil fraction of urban SOA, 71(+-3) %. The relative contribution of each source is uncertain by almost a factor of 2 depending on the parameterization used. In-basin biogenic VOCs are predicted to contribute only a few percent to SOA. A regional SOA background of approximately 2.1 μgm-3 is also present due to the long-distance transport of highly aged OA, likely with a substantial contribution from regional biogenic SOA. The percentage of SOA from diesel vehicle emissions is the same, within the estimated uncertainty, as reported in previous work that analyzed the weekly cycles in OA concentrations (Bahreini et al., 2012; Hayes et al., 2013). However, the modeling work presented here suggests a strong anthropogenic source of modern carbon in SOA, due to cooking emissions, which was not accounted for in those previous studies and which is higher on weekends. Lastly, this work adapts a simple two-parameter model to predict SOA concentration and O/C from urban emissions. This model successfully predicts SOA concentration, and the optimal parameter combination is very similar to that found for Mexico City. This approach provides a computationally inexpensive method for predicting urban SOA in global and climate models. We estimate pollution SOA to account for 26 Tg yr-1 of SOA globally, or 17% of global SOA, one third of which is likely to be non-fossil.

Predicting 5- and 10-year survival in older women with early-stage breast cancer: self-rated health and walking ability.

OBJECTIVES To determine life expectancy for older women with breast cancer. DESIGN Prospective longitudinal study with 10 years of follow-up data. SETTING Hospitals or collaborating tumor registries in four geographic regions (Los Angeles, California; Minnesota; North Carolina; Rhode Island). PARTICIPANTS Women aged 65 and older at time of breast cancer diagnosis with Stage I to IIIA disease with measures of self-rated health (SRH) and walking ability at baseline (N = 615; 17% aged ≥80, 52% Stage I, 58% with ≥2 comorbidities). MEASUREMENTS Baseline SRH, baseline self-reported walking ability, all-cause and breast cancer-specific estimated probability of 5- and 10-year survival. RESULTS At the time of breast cancer diagnosis, 39% of women reported poor SRH, and 28% reported limited ability to walk several blocks. The all-cause survival curves appear to separate after approximately 3 years, and the difference in survival probability between those with low SRH and limited walking ability and those with high SRH and no walking ability limitation was significant (0.708 vs 0.855 at 5 years, P ≤ .001; 0.300 vs 0.648 at 10 years, P < .001). There were no differences between the groups in breast cancer-specific survival at 5 and 10 years (P = .66 at 5 years, P = .16 at 10 years). CONCLUSION The combination of low SRH and limited ability to walk several blocks at diagnosis is an important predictor of worse all-cause survival at 5 and 10 years. These self-report measures easily assessed in clinical practice may be an effective strategy to improve treatment decision-making in older adults with cancer.

Denoising Your Monte Carlo Renders: Recent Advances in Image Space-Adaptive Sampling and Reconstruction

With the ongoing shift in the computer graphics industry toward Monte Carlo rendering, there is a need for effective, practical noise-reduction techniques that are applicable to a wide range of rendering effects and easily integrated into existing production pipelines. This course surveys recent advances in image-space adaptive sampling and reconstruction algorithms for noise reduction, which have proven very effective at reducing the computational cost of Monte Carlo techniques in practice. These approaches leverage advanced image-filtering techniques with statistical methods for error estimation. They are attractive because they can be integrated easily into conventional Monte Carlo rendering frameworks, they are applicable to most rendering effects, and their computational overhead is modest.