Air pollution evolution: Transport-derived emissions of volatile organic compounds (VOCs) have decreased owing to stricter controls on air pollution. This means that the relative importance of chemicals in pesticides, coatings, printing inks, adhesives, cleaning agents, and personal care products has increased. McDonald et al. show that these volatile chemical products now contribute fully one-half of emitted VOCs in 33 industrialized cities (see the Perspective by Lewis). Thus, the focus of efforts to mitigate ozone formation and toxic chemical burdens need to be adjusted. Science , this issue p. 760 ; see also p. 744

PH is a fundamental aerosol property that affects ambient particle concentration and composition, linking pH to all aerosol environmental impacts. Here, PM₁ and PM_2. 5 pH are calculated based on data from measurements during the California Research at the Nexus of Air Quality and Climate Change (CalNex) study from 15 May to 15 June 2010 in Pasadena, CA. Particle pH and water were predicted with the ISORROPIA-II thermodynamic model and validated by comparing predicted to measured gas–particle partitioning of inorganic nitrate, ammonium, and chloride. The study mean ± standard deviation PM₁ pH was 1.9 ± 0.5 for the SO₄²⁻–NO₃⁻–NH₄⁺–HNO₃–NH₃ system. For PM_2. 5, internal mixing of sea salt components (SO₄²⁻–NO₃⁻–NH₄⁺–Na⁺–Cl⁻–K⁺–HNO₃–NH₃–HCl system) raised the bulk pH to 2.7 ± 0.3 and improved predicted nitric acid partitioning with PM_2. 5 components. The results show little effect of sea salt on PM₁ pH, but significant effects on PM_2. 5 pH. A mean PM₁ pH of 1.9 at Pasadena was approximately one unit higher than what we have reported in the southeastern US, despite similar temperature, relative humidity, and sulfate ranges, and is due to higher total nitrate concentrations (nitric acid plus nitrate) relative to sulfate, a situation where particle water is affected by semi-volatile nitrate concentrations. Under these conditions nitric acid partitioning can further promote nitrate formation by increasing aerosol water, which raises pH by dilution, further increasing nitric acid partitioning and resulting in a significant increase in fine particle nitrate and pH. This study provides insights into the complex interactions between particle pH and nitrate in a summertime coastal environment and a contrast to recently reported pH in the eastern US in summer and winter and the eastern Mediterranean. All studies have consistently found highly acidic PM₁ with pH generally below 3.

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 μg m⁻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.

Anthropogenic secondary organic aerosol (ASOA), formed from anthropogenic emissions of organic compounds, constitutes a substantial fraction of the mass of submicron aerosol in populated areas around the world and contributes to poor air quality and premature mortality. However, the precursor sources of ASOA are poorly understood, and there are large uncertainties in the health benefits that might accrue from reducing anthropogenic organic emissions. We show that the production of ASOA in 11 urban areas on three continents is strongly correlated with the reactivity of specific anthropogenic volatile organic compounds. The differences in ASOA production across different cities can be explained by differences in the emissions of aromatics and intermediate- and semi-volatile organic compounds, indicating the importance of controlling these ASOA precursors. With an improved model representation of ASOA driven by the observations, we attribute 340 000 PM_2.5-related premature deaths per year to ASOA, which is over an order of magnitude higher than prior studies. A sensitivity case with a more recently proposed model for attributing mortality to PM_2.5 (the Global Exposure Mortality Model) results in up to 900 000 deaths. A limitation of this study is the extrapolation from cities with detailed studies and regions where detailed emission inventories are available to other regions where uncertainties in emissions are larger. In addition to further development of institutional air quality management infrastructure, comprehensive air quality campaigns in the countries in South and Central America, Africa, South Asia, and the Middle East are needed for further progress in this area.

Field studies in polluted areas over the last decade have observed large formation of secondary organic aerosol (SOA) that is often poorly captured by models. The study of SOA formation using ambient data is often confounded by the effects of advection, vertical mixing, emissions, and variable degrees of photochemical aging. An oxidation flow reactor (OFR) was deployed to study SOA formation in real-time during the California Research at the Nexus of Air Quality and Climate Change (CalNex) campaign in Pasadena, CA, in 2010. A high-resolution aerosol mass spectrometer (AMS) and a scanning mobility particle sizer (SMPS) alternated sampling ambient and reactor-aged air. The reactor produced OH concentrations up to 4 orders of magnitude higher than in ambient air. OH radical concentration was continuously stepped, achieving equivalent atmospheric aging of 0.8 days–6.4 weeks in 3 min of processing every 2 h. Enhancement of organic aerosol (OA) from aging showed a maximum net SOA production between 0.8–6 days of aging with net OA mass loss beyond 2 weeks. Reactor SOA mass peaked at night, in the absence of ambient photochemistry and correlated with trimethylbenzene concentrations. Reactor SOA formation was inversely correlated with ambient SOA and O_x, which along with the short-lived volatile organic compound correlation, indicates the importance of very reactive (τ_OH ∼ 0.3 day) SOA precursors (most likely semivolatile and intermediate volatility species, S/IVOCs) in the Greater Los Angeles Area. Evolution of the elemental composition in the reactor was similar to trends observed in the atmosphere (O : C vs. H : C slope ∼ −0.65). Oxidation state of carbon (OSc) in reactor SOA increased steeply with age and remained elevated (OS_C ∼ 2) at the highest photochemical ages probed. The ratio of OA in the reactor output to excess CO (ΔCO, ambient CO above regional background) vs. photochemical age is similar to previous studies at low to moderate ages and also extends to higher ages where OA loss dominates. The mass added at low-to-intermediate ages is due primarily to condensation of oxidized species, not heterogeneous oxidation. The OA decrease at high photochemical ages is dominated by heterogeneous oxidation followed by fragmentation/evaporation. A comparison of urban SOA formation in this study with a similar study of vehicle SOA in a tunnel suggests the importance of vehicle emissions for urban SOA. Pre-2007 SOA models underpredict SOA formation by an order of magnitude, while a more recent model performs better but overpredicts at higher ages. These results demonstrate the value of the reactor as a tool for in situ evaluation of the SOA formation potential and OA evolution from ambient air.

Mounting evidence from field and laboratory observations coupled with atmospheric model analyses shows that primary combustion emissions of organic compounds dynamically partition between the vapor and particulate phases, especially as near-source emissions dilute and cool to ambient conditions. The most recent version of the Community Multiscale Air Quality model version 5.2 (CMAQv5.2) accounts for the semivolatile partitioning and gas-phase aging of these primary organic aerosol (POA) compounds consistent with experimentally derived parameterizations. We also include a new surrogate species, potential secondary organic aerosol from combustion emissions (pcSOA), which provides a representation of the secondary organic aerosol (SOA) from anthropogenic combustion sources that could be missing from current chemical transport model predictions. The reasons for this missing mass likely include the following: (1) unspeciated semivolatile and intermediate volatility organic compound (SVOC and IVOC, respectively) emissions missing from current inventories, (2) multigenerational aging of organic vapor products from known SOA precursors (e.g., toluene, alkanes), (3) underestimation of SOA yields due to vapor wall losses in smog chamber experiments, and (4) reversible organic compounds–water interactions and/or aqueous-phase processing of known organic vapor emissions. CMAQ predicts the spatially averaged contribution of pcSOA to OA surface concentrations in the continental United States to be 38.6 and 23.6 % in the 2011 winter and summer, respectively. Whereas many past modeling studies focused on a particular measurement campaign, season, location, or model configuration, we endeavor to evaluate the model and important uncertain parameters with a comprehensive set of United States-based model runs using multiple horizontal scales (4 and 12 km), gas-phase chemical mechanisms, and seasons and years. The model with representation of semivolatile POA improves predictions of hourly OA observations over the traditional nonvolatile model at sites during field campaigns in southern California (CalNex, May–June 2010), northern California (CARES, June 2010), the southeast US (SOAS, June 2013; SEARCH, January and July, 2011). Model improvements manifest better correlations (e.g., the correlation coefficient at Pasadena at night increases from 0.38 to 0.62) and reductions in underprediction during the photochemically active afternoon period (e.g., bias at Pasadena from −5.62 to −2.42 µg m⁻³). Daily averaged predictions of observations at routine-monitoring networks from simulations over the continental US (CONUS) in 2011 show modest improvement during winter, with mean biases reducing from 1.14 to 0.73 µg m⁻³, but less change in the summer when the decreases from POA evaporation were similar to the magnitude of added SOA mass. Because the model-performance improvement realized by including the relatively simple pcSOA approach is similar to that of more-complicated parameterizations of OA formation and aging, we recommend caution when applying these more-complicated approaches as they currently rely on numerous uncertain parameters. The pcSOA parameters optimized for performance at the southern and northern California sites lead to higher OA formation than is observed in the CONUS evaluation. This may be due to any of the following: variations in real pcSOA in different regions or time periods, too-high concentrations of other OA sources in the model that are important over the larger domain, or other model issues such as loss processes. This discrepancy is likely regionally and temporally dependent and driven by interferences from factors like varying emissions and chemical regimes.