Atmospheric reduced nitrogen: Sources, transformations, effects, and management.

Human activities have increased atmospheric emissions and deposition of oxidized and reduced forms of nitrogen, but emission control programs have largely focused on oxidized nitrogen. As a result, in many regions of the world emissions of oxidized nitrogen are decreasing while emissions of reduced nitrogen are increasing. Emissions of reduced nitrogen largely originate from livestock waste and fertilizer application, with contributions from transportation sources in urban areas. Observations suggest a discrepancy between trends in emissions and deposition of reduced nitrogen in the U.S., likely due to an underestimate in emissions. In the atmosphere, ammonia reacts with oxides of sulfur and nitrogen to form fine particulate matter that impairs health and visibility and affects climate forcings. Recent reductions in emissions of sulfur and nitrogen oxides have limited partitioning with ammonia, decreasing long-range transport. Continuing research is needed to improve understanding of how shifting emissions alter formation of secondary particulates and patterns of transport and deposition of reactive nitrogen. Satellite remote sensing has potential for monitoring atmospheric concentrations and emissions of ammonia, but there remains a need to maintain and strengthen ground-based measurements and continue development of chemical transport models. Elevated nitrogen deposition has decreased plant and soil microbial biodiversity and altered the biogeochemical function of terrestrial, freshwater, and coastal ecosystems. Further study is needed on differential effects of oxidized versus reduced nitrogen and pathways and timescales of ecosystem recovery from elevated nitrogen deposition. Decreases in deposition of reduced nitrogen could alleviate exceedances of critical loads for terrestrial and freshwater indicators in many U.S. areas. The U.S. Environmental Protection Agency should consider using critical loads as a basis for setting standards to protect public welfare and ecosystems. The U.S. and other countries might look to European experience for approaches to control emissions of reduced nitrogen from agricultural and transportation sectors.Implications: In this Critical Review we synthesize research on effects, air emissions, environmental transformations, and management of reduced forms of nitrogen. Emissions of reduced nitrogen affect human health, the structure and function of ecosystems, and climatic forcings. While emissions of oxidized forms of nitrogen are regulated in the U.S., controls on reduced forms are largely absent. Decreases in emissions of sulfur and nitrogen oxides coupled with increases in ammonia are shifting the gas-particle partitioning of ammonia and decreasing long-range atmospheric transport of reduced nitrogen. Effort is needed to understand, monitor, and manage emissions of reduced nitrogen in a changing environment.

Modeling Emissions and Ozone Air Quality Impacts of Future Scenarios for Energy and Power Production in the Rocky Mountain States.

This study examines air quality impacts of scenarios for energy production and use in 2030 across Colorado, northern New Mexico, Utah, and Wyoming. Scenarios feature contrasting levels of oil and gas production and shares of electricity from coal, natural gas, and renewables. Hourly emissions are resolved for individual power plants; oil and gas emissions are basin-specific. Ozone decreased from 2011 to the 2030 baseline, with median and 90th percentile reductions in maximum daily 8 h average (MDA8) ozone across the four-state domain of 3.5 and 7.1 ppb, respectively, resulting in 200 fewer premature deaths annually. Relative to the 2030 baseline, MDA8 ozone increased in the "cheap gas" scenario, with median and 90th percentile increases of 0.1 and 1.0 ppb, and declined in a scenario with greenhouse gas (GHG) emissions fees, with median and 90th percentile reductions of 0.2 and 1.5 ppb. Reduced coal generation lowered SO2 emissions in all future scenarios compared to 2011. GHG emissions from electricity and oil and gas production declined by 4% (CO2-equivalent) from 2011 to the 2030 baseline, increased by 10% from the 2030 baseline to the cheap gas scenario, and declined by 28% from the 2030 baseline to the GHG fees scenario.

Emission Impacts of Electric Vehicles in the US Transportation Sector Following Optimistic Cost and Efficiency Projections.

This study investigates emission impacts of introducing inexpensive and efficient electric vehicles into the US light duty vehicle (LDV) sector. Scenarios are explored using the ANSWER-MARKAL model with a modified version of the Environmental Protection Agency's (EPA) 9-region database. Modified cost and performance projections for LDV technologies are adapted from the National Research Council (2013) optimistic case. Under our optimistic scenario (OPT) we find 15% and 47% adoption of battery electric vehicles (BEVs) in 2030 and 2050, respectively. In contrast, gasoline vehicles (ICEVs) remain dominant through 2050 in the EPA reference case (BAU). Compared to BAU, OPT gives 16% and 36% reductions in LDV greenhouse gas (GHG) emissions for 2030 and 2050, respectively, corresponding to 5% and 9% reductions in economy-wide emissions. Total nitrogen oxides, volatile organic compounds, and SO2 emissions are similar in the two scenarios due to intersectoral shifts. Moderate, economy-wide GHG fees have little effect on GHG emissions from the LDV sector but are more effective in the electricity sector. In the OPT scenario, estimated well-to-wheels GHG emissions from full-size BEVs with 100-mile range are 62 gCO2-e mi-1 in 2050, while those from full-size ICEVs are 121 gCO2-e mi-1.

Increasing the Use of Earth Science Data and Models in Air Quality Management.

In 2010, the U.S. National Aeronautics and Space Administration (NASA) initiated the Air Quality Applied Science Team (AQAST) as a 5-year, $17.5-million award with 19 principal investigators. AQAST aims to increase the use of Earth science products in air quality-related research and to help meet air quality managers' information needs. We conducted a Web-based survey and a limited number of follow-up interviews to investigate federal, state, tribal, and local air quality managers' perspectives on usefulness of Earth science data and models, and on the impact AQAST has had. The air quality managers we surveyed identified meeting the National Ambient Air Quality Standards for ozone and particulate matter, emissions from mobile sources, and interstate air pollution transport as top challenges in need of improved information. Most survey respondents viewed inadequate coverage or frequency of satellite observations, data uncertainty, and lack of staff time or resources as barriers to increased use of satellite data by their organizations. Managers who have been involved with AQAST indicated that the program has helped build awareness of NASA Earth science products, and assisted their organizations with retrieval and interpretation of satellite data and with application of global chemistry and climate models. AQAST has also helped build a network between researchers and air quality managers with potential for further collaborations. IMPLICATIONS: NASA's Air Quality Applied Science Team (AQAST) aims to increase the use of satellite data and global chemistry and climate models for air quality management purposes, by supporting research and tool development projects of interest to both groups. Our survey and interviews of air quality managers indicate they found value in many AQAST projects and particularly appreciated the connections to the research community that the program facilitated. Managers expressed interest in receiving continued support for their organizations' use of satellite data, including assistance in retrieving and interpreting data from future geostationary platforms meant to provide more frequent coverage for air quality and other applications.

Managing Carbon on Federal Public Lands: Opportunities and Challenges in Southwestern Colorado.

Federal lands in the United States have been identified as important areas where forests could be managed to enhance carbon storage and help mitigate climate change. However, there has been little work examining the context for decision making for carbon in a multiple-use public land environment, and how science can support decision making. This case study of the San Juan National Forest and the Bureau of Land Management Tres Rios Field Office in southwestern Colorado examines whether land managers in these offices have adequate tools, information, and management flexibility to practice effective carbon stewardship. To understand how carbon was distributed on the management landscape we added a newly developed carbon map for the SJNF-TRFO area based on Landsat TM texture information (Kelsey and Neff in Remote Sens 6:6407-6422. doi: 10.3390/rs6076407 , 2014). We estimate that only about 22 % of the aboveground carbon in the SJNF-TRFO is in areas designated for active management, whereas about 38 % is in areas with limited management opportunities, and 29 % is in areas where natural processes should dominate. To project the effects of forest management actions on carbon storage, staff of the SJNF are expected to use the Forest Vegetation Simulator (FVS) and extensions. While identifying FVS as the best tool generally available for this purpose, the users and developers we interviewed highlighted the limitations of applying an empirically based model over long time horizons. Future research to improve information on carbon storage should focus on locations and types of vegetation where carbon management is feasible and aligns with other management priorities.

Impacts of Foreign, Domestic, and State-Level Emissions on Ozone-Induced Vegetation Loss in the United States.

Exposure to elevated levels of ozone leads to yield reduction in agricultural crops and biomass loss in trees. Here, we quantify the impact of ozone pollution on two major U.S. crops, wheat and soybean, and two ozone-sensitive tree species, ponderosa pine and quaking aspen, using simulations with the GEOS-Chem model for 2010. Using previously established exposure-response functions, we estimate nationwide relative yield reductions of 4.9% for wheat and 6.7% for soybean, and relative biomass loss of 2.5% and 2.9% for ponderosa pine and aspen seedlings, respectively. Adjoint model sensitivities are used to estimate the impact of emissions sources from different locations, species, and sectors. We find that the nationwide relative loss in each vegetation type is influenced most by domestic anthropogenic NOx (>75%). Long-range transport from foreign sources is small relative to domestic influences. More than half of the anthropogenic NOx responsible for vegetation damage originates from outside the states where the damage occurs. Texas and Missouri are the highest contributors to the nationwide loss of wheat and soybean, respectively. California "exports" ozone damage for all types of vegetation studied, due to its location, high share of anthropogenic NOx, and a relatively low share of vegetation.

The short-term association of selected components of fine particulate matter and mortality in the Denver Aerosol Sources and Health (DASH) study.

BACKGROUND: Associations of short-term exposure to fine particulate matter (PM2.5) with daily mortality may be due to specific PM2.5 chemical components. Daily concentrations of PM2.5 components were measured over five years in Denver to investigate whether specific PM2.5 components are associated with daily mortality. METHODS: Daily counts of total and cause-specific deaths were obtained for the 5-county Denver metropolitan region from 2003 through 2007. Daily 24-hour concentrations of PM2.5, elemental carbon (EC), organic carbon (OC), sulfate and nitrate were measured at a central residential monitoring site. Using generalized additive models, we estimated relative risks (RRs) of daily death counts for daily PM2.5 and four PM2.5 component concentrations at single and distributed lags between the current and three previous days, while controlling for longer-term time trend and meteorology. RESULTS: RR of total non-accidental mortality for an inter-quartile increase of 4.55 µg/m(3) in PM2.5 distributed over 4 days was 1.012 (95 % confidence interval: 0.999, 1.025); RRs for EC and OC were larger (1.024 [1.005, 1.043] and 1.020 [1.000, 1.040] for 0.33 and 1.67 µg/m(3) increases, respectively) than those for sulfate and nitrate. We generally did not observe associations with cardiovascular and respiratory mortality except for associations with ischemic heart disease mortality at lags 3 and 0-3 depending on the component. In addition, there were associations with cancer mortality, particularly for EC and OC, possibly reflecting advanced deaths of a frail population. CONCLUSIONS: PM2.5 components possibly from combustion-related sources are more strongly associated with daily mortality than are secondary inorganic aerosols.

Emissions implications of future natural gas production and use in the U.S. and in the Rocky Mountain region.

Enhanced prospects for natural gas production raise questions about the balance of impacts on air quality, as increased emissions from production activities are considered alongside the reductions expected when natural gas is burned in place of other fossil fuels. This study explores how trends in natural gas production over the coming decades might affect emissions of greenhouse gases (GHG), volatile organic compounds (VOCs) and nitrogen oxides (NOx) for the United States and its Rocky Mountain region. The MARKAL (MARKet ALlocation) energy system optimization model is used with the U.S. Environmental Protection Agency's nine-region database to compare scenarios for natural gas supply and demand, constraints on the electricity generation mix, and GHG emissions fees. Through 2050, total energy system GHG emissions show little response to natural gas supply assumptions, due to offsetting changes across sectors. Policy-driven constraints or emissions fees are needed to achieve net reductions. In most scenarios, wind is a less expensive source of new electricity supplies in the Rocky Mountain region than natural gas. U.S. NOx emissions decline in all the scenarios considered. Increased VOC emissions from natural gas production offset part of the anticipated reductions from the transportation sector, especially in the Rocky Mountain region.

Unpacking the 'information barrier': comparing perspectives on information as a barrier to climate change adaptation in the interior mountain West.

Inadequate information has been repeatedly identified as a barrier to climate change adaptation planning and implementation. However less is known about how information functions as a barrier, and to what degree it prevents adaptation compared to other perceived barriers. In addition, the role of institutional context in mediating the demand for information in the context of adaptation has been less well studied. This paper helps to clarify the role that information plays in adaptation planning for two sectors of public employees working at similar scales, in similar locations, with similar challenges. We conducted surveys and semi-structured interviews to investigate the demand for information in support of adaptation implementation and planning from US federal public lands managers and municipal officials in the US interior West. We found that federal managers and municipal officials both consulted information frequently for decision making, and while both groups indicated that lack of information at relevant scales was a barrier to adaptation planning, this was seen as a much stronger barrier for federal managers than for communities. Uncertainty of information was raised as an issue, but results were mixed on whether or not this acted as a strong barrier. While peer-reviewed publications were seen as the "best available science," and correlated with adaptation planning, they were not accessed directly as frequently as other sources of information, including colleagues, the internet and reports. The strong connection between communities and adjacent federal lands may provide an opportunity for networking that could facilitate the flow of information relevant for adaptation.

Accounting for climate and air quality damages in future U.S. electricity generation scenarios.

The EPA-MARKAL model of the U.S. electricity sector is used to examine how imposing emissions fees based on estimated health and environmental damages might change electricity generation. Fees are imposed on life-cycle emissions of SO(2), nitrogen oxides (NO(x)), particulate matter, and greenhouse gases (GHG) from 2015 through 2055. Changes in electricity production, fuel type, emissions controls, and emissions produced under various fees are examined. A shift in fuels used for electricity production results from $30/ton CO(2)-equivalent GHG fees or from criteria pollutant fees set at the higher-end of the range of published damage estimates, but not from criteria pollutant fees based on low or midrange damage estimates. With midrange criteria pollutant fees assessed, SO(2) and NOx emissions are lower than the business as usual case (by 52% and 10%, respectively), with larger differences in the western U.S. than in the eastern U.S. GHG emissions are not significantly impacted by midrange criteria pollutant fees alone; conversely, with only GHG fees, NO(x) emissions are reduced by up to 11%, yet SO(2) emissions are slightly higher than in the business as usual case. Therefore, fees on both GHG and criteria pollutants may be needed to achieve significant reductions in both sets of pollutants.

Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods.

In studies of coarse particulate matter (PM10-2.5), mass concentrations are often estimated through the subtraction of PM2.5 from collocated PM10 tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM10-2.5 estimation when using the possible combinations of PM10 and PM2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural-Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM10-2.5 mass and spatial characteristics due to the very low semivolatile PM10-2.5 concentrations in Colorado. Estimation using either a PM2.5 or PM10 monitor without FDMS introduced absolute biases of 2.4 microg/m3 (25%) to -2.3 microg/m3 (-24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM2.5 TEOM and PM10 TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM2.5 from total PM2.5 concentrations. By estimating nonvolatile PM2.5 concentrations from this relationship, PM10-2.5 was calculated as the difference between nonvolatile PM10 and PM2.5 concentrations.

Positive matrix factorization of a 32-month series of daily PM2.5 speciation data with incorporation of temperature stratification.

This study presents source apportionment results for PM2.5 from applying positive matrix factorization (PMF) to a 32-month series of daily PM2.5 compositional data from Denver, CO, including concentrations of sulfate, nitrate, bulk elemental carbon (EC) and organic carbon (OC), and 51 organic molecular markers (OMMs). An optimum 8-factor solution was determined primarily based on the interpretability of the PMF results and rate of matching factors from bootstrapped PMF solutions with those from the base case solution. These eight factors were identified as inorganic ion, n-alkane, EC/sterane, light n-alkane/polycyclic aromatic hydrocarbon (PAH), medium alkane/alkanoic acid, PAH, winter/methoxyphenol and summer/odd n-alkane. The inorganic ion factor dominated the reconstructed PM2.5 mass (sulfate + nitrate + EC + OC) in cold periods (daily average temperature < 10 °C; 43.7% of reconstructed PM2.5 mass) whereas the summer/odd n-alkane factor dominated in hot periods (> 20 °C; 53.1%). The two factors had comparable relative contributions of 26.5% and 27.1% in warm periods with temperatures between 10 °C and 20 °C. Each of the seven factors resolved in a previous study (Dutton et al., 2010b) using a 1-year data set from the same location matches one factor from the current work based on comparing factor profiles. Six out of the seven matched pairs of factors are linked to similar source classes as suggested by the strong correlations between factor contributions (r = 0.89 - 0.98). Temperature-stratified source apportionment was conducted for three subsets of the data in the current study, corresponding to the cold, warm and hot periods mentioned above. The cold period (7-factor) solution exhibited a similar distribution of reconstructed PM2.5 mass as the full data set solution. The factor contributions of the warm period (7-factor) solution were well correlated with those from the full data set solution (r = 0.76 - 0.99). However, the reconstructed PM2.5 mass was distributed more to inorganic ion, n-alkane and medium alkane/alkanoic acid factors in the warm period solution than in the full data set solution. For the hot period (6-factor) solution, PM2.5 mass distribution was quite different from that of the full data set solution, as illustrated by regression slopes as low as 0.2 and as high as 4.8 of each matched pair of factors across the two solutions.

Positive matrix factorization of PM(2.5): comparison and implications of using different speciation data sets.

To evaluate the utility and consistency of different speciation data sets in source apportionment of PM(2.5), positive matrix factorization (PMF) coupled with a bootstrap technique for uncertainty assessment was applied to four different 1-year data sets composed of bulk species, bulk species and water-soluble elements (WSE), bulk species and organic molecular markers (OMM), and all species. The five factors resolved by using only the bulk species best reproduced the observed concentrations of PM(2.5) components. Combining WSE with bulk species as PMF inputs also produced five factors. Three of them were linked to soil, road dust, and processed dust, and together contributed 26.0% of reconstructed PM(2.5) mass. A 7-factor PMF solution was identified using speciated OMM and bulk species. The EC/sterane and summertime/selective aliphatic factors had the highest contributions to EC (39.0%) and OC (53.8%), respectively. The nine factors resolved by including all species as input data are consistent with those from the previous two solutions (WSE and bulk species, OMM and bulk species) in both factor profiles and contributions (r = 0.88-1.00). The comparisons across different solutions indicate that the selection of input data set may depend on the PM components or sources of interest for specific source-oriented health study.

Managing United States public lands in response to climate change: a view from the ground up.

Federal land managers are faced with the task of balancing multiple uses and goals when making decisions about land use and the activities that occur on public lands. Though climate change is now well recognized by federal agencies and their local land and resource managers, it is not yet clear how issues related to climate change will be incorporated into on-the-ground decision making within the framework of multiple use objectives. We conducted a case study of a federal land management agency field office, the San Juan Public Lands Center in Durango, CO, U.S.A., to understand from their perspective how decisions are currently made, and how climate change and carbon management are being factored into decision making. We evaluated three major management sectors in which climate change or carbon management may intersect other use goals: forests, biofuels, and grazing. While land managers are aware of climate change and eager to understand more about how it might affect land resources, the incorporation of climate change considerations into everyday decision making is currently quite limited. Climate change is therefore on the radar screen, but remains a lower priority than other issues. To assist the office in making decisions that are based on sound scientific information, further research is needed into how management activities influence carbon storage and resilience of the landscape under climate change.

Intra-urban spatial variability of PM2.5-bound carbonaceous components.

The Denver Aerosol Sources and Health (DASH) study was designed to evaluate associations between PM2.5 species and sources and adverse human health effects. The DASH study generated a five-year (2003-2007) time series of daily speciated PM2.5 concentration measurements from a single, special-purpose monitoring site in Denver, CO. To evaluate the ability of this site to adequately represent the short term temporal variability of PM2.5 concentrations in the five county Denver metropolitan area, a one year supplemental set of PM2.5 samples was collected every sixth day at the original DASH monitoring site and concurrently at three additional sites. Two of the four sites, including the original DASH site, were located in residential areas at least 1.9 km from interstate highways. The other two sites were located within 0.3 km of interstate highways. Concentrations of elemental carbon (EC), organic carbon (OC), and 58 organic molecular markers were measured at each site. To assess spatial variability, site pairs were compared using the Pearson correlation coefficient (r) and coefficient of divergence (COD), a statistic that provides information on the degree of uniformity between monitoring sites. Biweekly co-located samples collected from July 2004 to September 2005 were also analyzed and used to estimate the uncertainty associated with sampling and analytical measurement for each species. In general, the two near-highway sites exhibited higher concentrations of EC, OC, polycyclic aromatic hydrocarbons (PAHs), and steranes than did the more residential sites. Lower spatial heterogeneity based on r and COD was inferred for all carbonaceous species after considering their divergence and lack of perfect correlations in co-located samples. Ratio-ratio plots combined with available gasoline- and diesel-powered motor vehicle emissions profiles for the region suggested a greater impact to high molecular weight (HMW) PAHs from diesel-powered vehicles at the near-highway sites and a more uniformly distributed impact to ambient hopanes from gasoline-powered motor vehicles at all four sites.

Intra-urban spatial variability and uncertainty assessment of PM2.5 sources based on carbonaceous species.

To identify the sources of PM2.5 - bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5 compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th - 95th percentiles range) of factor contributions, expressed as reconstructed carbonaceous mass (EC + OC), were estimated from PMF solutions of replicate datasets generated by using a stationary block bootstrap technique. A seven-factor solution was resolved for a set of data pooled across the four sites, as it gave the most interpretable results and had the highest rate of neural network factor matching (76.9%). Identified factors were primarily associated with high plant wax, summertime emission, diesel vehicle emission, fossil fuel combustion, motor vehicle emission, lubricating oil combustion and wood burning. Pearson correlation coefficients (r) and coefficients of divergence (COD) were used to assess spatial variability of factor contributions. The summertime emission factor exhibited the highest spatial correlation (r = 0.74 - 0.88) and lowest CODs (0.32 - 0.38) among all resolved factors; while the three traffic dominated factors (diesel vehicle emission, motor vehicle emission and lubricating oil combustion) showed lower correlations (r = 0.47 - 0.55) and higher CODs (0.41 - 0.53) on average. Average total EC and OC mass were apportioned to each factor and showed a similar distribution across the four sites. Modeling uncertainties were defined as the 5th - 95th percentile range of the factor contributions derived from valid bootstrap PMF solutions, and were highly correlated with the median factor contribution in each factor (r = 0.77 - 0.98). Source apportionment was also performed on site specific datasets; the results exhibited similar factor profiles and temporal variation in factor contribution as those obtained for the pooled dataset, indicating that the four sites are primarily influenced by similar types of sources. On the other hand, differences were observed in absolute factor contributions between PMF solutions for the pooled versus site-specific datasets, likely due to the large uncertainties in EC and OC factor profiles derived from the site specific datasets with limited numbers of observations.

Effects of plug-in hybrid electric vehicles on ozone concentrations in Colorado.

This study explores how ozone concentrations in the Denver, CO area might have been different if plug-in hybrid electric vehicles (PHEVs) had replaced light duty gasoline vehicles in summer 2006. A unit commitment and dispatch model was used to estimate the charging patterns of PHEVs and dispatch power plants to meet electricity demand. Emission changes were estimated based on gasoline displacement and the emission characteristics of the power plants providing additional electricity. The Comprehensive Air Quality Model with extensions (CAMx) was used to simulate the effects of these emissions changes on ozone concentrations. Natural gas units provided most of the electricity used for charging PHEVs in the scenarios considered. With 100% PHEV penetration, nitrogen oxide (NO(x)) emissions were reduced by 27 tons per day (tpd) from a fleet of 1.7 million vehicles and were increased by 3 tpd from power plants; VOC emissions were reduced by 57 tpd. These emission changes reduced modeled peak 8-h average ozone concentrations by approximately 2-3 ppb on most days. Ozone concentration increases were modeled for small areas near central Denver. Future research is needed to forecast when significant PHEV penetration may occur and to anticipate characteristics of the corresponding power plant and vehicle fleets.

Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5).

Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been linked with a wide range of adverse health effects. Determination of the sources of PM(2.5) most responsible for these health effects could lead to improved understanding of the mechanisms of such effects and more targeted regulation. This has provided the impetus for the Denver Aerosol Sources and Health (DASH) study, a multi-year source apportionment and health effects study relying on detailed inorganic and organic PM(2.5) speciation measurements.In this study, PM(2.5) source apportionment is performed by coupling positive matrix factorization (PMF) with daily speciated PM(2.5) measurements including inorganic ions, elemental carbon (EC) and organic carbon (OC), and organic molecular markers. A qualitative comparison is made between two models, PMF2 and ME2, commonly used for solving the PMF problem. Many previous studies have incorporated chemical mass balance (CMB) for organic molecular marker source apportionment on limited data sets, but the DASH data set is large enough to use multivariate factor analysis techniques such as PMF.Sensitivity of the PMF2 and ME2 models to the selection of speciated PM(2.5) components and model input parameters was investigated in depth. A combination of diagnostics was used to select an optimum, 7-factor model using one complete year of daily data with pointwise measurement uncertainties. The factors included 1) a wintertime/methoxyphenol factor, 2) an EC/sterane factor, 3) a nitrate/polycyclic aromatic hydrocarbon (PAH) factor, 4) a summertime/selective aliphatic factor, 5) an n-alkane factor, 6) a middle oxygenated PAH/alkanoic acid factor and 7) an inorganic ion factor. These seven factors were qualitatively linked with known PM(2.5) emission sources with varying degrees of confidence. Mass apportionment using the 7-factor model revealed the contribution of each factor to the mass of OC, EC, nitrate and sulfate. On an annual basis, the majority of OC and EC mass was associated with the summertime/selective aliphatic factor and the EC/sterane factor, respectively, while nitrate and sulfate mass were both dominated by the inorganic ion factor. This apportionment was found to vary substantially by season. Several of the factors identified in this study agree well with similar assessments conducted in St. Louis, MO and Pittsburgh, PA using PMF and organic molecular markers.

After the clean air mercury rule: prospects for reducing mercury emissions from coal-fired power plants.

Recent court decisions have affected the EPA's regulation of mercury emissions from coal burning, but some state laws are helping to clear the air.