Photochemical Aging of Atmospheric Particulate Matter in the Aqueous Phase.

This study focused on the photoaging of atmospheric particulate matter smaller than 2.5 µm (PM2.5) in the aqueous phase. PM2.5 was collected during a winter, a spring, and a summer campaign in urban and rural settings in Colorado and extracted into water. The aqueous extracts were photoirradiated using simulated sunlight, and the production rate (r•OH) and the effects of hydroxyl radicals (•OH) were measured as well as the optical properties as a function of the photoaging of the extracts. r•OH was seen to have a strong seasonality with low mean values for the winter and spring extracts (4.8 and 14 fM s-1 mgC-1 L, respectively) and a higher mean value for the summer extracts (65.4 fM s-1 mgC-1 L). For the winter extracts, •OH was seen to mostly originate from nitrate photolysis while for the summer extracts, a correlation was seen between r•OH and iron concentration. The extent of photobleaching of the extracts was correlated with r•OH, and the correlation also indicated that non-•OH processes took place. Using the •OH measurements and singlet oxygen (1O2) measurements, the half-life of a selection of compounds was modeled in the atmospheric aqueous phase to be between 1.9 and 434 h.

Health impacts of a randomized biomass cookstove intervention in northern Ghana.

BACKGROUND: Household air pollution (HAP) from cooking with solid fuels has adverse health effects. REACCTING (Research on Emissions, Air quality, Climate, and Cooking Technologies in Northern Ghana) was a randomized cookstove intervention study that aimed to determine the effects of two types of "improved" biomass cookstoves on health using self-reported health symptoms and biomarkers of systemic inflammation from dried blood spots for female adult cooks and children, and anthropometric growth measures for children only. METHODS: Two hundred rural households were randomized into four different cookstove groups. Surveys and health measurements were conducted at four time points over a two-year period. Chi-square tests were conducted to determine differences in self-reported health outcomes. Linear mixed models were used to assess the effect of the stoves on inflammation biomarkers in adults and children, and to assess the z-score deviance for the anthropometric data for children. RESULTS: We find some evidence that two biomarkers of oxidative stress and inflammation, serum amyloid A and C-reactive protein, decreased among adult primary cooks in the intervention groups relative to the control group. We do not find detectable impacts for any of the anthropometry variables or self-reported health. CONCLUSIONS: Overall, we conclude that the REACCTING intervention did not substantially improve the health outcomes examined here, likely due to continued use of traditional stoves, lack of evidence of particulate matter emissions reductions from "improved" stoves, and mixed results for HAP exposure reductions. CLINICAL TRIAL REGISTRY: ClinicalTrials.gov (National Institutes of Health); Trial Registration Number: NCT04633135 ; Date of Registration: 11 November 2020 - Retrospectively registered. URL: https://clinicaltrials.gov/ct2/show/NCT04633135?term=NCT04633135&draw=2&rank=1.

Updated Emission Factors from Diffuse Combustion Sources in Sub-Saharan Africa and Their Effect on Regional Emission Estimates.

Diffuse emission sources outside of kitchen areas are poorly understood, and measurements of their emission factors (EFs) are sparse for regions of sub-Saharan Africa. Thirty-one in-field emission measurements were taken in northern Ghana from combustion sources common to rural regions worldwide. Sources sampled included commercial cooking, trash burning, kerosene lanterns, and diesel generators. EFs were calculated for carbon monoxide (CO), carbon dioxide (CO2), as well as carbonaceous particulate matter, specifically elemental carbon (EC) and organic carbon (OC). EC and OC emissions were measured from kerosene lighting events (EFEC = 25.1 g/kg-fuel SD = 25.7, EFOC = 9.5 g/kg-fuel SD = 10.0). OC emissions from trash burning events were large and highly variable (EFOC = 38.9 g/kg-fuel SD = 30.5). Combining our results with other recent in-field emission factors for rural Ghana, we explored updated emission estimates for Ghana using a region specific emissions inventory. Large differences are calculated for all updated source emissions, showing a 96% increase in OC and 78% decrease in EC compared to prior estimates for Ghana's emissions. Differences for carbon monoxide were small when averaged across all updated source types (-1%), though the household wood use and trash burning categories individually show large differences.

Prices, peers, and perceptions (P3): study protocol for improved biomass cookstove project in northern Ghana.

BACKGROUND: Despite their potential health and social benefits, adoption and use of improved cookstoves has been low throughout much of the world. Explanations for low adoption rates of these technologies include prices that are not affordable for the target populations, limited opportunities for households to learn about cookstoves through peers, and perceptions that these technologies are not appropriate for local cooking needs. The P3 project employs a novel experimental design to explore each of these factors and their interactive effects on cookstove demand, adoption, use and exposure outcomes. METHODS: The P3 study is being conducted in the Kassena-Nankana Districts of Northern Ghana. Leveraging an earlier improved cookstove study that was conducted in this area, the central design of the P3 biomass stove experiment involves offering stoves at randomly varying prices to peers and non-peers of households that had previously received stoves for free. Using household surveys, electronic stove use monitors, and low-cost, portable monitoring equipment, we measure how prices and peers' experience affect perceptions of stove quality, the decision to purchase a stove, use of improved and traditional stoves over time, and personal exposure to air pollutants from the stoves. DISCUSSION: The challenges that public health and development communities have faced in spreading adoption of potentially welfare-enhancing technologies, like improved cookstoves, have highlighted the need for interdisciplinary, multisectoral approaches. The design of the P3 project draws on economic theory, public health practice, engineering, and environmental sciences, to more fully grasp the drivers and barriers to expanding access to and uptake of cleaner stoves. Our partnership between academic institutions, in the US and Ghana, and a local environmental non-governmental organization creates unique opportunities to disseminate and scale up lessons learned. TRIAL REGISTRATION: ClinicalTrials.gov NCT03617952 7/31/18 (Retrospectively Registered).

Improving present day and future estimates of anthropogenic sectoral emissions and the resulting air quality impacts in Africa.

The African continent is undergoing immense social and economic change, particularly regarding population growth and urbanization, where the urban population in Africa is anticipated to increase by a factor of 3 over the next 40 years. To understand the potential health impacts from this demographical shift and design efficient emission mitigation strategies, we used improved Africa-specific emissions that account for inefficient combustion sources for a number of sectors such as transportation, household energy generation, waste burning, and home heating and cooking. When these underrepresented emissions sources are combined with the current estimates of emissions in Africa, ambient particulate matter concentrations from present-day anthropogenic activity contribute to 13 210 annual premature deaths, with the largest contributions (38%) coming from residential emissions. By scaling both the population and the emissions for projected national-scale levels of growth, the predicted health impact grows to approximately 78 986 annual premature deaths by 2030 with 45% now resulting from emissions related to energy combustion. In order to mitigate this resulting increase in premature deaths, three scenarios have been developed which reduce sector-specific future emissions based on prior targets for technological improvements and emission controls in transportation, energy production and residential activities. These targeted potential mitigation strategies can avoid up to 37% of the estimated annual premature deaths by 2030 with the largest opportunity being a reduction of 10 868 annual deaths from switching half of the energy generation in South Africa to renewable technologies.

New Emission Factors and Efficiencies from in-Field Measurements of Traditional and Improved Cookstoves and Their Potential Implications.

Household cooking using solid biomass fuels is a major global health and environmental concern. As part of the Research on Emissions Air quality Climate and Cooking Technologies in Northern Ghana study, we conducted 75 in-field uncontrolled cooking tests designed to assess emissions and efficiency of the Gyapa woodstove, Philips HD4012, threestone fire and coalpot (local charcoal stove). Emission factors (EFs) were calculated for carbon monoxide (CO), carbon dioxide (CO2), and particulate matter (PM). Moreover, modified combustion (MCE), heat transfer (HTE) and overall thermal efficiencies (OTE) were calculated across a variety of fuel, stove and meal type combinations. Mixed effect models suggest that compared to traditional stove/fuel combinations, the Philips burning wood or charcoal showed significant fuel and energy based EF differences for CO, but no significant PM changes with wood fuel. MCEs were significantly higher for Philips wood and charcoal-burning stoves compared to the threestone fire and coalpot. The Gyapa emitted significantly higher ratios of elemental to organic carbon. Fuel moisture, firepower and MCE fluctuation effects on stove performance were investigated with mixed findings. Results show agreement with other in-field findings and discrepancies with some lab-based findings, with important implications for estimated health and air quality impacts.

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.

Gas/particle partitioning of 2-methyltetrols and levoglucosan at an urban site in Denver.

In this study, a medium volume sampler incorporating quartz fiber filters (QFFs) and a polyurethane foam (PUF)/XAD/PUF sandwich (PXP) was used to collect 2-methyltetrols (isoprene tracer) and levoglucosan (biomass burning tracer) in gaseous and particle (PM2.5) phases. The measured gas/particle (G/P) partitioning coefficients (Kp,OMm) of 2-methyltetrols and levoglucosan were calculated and compared to their predicted G/P partitioning coefficients (Kp,OMt) based on an absorptive partitioning theory. The breakthrough experiments showed that gas-phase 2-methyltetrols and levoglucosan could be collected using the PXP or PUF adsorbent alone, with low breakthrough; however, the recoveries of levoglucosan in PXP samples were lower than 70% (average of 51.9­63.3%). The concentration ratios of 2-methyltetrols and levoglucosan in the gas phase to those in the particle phase were often close to or higher than unity in summer, indicating that these polar species are semi-volatile and their G/P partitioning should be considered when applying particle-phase data for source apportionment. The Kp,OMm values of 2-methyltetrols had small variability in summer Denver, which was ascribed to large variations in concentrations of particulate organic matter (5.14 ± 3.29 µg m­3) and small changes in ambient temperature (21.8 ± 4.05 °C). The regression between log Kp,OMm and log Kp,OMt suggested that the absorptive G/P partitioning theory could reasonably predict the measured G/P partitioning of levoglucosan in ambient samples.

Impact of gas/particle partitioning of semivolatile organic compounds on source apportionment with positive matrix factorization.

To quantify and minimize the influence of gas/particle (G/P) partitioning on receptor-based source apportionment using particle-phase semivolatile organic compound (SVOC) data, positive matrix factorization (PMF) coupled with a bootstrap technique was applied to three data sets mainly composed of "measured-total" (measured particle- + gas-phase), "particle-only" (measured particle-phase) and "predicted-total" (measured particle-phase + predicted gas-phase) SVOCs to apportion carbonaceous aerosols. Particle- (PM2.5) and gas-phase SVOCs were collected using quartz fiber filters followed by PUF/XAD-4/PUF adsorbents and measured using gas chromatography-mass spectrometry (GC-MS). Concentrations of gas-phase SVOCs were also predicted from their particle-phase concentrations using absorptive partitioning theory. Five factors were resolved for each data set, and the factor profiles were generally consistent across the three PMF solutions. Using a previous source apportionment study at the same receptor site, those five factors were linked to summertime biogenic emissions (odd n-alkane factor), unburned fossil fuels (light SVOC factor), road dust and/or cooking (n-alkane factor), motor vehicle emissions (PAH factor), and lubricating oil combustion (sterane factor). The "measured-total" solution was least influenced by G/P partitioning and used as reference. Two out of the five factors (odd n-alkane and PAH factors) exhibited consistent contributions for "particle-only" vs "measured-total" and "predicted-total" vs "measured-total" solutions. Factor contributions of light SVOC and n-alkane factors were more consistent for "predicted-total" vs "measured-total" than "particle-only" vs "measured-total" solutions. The remaining factor (sterane factor) underestimated the contribution by around 50% from both "particle-only" and "predicted-total" solutions. The results of this study confirm that when measured gas-phase SVOCs are not available, "predicted-total" SVOCs should be used to decrease the influence of G/P partitioning on receptor-based source apportionment.

Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere.

Bacteria and fungi are ubiquitous throughout the Earth's lower atmosphere where they often represent an important component of atmospheric aerosols with the potential to impact human health and atmospheric dynamics. However, the diversity, composition, and spatiotemporal dynamics of these airborne microbes remain poorly understood. We performed a comprehensive analysis of airborne microbes across two aerosol size fractions at urban and rural sites in the Colorado Front Range over a 14-month period. Coarse (PM10-2.5) and fine (PM2.5) particulate matter samples were collected at weekly intervals with both bacterial and fungal diversity assessed via high-throughput sequencing. The diversity and composition of the airborne communities varied across the sites, between the two size fractions, and over time. Bacteria were the dominant type of bioaerosol in the collected air samples, while fungi and plants (pollen) made up the remainder, with the relative abundances of fungi peaking during the spring and summer months. As bacteria made up the majority of bioaerosol particles, we analyzed the bacterial communities in greater detail using a bacterial-specific 16S rRNA gene sequencing approach. Overall, bacterial taxonomic richness and the relative abundances of specific bacterial taxa exhibited significant patterns of seasonality. Likewise, airborne bacterial communities varied significantly between sites and across aerosol size fractions. Source-tracking analyses indicate that soils and leaves represented important sources of bacteria to the near-surface atmosphere across all locations with cow fecal bacteria also representing an important source of bioaerosols at the more rural sites during early fall and early spring. Together, these data suggest that a complex set of environmental factors, including changes in atmospheric conditions and shifts in the relative importance of available microbial sources, act to control the composition of microbial bioaerosols in rural and urban environments.

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.

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 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.

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.

Temporal patterns in daily measurements of inorganic and organic speciated PM2.5 in Denver.

Airborne particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) has been linked to a wide range of adverse health effects and as a result is currently regulated by the U.S. Environmental Protection Agency. PM2.5 originates from a multitude of sources and has heterogeneous physical and chemical characteristics. These features complicate the link between PM2.5 emission sources, ambient concentrations and health effects. The goal of the Denver Aerosol Sources and Health (DASH) study is to investigate associations between sources and health using daily measurements of speciated PM2.5 in Denver. The datxa set being collected for the DASH study will be the longest daily speciated PM2.5 data set of its kind covering 5.5 years of daily inorganic and organic speciated measurements. As of 2008, 4.5 years of bulk measurements (mass, inorganic ions and total carbon) and 1.5 years of organic molecular marker measurements have been completed. Several techniques were used to reveal long-term and short-term temporal patterns in the bulk species and the organic molecular marker species. All species showed a strong annual periodicity, but their monthly and seasonal behavior varied substantially. Weekly periodicities appear in many compound classes with the most significant weekday/weekend effect observed for elemental carbon, cholestanes, hopanes, select polycyclic aromatic hydrocarbons (PAHs), heavy n-alkanoic acids and methoxyphenols. Many of the observed patterns can be explained by meteorology or anthropogenic activity patterns while others do not appear to have such obvious explanations. Similarities and differences in these findings compared to those reported from other cities are highlighted.

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.

PM(2.5) Characterization for Time Series Studies: Pointwise Uncertainty Estimation and Bulk Speciation Methods Applied in Denver.

Many studies have identified associations between adverse health effects and short-term exposure to particulate matter less than 2.5 microns in diameter (PM(2.5)). These effects, however, are not consistent across geographical regions. This may be due in part to variations in the chemical make-up of PM(2.5) resulting from unique combinations of sources, both primary and secondary, in different regions. The Denver Aerosol Sources and Health (DASH) study is a multi-year time series study designed to characterize the daily chemical composition of PM(2.5) in Denver, identify the major contributing sources, and investigate associations between sources and a broad array of adverse health outcomes.Measurement methodology, field blank correction, pointwise uncertainty estimation and detection limit consideration are discussed in the context of bulk speciation for the DASH study. Results are presented for the first 4.5 years of mass, inorganic ion and bulk carbon speciation. The derived measurement uncertainties were propagated using the root sum of squares method and show good agreement with precision estimates derived from bi-weekly duplicate samples collected on collocated samplers. Gravimetric mass has the most uncertainty of any measurement and reconstructed mass generated from the sum of the individual species shows less uncertainty than measured mass on average. The methods discussed provide a good framework for PM(2.5) speciation measurements and are generalizable to analysis of other environmental measures.