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.

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 (PM_2.5) in the aqueous phase. PM_2.5 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 mg_C^-1 L, respectively) and a higher mean value for the summer extracts (65.4 fM s^-1 mg_C^-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.

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 (EF_EC = 25.1 g/kg-fuel SD = 25.7, EF_OC = 9.5 g/kg-fuel SD = 10.0). OC emissions from trash burning events were large and highly variable (EF_OC = 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.

Light absorption of organic carbon and its sources at a southeastern U.S. location in summer

Light-absorbing organic carbon (OC), also referred to as "brown carbon" (BrC), has been intensively investigated in atmospheres impacted by biomass burning. However, other BrC sources (e.g., secondary formation in the atmosphere) are rarely studied in ambient aerosols. In the current work, forty-five PM_2.5 filter samples were collected in Research Triangle Park (RTP), NC, USA from June 1st to July 15th, 2013. The bulk carbonaceous components, including OC, elemental carbon (EC), water soluble OC (WSOC), and an array of organic molecular markers were measured; an ultraviolet/visible spectrometer was used to measure the light absorption of methanol extractable OC and WSOC. The average light absorption per OC and WSOC mass of PM_2.5 samples in summer RTP are 0.36 ± 0.16 m² gC^-1 and 0.29 ± 0.13 m² gC^-1, respectively, lower than the ambient aerosol samples impacted by biomass burning and/or fossil fuel combustion (0.7-1.6 m² gC^-1) from other places. Less than 1% of the aqueous extracts absorption is attributed to the light-absorbing chromophores (nitroaromatic compounds) identified in this work. To identify the major sources of BrC absorption in RTP in the summer, Positive Matrix Factorization (PMF) was applied to a dataset containing optical properties and chemical compositions of carbonaceous components in PM_2.5. The results suggest that the formation of biogenic secondary organic aerosol (SOA) containing organosulfates is an important BrC source, contributing up to half of the BrC absorption in RTP during the summertime.

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).

Electronic supplementary material

The online version of this article (10.1186/s12889-018-6116-z) contains supplementary material, which is available to authorized users.

Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a one-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesberg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area - highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

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.

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.

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.

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.

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 51.9–63.3%). 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 m–3) 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.

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.

The sensitivity of health effect estimates from time-series studies to fine particulate matter component sampling schedule

The US Environmental Protection Agency air pollution monitoring data have been a valuable resource commonly used for investigating the associations between short-term exposures to PM2.5 chemical components and human health. However, the temporally sparse sampling on every third or sixth day may affect health effect estimation. We examined the impact of non-daily monitoring data on health effect estimates using daily data from the Denver Aerosol Sources and Health (DASH) study. Daily concentrations of four PM2.5 chemical components (elemental and organic carbon, sulfate, and nitrate) and hospital admission counts from 2003 through 2007 were used. Three every-third-day time series were created from the daily DASH monitoring data, imitating the US Speciation Trend Network (STN) monitoring schedule. A fourth, partly irregular, every-third-day time series was created by matching existing sampling days at a nearby STN monitor. Relative risks (RRs) of hospital admissions for PM2.5 components at lags 0-3 were estimated for each data set, adjusting for temperature, relative humidity, longer term temporal trends, and day of week using generalized additive models, and compared across different sampling schedules. The estimated RRs varied somewhat between the non-daily and daily sampling schedules and between the four non-daily schedules, and in some instances could lead to different conclusions. It was not evident which features of the data or analysis were responsible for the variation in effect estimates, although seeing similar variability in resampled data sets with relaxation of the every-third-day constraint suggests that limited power may have had a role. The use of non-daily monitoring data can influence interpretation of estimated effects of PM2.5 components on hospital admissions in time-series studies.

Contrast and correlations between coarse and fine particulate matter in the United States

The characteristics of concentrations of PM₁₀₋₂.₅, PM₂.₅, and PM₁₀ at 77 sites in the United States are evaluated. PM₁₀ concentrations show strong spatial variability, with highest levels occurring in the southwestern United States, driven primarily by PM₁₀₋₂.₅. PM₁₀₋₂.₅ and PM₂.₅ concentrations show different spatial patterns. The highest concentrations of PM₁₀₋₂.₅ were observed at sites in the southwestern US, leading to the highest PM₁₀ concentrations there. The PM₂.₅ concentrations are the major contributors to the average PM₁₀ concentrations at many sites in the eastern United States. Poor correlations were generally found between PM₁₀₋₂.₅ and PM₂.₅, suggesting that PM₁₀₋₂.₅ and PM₂.₅ are generally influenced by different sources. PM₁₀₋₂.₅ is generally more variable than PM₂.₅ because PM₁₀₋₂.₅ has a higher deposition velocity and is primarily emitted from mechanical processes (e.g. agricultural harvest and construction) that are more influenced by factors including human operation and wind speed leading to a strong episodic nature. As a result of its high variability, PM₁₀₋₂.₅ acts as the major driver for PM₁₀ extremes. PM₁₀₋₂.₅ is significantly correlated with PM₁₀ at all investigated sites, with the average correlation value R(2)=0.79. Correlations of PM₂.₅ with PM₁₀ (average of 0.37) are overall considerably lower than those between PM₁₀₋₂.₅ and PM₁₀. Different seasonal, weekly, and diurnal patterns were observed between PM₁₀₋₂.₅ and PM₂.₅ at agricultural, on-road traffic, quarrying, airport, and marine sites. At investigated agricultural sites, while the concentrations of PM₂.₅ are higher in winter when there are few agricultural activities, PM₁₀₋₂.₅ concentrations are lower in winter months than in summer and autumn months, with highest levels corresponding to harvest and planting. The harvest and planting signatures were not observed in PM₂.₅ concentrations at any of these sites, suggesting that agricultural activities do not have a strong influence on 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.

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.

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.

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.

The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations

BACKGROUND

In air pollution time-series studies, the temporal pattern of the association of fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in aerodynamic diameter) and health end points has been observed to vary by disease category. The lag pattern of PM2.5 chemical constituents has not been well investigated, largely because daily data have not been available.

OBJECTIVES

We explored the lag structure for hospital admissions using daily PM2.5 chemical constituent data for 5 years in the Denver Aerosol Sources and Health (DASH) study.

METHODS

We measured PM2.5 constituents, including elemental carbon, organic carbon, sulfate, and nitrate, at a central residential site from 2003 through 2007 and linked these daily pollution data to daily hospital admission counts in the five-county Denver metropolitan area. Total hospital admissions and subcategories of respiratory and cardiovascular admissions were examined. We assessed the lag structure of relative risks (RRs) of hospital admissions for PM2.5 and four constituents on the same day and from 1 to 14 previous days from a constrained distributed lag model; we adjusted for temperature, humidity, longer-term temporal trends, and day of week using a generalized additive model.

RESULTS

RRs were generally larger at shorter lags for total cardiovascular admissions but at longer lags for total respiratory admissions. The delayed lag pattern was particularly prominent for asthma. Elemental and organic carbon generally showed more immediate patterns, whereas sulfate and nitrate showed delayed patterns.

CONCLUSION

In general, PM2.5 chemical constituents were found to have more immediate estimated effects on cardiovascular diseases and more delayed estimated effects on respiratory diseases, depending somewhat on the constituent.

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.

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.

PM(2.5) Characterization for Time Series Studies: Organic Molecular Marker Speciation Methods and Observations from Daily Measurements in Denver

Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been shown to have a wide range of adverse health effects and consequently is regulated in accordance with the US-EPA's National Ambient Air Quality Standards. PM(2.5) originates from multiple primary sources and is also formed through secondary processes in the atmosphere. It is plausible that some sources form PM(2.5) that is more toxic than PM(2.5) from other sources. Identifying the responsible sources could provide insight into the biological mechanisms causing the observed health effects and provide a more efficient approach to regulation. This is the goal of the Denver Aerosol Sources and Health (DASH) study, a multi-year PM(2.5) source apportionment and health study.The first step in apportioning the PM(2.5) to different sources is to determine the chemical make-up of the PM(2.5). This paper presents the methodology used during the DASH study for organic speciation of PM(2.5). Specifically, methods are covered for solvent extraction of non-polar and semi-polar organic molecular markers using gas chromatography-mass spectrometry (GC-MS). Vast reductions in detection limits were obtained through the use of a programmable temperature vaporization (PTV) inlet along with other method improvements. Results are presented for the first 1.5 years of the DASH study revealing seasonal and source-related patterns in the molecular markers and their long-term correlation structure. Preliminary analysis suggests that point sources are not a significant contributor to the organic molecular markers measured at our receptor site. Several motor vehicle emission markers help identify a gasoline/diesel split in the ambient data. Findings show both similarities and differences when compared with other cities where similar measurements and assessments have been made.

The Denver Aerosol Sources and Health (DASH) Study: Overview and Early Findings

Improved understanding of the sources of air pollution that are most harmful could aid in developing more effective measures for protecting human health. The Denver Aerosol Sources and Health (DASH) study was designed to identify the sources of ambient fine particulate matter (PM(2.5)) that are most responsible for the adverse health effects of short-term exposure to PM (2.5). Daily 24-hour PM(2.5) sampling began in July 2002 at a residential monitoring site in Denver, Colorado, using both Teflon and quartz filter samplers. Sampling is planned to continue through 2008. Chemical speciation is being carried out for mass, inorganic ionic compounds (sulfate, nitrate and ammonium), and carbonaceous components, including elemental carbon, organic carbon, temperature-resolved organic carbon fractions and a large array of organic compounds. In addition, water soluble metals were measured daily for 12 months in 2003. A receptor-based source apportionment approach utilizing positive matrix factorization (PMF) will be used to identify PM (2.5) source contributions for each 24-hour period. Based on a preliminary assessment using synthetic data, the proposed source apportionment should be able to identify many important sources on a daily basis, including secondary ammonium nitrate and ammonium sulfate, diesel vehicle exhaust, road dust, wood combustion and vegetative debris. Meat cooking, gasoline vehicle exhaust and natural gas combustion were more challenging for PMF to accurately identify due to high detection limits for certain organic molecular marker compounds. Measurements of these compounds are being improved and supplemented with additional organic molecular marker compounds. The health study will investigate associations between daily source contributions and an array of health endpoints, including daily mortality and hospitalizations and measures of asthma control in asthmatic children. Findings from the DASH study, in addition to being of interest to policymakers, by identifying harmful PM(2.5) sources may provide insights into mechanisms of PM effect.

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.

Source apportionment of fine (PM1.8) and ultrafine (PM0.1) airborne particulate matter during a severe winter pollution episode

Size-resolved samples of airborne particulate matter (PM) collected during a severe winter pollution episode at three sites in the San Joaquin Valley of California were extracted with organic solvents and analyzed for detailed organic compounds using GC-MS. Six particle size fractions were characterized with diameter (Dp) < 1.8 microm; the smallest size fraction was 0.056 < Dp < 0.1 microm which accounts for the majority of the mass in the ultrafine (PM0.1) size range. Source profiles for ultrafine particles developed during previous studies were applied to the measurements at each sampling site to calculate source contributions to organic carbon (OC) and elemental carbon (EC) concentrations. Ultrafine EC concentrations ranged from 0.03 microg m(-3) during the daytime to 0.18 microg m(-3) during the nighttime. Gasoline fuel, diesel fuel, and lubricating oil combustion products accounted for the majority of the ultrafine EC concentrations, with relatively minor contributions from biomass combustion and meat cooking. Ultrafine OC concentrations ranged from 0.2 microg m(-3) during the daytime to 0.8 microg m(-3) during the nighttime. Wood combustion was found to be the largest source of ultrafine OC. Meat cooking was also identified as a significant potential source of PM0.1 mass but further study is required to verify the contributions from this source. Gasoline fuel, diesel fuel, and lubricating oil combustion products made minor contributions to PM0.1 OC mass. Total ultrafine particulate matter concentrations were dominated by contributions from wood combustion and meat cooking during the current study. Future inhalation exposure studies may wish to target these sources as potential causes of adverse health effects.

Source apportionment of in vitro reactive oxygen species bioassay activity from atmospheric particulate matter

Recent atmospheric particulate matter health studies have suggested that the redox activity is an important factor in particulate matter toxicology, and that reactive oxygen species (ROS) activity may be an important characteristic of particulate matter that is associated with adverse health effects. In this study, associations between atmospheric particulate matter sources and in vitro ROS activities are investigated. Ambient concentrations of fine particle water-soluble elements and total organic and elemental carbon were measured daily in Denver for the 2003 calendar year. The data were used in a multivariate factor analysis source apportionment model, positive matrix factorization (PMF), to determine the contributions of nine sources or factors: a mobile source factor, a water soluble carbon factor, a sulfate factor, a soil dust source, an iron source, two point sources characterized by water soluble toxic metals, a pyrotechnique factor, and a platinum group metal factor. Aqueous leachates, including water soluble and colloidal components, as well as insoluble particles that pass through a 0.2 microm pore size filter, of 45 randomly selected PM samples, were assayed to quantify ROS activity using an in vitro rat alveolar macrophage assay. Results show that PM-stimulated in vitro ROS production was significantly positively correlated with the contributions from three sources: the iron source, the soil dust source and the water soluble carbon factor. The iron source accounted for the greatest fraction of the measured variability in redox activity, followed by the soil dust and the water-soluble carbon factor. Seventy-seven percent of the in vitro ROS activity was explained by a linear combination of these three source contributions.

Size-resolved source apportionment of airborne particle mass in a roadside environment

Airborne particulate hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs) were measured in six size fractions < 1.8 microm particle diameter at one site upwind and two sites downwind of the Interstate 5 freeway in San Diego, CA. The smallest size fraction collected was exclusively in the ultrafine size range (D(p) < 0.1 microm; PM0.1). Size distributions of hopanes, steranes, and PAHs peaked between 0.10-0.18 microm particle aerodynamic diameter with a tail extending into the PM0.1 size range. This pattern is similar to previous dynamometer studies of hopane, sterane, and PAH size distributions emitted from gasoline- and diesel-powered vehicles. Size-resolved source profiles were combined to form an "on-road" profile for motor oil, diesel, and gasoline contributions to EC and OC. The resulting equations were used to predict source contributions to the size distributions of EC and OC in the roadside environment. The method successfully accounted for the majority of the carbonaceous material in particles with diameter < 0.18 microm, with significant residual material in larger size fractions. The peak in both the measured and predicted EC size distribution occurred between 0.1-0.18 microm particle aerodynamic diameter. The predicted OC size distribution peaked between 0.1-0.18 microm particle diameter, butthe measured OC size distribution peaked between 0.56-1.0 microm particle diameter, possibly because of secondary organic aerosol formation. Predicted OC concentrations in particles with diameter < 0.18 microm were greater than measured values 18 m downwind of the roadway but showed good agreement 37 m downwind. The largest source contributions to the PM0.1 and PM0.18 size fractions were different. PM0.18 was dominated by diesel fuel and motor oil combustion products while PM0.1 was dominated by diesel fuel and gasoline fuel combustion products. Total source contributions to ultrafine (PM0.1) EC concentrations 37 m downwind of the roadway were 44 +/- 6% diesel fuel, 21 +/- 1% gasoline, 5 +/- 6% motor oil, and 30% unknown. Total source contributions to ultrafine (PM0.1) OC concentrations 37 m downwind of the roadway were 46 +/- 5% diesel fuel, 44 +/- 5% gasoline, 20 +/- 15% motor oil with a slight overprediction (11%). Diesel fuel appears to make the single largest contribution to ultrafine (PM0.1) particle mass given the fleet distribution during the current experiment.

Size distribution of trace organic species emitted from light-duty gasoline vehicles

Size distributions for particulate hopanes+steranes and nonvolatile polycyclic aromatic hydrocarbons (PAHs) emitted from five classes of light-duty gasoline-powered vehicles were measured using the federal test procedure (FTP), unified cycle (UC), and correction cycle (CC) driving cycles. 17alpha(H)-21beta(H)-29-norhopane, 17alpha(H)-21beta(H)-hopane, alpha beta beta-20R-stigmastane, and alpha beta beta-20S-stigmastane were highly correlated and behaved consistently across sampling methods. Coronene and benzo[ghi]perylene were the most ubiquitous heavy PAHs detected in the vehicle exhaust. The emission rates of hopanes, steranes, and PAHs contained in particles with aerodynamic diameters of less than 1.8 ,m varied by 2 orders of magnitude between the lowest- and highest-emitting vehicle classes. Hopane+sterane size distributions emitted from vehicles without an operating catalyst (including "cold-start" emissions from catalyst-equipped vehicles) were bimodal with one mode between 0.10 and 0.18 microm and the second mode >0.32 microm particle diameter. Hopane+sterane emissions released from vehicles with a catalyst at operating temperature had a single mode between 0.1 and 0.18 microm diameter. Hopane+sterane emissions from visibly smoking vehicles had a single mode between 0.18 and 0.32 microm diameter. Heavy PAH size distributions for all vehicle classes consistently had a single mode between 0.10 and 0.18 microm particle diameter (0.1-0.32 microm diameter for smoking vehicles). The geometric standard deviations for PAH size distributions were generally smaller than the corresponding hopane+sterane distributions. These trends suggest that hopanes+steranes and heavy PAHs act as tracers for separate processes of particulate organic carbon formation. PAH and hopane+sterane emissions shifted to smaller sizes during the more aggressive UC and CC driving cycles relative to the FTP. The fraction of PAH and hopane+sterane emissions in the ultrafine (Dp < 0.1 microm) range more than doubled during "warm-start" UC and CC cycles vs the FTP cycle. The enhancement of ultrafine PAHs during "cold-start" UC driving cycles was less pronounced.

Size distribution of trace organic species emitted from heavy-duty diesel vehicles

Size distributions of particulate hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs) were measured in the exhaust from four heavy-duty diesel vehicles (HDDVs) operated under idle, creep, transient, and two high-speed driving modes. Particulate matter was collected using a chassis dynamometer and a dilution sampling system equipped with cascade impactors and filter samplers. Samples were extracted using organic solvents and analyzed using gas chromatography-mass spectrometry. Size distributions of hopanes and steranes were functions of engine load conditions and vehicle technology. Hopanes and steranes peaked in size ranges larger than 0.18 microm aerodynamic particle diameter under light load conditions and less than 0.10 microm aerodynamic particle diameter under heavier load conditions. The eight hopane size distributions emitted from newertechnology (> 1998) vehicles were unimodal while the four hopane size distributions emitted from older technology vehicles (< 1992) were bimodal. Similar trends between older and newer vehicles were not observed for sterane size distributions. The PAH composition emitted from HDDVs was a function of driving cycle and vehicle technology. Light driving cycles produced quantifiable emissions of 3, 4, 5, and 6 ring PAHs (including coronene). Heavier driving cycles produced only the 3 and 4 ring PAHs in quantifiable amounts. PM1.8 and PM0.1 source profiles constructed using the relative abundance of hopanes and steranes to total organic carbon were functions of vehicle load condition. Increasing load reduced the relative abundance of motor oil tracers in the PM1.8 size fraction and increased the abundance of these tracers in the PM0.1 size fraction. The relative abundances of PAHs in the PM0.1 and PM1.8 size fractions emitted from the oldest vehicle tested (1985 HDDV) were significantly higher than for any other vehicle tested.

Use of synthetic data to evaluate positive matrix factorization as a source apportionment tool for PM2.5 exposure data

Positive matrix factorization (PMF) was applied to synthetic datasets that simulate personal exposures to airborne PM2.5 from 12 sources. Three differentfilter analysis scenarios using different analytical chemistry techniques were considered. The full suite scenario quantified elemental carbon, organic carbon, inorganic ions, trace elements, and trace organic species including carboxylic acids and organic compounds with -OH functionality. A second scenario excluded trace elements and a third assumed that derivatization steps to quantify polar organic compounds were not performed. Similar errors in source apportionment were seen with all three scenarios. In most cases, PMF failed to separate out factors corresponding to road dust and vegetative debris, two sources that made relatively uniform contributions to the synthetic exposures. Factors representing wood smoke, natural gas combustion, and meat cooking sources were difficult to identify due to a lack of unique tracers with concentrations reliably above the detection limits assumed in the study. Factors representing cigarette smoke, candle smoke, gasoline exhaust, and secondary aerosols were comparatively easy to identify. When contributions from a pair of sources, such as diesel and gasoline exhaust, were highly correlated in the synthetic datasets, a single factor corresponding to both sources was usually found.

Source contributions to the mutagenicity of urban particulate air pollution

Using organic compounds as tracers, a chemical mass balance model was employed to investigate the relationship between the mutagenicity of the urban organic aerosol sources and the mutagenicity of the atmospheric samples. The fine particle organic mass concentration present in the 1993 annual average Los Angeles-area composite sample was apportioned among eight emission source types. The largest source contributions to fine particulate organic compound mass concentration were identified as smoke from meat cooking, diesel-powered vehicle exhaust, wood smoke, and paved road dust. However, the largest source contributions to the mutagenicity of the atmospheric sample were natural gas combustion and diesel-powered vehicles. In both the human cell and bacterial assay systems, the combined mutagenicity of the composite of primary source effluents predicted to be present in the atmosphere was statistically indistinguishable from the mutagenicity of the actual atmospheric sample composite. Known primary emissions sources appear to be capable of emitting mutagenic organic matter to the urban atmosphere in amounts sufficient to account for the observed mutagenicity of the ambient samples. The error bounds on this analysis, however, are wide enough to admit to the possible importance of additional mutagenic organics that are formed by atmospheric reaction (e.g., 2-nitrofluoranthene has been identified as an important human cell mutagen in the atmospheric composite studied here, accounting for approximately 1% of the total sample mutagenic potency).

Indoor pollutant levels from the use of unvented natural gas fireplaces in Boulder, Colorado

High CO and NO2 concentrations have been documented in homes with unvented combustion appliances, such as natural gas fireplaces. In addition, polycyclic aromatic hydrocarbons (PAH) are emitted from incomplete natural gas combustion. The acute health risks of CO and NO2 exposure have been well established for the general population and for certain high-risk groups, including infants, the elderly, and people with heart disease or asthma. Health effects from PAH exposure are less well known, but may include increased risk of cancer. We monitored CO emissions during the operation of unvented natural gas fireplaces in two residences in Boulder, CO, at various times between 1997 and 2000. During 1999, we expanded our tests to include measurements of NO2 and PAH. Results show significant pollutant accumulation indoors when the fireplaces were used for extended periods of time. In one case, CO concentrations greater than 100 ppm accumulated in under 2 hr of operation; a person at rest exposed for 10 hr to this environment would get a mild case of CO poisoning with an estimated 10% carboxyhemoglobin level. Appreciable NO2 concentrations were also detected, with a 4-hr time average reaching 0.36 ppm. Similar time-average total PAH concentrations reached 35 ng/m3. The results of this study provide preliminary insights to potential indoor air quality problems in homes operating unvented natural gas fireplaces in Boulder.

Trends in fine particle concentration and chemical composition in southern California

Airborne fine particle mass concentrations in Southern California have declined in recent years. Trends in sulfate and elemental carbon (EC) particle concentrations over the period 1982-1993 are consistent with this overall improvement in air quality and help to confirm some of the reasons for the changes that are seen. Fine particle sulfate concentrations have declined as a strict sulfur oxides (SOx) emission control program adopted in 1978 was implemented over time. Fine particle elemental (black) carbon concentrations have declined over a period when newer diesel engines and improved diesel fuels have been introduced into the vehicle fleet. Organic aerosol concentrations have not declined as rapidly as the EC particle concentrations, despite the fact that catalyst-equipped cars having lower particle emission rates were introduced into the vehicle fleet alongside the diesel engine improvements mentioned above. This situation is consistent with the growth in population and vehicle miles traveled in the air basin over time. Fine particle ammonium nitrate in the Los Angeles area atmosphere contributes more than half of the fine aerosol mass concentration on the highest concentration days of the year, emphasizing both the need for accurate aerosol nitrate measurements and the likely importance of deliberate control of aerosol nitrate as a part of any serious further fine particle control program for the Los Angeles area.

Seasonal and spatial variation of the bacterial mutagenicity of fine organic aerosol in southern california

The bacterial mutagenicity of a set of 1993 urban particulate air pollution samples is examined using the Salmonella typhimurium TM677 forward mutation assay. Amibent fine particulate samples were collected for 24 hr every sixth day throughout 1993 at four urban sites, including Long Beach, central Los Angeles, Azusa, and Rubidoux, California, and at an upwind background site on San Nicolas Island. Long Beach and central Los Angeles are congested urban areas where air quality is dominated by fresh emissions from air pollution sources; Azuasa and Rubidoux are located farther downwind and receive transported air pollutants plus increased quantities of the products of atmospheric chemical reactions. Fine aerosol samples from Long Beach and Los Angeles show a pronounced seasonal variation in bacterial mutagenicity per cubic meter of- ambient air, with maximum in the winter and a minimum in the summer. The down-wind smog receptor site at Rubidoux shows peak mutagenicity (with postmitochondrial supernatant but no peak without postmitochondrial supernatant) during the September-October periods when direct transport from upwind sources can be expected. At most sites the mutagenicity per microgram of organic carbon from the aerosol is not obviously higher during the summer photochemical smog period than during the colder months. Significant spatial variation in bacterial mutagenicity is observed: mutagenicity per cubic meter of ambient air, on average, is more than an order of magnitude lower at San Nicolas Island than within the urban area. The highest mutagenicity values per microgram of organics supplied to the assay are found at the most congested urban sites at central Los Angeles and Long Beach. The highest annual average values of mutagenicity per cubic meter of air sampled occur at central Los Angeles. These findings stress the importance of proximity to sources of direct emissions of bacterial mutagens and imply that if important mutagen-forming atmospheric reactions occur, they likely occur in the winter and spring seasons as well as the photochemically more active summer and early fall periods.