Speciation Trends Network Research Articles

Source apportionment of PM2.5 in Baton Rouge, Louisiana during 2009-2014.

Particulate matter with aerodynamic diameter <2.5μm (PM2.5) chemical composition data from the Speciation Trends Network (STN) site located in Baton Rouge, Louisiana were analyzed using the receptor Positive Matrix Factorization (PMF) model version 5.0. The PM2.5 samples were collected every third day from January 2009 to December 2014. Seven sources were identified, including secondary sulfate, secondary nitrate, industrial emissions, traffic, crustal dust, road dust and sea salt. The contributions of these seven sources to PM2.5 total mass were 38.4%, 17.6%, 18.7%, 11.5%, 6.1%, 4.2% and 3.6%, respectively. Secondary sulfate, industrial emissions and secondary nitrate were the top three sources. The contributions of industrial emissions and crustal dust have been rising in recent years while that of traffic and sea salt were decreasing. Secondary sources were higher than primary sources during the winter. The crustal and road dust were dominant during the summer, while traffic was more significant during the fall compared to other seasons. During summer, traffic emission and crustal dust were driven by northeast-north winds, traffic is also driven by northeast-north winds in winter, while industry emissions and sea salt were driven by prevailing west and northwest winds during other seasons. PM mass clearly showed the synergetic effects of local sources and distance sources. Thus, measurements and strategies should focus on not only local sources, but also regional transport. Attention should also be paid to industrial and traffic sources since they also account for secondary sources in addition to the primary contributions.

Spatial and temporal variability of sources of ambient fine particulate matter (PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt;) in California

Abstract. To identify major sources of ambient fine particulate matter (PM2.5, dp &lt; 2.5 μm) and quantify their contributions in the state of California, a positive matrix factorization (PMF) receptor model was applied on Speciation Trends Network (STN) data, collected between 2002 and 2007 at eight distinct sampling locations, including El Cajon, Rubidoux, Los Angeles, Simi Valley, Bakersfield, Fresno, San Jose, and Sacramento. Between five to nine sources of fine PM were identified at each sampling site, several of which were common among multiple locations. Secondary aerosols, including secondary ammonium nitrate and ammonium sulfate, were the most abundant contributor to ambient PM2.5 mass at all sampling sites, except for San Jose, with an annual average cumulative contribution of 26 to 63%, across the state. On an annual average basis, vehicular emissions (including both diesel and gasoline vehicles) were the largest primary source of fine PM at all sampling sites in southern California (17–18% of total mass), whereas in Fresno and San Jose, biomass burning was the most dominant primary contributor to ambient PM2.5 (27 and 35% of total mass, respectively), in general agreement with the results of previous source apportionment studies in California. In Bakersfield and Sacramento, vehicular emissions and biomass burning displayed relatively equal annual contributions to ambient PM2.5 mass (12 and 25%, respectively). Other commonly identified sources at all sites included aged and fresh sea salt and soil, which contributed to 0.5–13%, 2–27%, and 1–19% of the total mass, respectively, across all sites and seasons. In addition, a few minor sources were identified exclusively at some of the sites (e.g., chlorine sources, sulfate-bearing road dust, and different types of industrial emissions). These sources overall accounted for a small fraction of the total PM mass across the sampling locations (1 to 15%, on an annual average basis).

Long-term source apportionment of ambient fine particulate matter (PM2.5) in the Los Angeles Basin: A focus on emissions reduction from vehicular sources

Positive Matrix Factorization (PMF) was utilized to quantify sources of ambient PM2.5 in central Los Angeles (LA) and Rubidoux, using the Speciation Trends Network data, collected between 2002 and 2013. Vehicular emissions (including gasoline and diesel vehicles) were the second major contributor to PM2.5, following secondary aerosols, with about 20% contribution to total mass in both sites. Starting in 2007, several major federal, state, and local regulations on vehicular emissions were implemented. To assess the effect of these regulations, daily-resolved vehicular source contributions from 2002 to 2006 were pooled together and compared to the combination of 2008 to 2012 datasets. Compared to the 2002–2006 dataset, the median values of vehicular emissions in 2008–2012 statistically significantly decreased by 24 and 21% in LA and Rubidoux, respectively. These reductions were noted despite an overall increase or similarity in the median values of the daily flow of vehicles after 2007, at the sites.

A ten–year source apportionment study of ambient fine particulate matter in San Jose, California

Fine particulate matter (PM2.5) composition data from the Speciation Trends Network (STN) site in San Jose, CA, were analyzed by positive matrix factorization (PMF) using U.S. Environmental Protection Agency (EPA) PMF version 5.0. These data were 24–h average mass concentrations and compositions obtained from samples taken every third day from October 2002 to February 2012. The eight identified sources include secondary sulfate, secondary nitrate, fresh sea salt, aged sea salt, diesel emission, road salt, gasoline vehicles, and wood combustion. The contributions to PM2.5 of these eight sources were 13.1%, 20.0%, 5.5%, 7.8%, 9.4%, 5.1%, 14.8, and 24.3%, respectively. The Ni–related industrial source, which was detected in previous PMF analysis, was not identified in our study and a sharp decrease in Ni concentrations was observed after the end of 2004. The contribution of road dust source decreased significantly after 2004 (Mann–Whitney test, p<0.01), which is probably the result of the city wide enhanced street sweeping programs starting in 2005. A 40% reduction in the wood combustion PM2.5 contribution between winter 2008 and winter 2009 was found. This decrease could be attributed to the San Francisco Bay Area Air Quality Management District (BAAQMD) wood burning rule implemented in July 2008. In the future, the effectiveness and benefits of the wood burning rule could be evaluated using the multi–wavelength aethalometer delta–c method.

PM2.5 Source Apportionment: Reconciling Receptor Models for U.S. Nonurban and Urban Long-Term Networks

ABSTRACT Chemical mass balance (CMB) and trajectory receptor models were applied to speciated particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) measurements from Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network across the state of Minnesota as part of the Minnesota PM2.5 Source Apportionment Study (MPSAS). CMB equations were solved by the Unmix, positive matrix factorization (PMF), and effective variance (EV) methods, giving collective source contribution and uncertainty estimates. Geological source profiles developed from local dust materials were either incorporated into the EV-CMB model or used to verify factors derived from Unmix and PMF. Common sources include soil dust, calcium (Ca)-rich dust, diesel and gasoline vehicle exhausts, biomass burning, secondary sulfate, and secondary nitrate. Secondary sulfate and nitrate aerosols dominate PM2.5 mass (50–69%). Mobile sources outweigh area sources at urban sites, and vice versa at rural sites due to traffic emissions. Gasoline and diesel contributions can be separated using data from the STN, despite significant uncertainties. Major differences between MPSAS and earlier studies on similar environments appear to be the type and magnitude of stationary sources, but these sources are generally minor (<7%) in this and other studies. Ensemble back-trajectory analysis shows that the lower Midwestern states are the predominant source region for secondary ammoniated sulfate in Minnesota. It also suggests substantial contributions of biomass burning and soil dust from out-of-state on occasions, although a quantitative separation of local and regional contributions was not achieved in the current study. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for a summary of input data, Unmix and PMF factor profiles, and additional maps. IMPLICATIONS The IMPROVE and STN data have been widely used for source apportionment by receptor models. Applying more than one model to available PM2.5 data reveals similarities and differences in source contribution estimates. The inter-model discrepancies identify areas for more focused investigation. Together, the information could lead to better-defined emission reduction strategies.

THE SENSITIVITY OF TIME-SERIES HEALTH EFFECT ESTIMATES TO FINE PARTICULATE MATTER (PM2.5) CHEMICAL COMPONENTS SAMPLING SCHEDULES

Background and Aims: The U.S. EPA Speciation Trends Network (STN) is a resource used to investigate the association between PM2.5 chemical components and health in time-series studies. However, the temporally-sparse sampling on every third or sixth day reduces the number of days available for analysis and may influence resulting associations and interpretations. The Denver Aerosol Sources and Health (DASH) study obtained daily speciated PM2.5 data over five years allowing us to examine the impact of less than daily monitoring data on health effect estimates. Methods: We performed a time-series analysis using daily PM2.5 chemical component concentrations and daily hospitalization counts from 2003 through 2007. Three every third-day sets of time series data for component concentrations were created from daily data, imitating the STN monitoring schedule. A fourth scenario was created using DASH data limited to the actual valid sampling days reported at the closest STN monitor. Four PM2.5 chemical components, elemental and organic carbon, sulfate, and nitrate, measured in the DASH study were examined. Relative risks (RRs per interquartile range increases) of hospitalizations for PM2.5 components at day lags 0 to 3 were estimated for each of the four systematically-missing and for the complete time-series using the Generalized Additive Model. Estimated RRs were compared across sampling schedules. Results: The estimated RRs often varied between limited and complete sampling schedules and between the four limited sampling schedules, depending on PM2.5 components and hospitalization diagnoses. For example, the association of asthma hospitalization and of nitrate at lag0 using complete time-series data (RR = 0.999; 95 % confidence interval (CI) = 0.998, 1.010; 0.86 µg/m3 increase) was elevated and statistically significant in one of the every-third-day scenarios (RR = 1.024; 95 % CI=1.005, 1.043). Conclusions: Estimated effects of PM2.5 components on hospitalization in time-series studies were sensitive to the completeness of daily sampling and the selection of sampling days.

Identifying Sources of Uncertainty from the Inter-Species Covariance of Measurement Errors

A standard metric of measurement precision in environmental monitoring is the variance of differences between duplicate (collocated) samples. With duplicate measurements of multiple species, we can extend this variance analysis to include the interspecies covariance of differences between duplicate samples; these covariances can provide clues about the sources of error. We illustrate the potential of such an analysis with atmospheric aerosol measurements from two national air quality monitoring networks: Interagency Monitoring of Protected Visual Environments (IMPROVE) and Speciation Trends Network (STN). These aerosol "speciation" networks provide the multivariate data sets needed to characterize error covariance by operating duplicate samplers at several of their monitoring locations and analyzing both the collected aerosol samples for multiple species. We observe covariance among the measurement differences for multiple species in both networks. The covariance among measurement differences for soil-derived elements suggests an error associated with the particle size discrimination step in sampling, which is not currently included in either network's uncertainty estimates. The multivariate statistical analyses of aerosol speciation data performed by standard source apportionment models assume that measurement errors in different species are independent of each other; the present analysis invalidates this assumption for several species measured by IMPROVE and STN.

Exploring summertime organic aerosol formation in the eastern United States using a regional‐scale budget approach and ambient measurements

We present a new method for estimating the overall organic aerosol (OA) formation rate at the regional scale using a chemical transport model (CTM), PMCAMx‐2008, and an extensive set of measurements (Speciation Trends Network, Interagency Monitoring of Protected Visual Environments, Pittsburgh Air Quality Study, Southeastern Aerosol Research and Characterization) for the eastern United States. PMCAMx‐2008 takes into account up‐to‐date OA formation theory including primary OA evaporation, updated secondary OA (SOA) yields from traditional volatile organic precursor gases and multigenerational oxidation chemistry (aging) of vapors from anthropogenic sources, which lowers the volatility of the OA distribution over time. An overall OA formation rate of 22 ± 5 ktons d−1 is consistent with available measurements for this summer time period. We perform an extensive sensitivity analysis of uncertain OA model processes to demonstrate the relationship between the estimated total OA production rate and model performance. Perturbing, within reasonable limits, emissions of volatile precursors, SOA yields from isoprene oxidation, and the solubility of organic vapors produces model predictions for total OA that deviate little from the base case performance. The fractional error and fractional bias vary by less than 6% and 13%, respectively. These cases also result in total OA formation rates within 5 ktons d−1 of the base case. Neglecting chemical aging of anthropogenic OA components results in OA levels significantly lower than the observations everywhere, while aging biogenic SOA with the same parameters used for the base case anthropogenic SOA aging results in overpredictions in both the South and Midwest United States. Aging biogenic and anthropogenic SOA together with a reduced aging reaction rate results in reasonable model performance and an OA formation rate of ∼23 ktons d−1. This suggests that even though uncertainties in the OA aging mechanism and other important parameters may lead to uncertainties in the contributions of specific OA formation pathways, the proposed approach may be used to infer upper and lower limits on the total OA mass formation rate.

Chemical mass balance source apportionment for combined PM 2.5 measurements from U.S. non-urban and urban long-term networks

The Minnesota Particulate Matter 2.5 (PM 2.5) Source Apportionment Study was undertaken to explore the utility of PM 2.5 mass, element, ion, and carbon measurements from long-term speciation networks for pollution source attribution. Ambient monitoring data at eight sites across the state were retrieved from the archives of the Interagency Monitoring of Protected Visual Environments (IMPROVE) and the Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and analyzed by an Effective Variance – Chemical Mass Balance (EV-CMB) receptor model with region-specific geological source profiles developed in this study. PM 2.5 was apportioned into contributions of fugitive soil dust, calcium-rich dust, taconite (low grade iron ore) dust, road salt, motor vehicle exhaust, biomass burning, coal-fired utility, and secondary aerosol. Secondary sulfate and nitrate contributed strongly (49–71% of PM 2.5) across all sites and was dominant (≥60%) at IMPROVE sites. Vehicle exhausts accounted for 20–70% of the primary PM 2.5 contribution, largely exceeding the proportion in the primary PM 2.5 emission inventory. The diesel exhaust contribution was separable from the gasoline engine exhaust contribution at the STN sites. Higher detection limits for several marker elements in the STN resulted in non-detectable coal-fired boiler contributions which were detected in the IMPROVE data. Despite the different measured variables, analytical methods, and detection limits, EV-CMB results from a nearby IMPROVE-STN non-urban/urban sites showed similar contributions from regional sources – including fugitive dust and secondary aerosol. Seasonal variations of source contributions were examined and extreme PM 2.5 episodes were explained by both local and regional pollution events.

Quantification of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; organic carbon sampling artifacts in US networks

Abstract. Field blanks (bQF) and backup filters (quartz-fiber behind quartz-fiber filter; QBQ) have been adopted by US long-term air quality monitoring networks to estimate PM2.5 organic carbon (OC) sampling artifacts. This study documents bQF and QBQ carbon levels for the: 1) Interagency Monitoring of Protected Visual Environments (IMPROVE); 2) Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]); and 3) Southeastern Aerosol Research and Characterization (SEARCH) networks and examines the similarities/differences associated with network-specific sampling protocols. A higher IMPROVE sample volume and smaller filter deposit area results in PM2.5 areal density (μg/cm2 on filter) 3–11 times those of STN/CSN and SEARCH samples for the same ambient PM2.5 concentrations, thus reducing the relative contribution of sampling artifacts from passive OC adsorption. A relatively short (1–15 min) passive exposure period of STN/CSN and SEARCH bQF OC (0.8–1 μg/cm2) underestimates positive and negative OC artifacts resulting from passive adsorption or evaporation of semi-volatile organic compounds on quartz-fiber filters. This is supported by low STN/CSN and SEARCH bQF levels and lack of temporal or spatial variability among the sites within the networks. With a much longer period, ~7 days of ambient passive exposure, average IMPROVE bQF and QBQ OC are comparable (2.4±0.5 and 3.1±0.8 μg/cm2, respectively) and more than twice levels found in the STN/CSN and SEARCH networks. Sampling artifacts in STN/CSN were estimated from collocated IMPROVE samples based on linear regression. At six of the eight collocated sites in this study, STN/CSN bQFs underestimated OC artifacts by 11–34%. Using a preceding organic denuder in the SEARCH network minimized passive adsorption on QBQ, but OC on QBQ may not be attributed entirely to the negative sampling artifact (e.g., evaporated or volatilized OC from the front filter deposits after sample collection).

Sources of fine particles in the South Coast area, California

PM 2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) speciation data collected between 2003 and 2005 at two United State Environmental Protection Agency (US EPA) Speciation Trends Network monitoring sites in the South Coast area, California were analyzed to identify major PM 2.5 sources as a part of the State Implementation Plan development. Eight and nine major PM 2.5 sources were identified in LA and Rubidoux, respectively, through PMF2 analyses. Similar to a previous study analyzing earlier data ( Kim and Hopke, 2007a), secondary particles contributed the most to the PM 2.5 concentrations: 53% in LA and 59% in Rubidoux. The next highest contributors were diesel emissions (11%) in LA and Gasoline vehicle emissions (10%) in Rubidoux. Most of the source contributions were lower than those from the earlier study. However, the average source contributions from airborne soil, sea salt, and aged sea salt in LA and biomass smoke in Rubidoux increased. To validate the apportioned sources in this study, PMF2 results were compared with those obtained from EPA PMF ( US EPA, 2005). Both models identified the same number of major sources and the resolved source profiles and contributions were similar at the two monitoring sites. The minor differences in the results caused by the differences in the least square algorithm and non-negativity constraints between two models did not affect the source identifications.

A Temperature Calibration Procedure for the Sunset Laboratory Carbon Aerosol Analysis Lab Instrument

The Sunset Laboratory Carbon Aerosol Analysis Lab Instrument is widely used for thermal-optical analysis (TOA) of ambient particulate matter samples to measure total carbon (TC), organic carbon (OC), and elemental carbon (EC), and often thermal sub-fractions of OC and EC. TOA operating protocols include a series of plateau temperatures at which the thermal sub-fractions evolve. The temperatures have conventionally been measured by a sensor located in the sample oven but away from the filter sample. However, the TOA protocol used by the Interagency Monitoring of Protected Visual Environments (IMPROVE) network and recently adopted by the U.S. Environmental Protection Agency (EPA) Speciation Trends Network (STN) and Chemical Speciation Network (CSN) specify temperatures to be achieved at the filter. Our goal was to develop a simple calibration method to obtain the target filter temperatures in a Sunset Instrument. This method showed good agreement with the IMPROVE/STN/CSN method and has the advantages of not damaging oven components and of providing a direct comparison of sample oven sensor and filter temperatures at the TOA protocol-specified temperatures. Calibrations performed on four Sunset Instruments yielded different sensor/filter temperature relationships. Ambient PM 2.5 samples analyzed using IMPROVE_A temperatures at the oven sensor compared to IMPROVE_A temperatures at the filter yielded statistically insignificant differences for TC, OC, and EC but statistically significant differences for the carbon sub-fraction concentrations. Temperature calibrations should be performed on each Sunset Instrument to ensure comparability in the carbon sub-fractions being reported, and a simple method has been provided for performing these calibrations.

Sensitivity of inverse estimation of 2004 elemental carbon emissions inventory in the United States to the choice of observational networks

Choice of observational networks for inverse re‐estimation of elemental carbon (EC) emissions in the United States impacts results. We convert the Thermal Optical Transmittance (TOT) EC measurements to the Thermal Optical Reflectance (TOR) equivalents to make full utilization of available networks in inverse modeling of EC using regional air quality model. Results show that using the Interagency Monitoring of Protected Visual Environments (IMPROVE) network gives significantly lower emissions estimate compared to using the Speciation Trends Network (STN) and other networks or using all available networks together. The re‐estimate obtained by using IMPROVE sites alone made overall model performance worse compared to the bottom‐up estimate of EC emissions, while both re‐estimates, using STN (and others) sites and using all sites together, significantly improved the performance, showing higher robustness. Further analysis suggests that site density with respect to geographical location (downwind of source) impacts the robustness of a network's inverse re‐estimate.

Seasonality of speciated aerosol transport over the Great Lakes region

The Community Multiscale Air Quality model (CMAQ) is used to simulate aerosol mass and composition in the Great Lakes region of North America in an annual study for 2002. Model predictions are evaluated against daily and weekly average speciated fine particle (PM2.5) and bulk (PM2.5 and PM10) mass concentration measurements taken throughout the region by the Interagency Monitoring of Protected Visual Environments (IMPROVE), Speciation Trends Network (STN), and Clean Air Status and Trends Network (CASTNet) monitoring networks, and number concentration is evaluated using hourly observations at a rural site. Through detailed evaluation of model‐measurement agreement over urban and remote areas, major features of aerosol seasonality are examined. Whereas nitrate (winter maximum) and sulfate (summer maximum) seasonal patterns are driven by climatic influence on aerosol thermodynamics, seasonality of ammonium and organic mass (OM) is driven by emissions. Production of anthropogenic secondary organic aerosol (SOA) and summertime ozone formation both reach regional maxima over the southern Great Lakes, where they are also most strongly temporally correlated. Although primary OM is more prevalent, insufficient SOA formation leads to summertime OM underprediction of more than 50%. By comparing temporal patterns in aerosol species between model and observations, we find that elemental carbon, OM, and PM2.5 are overly correlated in CMAQ, suggesting that the model misses chemical, transport, or emissions processes differentiating these constituents. In contrast, sulfate and PM2.5 are not sufficiently correlated in CMAQ, although CMAQ simulates sulfate with a high level of skill. Performance relative to ad hoc regional modeling goals and previous studies is average to excellent for most species throughout the year, and seasonal patterns are captured.

Source Apportionment of Airborne Particulate Matter for the Speciation Trends Network Site in Cleveland, OH

Aerosol composition data from the Speciation Trends Network (STN) site (East 14th Street) in Cleveland, OH, were analyzed by advanced receptor model methods for source apportionment as well as by the standard positive matrix factorization (PMF) using PMF2. These different models are used in combination to test model limitations. These data were 24-hr average mass concentrations and compositions obtained for samples taken every third day from 2001 to 2003. The Multilinear Engine (ME) was used to solve an expanded model to estimate the source profiles and source contributions and also to investigate the wind speed, wind direction, time-of-day, weekend/weekday, and seasonal effects. PMF2 was applied to the same data-set. Potential source contribution function (PSCF) and conditional probability function (CPF) analyses were used to locate the regional and local sources using the resolved source contributions and appropriate meteorological data. Very little difference was observed between the results of the expanded model and the PMF2 values for the profiles and source contribution time series. The identified sources were as ferrous smelter, secondary sulfate, secondary nitrate, soil/combustion mixture, steel mill, traffic, wood smoke, and coal burning. The CPF analysis was useful in helping to identify local sources, whereas the PSCF results were only useful for regional source areas. Both of these analyses were more useful than the wind directional factor derived from the expanded factor analysis. However, the expanded analysis provided direct information on seasonality and day-of-week behavior of the sources.

Supplemental Material for Source Apportionment of Airborne Particulate Matter for the Speciation Trends Network Sites in Cleveland, Oh

Supplemental Material for Source Apportionment of Airborne Particulate Matter for the Speciation Trends Network Sites in Cleveland, Oh

Semi-continuous speciation analyses for ambient air particulate matter: An urgent need for health effects studies

One of the most urgent needs for future progress in reducing the substantial impacts of ambient air particulate matter (PM) on human health is to determine which of its components are having the greatest effects. The EPA's Speciation Trends Network (STN) has been operating since 2000. It generates 24-h average fine PM component concentrations for sulfate and nitrate ions, elemental and organic carbon (EC/OC), and many elements on an every third or sixth day basis for one or a few sites in most large US cities. To date, a small number of research studies, summarized in this paper, have used available STN and other supplemental data to identify and quantify the influences of specific components or source-related mixtures on measures of health-related impacts. These pioneering studies have demonstrated the potential utility of using such data in analyses that can provide a sound basis for guiding future research and control activities on those PM sources that have the greatest public health relevance. Unfortunately, the STN data collection methods used are expensive, and data have therefore been too sparse for studies of short-term health effects, where semi-continuous data, or at least daily 24-h concentration data are needed, as well as for regional concentration distributions that are needed for definitive analyses. Furthermore, because of cost considerations, there is virtually no prospect of collecting the data needed by the health researchers for more definitive analyses as long as there is continued reliance on current FRM sampling and analysis methodologies. At the second EPA-HEI Workshop on "Air Quality and Health Researchers Working Together" in RTP, NC on 16 and 17 April 2008, many participants concluded that it was both desirable, and possibly technically and economically feasible, to re-equip the STN sites with an automated system of semi-continuous monitors for sulfate, nitrate, EC, OC, and semi-continuous multistage PM samplers for non-volatile elements, providing continuous records of PM components with an averaging time of approximately 6 h for both thoracic coarse mode PM, fine PM, and perhaps ultrafine PM as well. The availability of such data would greatly accelerate the accumulation of knowledge on PM component exposure-response relationships that would provide a sound basis more targeted air quality standards and pollution control measures.

Residual oil combustion: a major source of airborne nickel in New York City

On the basis of previous observations that: (1) both the nickel (Ni) concentration in ambient air fine particulate matter (PM(2.5)) and daily mortality rates in New York City (NYC) were much higher than in any other US city; and (2) that peaks in Ni concentration was strongly associated with cardiac function in a mouse model of atherosclerosis, we initiated a study of the spatial and seasonal distributions of Ni in NYC and vicinity to determine the feasibility of productive human population-based studies of the extent to which ambient fine particle Ni may account for cardiovascular health effects. Using available speciation data from previous studies at The New York University, Environmental Protection Agency's Speciation Trends Network; and the Interagency Monitoring of Protected Visual Environments network, we determined that Ni in NYC is on average 2.5 times higher in winter than in summer. This apparent seasonal gradient is absent, or much less pronounced, at NJ and CT speciation sites. Ni concentrations at a site on the east side of Manhattan and at two sites in the western portion of the Bronx were a factor of two higher than at a site on the west side of Manhattan, or at one at Queens College in eastern Queens County, indicating a strong spatial gradient within NYC. We conclude that the winter peaks of fine particle Ni indicate that space heating, which involves the widespread reliance on residual oil combustion in many older residential and commercial buildings in NYC, is a major source of ambient air Ni. Epidemiologic studies based on data generated by a network of speciation sites throughout NYC could effectively test the hypothesis that Ni could account for a significant portion of the excess mortality and morbidity that have been associated with elevated mass concentrations of PM(2.5).

A Macrophage-Based Method for the Assessment of the Reactive Oxygen Species (ROS) Activity of Atmospheric Particulate Matter (PM) and Application to Routine (Daily-24 h) Aerosol Monitoring Studies

Both short- and long-term exposure to particulate matter (PM) air pollution have been demonstrated to cause increases in cardiovascular disease, cancer, and respiratory disorders. Although the specific mechanisms by which exposure to PM cause these affects are unclear, significant evidence has accumulated to suggest that PM exposure leads to increased inflammation as the result of excessive production of reactive oxygen species (ROS) in critical cell types. In order to better understand how real-world PM exposure causes adverse health effects, there is a need to efficiently integrate metrics of PM toxicity into large scale air monitoring and health effects/epidemiology studies. Here we describe a rapid, inexpensive, method that can be employed to assess the potential of sub-mg masses of PM to generate oxidative stress in alveolar macrophage cells. Importantly, the approach is compatible with routine daily PM sampling programs such as those administered by EPA (Speciation trends network (STN), IMPROVE netw...

Simulating secondary organic aerosol formation using the volatility basis-set approach in a chemical transport model

The secondary organic aerosol (SOA) module in PMCAMx, a three-dimensional chemical transport model, has been updated based on laboratory results from recent smog chamber experiments. A new modeling framework is used based on the SOA volatility basis-set approach instead of the two-product approach used in existing models. The different scenarios are simulated for July 2001, and the predicted organic aerosol (OA) concentrations are compared to the available ambient measurements from the EPA Speciation Trends Network (STN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). The base-case OA simulation slightly overpredicts the ambient measured OA concentrations at the rural IMPROVE monitoring sites with a bias of 0.33 μg m −3. Compared to the urban STN sites, the base-case scenario still underpredicts the observed OA concentrations but is performing significantly better than the two-product module in PMCAMx compared to observations. The contribution of biogenic and anthropogenic SOA to the predicted organic aerosol concentrations and the potential role of chemical aging are investigated. The higher yields of SOA from anthropogenic precursors reported in the most recent studies improve the model performance in urban areas.