Development of a microscale emission factor model for particulate matter for predicting real-time motor vehicle emissions

The U.S. Environmental Protection Agency's National Exposure Research Laboratory is pursuing a project to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant exposure microenvironments. Current particulate matter (PM) emission models, particle emission factor model (used in the United States, except California) and motor vehicle emission factor model (used in California only), are suitable only for county-scale modeling and emission inventories. There is a need to develop a site-specific real-time emission factor model for PM emissions to support human exposure studies near roadways. A microscale emission factor model for predicting site-specific real-time motor vehicle PM (MicroFacPM) emissions for total suspended PM, PM less than 10 microm aerodynamic diameter, and PM less than 2.5 microm aerodynamic diameter has been developed. The algorithm used to calculate emission factors in MicroFacPM is disaggregated, and emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle-miles-traveled weighting of the emission factors for different vehicle classes. MicroFacPM requires input information necessary to characterize the site-specific real-time fleet being modeled. Other variables required include average vehicle speed, time and day of the year, ambient temperature, and relative humidity.     

Sensitivity analysis and evaluation of MicroFacPM: a microscale motor vehicle emission factor model for particulate matter emissions

A microscale emission factor model (MicroFacPM) for predicting real-time site-specific motor vehicle particulate matter emissions was presented in the companion paper titled "Development of a Microscale Emission Factor Model for Particulate Matter (MicroFacPM) for Predicting Real-Time Motor Vehicle Emissions". The emission rates discussed are in mass per unit distance with the model providing estimates of fine particulate matter (PM2.5) and coarse particulate matter. This paper complements the companion paper by presenting a sensitivity analysis of the model to input variables and evaluation model outputs using data from limited field studies. The sensitivity analysis has shown that MicroFacPM emission estimates are very sensitive to vehicle fleet composition, speed, and the percentage of high-emitting vehicles. The vehicle fleet composition can affect fleet emission rates from 8 mg/mi to 1215 mg/mi; an increase of 5% in the smoking (high-emitting) current average U.S. light-duty vehicle fleet (compared with 0%) increased PM2.5 emission rates by -272% for 2000; and for the current U.S. fleet, PM2.5 emission rates are reduced by a factor of -0.64 for speeds >50 miles per hour (mph) relative to a speed of 10 mph. MicroFacPM can also be applied to examine the contribution of emission rates per vehicle class, model year, and sources of PM. The model evaluation is presented for the Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA, and some limited evaluations at two locations: Sepulveda Tunnel, Los Angeles, CA, and Van Nuys Tunnel, Van Nuys, CA. In general, the performance of MicroFacPM has shown very encouraging results.     

Source contributions to fine particulate matter in an urban atmosphere

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM2.5 particles at an urban site in Seoul, Korea. 12-h PM2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM2.5 source impacts, 24-h PM2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO2 oxidation method. The 12-h PM2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM2.5 source categories were extracted with a combination of PM2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; chi2, R2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM2.5 mass (41.8 microg m-3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.     

Sensitivity analysis and evaluation of microFacCO: a microscale motor vehicle emission factor model for CO emissions

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting real-time site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by approximately 67% at ambient temperatures of 45 degrees F and > or = 95 degrees F in comparison with an ambient temperature of 75 degrees F. The input variable "emission failure standard rate" is more sensitive to estimating emission rates in the 1990s than in the 2000s. The estimation of emission rates is not very sensitive to relative humidity. MicroFacCO can also be applied to examine the contribution of emission rates per vehicle class and model year. The model evaluation is presented for tunnel studies at five locations. In general, this evaluation study found good agreement between the measured and the modeled emissions. These analyses and evaluations have identified the need for additional studies to update the high-speed (>35 mph) air conditioning (A/C) correction factor and to add effects due to road grades. MicroFacCO emission estimates are very sensitive to the emission standard failure rate. Therefore, the model performance can be greatly improved by using a local emission standard failure rate.     

Development and preliminary evaluation of a particulate matter emission factor model for European motor vehicles

Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 microns, either from published data or from user-defined size distributions. A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM10, PM5, PM2.5, and PM1] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway). A preliminary evaluation of PMFAC with an available dispersion model to predict the airborne concentration in the urban environment is presented. The trial was on the A6 trunk road where it passes through Loughborough, a medium-size town in the English East Midlands. This evaluation for TSP and PM10 was carried out for a range of traffic fleet compositions, speeds, and meteorological conditions. Given the limited basis of the evaluation, encouraging agreement was shown between predicted and measured concentrations.     

Development of an empirical model to estimate real-world fine particulate matter emission factors: the traffic air quality model

The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM2.5) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds < 50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flow-speeds > 80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.     

Using a dispersion model to estimate emission rates of particulate matter from paved roads

From January 1996 to June 1997, we carried out a series of measurements to estimate emissions of PM₁₀ from paved roads in Riverside County, California. The program involved the measurement of upwind and downwind vertical profiles of PM₁₀, in addition to meteorological variables such as wind speed and vertical turbulent intensity. This information was analyzed using a new dispersion model that incorporates current understanding of micrometeorology and dispersion. The emission rate was inferred by fitting model predictions to measurements. The inferred emission factors ranged from 0.2 g VKT^-1 for freeways to about 3 g VKT^-1 for city roads. The uncertainty in these factors is estimated to be approximately a factor of two since the contributions of paved road PM₁₀ emissions to ambient concentrations were comparable to the uncertainty in the mean value of the measurement. At this stage, our best estimate of emission factor lies between 0.1 and 10 g VKT^-1; there is some indication that it is about 0.1 g VKT^-1 for heavily traveled freeways, and is an order of magnitude higher for older city roads. We found that measured silt loadings were poor predictors of emission factors.The measured emission factors imply that paved road emissions may contribute about 30% to the total PM₁₀ emissions from a high traffic area such as Los Angeles. This suggests that it is necessary to develop methods that are more reliable than the upwind–downwind concentration difference technique.     

桂林地区大气细颗粒物(PM2.5)工业排放源清单

通过现场勘测以及走访调研的形式,获得桂林地区2011至2013年工业生产情况统计,参照国内外相关文献资料确定排放因子,并通过数据处理得到桂林地区工业排放源清单。结果表明,近三年桂林地区工业污染源年均向大气排放细颗粒物(PM2.5)10 751.01 t,其中以兴安县贡献量最大,达到5 024.92 t;永福县次之,为2 924.31 t。在企业类型中,以火力发电企业、水泥及砖瓦厂对桂林地区大气排放细颗粒物的贡献量较大,分别为2 540.81、6 544.51和555.13 t。同时,桂林地区以煤炭作为主要燃料,其对大气排放细颗粒物的年均贡献量达到2 672.17 t。     

Impact of biogenic emission uncertainties on the simulated response of ozone and fine particulate matter to anthropogenic emission reductions

The role of emissions of volatile organic compounds and nitric oxide from biogenic sources is becoming increasingly important in regulatory air quality modeling as levels of anthropogenic emissions continue to decrease and stricter health-based air quality standards are being adopted. However, considerable uncertainties still exist in the current estimation methodologies for biogenic emissions. The impact of these uncertainties on ozone and fine particulate matter (PM2.5) levels for the eastern United States was studied, focusing on biogenic emissions estimates from two commonly used biogenic emission models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emissions Inventory System (BEIS). Photochemical grid modeling simulations were performed for two scenarios: one reflecting present day conditions and the other reflecting a hypothetical future year with reductions in emissions of anthropogenic oxides of nitrogen (NOx). For ozone, the use of MEGAN emissions resulted in a higher ozone response to hypothetical anthropogenic NOx emission reductions compared with BEIS. Applying the current U.S. Environmental Protection Agency guidance on regulatory air quality modeling in conjunction with typical maximum ozone concentrations, the differences in estimated future year ozone design values (DVF) stemming from differences in biogenic emissions estimates were on the order of 4 parts per billion (ppb), corresponding to approximately 5% of the daily maximum 8-hr ozone National Ambient Air Quality Standard (NAAQS) of 75 ppb. For PM2.5, the differences were 0.1-0.25 microg/m3 in the summer total organic mass component of DVFs, corresponding to approximately 1-2% of the value of the annual PM2.5 NAAQS of 15 microg/m3. Spatial variations in the ozone and PM2.5 differences also reveal that the impacts of different biogenic emission estimates on ozone and PM2.5 levels are dependent on ambient levels of anthropogenic emissions.     

Evaluation of the European population intake fractions for European and Finnish anthropogenic primary fine particulate matter emissions

The intake fraction (iF) has been defined as the integrated incremental intake of a pollutant released from a source category or region summed over all exposed individuals. In this study we evaluated the iFs in the population of Europe for emissions of anthropogenic primary fine particulate matter (PM_2.5) from sources in Europe, with a more detailed analysis of the iF from Finnish sources. Parameters for calculating the iFs include the emission strengths, the predicted atmospheric concentrations, European population data, and the average breathing rate per person. Emissions for the whole of Europe and Finland were based on the inventories of the European Monitoring and Evaluation Programme (EMEP) and the Finnish Regional Emission Scenario (FRES) model, respectively. The atmospheric dispersion of primary PM_2.5 was computed using the regional-scale dispersion model SILAM. The iFs from Finnish sources were also computed separately for six emission source categories. The iFs corresponding to the primary PM_2.5 emissions from the European countries for the whole population of Europe were generally highest for the densely populated Western European countries, second highest for the Eastern and Southern European countries, and lowest for the Northern European and Baltic countries. For the entire European population, the iF values varied from the lowest value of 0.31 per million for emissions from Cyprus, to the highest value of 4.42 per million for emissions from Belgium. These results depend on the regional distribution of the population and the prevailing long-term meteorological conditions. Regarding Finnish primary PM_2.5 emissions, the iF was highest for traffic emissions (0.68 per million) and lowest for major power plant emissions (0.50 per million). The results provide new information that can be used to find the most cost-efficient emission abatement strategies and policies.     

Application of satellite remote-sensing data for source analysis of fine particulate matter transport events

Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative imagery and quantitative data, such as aerosol optical depth. Yet there has been limited application of these new datasets in the study of air pollutant sources relevant to public policy. One promising approach to more directly link satellite sensor data to air quality policy is to integrate satellite sensor data with air quality parameters and models. This paper presents a visualization technique to integrate satellite sensor data, ground-based data, and back trajectory analysis relevant to a new rule concerning the transport of particulate matter across state boundaries. Overlaying satellite aerosol optical depth data and back trajectories in the days leading up to a known fine particulate matter with an aerodynamic diameter of <2.5 microm (PM2.5) event may indicate whether transport or local sources appear to be most responsible for high PM2.5 levels in a certain location at a certain time. Events in five cities in the United States are presented as case studies. This type of analysis can be used to help understand the source locations of pollutants during specific events and to support regulatory compliance decisions in cases of long distance transport.     

人体室内外PM_(2.5)吸入暴露研究

介绍了室内外空气颗粒物吸入暴露的评价方法,选择PM2.5作为检测评价的对象,初步评价了上海市某区不同年龄段人员的PM2.5暴露水平。结果表明:(1)成人和老人的全年日平均PM2.5吸入暴露量均较高,并且成人的全年日平均PM2.5吸入暴露量变化曲线和儿童相似。(2)老人室内PM2.5吸入暴露量要明显高于室外,其主要原因是老人在室内时间较长。儿童和成人的室外PM2.5吸入暴露量高于室内。(3)不同人员的年平均PM2.5吸入暴露量的排序为成人老人儿童,其年平均PM2.5吸入暴露量分别为1.141、1.046、0.935mg。     

Determination of levoglucosan in atmospheric fine particulate matter

A microanalytical method suitable for the quantitative determination of the sugar anhydride levoglucosan in low-volume samples of atmospheric fine particulate matter (PM) has been developed and validated. The method incorporates two sugar anhydrides as quality control standards. The recovery standard sedoheptulosan (2,7-anhydro-beta-D-altro-heptulopyranose) in 20 microL solvent is added onto samples of the atmospheric fine PM and aged for 1 hr before ultrasonic extraction with ethylacetate/ triethylamine. The extract is reduced in volume, an internal standard is added (1,5-anhydro-D-mannitol), and a portion of the extract is derivatized with 10% by volume N-trimethylsilylimidazole. The derivatized extract is analyzed by gas chromatography/mass spectrometry (GC/MS). The recovery of levoglucosan using this procedure was 69 +/- 6% from five filters amended with 2 microg levoglucosan, and the reproducibility of the assay is 9%. The limit of detection is approximately 0.1 microg/mL, which is equivalent to approximately 3.5 ng/m3 for a 10 L/min sampler or approximately 8.7 ng/m3 for a 4 L/min personal sampler (assuming 24-hr integrated samples). We demonstrated that levoglucosan concentrations in collocated samples (expressed as ng/m3) were identical irrespective of whether samples were collected by PM with aerodynamic diameter < or = 2.5 microm or PM with aerodynamic diameter < or = 10 microm impactors. It was also demonstrated that X-ray fluorescence analysis of samples of atmospheric PM, before levoglucosan determinations, did not alter the levels of levoglucosan.     

Characterization of visibility impacts related to fine particulate matter in Canada

Canada has recently established standards for the management of particulate matter (PM) air quality. National networks currently measure PM mass concentrations and chemical speciation. Methods used in the U.S. IMPROVE network are applied to the 1994--2000 Canadian fine PM data to obtain a regional reconstruction of the visibility based on particle composition. Nationally, the greatest light extinction occurs in the Windsor-Quebec City corridor. Variations in the dominant chemical species responsible for the reduction in visibility are presented for regions across the country. In most regions, sulfate and nitrate contribute most greatly to reduced visibility. The visibility implications of achieving the Canada-Wide Standard (CWS) across the country are evaluated, with the greatest improvement in visibility associated with achieving the CWS in southern Ontario. Elsewhere in the country, achieving the CWS will actually result in deteriorating air quality. Improving current estimates of visibility requires higher spatially and temporally resolved measurements of organic and elemental carbon fractions and particulate nitrate.     

Regional contributions to airborne particulate matter in central California during a severe pollution episode

The externally-mixed source-oriented UCD/CIT air quality model was applied to determine the significance of inter-regional transport for primary and secondary particulate matter (PM) in California's Central Valley during a severe wintertime PM pollution episode from December 15, 2000 to January 7, 2001. The gases and primary PM emitted from eight different geographical sub-regions were tracked separately in a model simulation that included transport, physical and chemical transformation and deposition processes. The model results directly predict the contribution that each sub-region makes to PM concentrations throughout the entire model domain. The boundary layer was relatively stagnant during the simulated 3-week air quality episode, and no consistent transport pattern for primary PM was predicted. Several significant inter-regional transport events were identified that each lasted a few days. Each of these inter-regional events was characterized by transport of gas-phase precursors of nitrate that combined with local emissions of ammonia to produce particulate nitrate. Nitrate already in the particle phase was not transported efficiently due to higher dry deposition rates for particles relative to gas-phase nitrogen oxides. The distinctive pattern of transport for nitrate precursors reflects the relatively long timescales required to convert NO_x emissions to nitrate during winter conditions characterized by low temperatures, weak photolysis rates, and low oxidant concentrations. The equilibrium partitioning of nitrate and ammonia to the particle phase is relatively fast once the nitrate has been produced. The most-likely transport distance for nitrate during the current episode varied from 130–140 km for the northern portion of the Central Valley to 50–60 km in the southern portion of the Central Valley. Sub-regions further south in the Valley have smaller transport distances because of slower wind speeds and the greater abundance of ammonia in these areas, leading to faster conversion of gas-phase reactive nitrogen into particulate nitrate, which has a higher dry deposition rate than the gas-phase species. The most-likely transport distance for primary organic compounds (OC) was found to be less than that for nitrate, varying from 50 to 60 km for the northern portion of the Valley to 20–30 km for southern portion of the Valley. Overall, 68% of the particulate nitrate formed in the most polluted sub-regions of the Central Valley originates from emissions in those same sub-regions. Local emissions controls should therefore provide an effective strategy to reduce airborne particulate matter concentrations to acceptable levels.     

Theoretical study of the impact of particulate matter gravitational settling on ambient coarse particulate matter monitoring for agricultural emissions

The particle size distributions (PSDs) of particulate matter (PM) in the downwind plume from simulated sources of a cotton gin were analyzed to determine the impact of PM settling on PM monitoring. The PSD of PM in a plume varies as a function of gravitational settling. Gravitational settling has a greater impact on the downwind PSD from sources with PSDs having larger mass median diameters (MMDs). The change in PSD is a function of the source PSD of emitted PM, wind speed, and downwind distance. Both MMD and geometric standard deviation (GSD) in the downwind plume decrease with an increase in downwind distance and source MMD. The larger the source MMD, the greater the change in the downwind MMD and GSD. Also, the greater the distance from the source to the sampler, the greater the change in the downwind MMD and GSD. Variations of the PSD in the downwind plume significantly impact PM10 sampling errors associated with the U.S. Environmental Protection Agency (EPA) PM10 samplers. For the emission sources with MMD > 10 microm, the PM10 oversampling rate increases with an increase in downwind distance caused by the decrease of GSD of the PSD in the downwind plume. Gravitational settling of particles does not help reduce the oversampling problems associated with the EPA PM10 sampler. Furthermore, oversampling rates decrease with an increase of the wind speed.     

Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis

Fine particulate matter (PM2.5) concentrations associated with 202 24-hr samples collected at the National Energy Technology Laboratory (NETL) particulate matter (PM) characterization site in south Pittsburgh from October 1999 through September 2001 were used to apportion PM2.5 into primary and secondary contributions using Positive Matrix Factorization (PMF2). Input included the concentrations of PM2.5 mass determined with a Federal Reference Method (FRM) sampler, semi-volatile PM2.5 organic material, elemental carbon (EC), and trace element components of PM2.5. A total of 11 factors were identified. The results of potential source contributions function (PSCF) analysis using PMF2 factors and HYSPLIT-calculated back-trajectories were used to identify those factors associated with specific meteorological transport conditions. The 11 factors were identified as being associated with emissions from various specific regions and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. Three sources associated with transport from coal-fired power plants to the southeast, a combination of point sources to the northwest, and a steel mill and associated sources to the west were identified. In addition, two secondary-material-dominated sources were identified, one was associated with secondary products of local emissions and one was dominated by secondary ammonium sulfate transported to the NETL site from the west and southwest. Of these 11 factors, the four largest contributors to PM2.5 were the secondary transported material (dominated by ammonium sulfate) (47%), local secondary material (19%), diesel combustion emissions (10%), and gasoline combustion emissions (8%). The other seven factors accounted for the remaining 16% of the PM2.5 mass. The findings are consistent with the major source of PM2.5 in the Pittsburgh area being dominated by ammonium sulfate from distant transport and so decoupled from local activity emitting organic pollutants in the metropolitan area. In contrast, the major local secondary sources are dominated by organic material.     

Evaluation of gas well setback policy in the Marcellus Shale region of Pennsylvania in relation to emissions of fine particulate matter

Shale gas has become an important strategic energy source with considerable potential economic benefits and the potential to reduce greenhouse gas emissions in so far as it displaces coal use. However, there still exist environmental health risks caused by emissions from exploration and production activities. In the United States, states and localities have set different minimum setback policies to reduce the health risks corresponding to the emissions from these locations, but it is unclear whether these policies are sufficient. This study uses a Gaussian plume model to evaluate the probability of exposure exceedance from EPA concentration limits for PM2.5 at various locations around a generic wellsite in the Marcellus shale region. A set of meteorological data monitored at ten different stations across Marcellus shale gas region in Pennsylvania during 2015 serves as an input to this model. Results indicate that even though the current setback distance policy in Pennsylvania (500 ft. or 152.4 m) might be effective in some cases, exposure limit exceedance occurs frequently at this distance with higher than average emission rates and/or greater number of wells per wellpad. Setback distances should be 736 m to ensure compliance with the daily average concentration of PM2.5, and a function of the number of wells to comply with the annual average PM2.5 exposure standard.

Implications: The Marcellus Shale gas is known as a significant source of criteria pollutants and studies show that the current setback distance in Pennsylvania is not adequate to protect the residents from exceeding the established limits. Even an effective setback distance to meet the annual exposure limit may not be adequate to meet the daily limit. The probability of exceeding the annual limit increases with number of wells per site. We use a probabilistic dispersion model to introduce a technical basis to select appropriate setback distances.     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.