A Fuel-Based Motor Vehicle Emission Inventory

Abstract

A fuel-based methodology for calculating motor vehicle emission inventories is presented. In the fuel-based method, emission factors are normalized to fuel consumption and expressed as grams of pollutant emitted per gallon of gasoline burned. Fleet-average emission factors are calculated from the measured on-road emissions of a large, random sample of vehicles. Gasoline use is known at the state level from sales tax data, and may be disaggregated to individual air basins. A fuel-based motor vehicle CO inventory was calculated for the South Coast Air Basin in California for summer 1991. Emission factors were calculated from remote sensing measurements of more than 70,000 in-use vehicles. Stabilized exhaust emissions of CO were estimated to be 4400 tons/day for cars and 1500 tons/day for light-duty and medium- duty trucks, with an estimated uncertainty of ±20% for cars and ±30% for trucks. Total motor vehicle CO emissions, including incremental start emissions and emissions from heavy-duty vehicles were estimated to be 7900 tons/day. Fuelbased inventory estimates were greater than those of California's MVEI 7F model by factors of 2.2 for cars and 2.6 for trucks. A draft version of California's MVEI 7G model, which includes increased contributions from high-emitting vehicles and off-cycle emissions, predicted CO emissions which closely matched the fuel-based inventory. An analysis of CO mass emissions as a function of vehicle age revealed that cars and trucks which were ten or more years old were responsible for 58% of stabilized exhaust CO emissions from all cars and trucks.     

An Analysis of Michigan and California CO Remote Sensing Measurements

Abstract

Remote sensing measurements of CO emissions from on-road vehicles were made in California in 1991 and in Michigan in 1992. It was determined that both fleets had a small linear increase in the high emitter frequency (vehicles emitting more than 4% CO) as a function of vehicle age for 1986 and newer model vehicles. Although high emitting vehicles were only a small minority of the fleet, they had a dominant impact on the mean CO and total CO emitted by the fleet. In Michigan, the highest emitting 5% of passenger cars generated 45% of the CO from cars. In California, the highest emitting 5% of passenger cars generated 38% of the CO from cars. There was a high correlation between the mean CO emitted by each model year of vehicle and the frequency of high emitting vehicles within the model year for both the Michigan and California fleets. The frequency of high emitters within any model year had no obvious relation to that model year’s certification standards. The high emitter frequencies for vehicles less than nine years old were very similar for the California and Michigan fleets. An increase in the high emitter frequency in the ten-year-old and older Michigan passenger car fleet (relative to the California passenger car fleet), suggests, but does not conclusively demonstrate, that the rate of high emitters in Michigan and California is reduced by the inspection and maintenance (I/M) programs.     

Development of an individual exposure model for application to the Southern California children's health study

The Southern California Children's Health Study (CHS) investigated the relationship between air pollution and children's chronic respiratory health outcomes. Ambient air pollutant measurements from a single CHS monitoring station in each community were used as surrogates for personal exposures of all children in that community. To improve exposure estimates for the CHS children, we developed an Individual Exposure Model (IEM) to retrospectively estimate the long-term average exposure of the individual CHS children to CO, NO₂, PM₁₀, PM_2.5, and elemental carbon (EC) of ambient origin. In the IEM, pollutant concentrations due to both local mobile source emissions (LMSE) and meteorologically transported pollutants were taken into account by combining a line source model (CALINE4) with a regional air quality model (SMOG). To avoid double counting, local mobile sources were removed from SMOG and added back by CALINE4. Limited information from the CHS survey was used to group each child into a specific time-activity category, for which corresponding Consolidated Human Activity Database (CHAD) time-activity profiles were sampled. We found local traffic significantly increased within-community variability of exposure to vehicle-related pollutants. PM-associated exposures were influenced more by meteorologically transported pollutants and local non-mobile source emissions than by LMSE. The overall within-community variability of personal exposures was highest for NO₂ (±20–40%), followed by EC (±17–27%), PM₁₀ (±15–25%), PM_2.5 (±15–20%), and CO (±9–14%). Between-community exposure differences were affected by community location, traffic density, and locations of residences and schools in each community. Proper siting of air monitoring stations relative to emission sources is important to capture community mean exposures.     

Multispecies remote sensing measurements of vehicle emissions on Sherman Way in Van Nuys,California

As part of the 2010 Van Nuys tunnel study, researchers from the University of Denver measured on-road fuel-specific light-duty vehicle emissions from nearly 13,000 vehicles on Sherman Way (0.4 miles west of the tunnel) in Van Nuys, California, with its multispecies Fuel Efficiency Automobile Test (FEAT) remote sensor a week ahead of the tunnel measurements. The remote sensing mean gram per kilogram carbon monoxide (CO), hydrocarbon (HC), and oxide of nitrogen (NO_x) measurements are 8.9% lower, 41% higher, and 24% higher than the tunnel measurements, respectively. The remote sensing CO/NO_x and HC/NO_x mass ratios are 28% lower and 20% higher than the comparable tunnel ratios. Comparisons with the historical tunnel measurements show large reductions in CO, HC, and NO_x over the past 23 yr, but little change in the HC/NO_x mass ratio since 1995. The fleet CO and HC emissions are increasingly dominated by a few gross emitters, with more than a third of the total emissions being contributed by less than 1% of the fleet. An example of this is a 1995 vehicle measured three times with an average HC emission of 419 g/kg fuel (two-stroke snowmobiles average 475 g/kg fuel), responsible for 4% of the total HC emissions. The 2008 economic downturn dramatically reduced the number of new vehicles entering the fleet, leading to an age increase (>1 model year) of the Sherman Way fleet that has increased the fleet's ammonia (NH₃) emissions. The mean NH₃ levels appear little changed from previous measurements collected in the Van Nuys tunnel in 1993. Comparisons between weekday and weekend data show few fleet differences, although the fraction of light-duty diesel vehicles decreased from the weekday (1.7%) to Saturday (1.2%) and Sunday (0.6%).

Implications: On-road remote sensing emission measurements of light-duty vehicles on Sherman Way in Van Nuys, California, show large historical emission reductions for CO and HC emissions despite an older fleet arising from the 2008 economic downturn. Fleet CO and HC emissions are increasingly dominated by a few gross emitters, with a single 1995 vehicle measured being responsible for 4% of the entire fleet's HC emissions. Finding and repairing and/or scrapping as little as 2% of the fleet would reduce on-road tailpipe emissions by as much as 50%. Ammonia emissions have locally increased with the increasing fleet age.     

Three decades of on-road mobile source emissions reductions in South Los Angeles

In May 2018, the University of Denver repeated on-road optical remote sensing measurements at two locations in Lynwood, CA. Lynwood area vehicle tailpipe emissions were first surveyed in 1989 and 1991 because the area suffered from a large number of carbon monoxide (CO) air quality violations. These new measurements allow for the estimation of fuel-specific CO and total hydrocarbon (HC) emissions reductions, changes in the longevity of emission-control components, and the prevalence of high emitters in the current fleet. Since 1989 CO emissions decreased approximately factors of 10 (120 ± 8 to 12.3 ± 0.2 gCO/kg of fuel) and 20 (210 ± 8 to 10.4 ± 0.4 gCO/kg of fuel) at our I-710/Imperial Highway and Long Beach Blvd. sites, respectively. These reductions are also reflected in the local ambient air measurements. Tailpipe HC emissions have decreased by a factor of 25 (50 ± 4 to 2.1 ± 0.3 gHC/kg of fuel) since 1991 at the Long Beach Blvd. location. The decreases are so dramatic that the vast majority of vehicles now have HC measurements that are indistinguishable from zero. The decreases have increased the skewedness of the emissions distribution with the 99th percentile now responsible for more than 37% (CO) and 28% (HC) of the totals. Ammonia emissions collected in 2018 at both Lynwood locations peak with 20-year-old vehicles (1998 models), indicating long lifetimes for catalytic converters.

In 1989 and 1991, the on-road Lynwood fleets had significantly higher emissions than fleets observed in other locations within the South Coast Air Basin. The 2018 fleets now have means and emissions by model year that are consistent with those observed at other sites in Los Angeles and the U.S. This indicates that modern vehicle combustion management and after-treatment systems are achieving their goals regardless of community income levels.

Implications: Recent on-road vehicle emission measurements at two locations in the Lynwood, CA area, first visited in 1989, found significant fuel specific CO and HC emission reductions. CO emissions have decreased by a factor of 10 and 20 at each location and HC emissions have declined by a factor of 25. This has increased the skewedness in both species emissions distribution. The 2018 fleets have means and emissions by model year that are now consistent with those observed at other U.S. sites indicating that modern vehicle emissions control advancements are achieving their goals regardless of community income levels.     

Analysis of Remote Sensing Errors of Omission and Commission Under FTP Conditions

Abstract

Second-by-second modal emissions data from a 73-vehicle fleet of 1990 and 1991 light duty cars and trucks driven on the Federal Test Procedure (FTP) driving cycle were examined to determine remote sensing errors of commission in identifying high emissions vehicles. Results are combined with a similar analysis of errors of omission based on modal FTP data from high emissions vehicles. Extremely low errors of commission combined with modest errors of omission indicate that remote sensing should be very effective in isolating high CO and HC emitting vehicles in a fleet of late model vehicles on the road.     

Inhalation of Concentrated Ambient Particulate Matter near a Heavily Trafficked Road Stimulates Antigen-Induced Airway Responses in Mice

Abstract

The goal of this study was to test the following hypotheses:(1) exposure to mobile emissions from mobile sources close to a heavily trafficked roadway will exacerbate airway inflammation and allergic airway responses in a sensitized mouse model, and (2) the magnitude of allergic airway disease responses will decrease with increasing distance from the roadway. A particle concentrator and a mobile exposure facility were used to expose ovalbumin (OVA)-sensitized BALB/c mice to purified air and concentrated fine and concentrated ultrafine ambient particles at 50 m and 150 m downwind from a roadway that was heavily impacted by emissions from heavy duty diesel-powered vehicles. After exposure, we assessed interleukin (IL)-5, IL-13, OVA-specific immunoglobulin E, OVA-specific immunoglobulin G1, and eosinophil influx as biomarkers of allergic responses and numbers of polymorphonuclear leukocytes as a marker of inflammation. The study was performed over a two-year period, and there were differences in the concentrations and compositions of ambient particulate matter across those years that could have influenced our results. However, averaged over the two-year period, exposure to concentrated ambient particles (CAPs) increased the biomarkers associated with airway allergies (IL-5, immunoglobulin E, immunoglobulin G1 and eosinophils). In addition, mice exposed to CAPs 50 m downwind of the roadway had, on the average, greater allergic responses and showed greater indications of inflammation than did mice exposed to CAPs 150 m downwind. This study is consistent with the hypothesis that exposure to CAPs close to a heavily trafficked roadway influenced allergic airway responses.     

Models to predict emissions of health-damaging pollutants and global warming contributions of residential fuel/stove combinations in China

Residential energy use in developing countries has traditionally been associated with combustion devices of poor energy efficiency, which have been shown to produce substantial health-damaging pollution, contributing significantly to the global burden of disease, and greenhouse gas (GHG) emissions. Precision of these estimates in China has been hampered by limited data on stove use and fuel consumption in residences. In addition limited information is available on variability of emissions of pollutants from different stove/fuel combinations in typical use, as measurement of emission factors requires measurement of multiple chemical species in complex burn cycle tests. Such measurements are too costly and time consuming for application in conjunction with national surveys. Emissions of most of the major health-damaging pollutants (HDP) and many of the gases that contribute to GHG emissions from cooking stoves are the result of the significant portion of fuel carbon that is diverted to products of incomplete combustion (PIC) as a result of poor combustion efficiencies. The approximately linear increase in emissions of PIC with decreasing combustion efficiencies allows development of linear models to predict emissions of GHG and HDP intrinsically linked to CO2 and PIC production, and ultimately allows the prediction of global warming contributions from residential stove emissions. A comprehensive emissions database of three burn cycles of 23 typical fuel/stove combinations tested in a simulated village house in China has been used to develop models to predict emissions of HDP and global warming commitment (GWC) from cooking stoves in China, that rely on simple survey information on stove and fuel use that may be incorporated into national surveys. Stepwise regression models predicted 66% of the variance in global warming commitment (CO2, CO, CH4, NOx, TNMHC) per 1 MJ delivered energy due to emissions from these stoves if survey information on fuel type was available. Subsequently if stove type is known, stepwise regression models predicted 73% of the variance. Integrated assessment of policies to change stove or fuel type requires that implications for environmental impacts, energy efficiency, global warming and human exposures to HDP emissions can be evaluated. Frequently, this involves measurement of TSP or CO as the major HDPs. Incorporation of this information into models to predict GWC predicted 79% and 78% of the variance respectively. Clearly, however, the complexity of making multiple measurements in conjunction with a national survey would be both expensive and time consuming. Thus, models to predict HDP using simple survey information, and with measurement of either CO/CO2 or TSP/CO2 to predict emission factors for the other HDP have been derived. Stepwise regression models predicted 65% of the variance in emissions of total suspended particulate as grams of carbon (TSPC) per 1 MJ delivered if survey information on fuel and stove type was available and 74% if the CO/CO2 ratio was measured. Similarly stepwise regression models predicted 76% of the variance in COC emissions per MJ delivered with survey information on stove and fuel type and 85% if the TSPC/CO2 ratio was measured. Ultimately, with international agreements on emissions trading frameworks, similar models based on extensive databases of the fate of fuel carbon during combustion from representative household stoves would provide a mechanism for computing greenhouse credits in the residential sector as part of clean development mechanism frameworks and monitoring compliance to control regimes.     

Predicting black smoke levels from deposit gauge and SO2 data to estimate long-term exposure in the United Kingdom, 1956–1961

BackgroundIn the UK air quality has been monitored systematically since 1914, providing valuable data for studies of the long-term trends in air pollution and potentially for studies of health effects of air pollutants. There are, however, challenges in interpreting these data due to changes over time in the number and location of monitored sites, and in monitoring techniques. Particulate matter was measured as deposited matter (DM) using deposit gauge monitors until the 1950s when black smoke (BS) filters were introduced. Estimating long-term exposure to particulates using data from both deposit gauge and BS monitors requires an understanding of the relationships between DM, SO₂ and BS.AimsTo explore whether DM and/or SO₂, along with seasonal and location specific variables can be used to predict BS levels.MethodsAir quality data were abstracted from hard copies of the monthly Atmospheric Pollution Bulletins for the period April 1956–March 1961 for any sites with co-located DM, SO₂ and BS data for three or more consecutive years. The relationships between DM, SO₂, and BS were assessed using mixed models.ResultsThere were 34 eligible sites giving 1521 triplets of data. There was a consistent correlation between SO₂ and BS at all sites, but the association between DM and BS was less clear and varied by location. Mixed modelling allowing for repeat measurements at each site revealed that SO₂, year, rainfall and season of measurement explained 72% of the variability in BS levels.ConclusionsSO₂ can be used as a surrogate measure for BS in all monitoring locations. This surrogate can be improved upon by consideration of site specific characteristics, seasonal effects, rainfall and year of measurement. These findings will help in estimating historic, long-term exposure to particulates where BS or other measures are not available.     

Human Breath Emissions of VOCs

ABSTRACT

The medical community has long recognized that humans exhale volatile organic compounds (VOCs). Several studies have quantified emissions of VOCs from human breath, with values ranging widely due to variation between and within individuals. The authors have measured human breath concentrations of isoprene and pentane. The major VOCs in the breath of healthy individuals are isoprene (12580 ppb), acetone (1.2-1,880 ppb), ethanol (13-1,000 ppb), methanol (160-2,000 ppb) and other alcohols. In this study, we give a brief summary of VOC measurements in human breath and discuss their implications for indoor concentrations of these compounds, their contributions to regional and global emissions budgets, and potential ambient air sampling artifacts. Though human breath emissions are a negligible source of VOCs on regional and global scales (less than 4% and 0.3%, respectively), simple box model calculations indicate that they may become an important (and sometimes major) indoor source of VOCs under crowded conditions. Human breath emissions are generally not taken into account in indoor air studies, and results from this study suggest that they should be.     

Carbon Monoxide Trend,Meteorology, and Three-Way Catalysts in Mexico City

ABSTRACT

From the analysis of data of the Inspection/Maintenance (I/M) program, and of the long-term trend of ambient CO concentrations in Mexico City, it is inferred that three-way catalysts (TWCs) have a 45% efficiency, well below the expected 90% value. The most probable causes are sulfur poisoning, lead contamination, and ceramic breakage due to bumps and potholes on the streets. Also, we have found a ratio between the average daily peak value of atmospheric CO and gasoline consumption: (11 ± 1) ppbCO/MLm (million liters of gasoline per month) in 1988 decaying to (10 ±1) in 1991 for Mexico City before the introduction of TWCs. In addition, we found a correlation between the monthly averages of CO daily peak and meteorological variables, explaining most of the seasonal changes using only the intensity of the inversion layer and surface wind speed.     

Trends in on-road vehicle emissions and ambient air quality in Atlanta,Georgia, USA,from the late 1990s through 2009

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO_x), and volatile organic compounds (VOCs) during 1995–2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO_x, and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995–2009 period despite an increase in total vehicle distance traveled. The CO and NO_x emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO_x in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001–2009. Although this trend coexists with the declining trends in on-road NO_x, VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors.

Implications:Large reductions in on-road vehicle emissions of CO and NO_x in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO_x during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.     

Transportation-Related Volatile Hydrocarbon Source Profiles Measured in Atlanta

Abstract

Samples representative of transportation-related hydrocarbon emissions were collected as part of the 1990 Atlanta Ozone Precursor Monitoring Study. Motor vehicle emissions were sampled in canisters beside a roadway in a tunnel-like underpass during periods of heavy traffic. Airport and aircraft emissions were approximated by canister samples obtained at a major airport facility. Three octane grades of gasoline were purchased from six major vendors in Atlanta. Canister samples were prepared using these fuels to approximate the whole gasoline and gasoline vapor composition of the fuels in use during the study. All samples were analyzed by gas chromatography/flame ionization detection (GC/FID) for their hydrocarbon content. Detailed speciated hydrocarbon profiles were developed from this source sampling and analysis program for use in the Chemical Mass Balance (CMB) model. Profiles presented and discussed here represent the hydrocarbon composition of emissions from a roadway, composite headspace gasoline at two temperatures, composite whole gasoline, whole gasoline at three octane grades, and an airport. The roadway profile is compared with similar profiles in the literature, and recommendations are made regarding its use in the CMB model. The roadway and fuel profiles are discussed in the context of the MOBILE5 model outputs. The headspace gasoline vapor profile presented here is compared with a headspace gasoline vapor profile calculated from the whole gasoline profile by means of Raoult’s law. Agreement between the measured and calculated headspace profiles is excellent. The airport profile demonstrates the importance of high molecular weight volatile hydrocarbons in airport and aircraft emissions.     

Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of Individual Vehicle Exhaust with a Mobile Measurement Platform

ABSTRACT

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO_x), and particle mass with aerodynamic diameter below 2.5 μm (PM_2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO_x, black carbon (BC), PM_2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO_x and CO fleet-average emission factors and more than 60% of PM_2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM_2.5, and NO_x tended to cluster among the same vehicles.

IMPLICATIONS This study presents the characterization of on-road vehicle emissions in Wilmington, CA, by sampling individual vehicle plumes. Approximately 5% of the vehicles were high emitters, whose emissions were more than 5 times the fleet-average values. These high emitters were responsible for 30% and more than 50% of the average emission factors of LDGVs and HDDVs, respectively. It is likely that as the overall fleet becomes cleaner due to more stringent regulations, a small fraction of the fleet may contribute a growing and disproportionate share of the overall emissions. Therefore, long-term changes in on-road emissions need to be monitored.     

Reducing the Risk of Accidental Death Due to Vehicle-Related Carbon Monoxide Poisoning

ABSTRACT

Emissions of carbon monoxide (CO) from motor vehicles cause several hundred accidental fatal poisonings annually in the United States. The circumstances that could lead to fatal poisonings in residential settings with motor vehicles as the source of CO were explored. The risk of death in a garage (volume = 90 m³) and a single-family dwelling (400 m³) was evaluated using a Monte Carlo simulation with varying CO emission rates and ventilation rates. Information on emission rates was obtained from a survey of motor vehicle exhaust gas composition under warm idle conditions in California, and information on ventilation rates was obtained from a summary of published measurements in the U.S. housing stock. The risk of death ranged from 16 to 21% for a 3-hr exposure in a garage to 0% for a 1-hr exposure in a house. Older vehicles were associated with a disproportionately high risk of death. Removing all pre-1975 vehicles from the fleet would reduce the risk of death by one-fourth to two-thirds, depending on the exposure scenario. Significant efforts have been made to control CO emissions from motor vehicles with the goal of reducing CO concentrations in outdoor air. Substantial public health benefit could also be obtained if vehicle control measures were designed to take account of acute CO poisonings explicitly.     

Characterization of On-Road Vehicle NO Emissions by a TILDAS Remote Sensor

ABSTRACT

A tunable infrared laser differential absorption spectrometer (TILDAS) was used to remotely sense the nitric oxide (NO) emissions from 1,473 on-road vehicles. The real-world measurement precision of this instrument in the limit of low NO concentration is 5 ppm of the vehicle exhaust, which corresponds to a 3o detection limit of 15 ppm. Our analysis of the distribution of negative concentration measurements produced during this experiment supports this claim, showing that the instrumental noise for this set of measurements was at most 8 ppm in the limit of low NO concentration. The high sensitivity of this instrument allowed us to measure the NO emissions of even the cleanest vehicles. The measured vehicle fleet NO emissions closely fit a gamma distribution with 10% of the fleet contributing about 50% of the total fleet emissions. Newer vehicles had lower NO emissions than older ones, but high NO emitters were found in every vehicle age cohort. On a vehicle-by-vehicle basis, NO emissions correlated very weakly with vehicle velocity, acceleration, power per unit mass, carbon monoxide (CO) emissions, and hydrocarbon (HC) emissions. High NO emitting vehicles could not be identified by remote sensing of CO or HC emissions and vice versa. When we compared the NO emissions for 117 vehicles measured more than one time, about half of the high NO emitters were found to be very consistent, while the other half varied significantly.     

Characterization of air manganese exposure estimates for residents in two Ohio towns

This study was conducted to derive receptor-specific outdoor exposure concentrations of total suspended particulate (TSP) and respirable (d_ae ≤ 10 µm) air manganese (air-Mn) for East Liverpool and Marietta (Ohio) in the absence of facility emissions data, but where long-term air measurements were available. Our “site-surface area emissions method” used U.S. Environmental Protection Agency’s (EPA) AERMOD (AMS/EPA Regulatory Model) dispersion model and air measurement data to estimate concentrations for residential receptor sites in the two communities. Modeled concentrations were used to create ratios between receptor points and calibrated using measured data from local air monitoring stations. Estimated outdoor air-Mn concentrations were derived for individual study subjects in both towns. The mean estimated long-term air-Mn exposure levels for total suspended particulate were 0.35 μg/m³ (geometric mean [GM]) and 0.88 μg/m³ (arithmetic mean [AM]) in East Liverpool (range: 0.014–6.32 μg/m³) and 0.17 μg/m³ (GM) and 0.21 μg/m³ (AM) in Marietta (range: 0.03–1.61 μg/m³). Modeled results compared well with averaged ambient air measurements from local air monitoring stations. Exposure to respirable Mn particulate matter (PM₁₀; PM <10 μm) was higher in Marietta residents.

Implications: Few available studies evaluate long-term health outcomes from inhalational manganese (Mn) exposure in residential populations, due in part to challenges in measuring individual exposures. Local long-term air measurements provide the means to calibrate models used in estimating long-term exposures. Furthermore, this combination of modeling and ambient air sampling can be used to derive receptor-specific exposure estimates even in the absence of source emissions data for use in human health outcome studies.     

PM Emissions Emanating from Limited-Access Highways

ABSTRACT

Motor vehicle contributions to primary particulate matter (PM) emissions include exhaust, tire wear, brake and clutch wear, and resuspended road dust. Relatively few field studies have been conducted to quantify fleetaverage exhaust emissions for actual on-road conditions. Therefore, direct measurements of motor vehicle-related PM emissions are warranted. In this study, PM₁₀ and PM_2.5 mass concentrations were measured near two major highways in the St. Louis area over the period from February–April 1997. Samplers were deployed both upwind and downwind of the roadways to capture the transport and dispersion of PM with distance from the roadway. The observed microscale concentration fields were compared to estimates using the PART5 emission factor model together with the CALINE4 highway dispersion model. Traffic- induced PM mass concentrations observed downwind of the roadway were always less than PART5/CALINE4 predictions; average percent differences for observed traffic-induced mass concentrations compared to predicted values were ?34% for PM_2.5 and -70% for PM₁₀. In most cases, the observed PM concentration decay with increasing distance from the roadway was steeper than predicted by dispersion modeling. Motor vehicle-induced emission factors were reconstructed by fitting CALINE4 to the observed concentration data with the emission factor as the sole adjustable parameter. Reconstructed fleet-average motor vehicle emission factors for the urban interstate highway were 0.03–0.04 g/VMT for both PM_2.5 and PM₁₀, while the fleet-average emission factors for the rural interstate highway were 0.2 and 0.3 g/VMT for PM_2.5 and PM₁₀, respectively.     

Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability