Interpreting Urban Carbon Monoxide Concentrations by Means of a Computerized Blood COHb Model

A practical, inexpensive computer model for estimating the level of blood carboxyhemoglobin (percent COHb) as a function of time for measured carbon monoxide concentrations (ppm CO) was developed from data from published studies on the assimilation of CO into the blood of human subjects. The model was designed to consider more realistically the dynamic characteristics of urban CO concentrations measured continuously at air monitoring stations, and it was applied to a year's CO data measured at the San Jose CA, air monitoring station (8760 hourly values).

The results indicate that the model can be used by local air pollution control agencies to calculate and print out estimated COHb levels alongside continuous CO concentration data. According to the model, the National Ambient Air Quality Standards (NAAQS) for CO sometimes were violated in San Jose without exceeding 2% COHb, as well as the converse: 2% COHb was exceeded without violating the standards. The model's estimated COHb levels also provided an advance warning of impending violation of the 8-hr CO NAAQS, and analysis of the model's response to CO "spikes" suggests that averaging periods as short as 10 or 15 minutes are necessary to preserve completely the dynamic characteristics of ambient CO monitoring data. These findings suggest that the margin of safety included in the current CO NAAQS, would not be the same if the actual time variation of measured CO concentrations is taken into account.     

A Preliminary Study of Modeling the Air Pollution Effects fromTraffic Engineering Alternatives

Three separate mathematical models were combined to calculate the changes in carbon monoxide (CO) concentrations that might result from traffic engineering changes. The three models used were: (1) The Dynamic Highway Transportation model (DHTM) which relates traffic flow patterns to physical parameters and traffic signal characteristics of a network; (2) an emission model that predicts CO emissions from traffic flow parameters such as number of stops, idling time, etc; and (3) the APRAC-1A urban diffusion model which calculates CO concentrations from source distributions and meteorological factors. The composite model was applied to traffic in downtown Chicago for a specific set of meteorological conditions. Results are compared for two traffic signal control schemes. In those blocks where concentrations were highest, the model indicates a 20% reduction is possible through improved traffic signal controls. The model should be useful for testing other traffic control measures.     

Carbon Monoxide Exposures to Los Angeles Area Commuters

Carbon monoxide exposures to commuters were simulated in a 5-day study in Los Angeles County. Exposures were determined by measuring CO in three vehicles as they traveled typical commuter routes. The data collected during this study include measurements of vehicle speed and CO measurements in the interior and exterior of the three vehicles during the morning and evening peak traffic periods. In addition, hourly averaged CO measurements were taken from eight south coastal Air Quality Management District fixed-site monitoring stations and six California Department of Transportation vans in the proximity of the commuter routes. These data were used to investigate the relationship of CO exposures to meteorological parameters, fixed-site monitors, and traffic conditions.

The average ratio of interior CO concentrations to exterior CO concentrations was 0.92. Concentrations inside and outside the vehicles remained about the same even when the vehicles were driven with vents closed and windows up. Smoking was not permitted in the vehicles during the study. The average ratio of the hour average CO concentrations in the vehicles to fixed-site measurements was 3.9. However, this ratio decreases with increasing ambient CO levels. Although CO levels in the vehicles frequently exceeded 40 ppm and sometimes exceeded 60 ppm, the hour average CO concentrations did not exceed 35 ppm. Slow moving congested traffic is associated with higher CO levels in the vehicles than a high volume of traffic moving at a steady speed.     

Evaluation of a Probabilistic Exposure Model Applied

The probabilistic National Ambient Air Quality Standards (NAAQS) Exposure Model applied to carbon monoxide (pNEM/CO) was developed by the U.S. Environmental Protection Agency (EPA) to estimate frequency distributions of population exposure to carbon monoxide (CO) and the resulting carboxyhemoglobin (COHb) levels. To evaluate pNEM/CO, the model was set up to simulate CO exposure data collected during a Denver Personal Exposure Monitoring Study (PEM) conducted during the winter of 1982-1983.

This paper compares computer-simulated exposure distributions obtained by pNEM/CO with the observed cumulative

relative frequency distributions of population exposure to CO from 779 people in the Denver PEM study. The subjects were disaggregated into two categories depending upon whether they lived in a home with a gas stove or an electric stove. The observed and predicted population exposure frequency distributions were compared in terms of 1-hr daily maximum exposure (1DME) and 8-hr daily maximum moving average exposure (8DME) for people living in homes with gas stove or an electric stove. For 1DME, the computer-simulated results from pNEM/CO agreed most closely within the range of 6-13 ppm, but overestimated occurrences at low exposure (<6 ppm) and underestimated occurrences at high exposure (>13 ppm). For 8DME, the predicted exposures agreed best with observed exposures in the range of CO concentration between 5.5 and 7 ppm, and over-predicted occurrences below 5.5 ppm and under-predicted occurrences above 7 ppm.     

Evaluation of a Probabilistic Exposure Model Applied to Carbon Monoxide (pNEM/CO) Using Denver Personal Exposure Monitoring Data

The probabilistic National Ambient Air Quality Standards (NAAQS) Exposure Model applied to carbon monoxide (pNEM/CO) was developed by the U.S. Environmental Protection Agency (EPA) to estimate frequency distributions of population exposure to carbon monoxide (CO) and the resulting carboxyhemoglobin (COHb) levels. To evaluate pNEM/CO, the model was set up to simulate CO exposure data collected during a Denver Personal Exposure Monitoring Study (PEM) conducted during the winter of 1982-1983. This paper compares computer-simulated exposure distributions obtained by pNEM/CO with the observed cumulative relative frequency distributions of population exposure to CO from 779 people in the Denver PEM study.

The subjects were disaggregated into two categories depending upon whether they lived in a home with a gas stove or an electric stove. The observed and predicted population exposure frequency distributions were compared in terms of 1-hr daily maximum exposure (1DME) and 8-hr daily maximum moving average exposure (8DME) for people living in homes with gas stove or an electric stove. For 1DME, the

computer-simulated results from pNEM/CO agreed most closely within the range of 6-13 ppm, but overestimated occurrences at low exposure (<6 ppm) and underestimated occurrences at high exposure (>13 ppm). For 8DME, the predicted exposures agreed best with observed exposures in the range of CO concentration between 5.5 and 7 ppm, and over-predicted occurrences below 5.5 ppm and under-predicted occurrences above 7 ppm.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

Surface Air Pollutant Concentration Frequency Distributions: Implications for Urban Modeling

With the development of ambient air quality standards (AAQS), the need arises to describe the characteristics of regional surface air-pollutant concentration frequency distributions. In the evaluation of land use plans, numerous agencies will be concerned with evaluating the effectiveness of emission zoning and/or control actions. On a regional basis, one means of performing this assessment lies in determining the changes in the pollutant frequency distributions resulting from control actions.

This study presents new data concerning the surface air-pollutant concentration frequency distributions observed for area sources and continuous point sources, and compares these distributions with those of the pertinent meteorological variables describing the transport and diffusion of the pollutant. The observed surface air pollutant frequency distributions are compared to those corresponding to simple modeling concepts from either an urban area source or a continuous point source. For an urban source and a relatively inert pollutant like CO, we found that the observed frequency distribution for CO surface air concentration parallels the approximately log-normal frequency distribution of the reciprocal of the wind speed. We show that the constant relating these two well-correlated frequency distributions can be determined either experimentally or with a numerical simulation model of air pollution. The usefulness of numerical models in air pollution is discussed.     

Ozone formation in California's San Joaquin Valley: a critical assessment of modeling and data needs

Data from the 1990 San Joaquin Valley Air Quality Study/Atmospheric Utility Signatures, Predictions, and Experiments (SJVAQS/AUSPEX) field program in California's San Joaquin Valley (SJV) suggest that both urban and rural areas would have difficulty meeting an 8-hr average O3 standard of 80 ppb. A conceptual model of O3 formation and accumulation in the SJV is formulated based on the chemical, meteorological, and tracer data from SJVAQS/AUSPEX. Two major phenomena appear to lead to high O3 concentrations in the SJV: (1) transport of O3 and precursors from upwind areas (primarily the San Francisco Bay Area, but also the Sacramento Valley) into the SJV, affecting the northern part of the valley, and (2) emissions of precursors, mixing, transport (including long-range transport), and atmospheric reactions within the SJV responsible for regional and urban-scale (e.g., down-wind of Fresno and Bakersfield) distributions of O3. Using this conceptual model, we then conduct a critical evaluation of the meteorological model and air quality model. Areas of model improvements and data needed to understand and properly simulate O3 formation in the SJV are highlighted.     

Application of a Three-Layer Photochemical Box Model in an Athens Street Canyon

ABSTRACT

The aim of this paper is to show that a photochemical box model could describe the air pollution diurnal profiles within a typical street canyon in the city of Athens. As sophisticated three-dimensional dispersion models are computationally expensive and they cannot serve to simulate pollution levels in the scale of an urban street canyon, a suitably modified three-layer photochemical box model was applied. A street canyon of Athens with heavy traffic was chosen to apply the aforementioned model. The model was used to calculate pollutant concentrations during two days with meteorological conditions favoring pollutant accumulation. Road traffic emissions were calculated based on existing traffic load measurements. Meteorological data, as well as various pollutant concentrations, in order to compare with the model results, were provided by available measurements. The calculated concentrations were found to be in good agreement with measured concentration levels and show that, when traffic load and traffic composition data are available, this model can be used to predict pollution episodes. It is noteworthy that high concentrations persisted, even after additional traffic restriction measures were taken on the second day because of the high pollution levels.     

Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models

The Borman Expressway is a heavily traveled 16-mi segment of the Interstate 80/94 freeway through Northwestern Indiana. The Lake and Porter counties through which this expressway passes are designated as particulate matter < 2.5 microm (PM2.5) and ozone 8-hr standard nonattainment areas. The Purdue University air quality group has been collecting PM2.5, carbon monoxide (CO), wind speed, wind direction, pressure, and temperature data since September 1999. In this work, regression and neural network models were developed for forecasting hourly PM2.5 and CO concentrations. Time series of PM2.5 and CO concentrations, traffic data, and meteorological parameters were used for developing the neural network and regression models. The models were compared using a number of statistical quality indicators. Both models had reasonable accuracy in predicting hourly PM2.5 concentration with coefficient of determination -0.80, root mean square error (RMSE) <4 microg/m3, and index of agreement (IA) > 0.90. For CO prediction, both models showed moderate forecasting performance with a coefficient of determination -0.55, RMSE < 0.50 ppm, and IA -0.85. These models are computationally less cumbersome and require less number of predictors as compared with the deterministic models. The availability of real time PM2.5 and CO forecasts will help highway managers to identify air pollution episodic events beforehand and to determine mitigation strategies.     

Model for estimation of traffic pollutant levels in northern communities

Using models to estimate the contribution of traffic to air pollution levels from known traffic data typically requires the knowledge of model parameters such as emission factors and meteorological conditions. This paper presents a state-space model analysis method that does not require the knowledge of model parameters; these parameters are identified from measured traffic and ambient air quality data. This method was used to analyze carbon monoxide (CO) in downtown Fairbanks, AK, which is the community of focus for this paper. It was found that traffic contributed, on average, 53% to the total CO levels over the last six winters. The correlation coefficient between the measured and model-predicted daily profiles of the CO concentration was 0.98, and the results were in good agreement with earlier findings obtained via a thorough CO emission inventory. This justified the usability of the method and it was further used to analyze fine particulate matter (PM2.5) in downtown Fairbanks. It was found that traffic contributed, on average, approximately 30% to the total PM2.5 levels over the last six winters. The correlation coefficient between the measured and model-predicted daily profiles of the PM2.5 concentration was 0.98.     

Ozone Formation in California's San Joaquin Valley: A Critical Assessment of Modeling and Data Needs

ABSTRACT

Data from the 1990 San Joaquin Valley Air Quality Study/ Atmospheric Utility Signatures, Predictions, and Experiments (SJVAQS/AUSPEX) field program in California's San Joaquin Valley (SJV) suggest that both urban and rural areas would have difficulty meeting an 8-hr average O₃ standard of 80 ppb. A conceptual model of O₃ formation and accumulation in the SJV is formulated based on the chemical, meteorological, and tracer data from SJVAQS/ AUSPEX. Two major phenomena appear to lead to high O₃ concentrations in the SJV: (1) transport of O₃ and precursors from upwind areas (primarily the San Francisco Bay Area, but also the Sacramento Valley) into the SJV, affecting the northern part of the valley, and (2) emissions of precursors, mixing, transport (including long-range transport), and atmospheric reactions within the SJV responsible for regional and urban-scale (e.g., downwind of Fresno and Bakersfield) distributions of O₃. Using this conceptual model, we then conduct a critical evaluation of the meteorological model and air quality model. Areas of model improvements and data needed to understand and properly simulate O₃ formation in the SJV are highlighted.     

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     

Simulation of the Environmental Impact of an Airport on the Surrounding Air Quality

It is the purpose of this study to demonstrate the procedure involved in simulating those average and maximum pollutant concentrations at or around an airport which fall under the control of the Clean Air Act. The information is useful, when planning new or expanding existing airports, when estimating the impact of airports on the surrounding air quality, and when assessing the effectiveness of control procedures. Simulation of airport air quality requires the accurate assessment of the temporal and spatial emission patterns. This involves the tabulation of air traffic density by type and engine, make and model of aircraft, and engine mode number; the use of fuel by different aircraft; the pollutant emission rates by engine model and operational mode; the allocation of emission rates to the respective runways, turn-off points, taxi-ways, and parking areas, and the time each aircraft spent in the different operational modes. The resulting emission pattern for the Honolulu International Airport reflects scheduled and unscheduled commercial and military jet and piston aircraft and nonaircraft operations. Using this and the appropriate meteorological information average and maximum surface concentrations were calculated and compared with local ambient air quality standards. The calculation of concentrations is based on a newly developed diffusion model incorporating harmonic mean wind speeds for every degree of wind direction as determined by a Parzen maximum likelihood interpolation technique, and the assumption of log-normal concentration distributions. It is shown that for some pollutants the air quality standards are substantially exceeded, and it is concluded that airports may have a considerable adverse impact on their surrounding air quality.     

Carbon Monoxide Exposures Inside an Automobile Traveling on an Urban Arterial Highway

Carbon monoxide (CO) exposures were measured inside a motor vehicle during 88 standardized drives on a major urban arterial highway, El Camino Real (traffic volume of 30,500-45,000 vehicles per day), over a 13-1/2 month period. On each trip (lasting between 31 and 61 minutes), the test vehicle drove the same 5.9-mile segment of roadway in both directions, for a total of 11.8 miles, passing through 20 intersections with traffic lights (10 in each direction) in three California cities (Menlo Park, Palo Alto, and Los Altos). Earlier tests showed that the test vehicle was free of CO intrusion. For the 88 trips, the mean CO concentration was 9.8 ppm, with a standard deviation of 5.8 ppm. Of nine covariates that were examined to explain the variability in the mean CO exposures observed on the 88 trips (ambient CO at two fixed stations, atmospheric stability, seasonal trend function, time of day, average surrounding vehicle count, trip duration, proportion of time stopped at lights, and instrument type), a fairly strong seasonal trend was found. A model consisting of only a single measure of traffic volume and a seasonal trend component had substantial predictive power (R² = 0.68); by contrast, the ambient CO levels, although partially correlated with average exposures, contributed comparatively little predictive power to the model. The CO exposures experienced while drivers waited at the red lights at an intersection ranged from 6.8 to 14.9 ppm and differed considerably from intersection to intersection. A model also was developed to relate the short-term variability of exposures to averaging time for trip times ranging from 1 to 20 minutes using a variogram approach to deal with the serial autocorrelation. This study shows: (1) the mass balance equation can relate exterior CO concentrations as a function of time to interior CO concentrations; (2) CO exposures on urban arterial highways vary seasonally; (3) momentary CO exposures experienced behind red lights vary with the intersection; and (4) an averaging time model can simulate exposures during short trips (20 minutes or less) on urban arterial highways.     

Screening and Scenarios of Traffic Emissions at Trier,Germany

SCOPE AND BACKGROUND: In the course of the European Council Directive on permissible air pollutant limit values, valid starting from 2005 there is an urgent call for action, particularly for fine dust (PM10). Current investigations (Junk & Helbig 2003, Reuter & Baumüller 2003) show that the limit values in certain places in congested areas are exceeded. Only if it is possible to locate these Hot Spots purposeful measures to reduce the ambient air pollution can be conducted. For an efficient identification of these Hot Spots numerical computer models or establishing special measurements networks are too expensive. Using the statistical model STREET 5.0 (KTT 2003) a cost-effective screening of the air pollution situation caused by the traffic can be done. METHODS: STREET is based on the 3-dimensional micro-scale non-hydrostatic flow- and dispersion model MISCAM (Eichhorn 1989). The results of over 100.000 different calculations with MISCAM are stored in a Database and used to calculate the emissions with STREET. In collaboration with the city council of Trier more than 150 streets were investigated, mapped, and calculated. A special urban climate measuring network supplies the necessary meteorological input data about the wind field and precipitation events in the valley of the Moselle. Information about road width and road orientation as well as building density was derived from aerial photographs. Traffic censuses and mobile air pollutants measurements supplied the remaining input data. We calculated the mean annual air pollutant concentrations for NO2, CO, SO2, O3, benzene as well as PM10. RESULTS: A comparison of the model results with the values obtained from the stations of the central emission measuring network of Rhineland-Palatinate (ZIMEN, annual report 2002) shows very good agreements. The model was not only used to calculate the annual air pollutant but also for urban planning and management. The absolute level of the air pollutant is mainly dependent on the amount of traffic in the street canyons. Therefore four different case-scenarios with varying quantity of traffic were calculated and interpreted for each street. The results of the calculation show that on the basis of the mean values for both NO2 and benzene, it is not to be expected that the limits PERSPECTIVES: Furthermore the model can be used to find the maximum tolerable numbers of cars for a street without exceeding the air pollutant thresholds.     

Vehicle Occupant Exposure to Carbon Monoxide

Abstract

This paper focuses on the auto commuting micro-environment and presents typical carbon monoxide (CO) concentrations to which auto commuters in central Riyadh, Saudi Arabia were exposed. Two test vehicles traveling over four main arterial roadways were monitored for inside and outside CO levels during eighty peak and off-peak hours extending over an eight month period. The relative importance of several variables which explained the variability in CO concentrations inside autos was also assessed. It was found that during peak hours auto commuters were exposed to mean CO levels that ranged from 30 to 40 ppm over trips that typically took between 25 to 40 minutes. The mean ratio of inside to outside CO levels was 0.84. Results of variance component analyses indicated that the most important variables affecting CO concentrations inside autos were, in addition to the smoking of vehicle occupants, traffic volume, vehicle speed, period of day and wind velocity. An increase in traffic volume from 1,000 to 5,000 vehicles per hour (vph) increased mean CO level exposure by 71 percent. An increase in vehicle speed from 14 to 55 km/h reduced mean CO exposure by 36 percent. The number of traffic interruptions had a moderate effect on mean concentrations of CO inside vehicles.     

Urban CO Concentrations and Vehicle Emissions

The effect of the general growth of CO vehicular emissions in urban areas on the CAMP station measurements in downtown areas, where vehicular traffic is saturated is considered. With the assumption that the street-level CO concentration is derived from the sum of an urban background term and a local street-effect term, the urban background CO concentration is computed with a diffusion model by introducing a simple area source distribution. The local street-effect term is taken to be constant at a saturation emission level corresponding to a saturation traffic density when the emission per vehicle-mile and meteorological conditions are fixed. The present analysis indicates that the local street-effect term, AC, has a major role in determining street-level concentrations for pollutants, such as CO, whose air quality standard is based on maximum concentrations with averaging times of 1 hour and 8 hours. The relevance of this analysis to the abatement requirements of the Clean Air Amendments and to the driving cycle adopted is discussed.     

The Exposure to Carbon Monoxide of Occupants of Vehicles Moving in Heavy Traffic

Carbon monoxide and hydrocarbons were sampled at operator’s nose height inside vehicles moving in moderate to heavy traffic in six cities. The samples were integrated over 20-30 minutes by collection in Mylar bags. Carbon monoxide and hydrocarbons were analyzed by infrared and flame ionization, respectively, with instruments at the Continuous Air Monitoring Program (CAMP) station in each city. Detector tubes for carbon monoxide were also used to determine 5-min concentrations at suspected high points in the field. Estimates of traffic density were made. Three types of traffic arteries were considered: (7) heavily traveled, wide expressways, (2) main city streets with moderately rapid vehicular traffic, and (3) center city streets with slow-moving traffic. Integrated half-hour CO concentrations obtained within the vehicles while in traffic were generally considerably higher than the concurrent concentrations measured at the CAMP sites. In-traffic CO values in all cities sampled exceeded 30 ppm in at least 10% of the integrated samples. The range of city averages was 21–39 ppm carbon monoxide and the range of individual integrated samples was 7–77 ppm of carbon monoxide.     

Roadside particle number distributions and relationships between number concentrations, meteorology, and traffic along a northern California freeway

Particle number distributions were measured simultaneously upwind and downwind of a suburban-agricultural freeway to determine relationships with traffic and meteorological parameters. Average traffic volumes were 6330 vehicles/hr with 10% heavy-duty vehicles, and volumes were higher in July than November. Most downwind particle number distributions were bimodal, with a primary mode at approximately 10-25 nm, indicating that newly formed particles were sampled. Total downwind 6-237 nm particle number concentrations (Ntot) ranged from 9.3 x 10(3) to 2.5 x 10(5) cm(-3), with higher daily average concentrations in November compared with July. Ntot correlated with wind speed, temperature, and relative humidity. Upwind photochemically initiated nucleation likely led to elevated background nanoparticle concentrations in July, as evidenced by increasing upwind distribution modal diameter with increasing temperature and a strong correlation between upwind Ntot and solar radiation. Also in summer, Ntot showed stronger correlation with heavy-duty vehicle volumes than wind speed, temperature, and relative humidity. These results indicate the importance of measuring background particle size distributions simultaneously with roadside distributions. There may be a minimum vehicle volume from which useful real-world vehicle particle number distributions can be measured at roadside, even when collecting samples within 10 m of the traveled lanes.