Persistence Factors for Mobile Source (Roadway) Carbon Monoxide Modeling

A critical step in the modeling of the carbon monoxide (CO) impacts of mobile sources is predicting an 8-hour CO concentration given a modeled "worst-case" 1-hour concentration. Often, this is done by a multiplicative persistence factor. A meteorological persistence factor (MPF) accounts for the variability over 8 hours of wind speed, wind direction, stability class, and temperature. A vehicular persistence factor (VPF) reflects the lower traffic volumes during the off-peak hours.

Hourly meteorological data for ten years for four cities in Florida were obtained from the National Climatic Data Center. The CALINE3 model was used to obtain hourly CO concentrations, which were combined to derive MPFs for each city. Similarly, VPFs were derived from hourly vehicle counts from one busy roadway in each city. The mean VPF multiplied by the second highest MPF was defined as the worst-case total persistence factor (TPF). These worst-case TPFs increased significantly as more hours of nighttime were included in the 8- hour averaging time, but were fairly consistent from city to city. In general, the results suggest worst-case TPFs in the range of 0.4 to 0.5, lower than has been recommended by EPA in the past.     

Which meteorological conditions produce worst-case odors from area sources?

Two competing meteorological factors influence atmospheric concentrations of pollutants from open liquid area sources such as wastewater treatment plant units: temperature and stability. High temperatures in summer produce greater emissions from liquid area sources because of increased compound volatility; however, these emissions tend to disperse more readily because of frequent occurrence of unstable conditions. An opposite scenario occurs in winter, with lesser emissions due to lower temperatures, but also frequently less dispersion, due to stable atmospheric conditions. The primary objective of this modeling study was thus to determine whether higher atmospheric concentrations from open liquid area sources occur more frequently in summer, when emissions are greater but so is dispersion, or in winter, when emissions are lesser but so is dispersion. The study utilized a rectangular clarifier emitting hydrogen sulfide as a sample open liquid area source. Dispersion modeling runs were conducted using ISCST3 and AERMOD, encompassing 5 yr of hourly meteorological data divided by season. Emission rates were varied hourly on the basis of a curve-fit developed from previously collected field data. Model output for each season was used to determine (1) maximum 2-min average concentrations, (2) the number of odor events (2-min average concentrations greater than odor detection thresholds), and (3) areas of impact. On the basis of these 3 types of output, it was found that the worst-case odors were associated with summer, considering impacts of meteorology upon both emissions and dispersion. Not accounting for the impact of meteorology on emissions (using a constant worst-case emission rate) caused concentrations to be overpredicted compared with a variable emission rate case. The highest concentrations occurred during stability classes D, E, and F, as anticipated. A comparison of ISCST3 and AERMOD found that for the area source modeled, ISCST3 predicted higher concentrations and more odor events for all seasons.     

motor vehicle emissions and atmospheric lead concentrations in the los angeles area

This paper is directed to those concerned with the effect of changes in lead consumption by motor vehicles on atmospheric lead concentrations. Atmospheric lead concentrations in the Los Angeles area have been found to be dependent on lead consumption, meteorology, and source-receptor relationships. Mathematical relationships between these variables for selected Los Angeles area sites are derived. Los Angeles County experienced a 50% reduction in annual average atmospheric lead concentrations between 1971 and 1976, which was found to be due to a decrease in vehicular emissions of lead. Seasonal variations in lead concentration were found to be mainly a product of seasonal variations in weather. Projections of atmospheric lead concentrations for the next decade show a continued downtrend, based on regulation of fuel lead content and introduction of additional catalyst equipped vehicles. By the mid-1980's most locations in the Los Angeles area are expected to attain the California and federal ambient air quality standards for lead, 1.5 micrograms per cubic meter, based on a monthly average.     

Characterization of Air Quality Problems in Five Finnish Indoor Ice Arenas

ABSTRACT

The air quality in five Finnish ice arenas with different volumes, ventilation systems, and resurfacer power sources (propane, gasoline, electric) was monitored during a usual training evening and a standardized, simulated ice hockey game. The measurements included continuous recording of carbon monoxide (CO), nitric oxide (NO), and nitrogen dioxide (NO₂) concentrations, and sampling and analysis of volatile organic compounds (VOCs). Emissions from the ice resurfacers with combustion engines caused indoor air quality problems in all ice arenas. The highest 1-hour average CO and NO₂ concentrations ranged from 20 to 33 mg/m³ (17 to 29 ppm) and 270 to 7440 µg/m³ (0.14 to 3.96 ppm), respectively. The 3-hour total VOC concentrations ranged from 150 to 1200 µg/m³. The highest CO and VOC levels were measured in the arena in which a gasoline-fueled resurfacer was used. The highest NO₂ levels were measured in small ice arenas with propane-fueled ice resurfacers and insufficient ventilation.

In these arenas, the indoor NO₂ levels were about 100 times the levels measured in ambient outdoor air, and the highest 1-hour concentrations were about 20 times the national and World Health Organization (WHO) health-based air quality guidelines. The air quality was fully acceptable only in the arena with an electric resurfacer. The present study showed that the air quality problems of indoor ice arenas may vary with the fuel type of resurfacer and the volume and ventilation of arena building. It also confirmed that there are severe air quality problems in Finnish ice arenas similar to those previously described in North America.     

A field study of factors influencing the concentrations of a traffic-related pollutant in the vicinity of a complex urban junction

The paper describes a field study focused on the dispersion of a traffic-related pollutant within an area close to a busy intersection between two street canyons in Central London. Simultaneous measurements of airflow, traffic flow and carbon monoxide concentrations ([CO]) are used to explore the causes of spatial variability in [CO] over a full range of background wind directions. Depending on the roof-top wind direction, evidence of both flow channelling and recirculation regimes were identified from data collected within the main canyon and the intersection. However, at the intersection, the merging of channelled flows from the canyons increased the flow complexity and turbulence intensity. These features, coupled with the close proximity of nearby queuing traffic in several directions, led to the highest overall time-average measured [CO] occurring at the intersection. Within the main street canyon, the data supported the presence of a helical flow regime for oblique roof-top flows, leading to increased [CO] on the canyon leeward side. Predominant wind directions led to some locations having significantly higher diurnal average [CO] due to being mostly on the canyon leeward side during the study period. For all locations, small changes in the background wind direction could cause large changes in the in-street mean wind angle and local turbulence intensity, implying that dispersion mechanisms would be highly sensitive to small changes in above roof flows. During peak traffic flow periods, concentrations within parallel side streets were approximately four times lower than within the main canyon and intersection which has implications for controlling personal exposure. Overall, the results illustrate that pollutant concentrations can be highly spatially variable over even short distances within complex urban geometries, and that synoptic wind patterns, traffic queue location and building topologies all play a role in determining where pollutant hot spots occur.     

Detecting ozone and demonstrating its phytotoxicity in forested areas of Poland: a pilot study

Ambient concentrations of ozone (O(3)) were measured and O(3) phytotoxicity to tobacco (Nicotiana tabacum L.) was demonstrated in several forest locations in Poland during a pilot study from July-October, 1991. At southern and central locations in Poland, the 24-hour average O(3) concentrations measured with a UV absorption photometer were in the range of 32-55 ppb, and the corresponding 1-hour maxima in the range of 39-83 ppb. At these locations longer period (four to fifteen days) average concentrations were determined using O(3) passive samplers (DGA, Inc.) and were reaching 60 ppb, while at Bialowieza in eastern Poland O(3) concentrations averaged less than 40 ppb. In Szarow, near the Niepolomice Forest in southern Poland, 1-hour O(3) maxima estimated from the data obtained using passive samplers were about 105 ppb in early September. At several locations in southern and central Poland, extensive O(3) injury was determined on O(3)-sensitive Bel W-3 tobacco plants; such injury did not occur in the Bialowieza Forest of eastern Poland. The results of this pilot study indicate that O(3) is present at phytotoxic levels in southern and central Poland.     

Vertical gradients of ozone and carbon dioxide within a deciduous forest in Central Pennsylvania

Ambient concentrations of ozone (O(3)) and carbon dioxide (CO(2)) were measured at locations from the forest floor to the top of the canopy in a deciduous forest at the Moshannon State Forest in northcentral Pennsylvania. O(3) concentrations were measured from May-September for three years (1993-1995) while CO(2) concentrations were measured only during July and August of 1994. O(3) concentrations increased steadily during the day at all locations, peaking during the middle to late afternoon hours. O(3) concentrations then steadily declined to their lowest point, just before dawn. Vertical O(3) concentration gradients varied seasonally and among years. However, O(3) concentrations were highest within the forest canopy and lowest at the forest floor, with an average difference of approximately 13%. Differences in O(3) concentrations between the canopy and forest floor were greatest at night. O(3) concentrations were slightly higher at locations within the canopy than above the canopy. CO(2) concentrations were consistenly higher near the forest floor and were higher above the canopy than within the canopy. CO(2) concentrations were higher at night than during the day at all locations, especially near the forest floor.     

Carbon monoxide modeling from transportation sources.

Street canyon module and gaussian line source module of a regional-scale dispersion model Indic Airviro were used to simulate ambient carbon monoxide (CO) concentrations due to traffic flow at two roadside monitoring locations in Singapore. The fleet average emission factors for each vehicle category was estimated from US EPA MOBILE 5 A guidelines as a function of speed, vehicle deterioration rates and model years. 1-h CO concentrations and worst case 8-h levels have been simulated and compared with measured readings. This study used model-simulated rooftop concentration levels from non-localized sources as background levels at the two sites. The resulting CO concentrations correlate well with actual measured levels and provide a unique approach to predict the impact of CO from transportation.     

Near-field dispersion modeling for regulatory applications

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20 m of the source. Modeling results indicated that Industrial Source Complex-Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model-Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field.     

An assessment of the impact of California's Phase 2 Reformulated Gasoline on ozone air quality

California's Phase 2 Reformulated Gasoline (CaRFG), introduced early in 1996, represents an important step toward attainment of ozone standards. Studies of vehicle emissions and ambient air quality data have reported substantial reductions of ozone precursors due to CaRFG. This study uses daily measurements of regional ozone and meteorology to estimate the effect of CaRFG on ozone concentrations in three areas of California. In each area, a regression model was used to partially account for the daily effects of meteorology on area-wide ozone maxima for May-October. The statistical models are based on combinations of air temperature aloft (approximately 5000 ft), surface air temperatures, and surface wind speeds. Estimated ozone benefits were attributed to CaRFG after accounting for meteorology, which improved the precision of the estimates by approximately 37-57% based on a resampling analysis. The ozone benefits were calculated as the difference in ozone times the proportion of the reductions of hydrocarbons and nitrogen oxides attributed to CaRFG by the best available emission inventories. Ozone benefits attributed to CaRFG (with approximately 90% confidence) are 8-13% in the Los Angeles area, -2-6% in the San Francisco Bay area overall with greater benefits in two major subregions, and 3-15% in the Sacramento area.     

The effect of reformulated gasoline on ambient carbon monoxide concentrations in southeastern Wisconsin

Reformulated gasoline (RFG) contains oxygen additives such as methyl tertiary butyl ether or ethanol. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of carbon monoxide (CO) and volatile organic compounds (VOCs). Because VOCs react with sunlight to form ozone (O3), the Clean Air Act requires severe O3 nonattainment areas such as southeastern Wisconsin to use RFG. On July 17, 2001, the U.S. Environmental Protection Agency (EPA) granted Milwaukee, WI, and Chicago, IL, a waiver from the VOC reduction requirement of Phase II RFG. The VOC reduction requirement was lowered from 27.4% of the 1990 baseline fuel to 25.4%. The assumption was that ethanol-blended RFG would lower summertime CO concentrations sufficiently to offset the increased VOC emissions. The waiver is estimated to increase VOC emissions by approximately 0.8%, or 0.4 t of VOC on a hot summer weekday. This study evaluates whether RFG has been effective in lowering southeastern Wisconsin ambient CO concentrations. Three years of ambient CO data before RFG was introduced were compared with the first three years of ambient CO data after RFG was introduced. This paper also evaluates how the meteorology, vehicle inspection/maintenance program, vehicle miles traveled, and stationary source emissions influence CO concentrations. The winter decrease in ambient CO concentrations was found to be statistically significant, while the summer data showed no statistically significant change, indicating that RFG is most effective lowering ambient CO concentrations in cold weather.     

Near-Field Dispersion Modeling for Regulatory Applications

Abstract

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20 m of the source. Modeling results indicated that Industrial Source Complex–Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model–Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field.     

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.     

The Effect of Reformulated Gasoline on Ambient Carbon Monoxide Concentrations in Southeastern Wisconsin

Abstract

Reformulated gasoline (RFG) contains oxygen additives such as methyl tertiary butyl ether or ethanol. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of carbon monoxide (CO) and volatile organic compounds (VOCs). Because VOCs react with sunlight to form ozone (O₃), the Clean Air Act requires severe O₃ nonattainment areas such as southeastern Wisconsin to use RFG. On July 17, 2001, the U.S. Environmental Protection Agency (EPA) granted Milwaukee, WI, and Chicago, IL, a waiver from the VOC reduction requirement of Phase II RFG. The VOC reduction requirement was lowered from 27.4% of the 1990 baseline fuel to 25.4%. The assumption was that ethanol-blended RFG would lower summertime CO concentrations sufficiently to offset the increased VOC emissions. The waiver is estimated to increase VOC emissions by ～0.8%, or 0.4 t of VOC on a hot summer weekday. This study evaluates whether RFG has been effective in lowering southeastern Wisconsin ambient CO concentrations. Three years of ambient CO data before RFG was introduced were compared with the first three years of ambient CO data after RFG was introduced. This paper also evaluates how the meteorology, vehicle inspection/maintenance program, vehicle miles traveled, and stationary source emissions influence CO concentrations. The winter decrease in ambient CO concentrations was found to be statistically significant, while the summer data showed no statistically significant change, indicating that RFG is most effective lowering ambient CO concentrations in cold weather.     

Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona

We perform a climatology of factors influencing ambient carbon monoxide (CO), in which we examine the relationships between meteorology, traffic patterns, and CO at seasonal, weekly, and diurnal time scales in Phoenix, Arizona. From this analysis we identify a range of potentially important variables for statistical CO modeling. Using stepwise multivariate regression, we create a suite of models for hourly and 8-h ambient CO designed for daily operational forecasting purposes. The resulting models include variables and interaction terms related to anticipated nocturnal atmospheric stability as well as antecedent and climatological CO behavior. The models are evaluated using a range of error statistics and skill measures. The most successful approach employs a two-stage modeling strategy in which an initial prediction is made that may, depending on the forecast value, be followed by a second prediction that improves upon the first. The best models provide accurate daily forecasts of CO, with explained variances approaching 0.9 and errors under 1 ppm.     

Detecting and characterising small changes in urban nitrogen dioxide concentrations

Detecting and quantifying abnormal changes in the concentration of urban air pollutants can be difficult due to the influence of meteorology and atmospheric chemistry. This study presents methods to detect and characterise small changes in the concentration of nitrogen dioxide (NO₂) that deviate from expected behaviour. Generalized additive models (GAMs) are used to describe daily mean NO₂ concentrations at roadside monitoring sites to determine how concentrations deviate from measured concentrations. Structural change methods are applied to time series describing this difference to identify change-points, where concentrations of NO₂ deviate significantly from expected behaviour. Methods are also used which quantify the timing and uncertainty associated in the timing of these change-points. For most time series data considered in London, concentrations of NO₂ underwent relatively abrupt changes rather than smoothly varying increases; changes which remain largely undetected in raw time series data. Most change-points occurred in late 2002 or early 2003, and the factors, which may have contributed to them, are discussed. These methods can also identify technical problems with monitoring equipment and are applicable to other instances where detecting sudden atmospheric composition change is important.     

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.     

Modeling of Carbon Monoxide Hot Spots

This paper describes the results of a measurement and modeling study of carbon monoxide (CO) concentrations In the proximity of intersections. Analysis for model performance of paired observed and predicted CO concentrations are presented. Two methodologies of pollutant prediction were used: the Intersection Midblock Model (IMM) and a statistical multiple linear regression. The results showed that both methods underpredicted frequently and dispensed results that were site specific. In addition, correlations of IMM predicted concentrations to observed concentrations were poor (typically r² values <0.25). Various explanations for this observation are proposed. The statistical approach exhibited an improved accuracy over that of IMM. However, some of the independent variables used might be difficult to obtain as a routine measurement, and use of a one or two independent parameter model yielded adjusted R² values comparable to the r² values observed with IMM. Based on these results, an Intersection model applicable under a wide range of conditions of traffic, meteorology, and geometry is not available. Research Is needed to develop one, since its use would often be called on in the development of air quality sections of Environmental Assessments or Environmental Impact Statements.     

Comparison of Predicted and Measured Concentrations for 58 Alternative models of Plume Transport in Complex Terrain

The purpose of this study was to evaluate alternative prediction models for the SO₂ concentrations produced in the vicinity of the Ohio Edison Company Sammis Power Plant. The plant is situated in the northeastern portion of the Ohio River Valley in complex terrain. Comparisons of the 16 highest predicted and measured short-term SO₂ concentrations were conducted for a one year period for 58 alternative models. Several models were found to predict reasonably accurately the 16 highest measured 24-hour SO₂ concentrations. Each of these models requires an upward adjustment in the plume centerline location as the plume is transported downwind in rising terrain. These same models overpredict by substantial margins the 16 highest measured 3-hour SO₂ concentrations. Improvements in emissions inventory data and improvements in the prediction models used are believed necessary to increase prediction accuracy further.     

Workplace and Environmental Air Contaminant Concentrations Measured by Open Path Fourier Transform Infrared Spectroscopy: A Statistical Process Control Technique to Detect Changes from Normal Operating Conditions

Open path Fourier transform infrared (OP-FTIR) spectroscopy is a new air monitoring technique that can be used to measure concentrations of air contaminants in real or near-real time. OP-FTIR spectroscopy has been used to monitor workplace gas and vapor exposures, emissions from hazardous waste sites, and to track emissions along fence lines. This paper discusses a statistical process control technique that can be used with air monitoring data collected with an OP-FTIR spectrometer to detect departures from normal operating conditions in the workplace or along a fence line. Time series data, produced by plotting consecutive air sample concentrations in time, were analyzed. Autocorrelation in the time series data was removed by fitting dynamic models. Control charts were used with the residuals of the model fit data to determine if departures from defined normal operating conditions could be rapidly detected. Shewhart and exponentially weighted moving average (EWMA) control charts were evaluated for use with data collected under different room air flow and mixing conditions.

Under rapidly changing conditions the Shewhart control chart was able to detect a leak in a simulated process area. The EWMA control chart was found to be more sensitive to drifts and slowly changing concentrations in air monitoring data. The time series and statistical process control techniques were also applied to data obtained during a field study at a chemical plant. A production area of an acrylonitrile, 1,3-butadiene, and styrene (ABS) polymer process was monitored in near-real time. Decision logics based on the time series and statistical process control technique introduced suggest several applications in workplace and environmental monitoring. These applications might include signaling of an alarm or warning, increasing levels of worker respiratory protection, or evacuation of a community, when gas and vapor concentrations are determined to be out-of-control.