Daily Variability of Motor Vehicle Emissions Derived from Traffic Counter Data

ABSTRACT

This work studied the daily variability of mobile sources in rural and urban areas, in and around the Atlanta Metropolitan Area. Traffic counter data collected during the 1992 Southern Oxidants Study Atlanta Intensive Study were used to analyze the spatial and temporal distribution of traffic volume. A simple method to study the daily variability of mobile emissions from the different types of urban and rural roads is presented. The method is based on hourly traffic volume data and emission factors and it has been generalized to describe the daily variability of mobile emissions for urban and rural areas and for the whole modeling domain. Implications of this study for improving mobile emission inventories are also discussed.     

Characterization of Near-Road Pollutant Gradients Using Path-Integrated Optical Remote Sensing

Abstract

Understanding motor vehicle emissions, near-roadway pollutant dispersion, and their potential impact to near-roadway populations is an area of growing environmental interest. As part of ongoing U.S. Environmental Protection Agency research in this area, a field study was conducted near Interstate 440 (I-440) in Raleigh, NC, in July and August of 2006. This paper presents a subset of measurements from the study focusing on nitric oxide (NO) concentrations near the roadway. Measurements of NO in this study were facilitated by the use of a novel path-integrated optical remote sensing technique called deep ultraviolet differential optical absorption spectroscopy (DUV-DOAS). This paper reviews the development and application of this measurement system. Time-resolved near-road NO concentrations are analyzed in conjunction with wind and traffic data to provide a picture of emissions and near-road dispersion for the study. Results show peak NO concentrations in the 150 ppb range during weekday morning rush hours with winds from the road accompanied by significantly lower afternoon and weekend concentrations. Traffic volume and wind direction are shown to be primary determinants of NO concentrations with turbulent diffusion and meandering accounting for significant near-road concentrations in off-wind conditions. The enhanced source capture performance of the open-path configuration allowed for robust comparisons of measured concentrations with a composite variable of traffic intensity coupled with wind transport (R² = 0.84) as well as investigations on the influence of wind direction on NO dilution near the roadway. The benefits of path-integrated measurements for assessing line source impacts and evaluating models is presented. The advantages of NO as a tracer compound, compared with nitrogen dioxide, for investigations of mobile source emissions and initial dispersion under crosswind conditions are also discussed.     

An Integrated Modeling System for the Estimation of Motor Vehicle Emissions

ABSTRACT

The paper provides a summary of accomplished and ongoing activities in the field of motor vehicle emission modeling in Europe. These activities have led to the development of a system of methods and conesponding computer models that address all the issues related to motor vehicle emissions that are of interest to policy-makers, institutions, and the automotive and oil industries. The Coordination of Information on Air Emissions/Computer Program to Calculate Emissions from Road Traffic (CORLNAIR/COPERT) methodology for the estimation of emissions from road vehicles is presented and compared with other models. A COPERT-based approach for microscale traffic emission estimation, with direct application in regional and urban emission inventories, is outlined, and relevant case studies are briefly discussed. The FOREMOVE model, developed for forecasts of motor vehicle emissions, is presented, together with some results from its application in the European Auto/Oil program. Particular attention is given to modeling the deterioration of in-use vehicles. Finally, the major areas of further research in the field of vehicle emissions in Europe are indicated.     

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation.

Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .     

Development,Evaluation, and Application of a Primary Aerosol Model

ABSTRACT

The Segmented-Plume Primary Aerosol Model (SPPAM) has been developed over the past several years. The earlier model development goals were simply to generalize the widely used Industrial Source Complex Short-Term (ISCST) model to simulate plume transport and dispersion under light wind conditions and to handle a large number of roadway or line sources. The goals have been expanded to include development of improved algorithm for effective plume transport velocity, more accurate and efficient line and area source dispersion algorithms, and recently, a more realistic and computationally efficient algorithm for plume depletion due to particle dry deposition. A performance evaluation of the SPPAM has been carried out using the 1983 PNL dual tracer experimental data. The results show the model predictions to be in good agreement with observations in both plume advection-dispersion and particulate matter (PM) depletion by dry deposition. For PM_2.5 impact analysis, the SPPAM has been applied to the Rubidoux area of California. Emission sources included in the modeling analysis are: paved road dust, diesel vehicular exhaust, gasoline vehicular exhaust, and tire wear particles from a large number of roadways in Rubidoux and surrounding areas. For the selected modeling periods, the predicted primary PM_2.5 to primary PM10 concentration ratios for the Rubidoux sampling station are in the range of 0.39–0.46. The organic fractions of the primary PM_2.5 impacts are estimated to be at least 34–41%. Detailed modeling results indicate that the relatively high organic fractions are primarily due to the proximity of heavily traveled roadways north of the sampling station. The predictions are influenced by a number of factors; principal among them are the receptor locations relative to major roadways, the volume and composition of traffic on these roadways, and the prevailing meteorological conditions.     

Use of n-Fold Cross-Validation to Evaluate Three Methods to Calculate Heavy Truck Annual Average Daily Traffic and Vehicle Miles Traveled

Abstract

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.     

A New Screening Measure to Identify Potential Carbon Monoxide Hotspots

ABSTRACT

To demonstrate conformity of transportation projects to National Ambient Air Quality Standards in accordance with State Implementation Plans, the U.S. Environmental Protection Agency (EPA) uses intersection level of service (LOS) as one of its major criteria for screening for potential carbon monoxide (CO) hotspots. Although intersection LOS is a measure of traffic volume, signal timing, and related congestion and delay, the assigned level reflects only the computed averaged stopped delay (ASD) per vehicle at the intersection. Thus, intersections can often operate at the same LOS but produce vastly different levels of predicted CO concentrations. For example, a two-lane approach operating at LOS D will produce very different levels of CO than a five-lane approach also operating at LOS D.

This study explores the effectiveness of the LOS D criterion as a screen for identifying potential CO hotspots. The study results indicate that LOS is a poor predictor of potential CO hotspots when compared to results generated with the EPA-recommended micro-scale model CAL3QHCr. To more consistently screen out those intersections that will not be identified as CO hotspots using the micro-scale models, a new criterion, equivalent red-time vehicles (ERTV), is introduced. The modeling results using ERTV suggest that it is a more robust measure for identifying potential CO hotspots, and conversely, screening out those intersections that are not likely to be identified as hotspots using micro-scale simulation results.     

Exposure to ultrafine and fine particles and noise during cycling and driving in 11 Dutch cities

Recent studies have suggested that exposures during traffic participation may be associated with adverse health effects. Traffic participation involves relatively short but high exposures. Potentially relevant exposures include ultrafine particles, fine particles (PM_2.5) and noise.Simultaneously, detailed real time exposure of particle number concentration (PNC), PM_2.5 and noise has been measured while driving and cycling 12 predefined routes of approximately 10–20 min duration. Sampling took place in eleven medium-sized Dutch cities on eleven weekdays in August till October 2006. To investigate variability in cyclists exposure, we systematically collected information on meteorology, GPS coordinates, type of road, traffic intensity, passing vehicles and mopeds while cycling.The overall mean PNC of car drivers was 5% higher than the mean PNC of cyclists. The overall mean concentration of PM_2.5 in the car was 11% higher than during cycling. Slightly higher 1-min peak concentrations were measured in the car (PNC 14%; PM_2.5 29% for 95-percentiles). Shorter duration peaks of PNC were higher during cycling (43% for 99-percentile of 1-s averages). Peaks in PNC typically last for less than 10 s. A large variability of exposure was found within and between routes. Factors that significantly predicted PNC variability during cycling were: passing vehicles (mopeds, cars), waiting for traffic lights, passing different types of (large) intersections and bicycle lanes and bike paths close to motorized traffic. No relation was found between PM_2.5 and those predictor variables. The correlation between PNC and noise was moderate (median 0.34). PM_2.5 had very low correlations with PNC and noise.PNC and PM_2.5 exposure of car drivers was slightly higher than that of cyclists. PNC was largely uncorrelated with PM_2.5 and reflected local traffic variables more than PM_2.5. Different factors were associated with high PNC and high noise exposures.     

Traffic noise exposure of high-rise residential buildings in urban area

Noise pollution is a major factor of environmental complaints in many cities, which has significant impacts on human health. As a dominating source of environmental noise, the impact of road traffic noise is increasing. Residents living in high-rise buildings along the main road are severely affected by traffic noise. In order to assess the noise level of urban area along the main road in Guangzhou, three buildings were selected to conduct traffic noise measurements, and the questionnaire about traffic noise impact on human being was completed. Through the questionnaire, around 70% of participants consider the traffic noise has negative effect, and about 60% of participants consider the noise has moderate or much higher impact on physical comfort. Around 65% of participants consider the noise had moderately or much higher impact on their psychological comfort. By analyzing the measured data, all of the measured noise levels in three buildings exceed the recommended limit of 55 dB (A) in the daytime and 45 dB (A) in the night for residence, and the exceeded value can be up to 16 dB (A). By comparing the fitting curve of noise level transfer function on each floor relative to the reference floor, the quadratic polynomial was selected to plot the transfer function rather than cubic polynomial.

     

A Mathematical Model for Predicting Trends in Carbon Monoxide Emissions and Exposures on Urban Arterial Highways

Abstract

The roadway is one of the most important microenvironments for human exposure to carbon monoxide (CO). To evaluate long-term changes in pollutant exposure due to in-transit activities, a mathematical model has been developed to predict average daily vehicular emissions on highways. By utilizing measurements that are specific for a given location and year (e.g., traffic counts, fleet composition), this model can predict emissions for a specific roadway during various time periods of interest, allowing examination of long-term trends in human exposure to CO. For an arterial highway in northern California, this model predicts that CO emissions should have declined by 58% between 1980 and 1991, which agrees fairly well with field measurements of human exposure taken along that roadway during those two years. An additional reduction of up to 60% in CO emissions is predicted to occur between 1991 and 2002, due solely to the continued replacement of older cars with newer, cleaner vehicles.     

Measurement and Three-Dimensional Modeling of Airflow and Pollutant Dispersion in an Undersea Traffic Tunnel

Abstract

Airflow and pollutant dispersion in a cross-harbor traffic tunnel were experimentally and numerically studied. Concentrations of the gaseous pollutants CO, NO_x, and total hydrocarbons (THC) at three axial locations in the tunnel, together with traffic flow rate, traffic speed, and types of vehicle were measured. Three-dimensional (3D) turbulent flow and dispersion of air pollutants in the tunnel were modeled and solved numerically using the finite volume method. Traffic emissions were modeled accordingly as banded line sources along the tunnel floor. The results reveal that cross-sectional concentrations are nonuniformly distributed and that concentrations rise with downstream distance. The piston effect of vehicles alone can provide 9–23% dilution of air pollutants in the tunnel, compounded to a 23–74% dilution effect according to the ventilation condition.     

Lagrangian Dispersion Modeling of Vehicular Emissions from a Highway in Complex Terrain

Abstract

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

PM2.5 concentrations in London for 2008–A modeling analysis of contributions from road traffic

We report on the analysis of contributions from road traffic emissions to fine particulate matter (PM_2.5) concentrations within London for 2008 with the OSCAR Air Quality Assessment System. A spatiotemporal evaluation of the OSCAR system has been conducted with measurements from the London air quality network (LAQN). For the predicted and measured hourly time series of concentrations at 18 sites in London, the medians of correlation, mean absolute error, index of agreement, and factor of two (FAC2) of all stations were 0.80, 4.1 μg/m³, 0.86, and 74%, respectively. Spatial evaluation of modeled and observed annual mean concentrations also showed a fairly good agreement, with all the values falling within the FAC2 range. According to model predictions, the urban increment (including the contributions from urban traffic and other urban sources) was evaluated to be on the average 18%, 33%, 39%, and 43% of the total PM_2.5 in suburban environments, in the urban background, near roads, and near busy roads, respectively. However, the highest values of the urban traffic increment can be around 50% of the total PM_2.5 concentrations near motorways and major roads. The total concentrations (including regional background, and the contributions from urban traffic and other urban sources) can therefore be almost three times the regional background. The total urban increment close to busy roads was around 7–8 μg/m³, in which the estimated traffic contribution is more than 2 μg/m³. On the average, urban traffic contributes approximately 1 μg/m³ of PM_2.5 to the urban background across London. According to modeling, approximately two-thirds of the traffic increment originated from exhaust emissions and most of the rest was due to brake and tire wear.

Implications: The urban increment and traffic contribution to the total PM_2.5 are significant and spatially heterogeneous across London. The highly heterogeneous distribution of PM_2.5 hence requires detailed modeling studies to be carried out at high spatial resolution, which can be particularly important for exposure and health impact assessment. This type of information can be used to quantify health impacts resulting from specific sources of PM_2.5 such as traffic emissions, to aid city and national decision makers when formulating pollution control strategies.     

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.     

A Comparison of Model Performance between AERMOD and AUSTAL2000

ABSTRACT

In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gemäβ der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.

IMPLICATIONS This study contributes to the understanding of dispersion modeling and demonstrates the advantage of detailed meteorological data. It also helps air quality regulators and planners to identify the most appropriate model to use. It is indicated that AERMOD is more suitable for air quality planning and regulation, when all required meteorological information is available, because its predictions are mostly closer to field observations. Furthermore AUSTAL2000 predicted concentrations that showed a narrow range and triggered far less impacts from the source.     

Traffic pollution in a downtown site of Buenos Aires City

It has recently been recognized that air and noise pollution constitutes an extended problem over the densely populated city of Buenos Aires. Traffic emissions are of paramount concern, especially along narrow and main traffic arteries. In spite of these considerations, few systematic studies have been undertaken to evaluate the air quality in the metropolitan area of the city. In 1996, concentrations of carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO₂) and ozone (O₃) were simultaneously measured for the first time using a continuous monitoring station. This station was placed in a building at Belgrano Avenue, which is a heavy traffic street in the downtown area of the city (Bogo et al., Atmospheric Environment 33 (1999) 2587. In this work, we analyze the dependence of the measured primary pollutants, CO and the mixture of nitrogen oxides (NO_x), with meteorological conditions, traffic emissions and monitoring location. We compare the registered values with the results obtained from modeling the dispersion of the pollutants emitted from mobile and area sources. We also discuss the relevance of street canyon effects compared with background concentrations of these pollutants.     

A Quantitative Approach to the Traffic Air Quality Problem: The Traffic Air Quality Index

Abstract

The Traffic Air Quality (TAQ) model is a simple tool to estimate traffic fine particulate emissions on roadways (g/km) and can be used for both real-time analysis and for localized conformity analysis (“hot-spot” analysis for nonattainment areas) as defined by 40 CFR 93.123. This paper is a follow-up to a study published earlier regarding the development of the TAQ model. This paper shows how local air quality levels can be a factor in traffic management in nonattainment areas. Similar to the industrial source quotas measured in tons per year, it is proposed that road segments are to be assigned emission quotas (or TAQ indices) measured in pollutant mass emitted per road length (g/km) above which traffic-measures have to be taken to reduce the fine-particulates emissions on such road links. The TAQ model as well as traffic-rerouting measures along with the Intelligent Transportation System (ITS) protocols can be used to have a real-time control of the traffic conditions along expressways to maintain the fine-particulates emissions below the quota assigned per road link and consequently improving the over all local air quality in nonattainment areas.     

A wind tunnel study of the effect of roadway configurations on the dispersion of traffic-related pollution

In this paper we examine the effect of different roadway configurations, including noise barriers and roadway elevation or depression relative to the surrounding terrain, on the dispersion of traffic-related pollutants for winds perpendicular to the roadway. A wind tunnel experiment modeling 12 different configurations was performed to study the flow fields and the concentration distributions resulting from emissions from a simulated six-lane highway. All of the configurations examined here reduced the downwind ground-level concentrations relative to that for a flat, unobstructed roadway; however, the degree to which the concentrations were reduced varied widely depending on the particular situation.Ground-level concentration data from the cases considered in this research indicate that a constant entrainment velocity can be used over the region beginning downwind of any initial disturbance to the flow resulting from the roadway configuration (e.g., a recirculation region behind a noise barrier) and extending at least to the end of our measurements. For example, for the case of a single noise barrier on the downwind side of the road, this region extends from approximately four barrier heights downwind of the roadway to 40 barrier heights. It was also found that the virtual origin concept is useful in describing the initial mixing created by the particular roadway configuration. To effectively model the influence of the roadway configuration on the dispersion, a combination of a virtual origin and an entrainment velocity may be effective. The magnitude of the virtual origin shift appears to depend on the particular roadway configuration, while the entrainment velocity appears to be a function of the friction velocity and the roadway geometry. These results suggest that road configuration must be taken into account in modeling near-road air quality.     

Monitoring study of the near-road PM2.5 concentrations in Maryland

The Maryland State Highway Administration (SHA) monitoring program monitored the impact of vehicular emissions on the concentrations of the fine particles smaller than 2.5 microns (PM2.5). PM2.5 concentrations were monitored in close proximity to a highway in order to determine whether traffic conditions on the roadway impact concentrations at this location. The monitoring program attempted to connect monitored concentrations with the roadway traffic exhaust or with the other sources of PM2.5. PM2.5 concentrations were collected near the Capital Beltway (I-495/I-95) in Largo, Maryland. The monitoring program was launched on May 13, 2009 and continued through the end of 2012. Two co-located monitors, one for continuous PM2.5 measurements and the other for speciation measurements, were used in this program. Meteorological and traffic information was also continuously collected at or near the monitoring site. Additionally, data from the two other monitoring locations, one at the Howard University-Beltsville, MD and one at McMillan Reservoir, DC, was used for comparison with the data collected at the SHA monitoring location. The samples collected by the speciation monitor were analyzed at the RTI and DRI Laboratories to determine the composition and the sources of the collected PM2.5 samples. Based on the apportionment analysis, the contribution of roadway sources is about 12 to 17 percent of PM2.5 at the near-road site.

Implications: PM_2.5 monitoring at 150 m (approximately 500 feet) from a major highway in Maryland near Washington, DC, demonstrated that roadway traffic contributes to the total PM_2.5 concentration near the roadway, but the contribution at such distance is small, in the order of 12–17% of the total.     

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.