Resolving local-scale emissions for modeling air quality near roadways

A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a "bottom-up" approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.     

National estimates of outdoor air toxics concentrations

The Clean Air Act identifies 189 hazardous air pollutants (HAPs), or "air toxics," associated with a wide range of adverse human health effects. The U.S. Environmental Protection Agency has conducted a modeling study with the Assessment System for Population Exposure Nationwide (ASPEN) to gain a greater understanding of the spatial distribution of concentrations of these HAPs resulting from contributions of multiple emission sources. The study estimates year 1990 long-term outdoor concentrations of 148 air toxics for each census tract in the continental United States, utilizing a Gaussian air dispersion modeling approach. Ratios of median national modeled concentrations to estimated emissions indicate that emission totals without consideration of emission source type can be a misleading indicator of air quality. The results also indicate priorities for improvements in modeling methodology and emissions identification. Model performance evaluation suggests a tendency for underprediction of observed concentrations, which is likely due, at least in part, to a number of limitations of the Gaussian modeling formulation. Emissions estimates for HAPs have a high degree of uncertainty and contribute to discrepancies between modeled and monitored concentration estimates. The model's ranking of concentrations among monitoring sites is reasonably good for most of the gaseous HAPs evaluated, with ranking accuracy ranging from 66 to 100%.     

Refining emission rate estimates using a coupled receptor–dispersion modeling approach

Receptor modeling techniques like chemical mass balance are used to attribute pollution levels at a point to different sources. Here we analyze the composition of particulate matter and use the source profiles of sources prevalent in a region to estimate quantitative source contributions. In dispersion modeling on the other hand the emission rates of various sources together with meteorological conditions are used to determine the concentrations levels at a point or in a region. The predictions using these two approaches are often inconsistent. In this work these differences are attributed to errors in emission inventory. Here an algorithm for coupling receptor and dispersion models is proposed to reduce the differences of the two predictions and determine the emission rates accurately. The proposed combined approach helps reconcile the differences arising when the two approaches are used in a stand-alone mode. This work is based on assuming that the models are perfect and uses a model-to-model comparison to illustrate the concept.     

Modeling of Personal Exposures to Ambient Air Toxics in Camden,New Jersey: An Evaluation Study

Abstract

This study presents the Individual Based Exposure Modeling (IBEM) application of MENTOR (Modeling ENvironment for TOtal Risk studies) in a hot spot area, where there are concentrated local sources on the scale of tens to hundreds of meters, and an urban reference area in Camden, NJ, to characterize the ambient concentrations and personal exposures to benzene and toluene from local ambient sources. The emission-based ambient concentrations in the two neighborhoods were first estimated through atmospheric dispersion modeling. Subsequently, the calculated and measured ambient concentrations of benzene and toluene were separately combined with the time-activity diaries completed by the subjects as inputs to MENTOR/IBEM for estimating personal exposures resulting from ambient sources. The modeling results were then compared with the actual personal measurements collected from over 100 individuals in the field study to identify the gaps in modeling personal exposures in a hot spot. The modeled ambient concentrations of benzene and toluene were generally in agreement with the neighborhood measurements within a factor of 2, but were underestimated at the high-end percentiles. The major local contributors to the benzene ambient levels are from mobile sources, whereas mobile and stationary (point and area) sources contribute to the toluene ambient levels in the study area. This finding can be used as guidance for developing better air toxic emission inventories for characterizing, through modeling, the ambient concentrations of air toxics in the study area. The estimated percentage contributions of personal exposures from ambient sources were generally higher in the hot spot area than the urban reference area in Camden, NJ, for benzene and toluene. This finding demonstrates the hot spot characteristics of stronger local ambient source impacts on personal exposures. Non-ambient sources were also found as significant contributors to personal exposures to benzene and toluene for the population studied.     

Source apportionment of indoor air pollution

An understanding of the relative contributions from important pollutant sources to human exposures is necessary for the design and implementation of effective control strategies. In the past, societal efforts to control air pollution have focused almost exclusively on the outdoor (ambient) environment. As a result, substantial amounts of time and money have been spent to limit airborne discharges from mobile and stationary sources. Yet it is now recognized that exposures to elevated pollutant concentrations often occur as a result of indoor, rather than outdoor, emissions. While the major indoor sources have been identified, their relative impacts on indoor air quality have not been well defined. Application of existing source apportionment models to nonindustrial indoor environments is only just beginning. It is possible that these models might be used to distinguish between indoor and outdoor emissions, as well as to distinguish among indoor sources themselves. However, before the feasibility and suitability of source-apportionment methods for indoor applications can be assessed adequately, it is necessary to take account of model assumptions and associated data requirements. This paper examines the issue of indoor source apportionment and reviews the need for emission characterization studies to support such source-apportionment efforts.     

Modeling exposure to particulate matter

Exposure assessment, a component of risk assessment, links sources of pollution with health effects. Exposure models are scientific tools used to gain insights into the processes affecting exposure assessment. The purpose of this paper is to review the process and methodology of estimating inhalation exposure to particulate matter (PM) using various types of models. Three types of models are discussed in the paper. Indirect type of models are physical models that employ inventories of outdoor and indoor sources and their emission rates to identify major sources contributing to exposure to PM, and use fate and transport and indoor air quality models to estimate PM concentrations at receptor sites. PM concentrations and time spent by a subject at each receptor site are input variables to the conventional exposure model that estimates the desired exposure levels. Direct type models use measured exposure or exposure concentrations in conjunction with information obtained from questionnaires to formulate exposure regression models. Stochastic models use exposure measurements, estimates can also be used, to formulate exposure population distributions and investigate associated uncertainty and variability. Since models developed using databases from western countries are not necessarily applicable in developing countries, the difference in requirements among western and developing countries is highlighted in the paper. Employment of exposure modeling methods in developing countries requires development of local information. Such information includes local outdoor and indoor source inventories, local or regional meteorological conditions, adjustment of indoor models to reflect local building construction conditions, and use of questionnaires to obtain local time budget and activity patterns of the subject population.     

Field monitoring design considerations for assessing indoor exposures to combustion pollutants

Laboratory and controlled field studies of indoor air quality (IAQ) have characterized pollutant emission rates from combustion sources and have measured other key indoor air pollution parameters such as air exchange rates and indoor reactivity rates for the houses investigated. In addition, several field studies have attempted to measure, with varying degrees of success, pollutant exposures, indoor pollutant concentrations, and other parameters in large populations. To date, there exists no comprehensive strategy for assessing distributions of exposures to combustion pollutants and distributions of factors that affect such exposures in large populations. This paper outlines important parameters that affect combustion-related indoor air pollution concentrations and exposures, delineates weaknesses in our current understanding of exposures and field sampling methodologies, and mentions important considerations in planning appropriate field sampling strategies.     

Analysis of coupled model uncertainties in source-to-dose modeling of human exposures to ambient air pollution: A PM2.5 case study

Quantitative assessment of human exposures and health effects due to air pollution involve detailed characterization of impacts of air quality on exposure and dose. A key challenge is to integrate these three components on a consistent spatial and temporal basis taking into account linkages and feedbacks. The current state-of-practice for such assessments is to exercise emission, meteorology, air quality, exposure, and dose models separately, and to link them together by using the output of one model as input to the subsequent downstream model. Quantification of variability and uncertainty has been an important topic in the exposure assessment community for a number of years. Variability refers to differences in the value of a quantity (e.g., exposure) over time, space, or among individuals. Uncertainty refers to lack of knowledge regarding the true value of a quantity. An emerging challenge is how to quantify variability and uncertainty in integrated assessments over the source-to-dose continuum by considering contributions from individual as well as linked components. For a case study of fine particulate matter (PM_2.5) in North Carolina during July 2002, we characterize variability and uncertainty associated with each of the individual concentration, exposure and dose models that are linked, and use a conceptual framework to quantify and evaluate the implications of coupled model uncertainties. We find that the resulting overall uncertainties due to combined effects of both variability and uncertainty are smaller (usually by a factor of 3–4) than the crudely multiplied model-specific overall uncertainty ratios. Future research will need to examine the impact of potential dependencies among the model components by conducting a truly coupled modeling analysis.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Model and input uncertainty in multi-media fate modeling: benzo[a]pyrene concentrations in Europe

This paper evaluates the contribution of (i) uncertainty in substance properties, (ii) lack of spatial variability, (iii) intermodel differences and (iv) neglecting sorption to black carbon (BC) to the uncertainty of Benzo[a]pyrene (BaP) concentrations in European air, soil and fresh water predicted by the multi-media fate model Simplebox. Uncertainty in substance properties was quantified using probabilistic modeling. The influence of spatial variability was quantified by estimating variation in predicted concentrations with three spatially explicit fate models (Impact 2002, EVn BETR and BETR Global). Intermodel differences were quantified by comparing concentration estimates of Simplebox, Impact 2002, EVn BETR and the European part of BETR Global. Finally, predictions of a BC-inclusive version of Simplebox were compared with predictions of a BC-exclusive version. For air concentrations of BaP, the lack of spatial variability in emissions was most influential. For freshwater concentrations of BaP, intermodel differences and lack of spatial variability in dimensions of fresh water bodies were the dominant sources of uncertainty. For soil, all sources of uncertainty were of comparable magnitude. Our results indicate that uncertainty in Simplebox can be as large as three orders of magnitude for BaP concentrations in the environment and would be substantially underestimated by focusing on one source of uncertainty only.     

Stochastic analysis to assess the spatial distribution of groundwater nitrate concentrations in the Po catchment (Italy)

A large database including temporal trends of physical, ecological and socio-economic data was developed within the EUROCAT project. The aim was to estimate the nutrient fluxes for different socio-economic scenarios at catchment and coastal zone level of the Po catchment (Northern Italy) with reference to the Water Quality Objectives reported in the Water Framework Directive (WFD 2000/60/CE) and also in Italian legislation. Emission data derived from different sources at national, regional and local levels are referred to point and non-point sources. While non-point (diffuse) sources are simply integrated into the nutrient flux model, point sources are irregularly distributed. Intensive farming activity in the Po valley is one of the main Pressure factors Driving groundwater pollution in the catchment, therefore understanding the spatial variability of groundwater nitrate concentrations is a critical issue to be considered in developing a Water Quality Management Plan. In order to use the scattered point source data as input in our biogeochemical and transport models, it was necessary to predict their values and associated uncertainty at unsampled locations. This study reports the spatial distribution and uncertainty of groundwater nitrate concentration at a test site of the Po watershed using a probabilistic approach. Our approach was based on geostatistical sequential Gaussian simulation used to yield a series of stochastic images characterized by equally probable spatial distributions of the nitrate concentration across the area. Post-processing of many simulations allowed the mapping of contaminated and uncontaminated areas and provided a model for the uncertainty in the spatial distribution of nitrate concentrations.     

A modeling study of coarse particulate matter pollution in Beijing: regional source contributions and control implications for the 2008 Summer Olympics

In the last 10 yr, Beijing has made a great effort to improve its air quality. However, it is still suffering from regional coarse particulate matter (PM10) pollution that could be a challenge to the promise of clean air during the 2008 Olympics. To provide scientific guidance on regional air pollution control, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality Model (CMAQ) air quality modeling system was used to investigate the contributions of emission sources outside the Beijing area to pollution levels in Beijing. The contributions to the PM10 concentrations in Beijing were assessed for the following sources: power plants, industry, domestic sources, transportation, agriculture, and biomass open burning. In January, it is estimated that on average 22% of the PM10 concentrations can be attributed to outside sources, of which domestic and industrial sources contributed 37 and 31%, respectively. In August, as much as 40% of the PM10 concentrations came from regional sources, of which approximately 41% came from industry and 31% from power plants. However, the synchronous analysis of the hourly concentrations, regional contributions, and wind vectors indicates that in the heaviest pollution periods the local emission sources play a more important role. The implications are that long-term control strategies should be based on regional-scale collaborations, and that emission abatement of local sources may be more effective in lowering the PM10 concentration levels on the heavy pollution days. Better air quality can be attained during the Olympics by placing effective emission controls on the local sources in Beijing and by controlling emissions from industry and power plants in the surrounding regions.     

Quantification of personal exposure concentrations to gasoline vehicle emissions in high-end exposure microenvironments: Effects of fuel and season

Mobile-source air toxic (MSAT) levels increase in confining microenvironments (MEs) with numerous emission sources of vehicle exhaust or evaporative emissions or during high-load and cold-start conditions. Reformulated fuels are expected to reduce MSAT and ozone precursor emissions. This study, required under the Clean Air Act Section 211b, evaluated high-end exposures in cities using reformulated (methyl tertiary-butyl ether [MTBE] or ethanol [EtOH]) fuels and conventional gasoline blends. The study investigates 13 high-end MEs, sampling under enhanced exposure conditions expected to result in maximal fuel and exhaust component exposures to carbon monoxide (CO), carbon dioxide (CO₂), BTEX (benzene, toluene, ethylbenzene, xylenes), MTBE, 1,3-butadiene (1,3-BD), EtOH, formaldehyde (HCHO), and acetaldehyde (CH₃CHO). The authors found that day-to-day ME variations in high-end benzene, 1,3-BD, HCHO, and CO concentrations are substantial, but independent of gasoline composition and season, and related to the activity and emission rates of ME sources, which differ from day to day.

Implications: Mobile-source air toxic (MSAT) levels increase in confining microenvironments (MEs) in the presence of vehicular exhaust or evaporative emissions. This study, required under the Clean Air Act Section 211b, evaluated high-end exposures in cities using oxygenated (methyl tertiary-butyl ether or ethanol) and conventional gasoline blends. Personal exposure concentrations were quantified in selected MEs representing the upper end of the frequency distribution of potential population exposures. This work presents the first systematic look at high-end/maximal exposures to multiple contaminants, in multiple microenvironments, in multiple cities, over two seasons, for multiple fuels, making it a very complete evaluation of reformulated fuel impacts on MSAT concentrations in confined microenvironments. The study found that day-to-day ME variations of high-end pollutant concentrations are substantial, but independent of gasoline composition and season, and related to the variable daily activity and emission rates of ME sources. The data collected in this study may be used in bounding exposure modeling estimates that account for time spent in similar confining MEs.     

Effective pollutant emission heights for atmospheric transport modelling based on real-world information

Emission data needed as input for the operation of atmospheric models should not only be spatially and temporally resolved. Another important feature is the effective emission height which significantly influences modelled concentration values. Unfortunately this information, which is especially relevant for large point sources, is usually not available and simple assumptions are often used in atmospheric models. As a contribution to improve knowledge on emission heights this paper provides typical default values for the driving parameters stack height and flue gas temperature, velocity and flow rate for different industrial sources. The results were derived from an analysis of the probably most comprehensive database of real-world stack information existing in Europe based on German industrial data. A bottom-up calculation of effective emission heights applying equations used for Gaussian dispersion models shows significant differences depending on source and air pollutant and compared to approaches currently used for atmospheric transport modelling.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

Air quality modeling of interpollutant trading for ozone precursors in an urban area

Emission trading is a market-based approach designed to improve the efficiency and economic viability of emission control programs; emission trading has typically been confined to trades among single pollutants. Interpollutant trading (IPT), as described in this work, allows for trades among emissions of different compounds that affect the same air quality end point, in this work, ambient ozone (O3) concentrations. Because emissions of different compounds impact air quality end points differently, weighting factors or trading ratios (tons of emissions of nitrogen oxides (NO(x)) equivalent to a ton of emissions of volatile organic compounds [VOCs]) must be developed to allow for IPT. In this work, IPT indices based on reductions in O3 concentrations and based on reductions in population exposures to O3 were developed and evaluated using a three-dimensional gridded photochemical model for Austin, TX, a city currently on the cusp of nonattainment with the National Ambient Air Quality Standards for O3 concentrations averaged over 8 hr. Emissions of VOC and NO(x) from area and mobile sources in Austin are larger than emissions from point sources. The analysis indicated that mobile and area sources exhibited similar impacts. Trading ratios based on maximum O3 concentration or population exposure were similar. In contrast, the trading ratios did exhibit significant (more than a factor of two) day-to-day variability. Analysis of the air quality modeling indicated that the daily variability in trading ratios could be attributed to daily variations in both emissions and meteorology.     

Air quality at Santiago,Chile: a box modeling approach II. PM2.5, coarse and PM10 particulate matter fractions

Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The box modeling approach was applied to PM₁₀, PM_2.5 and coarse (PM₁₀–PM_2.5) particulate matter (PM) fractions; the period analyzed is 1989–1999. A linear model for each PM fraction was obtained, having as independent variables CO and SO₂ concentrations, plus a term proportional to (wind speed)⁻¹ that lumps together non-combustion emissions and secondary generation terms; wet scavenging is included as another independent variable. Model identification results show good agreement for the different parameters across monitoring stations. The washout ratios and scavenging coefficients agree with data published in the literature, being higher for the coarse PM fraction. The CO and SO₂ coefficients fitted for 1989–1995 agree with a priori estimates for the same period. Background estimates for the PM fractions are in agreement with measurement campaigns in upwind sites. Results show that transportation sources have become the dominant contributors to ambient PM levels, while stationary sources have decreased their contributions in the last years. The relative importance of mobile sources to PM_2.5 ambient concentrations has doubled in the last 10 years, whereas stationary sources have reduced their relative contributions to half the value in the early 1990s. Model estimates of regional background of PM_2.5 and PM₁₀ have decreased 50% and 22% in the last decade, respectively; coarse background has shown no significant change. The final conclusion is that there is room and need for a more intensive emission reduction strategy for Santiago, focusing on mobile sources. The approach pursued in this work is feasible for cities or regions where comprehensive, transport and chemistry models are not available yet, but estimates of air quality contributions are needed for policy purposes. The methodology requires data on ambient air quality measurements and surface meteorology.     

Mesoscale modeling of wintertime particulate matter episodes in eastern Washington, USA

The Spokane, Washington area is classified as a non-attainment area for the 24-h PM₁₀ standard due to a history of high particulate matter concentrations. A Eulerian regional air quality model (CALMET/CALGRID) has been used to characterize the emission, transport and dispersion of PM₁₀ and PM_2.5 in Spokane. Observations from a residential site (Rockwood, RW) and an industrial site (Crown Zellerbach, CZ), spanning July 1994–August 1996 were used to evaluate the current emission inventory. Two major tasks were devised to conduct the objectives of this investigation. First, a simple and efficient urban dispersion model (WYNDValley) was used to simulate important episodes characterized by the highest PM₁₀ and PM_2.5 concentrations. The selected episodes included four days with wet conditions for which no roads would have been emitting and seven days with dry conditions for which roads would emit. In the second step, a single road-emitting event was selected from the previous predicted results for further analysis using the Eulerian regional air quality model to examine the emission inventory. The urban and regional models predicted the observed concentration distributions reasonably well for the source emissions inventoried in Spokane. The mass concentrations of PM₁₀ were well predicted for the roads emitting case examined by both models indicating that the emission inventory based primarily upon area sources including roads is reasonably well characterized, at least at the RW site. The area sources around CZ are less well characterized, so that the PM₁₀ concentrations are underpredicted at CZ. The models appear unable to reach an equilibrium mass balance status at the beginning of the simulation, and the urban model seems unable to properly resolve the nocturnal boundary layer.     

Evaluation of land-use regression models used to predict air quality concentrations in an urban area

Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors. However, it is often difficult to collect concentration information at each study subject location. In the absence of complete subject-specific measurements, land-use regression (LUR) models have frequently been used for estimating individual levels of exposures to ambient air pollution. The LUR models, however, have several limitations mainly dealing with extensive monitoring data needs and challenges involved in their broader applicability to other locations. In contrast, air quality models can provide high-resolution source–concentration linkages for multiple pollutants, but require detailed emissions and meteorological information. In this study, first we predicted air quality concentrations of PM_2.5, NO_x, and benzene in New Haven, CT using hybrid modeling techniques based on CMAQ and AERMOD model results. Next, we used these values as pseudo-observations to develop and evaluate the different LUR models built using alternative numbers of (training) sites (ranging from 25 to 285 locations out of the total 318 receptors). We then evaluated the fitted LUR models using various approaches, including: 1) internal “Leave-One-Out-Cross-Validation” (LOOCV) procedure within the “training” sites selected; and 2) “Hold-Out” evaluation procedure, where we set aside 33–293 tests sites as independent datasets for external model evaluation. LUR models appeared to perform well in the training datasets. However, when these LUR models were tested against independent hold out (test) datasets, their performance diminished considerably. Our results confirm the challenges facing the LUR community in attempting to fit empirical response surfaces to spatially- and temporally-varying pollution levels using LUR techniques that are site dependent. These results also illustrate the potential benefits of enhancing basic LUR models by utilizing air quality modeling tools or concepts in order to improve their reliability or transferability.