Weighted road density: A simple way of assigning traffic-related air pollution exposure

BackgroundExisting traffic variables used for predicting NO₂ in epidemiological studies are either difficult to acquire or explain only a small proportion of the variance. The purpose of this study was to develop and validate a new predictor, weighted road density, which combines the maximum amount of information related to traffic into a single variable without the requirement of obtaining traffic counts for a given area.MethodTwo week NO₂ samples were collected using the readings of up to 32 passive samplers on 3 separate rounds between September and December 2006 and again in 2007. Several types of traffic related explanatory variables based on traffic counts, distance to main road and the proposed weighted road density were constructed using GIS software, and tested for association with the NO₂ samplers. Assessment of the best model was based on R² values, as well as leave-one-out cross validation.ResultsThe weighted road density variable and the density variable based on traffic counts resulted in a similar R² (0.59) for predicting NO₂, although weighted road density was much easier to construct and outperformed other variables such as distance to main road.ConclusionAs well as being a powerful predictor for use in a land use regression model, weighted road density can be used as a proxy for exposure to traffic-related pollution, for use in circumstances where direct measurements of pollutant levels are not feasible or are not required.     

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.     

Assessing spatial variability of ambient nitrogen dioxide in Montréal, Canada, with a land-use regression model

The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO2) in Montreal, Quebec, Canada. These estimates of concentrations of NO2 will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO2 was measured for 14 consecutive days at 67 sites across the city using Ogawa passive diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). Linear regression analysis was used to assess the association between logarithmic concentrations of NO2 and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO2 was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads showed-no association with NO2. In multiple regression analyses, distance from the nearest highway, traffic count on the nearest highway, length of highways and major roads within 100 m, and population density showed significant associations with NO2; the best-fitting regression model had a R2 of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies.     

Predicting residential indoor concentrations of nitrogen dioxide,fine particulate matter,and elemental carbon using questionnaire and geographic information system based data

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3–4 day samples of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM_2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO₂ with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50 m buffer of a home and distance from a truck route as important contributors to indoor levels of NO₂ and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.     

Modeling the intra-urban variability of outdoor traffic pollution in Oslo,Norway—A GA2LEN project

Traffic is a major source of air pollutants in urban environments, and exposure to these pollutants may be associated with adverse health effects. However, inconsistencies in observational epidemiological studies may be caused by differential measurement errors in various approaches in assessing exposure.We aimed to evaluate a simple method for assessing outdoor air pollutant concentrations in Oslo, Norway, through a land-use regression method.Samples of nitrogen oxides (NO_x) were collected in two different weeks using Ogawa passive diffusion samplers simultaneously at 80 locations across Oslo. Independent variables used in subsequent regression models as predictors of the pollutants were derived using the Arc 9 geographic information system (GIS) software. Indicators of land use, traffic, population density, and physical geography were tested.The final regression model yielded an adjusted coefficient of determination (R²) of 0.77 for nitrogen dioxide (NO₂), 0.66 for nitric oxide (NO), and 0.73 for NO_x.The results suggest that a good predictive exposure model can be derived from this approach, which can be used to estimate long-term small-area variation in concentrations for individual exposure assessment in epidemiological studies in a highly cost-effective way. These small-area variations in traffic pollution are important since they may have associations with health effects.     

Estimated long-term outdoor air pollution concentrations in a cohort study

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods.Long-term exposure to nitrogen dioxide (NO₂), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO₂) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated.The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO₂, NO, BS, and SO₂ concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO₂, NO, BS and SO₂, respectively.We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.     

Indoor pollutant levels from the use of unvented natural gas fireplaces in Boulder, Colorado

High CO and NO2 concentrations have been documented in homes with unvented combustion appliances, such as natural gas fireplaces. In addition, polycyclic aromatic hydrocarbons (PAH) are emitted from incomplete natural gas combustion. The acute health risks of CO and NO2 exposure have been well established for the general population and for certain high-risk groups, including infants, the elderly, and people with heart disease or asthma. Health effects from PAH exposure are less well known, but may include increased risk of cancer. We monitored CO emissions during the operation of unvented natural gas fireplaces in two residences in Boulder, CO, at various times between 1997 and 2000. During 1999, we expanded our tests to include measurements of NO2 and PAH. Results show significant pollutant accumulation indoors when the fireplaces were used for extended periods of time. In one case, CO concentrations greater than 100 ppm accumulated in under 2 hr of operation; a person at rest exposed for 10 hr to this environment would get a mild case of CO poisoning with an estimated 10% carboxyhemoglobin level. Appreciable NO2 concentrations were also detected, with a 4-hr time average reaching 0.36 ppm. Similar time-average total PAH concentrations reached 35 ng/m3. The results of this study provide preliminary insights to potential indoor air quality problems in homes operating unvented natural gas fireplaces in Boulder.     

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

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

Screening and Scenarios of Traffic Emissions at Trier,Germany

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

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto,Canada

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO₂) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO₂ levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO₂ model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO₂ and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.     

Synthesis of Harvard Environmental Protection Agency (EPA) Center studies on traffic-related particulate pollution and cardiovascular outcomes in the Greater Boston Area

The association between particulate pollution and cardiovascular morbidity and mortality is well established. While the cardiovascular effects of nationally regulated criteria pollutants (e.g., fine particulate matter [PM_2.5] and nitrogen dioxide) have been well documented, there are fewer studies on particulate pollutants that are more specific for traffic, such as black carbon (BC) and particle number (PN). In this paper, we synthesized studies conducted in the Greater Boston Area on cardiovascular health effects of traffic exposure, specifically defined by BC or PN exposure or proximity to major roadways. Large cohort studies demonstrate that exposure to traffic-related particles adversely affect cardiac autonomic function, increase systemic cytokine-mediated inflammation and pro-thrombotic activity, and elevate the risk of hypertension and ischemic stroke. Key patterns emerged when directly comparing studies with overlapping exposure metrics and population cohorts. Most notably, cardiovascular risk estimates of PN and BC exposures were larger in magnitude or more often statistically significant compared to those of PM_2.5 exposures. Across multiple exposure metrics (e.g., short-term vs. long-term; observed vs. modeled) and different population cohorts (e.g., elderly, individuals with co-morbidities, young healthy individuals), there is compelling evidence that BC and PN represent traffic-related particles that are especially harmful to cardiovascular health. Further research is needed to validate these findings in other geographic locations, characterize exposure errors associated with using monitored and modeled traffic pollutant levels, and elucidate pathophysiological mechanisms underlying the cardiovascular effects of traffic-related particulate pollutants.

Implications: Traffic emissions are an important source of particles harmful to cardiovascular health. Traffic-related particles, specifically BC and PN, adversely affect cardiac autonomic function, increase systemic inflammation and thrombotic activity, elevate BP, and increase the risk of ischemic stroke. There is evidence that BC and PN are associated with greater cardiovascular risk compared to PM_2.5. Further research is needed to elucidate other health effects of traffic-related particles and assess the feasibility of regulating BC and PN or their regional and local sources.     

Assessing Spatial Variability of Ambient Nitrogen Dioxide in Montréal,Canada, with a Land-Use Regression Model

Abstract

The purpose of this study was to derive a land-use regression model to estimate on a geographical basis ambient concentrations of nitrogen dioxide (NO₂) in Montréal, Quebec, Canada. These estimates of concentrations of NO₂ will be subsequently used to assess exposure in epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, NO₂ was measured for 14 consecutive days at 67 sites across the city using Ogawa passive diffusion samplers. Concentrations ranged from 4.9 to 21.2 ppb (median 11.8 ppb). Linear regression analysis was used to assess the association between logarithmic concentrations of NO₂ and land-use variables derived using the ESRI Arc 8 geographic information system. In univariate analyses, NO₂ was negatively associated with the area of open space and positively associated with traffic count on nearest highway, the length of highways within any radius from 100 to 750 m, the length of major roads within 750 m, and population density within 2000 m. Industrial land-use and the length of minor roads showed no association with NO₂. In multiple regression analyses, distance from the nearest highway, traffic count on the nearest highway, length of highways and major roads within 100 m, and population density showed significant associations with NO₂; the best-fitting regression model had a R² of 0.54. These analyses confirm the value of land-use regression modeling to assign exposures in large-scale epidemiologic studies.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Variability of Nox and No2 concentrations observed at pedestrian level in the city centre of a medium sized urban area

NO_x and NO₂ concentrations were measured at different locations in a city centre of an urban zone (Population 450 000) in order to study the variation of the outdoor exposure at pedestrian level. These measurements were carried out to understand the influence of traffic emissions at each measured site. The observations were done during four weeks in winter, including several days with high pollution levels. The results at different locations have been used to analyse criteria recommended for locating observation sites in a monitoring network. No large differences in background pollution averaged over several weeks have been found throughout the city centre, even during pollution peaks. Measurements were also carried out inside one street canyon. The contribution of the street traffic to the NO=NO_x−NO₂ concentrations observed at side-walk has been found important, i.e., several times the background level. On the other hand, the majority of observed NO₂ pollution is due to the contribution of background pollution within the street. The pollutant excess at pedestrian level is strongly correlated to the street traffic emission and to the atmospheric turbulence observed at roof level. Application of a box model to the street data demonstrates that such models can be useful to estimate the pollutant accumulation within the street.     

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.     

Evaluation of traffic-producing turbulence schemes within Operational Street Pollution Models using roadside measurements

Low wind scenarios are associated with the worst air pollution episodes in urban street canyons. Under these conditions, operational dispersion models often over-predict pollutant concentration. Traffic-producing turbulence (TPT) becomes dominant in mixing and diluting traffic-related pollutants under low wind speed conditions. Determining the TPT effect on the flow and dispersion patterns within urban street canyons is crucial for the development of detailed operational dispersion models for assessing urban air quality. Several spatially averaged TPT formulations have been recently proposed in the literature. However, only a few attempts have been made so far to incorporate different TPT schemes into operational dispersion models and evaluate their performance using measurements.In this paper, several TPT schemes presented in literature were evaluated. Two TPT schemes were implemented in the well-validated Windows version of the Danish Operational Street Pollution Model (WinOSPM). Both formulations were evaluated using six independent datasets of roadside CO concentrations collected in European cities. Statistical and sensitivity analyses were undertaken to test the performance of the different formulations. The results showed that the overall model performance was significantly sensitive to the TPT schemes adopted. The model performance improved when a detailed characterisation of the TPT, depending on the density of road traffic, was used.     

Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.     

Concentrations of ammonia and nitrogen dioxide at roadside verges, and their contribution to nitrogen deposition

Bimonthly integrated measurements of NO2 and NH3 have been made over one year at distances up to 10 m away from the edges of roads across Scotland, using a stratified sampling scheme in terms of road traffic density and background N deposition. The rate of decrease in gas concentrations away from the edge of the roads was rapid, with concentrations falling by 90% within the first 10 m for NH3 and the first 15 m for NO2. The longer transport distance for NO2 reflects the production of secondary NO2 from reaction of emitted NO and O3. Concentrations above the background, estimated at the edge of the traffic lane, were linearly proportional to traffic density for NH3 (microg NH3 m(-3) = 1 x 10(-4) x numbers of cars per day), reflecting emissions from three-way catalysts. For NO2, where emissions depend strongly on vehicle type and fuel, traffic density was calculated in terms of 'car equivalents'; NO2 concentrations at the edge of the traffic lane were proportional to the number of car equivalents (microg NO2 m(-3) = 1 x 10(-4) x numbers of car equivalents per day). Although absolute concentrations (microg m(-3)) of NH3 were five times smaller than for NO2, the greater deposition velocity for NH3 to vegetation means that approximately equivalent amounts of dry N deposition to road side vegetation from vehicle emissions comes from NH3 and NO2. Depending on traffic density, the additional N deposition attributable to vehicle exhaust gases is between 1 and 15 kg N ha(-1) y(-1) at the edge of the vehicle lane, falling to 0.2-10 kg N ha(-1) y(-1) at 10 m from the edge of the road.     

Increasing concentrations of nitrogen dioxide pollution in rural Wales

Monitoring of nitrogen dioxide pollution was carried out in rural environments throughout Wales during a 1-year survey to quantify any changes in background concentrations and distribution of the pollutant since an earlier survey in 1986. There were 23 sites in the present survey of which 16 had been monitored during the 1986 survey. The remaining 7 sites were based on moorland in mid-Wales within map squares for which critical loads for soil acidification are expected to be exceeded by the year 2005. All sites were chosen so as to be remote from major local sources of NO(2) and the values obtained were deemed to be minimum concentrations for the different regions. Measurements were made using diffusion tubes which aimed to provide mean concentrations of NO(2) for 2-week exposure periods. Concentrations of NO(2) were found to be higher in the winter months for most sites and this is probably related to a greater use of fossil fuels for heating buildings at this time of year. The exception was the high concentrations of NO(2) in May and June for several sites in North Wales, and in July and August for a site on Mount Snowdon. These high summer concentrations in North Wales are thought to be related to increased traffic associated with tourism. It is apparent that there has been a substantial increase in rural concentrations of NO(2) throughout Wales since the earlier survey of 1986. As an average of all 16 sites used in both surveys, there was a 53% increase in the annual mean concentration of NO(2). Also, it is evident that, since 1986, there has been a substantial increase in the area of south-eastern Wales which has a background level in excess of 10 ppb NO(2) and a notable reduction in land area with concentrations below 6 ppb NO(2) as an annual mean concentration. The possible future impact of increasing rural concentrations of NO(2) on Welsh vegetation is discussed with references to estimates of critical levels of NO(2) for adverse effects on plants.     

A land use regression for predicting fine particulate matter concentrations in the New York City region

We developed regression equations to predict fine particulate matter (PM_2.5) at air monitoring locations in the New York City region using data on nearby traffic and land use patterns. Three-year averages (1999–2001) of PM_2.5 at US Environmental Protection Agency (EPA) monitors in the 28 counties including and surrounding New York City were calculated using daily data from the EPA's Air Quality Subsystem. As the secondary contribution to PM_2.5 concentrations is lowest in the winter, we also calculated and modeled average winter 2000 PM_2.5 to conduct a preliminary evaluation of model sensitivity to source contribution. Candidate predictor variables included traffic, land use, census and emissions data from local, state and national sources and were tabulated for a series of circular buffer regions at varying distances around the monitors using a geographic information system. In total, more than 25 variables at 5 different buffer distances were considered for inclusion in the model. Before evaluating the variables we removed several samples from the modeling for validation. For comparison and validation purposes we computed both a model using data for the full 28-county region as well as a more urbanized 9-county region. We found that traffic within a buffer of 300 or 500 m explains the greatest proportion of variance (37–44%) in all 3 models. Measures of urbanization, specifically population density, explain a significant amount of the residual variation (7–18%) after including a traffic variable. Finally, a measure of industrial land use further improves the 28-county and 9-county models based on the 3-yr annual averages, explaining an additional 4% and 11% of the variation, respectively, while vegetative land use improves the winter model explaining an additional 6%. The final models predicted well at validation locations. In total, the final land use regression models explain between 61% and 64% of the variation in PM_2.5.