Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO2) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.
Compliance-oriented air pollution monitoring, even for population-oriented monitors, is generally conducted at only a few locations in a city to reflect higher population exposures. This provides limited information on spatial variability of urban air pollution [
Group types for potential predictor variablesa.
Predictor variable groups and subgroups from GIS | El Paso | Detroit | Dallas | Cleveland |
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(1) Traffic | ||||
Distance to nearest low-traffic road (m)b | Xc | |||
Distance to nearest medium-traffic road (m) | Xd | Xe | ||
Distance to nearest high-traffic road (m) | Xf | Xf | Xg | Xh |
Traffic intensity within set buffers (vehicles per day/km) | X | X | X | X |
Length of local roads within set buffers (m) | X | |||
Length of secondary roads within set buffers (m) | X | |||
(2) Area and point | ||||
Open area within set radii (km2) | X | |||
Population density within census block group or set radii | X | X | X | X |
Point source emitters (categorical or continuous) | Xi | Xi | Xi | Xj |
(3) City specific | ||||
Elevation (m) | X | |||
Distance to nearest international border crossing (m) | X | X | ||
Distance to airport (km) | X | |||
Distance to lake (km) | X | |||
(4) Season | X | X |
aSpecific variables and their sources are detailed elsewhere for El Paso [
The US Environmental Protection Agency (EPA) has been involved in LUR studies in El Paso, Detroit, Dallas, and Cleveland (referred to here as the four cities) to support air quality and respiratory health studies [
El Paso and Dallas are in the US state of Texas. El Paso is on the western tip of Texas and sits between the Rio Grande River and the Franklin Mountains. The Rio Grande River is part of the US-Mexico border region; Ciudad Juárez, Mexico’s fourth largest city, is adjacent to El Paso. Dallas, part of the Dallas-Fort Worth metroplex, is in north-central Texas and has flat terrain. Detroit, MI and Cleveland, OH are Great Lakes cities with heavy industry such as automobile and iron and steel production and have flat to gently-rolling terrain; Detroit is a US-Canada border city adjacent to Windsor, ON. As encountered for many urbanized areas, mobile sources are a major source of air pollution in the four cities.
LURs were constructed separately in the four cities. A GIS platform was used to develop predictor variables to be used in the regression analyses and to select monitoring sites. In the LURs, the general groups of variables were distance to roadways, traffic intensity, population density, land use, emissions levels, and city-specific variables such as distance to border crossings or distance to Lake Erie (Table
A large number of GIS variables (typically > 40) were developed from the databases. For use in the LURs, potential explanatory variables were selected within their appropriate variable group to exhibit a reasonable amount of variability across the geographic study area and have low correlation with other potential predictors. To select the variables, separate correlation analyses for variable groups were conducted, and the correlations were examined between variables from different types of groups (e.g., population density and traffic intensity). Table
Variables chosen as potential predictors were also used to select monitoring locations. In the LURs, schools or fire stations were used to represent neighborhood-scale, ambient exposures. Such sites had secure, free air-flow sampling locations, and similar sampling heights of approximately 1.5–2 m. Sites were ranked on each potential predictor and ultimately selected based on their joint predictor variable ranges and variabilities. Chosen sites had similar correlation structure among potential predictors as the unmonitored sites. Cluster analysis was also used to ensure that the chosen sites adequately covered the mathematical space defined by the potential predictors. (The mathematical space is established by the variables’ ranges and their overall correlation structure.) Figure
Median pollutant concentrations (all above method detection limits) in the four citiesa.
Pollutant | El Paso (22 schools) | Detroit (25 schools) | Dallas (24 fire stations) | Cleveland (22 fire stations) |
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NO2 | 22 (11, 37) | 16 (11, 24) | 12 (4, 25)b | 10 (2, 29)d |
14 (2, 22)c | 18 (0, 25)e | |||
Benzene | 777 (489, 1531) | 466 (338, 698) | 232 (83, 388)b | Not measured |
357 (247, 538)c |
aMedians calculated over all sites and weeks. Units for NO2 in ppb; benzene in ppt. Minimum and maximum values in parentheses.
bSummer 2006.
cWinter 2008.
dSummer 2009.
eWinter 2010.
Example of El Paso school sites chosen (red) to be representative of all other school sites (green) for the variables of distance to petroleum facility point source (OIL_DIST, m), distance to nearest road segment
Passive sampling methods, which are typically employed in LUR studies since they are field portable and economical, were used. NO2 was sampled with Ogawa badges (Ogawa & Co., Pompano Beach, FL, USA). VOC samples were collected using 3 M OVM samplers in El Paso and PE tubes packed with Carbopack X sorbent (Supelco, Inc., Bellefonte, PA, USA) in Detroit and Dallas; no passive VOCs were collected in Cleveland. (Ammonia and passive aerosol sampling were conducted in Cleveland; see [
Passive samplers were deployed for week-long sampling integrals to represent chronic exposures. During the given studies, passive samples were deployed concurrently at all sites. Monitoring time frames typically lasted five weeks during a season; however, sampling in El Paso lasted two weeks. Ambient monitoring was conducted in El Paso in November/December 1999, Detroit in summer 2005, Dallas in summer 2006 and winter 2008, and Cleveland in summer 2009 and winter 2010. All samplers were deployed concurrently during study periods and housed in appropriate shelters. Further details on the field sampling and lab analysis methods are presented elsewhere [
Summary statistics of air pollution data at monitoring sites from the four cities are shown in Table
Based on visual inspection of plots of the air pollution data versus predictor variables and residual analyses, multiple linear regression models were used for LURs in Detroit, Dallas, and Cleveland, and semiparametric regressions (as generalized additive models) were applied in El Paso. Significant variables (5% level) and model predictive capacity (as
Model
Model |
El Paso | Detroit | Dallas | Cleveland | |||
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NO2 | benzene | NO2 | benzene | NO2 | benzene | NO2 | |
97 | 93 | 82 | 43 | 34a/48b | 72a/49b | 96 | |
Distance to nearest low traffic road | |||||||
Distance to nearest medium traffic road |
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Distance to nearest high traffic road | ▲d |
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Traffic intensity within set buffers | ▲ | ▲/▲ | ▲/ | ▲ | |||
Length of local roads within set buffers |
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Length of secondary roads within set buffers |
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Open area within set radii |
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Population density within census block group or set radii | ▲ | ▲ | ▲ | ||||
Point source (categorical or continuous) |
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♦f | |
Elevation | |||||||
Distance to nearest international border crossing |
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Season | ♦ | ||||||
Seasonal interaction of point source and population density categories | ♦ |
aSummer.
bWinter.
cSignificant (5% level) decrease.
dSignificant (5% level) increase.
eDecrease followed by increase.
fCategorical variables (significant 5% level).
As shown in Table
Table
These apparent inconsistencies may in part be due to characteristics of the local road networks within the cities, partly to the varying definition of the predictor variables between the cities, and seasonal effects. For example, in Detroit and Dallas, NO2 levels are influenced by distance to both medium and high traffic volume roads, but the effects are in opposite directions in the two cities. In Detroit, NO2 increases as distance to a high traffic road increases but decreases the farther from a medium traffic road; however, in Dallas, the roles of the roadways are reversed in summer. This may simply be a reflection of both the overall numbers of medium and high traffic roads as well as their relative locations in the two cities. Seasonal effects were an influencing factor in Cleveland LURs. Dallas may have indicated seasonal effects, but the seasonal data were from different years. El Paso and Detroit sampling was for only one season. Data collection in these from two seasons within the same year may have tempered the inconsistencies noted above.
Another factor contributing to the apparent inconsistencies in Table
LUR modeling revealed spatial gradients for all pollutants. For example, NO2 was generally higher in downtown, industrial, central valley, and high traffic areas of cities where such emission activities would be located (Figure
LUR predicted NO2 concentrations: (a) El Paso; (b) Detroit; (c) Dallas summer; (d) Dallas winter; (e) Cleveland (average of summer and winter). NO2 gradients are the same scale in all cities for comparison.
Transferability of LURs to different study areas has been suggested as a cost-effective alternative to developing new LURs; LURs transferred to similar types of cities have been evaluated with limited success [
To this end, we evaluated common variables considered for LURs in Detroit and Cleveland to determine whether LUR variables had similar values that could be transferable between the two cities. We did this comparison using these cities since they were geographically similar and had similar emission sources. NO2 was used with the variables to evaluate distribution. Summer data from Cleveland were compared with Detroit measurements that were also collected during summer.
Figures
NO2 concentration using common variables in Detroit (D) and Cleveland (C).
LURs were successfully developed from passive sampling networks in the four cities. Considered conjointly, the studies confirm flexibility and universality of traffic and other urban source variables in LURs for predicting air pollutant concentrations. As with the measured pollutants, predictor variables should be collected from the local study area for reliable spatial predictions.
Gaseous air pollutants were generally similar across the cities, but higher levels in El Paso may have been due to complex terrain concentrating pollutants from El Paso and Ciudad Juárez. Traffic, point source, and population counts were important predictors in the LURs despite major differences in geographic characteristics between the four cities. These variable groups were similar to those used in other LURs [
LURs were developed during their respective monitoring periods, and prior experience was used to inform the subsequent efforts. For example, traffic variables in El Paso, Detroit and Dallas used distance to roads carrying various vehicle counts and traffic intensity. In Cleveland, categories of local and secondary road lengths within various buffers were added to better capture the potential total impact of traffic. Point source emission variables in El Paso, Detroit, and Dallas included the distance from the nearest large emitters of a given pollutant; this was revised for Cleveland by using emissions densities within various buffer sizes, thus incorporating all available emissions information.
The potential of differential seasonal impacts was explored in Dallas, though unfortunately the relatively large gap between the actual field monitoring periods precluded a definitive conclusion regarding potential seasonal effects. In Cleveland, however, season was explicitly used as a predictor itself and as an interacting factor with other predictors. El Paso and Detroit LURs could not use season as a predictor since data were only measured during winter and summer, respectively.
Finally, common types of predictor variables can be applicable in LURs from city to city. However, coefficients in LUR models can be significant or not, and even common significant predictor variables (e.g., distance to nearest road) can have opposite effects depending on city-to-city differences in source and pollutant measures. In addition, transferability of variables or LURs from one city to another may be problematic due to differences in how GIS data are defined. Differences in roadway characteristics may not be incorporated into the definition of the predictor variables. For example, considerations such as elevated and depressed roadways, tunnels, or overpasses were not considered in defining the GIS variables used in these four cities. In addition, it was noted that the definition of local and secondary road categories was different between Detroit and Cleveland, despite their geographic and emission similarities. Though extracted from the same standard ArcGIS databases routinely used to develop road network variables for LURs, it was apparent that different criteria had been used to categorize roads as local or secondary in the two cities. Inherent misclassification of roads could only be rectified by transportation surveys. Caution should be exercised when evaluating similarities or differences of such variables from city to city. Another complicating factor for the transferability question is the importance of city-specific factors; for example, El Paso and Detroit have border crossings unlike Dallas and Cleveland.
In conclusion, neighborhood-scale spatial gradients were encountered in the pollutants confirming the influence of traffic and other urban influences. Traffic and other urban variables were important predictors in the LURs although city-specific influences and season of the year may also be important. However, transferability of specific variables or LUR predictive equations from one city to another may be problematic due to intercity differences and data availability or comparability. Thus developing common predictors in future LURs may be difficult.
For the roles in various aspects of these studies, the authors thank the following: Kuenja Chung of EPA Region 6; Karen Oliver and Davyda Hammond of EPA/ORD; Casson Stallings, Hunter Daughtrey, David Welch, Dennis Williams, Mariko Porter, and Mike Wheeler of Alion Science and Technology. They also thank the Dallas and Cleveland area fire departments and the El Paso and Detroit area schools for allowing us to use their properties. They thank Michael Breen and David Olson of EPA/ORD for reviewing the paper. The US Environmental Protection Agency through its Office of Research and Development funded and managed the research described here under contract EP-D-10-070 to Alion. The paper has been subjected to Agency review and approved for publication. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use.