Application of Land Use Regression to Estimate Long-Term Concentrations of Traffic-Related Nitrogen Oxides and Fine Particulate Matter

Henderson, Sarah B.; Beckerman, Bernardo; Jerrett, Michael; Bräuer, Michael

doi:10.1021/es0606780

Cited by 466 publications

(410 citation statements)

References 23 publications

Supporting

Mentioning

393

Contrasting

Unclassified

Order By: Relevance

“…By means of establishing a statistical relationship between pollutant concentrations measured at 20-100 sites and potential influencing factors such as land use, traffic, and population density, LUR is able to predict concentrations at unsampled locations throughout a given domain within the framework of GIS (Henderson et al 2007;Hoek et al 2008). LUR has achieved great success in predicting concentrations of major air pollutants, mainly in Europe and North America (Hoek et al 2008), yet it has seldom been applied to China (Li et al 2010a, b), one of the most seriously air-polluted regions in the world.…”

Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

“…In the absence of traffic intensity data, we used the length of specific road types as a reasonable proxy (Henderson et al 2007). Some studies (Dong et al 2014;Feng et al 2013) have shown that average traffic volume on major roads in Beijing is 97 vehicle/km, which is close to volume in the study implemented in New York (Ross et al 2007).…”

Section: Development Of Predictor Variablesmentioning

confidence: 99%

“…We used the model-building algorithm developed by Henderson et al (2007). A priori assumptions of a required sign of regression coefficients for specific variables (e.g., the coefficient of road length is positive to PM 2.5 concentrations) were made so as to increase the applicability of LUR models beyond the monitoring sites (Henderson et al 2007;Hoek et al 2008), which is described in Table 1.…”

Section: Model Development and Evaluationmentioning

confidence: 99%

“…In order to limit the estimated concentrations to a reasonable range, predicted concentrations at the low and high ends were truncated at a certain percentage of measured maximum/minimum values in some LUR studies (Abernethy et al 2013;Amini et al 2014;Henderson et al 2007). There is no rigorous standard for cutting the range of predicted concentrations.…”

Section: Regression Mapping and Population Exposurementioning

confidence: 99%

See 3 more Smart Citations

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

View full text Add to dashboard Cite

Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

show abstract

Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

Section: Development Of Predictor Variablesmentioning

confidence: 99%

Section: Model Development and Evaluationmentioning

confidence: 99%

Section: Regression Mapping and Population Exposurementioning

confidence: 99%

See 2 more Smart Citations

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

View full text Add to dashboard Cite

show abstract

“…Jerrett et al (2005b) review several exposure modeling approaches that may be useful for specific traffic-related pollutants that exhibit strong gradients within urban areas, such as in congested urban centers or downwind of major highways. One such modeling approach currently receiving attention is ''land-use regression'' (Henderson et al, 2007;Jerrett et al, 2007;Moore et al, 2007;Rosenlund et al, 2007a), a method intended to predict local variations in specific air quality measures by implicitly accounting for the types of local sources and sinks typically found within a city. However, each type of pollution source (traffic, space heating, major industrial point sources) is characterized by a different group of co-emitted pollutants, so that although the resulting spatial map of the target pollutant is intended to represent that pollutant (alone), it actually includes all of the copollutants for the mix of source types involved.…”

Section: Exposures Based On ''Traffic-related'' Ambient Air Qualitymentioning

confidence: 99%

On exposure and response relationships for health effects associated with exposure to vehicular traffic

Lipfert

Wyzga

2008

J Expo Sci Environ Epidemiol

View full text Add to dashboard Cite

This work examines various metrics and models that have been used to estimate long-term health effects of exposure to vehicular traffic. Such health impacts may include effects of air pollution due to emissions of combustion products and from vehicle or roadway wear, of noise, stress, or from socioeconomic effects associated with preferred residential locations. Both categorical and continuous exposure metrics are considered, typically for distances between residences and roadways, or for traffic density or intensity. It appears that continuous measures of exposure tend to yield lower risk estimates that are also more precise than categorical measures based on arbitrary criteria. The selection of appropriate exposure increments to characterize relative risks is also important in comparing pollutants and other agents. Confounding and surrogate variables are also important issues, since studies of traffic proximity or density cannot identify the specific agents related to traffic exposures that might be responsible for the various health endpoints that have been implicated. Studies based on ambient air quality measurements are necessarily restricted to species for which data are available, some of which may be serving as markers for the actual agents of harm. Studies based on modeled air quality are limited by the accuracy of mobile source emission inventories, which may not include poorly maintained (high emitting) vehicles. Additional exposure modeling errors may result from precision limitations of geocoding methods. Studies of the health effects of traffic are progressing from establishing the existence of relationships to describing them in more detail, but effective remedies or control strategies have generally not yet been proposed in the context of these epidemiological studies. Resolution of these dose-response uncertainties is important for the development of effective public health strategies for the future.

show abstract

Association of Prenatal Exposure to Air Pollution With Autism Spectrum Disorder

et al. 2019

Self Cite

View full text Add to dashboard Cite

IMPORTANCEThe etiology of autism spectrum disorder (ASD) is poorly understood, but prior studies suggest associations with airborne pollutants.OBJECTIVE To evaluate the association between prenatal exposures to airborne pollutants and ASD in a large population-based cohort. DESIGN, SETTING, AND PARTICIPANTSThis population-based cohort encompassed nearly all births in Metro Vancouver, British Columbia, Canada, from 2004 through 2009, with follow-up through 2014. Children were diagnosed with ASD using a standardized assessment with the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule. Monthly mean exposures to particulate matter with a diameter less than 2.5 μm (PM 2.5 ), nitric oxide (NO), and nitrogen dioxide (NO 2 ) at the maternal residence during pregnancy were estimated with temporally adjusted, high-resolution land use regression models. The association between prenatal air pollution exposures and the odds of developing ASD was evaluated using logistic regression adjusted for child sex, birth month, birth year, maternal age, maternal birthplace, and neighborhood-level urbanicity and income band. Data analysis occurred from June 2016 to May 2018.EXPOSURES Mean monthly concentrations of ambient PM 2.5 , NO, and NO 2 at the maternal residence during pregnancy, calculated retrospectively using temporally adjusted, high-resolution land use regression models. MAIN OUTCOMES AND MEASURESAutism spectrum disorder diagnoses based on standardized assessment of the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule. The hypothesis being tested was formulated during data collection. RESULTSIn a cohort of 132 256 births, 1307 children (1.0%) were diagnosed with ASD by the age of 5 years. The final sample size for the PM 2.5 -adjusted model was 129 439 children, and for NO and NO 2 , it was 129 436 children; of these, 1276 (1.0%) were diagnosed with ASD. Adjusted odds ratios for ASD per interquartile range (IQR) were not significant for exposure to PM 2.5 during pregnancy (1.04 [95% CI, 0.98-1.10] per 1.5 μg/m 3 increase [IQR] in PM 2.5 ) or NO 2 (1.06 [95% CI, 0.99-1.12] per 4.8 ppb [IQR] increase in NO 2 ) but the odds ratio was significant for NO (1.07 [95% CI, 1.01-1.13] per 10.7 ppb [IQR] increase in NO). Odds ratios for male children were 1.04 (95% CI, 0.98-1.10) for PM 2.5 ; 1.09 (95% CI, 1.02-1.15) for NO; and 1.07 (95% CI, 1.00-1.13) for NO 2 . For female children, they were for 1.03 (95% CI, 0.90-1.18) for PM 2.5 ; 0.98 (95% CI, 0.83-1.13) for NO; and 1.00 (95% CI, 0.86-1.16) for NO 2 .CONCLUSIONS AND RELEVANCE In a population-based birth cohort, we detected an association between exposure to NO and ASD but no significant association with PM 2.5 and NO 2 .

show abstract

Application of Land Use Regression to Estimate Long-Term Concentrations of Traffic-Related Nitrogen Oxides and Fine Particulate Matter

Cited by 466 publications

References 23 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

On exposure and response relationships for health effects associated with exposure to vehicular traffic

Association of Prenatal Exposure to Air Pollution With Autism Spectrum Disorder

Contact Info

Product

Resources

About