Seasonal effects in land use regression models for nitrogen dioxide, coarse particulate matter, and gaseous ammonia in Cleveland, Ohio

Mukerjee, Shaibal; Willis, Robert D.; Walker, John S.; Hammond, Davyda; Norris, Gary A.; Smith, Luther; Welch, David P.; Peters, Thomas M.

doi:10.5094/apr.2012.039

Cited by 20 publications

(26 citation statements)

References 33 publications

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“…Higher NO 2 wintertime concentrations have also been found in other LUR studies in Dallas and Cleveland during winter and summer seasons using the same sampling methods (Smith et al, 2011, Mukerjee et al, 2011). Both the 2006–7 and 1999 LURs were developed using NO 2 concentrations measured during the cool season months and their NO 2 predictions were most highly correlated for these months (ρ = 0.91).…”

Section: Discussionsupporting

confidence: 73%

“…The over prediction could be partially explained by overall lower regional NO 2 ambient levels in 2006, as reported by the EPA National Air Trends website. Nonetheless, explicit distinction between warm and cool seasons in the LURs developed here has also been found to be significant in LURs developed in Cleveland (Mukerjee et al, 2011). If the health issue being studied has a seasonal aspect, it would be beneficial for the corresponding LURs to account for this distinction.…”

Section: Discussionmentioning

confidence: 58%

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Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Gonzales¹,

Myers²,

Smith³

et al. 2012

Science of The Total Environment

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Developing suitable exposure estimates for air pollution health studies is problematic due to spatial and temporal variation in concentrations and often limited monitoring data. Though land use regression models (LURs) are often used for this purpose, their applicability to later periods of time, larger geographic areas, and seasonal variation is largely untested. We evaluate a series of mixed model LURs to describe the spatial-temporal gradients of NO2 across El Paso County, Texas based on measurements collected during cool and warm seasons in 2006–2007 (2006–7). We also evaluated performance of a general additive model (GAM) developed for central El Paso in 1999 to assess spatial gradients across the County in 2006–7. Five LURs were developed iteratively from the study data and their predictions were averaged to provide robust nitrogen dioxide (NO2) concentration gradients across the county. Despite differences in sampling time frame, model covariates and model estimation methods, predicted NO2 concentration gradients were similar in the current study as compared to the 1999 study. Through a comprehensive LUR modeling campaign, it was shown that the nature of the most influential predictive variables remained the same for El Paso between the 1999 and 2006–7. The similar LUR results obtained here demonstrate that, at least for El Paso, LURs developed from prior years may still be applicable to assess exposure conditions in subsequent years and in different seasons when seasonal variation is taken into consideration.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 58%

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Gonzales¹,

Myers²,

Smith³

et al. 2012

Science of The Total Environment

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“…(Eeftens et al 2012b). In a recent study from Ohio, Mukerjee et al (2012) reported an R 2 of 0.78 for their LUR model. Because this was not derived from cross-validation in which some data are withheld from the model-building step for validation, however, the predictive power would be inflated due to overfitting.…”

Section: Discussionmentioning

confidence: 99%

Characterizing Spatial Patterns of Airborne Coarse Particulate (PM _10–2.5 ) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis

Zhang

Larson²,

Gassett³

et al. 2014

Environ Health Perspect

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Background: The long-term health effects of coarse particular matter (PM10–2.5) are challenging to assess because of a limited understanding of the spatial variation in PM10–2.5 mass and its chemical components.Objectives: We conducted a spatially intensive field study and developed spatial prediction models for PM10–2.5 mass and four selected species (copper, zinc, phosphorus, and silicon) in three American cities.Methods: PM10–2.5 snapshot campaigns were conducted in Chicago, Illinois; St. Paul, Minnesota; and Winston-Salem, North Carolina, in 2009 for the Multi-Ethnic Study of Atherosclerosis and Coarse Airborne Particulate Matter (MESA Coarse). In each city, samples were collected simultaneously outside the homes of approximately 40 participants over 2 weeks in the winter and/or summer. City-specific and combined prediction models were developed using land use regression (LUR) and universal kriging (UK). Model performance was evaluated by cross-validation (CV).Results: PM10–2.5 mass and species varied within and between cities in a manner that was predictable by geographic covariates. City-specific LUR models generally performed well for total mass (CV R2, 0.41–0.68), copper (CV R2, 0.51–0.86), phosphorus (CV R2, 0.50–0.76), silicon (CV R2, 0.48–0.93), and zinc (CV R2, 0.36–0.73). Models pooled across all cities inconsistently captured within-city variability. Little difference was observed between the performance of LUR and UK models in predicting concentrations.Conclusions: Characterization of fine-scale spatial variability of these often heterogeneous pollutants using geographic covariates should reduce exposure misclassification and increase the power of epidemiological studies investigating the long-term health impacts of PM10–2.5.Citation: Zhang K, Larson TV, Gassett A, Szpiro AA, Daviglus M, Burke GL, Kaufman JD, Adar SD. 2014. Characterizing spatial patterns of airborne coarse particulate (PM10–2.5) mass and chemical components in three cities: the Multi-Ethnic Study of Atherosclerosis. Environ Health Perspect 122:823–830; http://dx.doi.org/10.1289/ehp.1307287

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“…Mukerjee et al (2012), Reche et al (2012), Zhu et al (2011), andVallius et al (2004) examine the measurement studies carried out only one period (summer or winter) during different periods. Similarly, these studies suggest that the level of air pollution differs according to the measurement period.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The results of these studies differ according to the size of study area, land use, meteorological and topographical factors and population density in the regions. Fan et al (2012), Mansha et al (2012), Mukerjee (2012), Pits et al (2008), Moore et al (2007) and Querola et al (2001) focus on urban studies which observe the level of urban air pollution in different cities all around the world. They suggest that the concentrations of air pollutants in urban areas increase various factors such as because of dense building and population, widespread use of automobiles instead of public transportation, the location of industrial facilities near city centers and the negative effects of land use decisions on the level of air quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Examination of the Relationship between Urban Air Pollution and Urban Planning Decisions in Cigli Case, Izmir (Turkey)

Özcan¹,

Cubukcu²

2016

E-BPJ

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The air pollution problem remains, although significant improvements have been seen in urban air quality over the last years. Despite the size and variety of studies on urban air pollution, the usage of spatial statistics has been extremely limited. This study aims to show that the spatial statistic techniques can well be used to examine and explain the air pollution levels in urban areas. The data for the sulphur dioxide (SO2) and nitrogen dioxide (NO2) concentrations are measures using passive diffusion tubes at the 67 monitoring sites in the district of Çiğli (Izmir), which are selected through a spatial systematic sampling process. The tubes were exposed for a 2-week period in August 2015. For the determination of the level of clustering for high values and low values of SO2 and NO2 concentrations, Getis-Ord G* local statistics are calculated. There are five points with high values of SO2 surrounded by low values, three points of NO2, where the results are statistically significant at the 0.10 level. The findings indicate that the presence of the industrial zone, the form of fossil fuels (coal or natural gas) used in heating, and topography are the strong determinants urban air pollution.

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Seasonal effects in land use regression models for nitrogen dioxide, coarse particulate matter, and gaseous ammonia in Cleveland, Ohio

Cited by 20 publications

References 33 publications

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Characterizing Spatial Patterns of Airborne Coarse Particulate (PM _10–2.5 ) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis

Examination of the Relationship between Urban Air Pollution and Urban Planning Decisions in Cigli Case, Izmir (Turkey)

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Seasonal effects in land use regression models for nitrogen dioxide, coarse particulate matter, and gaseous ammonia in Cleveland, Ohio

Cited by 20 publications

References 33 publications

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

Characterizing Spatial Patterns of Airborne Coarse Particulate (PM 10–2.5 ) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis

Examination of the Relationship between Urban Air Pollution and Urban Planning Decisions in Cigli Case, Izmir (Turkey)

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Characterizing Spatial Patterns of Airborne Coarse Particulate (PM _10–2.5 ) Mass and Chemical Components in Three Cities: The Multi-Ethnic Study of Atherosclerosis