Intraurban Variation of Fine Particle Elemental Concentrations in New York City

Few past studies have collected and analyzed within-city variation of fine particulate matter (PM_2.5) elements. We developed land-use regression (LUR) models to characterize spatial variation of 15 PM_2.5 elements collected at 150 street-level locations in New York City during December 2008–November 2009: aluminum, bromine, calcium, copper, iron, potassium, manganese, sodium, nickel, lead, sulfur, silicon, titanium, vanadium, and zinc. Summer- and winter-only data available at 99 locations in the subsequent 3 years, up to November 2012, were analyzed to examine variation of LUR results across years. Spatial variation of each element was modeled in LUR including six major emission indicators: boilers burning residual oil; traffic density; industrial structures; construction/demolition (these four indicators in buffers of 50 to 1000 m), commercial cooking based on a dispersion model; and ship traffic based on inverse distance to navigation path weighted by associated port berth volume. All the elements except sodium were associated with at least one source, with <i>R</i>² ranging from 0.2 to 0.8. Strong source-element associations, persistent across years, were found for residual oil burning (nickel, zinc), near-road traffic (copper, iron, and titanium), and ship traffic (vanadium). These emission source indicators were also significant and consistent predictors of PM_2.5 concentrations across years

Additional file 1: Table S1 of The associations between daily spring pollen counts, over-the-counter allergy medication sales, and asthma syndrome emergency department visits in New York City, 2002-2012

Correlation matrix of log-transformed tree pollens, weather, and air pollution variables. Figure S1: Year-round daily time-series of over-the-counter allergy medications sales, asthma ED visits syndrome, and log-transformed ash pollen concentrations. Figure S2: Year-to-year average peak date correspondence between tree pollens and OTC allergy medication sales. Figure S3: Individual lag days’ rate ratios for OTC allergy medication sales and asthma syndrome ED visits. Figure S4: Sensitivity analysis using alternative pollen metrics for OTC allergy medication sales and asthma syndrome ED visits. Figure S5. Sensitivity analysis using alternative model specifications for OTC allergy medication sales and asthma syndrome ED visits. (PDF 1467 kb

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM_2.5 in the Contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM_2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM_2.5, land use and traffic indicators. Normalized cross-validated <i>R</i>² values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated <i>R</i>² were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM_2.5 at multiple scales over the contiguous U.S

A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM_2.5 in the Contiguous United States

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM_2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM_2.5, land use and traffic indicators. Normalized cross-validated <i>R</i>² values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated <i>R</i>² were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM_2.5 at multiple scales over the contiguous U.S

Characteristics of the sample of people who inject drugs (PWID), drawn from the 2009 Centers for Disease Control and Prevention’s National HIV Behavior Surveillance.

<p>Characteristics of the sample of people who inject drugs (PWID), drawn from the 2009 Centers for Disease Control and Prevention’s National HIV Behavior Surveillance.</p

Components Generated by the Principal Components Analysis.

<p>Components Generated by the Principal Components Analysis.</p

Bivariate associations between HIV negative status and participant characteristics¹.

<p>Bivariate associations between HIV negative status and participant characteristics<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150410#t006fn001" target="_blank">¹</a>.</p

Characteristics of the ZIP Codes (N = 968), Counties (N = 51), and Metropolitan Statistical Areas (MSAs; N = 19) where the 9,077 participants of the 2009 Centers for Disease Control and Prevention’s National HIV Behavioral Surveillance Lived, by Racial/Ethnic Group¹.

<p>Characteristics of the ZIP Codes (N = 968), Counties (N = 51), and Metropolitan Statistical Areas (MSAs; N = 19) where the 9,077 participants of the 2009 Centers for Disease Control and Prevention’s National HIV Behavioral Surveillance Lived, by Racial/Ethnic Group<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150410#t004fn001" target="_blank">¹</a>.</p

Multivariable hierarchical generalized linear models regressing the odds of HIV negative status on characteristics of the environments where people who inject drugs (N = 9,077) lived when participating in the National HIV Behavioral Surveillance in 2009¹.

<p>Multivariable hierarchical generalized linear models regressing the odds of HIV negative status on characteristics of the environments where people who inject drugs (N = 9,077) lived when participating in the National HIV Behavioral Surveillance in 2009<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150410#t007fn001" target="_blank">¹</a>.</p

Bivariate associations between HIV negative status and (a) features of the environments where people who inject drugs (N = 9,077) lived when participating in the 2009 National HIV Behavioral Surveillance, and (b) participant characteristics¹.

<p>Bivariate associations between HIV negative status and (a) features of the environments where people who inject drugs (N = 9,077) lived when participating in the 2009 National HIV Behavioral Surveillance, and (b) participant characteristics<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0150410#t005fn001" target="_blank">¹</a>.</p