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Intra-urban variation of fine particle elemental concentrations in New York City Kazuhiko Ito, Sarah Johnson, Iyad Kheirbek, jane clougherty, Grant Pezeshki, Zev Ross, Holger Eisl, and Thomas D. Matte Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b00599 • Publication Date (Web): 22 Jun 2016 Downloaded from http://pubs.acs.org on June 22, 2016

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Environmental Science & Technology

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Title: Intra-urban variation of fine particle elemental concentrations in New York City

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Kazuhiko Ito*,†, Sarah Johnson†, Iyad Kheirbek†, Jane Clougherty‡, Grant Pezeshki†, Zev Ross§,

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Holger Eisl∥, Thomas D. Matte†

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†

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Surveillance and Policy, New York, NY 10013.

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‡

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School of Public Health, Pittsburgh, PA 15219.

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§

New York City Department of Health and Mental Hygiene, Bureau of Environmental

Department of Environmental and Occupational Health, University of Pittsburgh, Graduate

ZevRoss Spatial Analysis, Ithaca, NY 14850.

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∥Barry

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New York, Flushing, NY 11367.

Commoner Center for Health and the Environment, Queens College, City University of

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1 ACS Paragon Plus Environment


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ABSTRACT

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Few past studies have collected and analyzed within-city variation of fine particulate matter

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(PM2.5) elements. We developed land-use regression (LUR) models to characterize spatial

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variation of fifteen PM2.5 elements collected at 150 street-level locations in New York City

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during December 2008 - November 2009: aluminum, bromine, calcium, copper, iron, potassium,

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manganese, sodium, nickel, lead, sulfur, silicon, titanium, vanadium, and zinc. Summer- and

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winter-only data available at 94 locations in the subsequent 3 years, up to November 2012, were

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analyzed to examine variation of LUR results across years. Spatial variation of each element was

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modeled in LUR including six major emission indicators: boilers burning residual oil; traffic

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density; industrial structures; construction/demolition (these four indicators in buffers of 50 to

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1000m), commercial cooking based on a dispersion model; and ship traffic based on inverse

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distance to navigation path weighted by associated port berth volume. All the elements except

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sodium were associated with at least one source, with R-squared ranging from 0.2 to 0.8. Strong

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source-element associations, persistent across years, were found for: residual oil burning (nickel,

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zinc); near-road traffic (copper, iron, and titanium); and, ship traffic (vanadium). These emission

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source indicators were also significant and consistent predictors of PM2.5 concentrations across

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years.

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INTRODUCTION As an increasing number of observational epidemiological studies have reported

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associations between particulate matter (PM) and adverse health outcomes, identifying PM

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chemical component(s) responsible for the observed effects has become a high-priority research

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area.1,2 In the U.S., two national air quality network programs established by the Environmental

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Protection Agency (EPA) have been collecting fine particle (PM2.5) chemical components

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including major anions, carbonaceous material, and trace elements in both urban and rural areas,3

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and these data have been used for epidemiological investigations. 4,5,6,7,8 However, since fine

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particle composition may vary within a city, as do population characteristics associated with

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susceptibility, the low density of sites in these networks (i.e., several sites in a city, at most),

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limits the ability to study the relative importance of PM2.5 mass and its components.9,10

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Only a limited number of studies have conducted land-use regression (LUR) analysis to

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model within-city variation of PM2.5 elements to date.11,12,13,14, 15In New York City (NYC),

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source apportionment studies analyzing temporal variations of EPA’s chemical speciation data

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found that a major fraction of PM2.5 mass was regional sulfate and nitrate but also identified

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contributions from local traffic and residual oil burning.9,16,17 A study of PM2.5 elements

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measured outside 10 NYC apartment buildings found that nickel (Ni) and vanadium (V)

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exhibited different spatial and seasonal patterns, and the authors speculated that, while space-

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heating boilers were the major source for Ni, ship emissions from the Port of New York were a

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likely source of V.18 Based on EPA’s National Emissions Inventory (NEI),19 we also expected

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significant contributions to PM2.5 mass from other emission sources such as construction dust

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and commercial cooking in NYC. Since 2008, NYC Department of Health and Mental Hygiene

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(NYCDOHMH) has measured PM2.5 and gaseous pollutants at up to 155 locations in NYC to 3 ACS Paragon Plus Environment


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characterize intra-urban variation in major sources and to monitor the relationships between the

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changes in emissions and ambient concentrations.20 The first winter’s data for PM2.5, black

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carbon, nitrogen dioxide, and sulfur dioxide have been characterized in LUR models.21 In this

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study, we develop LUR models for PM2.5 elements, to better assess spatial variation and

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important source contributions.

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MATERIALS AND METHODS

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PM2.5 Elements Data. Details of the design and implementation of New York City

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Community Air Survey (NYCCAS) can be found elsewhere.20 The basic study design follows

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those in the past studies,22,23 including sampling at multiple “distributed” sites to measure within-

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city variation of air pollution at intermittent frequencies (for feasibility) while sampling

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continuously at a “reference” site(s) to adjust for the influence from temporal factors (e.g.,

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temperature inversion). Briefly, 150 street-level (distributed) sites were chosen to represent a

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range of traffic, land-use and other characteristics. The number of street-level sampling sites was

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reduced to 100 in the 3rd year. Integrated samples are collected at each street-level site for one

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two-week session each season and in every two-week period at five reference locations. The five

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reference sites were located in parks or on rooftops away from major roadways in each of the

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five boroughs, to track city-wide temporal variation (see “Temporal Adjustment” in the method

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section). The PM2.5 filters were analyzed for 50 elements using X-ray fluorescence (XRF) by

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Desert Research Institute for the first full year of data (December 2008 through November 2009)

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and for the winter (late December to February) and summer (June through August) periods

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starting second year. Of the 50 elements (see the complete list of elements in Supplemental

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Information, List S1), we retained 15 elements with 70% of measurements above the reported

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sample uncertainty levels (which incorporate variability in lab blank measurements and the low 4 ACS Paragon Plus Environment

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concentration standards) that are of interest for source-element associations: aluminum (Al),

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bromine (Br), calcium (Ca), copper (Cu), iron (Fe), potassium (K), manganese (Mn), sodium

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(Na), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), titanium (Ti), vanadium (V), and zinc (Zn).

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We focused our main LUR analysis on the first year of PM2.5 element data because they

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were available for the full year at 150 sites and because most of the emission indicators were

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developed for the first year. However, to characterize changes in PM2.5 element concentrations

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and correlation of their spatial variation patterns over time, and to assess the consistency of LUR

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results across years, we also analyzed a subset 94 sites (of up to 100 possible sites available

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during the period) whose winter-only and summer-only data were complete across four years

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(December 2008 to November 2012). Thus, we also estimated the annual average spatial pattern

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by averaging the temporally-adjusted winter-only and summer-only data and applied LUR

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models to each of the four years (i.e., using the winter- and summer-only data from the 1st year

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for this subset analysis, for consistency). The locations of the 150 and 94 subset sites are shown

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in Supporting Information Figure S1.

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In Supporting Information Figure S2, we also briefly summarize the result from a limited

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study to measure coarse and fine elemental concentrations at 10 locations, to aid interpretation of

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the combustion vs. non-combustion related elements.

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Ratios of PM2.5 elements for which emission factors have been reported in past studies

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were also examined for their relationship to emission indicators for interpretation of LUR results

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and shown in Supporting Information Figures S3, S4 and associated text.

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Spatial Emission Source Indicator Variables. Based on the PM2.5 LUR result of the

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first winter data21 and expected importance of sources to PM2.5 elements, we selected a set of

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available emission indicators: (1) the number of permitted combustion source for fuel type #4



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and 6 (i.e., residual oil); (2) kernel-weighted traffic density; and (3) land-use area of industrial

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structures, within 15 buffers of 50-1000m range around the monitoring site. In addition, we

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created two new emission indicators for other sources that we considered potentially important in

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explaining spatial variation of PM2.5 elements based on the 2008 NEI data19 for NYC:

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construction and demolition activity-related emissions and ship emissions. We also used

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estimates of contributions of commercial charbroiling to PM2.5 concentrations from data

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generated in a prior analysis of the health impacts of this source, which was conducted as part of

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an evaluation of sources considered in an update of the local air pollution control code. We

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describe development of these variables below.

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We created a spatial indicator for emissions associated with construction or demolition

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activities for a given year by computing the absolute difference in interior built space per square-

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foot of tax lot between the two annual releases encompassing that year. To compute the interior

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built space, we used the NYC Department of City Planning’s MapPLUTOTM data at 50 to 1000m

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buffers, assuming that both increasing and decreasing square footage of built space would be

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associated with construction-related emissions. For example, for the first year of data (for which

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sampling took place between December 2008 and November 2009), we used the 2009 and 2010

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annual release data.

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The ship emissions indicator was created by first developing a flow network of navigation

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channels in New York Harbor using the data from National Oceanic and Atmospheric

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Administration navigation charts, with a focus on North Atlantic traffic, then calculating the sum

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of upstream berth volume for all active ports based on National Transportation Atlas Database

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201024 within 2 miles of the navigation channel network. For each NYCCAS site, we computed


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the inverse distance to the closest navigation channel weighted by the sum of upstream berth

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volume at that point in the network.

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The PM2.5 levels associated with commercial charbroiling operations were estimated

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using an atmospheric dispersion model, AERMOD,25 to predict at receptors placed at XY

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coordinates of each NYCCAS site.

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first obtaining county-level primary PM2.5 emissions from the 2008 NEI19 for the under-fired

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charbroiling and conveyorized charbroiling categories. To better assess spatial distribution of

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these sources, NYCDOHMH restaurant inspectors administered a survey of 5,244 restaurants

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(23% of all city restaurants), collecting information on the number and grill area of under-fired

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and conveyorized char-broilers in restaurants of varying cuisine type. We then calculated total

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charbroiling area per restaurant by cuisine type and applied this to estimate charbroiling area for

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all of the 23,297 restaurants throughout the city, based on their cuisine type. The estimated

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restaurant-level charbroiling grill areas were then aggregated to 1-km by 1-km resolution grid

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cells and used to spatially allocate emissions by multiplying the county-level NEI emissions by

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the ratio of grill area in the grid cell by total grill area in the county. We assumed a uniform

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temporal emissions rate over the year and treated emissions as an area source. AERMOD was

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run for 5 summers of data, June 1 – August 31st, 2006-2010.

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We estimated emissions from commercial charbroiling by

Temporal Adjustment. Air pollution levels in a given two-week sampling period are

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influenced by prevailing weather conditions (e.g., temperature inversion leading to air stagnation

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and pollution buildup) during that period and by temporal variation of regional air pollutants

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(e.g., sulfate), which tend to be spatially uniform and can obscure within-city variation of air

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pollutants. The continuous two-week samples collected at the five reference sites were used to

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adjust the two-week samples collected at the 150 street-level sites, as was done in the past LUR 7 ACS Paragon Plus Environment


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studies.22,23 To account for such confounding temporal variations, we estimated the annual

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average level of individual PM2.5 elements at each site by fitting a generalized additive model

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(GAM) with session and site specific concentrations (including the five reference sites) as the

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dependent variable and a smooth function of the two-week session number (i.e., 1 through 25 in

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the full year) using a penalized splines in GAM as implemented in the statistical software R (R

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Foundation for Statistical Computing, Vienna, Austria) version 3.2.0 with the ‘mgcv’ package,26

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allowing up to 24 degrees of freedom and the unique site identifier (ID) as a factor. Allowing up

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to 24 degrees of freedom for the smooth function of time (session) ensured that even a single

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two-week period with a citywide high air pollution levels can be adjusted for in estimating the

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average spatial variation of a pollutant. Including the five reference sites’ data with continuous

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two-week measurements in the model also emphasized the influence of the citywide temporal

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pattern in the fit. The resulting coefficients for the site ID’s are thus the estimated annual

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average values. We used the estimated annual average values for the street-level sites as the

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dependent variable for LUR models. For the LUR analysis using the winter- and summer-only

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data for the 94 sites analysis, we applied the same temporal adjustment approach for each season

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but using up to six degrees of freedom for temporal splines.

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Land-Use Regression Model for the full first year data at 150 sites. We followed a

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similar LUR model building approach previously used in our analysis of the first winter PM2.5

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and gaseous pollutants21 and a full year analysis of PM2.5 and nitrogen dioxide for exposure

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assignment in a birth cohort.27 These LUR methods were built on the methods developed in

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previous studies.22,23 We have modified these analyses by considering three additional emission

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indicators described above and also used alternative methods for temporal adjustment of the raw

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data and fitting residual spatial auto-correlation in the LUR models, as described below. We first 8 ACS Paragon Plus Environment

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examined correlations of the PM2.5 elements with each emissions indicator at 50 to 1000 m

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buffers in the full first year data. Then, we chose one buffer distance for each of the buffer-based

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emission indicators that was most highly correlated with the elements of interest (e.g.,

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consistency with prior knowledge on signature element(s) of the source) for the subsequent LUR

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models. Using these buffer-specific emission indicators along with non-buffer based emission

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indicators, forward stepwise regression models were developed with the emission indicators

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entering the model in the following order based on their perceived a priori importance: (1) the

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number of boiler burning residual oil; (2) kernel-weighted traffic density; (3) percent of floor

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area used for industrial structures; (4) construction and demolition; (5) commercial cooking; and,

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(6) ship emissions.

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To be retained in the model, an emission indicator variable had to: (a) yield a positive

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regression coefficient; (b) be significant at alpha=0.05 level; (c) increase the model R-squared by

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at least two percent from the previous model; and, (d) yield a model with variance inflation

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factors for all the covariates less than 1.5. After this step, we examined regression residuals for

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their normality, outliers, and influential points. For each element, we identified any site that

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yielded Cook’s distance larger than one. We repeated the above forward regression steps by

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excluding these sites. We assessed the impacts of these “influential” sites in interpreting the

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results with all the sites in the model. To model any remaining spatial auto-correlation in the

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residuals, we re-fitted these models including a thin-plate smooth function of XY coordinates in

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a GAM, allowing up to 20 degrees of freedom. We evaluated spatial auto-correlation of the

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regression residuals using Moran’s I before and after the inclusion of the smooth term to ensure

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that there was no significant spatial auto-correlation in the final model. If the spatial auto-

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correlation was not substantive (i.e., p-value of Moran’s I > 0.2) in the model with emission 9 ACS Paragon Plus Environment


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indicators only, then the model was considered the final model without a spatial auto-correlation

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term. Including the spatial auto-correlation term attenuated the significance of the emission

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indicators in some cases, but we kept these indicators and considered the model to be final

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because the regression coefficients did not change substantively. To evaluate the robustness of

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these final LUR models, we conducted leave-one-out cross validation (LOOCV) and computed

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squared correlation of the observed values vs. LOOCV-predicted value (i.e., LOOCV R-

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squared). We also computed normalized mean square error and fractional bias of these LOOCV

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results.

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To summarize the LUR models across the PM2.5 elements, for each source emission

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indicator, we computed the change in the element’s concentration, as a percentage of its mean

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annual concentration, per one standard deviation change in the source emission indicator. For

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comparison, we repeated the same LUR procedure for PM2.5 mass concentrations, except for the

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condition that the predictor needs to yield a two percent incremental increase in R-squared,

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because of the smaller spatial variation of PM2.5 mass (due to large regional contributions)

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compared to its elements.

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To illustrate the spatial pattern of predicted PM2.5 elemental concentrations from the final

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LUR models, we predicted the elemental concentrations for Cu and Ni, the elements associated

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with two major sources of interest (i.e., residual oil burning from large buildings and traffic).

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Using the final LUR model specification and the predictors’ (emission indicators) values at 100

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m x 100 m lattice points within the city (there are 82,746 lattices), we predicted the elemental

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concentrations at these points. The resulting values were further smoothed using inverse distance

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weighting with power of two. We assigned levels of colors, yellow to dark brown, to the

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predicted concentrations at each lattice and created a scatter plot of XY coordinates of the lattice 10 ACS Paragon Plus Environment

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points with the assigned colors over the New York City boundary map. The map shape file was

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provided from New York City Department of Information Technology and Telecommunications.

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These figures were created in R using packages ‘maptools’, ‘sp’, and ‘GISTools’.

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Land-Use Regression Models for the winter/summer averages at 94 sites in Years 1

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through 4. Using the winter and summer-only PM2.5 elements data available for the 94 sites for

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years 2008-2012, we conducted LUR analyses for each of the four years using the average of the

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temporally-adjusted winter and summer data, assuming that the average values of the summer

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and winter average values represent the annual average values. For consistency, the Year 1 data

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in this set of analysis was the average of winter- and summer-only data, even though full year of

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data were available. To each of the four years, we applied the same LUR method as we used for

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the full year 2008-2009 data using the same buffer distance for the buffer-based emission

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indicators.

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RESULTS

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Of the 150 street-level sites, 20 sites had one missing session. The temporal adjustment

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regression models were run without imputing these missing values. In the temporal adjustment

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regression, most of the 15 elements exhibited strong temporal patterns, with the highest R-

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squared of 0.94 for S, clearly reflecting the seasonality of secondary sulfate (Table 1). The

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degree of smoothness of temporal variation ranged from 12 to 23 effective degrees of freedom

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per year (see Supporting Information, Figure S5, for fitted temporal variation). Ni, Ca, and Zn

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showed higher concentrations in winter, apparently reflecting increased residual oil burning for

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space heating. Table 2 shows the distribution of temporally-adjusted PM2.5 elements. The

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spatial coefficient of variation (C.V.) ranged from 0.07 (S) to 0.71 (Zn).



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Among the correlations between PM2.5 elemental concentrations and buffer-based

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emission indicators (Figure 1), Ni showed the strongest correlation with the residual oil

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indicator, Cu with the traffic density, Si with the percent industrial structure floor space, and K

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with the construction/demolition indicator. The pattern of correlation increase/decay as a

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function of buffer size varied across the emission indicators. For the residual oil indicator, the

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correlations increased for Ni and Zn as buffer size increased. In contrast, for traffic density, the

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correlations for Cu, Fe, and Ti were highest at 100m but decreased gradually as buffer size

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increased. For the percent of floor area for industrial structure, with the exception of Si (for

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which the correlation was higher at smaller buffer size), correlations were not dependent on the

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buffer distance. For construction/demolition, K, Mn, and Br showed the first peak at 250 m.

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Based on these observations, we chose one buffer size for each emission indicator to be included

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in the LUR model: 1000 m for residual oil; 100 m for traffic density (and thus this indicator may

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be called “near-road”); 150 m for percent floor area of industrial structure; and 250 m for

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construction/demolition. The mean and standard deviation of these emission indicators are

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shown in Supporting Information Table S1.

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The correlation among the emissions indicators (Supporting Information Table S2)

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ranged from -0.13 (residual oil vs. ships) to 0.47 (residual oil vs. construction/demolition). R-

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squared values for LUR models are also shown in Table 1. Na was the only element whose

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spatial variation was not significantly explained by any of the emission indicators or spatial auto-

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correlation. We did not specifically prepare a source indicator for sea salt because it was not policy

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relevant, and thus it is not possible to say that if sodium was associated with sea salt or not. However,

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the fact that no spatial auto-correlation was detected in Na spatial variation would suggest that a potential

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indicator of sea salt (e.g., distance to the ocean) is unlikely to explain Na spatial variation. The adjusted-


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R2 with these emission indicators ranged from 0.24 (Pb) to 0.75 (Ca). However, for Br, a site

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(near the construction site of the new World Trade Center at the time) with a high value was

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influential on its regression coefficient for the construction/demolition indicator. Removing this

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site from the Br model reduced its coefficient by nearly a factor of two and R2 from 0.43 to 0.23.

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Residual spatial auto-correlations were not significant for Br, Fe, Mn, and Ti. Including a

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spatial auto-correlation term increased the adjusted-R2 only modestly for most of the elements

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except Zn and also made some of the emission indicators (near-road and industrial structures for

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Pb; industrial structures and ship for PM2.5 mass) no longer significant. The LOOCV R-squared

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values were generally very close to those of the final models, except for Br and Mn, suggesting

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that most of the final models were not over-specified. The scatter plots of observed vs. predicted

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from the LOOCV analysis with associated statistics are shown in Supporting Information, Figure

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S6. Br and Mn showed the largest NMSE. Most of the models tended to under-predict at higher

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values. The regression coefficients and their standard errors for the main models are presented in

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Supporting Information, Table S3.

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Figure 2 shows the percent increase in PM2.5 elements (as a fraction of its mean) per one

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standard deviation increase in the emission indicator and its significance for the final LUR

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models in the 150-site full first year data set (numerical results are shown in Supporting

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Information, Table S4). The residual oil boiler indicator significantly explained spatial variation

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in all the elements except Br and Na. The near-road indicator was the most significant predictor

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for Cu, Fe, and Ti. The indicator for industrial structures was the most significant predictor of

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the spatial variation of Si but was also associated with multiple other elements. The

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construction/demolition indicator was significantly associated with Br, K, and Mn. The

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commercial cooking indicator was a significant predictor for Ca, Fe, K, and S. The ship indicator 13 ACS Paragon Plus Environment


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explained the spatial variation of V more significantly than residual oil indicator did. All of

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these emission indicators were significant predictors of PM2.5 mass concentrations in the model

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together, though including the spatial auto-correlation term reduced significance for the

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indicators of industrial structures and ships. Cu and Ni concentrations predicted from the final

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LUR models at 100 m x 100 m lattice points were mapped and shown in the abstract figure.

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During the four years at 94 sites, the average Ni and V levels showed the largest

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reductions at Year 4 (59 and 58 percent of the Year 1 level, respectively), while Zn and Ti levels

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increased from Year 1 and were higher at Year 4 (150 and 128 percent of the Year 1 level,

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respectively; Supporting Information Figure S7-A). Despite its concentration reduction (likely

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due to changes in fuel oil types in both power sector and buildings), Ni showed the strongest

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correlations of spatial patterns across years (r > 0.9), followed by Fe (r > 0.87) (Supporting

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Information Figure S7-B). When LUR analysis was conducted for each of these four years at the

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94 sites (Figure 3), the results were generally consistent with that from the full Year 1 data at 150

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sites. Based on the consistency of associations across years, the following patterns were seen.

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The residual oil indicator was strongly associated with Ni and Zn; the traffic (near-road)

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indicator was strongly associated with Cu, Fe, and Ti; the indicator for industrial structures was

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associated with Ca and Si; the commercial cooking indicator was consistently associated with

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Fe; the ship emission indicator was strongly associated with V.

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DISCUSSION

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Residual oil burning in buildings. The counts of boilers in buildings burning #4 or #6

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residual oil within 1000m were associated with a number of PM2.5 elements but most strongly

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with Ni, followed by Zn, in the 150 site data set. This pattern was also seen across the four years


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in the 94-site data set. Based on the sites with both coarse and fine elemental concentrations (see

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Supporting Information, Figure S2), the majority of Ni and Zn are associated with the fine

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fraction, implying that their spatial extent (i.e., how far the associated particles travel from the

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source) is large. This is consistent with our result that correlations between these elements with

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the residual oil emission indicator increased steeply between 50 m and 400 m, likely due to the

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increased accuracy of multiple sources’ contributions to the sampling location as the buffer

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distance increases to encompass the spatial extent of these elements. The finding that these

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elements are significantly associated with the residual oil burning indicator in the LUR models is

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also consistent with the emission factors data showing significant amounts of Cu, Pb, Mn, Ni, V,

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and Zn28; combustion experiments of #6 residual oil indicating the presence of Fe, Cu, Zn, Pb,

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Ca, Ni, V, and Zn in sulfate form;29 and chemical-physical characterization of residual oil fly ash

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as part of a toxicological study showing oxide form of Al, Si, K, Ca, Ti, Fe, V, Ni, Mn, and Pb.30

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Ni-to-V ratios in our data also increased as the residual oil indicator increased (Supporting

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Information Figure S3). The seasonal pattern of Ni and Zn (along with Ca, Mn, and Pb, which

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were also associated with the residual oil indicator) also exhibited higher winter concentrations

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(see Supporting Information, Figure S5), which is consistent with the increased fuel usage for

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space heating during the winter. Thus, while these elements also showed associations with other

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emission indicators, the residual oil indicator’s particularly strong association with Ni and Zn

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supports a strong link between these elements and residual oil burning at buildings.

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Traffic-related pollution. Kernel-weighted traffic density within 100 m buffer was

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significantly and consistently associated with Cu, Fe, and Ti in the 150-site and 94-site LUR

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models. Among the 15 elements, these three elements also showed a very distinct pattern of

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decay with increased buffer distance in correlations with the traffic density indicator. This 15 ACS Paragon Plus Environment


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pattern may be explained by the size distribution of these elements, which tends more towards

334

the coarse fraction rather than the fine (see Supporting Information Figure S2), and is consistent

335

with a reported size distribution of brake dust.31 Because the concentration of these larger

336

particles decays more rapidly with distance from the source, increasing the buffer size for traffic

337

density (i.e., considering the influence from multiple roads that are away from the sampling

338

location) would result in poorer correlations. The Cu-to-antimony (Sb) ratios observed in our

339

separate study (Supporting Information Figure S4) were closer to those reported (~5) in a review

340

of non-exhaust particles from road traffic, which concludes that Cu and Sb are reliable tracers of

341

brake-related particles32 and a more recent review, which also found the ratios to be much lower

342

than those found in crustal materials. 33 Fe and Ti are also reported to be major fractions of brake

343

wear dust.34 The LUR results, combined with available external information, therefore suggest

344

that the spatial variation of Cu in NYC represents non-exhaust traffic emissions, in particular

345

brake wear.

346

Ship emission. The ship emission indicator developed for this analysis was strongly

347

associated with V and marginally significantly associated with S in the LUR models. The studies

348

that measured emissions from operating ocean going vessels found S, V, and Ni to be

349

particularly abundant elements in the exhaust from engines running on heavy fuel oil.35,36,37,38

350

The Ni-to-V ratios in our data also decreased as the ship emission indicator value increased

351

(Supplemental Information Figure S3 (B)) with the ratios consistent with these emission studies.

352

This result supports the conclusion that the spatial pattern of V in our study is strongly

353

influenced by ship emissions.

354 355

Construction and demolition. Spatial variation of Br, K, and Mn were associated with the indicator for construction/demolition at 250 m buffer in the full year 150-site data set. 16 ACS Paragon Plus Environment

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356

However, this pattern of association was not consistently observed in the 94-site data set. In the

357

94-site data, the strong association between Br and the construction/demolition indicator was

358

observed only in the first year. Also, Si and Ca (i.e., cement), the elements that we expected to

359

see associations with this indicator, based on emissions inventory with construction dusts,39 were

360

not consistently associated. The transient nature of construction and demolition occurrences

361

makes estimating the influence of these “events” on spatial variation of PM2.5 elements

362

challenging.

363

Commercial cooking. Fe was most consistently associated with the commercial cooking

364

indicator in the 150-site data set and also consistently across the four years in the 94-site data set.

365

However, Fe is also the element that showed associations with the most number of emission

366

indicators (most strongly associated with the traffic indicator). A study of emissions from

367

charbroiling and grilling of chicken and beef indicates that multiple elements, including Na, S,

368

K, and Fe, were emitted from charbroiling of hamburgers, but the emitted mass was essentially

369

all due to organic carbon compounds.40 Given that there are good organic markers of commercial

370

cooking (e.g., cholesterol) 41,42 and that we did not have organic speciation data for these data

371

sets, our result of the association between Fe and the cooking indicator leaves uncertainty.

372

Spatially dense particle sampling for organic constituents is needed to better assess spatial

373

patterns in ambient PM from cooking emissions.

374

Industrial structures. The indicator for areas of industrial structures within 150 m buffer

375

was significantly associated with multiple elements (Al, Ca, Cu, Fe, K, Mn, Pb, Si, and Ti; most

376

strongly with Si) in the 150-site data set. Note that Si was the only element that showed a pattern

377

of decay in correlation as a function of buffer distance, indicating that the correlations with these

378

other elements have weaker physical interpretations. In the analysis of 94-site data set, only Ca 17 ACS Paragon Plus Environment


379

and Si were consistently associated with this indicator in all four years. Limitations to this

380

indicator includes the wide range of industrial, manufacturing and commercial activities present

381

in New York City industrial areas,43 the number of vacant or repurposed industrial buildings,

382

and the association of this land use with other emission source activity (such as trucks, truck

383

idling and non-road mobile).

384

PM2.5 mass concentrations. In the full-year 150-site LUR analysis, all the emission

385

indicators were predictors of PM2.5 mass. However, in the 94-site subset LUR analysis, only the

386

indicators for residual oil, traffic density, and ships were consistently significant predictors of

387

PM2.5 mass in all of the four years. The studies that characterized chemical compositions of

388

PM2.5 from residual oil burning found that a major fraction of PM2.5 mass was sulfate and that Ni

389

as well as other metals (Fe, Cu, Zn, Pb, and V) were in sulfate form.29,44 Thus, combined with the

390

result that S was also significantly associated with the residual oil indicator in both the 150- and

391

94-site analyses, these elements are likely associated with sulfate that contribute to PM2.5 mass.

392

Similarly, the ship emission studies indicate that a substantive fraction of PM2.5 mass emitted

393

from the ships was associated with hydrated sulfate,35,36,38 possibly due to enhanced catalytic

394

aqueous phase reactions in V-rich particles.45 This is also consistent with our finding that the

395

ship emission indicator was associated with PM2.5 mass, V, and S (though S was not significant

396

in the fourth year in the 94-site analysis). PM2.5 mass was also significantly associated with the

397

near-road indicator, but the mass contribution is likely due to primary carbonaceous compounds

398

in the fine particle size range from traffic exhaust21 rather than the non-exhaust particles

399

associated with Cu, Fe, or Ti that are more abundant in the coarse size range.

400 401

Limitations. As with any LUR analysis, a spatial correlation between an emission indicator and a PM2.5 element by itself does not establish a causal link. Such correlations found 18 ACS Paragon Plus Environment

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402

in LUR models also depend on the quality of both emission indicator variables and the measured

403

pollutant, which may also vary across the indicators and elements, leaving possibility of residual

404

confounding. To address these issues, we limited our analysis to the elements that have relatively

405

large fractions of measurements above sample uncertainties and developed LUR models with

406

emission source indicators that are expected to significantly contribute to PM2.5 mass and its

407

elements. The spatial correlations among the emission indicators in this analysis were also

408

relatively low, though some localized correlation may still influence the result. We did not

409

conduct extensive sets of validation analysis in terms of statistical model specifications, but our

410

conclusion regarding the source-elements associations was supported by the multi-year LUR results. In

411

interpreting the LUR results, we also considered additional information such as coarse-to-fine

412

mass ratios, seasonal pattern, and the information from literature on emission factors for PM2.5

413

elements. While some of the sources investigated in this study (e.g., residual oil burning) may

414

not be common in other major cities, the construction of emission indicators and the general

415

methods used in this analysis should be applicable in future studies of associations between

416

PM2.5 elements and emission sources.

417

Overall, we found strong source-element associations for: residual oil burning (Ni, Zn);

418

near-road non-exhaust traffic (Cu, Fe, and Ti); and, ships (V). Despite the reductions in PM2.5

419

elemental concentrations associated with residual oil burning and ship emission over the years,

420

these source-element associations were consistently seen across the four years. These emission

421

source indicators were also significant predictors of PM2.5 mass concentrations during the study

422

period. As the NYCCAS sampling program continues, we will be able to assess any future

423

change in relative impacts from these emission sources using LUR we developed. The method



424

also allows estimation of community-level exposures to PM2.5 elements for epidemiological

425

analyses.

426

ASSOCIATED CONTENT

427

Supporting Information

428

(1) List of 50 elements analyzed by X-ray fluorescence (2) mean and standard deviation of the

429

emission indicators used in LUR models, (3) correlation among the emission indicators used in

430

LUR models, (4) regression coefficients for the 150-site full year LUR models, (5) numerical

431

result for Figure 2, (6) map of location of 150 street-level sites and 94 subset sites in New York

432

City , (7) distribution (boxplot) of coarse-to-fine elemental mass ratios across 10 sites in a special

433

study, (8) nickel-to-vanadium ratios vs. residual oil and ship emission indicators at 150 sites, (9)

434

distribution of copper-to-antimony ratios in coarse and fine fractions measured at 10 sites, (10)

435

fitted temporal trend of Year 1 PM2.5 elements, (11) observed vs. predicted values from leave-

436

one-out cross-validation analysis for PM2.5 elements and mass concentrations, and (12) trend in

437

the average of elemental concentrations across 94 sites and spatial correlation across years

438

during 2008-2013. This information is available free of charge via the Internet at

439

http://pubs.acs.org/.

440

AUTHOR INFORMATION

441

Corresponding Author

442

* Phone number: 646-632-6539; E-mail: [email protected]

443

Notes

444

The authors declare no competing financial interest. 20 ACS Paragon Plus Environment

Page 20 of 32

Page 21 of 32


445

Author Contributions

446

The manuscript was written through contributions of all authors. All authors have given approval

447

to the final version of the manuscript.

448

ACKNOWLEDGMENT

449

This work was supported by U.S. Environmental Protection Agency grant RD83489801, the City

450

of New York tax levy funds, and NIEHS ES00260. Although the research described in the article

451

has been funded in part by the U.S. Environmental Protection Agency's STAR program, it has

452

not been subjected to any EPA review and therefore does not necessarily reflect the views of the

453

Agency, and no official endorsement should be inferred.

454 455

REFERENCES

456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477

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23. Hoek, G.; Beelen, R.; de Hoogh, K.; Vienneau, D.; Gulliver, J.; Fischer, P.; Briggs, D., A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmospheric Environment 2008, 42 (33), 7561-7578. 24. U.S. Department of Transportation National Transportation Atlas Database. http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/ 2012/index.html. 25. U. S. Environmental Protection Agency AERMOD: Description of Model Formulation; U.S. Environmental Protection Agency: North Carolina, 2004. 26. Wood, S. N., Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models. Journal of the American Statistical Association 2004, 99 (467), 673-686. 27. Ross, Z.; Ito, K.; Johnson, S.; Yee, M.; Pezeshki, G.; Clougherty, J. E.; Savitz, D.; Matte, T., Spatial and temporal estimation of air pollutants in New York City: exposure assignment for use in a birth outcomes study. Environmental health : a global access science source 2013, 12, 51. 28. U.S. Environmental Protection Agency Compilation of Air Pollutant Emission Factors; Research Triangle Park, NC, January, 1995, 1995. 29. Huffman, G. P.; Huggins, F. E.; Shah, N.; Huggins, R.; Linak, W. P.; Miller, C. A.; Pugmire, R. J.; Meuzelaar, H. L.; Seehra, M. S.; Manivannan, A., Characterization of fine particulate matter produced by combustion of residual fuel oil. J Air Waste Manag Assoc 2000, 50 (7), 1106-14. 30. Dreher, K. L.; Jaskot, R. H.; Lehmann, J. R.; Richards, J. H.; McGee, J. K.; Ghio, A. J.; Costa, D. L., Soluble transition metals mediate residual oil fly ash induced acute lung injury. Journal of toxicology and environmental health 1997, 50 (3), 285-305. 31. Harrison, R. M.; Jones, A. M.; Gietl, J.; Yin, J.; Green, D. C., Estimation of the Contributions of Brake Dust, Tire Wear, and Resuspension to Nonexhaust Traffic Particles Derived from Atmospheric Measurements. Environmental science & technology 2012, 46 (12), 6523-6529. 32. Thorpe, A.; Harrison, R. M., Sources and properties of non-exhaust particulate matter from road traffic: A review. Science of The Total Environment 2008, 400 (1–3), 270-282. 33. Pant, P.; Harrison, R. M., Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: A review. Atmospheric Environment 2013, 77, 78-97. 34. Grigoratos, T.; Martini, G., Brake wear particle emissions: a review. Environ Sci Pollut Res 2015, 22 (4), 2491-2504. 35. Agrawal, H.; Welch, W. A.; Miller, J. W.; Cocker, D. R., Emission Measurements from a Crude Oil Tanker at Sea. Environmental science & technology 2008, 42 (19), 7098-7103. 36. Agrawal, H.; Malloy, Q. G. J.; Welch, W. A.; Wayne Miller, J.; Cocker Iii, D. R., In-use gaseous and particulate matter emissions from a modern ocean going container vessel. Atmospheric Environment 2008, 42 (21), 5504-5510. 37. Viana, M.; Amato, F.; Alastuey, A.; Querol, X.; Moreno, T.; García Dos Santos, S.; Herce, M. D.; Fernández-Patier, R., Chemical Tracers of Particulate Emissions from Commercial Shipping. Environmental science & technology 2009, 43 (19), 7472-7477. 38. Celo, V.; Dabek-Zlotorzynska, E.; McCurdy, M., Chemical Characterization of Exhaust Emissions from Selected Canadian Marine Vessels: The Case of Trace Metals and Lanthanoids. Environmental science & technology 2015, 49 (8), 5220-5226. 39. Reff, A.; Bhave, P. V.; Simon, H.; Pace, T. G.; Pouliot, G. A.; Mobley, J. D.; Houyoux, M., Emissions Inventory of PM2.5 Trace Elements across the United States. Environmental science & technology 2009, 43 (15), 5790-5796. 40. McDonald, J. D.; Zielinska, B.; Fujita, E. M.; Sagebiel, J. C.; Chow, J. C.; Watson, J. G., Emissions from charbroiling and grilling of chicken and beef. J Air Waste Manag Assoc 2003, 53 (2), 185-94. 41. Rogge, W. F.; Hildemann, L. M.; Mazurek, M. A.; Cass, G. R.; Simoneit, B. R. T., Sources of fine organic aerosol. 1. Charbroilers and meat cooking operations. Environmental science & technology 1991, 25 (6), 1112-1125.



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42. Robinson, A. L.; Subramanian, R.; Donahue, N. M.; Bernardo-Bricker, A.; Rogge, W. F., Source Apportionment of Molecular Markers and Organic Aerosol. 3. Food Cooking Emissions. Environmental science & technology 2006, 40 (24), 7820-7827. 43. New York City Department of City Planning Department of City Planning, Zoning districts Manufacturing Districts. http://www.nyc.gov/html/dcp/html/zone/zh_manudistricts.shtml. 44. Pattanaik, S.; Huggins, F. E.; Huffman, G. P.; Linak, W. P.; Miller, C. A., XAFS studies of nickel and sulfur speciation in residual oil fly-ash particulate matters (ROFA PM). Environmental science & technology 2007, 41 (4), 1104-10. 45. Ault, A. P.; Gaston, C. J.; Wang, Y.; Dominguez, G.; Thiemens, M. H.; Prather, K. A., Characterization of the Single Particle Mixing State of Individual Ship Plume Events Measured at the Port of Los Angeles. Environmental science & technology 2010, 44 (6), 1954-1961.

589 590

591


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592

Table 1. R-squared values for temporal adjustment model and LUR models developed using the

593

data from 150 sites during the first year of sampling (December, 2008 – November, 2009). Spatial Land-use regression (N=150) With

Leave-

spatial

one-outcross-

Temporal trends

Emission

auto-

(N=703)

indicators only

correlation validation

Al

0.43

0.45

0.48

0.50

Br

0.62

0.43

n/a

0.24

Ca

0.74

0.75

0.82

0.85

Cu

0.69

0.58

0.64

0.68

Fe

0.86

0.63

n/a

0.60

K

0.68

0.55

0.61

0.64

Mn

0.55

0.50

n/a

0.36

Na

0.53

n/a

n/a

n/a

Ni

0.69

0.72

0.84

0.85

Pb

0.35

0.24

0.33

0.38

S

0.94

0.52

0.63

0.67

Si

0.65

0.64

0.67

0.69

Ti

0.60

0.51

n/a

0.49

V

0.68

0.67

0.80

0.82

Zn

0.73

0.54

0.77

0.80



PM2.5

0.93

0.74

0.80

594

595


0.83

Page 26 of 32

Page 27 of 32


596

597

Table 2. Distribution of temporally-adjusted annual average PM2.5 elemental concentrations (in

598

ng/m3) and PM2.5 mass concentration (in µg/m3) across 150 street-level sampling locations using

599

the data collected in the first year of sampling (December, 2008 – November, 2009). Spatial Mean

S.D.

C.V.

Min.

25%

Median 75%

Max.

Al

26.60

8.99

0.34

9.88

20.24

25.65

31.46

66.49

Br

1.82

0.69

0.38

0.71

1.51

1.77

1.95

8.56

Ca

85.98

45.81

0.53

23.36

52.47

73.15

105.90

273.10

Cu

7.07

2.66

0.38

2.95

5.16

6.51

8.42

17.64

Fe

163.90

79.64

0.49

58.07

103.50

144.90

194.10

540.80

K

49.94

12.26

0.25

29.13

43.15

46.98

52.18

134.80

Mn

3.83

2.60

0.68

0.74

2.21

3.09

4.85

17.46

Na

324.70

63.10

0.19

195.00

280.10

321.10

355.30

564.20

Ni

7.19

5.01

0.70

0.46

3.83

5.35

8.88

24.30

Pb

3.40

1.52

0.45

1.22

2.43

3.11

3.90

10.98

S

978.80

65.92

0.07

841.20

929.70

Si

65.67

27.13

0.41

31.04

47.76

982.20 1014.00 1149.00 59.18


74.50

245.10


Page 28 of 32

Ti

6.38

2.40

0.38

3.12

4.69

5.91

7.27

17.80

V

4.69

2.18

0.47

1.31

3.27

4.44

5.57

19.16

Zn

26.73

18.85

0.71

4.27

14.28

20.65

32.88

99.22

PM2.5

11.40

2.05

0.18

8.27

9.99

11.10

12.13

19.99

600

601


Page 29 of 32


602

603

Figure 1. Correlation between the first year annual average PM2.5 elemental concentrations at

604

150 sampling locations with buffer-based emission indicators.

605 606



607

Figure 2. Land-use regression result with 150 sites: percentage increase in elemental

608

concentrations (as fraction of its mean) per 1 standard deviation change in the emission indicator.

609

610

611


Page 30 of 32

Page 31 of 32


612

Figure 3. Land-use regression result with 94 sites with the annual average concentrations

613

estimated from the winter and summer data for each of the four years. X-axis: percentage

614

increase in elemental and PM2.5 mass concentrations (as fraction of its annual mean) per 1

615

standard deviation change in the emission indicator and their 95% confidence intervals. A

616

different scale was used for S and PM2.5 because their relative baseline concentrations are higher

617

due to higher influence of regional sources. The Year 1 elemental concentrations in this analysis

618

were computed using the winter- and summer-only data for consistency.

619

620

621 31 ACS Paragon Plus Environment


622

TOC Figure:

623

624


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