Holger Eisl

Monitoring intraurban spatial patterns of multiple combustion air pollutants in New York City: Design and implementation

Routine air monitoring provides data to assess urban scale temporal variation in pollution concentrations in relation to regulatory standards, but is not well suited to characterizing intraurban spatial variation in pollutant concentrations from local sources. To address these limitations and inform local control strategies, New York City developed a program to track spatial patterns of multiple air pollutants in each season of the year. Monitor locations include 150 distributed street-level sites chosen to represent a range of traffic, land-use and other characteristics. Integrated samples are collected at each distributed site for one 2-week session each season and in every 2-week period at five reference locations to track city-wide temporal variation. Pollutants sampled include PM2.5 and constituents, nitrogen oxides, black carbon, ozone (summer only) and sulfur dioxide (winter only). During the first full year of monitoring more than 95% of designed samples were completed. Agreement between colocated samples was good (absolute mean % difference 3.2–8.9%). Street-level pollutant concentrations spanned a much greater range than did concentrations at regulatory monitors, especially for oxides of nitrogen and sulfur dioxide. Monitoring to characterize intraurban spatial gradients in ambient pollution usefully complements regulatory monitoring data to inform local air quality management.

Intra-urban spatial variability in wintertime street-level concentrations of multiple combustion-related air pollutants: the New York City Community Air Survey (NYCCAS).

Although intra-urban air pollution differs by season, few monitoring networks provide adequate geographic density and year-round coverage to fully characterize seasonal patterns. Here, we report winter intra-urban monitoring and land-use regression (LUR) results from the New York City Community Air Survey (NYCCAS). Two-week integrated samples of fine particles (PM2.5), black carbon (BC), nitrogen oxides (NOx) and sulfur dioxide (SO2) were collected at 155 city-wide street-level locations during winter 2008–2009. Sites were selected using stratified random sampling, randomized across sampling sessions to minimize spatio-temporal confounding. LUR was used to identify GIS-based source indicators associated with higher concentrations. Prediction surfaces were produced using kriging with external drift. Each pollutant varied twofold or more across sites, with higher concentrations near midtown Manhattan. All pollutants were positively correlated, particularly PM2.5 and BC (Spearman’s r=0.84). Density of oil-burning boilers, total and truck traffic density, and temporality explained 84% of PM2.5 variation. Densities of total traffic, truck traffic, oil-burning boilers and industrial space, with temporality, explained 65% of BC variation. Temporality, built space, bus route location, and traffic density described 67% of nitrogen dioxide variation. Residual oil-burning units, nighttime population and temporality explained 77% of SO2 variation. Spatial variation in combustion-related pollutants in New York City was strongly associated with oil-burning and traffic density. Chronic exposure disparities and unique local sources can be identified through year-round saturation monitoring.

Noise, air pollutants and traffic: continuous measurement and correlation at a high-traffic location in New York City.

BACKGROUND Epidemiological studies have linked both noise and air pollution to common adverse health outcomes such as increased blood pressure and myocardial infarction. In urban settings, noise and air pollution share important sources, notably traffic, and several recent studies have shown spatial correlations between noise and air pollution. The temporal association between these exposures, however, has yet to be thoroughly investigated despite the importance of time series studies in air pollution epidemiology and the potential that correlations between these exposures could at least partly confound statistical associations identified in these studies. METHODS An aethelometer, for continuous elemental carbon measurement, was co-located with a continuous noise monitor near a major urban highway in New York City for six days in August 2009. Hourly elemental carbon measurements and hourly data on overall noise levels and low, medium and high frequency noise levels were collected. Hourly average concentrations of fine particles and nitrogen oxides, wind speed and direction and car, truck and bus traffic were obtained from nearby regulatory monitors. Overall temporal patterns, as well as day-night and weekday-weekend patterns, were characterized and compared for all variables. RESULTS Noise levels were correlated with car, truck, and bus traffic and with air pollutants. We observed strong day-night and weekday-weekend variation in noise and air pollutants and correlations between pollutants varied by noise frequency. Medium and high frequency noise were generally more strongly correlated with traffic and traffic-related pollutants than low frequency noise and the correlation with medium and high frequency noise was generally stronger at night. Correlations with nighttime high frequency noise were particularly high for car traffic (Spearman rho=0.84), nitric oxide (0.73) and nitrogen dioxide (0.83). Wind speed and direction mediated relationships between pollutants and noise. CONCLUSIONS Noise levels are temporally correlated with traffic and combustion pollutants and correlations are modified by the time of day, noise frequency and wind. Our results underscore the potential importance of assessing temporal variation in co-exposures to noise and air pollution in studies of the health effects of these urban pollutants.

Air Pollution and Activity During Transportation by Car, Subway, and Walking

BACKGROUND Little evidence exists about the health risks and benefits associated with using public buses and subways rather than cars. The objective of the current study was to assess the magnitude and variance of personal exposure to particulate matter 2.5 microns or smaller (PM(2.5)) and concomitant physical activity energy expenditure (PAEE) for transportation by car, subway, or walking. METHODS Twenty nonsmoking volunteers from New York City traveled on predetermined routes by car, subway, and walking, for up to 8 hours on 3 different days, between October 2007 and February 2008. Outfitted with a personal monitor with PM(2.5) aerosol inlet, and a GPS receiver, they completed a detailed physical activity diary for each route. Both metabolic equivalent (MET) and PAEE rates (Kcal/min) were computed from GPS-derived activity durations and speeds, activity-specific METs, and measured body weight. RESULTS Total PM(2.5) exposures did not differ among car, subway, and walking arms (respectively, 21.4, 30.6, and 26.5 microg/m(3) x min, p=0.19); but average MET values (respectively, 1.51, 2.03, and 2.60 Kcal/kg x hr, p<0.0001) and PAEE rates (1.74, 2.35, and 3.04 Kcal/min, p<0.0001) did. After correction for the humidity factor, exposure to PM(2.5) appeared to be lower for the car arm (13.1 microg/m(3) x min) than for the subway (19.6 microg/m(3) x min) or walking (23.9 microg/m(3) x min, p=0.004) arms. CONCLUSIONS Driving cars was associated with less physical activity but not necessarily less exposure to PM(2.5) than riding subways or walking in an urban environment. These effect sizes and variances can be used to design larger experiments assessing the health effects of urban transportation.

Potential Health Impact of Switching From Car to Public Transportation When Commuting to Work

We assessed humidity-corrected particulate matter (PM(2.5)) exposure and physical activity (using global positioning system monitors and diaries) among 18 people who commuted by car to Queens College, New York, New York, for 5 days, and then switched to commuting for the next 5 days via public transportation. The PM(2.5) differed little between car and public transportation commutes (1.41 μg/M(3)·min; P = .226). Commuting by public transportation rather than by car increased energy expenditure (+124 kcal/day; P < .001) equivalent to the loss of 1 pound of body fat per 6 weeks.

Spatial variability in levels of benzene, formaldehyde, and total benzene, toluene, ethylbenzene and xylenes in New York City: a land-use regression study

BackgroundHazardous air pollutant exposures are common in urban areas contributing to increased risk of cancer and other adverse health outcomes. While recent analyses indicate that New York City residents experience significantly higher cancer risks attributable to hazardous air pollutant exposures than the United States as a whole, limited data exist to assess intra-urban variability in air toxics exposures.MethodsTo assess intra-urban spatial variability in exposures to common hazardous air pollutants, street-level air sampling for volatile organic compounds and aldehydes was conducted at 70 sites throughout New York City during the spring of 2011. Land-use regression models were developed using a subset of 59 sites and validated against the remaining 11 sites to describe the relationship between concentrations of benzene, total BTEX (benzene, toluene, ethylbenzene, xylenes) and formaldehyde to indicators of local sources, adjusting for temporal variation.ResultsTotal BTEX levels exhibited the most spatial variability, followed by benzene and formaldehyde (coefficient of variation of temporally adjusted measurements of 0.57, 0.35, 0.22, respectively). Total roadway length within 100 m, traffic signal density within 400 m of monitoring sites, and an indicator of temporal variation explained 65% of the total variability in benzene while 70% of the total variability in BTEX was accounted for by traffic signal density within 450 m, density of permitted solvent-use industries within 500 m, and an indicator of temporal variation. Measures of temporal variation, traffic signal density within 400 m, road length within 100 m, and interior building area within 100 m (indicator of heating fuel combustion) predicted 83% of the total variability of formaldehyde. The models built with the modeling subset were found to predict concentrations well, predicting 62% to 68% of monitored values at validation sites.ConclusionsTraffic and point source emissions cause substantial variation in street-level exposures to common toxic volatile organic compounds in New York City. Land-use regression models were successfully developed for benzene, formaldehyde, and total BTEX using spatial indicators of on-road vehicle emissions and emissions from stationary sources. These estimates will improve the understanding of health effects of individual pollutants in complex urban pollutant mixtures and inform local air quality improvement efforts that reduce disparities in exposure.

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 (PM2.5) elements. We developed land-use regression (LUR) models to characterize spatial variation of 15 PM2.5 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 R(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 PM2.5 concentrations across years.