Sources of ambient PM2.5 exposure in 96 global cities.

To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM2.5 exposure caused by within-city emissions varies widely (µ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.

Using Satellites to Track Indicators of Global Air Pollution and Climate Change Impacts: Lessons Learned From a NASA-Supported Science-Stakeholder Collaborative.

The 2018 NASA Health and Air Quality Applied Science Team (HAQAST) "Indicators" Tiger Team collaboration between NASA-supported scientists and civil society stakeholders aimed to develop satellite-derived global air pollution and climate indicators. This Commentary shares our experience and lessons learned. Together, the team developed methods to track wildfires, dust storms, pollen counts, urban green space, nitrogen dioxide concentrations and asthma burdens, tropospheric ozone concentrations, and urban particulate matter mortality. Participatory knowledge production can lead to more actionable information but requires time, flexibility, and continuous engagement. Ground measurements are still needed for ground truthing, and sustained collaboration over time remains a challenge.

Assessing Air Quality and Public Health Benefits of New York City's Climate Action Plans.

Strategies that reduce greenhouse gas (GHG) emissions may also provide significant public health benefits and their estimation can help prioritize the case for climate change mitigation policies. In 2014, New York City (NYC) committed to reduce GHG emissions by 80% by 2050 (80 × 50). In this analysis we quantified the air quality-related public health benefits of the policies outlined in the 80 × 50 strategy, compared sector-specific (buildings, energy, transportation) policy impacts, and assessed variations in benefits across NYC neighborhoods. We applied air quality modeling and health impact assessment tools to estimate expected changes in ambient PM2.5 and related health outcomes by Zip Code Tabulation Areas (ZCTA). Full implementation of 80 × 50 strategies would reduce PM2.5 (fine particulates) concentrations across NYC ZCTAs by 7% (3%, 10%) (ZCTA median, 10th, 90th percentile), avoiding between 160 and 390 premature deaths and 460 hospitalizations and emergency department visits for respiratory and cardiovascular disease each year, valued at $3.4 billion annually. Across all the policy scenarios we estimated 10 times more avoided asthma emergency department visits in low-income neighborhoods as compared to the wealthiest neighborhoods even though median declines in ambient PM2.5 were similar. Consideration of public health benefits helps to prioritize climate policy implementation and identify priority neighborhoods.

Levels and determinants of tree pollen in New York City.

Exposure to allergenic tree pollen is a risk factor for multiple allergic disease outcomes. Little is known about how tree pollen levels vary within cities and whether such variation affects the development or exacerbation of allergic disease. Accordingly, we collected integrated pollen samples at uniform height at 45 sites across New York City during the 2013 pollen season. We used these monitoring results in combination with adjacent land use data to develop a land use regression model for tree pollen. We evaluated four types of land use variables for inclusion in the model: tree canopy, distributed building height (a measure of building volume density), elevation, and distance to water. When included alone in the model, percent tree canopy cover within a 0.5 km radial buffer explained 39% of the variance in tree pollen (1.9% increase in tree pollen per one-percentage point increase in tree canopy cover, P<0.0001). The inclusion of additional variables did not improve model fit. We conclude that intra-urban variation in tree canopy is an important driver of tree pollen exposure. Land use regression models can be used to incorporate spatial variation in tree pollen exposure in studies of allergic disease outcomes.

Using Tracking Data to Promote Environmental Public Health Through Regulatory and Legislative Processes in New York City.

Legislation and regulation are powerful tools for decreasing health risks from environmental hazards. Legislation is enacted by an elected body, and regulations are issued by an agency in the executive branch delegated authority by the legislature to carry out enacted laws. The New York City (NYC) Environmental Public Health Tracking Program makes data and analytic findings available to policy makers to inform development of sound and effective environmental health legislation and regulation. Tracking data and associated science create awareness of environmental hazards and health impacts, guide strategies for mitigating hazards, and sustain support for effective law by documenting beneficial impacts on the environment and health. We describe how environmental and health surveillance data and analytic findings have informed legislation and regulations related to restaurant food safety and air pollution control in NYC. Using data to guide legislative and regulatory processes helps ensure that policy decisions and directions are based on objective evidence.

The contribution of motor vehicle emissions to ambient fine particulate matter public health impacts in New York City: a health burden assessment.

BACKGROUND: On-road vehicles are an important source of fine particulate matter (PM2.5) in cities, but spatially varying traffic emissions and vulnerable populations make it difficult to assess impacts to inform policy and the public. METHODS: We estimated PM2.5-attributable mortality and morbidity from on-road vehicle generated air pollution in the New York City (NYC) region using high-spatial-resolution emissions estimates, air quality modeling, and local health incidence data to evaluate variations in impacts by vehicle class, neighborhood, and area socioeconomic status. We developed multiple 'zero-out' emission scenarios focused on regional and local cars, trucks, and buses in the NYC region. We simulated PM2.5 concentrations using the Community Multi-scale Air Quality Model at a 1-km spatial resolution over NYC and combined modeled estimates with monitored data from 2010 to 2012. We applied health impact functions and local health data to quantify the PM2.5-attributable health burden on NYC residents within 42 city neighborhoods. RESULTS: We estimate that all on-road mobile sources in the NYC region contribute to 320 (95 % Confidence Interval (CI): 220-420) deaths and 870 (95 % CI: 440-1280) hospitalizations and emergency department visits annually within NYC due to PM2.5 exposures, accounting for 5850 (95 % CI: 4020-7620) years of life lost. Trucks and buses within NYC accounted for the largest share of on-road mobile-attributable ambient PM2.5, contributing up to 14.9 % of annual average levels across 1-km grid cells, and were associated with 170 (95 % CI: 110-220) PM2.5-attributable deaths each year. These contributions were not evenly distributed, with high poverty neighborhoods experiencing a larger share of the exposure and health burden than low poverty neighborhoods. CONCLUSION: Reducing motor vehicle emissions, especially from trucks and buses, could produce significant health benefits and reduce disparities in impacts. Our high-spatial-resolution modeling approach could improve assessment of on-road vehicle health impacts in other cities.

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.

The public health benefits of reducing fine particulate matter through conversion to cleaner heating fuels in New York City.

In recent years, both New York State and City issued regulations to reduce emissions from burning heating oil. To assess the benefits of these programs in New York City, where the density of emissions and vulnerable populations vary greatly, we simulated the air quality benefits of scenarios reflecting no action, partial, and complete phase-out of high-sulfur heating fuels using the Community MultiScale Air Quality (CMAQ) model conducted at a high spatial resolution (1 km). We evaluated the premature mortality and morbidity benefits of the scenarios within 42 city neighborhoods and computed benefits by neighborhood poverty status. The complete phase-out scenario reduces annual average fine particulate matter (PM2.5) by an estimated 0.71 µg/m(3) city-wide (average of 1 km estimates, 10-90th percentile: 0.1-1.6 µg/m(3)), avoiding an estimated 290 premature deaths, 180 hospital admissions for respiratory and cardiovascular disease, and 550 emergency department visits for asthma each year. The largest improvements were seen in areas of highest building and population density and the majority of benefits have occurred through the partial phase out of high-sulfur heating fuel already achieved. While emissions reductions were greatest in low-poverty neighborhoods, health benefits are estimated to be greatest in high-poverty neighborhoods due to higher baseline morbidity and mortality rates.

Spatial variation in environmental noise and air pollution in New York City.

Exposure to environmental noise from traffic is common in urban areas and has been linked to increased risks of adverse health effects including cardiovascular disease. Because traffic sources also produce air pollutants that increase the risk of cardiovascular morbidity, associations between traffic exposures and health outcomes may involve confounding and/or synergisms between air pollution and noise. While prior studies have characterized intraurban spatial variation in air pollution in New York City (NYC), limited data exists on the levels and spatial variation in noise levels. We measured 1-week equivalent continuous sound pressure levels (Leq) at 56 sites during the fall of 2012 across NYC locations with varying traffic intensity and building density that are routinely monitored for combustion-related air pollutants. We evaluated correlations among several noise metrics used to characterize noise exposures, including Leq during different time periods (night, day, weekday, weekend), Ldn (day-night noise), and measures of intermittent noise defined as the ratio of peak levels to median and background levels. We also examined correlations between sound pressure levels and co-located simultaneous measures of nitric oxide (NO), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and black carbon (BC) as well as estimates of traffic and building density around the monitoring sites. Noise levels varied widely across the 56 monitoring sites; 1-week Leq varied by 21.6 dBA (range 59.1-80.7 dBA) with the highest levels observed during the weekday, daytime hours. Indices of average noise were well correlated with each other (r > 0.83), while indices of intermittent noise were not well correlated with average noise levels (r < 0.41). One-week Leq correlated well with NO, NO2, and EC levels (r = 0.61 to 0.68) and less so with PM2.5 levels (r = 0.45). We observed associations between 1-week noise levels and traffic intensity within 100 m of the monitoring sites (r = 0.58). The high levels of noise observed in NYC often exceed recommended guidelines for outdoor and personal exposures, suggesting unhealthy levels in many locations. Associations between noise, traffic, and combustion air pollutants suggest the possibility for confounding and/or synergism in intraurban epidemiological studies of traffic-related health effects. The different spatial pattern of intermittent noise compared to average noise level may suggest different sources.

PM2.5 and ozone health impacts and disparities in New York City: sensitivity to spatial and temporal resolution.

Air quality health impact assessment (HIA) synthesizes information about air pollution exposures, health effects, and population vulnerability for regulatory decision-making and public engagement. HIAs often use annual average county or regional data to estimate health outcome incidence rates that vary substantially by season and at the subcounty level. Using New York City as an example, we assessed the sensitivity of estimated citywide morbidity and mortality attributable to ambient fine particulate matter (PM2.5) and ozone to the geographic (county vs. neighborhood) and temporal (seasonal vs. annual average) resolution of health incidence data. We also used the neighborhood-level analysis to assess variation in estimated air pollution impacts by neighborhood poverty concentration. Estimated citywide health impacts attributable to PM2.5 and ozone were relatively insensitive to the geographic resolution of health incidence data. However, the neighborhood-level analysis demonstrated increasing impacts with greater neighborhood poverty levels, particularly for PM2.5-attributable asthma emergency department visits, which were 4.5 times greater in high compared to low-poverty neighborhoods. PM2.5-attributable health impacts were similar using seasonal and annual average incidence rates. Citywide ozone-attributable asthma morbidity was estimated to be 15 % lower when calculated from seasonal, compared to annual average incidence rates, as asthma morbidity rates are lower during the summer ozone season than the annual average rate. Within the ozone season, 57 % of estimated ozone-attributable emergency department for asthma in children occurred in the April-June period when average baseline incidence rates are higher than in the July-September period when ozone concentrations are higher. These analyses underscore the importance of utilizing spatially and temporally resolved data in local air quality impact assessments to characterize the overall city burden and identify areas of high vulnerability.

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 PM(2.5) 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 (PM(2.5)), black carbon (BC), nitrogen oxides (NO(x)) and sulfur dioxide (SO(2)) 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 PM(2.5) and BC (Spearman's r=0.84). Density of oil-burning boilers, total and truck traffic density, and temporality explained 84% of PM(2.5) 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 SO(2) 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.

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

BACKGROUND: Hazardous 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. METHODS: To 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. RESULTS: Total 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. CONCLUSIONS: Traffic 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.

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.