BackgroundOn-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.MethodsWe 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.ResultsWe 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.ConclusionReducing 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0172-6) contains supplementary material, which is available to authorized users.

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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.

[1] Modeling systems that are designed to investigate tropospheric air quality concerns must address several issues simultaneously: ozone, particulate matter, deposition and visibility. These modeling systems consist of three components: meteorology, emissions and air quality. A simulation was conducted for the July 1999 Southern Oxidants Study to evaluate one such modeling system, Models-3. Performance of the meteorological component, MM5, was evaluated against observations. Consistency of two emissions models, SMOKE and EPS 2.5, was evaluated by comparing their outputs. For comparison, the performance of CMAQ and three additional models (CMAQ-MADRID 1, CMAQ-MADRID 2, and REMSAD) was evaluated for the same time period. Nested simulations for a 32-km and an 8-km grid were conducted for CMAQ and CMAQ-MADRID 1. Results for CMAQ-MADRID 2 and REMSAD are available only for the 8-km grid. Performance was evaluated for PM and its components, ozone and wet deposition. Differences in model performance for PM 2.5 and its components were greatest for OC and total PM 2.5 ; performance was more consistent for the other components. Model performance was generally better on the 32-km grid than the 8-km grid for PM 2.5 and its components. R 2 values ranged from 20 to 50% for NH 4 + and EC and were lower for other PM 2.5 components, indicating that the predictive capabilities of the models for PM 2.5 are limited. Model performance for ozone met EPA guidance for MNB and MNE when a 60 ppb cutoff was used and was better on the 8-km grid than on the 32-km grid. Performance for wet deposition was good.

Three mathematical models of air quality (CMAQ, CMAQ-MADRID, and REMSAD) are applied to simulate the response of atmospheric fine particulate matter (PM2.5) concentrations to reductions in the emissions of gaseous precursors for a 10 day period of the July 1999 Southern Oxidants Study (SOS) in Nashville. The models are shown to predict similar directions of the changes in PM2.5 mass and component (sulfate, nitrate, ammonium, and organic compounds) concentrations in response to changes in emissions of sulfur dioxide (SO2), nitrogen oxides (NO(x)), and volatile organic compounds (VOC), except for the effect of SO2 reduction on nitrate and the effect of VOC reduction on PM2.5 mass. Furthermore, in many cases where the directional changes are consistent, the magnitude of the changes are significantly different among models. Examples are the effects of SO2 and NO(x) reductions on nitrate and PM2.5 mass and the effects of VOC reduction on organic compounds, sulfate and nitrate. The spatial resolution significantly influences the results in some cases. Operational model performance for a PM2.5 component appears to provide some useful indication on the reliability of the relative response factors (RRFs) for a change in emissions of a direct precursor, as well as for a change in emissions of a compound that affects this component in an indirect manner, such as via oxidant formation. However, these results need to be confirmed for other conditions and caution is still needed when applying air quality models for the design of emission control strategies. It is advisable to use more than one air quality model (or more than one configuration of a single air quality model) to span the full range of plausible scientific representations of atmospheric processes when investigating future air quality scenarios.