Full-chain health impact assessment of traffic-related air pollution and childhood asthma

Highlights

• Traffic-related air pollution (TRAP) may be an important exposure contributing to childhood asthma development

• We estimated the burden of childhood asthma attributable to ambient NO₂ and NO_x and specifically traffic-related NO₂ and NO_x in Bradford, UK, a deprived and multi-ethnic city

• We used a newly developed full-chain model knitting together distinct traffic, vehicle emission, atmospheric dispersion and health impact assessment models

• We compared the full-chain model's estimates with burden of disease estimates using the ESCAPE's land-use regression model

• We estimated that up to 687 (or 38% of all) annual childhood asthma cases in Bradford may be attributable to NO_x and NO₂ air pollution. Up to 109 cases (6%) and 219 cases (12%) may be specifically attributable to traffic-related NO_x and NO₂, with and without minor roads and cold starts, respectively

Abstract

Background

Asthma is the most common chronic disease in children. Traffic-related air pollution (TRAP) may be an important exposure contributing to its development. In the UK, Bradford is a deprived city suffering from childhood asthma rates higher than national and regional averages and TRAP is of particular concern to the local communities.

Aims

We estimated the burden of childhood asthma attributable to air pollution and specifically TRAP in Bradford. Air pollution exposures were estimated using a newly developed full-chain exposure assessment model and an existing land-use regression model (LUR).

Methods

We estimated childhood population exposure to NO_x and, by conversion, NO₂ at the smallest census area level using a newly developed full-chain model knitting together distinct traffic (SATURN), vehicle emission (COPERT) and atmospheric dispersion (ADMS-Urban) models. We compared these estimates with measurements and estimates from ESCAPE's LUR model. Using the UK incidence rate for childhood asthma, meta-analytical exposure-response functions, and estimates from the two exposure models, we estimated annual number of asthma cases attributable to NO₂ and NO_x in Bradford, and annual number of asthma cases specifically attributable to traffic.

Results

The annual average census tract levels of NO₂ and NO_x estimated using the full-chain model were 15.41 and 25.68 μg/m³, respectively. On average, 2.75 μg/m³ NO₂ and 4.59 μg/m³ NO_x were specifically contributed by traffic, without minor roads and cold starts. The annual average census tract levels of NO₂ and NO_x estimated using the LUR model were 21.93 and 35.60 μg/m³, respectively. The results indicated that up to 687 (or 38% of all) annual childhood asthma cases in Bradford may be attributable to air pollution. Up to 109 cases (6%) and 219 cases (12%) may be specifically attributable to TRAP, with and without minor roads and cold starts, respectively.

Conclusions

This is the first study undertaking full-chain health impact assessment of TRAP and childhood asthma in a disadvantaged population with public concern about TRAP. It further adds to scarce literature exploring the impact of different exposure assessments. In conservative estimates, air pollution and TRAP are estimated to cause a large, but largely preventable, childhood asthma burden. Future progress with childhood asthma requires a move beyond the prevalent disease control-based approach toward asthma prevention.

Introduction

Asthma is a chronic disease of the air passages leading to and from the lung, and is a condition that is often cited as the most common chronic disease of childhood (Gasana et al., 2012; Fabian et al., 2012; Gaffin and Phipatanakul, 2014). A recent meta-analysis showed statistically significant exposure-response relationships between traffic-related air pollution (TRAP) and development of asthma in children from birth to 18 years of age (Khreis et al., 2017c). The public health relevance of these relationships is largely unknown and the impact of TRAP exposures on the burden of childhood asthma is poorly documented. Due to the ubiquity of TRAP and the number of exposed children, the relatively small individual risks of TRAP-associated asthma could translate into significant public health impact.

Little work has been undertaken to estimate the burden of childhood asthma attributable to TRAP. Only four published studies, coming from the same research group, quantified the number of prevalent asthma cases attributable to TRAP (Perez et al., 2009; Perez et al., 2013; Künzli et al., 2008; Perez et al., 2012). Three of these studies were conducted in California, in Long Beach, Riverside and Los Angeles county (Künzli et al., 2008; Perez et al., 2009; Perez et al., 2012). The fourth study was conducted in 10 European cities (Perez et al., 2013). All four studies estimated the impacts of exposure to TRAP, characterized by proximity to major roadways, on asthma prevalence in children between birth and 18 years old. These studies suggested that 6% to 14% of prevalent childhood asthma cases were attributable to TRAP exposures; as characterized by traffic proximity (Table S1).

Despite pioneering in studying asthma as an outcome in the burden of disease assessment of TRAP, these studies relied on residential proximity to major roadways as the TRAP exposure metric. Proximity to major roadways is a crude exposure metric (Beevers et al., 2013; Jerrett et al., 2005) and alternative improved approaches are now more readily available (Khreis and Nieuwenhuijsen, 2017). Individual measurements are the preferred exposure assessment method, but since it is often not possible to measure air pollution exposures for the large populations included in health impact assessment and most epidemiological studies, many rely on less costly and more practical modeling approaches. Land-use regression (LUR) (Eeftens et al., 2012; Beelen et al., 2013; De Hoogh et al., 2014) and atmospheric dispersion (AD) modeling (Rancière et al., 2017; Yamazaki et al., 2014; De Hoogh et al., 2014) are two common modeling methods used to obtain air pollution exposure estimates for relatively large areas and number of people.

These two exposure modeling methods are fundamentally different and vary in their spatial and temporal resolution, specificity to traffic and advantages and disadvantages (Khreis and Nieuwenhuijsen (2017). AD models rely on mathematical formula and an understanding of underlying emission and dispersion processes to estimate air pollution exposures (Nieuwenhuijsen, 2015). On the other hand, LUR is an empirical method that uses least squares regression to combine air pollution measurements with geographic information system (GIS)-based predictor variables which reflect pollutant sources (for example, road, traffic and buildings density, green space etc.). The practical and policy advantage of AD modeling is that it allows for easier estimation of the contribution of different sources, such as traffic, to air pollution exposure estimates. On the other hand, the true contribution of traffic to the regression in LUR models is not always known or reported (Health Effects Institute, 2010).

In this study, we aimed to construct a full-chain health impact assessment model (Nieuwenhuijsen et al., 2017), to estimate the annual number of childhood asthma cases in Bradford, UK, attributable to air pollution, and specifically to TRAP. In the full-chain health impact assessment model, we combined four distinct models of traffic, emission, AD and health impact assessment (HIA), which covered the full-chain from the source of air pollution to the health impacts (Fig. 1). We then compared the burden of disease estimates obtained using the full-chain model with those obtained using exposure estimates from a LUR model, instead.