Measuring the health effects of air pollution : to what extent can we really say people are dying from bad air?

Estimation of the effects of environmental impacts is a major focus of current theoretical and policy research in environmental economics. Such estimates are used to set regulatory standards for pollution exposure; design appropriate environmental protection and damage mitigation strategies; guide the assessment of environmental impacts; and measure public willingness to pay for environmental amenities. It is a truism that the effectiveness of such strategies depends crucially on the quality of the estimates used to inform them. However, this paper argues that in respect to at least one area of the empirical literature - the estimation of the health impacts of air pollution using daily time series data - existing estimates are questionable and thus have limited relevance for environmental decision-making. By neglecting the issue of model uncertainty - or which models, among the myriad of possible models researchers should choose from to estimate health effects - most studies overstate confidence in their chosen model and underestimate the evidence from other models, thereby greatly enhancing the risk of obtaining uncertain and inaccurate results. This paper discusses the importance of model uncertainty for accurate estimation of the health effects of air pollution and demonstrates its implications in an exercise that models pollution-mortality impacts using a new and comprehensive data set for Toronto, Canada. The main empirical finding of the paper is that standard deviations for air pollution-mortality impacts become very large when model uncertainty is incorporated into the analysis. Indeed they become so large as to question the plausibility of previously measured links between air pollution and mortality. Although applied to the estimation of the effects of air pollution, the general message of this paper - that proper treatment of model uncertainty critically determines the accuracy of the resulting estimates - applies to many studies that seek to estimate environmental effects

Development and evaluation of the RapidAir® dispersion model, including the use of geospatial surrogates to represent street canyon effects

We developed a dispersion model (RapidAir®) to estimate air pollution concentrations at fine spatial resolution over large geographical areas with fast run times. Concentrations were modelled at 5 m spatial resolution over an area of ∼3500 km2 in <10 min. RapidAir® was evaluated by estimating NOx and NO2 concentrations at 86 continuous monitoring sites in London, UK during 2008. The model predictions explained 66% of the spatial variation (r = 0.81) in annual NOx concentrations observed at the monitoring sites. We included discrete canyon models or geospatial surrogates (sky view factor, hill shading and wind effect) to improve the accuracy of model predictions at kerbside locations. Geospatial surrogates provide alternatives to discrete street canyon models where it is impractical to run canyon models for thousands of streets within a large city dispersion model (with advantages including: ease of operation; faster run times; and more complete treatment of building effects)

Measuring the health effects of air pollution: to what extent can we really say that people are dying of bad air

Abstract Estimation of the effects of environmental impacts is a major focus of current theoretical and policy research in environmental economics. Such estimates are used to set regulatory standards for pollution exposure; design appropriate environmental protection and damage mitigation strategies; guide the assessment of environmental impacts; and measure public willingness to pay for environmental amenities. It is a truism that the effectiveness of such strategies depends crucially on the quality of the estimates used to inform them. However, this paper argues that in respect to at least one area of the empirical literaturethe estimation of the health impacts of air pollution using daily time series data-existing estimates are questionable and thus have limited relevance for environmental decision-making. By neglecting the issue of model uncertainty-or which models, among the myriad of possible models researchers should choose from to estimate health effects-most studies overstate confidence in their chosen model and underestimate the evidence from other models, thereby greatly enhancing the risk of obtaining uncertain and inaccurate results. This paper discusses the importance of model uncertainty for accurate estimation of the health effects of air pollution and demonstrates its implications in an exercise that models pollution-mortality impacts using a new and comprehensive data set for Toronto, Canada. The main empirical finding of the paper is that standard deviations for air pollution-mortality impacts become very large when model uncertainty is incorporated into the analysis. Indeed they become so large as to question the plausibility of previously measured links between air pollution and mortality. Although applied to the estimation of the effects of air pollution, the general message of this paper-that proper treatment of model uncertainty critically determines the accuracy of the resulting estimates-applies to many studies that seek to estimate environmental effects.

Traffic-Related Air Pollution and All-Cause Mortality during Tuberculosis Treatment in California.

BackgroundAmbient air pollution and tuberculosis (TB) have an impact on public health worldwide, yet associations between the two remain uncertain.ObjectiveWe determined the impact of residential traffic on mortality during treatment of active TB.MethodsFrom 2000-2012, we enrolled 32,875 patients in California with active TB and followed them throughout treatment. We obtained patient data from the California Tuberculosis Registry and calculated traffic volumes and traffic densities in 100- to 400-m radius buffers around residential addresses. We used Cox models to determine mortality hazard ratios, controlling for demographic, socioeconomic, and clinical potential confounders. We categorized traffic exposures as quintiles and determined trends using Wald tests.ResultsParticipants contributed 22,576 person-years at risk. There were 2,305 deaths during treatment for a crude mortality rate of 1,021 deaths per 10,000 person-years. Traffic volumes and traffic densities in all buffers around patient residences were associated with increased mortality during TB treatment, although the findings were not statistically significant in all buffers. As the buffer size decreased, fifth-quintile mortality hazards increased, and trends across quintiles of traffic exposure became more statistically significant. Increasing quintiles of nearest-road traffic volumes in the 100-m buffer were associated with 3%, 14%, 19%, and 28% increased risk of death during TB treatment [first quintile, referent; second quintile hazard ratio (HR)=1.03 [95% confidence interval (CI): 0.86, 1.25]; third quintile HR=1.14 (95% CI: 0.95, 1.37); fourth quintile HR=1.19 (95% CI: 0.99, 1.43); fifth quintile HR=1.28 (95% CI: 1.07, 1.53), respectively; p-trend=0.002].ConclusionsResidential proximity to road traffic volumes and traffic density were associated with increased all-cause mortality in patients undergoing treatment for active tuberculosis even after adjusting for multiple demographic, socioeconomic, and clinical factors, suggesting that TB patients are susceptible to the adverse health effects of traffic-related air pollution. https://doi.org/10.1289/EHP1699

The Effects of Outdoor Air Pollutants on the Costs of Stroke Hospitalizations in China

Stroke, the most frequent cause of severe disability and the second cause of death among adults in the world, brings tremendous mental and economic burden to patients and their families. Emerging evidence indicates that the air pollution mixture contributes to strokes. Knowing the relationship between the air pollution and the hospital costs of stroke can help us predict the costs due to air pollution, provide grounds for the allocation of medical insurance funds, and provide better working arrangements for CDC. However, few studies have examined this connection. We used time series analysis with a generalized additive model to estimate the associations between ambient air pollutions and hospital costs between the period of 2015–2017. We were surprised to find that although same-day air pollutions were positively associated with stroke mortality hospital costs were found to have a negatively association. Suggestive evidence of an association between fine particles and the costs of stroke were found: more serious air pollution increases the risk of stroke, but has a dampening effect on hospital costs. This study is the first step in optimizing medical resources, which is essential for policy making, service planning, and cost-effectiveness analysis of new therapeutic strategies

Diesel Exhaust Activates & Primes Microglia: Air Pollution, Neuroinflammation, & Regulation of Dopaminergic Neurotoxicity

BACKGROUND: Air pollution is linked to central nervous system disease, but the mechanisms responsible are poorly understood. OBJECTIVES: Here, we sought to address the brain-region-specific effects of diesel exhaust (DE) and key cellular mechanisms underlying DE-induced microglia activation, neuroinflammation, and dopaminergic (DA) neurotoxicity. METHODS: Rats were exposed to DE (2.0, 0.5, and 0 mg/m3) by inhalation over 4 weeks or as a single intratracheal administration of DE particles (DEP; 20 mg/kg). Primary neuron-glia cultures and the HAPI (highly aggressively proliferating immortalized) microglial cell line were used to explore cellular mechanisms. RESULTS: Rats exposed to DE by inhalation demonstrated elevated levels of whole-brain IL-6 (interleukin-6) protein, nitrated proteins, and IBA-1 (ionized calcium-binding adaptor molecule 1) protein (microglial marker), indicating generalized neuroinflammation. Analysis by brain region revealed that DE increased TNFα (tumor necrosis factor-α), IL-1β, IL-6, MIP-1α (macrophage inflammatory protein-1α) RAGE (receptor for advanced glycation end products), fractalkine, and the IBA-1 microglial marker in most regions tested, with the midbrain showing the greatest DE response. Intratracheal administration of DEP increased microglial IBA-1 staining in the substantia nigra and elevated both serum and whole-brain TNFα at 6 hr posttreatment. Although DEP alone failed to cause the production of cytokines and chemokines, DEP (5 μg/mL) pretreatment followed by lipopolysaccharide (2.5 ng/mL) in vitro synergistically amplified nitric oxide production, TNFα release, and DA neurotoxicity. Pretreatment with fractalkine (50 pg/mL) in vitro ameliorated DEP (50 μg/mL)-induced microglial hydrogen peroxide production and DA neurotoxicity. CONCLUSIONS: Together, these findings reveal complex, interacting mechanisms responsible for how air pollution may cause neuroinflammation and DA neurotoxicity

Residential proximity to major roadways and prevalent hypertension among postmenopausal women: results from the Women's Health Initiative San Diego Cohort.

BackgroundLiving near major roadways has been linked with increased risk of cardiovascular events and worse prognosis. Residential proximity to major roadways may also be associated with increased risk of hypertension, but few studies have evaluated this hypothesis.Methods and resultsWe examined the cross-sectional association between residential proximity to major roadways and prevalent hypertension among 5401 postmenopausal women enrolled into the San Diego cohort of the Women's Health Initiative. We used modified Poisson regression with robust error variance to estimate the association between prevalence of hypertension and residential distance to nearest major roadway, adjusting for participant demographics, medical history, indicators of individual and neighborhood socioeconomic status, and for local supermarket/grocery and fast food/convenience store density. The adjusted prevalence ratios for hypertension were 1.22 (95% CI: 1.07, 1.39), 1.13 (1.00, 1.27), and 1.05 (0.99, 1.12) for women living ≤100, &gt;100 to 200, and &gt;200 to 1000 versus &gt;1000 m from a major roadway (P for trend=0.006). In a model treating the natural log of distance to major roadway as a continuous variable, a shift in distance from 1000 to 100 m from a major roadway was associated with a 9% (3%, 16%) higher prevalence of hypertension.ConclusionsIn this cohort of postmenopausal women, residential proximity to major roadways was positively associated with the prevalence of hypertension. If causal, these results suggest that living close to major roadways may be an important novel risk factor for hypertension