Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research

Abstract

Exposure to traffic related nitrogen dioxide (NO) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO. The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

Pengaruh Faktor Meteorologi Terhadap Konsentrasi NO2 di Udara Ambien (Studi Kasus Bundaran Hotel Indonesia DKI Jakarta)

“…Sumber NO 2 di udara ambien dapat berupa proses alami di atmosfer dan permukaan bumi seperti produksi oleh tanaman, tanah, dan air [8]. Selain itu NO 2 juga dapat berasal dari aktivitas manusia, seperti pembakaran bahan bakar fosil, lalu lintas atau knalpot kendaraan bermotor [9], penyulingan bensin dan logam, pembangkit listrik tenaga batu bara [10], industri manufaktur, dan penggunaan perumahan [8]. NO 2 berkontribusi dalam pembentukan kabut fotokimia yang memberikan dampak terhadap kesehatan manusia dan lingkungan [7].…”

Comparison of model estimates from an intra-city land use regression model with a national satellite-LUR and a regional Bayesian Maximum Entropy model, in estimating NO2 for a birth cohort in Sydney, Australia

“…In an effort to reduce the uncertainty associated with these individual approaches, recent work has sought to combine or "blend" separate exposure model estimates with varying spatial and temporal resolutions (Akita et al 2014;Buteau et al 2017;Hanigan et al 2017). Hanigan et al (2017) used a Bayesian Maximum Entropy (BME) model to blend estimates from an Australian LUR model using satellite data sat-LUR (Knibbs et al 2014), a CTM (Cope et al 2014), and measurements from fixed site regulatory monitors, to produce NO 2 estimates for Sydney. The BME model resulted in a 6% improvement in Root Mean Square Error (RMSE) compared to the sat-LUR model and 16% improvement compared to the CTM model.…”

“…Our second aim to compare NO 2 cohort estimates calculated from the three different spatial models is important, especially for settings where there may be limited capacity to develop more complex exposure models. To do this, we assessed the agreement between three estimates of NO 2 for each cohort address, derived from the standard LUR model and two alternative models: a national sat-LUR model (Knibbs et al 2014); and an ensemble BME regional model (Hanigan et al 2017). While a number of studies (Table S1) have compared pollutant estimates derived from different hybrid models, to our knowledge there are no studies that have examined the agreement between three such models with substantially varying spatial scales.…”

“…Of note, NO 2 data from the fixed site monitors showed only minimal change during the period 2011-2014. The BME reported 6% and 16% improvements in root mean square error (RMSE) compared with the Sat-LUR and CTM models respectively, when compared using a Sydney wide study area for that analysis (Hanigan et al 2017).…”

A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

“…Research into 'data fusion' across existing air quality networks and future sensor networks that could include hot spot measurements, satellite retrievals, and model outputs (including chemical transport and land use regression models) for a more comprehensive air quality and exposure mapping, etc. [205].…”