Marissa N. DeLang

BACKGROUND: Data on long-term trends of ozone exposure and attributable mortality across urban-rural catchment areas worldwide are scarce, especially for low-income and middle-income countries. This study aims to estimate trends in ozone concentrations and attributable mortality for urban-rural catchment areas worldwide. METHODS: In this modelling study, we used a health impact function to estimate ozone concentrations and ozone-attributable chronic respiratory disease mortality for urban areas worldwide, and their surrounding peri-urban, peri-rural, and rural areas. We estimated ozone-attributable respiratory health outcomes using a modified Global Burden of Diseases, Injuries, and Risk Factors 2019 Study approach. We evaluate long-term trends with linear regressions of annual ozone concentrations and ozone-attributable mortality against time in years, and examined the influence of each health impact function input parameter to temporal changes in ozone-attributable disease burden estimates for 12 946 cities worldwide by region, from 2000 to 2019. FINDINGS: ) increased from 11 568 (89%) of 12 946 cities in 2000 to 12 433 (96%) cities in 2019. Percent change in ozone-attributable mortality averaged across 11 032 cities within each region from 2000 to 2019 ranged from -62% in eastern Europe to 350% in tropical Latin America. The contribution of ozone concentrations, population size, and baseline chronic respiratory disease rates to the change in ozone-attributable mortality differed regionally. INTERPRETATION: Ozone exposure is increasing worldwide, contributing to disproportionate ozone mortality in peri-urban areas and increasing ozone exposure and attributable mortality in urban areas worldwide. Reducing ozone precursor emissions in areas affecting urban and peri-urban exposure can yield substantial public health benefits. FUNDING: NASA Health and Air Quality Applied Sciences Team, the National Institute for Occupational Safety and Health, and the NOAA Co-operative Agreement with the Cooperative Institute for Research in Environmental Sciences.

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multimodel composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8834 sites globally) in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multimodel composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2 = 0.81 at the test point, 0.63 at 0.1°, and 0.62 at 0.5°) than the multimodel mean (R2 = 0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

Abstract City-level estimates of ambient ozone concentrations and associated disease burdens are sparsely available, especially for low and middle-income countries. Recently available high-resolution gridded global ozone concentration estimates allow for estimating ozone concentrations and mortality at urban scales and for urban-rural catchment areas worldwide. We applied existing fine resolution global surface ozone estimates, developed by integrating observations (8834 sites globally) with nine atmospheric chemistry models, in an epidemiologically-derived health impact function to estimate chronic respiratory disease mortality worldwide in 2019. We compared ozone season daily maximum 8 h mixing ratio concentrations and ozone-attributable mortality for urban areas worldwide (including cities and densely-populated towns), and their surrounding peri-urban, peri-rural, and rural areas. In 2019, population-weighted mean ozone among all urban-rural catchment areas was greatest in peri-urban areas (52 ppb), followed by urban areas (cities and towns; 49 ppb). Of 423 100 estimated global ozone-attributable deaths, 37% (147 100) occurred in urban areas, where 40% of the world’s population resides, and 56% (254 000) occurred in peri-urban areas (<1 h from an urban area), where 47% of the world’s population resides. Across 12 946 cities (excluding towns), average population-weighted mean ozone was 51 ppb (sd = 13 ppb, range = 10–78 ppb). Three quarters of the ozone-attributable deaths worldwide (77%; 112 700) occurred in cities of South and East Asia. City-level ozone-attributable mortality rates varied by a factor of 10 across world regions. Ozone levels and attributable mortality were greatest in Asian and African cities; however, cities of higher-income regions, like high-income Asia Pacific and North America, continue to experience high ozone concentrations and attributable mortality rates, despite successful national air quality measures for reducing ozone precursor emissions. The disproportionate magnitude of ozone mortality compared with population size in peri-urban areas indicates that reducing ozone precursor emissions in places that influence peri-urban concentrations can yield substantial health benefits in these areas.

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.