Relationships between Changes in Urban Characteristics and Air Quality in East Asia from 2000 to 2010

Characteristics of urban areas, such as density and compactness, are associated with local air pollution concentrations. The potential for altering air pollution through changing urban characteristics, however, is less certain, especially for expanding cities within the developing world. We examined changes in urban characteristics from 2000 to 2010 for 830 cities in East Asia to evaluate associations with changes in nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) air pollution. Urban areas were stratified by population size into small (100 000–250 000), medium, (250 000–1 000 000), and large (>1 000 000). Multivariate regression models including urban baseline characteristics, meteorological variables, and change in urban characteristics explained 37%, 49%, and 54% of the change in NO₂ and 29%, 34%, and 37% of the change in PM_2.5 for small, medium and large cities, respectively. Change in lights at night strongly predicted change in NO₂ and PM_2.5, while urban area expansion was strongly associated with NO₂ but not PM_2.5. Important differences between changes in urban characteristics and pollutant levels were observed by city size, especially NO₂. Overall, changes in urban characteristics had a greater impact on NO₂ and PM_2.5 change than baseline characteristics, suggesting urban design and land use policies can have substantial impacts on local air pollution levels

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO₂ exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO₂) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO₂ variation, with a mean absolute error of 3.7 ppb. Regional performance varied from <i>R</i>² = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (<i>n</i> = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted <i>R</i>² within 2%) but not for Africa and Oceania (adjusted <i>R</i>² within 11%) where NO₂ monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO₂ concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO₂ were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO₂ monitoring data or models

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO₂ exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO₂) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO₂ variation, with a mean absolute error of 3.7 ppb. Regional performance varied from <i>R</i>² = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (<i>n</i> = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted <i>R</i>² within 2%) but not for Africa and Oceania (adjusted <i>R</i>² within 11%) where NO₂ monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO₂ concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO₂ were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO₂ monitoring data or models