Developing intake fraction estimates with limited data: Comparison of methods in Mexico City |
| |
| Institution: | 1. Department of Environmental Health, Harvard School of Public Health, Landmark Center, P.O. Box 15677, Boston, MA 02215, USA;2. Harvard Initiative for Global Health, 104 Mount Auburn St., Cambridge, MA 02138, USA;3. Molina Center for Energy and the Environment, 3262 Holiday Ct. Suite 201, La Jolla, CA 92037, USA;4. Program in Atmospheric and Oceanic Sciences, and Woodrow Wilson School of Public and International Affairs, Princeton University, 403 Robertson Hall, Princeton, NJ 08544, USA;1. CNR-ISAC, Via Fosso del Cavaliere, Rome, Italy;2. ENEA, Frascati, Rome, Italy;1. Climate Systems Analysis Group, Department of Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa;2. African Centre for Cities, Department of Environmental and Geographical Science, University of Cape Town, South Africa;1. Department of Physics and Astronomy – University of Florence, Italy;2. National Institute of Nuclear Physics (INFN) – Florence, Via G. Sansone 1, 50019 Sesto Fiorentino (Fi), Italy;3. Department of Chemistry – University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino (Fi), Italy;4. Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain;5. Centre for Environmental and Marine Studies (CESAM), Department of Environment, University of Aveiro, 3810-193 Aveiro, Portugal;6. Environmental Radioactivity Laboratory, N.C.S.R. Demokritos Univ., 15341 Ag. Paraskevi, Attiki, Greece;1. State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China;2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;3. Department of Computer Science, University of Georgia, Athens, GA, USA |
| |
| Abstract: | In order to estimate the health benefits of reducing mobile source emissions, analysts typically use detailed atmospheric models to estimate the change in population exposure that results from a given change in emissions. However, this may not be feasible in settings where data are limited or policy decisions are needed in the short term. Intake fraction (iF), defined as the fraction of emissions of a pollutant or its precursor that is inhaled by the population, is a metric that can be used to compare exposure assessment methods in a health benefits analysis context. To clarify the utility of rapid-assessment methods, we calculate particulate matter iFs for the Mexico City Metropolitan Area using five methods, some more resource intensive than others. First, we create two simple box models to describe dispersion of primary fine particulate matter (PM2.5) in the Mexico City basin. Second, we extrapolate iFs for primary PM2.5, ammonium sulfate, and ammonium nitrate from US values using a regression model. Third, we calculate iFs by assuming a linear relationship between emissions and population-weighted concentrations of primary PM2.5, ammonium nitrate, and ammonium sulfate (a particle composition method). Finally, we estimate PM iFs from detailed atmospheric dispersion and chemistry models run for only a short period of time. Intake fractions vary by up to a factor of five, from 23 to 120 per million for primary PM2.5. Estimates of 60, 7, and 0.7 per million for primary PM, secondary ammonium sulfate, and secondary ammonium nitrate, respectively, represent credible central estimates, with an approximate factor of two uncertainty surrounding each estimate. Our results emphasize that multiple rapid-assessment methods can provide meaningful estimates of iFs in resource-limited environments, and that formal uncertainty analysis, with special attention to model biases and uncertainty, would be important for health benefits analyses. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
