Matthew Shupler1, William Godwin2, Joseph Frostad2, Paul Gustafson3, Raphael E Arku4, Michael Brauer5. 1. School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada. Electronic address: mshupler@gmail.com. 2. Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, United States of America. 3. Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada. 4. School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada; School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States of America. 5. School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada; Institute for Health Metrics & Evaluation, University of Washington, Seattle, WA, United States of America.
Abstract
BACKGROUND: Exposure to household air pollution (HAP) from cooking with dirty fuels is a leading health risk factor within Asia, Africa and Central/South America. The concentration of particulate matter of diameter ≤ 2.5 μm (PM2.5) is an important metric to evaluate HAP risk, however epidemiological studies have demonstrated significant variation in HAP-PM2.5 concentrations at household, community and country levels. To quantify the global risk due to HAP exposure, novel estimation methods are needed, as financial and resource constraints render it difficult to monitor exposures in all relevant areas. METHODS: A Bayesian, hierarchical HAP-PM2.5 global exposure model was developed using kitchen and female HAP-PM2.5 exposure data available in peer-reviewed studies from an updated World Health Organization Global HAP database. Cooking environment characteristics were selected using leave-one-out cross validation to predict quantitative HAP-PM2.5 measurements from 44 studies. Twenty-four hour HAP-PM2.5 kitchen concentrations and male, female and child exposures were estimated for 106 countries in Asia, Africa and Latin America. RESULTS: A model incorporating fuel/stove type (traditional wood, improved biomass, coal, dung and gas/electric), urban/rural location, wet/dry season and socio-demographic index resulted in a Bayesian R2 of 0.57. Relative to rural kitchens using gas or electricity, the mean global 24-hour HAP-PM2.5 concentrations were 290 μg/m3 higher (range of regional averages: 110, 880) for traditional stoves, 150 μg/m3 higher (range of regional averages: 50, 290) for improved biomass stoves, 850 μg/m3 higher (range of regional averages: 310, 2600) for animal dung stoves, and 220 μg/m3 higher (range of regional averages: 80, 650) for coal stoves. The modeled global average female/kitchen exposure ratio was 0.40. Average modeled female exposures from cooking with traditional wood stoves were 160 μg/m3 in rural households and 170 μg/m3 in urban households. Average male and child rural area exposures from traditional wood stoves were 120 μg/m3 and 140 μg/m3, respectively; average urban area exposures were identical to average rural exposures among both sub-groups. CONCLUSIONS: A Bayesian modeling approach was used to generate unique HAP-PM2.5 kitchen concentrations and personal exposure estimates for all countries, including those with little to no available quantitative HAP-PM2.5 exposure data. The global exposure model incorporating type of fuel-stove combinations can add specificity and reduce exposure misclassification to enable an improved global HAP risk assessment.
BACKGROUND: Exposure to household air pollution (HAP) from cooking with dirty fuels is a leading health risk factor within Asia, Africa and Central/South America. The concentration of particulate matter of diameter ≤ 2.5 μm (PM2.5) is an important metric to evaluate HAP risk, however epidemiological studies have demonstrated significant variation in HAP-PM2.5 concentrations at household, community and country levels. To quantify the global risk due to HAP exposure, novel estimation methods are needed, as financial and resource constraints render it difficult to monitor exposures in all relevant areas. METHODS: A Bayesian, hierarchical HAP-PM2.5 global exposure model was developed using kitchen and female HAP-PM2.5 exposure data available in peer-reviewed studies from an updated World Health Organization Global HAP database. Cooking environment characteristics were selected using leave-one-out cross validation to predict quantitative HAP-PM2.5 measurements from 44 studies. Twenty-four hour HAP-PM2.5 kitchen concentrations and male, female and child exposures were estimated for 106 countries in Asia, Africa and Latin America. RESULTS: A model incorporating fuel/stove type (traditional wood, improved biomass, coal, dung and gas/electric), urban/rural location, wet/dry season and socio-demographic index resulted in a Bayesian R2 of 0.57. Relative to rural kitchens using gas or electricity, the mean global 24-hour HAP-PM2.5 concentrations were 290 μg/m3 higher (range of regional averages: 110, 880) for traditional stoves, 150 μg/m3 higher (range of regional averages: 50, 290) for improved biomass stoves, 850 μg/m3 higher (range of regional averages: 310, 2600) for animal dung stoves, and 220 μg/m3 higher (range of regional averages: 80, 650) for coal stoves. The modeled global average female/kitchen exposure ratio was 0.40. Average modeled female exposures from cooking with traditional wood stoves were 160 μg/m3 in rural households and 170 μg/m3 in urban households. Average male and child rural area exposures from traditional wood stoves were 120 μg/m3 and 140 μg/m3, respectively; average urban area exposures were identical to average rural exposures among both sub-groups. CONCLUSIONS: A Bayesian modeling approach was used to generate unique HAP-PM2.5 kitchen concentrations and personal exposure estimates for all countries, including those with little to no available quantitative HAP-PM2.5 exposure data. The global exposure model incorporating type of fuel-stove combinations can add specificity and reduce exposure misclassification to enable an improved global HAP risk assessment.
Authors: Win Le Shwe Sin Ei; Than Lwin Tun; Chit Htun; Etienne Gignoux; Kyaw Thu Swe; Andrea Incerti; Derek C Johnson Journal: PLoS One Date: 2019-05-14 Impact factor: 3.240
Authors: Chris Kypridemos; Elisa Puzzolo; Borgar Aamaas; Lirije Hyseni; Matthew Shupler; Kristin Aunan; Daniel Pope Journal: Environ Health Perspect Date: 2020-04-01 Impact factor: 9.031
Authors: Matthew Shupler; Perry Hystad; Aaron Birch; Daniel Miller-Lionberg; Matthew Jeronimo; Raphael E Arku; Yen Li Chu; Maha Mushtaha; Laura Heenan; Sumathy Rangarajan; Pamela Seron; Fernando Lanas; Fairuz Cazor; Patricio Lopez-Jaramillo; Paul A Camacho; Maritza Perez; Karen Yeates; Nicola West; Tatenda Ncube; Brian Ncube; Jephat Chifamba; Rita Yusuf; Afreen Khan; Bo Hu; Xiaoyun Liu; Li Wei; Lap Ah Tse; Deepa Mohan; Parthiban Kumar; Rajeev Gupta; Indu Mohan; K G Jayachitra; Prem K Mony; Kamala Rammohan; Sanjeev Nair; P V M Lakshmi; Vivek Sagar; Rehman Khawaja; Romaina Iqbal; Khawar Kazmi; Salim Yusuf; Michael Brauer Journal: Lancet Planet Health Date: 2020-10
Authors: Kuan Ken Lee; Rong Bing; Joanne Kiang; Sophia Bashir; Nicholas Spath; Dominik Stelzle; Kevin Mortimer; Anda Bularga; Dimitrios Doudesis; Shruti S Joshi; Fiona Strachan; Sophie Gumy; Heather Adair-Rohani; Engi F Attia; Michael H Chung; Mark R Miller; David E Newby; Nicholas L Mills; David A McAllister; Anoop S V Shah Journal: Lancet Glob Health Date: 2020-11 Impact factor: 26.763
Authors: Nelson Augusto Rosário Filho; Marilyn Urrutia-Pereira; Gennaro D'Amato; Lorenzo Cecchi; Ignacio J Ansotegui; Carmen Galán; Anna Pomés; Margarita Murrieta-Aguttes; Luis Caraballo; Philip Rouadi; Herberto J Chong-Neto; David B Peden Journal: World Allergy Organ J Date: 2021-01-07 Impact factor: 4.084
Authors: Jiawen Liao; Miles A Kirby; Ajay Pillarisetti; Ricardo Piedrahita; Kalpana Balakrishnan; Sankar Sambandam; Krishnendu Mukhopadhyay; Wenlu Ye; Ghislaine Rosa; Fiona Majorin; Ephrem Dusabimana; Florien Ndagijimana; John P McCracken; Erick Mollinedo; Oscar de Leon; Anaité Díaz-Artiga; Lisa M Thompson; Katherine A Kearns; Luke Naeher; Joshua Rosenthal; Maggie L Clark; Kyle Steenland; Lance A Waller; William Checkley; Jennifer L Peel; Thomas Clasen; Michael Johnson Journal: Environ Pollut Date: 2021-09-21 Impact factor: 8.071