TY - JOUR
T1 - Sources of ambient PM2.5 exposure in 96 global cities
AU - Tessum, Mei W.
AU - Anenberg, Susan C.
AU - Chafe, Zoe A.
AU - Henze, Daven K.
AU - Kleiman, Gary
AU - Kheirbek, Iyad
AU - Marshall, Julian D.
AU - Tessum, Christopher W.
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/10/1
Y1 - 2022/10/1
N2 - To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM2.5 exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.
AB - To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM2.5 exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.
KW - Air quality
KW - Air quality modeling
KW - Chemical transport modeling
KW - Environmental policy
KW - Fine particulate matter
KW - Metropolitan
KW - Pollution
UR - http://www.scopus.com/inward/record.url?scp=85132537858&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132537858&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2022.119234
DO - 10.1016/j.atmosenv.2022.119234
M3 - Article
C2 - 36193038
AN - SCOPUS:85132537858
SN - 1352-2310
VL - 286
JO - Atmospheric Environment
JF - Atmospheric Environment
M1 - 119234
ER -