fine particulate
Recently Published Documents


TOTAL DOCUMENTS

3274
(FIVE YEARS 1280)

H-INDEX

105
(FIVE YEARS 19)

2022 ◽  
Vol 26 ◽  
pp. 101281
Author(s):  
Ko-Chih Lin ◽  
Jia-Wei Yang ◽  
Pei-Yi Ho ◽  
Chun-Zai Yen ◽  
Hao-Wei Huang ◽  
...  

2022 ◽  
Vol 160 ◽  
pp. 107053
Author(s):  
Jovine Bachwenkizi ◽  
Cong Liu ◽  
Xia Meng ◽  
Lina Zhang ◽  
Weidong Wang ◽  
...  

2022 ◽  
Vol 160 ◽  
pp. 107082
Author(s):  
Yang Xie ◽  
Ying Wang ◽  
Yichi Zhang ◽  
Wenhong Fan ◽  
Zhaomin Dong ◽  
...  

2022 ◽  
Author(s):  
Ashwini Sankar ◽  
Andrew Goodkind ◽  
Jay Coggins

Abstract Chronic exposure to ambient fine particulate matter (PM2.5) represents one of the largest global public health risks, leading to millions of premature deaths annually. For a country facing high and spatially variable exposures, prioritizing where to reduce PM2.5 concentrations leads to an inherent tradeoff between saving the most lives and reducing inequality of exposure. This tradeoff results from the shape of the concentration-response function between exposure to PM2.5 and mortality, which indicates that the additional lives saved per unit reduction in PM2.5 declines as concentrations increase. We estimate this concentration-response function for urban areas of India, finding that a 10 unit reduction in PM2.5 in already-clean locations will reduce the mortality rate substantially (4.2% for a reduction from 30 to 20 µgm-3), while a 10 unit reduction in the dirtiest locations will reduce mortality only modestly (1.2% for a reduction from 90 to 80 µgm-3). We explore the implications of this PM2.5/mortality relationship by considering a thought experiment. If India had a fixed amount of resources to devote to PM2.5 concentration reductions across urban areas, what is the lives saved/inequality of exposure tradeoff from three different methods of employing those resources? Across our three scenarios—1) which reduces exposures for the dirtiest districts, 2) which reduces exposures everywhere equally, and 3) which reduces exposures to save the most lives—scenario 1 saves 18,000 lives per year while reducing the inequality of exposure by 65%, while scenario 3 saves 126,000 lives per year, but increases inequality by 19%.


2022 ◽  
Vol 194 (2) ◽  
Author(s):  
Elson Silva Galvão ◽  
Jane Meri Santos ◽  
Neyval Costa Reis Junior ◽  
Rita de Cassia Feroni ◽  
Marcos Tadeu D’Azeredo Orlando

Sign in / Sign up

Export Citation Format

Share Document