Source contributions and potential reductions to health effects of particulate matter in India
Abstract
Health effects of exposure to fine particulate matter (PM_(2.5)) in India were estimated in this study based on a source-oriented version of the Community Multi-scale Air Quality (CMAQ) model. Contributions of different sources to premature mortality and years of life lost (YLL) were quantified in 2015. Premature mortality due to cerebrovascular disease (CEVD) was the highest in India (0.44million), followed by ischaemic heart disease (IHD, 0.40million), chronic obstructive pulmonary disease (COPD, 0.18million), and lung cancer (LC, 0.01million), with a total of 1.04million deaths. The states with highest premature mortality were Uttar Pradesh (0.23million), Bihar (0.12million), and West Bengal (0.10million). The highest total YLL was 2 years in Delhi, and the Indo-Gangetic plains and eastern India had higher YLL ( ∼ 1 years) than other regions. The residential sector was the largest contributor to PM_(2.5) concentrations ( ∼ 40 µg m^(−3)), total premature mortality (0.58 million), and YLL ( ∼ 0.2 years). Other important sources included industry ( ∼ 20 µg m^(−3)), agriculture ( ∼ 10 µg m^(−3)), and energy ( ∼ 5 µg m^(−3)) with their national averaged contributions of 0.21, 0.12, and 0.07 million to premature mortality, and 0.12, 0.1, and 0.05 years to YLL. Reducing PM_(2.5) concentrations would lead to a significant reduction of premature mortality and YLL. For example, premature mortality in Uttar Pradesh (including Delhi) due to PM_(2.5) exposures would be reduced by 79% and YLL would be reduced by 83% when reducing PM_(2.5) concentrations to 10 µg m^(−3).
Additional Information
© Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 14 May 2018 – Discussion started: 08 Jun 2018 – Revised: 30 Sep 2018 – Accepted: 02 Oct 2018 – Published: 23 Oct 2018. Portions of this research were conducted with high-performance computing resources provided by Louisiana State University (http://www.hpc.lsu.edu, last access: 20 September 2018.). The project is funded by the Competitiveness Subprogram (RCS) from Louisiana Board of Regents (LEQSF(2016-19)-RDA-14) and the European Climate Foundation (G-1606-00917). Jianlin Hu would like to thank the support from the National Natural Science Foundation of China (41675125) and Natural Science Foundation of Jiangsu Province (BK20150904), Jiangsu Six Major Talent Peak Project (2015-JNHB-010). Author contributions. HG and HZ designed the research, modified the health model, and conducted simulations. SK and SS offered local data and helped in modifying codes. JH, QY, YW and HZ contributed to model development and configuration. HG and KC analyzed the data. HG prepared the paper and all co-authors helped improve the paper. Data availability. Data used in this paper can be provided upon request by e-mail to the corresponding author (hlzhang@lsu.edu). The authors declare that they have no conflict of interest.Attached Files
Published - acp-18-15219-2018.pdf
Supplemental Material - acp-18-15219-2018-supplement.pdf
Files
Name | Size | Download all |
---|---|---|
md5:6a056703c14dd06a502bfddf6e1579f5
|
643.3 kB | Preview Download |
md5:08fc8e3b9a62d8a9b5e2ea3546537e43
|
3.1 MB | Preview Download |
Additional details
- Eprint ID
- 90901
- Resolver ID
- CaltechAUTHORS:20181114-153057963
- LEQSF(2016-19)-RD-A-14
- Louisiana Board of Regents
- G-1606-00917
- European Climate Foundation
- 41675125
- National Natural Science Foundation of China
- BK20150904
- Natural Science Foundation of Jiangsu Province
- 2015-JNHB-010
- Jiangsu Six Major Talent Peak Project
- Created
-
2018-11-14Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field