Literature DB >> 26197060

Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices.

Jianlin Hu1, Qi Ying2, Yungang Wang3, Hongliang Zhang4.   

Abstract

Multi-pollutant air pollution (i.e., several pollutants reaching very high concentrations simultaneously) frequently occurs in many regions across China. Air quality index (AQI) is used worldwide to inform the public about levels of air pollution and associated health risks. The current AQI approach used in China is based on the maximum value of individual pollutants, and does not consider the combined health effects of exposure to multiple pollutants. In this study, two novel alternative indices--aggregate air quality index (AAQI) and health-risk based air quality index (HAQI)--were calculated based on data collected in six megacities of China (Beijing, Shanghai, Guangzhou, Shjiazhuang, Xi'an, and Wuhan) during 2013 to 2014. Both AAQI and HAQI take into account the combined health effects of various pollutants, and the HAQI considers the exposure (or concentration)-response relationships of pollutants. AAQI and HAQI were compared to AQI to examine the effectiveness of the current AQI in characterizing multi-pollutant air pollution in China. The AAQI and HAQI values are higher than the AQI on days when two or more pollutants simultaneously exceed the Chinese Ambient Air Quality Standards (CAAQS) 24-hour Grade II standards. The results of the comparison of the classification of risk categories based on the three indices indicate that the current AQI approach underestimates the severity of health risk associated with exposure to multi-pollutant air pollution. For the AQI-based risk category of 'unhealthy', 96% and 80% of the days would be 'very unhealthy' or 'hazardous' if based on AAQI and HAQI, respectively; and for the AQI-based risk category of 'very unhealthy', 67% and 75% of the days would be 'hazardous' if based on AAQI and HAQI, respectively. The results suggest that the general public, especially sensitive population groups such as children and the elderly, should take more stringent actions than those currently suggested based on the AQI approach during high air pollution events. Sensitivity studies were conducted to examine the assumptions used in the AAQI and HAQI approaches. Results show that AAQI is sensitive to the choice of pollutant irrelevant constant. HAQI is sensitive to the choice of both threshold values and pollutants included in total risk calculation.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Air quality index; China; Health risk; Multi-pollutant air pollution; PM(2.5)

Mesh:

Substances:

Year:  2015        PMID: 26197060     DOI: 10.1016/j.envint.2015.06.014

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  17 in total

1.  Difference of performance in response to disease admissions between daily time air quality indices and those derived from average and entropy functions.

Authors:  Li-Wei Lai; Wan-Li Cheng
Journal:  Environ Sci Pollut Res Int       Date:  2017-05-08       Impact factor: 4.223

2.  The Coordinated Development and Regulation Research on Public Health, Ecological Environment and Economic Development: Evidence from the Yellow River Basin of China.

Authors:  Wei Wei; Chenggong Jin; Ying Han; Zhenhui Huang; Tong Niu; Jinkai Li
Journal:  Int J Environ Res Public Health       Date:  2022-06-06       Impact factor: 4.614

3.  Photochemical Conversion of Surrogate Emissions for Use in Toxicological Studies: Role of Particulate- and Gas-Phase Products.

Authors:  Jonathan D Krug; Michael Lewandowski; John H Offenberg; John M Turlington; William A Lonneman; Nabanita Modak; Q Todd Krantz; Charly King; Stephen H Gavett; M Ian Gilmour; David M DeMarini; Tadeusz E Kleindienst
Journal:  Environ Sci Technol       Date:  2018-02-13       Impact factor: 9.028

4.  Detecting the causality influence of individual meteorological factors on local PM2.5 concentration in the Jing-Jin-Ji region.

Authors:  Ziyue Chen; Jun Cai; Bingbo Gao; Bing Xu; Shuang Dai; Bin He; Xiaoming Xie
Journal:  Sci Rep       Date:  2017-01-27       Impact factor: 4.379

5.  Estimating the Excess Mortality Risk during Two Red Alert Periods in Beijing, China.

Authors:  Weilin Zeng; Lingling Lang; Yue Li; Lingchuan Guo; Hualiang Lin; Yonghui Zhang; Tao Liu; Jianpeng Xiao; Xing Li; Yanjun Xu; Xiaojun Xu; Lauren D Arnold; Erik J Nelson; Zhengmin Qian; Wenjun Ma
Journal:  Int J Environ Res Public Health       Date:  2017-12-29       Impact factor: 3.390

6.  Directional dependence between major cities in China based on copula regression on air pollution measurements.

Authors:  Jong-Min Kim; Namgil Lee; Xingyao Xiao
Journal:  PLoS One       Date:  2019-03-14       Impact factor: 3.240

7.  Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak.

Authors:  Pengfei Wang; Kaiyu Chen; Shengqiang Zhu; Peng Wang; Hongliang Zhang
Journal:  Resour Conserv Recycl       Date:  2020-03-23       Impact factor: 10.204

8.  Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017.

Authors:  Hong Guo; Xingfa Gu; Guoxia Ma; Shuaiyi Shi; Wannan Wang; Xin Zuo; Xiaochuan Zhang
Journal:  Sci Rep       Date:  2019-10-23       Impact factor: 4.379

9.  Mortality effects of heat waves vary by age and area: a multi-area study in China.

Authors:  Lingyan Zhang; Zhao Zhang; Tao Ye; Maigeng Zhou; Chenzhi Wang; Peng Yin; Bin Hou
Journal:  Environ Health       Date:  2018-06-11       Impact factor: 5.984

10.  Personal strategies to minimise effects of air pollution on respiratory health: advice for providers, patients and the public.

Authors:  Christopher Carlsten; Sundeep Salvi; Gary W K Wong; Kian Fan Chung
Journal:  Eur Respir J       Date:  2020-06-04       Impact factor: 16.671

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.