Urbanization-induced population migration has reduced ambient PM2

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Presentation transcript:

Urbanization-induced population migration has reduced ambient PM2 Urbanization-induced population migration has reduced ambient PM2.5 concentrations in China by Huizhong Shen, Shu Tao, Yilin Chen, Philippe Ciais, Burak Güneralp, Muye Ru, Qirui Zhong, Xiao Yun, Xi Zhu, Tianbo Huang, Wei Tao, Yuanchen Chen, Bengang Li, Xilong Wang, Wenxin Liu, Junfeng Liu, and Shuqing Zhao Science Volume 3(7):e1700300 July 19, 2017 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 1 Illustration of the changes in urban and rural RTC emissions and PM2.5 concentrations due to population migration. Illustration of the changes in urban and rural RTC emissions and PM2.5 concentrations due to population migration. Distributions of RTC emissions and PM2.5 concentrations before (A) and after (B) migration. Compared with rural areas, urban areas are associated with lower per-capita RTC emissions due to cleaner RTC energy mix used by the urban population. A consequence of the population migration is the decrease in rural population density, which leads to decreases in RTC emissions and PM2.5 concentration in rural areas. In contrast, migration-induced change in urban concentration is a competition between increased urban emissions and declined background concentrations contributed by decreased rural emissions. In addition, migration to cities causes more people to be exposed to polluted urban air. The overall change in population exposure concentration and premature deaths across China due to migration is the consequence of the concurrent changes in both concentrations and population distributions. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 2 The rapidly changing population in China from 1980 to 2030. The rapidly changing population in China from 1980 to 2030. (A) Temporal trends of the population of four major categories. (B) Temporal trends of the urban population density and the fraction of urban areas to total area. (C) Population frequency distributions at different levels of population densities in 1980, 1990, 2000, 2010, and 2030. The gray and orange regions illustrate the population density ranges containing most of the rural and urban population, respectively. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 3 Disparities in RTC energy consumption among RRs, MUs, and URs. Disparities in RTC energy consumption among RRs, MUs, and URs. (A) Per-capita energy mix of RRs, MUs, and URs in Beijing (a representative for the north of China) and Guangzhou (a representative for the south of China). Beijing and Guangzhou are the two cities where our questionnaire surveys were conducted. The per-capita energy consumption of RRs is calculated as the weighted average of per-capita rural energy consumption of all home provinces of the rural-urban migrants in that city. (B) Temporal trend of total RTC energy consumption by RRs in China. (C) Temporal trend of total RTC energy consumption by MUs in China. (D) Temporal trend of total RTC energy consumption by URs in China. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 4 Per-capita RTC emissions of primary PM pollutants and secondary PM precursors in China by fuel. Per-capita RTC emissions of primary PM pollutants and secondary PM precursors in China by fuel. Per-capita RTC emissions of RRs (A), MUs (B), and URs (C). The axis ranges of different pollutants are different, as shown in the legend. The per-capita emissions shown in the graphs are national averages in 2010. See figs. S4 and S5 for the per-capita emissions in 1990 and 2030. NMVOC, non-methane volatile organic compounds; TSP, total suspended particulate matter. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 5 Migration-induced spatial changes in PM2 Fig. 5 Migration-induced spatial changes in PM2.5 emissions and concentrations in China. Migration-induced spatial changes in PM2.5 emissions and concentrations in China. Spatial changes in (A) annual primary PM2.5 emissions and (B) annual average PM2.5 concentrations (primary + secondary) in 2010 in mainland China caused by the population migration in the last 30 years. (A) The bubbles represent the changes in emissions within the urban areas of individual cities. The areas of the bubbles are proportional to emission increase. The color of the bubbles shows the change in urban emission density. Emission change in rural area is shown in the background. Because of population migration since 1980, emissions from RTC were spatially relocated. Rural emissions decreased pervasively while urban emissions increased. The national total emissions were also changed by migration because migrants got easier access to clean fuels and spontaneously shifted their energy mix after migrating into urban areas. (B) Migration-induced increase in urban emissions cannot compensate the extensive decrease in rural areas, leading to a reduction of PM2.5 concentrations across the east part of the country. There are some exceptions such as those four megacities. Serving as the destinations of massive migration, these cities showed an increase in PM2.5 concentrations due to increased local emissions by migration. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 6 Changes of PM2.5 exposure concentrations in urban areas of individual cities as a result of population migration in 2010. Changes of PM2.5 exposure concentrations in urban areas of individual cities as a result of population migration in 2010. Each bubble represents an individual city. The areas of the bubbles are proportional to urban population in the city. The shades of the bubbles represent the increase in urban population since 1980. (A) Relationship between the migration-induced change in PM2.5 exposure concentrations with GDPcap. (B) Relationship between the migration-induced change in PM2.5 exposure concentrations with HDD. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

Fig. 7 Influences of individual non–migration-related and migration-related factors on PM2.5 emissions, PM2.5 exposure concentrations, and annual deaths from exposure to ambient PM2.5 in China in 2010. Influences of individual non–migration-related and migration-related factors on PM2.5 emissions, PM2.5 exposure concentrations, and annual deaths from exposure to ambient PM2.5 in China in 2010. The results are presented as changes in PM2.5 emissions (A), PM2.5 exposure concentrations (B), and annual deaths from exposure to ambient PM2.5 (C) caused by the change of individual non–migration-related and migration-related factors between 1980 and 2010. Influences on urban, rural, and total areas are illustrated using bars with different colors. In urban areas, total migration-related factors led to a decrease in PM2.5 exposure concentration but an increase in annual deaths because the urban population base was increased as a result of migration. Huizhong Shen et al. Sci Adv 2017;3:e1700300 Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).