Mapping Social Vulnerability to Air Pollution: A Case Study of the Yangtze River Delta Region, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Case Study: the Yangtze River Delta Region
2.3. Research Method: the Projection Pursuit Cluster (PPC) Model
3. Results and Discussion
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Factors | Indicator Names | Description |
---|---|---|
Age | Children and Elderly | Children and the elderly are especially sensitive to air pollution. Physiologic immaturity and developmental changes account for children’s susceptibility to air pollutants. For older people, comorbidity, physical fragility and less appropriate immune responses decrease their coping capacity. Source: [10,29,53,54,55,56]. |
Gender | Female | Women can have a more difficult time during recovery than men, often due to lower wages, and family care responsibilities. Source: [27,29,32] |
Ethnicity | Ethnicity | Imposes language and cultural barriers that affect the ability to seek, find or understand warning information and access recovery information. Source: [29] |
Education | Illiterate and Educated | People highly educated are more likely to have better employment prospects, which results in better economic conditions and more resources to take precaution against air pollution. Source: [29,57,58,59] |
Individual economic status | Unemployed;Poor | Low-income individuals often exposure to hazardous pollution environment or can’t take enough actions to protect themselves against air pollution. Source: [2,60,61,62,63,64,65] |
Population exposure | Urban resident; Employees in 2nd industry, mining, manufactory and construction; GDP in secondary sector; Population density | Urban residents expose to severer air pollution for ambient heavy traffic. High-exposure occupations lead to high health risk for potential cumulative effects in air pollution. The boom and bust economy of secondary sector may create more high-exposure occupation opportunities. Population density illustrates discrepancy of average exposure among regions. Source: [2,17,51,59,66]. |
Regional resource | GDP; Green space coverage | “GDP” and “Green space coverage” demonstrate potential resources available for absorbing, reducing the adverse impact and recovering from losses more quickly. Source: [17,25,29,57] |
Medical and management services | Beds and Physicians in hospital; Employees in management sector | Public medical services can help for recovery and mitigation. Employees in the sectors of management can reflect the capacity of environmental governance. Source: [25,29,59]. |
No. | Indicator | Name | Description | Dimension of SVI | Impact to SVI |
---|---|---|---|---|---|
1 | Children | CHILD | Percentage of population under 14 years old | Susceptibility | + |
2 | Elderly | ELD | Percentage of population over 65 years old | Susceptibility | + |
3 | Female | FEMALE | Percentage of female | Susceptibility | + |
4 | Ethnicity | ETHNICITY | Percentage of Ethnicity | Susceptibility | + |
5 | Illiterate | ILLITERATE | Percentage of illiterates among those aged 15 and over | Susceptibility | + |
6 | Poor | POOR | Percentage of recipients of subsistence allowances | Susceptibility | + |
7 | Unemployed | UNEMPLOY | Percentage of unemployed | Susceptibility | + |
8 | Population density | POPDENSITY | Population density | Exposure | + |
9 | Urban resident | URBAN | Percentage of urban residents | Exposure | + |
10 | Employees in 2nd industry | SECWORKER | Percentage of employed in secondary industry | Exposure | + |
11 | Employees in mining | MINING | Percentage of employed in mining | Exposure | + |
12 | Employees in manufactory | MANUFACT | Percentage of employed in manufactory | Exposure | + |
13 | Employees in construction | CONSTRUCT | Percentage of employed in construction | Exposure | + |
14 | GDP in secondary sector | INDUSTRY | Percentage of GDP in secondary sector | Exposure | + |
15 | GDP | P_GDP | Gross domestic product per capita | Adaptability | − |
16 | Educated | EDUCATE | Percentage higher education graduates | Adaptability | − |
17 | Beds in hospital | HOSBED | Number of beds in hospital per 1000 people | Adaptability | − |
18 | Physicians in hospital | HOSPHY | Number of physicians in hospital per 1000 people | Adaptability | − |
19 | Employees in management sector | ENWORKER | Percentage employees in the sectors of water conservancy, environment and public management | Adaptability | − |
20 | Green space coverage | GREEN | Ratio of open green space coverage | Adaptability | − |
No. | Indicators | Weighting Values | No. | Indicators | Weighting Values |
---|---|---|---|---|---|
1 | Children | 0.297 | 11 | Employees in mining | 0.274 |
2 | Elderly | 0.120 | 12 | Employees in construction | 0.166 |
3 | Female | 0.142 | 13 | GDP in secondary sector | 0.255 |
4 | Ethnicity | 0.082 | 14 | GDP | 0.188 |
5 | Illiterate | 0.245 | 15 | Educated | 0.416 |
6 | Poor | 0.063 | 16 | Beds in hospital | 0.283 |
7 | Unemployed | 0.040 | 17 | Physicians in hospital | 0.365 |
8 | Population density | 0.060 | 18 | Employees in management sector | 0.355 |
9 | Urban resident | 0.024 | 19 | Green space coverage | 0.044 |
10 | Employees in 2nd industry | 0.288 |
SVI | YRD Region | Shanghai Municipality | Jiangsu Province | Zhejiang Province |
---|---|---|---|---|
Average | 1.970 | 1.474 | 1.989 | 2.110 |
Minimum | 0.916 | 0.992 | 0.916 | 1.002 |
Maximum | 2.516 | 2.021 | 2.429 | 2.516 |
Level | SVI | SVI Dimension 1 | SVI Dimension 2 | SVI Dimension 3 | ||||
---|---|---|---|---|---|---|---|---|
Susceptibility | Exposure | Adaptability | ||||||
Count | Percentage | Count | Percentage | Count | Percentage | Count | Percentage | |
High | 9 | 6.47% | 7 | 5.04% | 11 | 7.91% | 54 | 38.85% |
High-medium | 75 | 53.96% | 39 | 28.06% | 50 | 35.97% | 32 | 23.02% |
Medium | 30 | 21.58% | 28 | 20.14% | 36 | 25.90% | 25 | 17.99% |
Medium-low | 15 | 10.79% | 47 | 33.81% | 24 | 17.27% | 17 | 12.23% |
Low | 10 | 7.20% | 18 | 12.95% | 18 | 12.95% | 11 | 7.91% |
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Ge, Y.; Zhang, H.; Dou, W.; Chen, W.; Liu, N.; Wang, Y.; Shi, Y.; Rao, W. Mapping Social Vulnerability to Air Pollution: A Case Study of the Yangtze River Delta Region, China. Sustainability 2017, 9, 109. https://doi.org/10.3390/su9010109
Ge Y, Zhang H, Dou W, Chen W, Liu N, Wang Y, Shi Y, Rao W. Mapping Social Vulnerability to Air Pollution: A Case Study of the Yangtze River Delta Region, China. Sustainability. 2017; 9(1):109. https://doi.org/10.3390/su9010109
Chicago/Turabian StyleGe, Yi, Haibo Zhang, Wen Dou, Wenfang Chen, Ning Liu, Yuan Wang, Yulin Shi, and Wenxin Rao. 2017. "Mapping Social Vulnerability to Air Pollution: A Case Study of the Yangtze River Delta Region, China" Sustainability 9, no. 1: 109. https://doi.org/10.3390/su9010109