Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors
Abstract
:1. Introduction
1.1. Meteorological Factors
1.2. Human Activities
2. Indicator Selection and Data Sources
2.1. Indicator Selection
2.1.1. Meteorological Factors
2.1.2. Human Activities
2.2. Data Sources
3. Model Construction
4. Results and Analyses
4.1. Stochastic DEA Results for 2013–2016
4.1.1. Year 2013
4.1.2. Year 2014
4.1.3. Year 2015
4.1.4. Year 2016
4.2. Regional Stochastic DEA Results
4.2.1. Southern Jiangsu Province
4.2.2. Central Jiangsu Province
4.2.3. Northern Jiangsu Province
4.2.4. Coastal Area
4.2.5. Inland Area
5. Results Comparison
6. Conclusions and Policy Recommendations
6.1. Conclusions
- (1)
- Consider the distribution characteristics of data.
- (2)
- Comprehensively investigate the meteorological factors and human activities.
- (3)
- Differentiate multiple effective decision units.
6.2. Policy Recommendations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Disaster Point Days | 2013 | 2014 | 2015 | 2016 |
---|---|---|---|---|
wind speed (<1.5 m/s) | 0.5967 | 0.5237 | 0.5955 | 0.6250 |
no precipitation day (= 0 mm) | 0.8450 | 0.7868 | 0.7905 | 0.7684 |
positive temperature change (℃) | 0.8108 | 0.8282 | 0.6977 | 0.6364 |
negative pressure change (hpa) | 0.8545 | 0.8167 | 0.7033 | 0.6374 |
relative humidity (60–90%, excluding precipitation days) | 0.6327 | 0.6760 | 0.6122 | 0.5788 |
DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|
Nanjing | 0.6957 | 0.6098 | |||||
Wuxi | 1.0000 | 0.5222 | |||||
Xuzhou | 1.0000 | 0.6689 | |||||
Changzhou | 0.5389 | 0.5234 | 0.5361 | ||||
Suzhou | 0.5025 | 0.5047 | 0.4561 | 0.4770 | |||
Nantong | 1.0000 | 0.3982 | 0.4915 | ||||
Lianyungang | 1.0000 | ||||||
Huai’an | 1.0000 | 0.5820 | 0.7774 | ||||
Yancheng | 1.0000 | 0.3721 | |||||
Yangzhou | 0.5401 | 0.5064 | |||||
Zhenjiang | 1.0000 | 0.5987 | |||||
Taizhou | 0.8035 | 0.7725 | 0.7753 | 0.7675 | |||
Suqian | 1.0000 |
DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|
Nanjing | 0.1912 | 0.1761 | |||||
Wuxi | 1.0000 | 0.4562 | |||||
Xuzhou | 0.8004 | ||||||
Changzhou | 1.0000 | 0.3855 | |||||
Suzhou | 0.4761 | 0.4666 | 0.4909 | 0.4849 | |||
Nantong | 0.5489 | 0.5007 | |||||
Lianyungang | 1.0000 | 0.4622 | |||||
Huai’an | 1.0000 | 0.4864 | |||||
Yancheng | 1.0000 | 0.3725 | |||||
Yangzhou | 0.3427 | 0.3393 | 0.3151 | ||||
Zhenjiang | 1.0000 | 0.3984 | |||||
Taizhou | 0.5322 | 0.5251 | |||||
Suqian | 1.0000 |
DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|
Nanjing | 0.0195 | 0.0187 | |||||
Wuxi | 1.0000 | 0.3156 | |||||
Xuzhou | 0.7436 | 0.7186 | |||||
Changzhou | 0.2706 | 0.2615 | 0.2499 | ||||
Suzhou | 0.2991 | 0.2833 | 0.2641 | 0.2886 | |||
Nantong | 1.0000 | 0.2984 | 0.3226 | 0.3226 | |||
Lianyungang | 1.0000 | ||||||
Huai’an | 1.0000 | 0.4609 | |||||
Yancheng | 1.0000 | 0.3222 | |||||
Yangzhou | 0.3362 | 0.3361 | 0.3424 | 0.3362 | 0.3217 | ||
Zhenjiang | 1.0000 | 0.1833 | |||||
Taizhou | 0.4671 | 0.4581 | |||||
Suqian | 1.0000 |
DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR |
Nanjing | 0.6957 | 0.6494 | 0.6098 | |||||
Wuxi | 1 | |||||||
Xuzhou | 1 | 0.679 | ||||||
Changzhou | 0.5389 | |||||||
Suzhou | 0.5025 | 0.5047 | ||||||
Nantong | 1 | 0.3982 | ||||||
Lianyungang | 1 | |||||||
Huai’an | 1 | |||||||
Yancheng | 1 | |||||||
Yangzhou | 0.5401 | |||||||
Zhenjiang | 1 | |||||||
Taizhou | 0.8035 | 0.7725 | ||||||
Suqian | 1 | |||||||
DMUs | a = 0.95 | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
Nanjing | 0.6957 | |||||||
Wuxi | 1 | 0.5222 | ||||||
Xuzhou | 1 | |||||||
Changzhou | 0.5389 | 0.5234 | 0.5361 | |||||
Suzhou | 0.5025 | 0.4561 | 0.477 | |||||
Nantong | 1 | 0.4915 | ||||||
Lianyungang | 1 | |||||||
Huai’an | 1 | 0.6079 | 0.7774 | |||||
Yancheng | 1 | 0.4315 | ||||||
Yangzhou | 0.5401 | 0.5064 | ||||||
Zhenjiang | 1 | 0.5987 | ||||||
Taizhou | 0.8035 | 0.7837 | 0.7675 | |||||
Suqian | 1 |
DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Nanjing | 0.1912 | 0.1761 | |||||||||||||
Wuxi | 1.0000 | 0.4562 | |||||||||||||
Xuzhou | 0.8004 | ||||||||||||||
Changzhou | 1.0000 | 0.3920 | |||||||||||||
Suzhou | 0.4761 | 0.4618 | 0.4842 | 0.4761 | 0.4757 | 0.4909 | 0.4849 | ||||||||
Nantong | 0.5489 | 0.5221 | 0.5364 | ||||||||||||
Lianyungang | 1.0000 | 0.4622 | |||||||||||||
Huai’an | 1.0000 | 0.4883 | |||||||||||||
Yancheng | 1.0000 | 0.4026 | |||||||||||||
Yangzhou | 0.3427 | 0.3393 | 0.3151 | ||||||||||||
Zhenjiang | 1.0000 | 0.3984 | |||||||||||||
Taizhou | 0.5322 | 0.5251 | |||||||||||||
Suqian | 1.0000 |
DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Nanjing | 0.0195 | 0.0187 | |||||||||||||
Wuxi | 1.0000 | 0.3156 | |||||||||||||
Xuzhou | 0.7436 | 0.7186 | |||||||||||||
Changzhou | 0.2706 | 0.2681 | 0.2564 | ||||||||||||
Suzhou | 0.2991 | 0.2983 | 1.0000 | 0.2641 | 0.2886 | ||||||||||
Nantong | 1.0000 | 0.3030 | 0.3060 | 0.3820 | 0.3226 | 0.3226 | |||||||||
Lianyungang | 1.0000 | ||||||||||||||
Huai’an | 1.0000 | 0.4609 | 0.5513 | ||||||||||||
Yancheng | 1.0000 | 0.3488 | |||||||||||||
Yangzhou | 0.3362 | 0.3361 | 0.3362 | 0.3424 | 0.3362 | 0.3424 | 0.3362 | 0.3217 | |||||||
Zhenjiang | 1.0000 | ||||||||||||||
Taizhou | 0.4671 | 0.4581 | |||||||||||||
Suqian | 1.0000 |
Years | DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|---|
2013 | Nanjing | 0.6294 | 0.5882 | |||||
Wuxi | 1 | 0.6848 | 0.6259 | |||||
Xuzhou | 1 | 0.6755 | 0.6428 | |||||
Suzhou | 0.5162 | 0.5055 | 0.5146 | 0.4946 | ||||
Zhenjiang | 1 | 0.6199 | ||||||
2014 | Nanjing | 0.6957 | 0.6098 | |||||
Wuxi | 1.0000 | 0.5222 | ||||||
Xuzhou | 0.5389 | 0.5234 | 0.5361 | |||||
Suzhou | 0.5025 | 0.5047 | 0.4561 | 0.4770 | ||||
Zhenjiang | 1.0000 | 0.5987 | ||||||
2015 | Nanjing | 0.1912 | 0.1761 | |||||
Wuxi | 1.0000 | 0.4562 | ||||||
Xuzhou | 1.0000 | 0.3855 | ||||||
Suzhou | 0.4761 | 0.4666 | 0.4909 | 0.4849 | ||||
Zhenjiang | 1.0000 | 0.3984 | ||||||
2016 | Nanjing | 0.0195 | 0.0187 | |||||
Wuxi | 1.0000 | 0.3156 | ||||||
Xuzhou | 0.2706 | 0.2615 | 0.2499 | |||||
Suzhou | 0.2991 | 0.2833 | 0.2641 | 0.2886 | ||||
Zhenjiang | 1.0000 | 0.1833 |
Years | DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|---|
2013 | Nantong | 0.5776 | 0.6129 | 0.5708 | 1 | |||
Yangzhou | 0.6181 | 0.6062 | 0.6122 | |||||
Taizhou | 0.7197 | 0.6883 | 0.7265 | 0.641 | 0.722 | |||
2014 | Nantong | 1.0000 | 0.3982 | 0.4915 | ||||
Yangzhou | 0.5401 | 0.5064 | ||||||
Taizhou | 0.8035 | 0.7725 | 0.7753 | 0.7675 | ||||
2015 | Nantong | 0.5489 | 0.5007 | |||||
Yangzhou | 0.3427 | 0.3393 | 0.3151 | |||||
Taizhou | 0.5322 | 0.5251 | ||||||
2016 | Nantong | 1.0000 | 0.2984 | 0.3226 | 0.3226 | |||
Yangzhou | 0.3362 | 0.3361 | 0.3424 | 0.3362 | 0.3217 | |||
Taizhou | 0.4671 | 0.4581 |
Years | DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|---|
2013 | Xuzhou | 1.0000 | 0.7798 | |||||
Lianyungang | 1.0000 | |||||||
Huai’an | 1.0000 | 0.6927 | 0.8089 | |||||
Yancheng | 1.0000 | 0.6284 | ||||||
Suqian | 1.0000 | |||||||
2014 | Xuzhou | 1.0000 | 0.6689 | |||||
Lianyungang | 1.0000 | |||||||
Huai’an | 1.0000 | 0.5820 | 0.7774 | |||||
Yancheng | 1.0000 | 0.3721 | ||||||
Suqian | 1.0000 | |||||||
2015 | Xuzhou | 0.8004 | ||||||
Lianyungang | 1.0000 | 0.4622 | ||||||
Huai’an | 1.0000 | 0.4864 | ||||||
Yancheng | 1.0000 | 0.3725 | ||||||
Suqian | 1.0000 | |||||||
2016 | Xuzhou | 0.7436 | 0.7186 | |||||
Lianyungang | 1.0000 | |||||||
Huai’an | 1.0000 | 0.4609 | ||||||
Yancheng | 1.0000 | 0.3222 | ||||||
Suqian | 1.0000 |
Years | DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|---|
2013 | Nantong | 0.5776 | 0.6129 | 0.5708 | 1.0000 | |||
Lianyungang | 1.0000 | |||||||
Yancheng | 1.0000 | 0.6284 | ||||||
2014 | Nantong | 1.0000 | 0.3982 | 0.4915 | ||||
Lianyungang | 1.0000 | |||||||
Yancheng | 1.0000 | 0.3721 | ||||||
2015 | Nantong | 0.5489 | 0.5007 | |||||
Lianyungang | 1.0000 | 0.4622 | ||||||
Yancheng | 1.0000 | 0.3725 | ||||||
2016 | Nantong | 1.0000 | 0.2984 | 0.3226 | 0.3226 | |||
Lianyungang | 1.0000 | |||||||
Yancheng | 1.0000 | 0.3222 |
Years | DMUs | a = 0.95 | Delete MF | Delete ID | Delete SP | Delete T | Delete EU | Delete EP |
---|---|---|---|---|---|---|---|---|
2013 | Nanjing | 0.6294 | 0.5882 | |||||
Xuzhou | 1 | 0.7798 | ||||||
Changzhou | 1 | 0.6755 | 0.6428 | |||||
Suzhou | 0.5162 | 0.5055 | 0.5146 | 0.4946 | ||||
Yangzhou | 0.6181 | 0.6062 | 0.6122 | |||||
Taizhou | 0.7197 | 0.6883 | 0.7265 | 0.641 | 0.722 | |||
Wuxi | 1 | 0.6848 | 0.6259 | |||||
Zhenjiang | 1 | 0.6199 | ||||||
Huai’an | 1 | 0.6927 | 0.8089 | |||||
Suqian | 1 | |||||||
2014 | Nanjing | 0.6957 | 0.6098 | |||||
Xuzhou | 1.0000 | 0.6689 | ||||||
Changzhou | 0.5389 | 0.5234 | 0.5361 | |||||
Suzhou | 0.5025 | 0.5047 | 0.4561 | 0.4770 | ||||
Yangzhou | 0.5401 | 0.5064 | ||||||
Taizhou | 0.8035 | 0.7725 | 0.7753 | 0.7675 | ||||
Wuxi | 1.0000 | 0.5222 | ||||||
Zhenjiang | 1.0000 | 0.5987 | ||||||
Huai’an | 1.0000 | 0.5820 | 0.7774 | |||||
Suqian | 1.0000 | |||||||
2015 | Nanjing | 0.1912 | 0.1761 | |||||
Xuzhou | 0.8004 | |||||||
Changzhou | 1.0000 | 0.3855 | ||||||
Suzhou | 0.4761 | 0.4666 | 0.4909 | 0.4849 | ||||
Yangzhou | 0.3427 | 0.3393 | 0.3151 | |||||
Taizhou | 0.5322 | 0.5251 | ||||||
Wuxi | 1.0000 | 0.4562 | ||||||
Zhenjiang | 1.0000 | 0.3984 | ||||||
Huai’an | 1.0000 | 0.4864 | ||||||
Suqian | 1.0000 | |||||||
2016 | Nanjing | 0.0195 | 0.0187 | |||||
Xuzhou | 0.7436 | 0.7186 | ||||||
Changzhou | 0.2706 | 0.2615 | 0.2499 | |||||
Suzhou | 0.2991 | 0.2833 | 0.2641 | 0.2886 | ||||
Yangzhou | 0.3362 | 0.3361 | 0.3424 | 0.3362 | 0.3217 | |||
Taizhou | 0.4671 | 0.4581 | ||||||
Wuxi | 1.0000 | 0.3156 | ||||||
Zhenjiang | 1.0000 | 0.1833 | ||||||
Huai’an | 1.0000 | 0.4609 | ||||||
Suqian | 1.0000 |
Years | DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2013 | Nanjing | 0.6294 | 0.5834 | 1.0000 | ||||||||||||
Wuxi | 1.0000 | 0.6760 | 0.6259 | |||||||||||||
Xuzhou | 1.0000 | 0.6866 | 0.6755 | 0.6805 | ||||||||||||
Suzhou | 0.5162 | 0.5055 | 0.5146 | 0.4946 | ||||||||||||
Zhenjiang | 1.0000 | 0.6199 | ||||||||||||||
2014 | Nanjing | 0.6957 | 0.6494 | 0.6098 | ||||||||||||
Wuxi | 1.0000 | 0.5222 | ||||||||||||||
Xuzhou | 0.5389 | 0.5234 | 0.5361 | |||||||||||||
Suzhou | 0.5025 | 0.5047 | 0.4561 | 0.4770 | ||||||||||||
Zhenjiang | 1.0000 | 0.5987 | ||||||||||||||
2015 | Nanjing | 0.1912 | 0.1761 | |||||||||||||
Wuxi | 1.0000 | 0.4562 | ||||||||||||||
Xuzhou | 1.0000 | 0.3920 | ||||||||||||||
Suzhou | 0.4761 | 0.4618 | 0.4842 | 0.4761 | 0.4757 | 0.4909 | 0.4849 | |||||||||
Zhenjiang | 1.0000 | 0.3984 | ||||||||||||||
2016 | Nanjing | 0.0195 | 0.0187 | |||||||||||||
Wuxi | 1.0000 | 0.3156 | ||||||||||||||
Xuzhou | 0.2706 | 0.2681 | 0.2564 | |||||||||||||
Suzhou | 0.2991 | 0.2983 | 1.0000 | 0.2641 | 0.2886 | |||||||||||
Zhenjiang | 1.0000 |
Years | DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2013 | Nantong | 0.5776 | 0.6129 | 0.5708 | 1.0000 | |||||||||||
Yangzhou | 0.6181 | 0.6062 | 0.6122 | |||||||||||||
Taizhou | 0.7197 | 0.6883 | 0.7265 | 0.6410 | 0.7220 | |||||||||||
2014 | Nantong | 1.0000 | 0.3982 | 0.4915 | ||||||||||||
Yangzhou | 0.5401 | 0.5064 | ||||||||||||||
Taizhou | 0.8035 | 0.7725 | 0.7837 | 0.7675 | ||||||||||||
2015 | Nantong | 0.5489 | 0.5221 | 0.5364 | ||||||||||||
Yangzhou | 0.3427 | 0.3393 | 0.3151 | |||||||||||||
Taizhou | 0.5322 | 0.5251 | ||||||||||||||
2016 | Nantong | 1.0000 | 0.3030 | 0.3060 | 0.3820 | 0.3226 | 0.3226 | |||||||||
Yangzhou | 0.3362 | 0.3361 | 0.3362 | 0.3424 | 0.3362 | 0.3424 | 0.3362 | 0.3217 | ||||||||
Taizhou | 0.4671 | 0.4581 |
Years | DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2013 | Xuzhou | 1.0000 | 0.8123 | |||||||||||||
Lianyungang | 1.0000 | |||||||||||||||
Huai’an | 1.0000 | 0.7173 | 0.8089 | |||||||||||||
Yancheng | 1.0000 | |||||||||||||||
Suqian | 1.0000 | |||||||||||||||
2014 | Xuzhou | 1.0000 | 0.6790 | |||||||||||||
Lianyungang | 1.0000 | |||||||||||||||
Huai’an | 1.0000 | 0.6079 | 0.7774 | |||||||||||||
Yancheng | 1.0000 | 0.4315 | ||||||||||||||
Suqian | 1.0000 | |||||||||||||||
2015 | Xuzhou | 0.8004 | ||||||||||||||
Lianyungang | 1.0000 | 0.4622 | ||||||||||||||
Huai’an | 1.0000 | 0.4883 | ||||||||||||||
Yancheng | 1.0000 | 0.4026 | ||||||||||||||
Suqian | 1.0000 | |||||||||||||||
2016 | Xuzhou | 0.7436 | 0.7186 | |||||||||||||
Lianyungang | 1.0000 | |||||||||||||||
Huai’an | 1.0000 | 0.4609 | 0.5513 | |||||||||||||
Yancheng | 1.0000 | 0.3488 | ||||||||||||||
Suqian | 1.0000 |
Years | DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2013 | Nantong | 0.5776 | 0.6129 | 0.5708 | 1.0000 | |||||||||||
Lianyungang | 1.0000 | |||||||||||||||
Yancheng | 1.0000 | |||||||||||||||
2014 | Nantong | 1.0000 | 0.3982 | 0.4915 | ||||||||||||
Lianyungang | 1.0000 | |||||||||||||||
Yancheng | 1.0000 | 0.4315 | ||||||||||||||
2015 | Nantong | 0.5489 | 0.5221 | 0.5364 | ||||||||||||
Lianyungang | 1.0000 | 0.4622 | ||||||||||||||
Yancheng | 1.0000 | 0.4026 | ||||||||||||||
2016 | Nantong | 1.0000 | 0.3030 | 0.3060 | 0.3820 | 0.3226 | 0.3226 | |||||||||
Lianyungang | 1.0000 | |||||||||||||||
Yancheng | 1.0000 | 0.3488 |
Years | DMUs | a = 0.95 | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2013 | Nanjing | 0.6294 | 0.5834 | 1.0000 | ||||||||||||
Xuzhou | 1.0000 | 0.8123 | ||||||||||||||
Changzhou | 1.0000 | 0.6866 | 0.6755 | 0.6805 | ||||||||||||
Suzhou | 0.5162 | 0.5055 | 0.5146 | 0.4946 | ||||||||||||
Yangzhou | 0.6181 | 0.6062 | 0.6122 | |||||||||||||
Taizhou | 0.7197 | 0.6883 | 0.7265 | 0.6410 | 0.7220 | |||||||||||
Wuxi | 1.0000 | 0.6760 | 0.6259 | |||||||||||||
Zhenjiang | 1.0000 | 0.6199 | ||||||||||||||
Huai’an | 1.0000 | 0.7173 | 0.8089 | |||||||||||||
Suqian | 1.0000 | |||||||||||||||
2014 | Nanjing | 0.6957 | 0.6494 | 0.6098 | ||||||||||||
Xuzhou | 1.0000 | 0.6790 | ||||||||||||||
Changzhou | 0.5389 | 0.5234 | 0.5361 | |||||||||||||
Suzhou | 0.5025 | 0.5047 | 0.4561 | 0.4770 | ||||||||||||
Yangzhou | 0.5401 | 0.5064 | ||||||||||||||
Taizhou | 0.8035 | 0.7725 | 0.7837 | 0.7675 | ||||||||||||
Wuxi | 1.0000 | 0.5222 | ||||||||||||||
Zhenjiang | 1.0000 | 0.5987 | ||||||||||||||
Huai’an | 1.0000 | 0.6079 | 0.7774 | |||||||||||||
Suqian | 1.0000 | |||||||||||||||
2015 | Nanjing | 0.1912 | 0.1761 | |||||||||||||
Xuzhou | 0.8004 | |||||||||||||||
Changzhou | 1.0000 | 0.3920 | ||||||||||||||
Suzhou | 0.4761 | 0.4618 | 0.4842 | 0.4761 | 0.4757 | 0.4909 | 0.4849 | |||||||||
Yangzhou | 0.3427 | 0.3393 | 0.3151 | |||||||||||||
Taizhou | 0.5322 | 0.5251 | ||||||||||||||
Wuxi | 1.0000 | 0.4562 | ||||||||||||||
Zhenjiang | 1.0000 | 0.3984 | ||||||||||||||
Huai’an | 1.0000 | 0.4883 | ||||||||||||||
Suqian | 1.0000 | |||||||||||||||
2016 | Nanjing | 0.0195 | 0.0187 | |||||||||||||
Xuzhou | 0.7436 | 0.7186 | ||||||||||||||
Changzhou | 0.2706 | 0.2681 | 0.2564 | |||||||||||||
Suzhou | 0.2991 | 0.2983 | 1.0000 | 0.2641 | 0.2886 | |||||||||||
Yangzhou | 0.3362 | 0.3361 | 0.3362 | 0.3424 | 0.3362 | 0.3424 | 0.3362 | 0.3217 | ||||||||
Taizhou | 0.4671 | 0.4581 | ||||||||||||||
Wuxi | 1.0000 | 0.3156 | ||||||||||||||
Zhenjiang | 1.0000 | |||||||||||||||
Huai’an | 1.0000 | 0.4609 | 0.5513 | |||||||||||||
Suqian | 1.0000 |
References
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Concentration of PM2.5 | Nanjing | Wuxi | Xuzhou | Changzhou | Suzhou | Nantong | Lianyungang | |||||||
S | W | S | W | S | W | S | W | S | W | S | W | S | W | |
Maximum concentration of PM2.5 | 98 | 184 | 85 | 161 | 77 | 282 | 66 | 171 | 69 | 163 | 66 | 159 | 52 | 211 |
Minimum concentration of PM2.5 | 10 | 15 | 11 | 23 | 13 | 16 | 12 | 24 | 10 | 23 | 10 | 18 | 9 | 13 |
Concentration of PM2.5 | Huai’an | Yancheng | Yangzhou | Zhenjiang | Taizhou | Suqian | ||||||||
S | W | S | W | S | W | S | W | S | W | S | W | |||
Maximum concentration of PM2.5 | 73 | 206 | 67 | 216 | 76 | 187 | 68 | 187 | 90 | 185 | 67 | 218 | ||
Minimum concentration of PM2.5 | 13 | 19 | 6 | 16 | 13 | 23 | 8 | 20 | 11 | 18 | 12 | 23 |
Research Variables | Grouping Variable (Abbreviation) | Single Input Variable | Abbreviation | Unit |
---|---|---|---|---|
Input variables | Meteorological Factors (MF) | Wind Speed (< 1.5 m/s) | WS | days |
No Precipitation Day | NPD | |||
Positive Temperature Change | PTC | |||
Negative Pressure Change | NPC | |||
Relative Humidity (60–90%, excluding precipitation days) | RH | |||
Industrial Development (ID) | Gross Output Value of Industrial Enterprises above Designated Size | GOVIE | hundred million | |
Social Progress (SP) | Urbanization Rate | UR | % | |
Population Density | PD | people per square kilometer | ||
Building Construction Area | BCA | Ten thousand square meters | ||
Transportation (T) | Civil Car Ownership | CCO | Ten thousand cars | |
Number of Public Transportation Vehicles under Operation | NPTVO | Standard number | ||
Energy Utilization (EU) | Energy Consumption of per 10,000 Yuan Industrial Cross Output Value | EC | Ton of standard coal per ten thousand yuan | |
Total Coal Consumption | TCC | Ton of standard coal | ||
Ecological Protection (EP) | Green Coverage Rate of Built-up Areas | GCRBA | % | |
Output variable | Haze Pollution | PM2.5 Pollution Days | - | days |
DMUs | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR |
2013 | 5 | 1 | 1 | 0 | 6 | 0 | 1 |
2014 | 4 | 3 | 1 | 2 | 3 | 0 | 0 |
2015 | 4 | 4 | 0 | 0 | 6 | 1 | 0 |
2016 | 4 | 5 | 2 | 0 | 5 | 1 | 0 |
DMUs | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
2013 | 3 | 2 | 1 | 2 | 2 | 2 | 2 |
2014 | 3 | 4 | 1 | 3 | 1 | 2 | 2 |
2015 | 5 | 5 | 1 | 2 | 1 | 1 | 2 |
2016 | 4 | 2 | 2 | 3 | 2 | 2 | 2 |
DMUs | Delete WS | Delete NPD | Delete PTC | Delete NPC | Delete RH | Delete GOVIE | Delete UR |
2013 | 5 | 1 | 1 | 0 | 6 | 0 | 1 |
2014 | 4 | 3 | 1 | 2 | 3 | 0 | 0 |
2015 | 4 | 4 | 0 | 0 | 6 | 1 | 0 |
2016 | 4 | 5 | 2 | 0 | 5 | 1 | 0 |
DMUs | Delete PD | Delete BCA | Delete CCO | Delete NPTVO | Delete EC | Delete TCC | Delete GCRBD |
2013 | 3 | 2 | 1 | 2 | 2 | 2 | 2 |
2014 | 3 | 4 | 1 | 3 | 1 | 2 | 2 |
2015 | 5 | 5 | 1 | 2 | 1 | 1 | 2 |
2016 | 4 | 2 | 2 | 3 | 2 | 2 | 2 |
Deleting Single Variable | Risk Level | City with Changing Value | Result Analysis |
---|---|---|---|
Delete WS | α = 0.95 | Nanjing, Changzhou, Nantong and Taizhou | The wind speed (<1.5 m/s) of these cities was related to the local haze pollution occurrence. Stable weather with low wind speed is not conducive to the diffusion of pollutants, thus exacerbating the formation of pollution days. |
α = 0.9 | Nanjing, Wuxi, Nantong and Taizhou | ||
α = 0.8 | |||
Delete NPD | α = 0.95 | - | The no precipitation day affected the PM2.5 pollution days in Nantong. |
α = 0.9 | Nantong | ||
α = 0.8 | - | ||
Delete PTC | α = 0.95 | - | The positive temperature change affected the PM2.5 pollution days in Nantong. |
α = 0.9 | - | ||
α = 0.8 | Nantong | ||
Delete RH | α = 0.95 | Nanjing, Wuxi, Xuzhou, Changzhou, Suzhou and Yangzhou | When the relative humidity of these cities is between 60 and 90%, and no precipitation, there is a greater chance of haze pollution. |
α = 0.9 | Wuxi, Xuzhou, Suzhou and Yangzhou | ||
α = 0.8 | Wuxi, Xuzhou, Changzhou and Yangzhou | ||
Delete UR | α = 0.95 | Nantong | The urbanization rate in Nantong has impact on the local PM2.5 pollution days. |
α = 0.9 | |||
α = 0.8 | |||
Delete PD | α = 0.95 | Suzhou, Huai’an and Yangzhou | The population density has impact on the PM2.5 pollution days in these cities. |
α = 0.9 | |||
α = 0.8 | |||
Delete BCA | α = 0.95 | Wuxi and Zhenjiang | The pollutions caused by the building construction area in the two cities had certain relationship with the local PM2.5 pollution days. |
α = 0.9 | - | ||
α = 0.8 | - | ||
Delete CCO | α = 0.95 | Huai’an | The civil car ownership has impact on the local PM2.5 pollution days in Huai’an. |
α = 0.9 | |||
α = 0.8 | |||
Delete NPTVO | α = 0.95 | Nantong and Taizhou | The bus operations in these cities were related to the local PM2.5 pollution days. |
α = 0.9 | |||
α = 0.8 | - | ||
Delete EC | α = 0.95 | Taizhou | The energy utilization of the two cities affected the local PM2.5 pollution days. |
α = 0.9 | |||
α = 0.8 | Nantong and Taizhou | ||
Delete TCC | α = 0.95 | Changzhou | The local coal consumption in these cities affected the local PM2.5 pollution days. |
α = 0.9 | Yangzhou | ||
α = 0.8 | |||
Delete GCRBA | α = 0.95 | - | - |
α = 0.9 | Suzhou and Taizhou | The local greening situation in the two cities affected the local PM2.5 pollution days. | |
α = 0.8 |
Classification | Generality | Personality |
---|---|---|
Years | With the risk level decrease, the influencing factors of PM2.5 pollution days reduced. | With the risk level change, the specific factors affecting PM2.5 pollution days were different. |
2013–2016, the number of cities with values of 1 decreased, and the higher the risk level, the fewer cities the values were effective. | At 95% risk level, there were more cities’ PM2.5 pollution days affected by transportation in 2013–2014 than in 2015–2016. | |
In 2013–2016, PM2.5 pollution days of 13 cities in Jiangsu Province were affected by meteorological factors and social progress. | Wind speed and relative humidity had a significant impact on PM2.5 pollution days in 2013–2014; no precipitation days had greater impact on PM2.5 pollution days in 2015–2016. | |
Areas | Stochastic DEA effective regional sorting: Northern Jiangsu Province, Southern Jiangsu Province, Central Jiangsu Province. | The stochastic efficiencies of Yangzhou and Taizhou in Central Jiangsu Province were invalid. |
The PM2.5 pollution days in Southern and Central Jiangsu Province were closely related to meteorological factors and social progress. | The PM2.5 pollution days in Northern Jiangsu Province were closely related to social progress. | |
The PM2.5 pollution days in Southern and Central Jiangsu Province were affected by most of the input variables. | The PM2.5 pollution days in Northern Jiangsu Province is only related to relative humidity, population density and civil car ownership. | |
The PM2.5 pollution days in coastal and inland area were affected by meteorological factors, social progress, transportation and energy utilization, less affected by industrial development. | The PM2.5 pollution days in inland area was also related to ecological protection. | |
The specific factors affecting the PM2.5 pollution days in coastal and inland areas were wind speed, no precipitation day, relative humidity, and population density. | The factors affecting the PM2.5 pollution days in inland area also included: building construction area, civil car ownership, total coal consumption and green coverage rate of built-up areas. |
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Sun, R.; Zhou, Y.; Wu, J.; Gong, Z. Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors. Int. J. Environ. Res. Public Health 2019, 16, 3891. https://doi.org/10.3390/ijerph16203891
Sun R, Zhou Y, Wu J, Gong Z. Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors. International Journal of Environmental Research and Public Health. 2019; 16(20):3891. https://doi.org/10.3390/ijerph16203891
Chicago/Turabian StyleSun, Ruiling, Yi Zhou, Jie Wu, and Zaiwu Gong. 2019. "Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors" International Journal of Environmental Research and Public Health 16, no. 20: 3891. https://doi.org/10.3390/ijerph16203891