DOCSLIB.ORG

Stable Hydrogen and Oxygen Isotope Characteristics of Bottled Water in China: a Consideration of Water Source

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Stable Hydrogen and Oxygen Isotope Characteristics of Bottled Water in China: A Consideration of Water Source

Supplementary Materials:

Table 1. Inventory of sampling stations.

  • Ave.(‰)
  • σ(‰)

  • Brand
  • Source site
  • Origin
  • Lon./°
  • Lat./°
  • Alt/m
  • Date

  • 2
  • 18
  • 2
  • 18

δ H

−31.3 −46.3

δ O

−5.53 −6.99

δ H

−

δ O

Daqishanquan Bamaboquan
Tonglu Bama spring spring
119.72 107.25
29.77 24.14
158.41 244.08 1626.9
5

−−

2015.5.22 2015.6.6

−

  • Baishite
  • Lanzhou
  • spring
  • 103.81
  • 36.02
  • −63.06
  • −9.61

  • −
  • −

2015.4.22 2015.3.11
Wudangshanqu an
Xiaoyushanquan
Danjiangkou Guantao spring spring
111.48 115.30
32.55 36.53
147.41 43.33

  • −18.75
  • −3.86

−−
−−

  • −65.01
  • −9.24
  • 2015.7.05

2015.4.24 2015.5.31 2015.7.16 2015.5.20 2015.6.27 2015.5.20 2015.6.09 2015.1.07 2015.3.29 2015.5.07 2015.6.04 2015.6.01 2015.8.09 2015.8.10 2014.11.04 2015.3.06 2015.4.11 2015.8.17 2015.4.02 2015.7.09 2015.7.10 2015.9.06 2015.6.04 2015.3.20 2015.8.08

  • Lao Shan (4)
  • Qingdao
  • spring
  • 120.68
  • 36.20
  • 4.06
  • −50.87
  • −7.65
  • 0.36
  • 0.09

  • Shenshuiyu
  • Sishui
  • spring

spring
117.30 110.01
35.55 19.7
340.20 63.46

  • −63.66
  • −8.88
  • -
  • -

  • HOSANMI (2)
  • Haikou
  • −45.80
  • −6.99
  • 0.05
  • 0.00

1062.5
8
−107.0
8

  • AER (3)
  • Arxan
  • spring
  • 119.94
  • 47.19
  • −14.58
  • 1.96
  • 0.71

2695.2
3

  • Angsiduo (2)
  • Haidong
  • spring

spring
102.13 127.84
36.20 42.52

  • −57.37
  • −8.73
  • 0.08

0.33
0.08 0.04
Nongfu Spring
(2)
Changbai Shan

  • 738.31
  • −94.15
  • −13.6

Evergrande ice spring (4)
Changbai Shan
2720.5
2spring spring
127.88 128.17
41.99 42.48

  • −86.00
  • −12.22
  • 0.34

0.02
0.11

  • 0.02
  • ALKAQUA (4)
  • Antu
  • 655.72
  • −98.68
  • −13.88

Master Kong Yaquan Wahaha
Yanbian Hotan Xianyang spring spring spring
129.52 79.92 108.91
42.90 37.12 34.63
183.16
1379
423.92 1316.2
3
−85.54 −43.23 −51.88
−12.06 −7.12 −6.78
---
---

  • Wahaha
  • Tianshui
  • spring
  • 105.68
  • 34.55
  • −57.68
  • −9.21
  • 1.13
  • 0.07
  • 2015.3.04

1208.8
6
2015.6.20 2015.10.22 2015.6.30 2015.7.30 2015.8.09

  • Beijixiong (2)
  • Guiyang
  • spring
  • 106.65
  • 26.41
  • −45.76
  • −7.03
  • 2.18
  • 0.46

Jianlong (2) Shihanquan Guashui
Wuda Lianchi
Qiqihar spring spring spring spring spring
126.21 126.21 116.19 103.51 104.13
48.75 48.17 31.14 29.62 30.71

  • 283.99
  • −83.00

−84.12 −38.91 −47.63 −82.35
−11.05 −11.75 −6.56

  • 0.43
  • 0.03

252.72 1287.5
0

  • 0.14
  • 0.18

  • Huoshan
  • -

--
---
2015.6.02 2015.7.19 2015.9.09
Nongfu Spring Xuenanshanqua n
Emei Shan Kunming

  • 442.23
  • −8.38

529.14

37.25
−12.41
2015.5.20 2015.9.27 2015.3.28 2015.6.20 2015.6.22 2015.6.30

  • Ganten (2)
  • Guangzhou
  • spring
  • 113.42
  • 23.60
  • −35.80
  • −5.82
  • 0.17
  • 0.02

Ganten (3) Ganten

  • Huizhou
  • spring

spring
114.30 113.15
23.18 28.32
22.42 45.20

  • −39.66
  • −6.29
  • 1.98

-
0.15

  • -
  • Changsha
  • −19.28
  • −4.08

Yearround Spring
Shijiazhuang Tongren spring spring spring spring spring
114.51 109.12 78.28
38.00 27.72 37.62 28.68 44.62

  • 75.26
  • −62.84

−36.74 −43.23
−8.58 −6.54
-
1.47
-
-
0.09
-
2015.7.23 2015.6.22 2015.7.17 2015.9.07
Nongfu Spring
(3)
379.25 1371.3
5

  • Yaquan
  • Pishan
  • −7.12
  • 2015.6.05

Everest Glacier Natural Living
Water
Yulonghanzhu
(2)
4595.3
0
−163.9
8

  • Xigazê
  • 86.71
  • −20.85

−10.55

  • -
  • -
  • 2015.4.03

2015.7.04 2015.7.24

  • Mudanjiang
  • 129.61
  • 249.15
  • −80.95
  • 0.08
  • 0.08

Huoshanyan Royalquan (3)

Bamalilang
Yeshu

Wuda Lianchi Wuda Lianchi

Bama

spring spring

spring spring spring

126.20 126.11

107.32 110.21 107.31

48.52 48.71

24.12 20.01 24.11

248.00
322

421 12

−86.80 −82.92

−49.90 −43.96 −50.84

−11.56 −10.91

−7.14 −6.31 −7.57

  • -
  • -
  • 2015.7.20

  • 0.85
  • 0.24
  • -

---
-
---
---

Haikou

  • Bama
  • Alkaqua
  • 421

Changbai Shan

  • Quanyangquan
  • spring
  • 128.13
  • 42.02
  • 2302
  • −93.52
  • −13.13

  • -
  • -
  • -

Runtian Jingtian
Beidahuangguo
Kelan Lanjian Meizhi S.Z.Spring Cestbon Nestle

  • Laiwu
  • spring

spring spring spring spring spring spring spring spring spring spring
117.71 120.91 126.12 120.40 116.10 116.72 102.52 113.31 117.46 126.13 127.55
36.21 32.03 48.71 36.12 39.92 39.53 24.46 23.15 40.23 48.71 50.22
204 11 307 112 102 29
1696
17 23 307 125

3562.3
1
6462.6
7
3764.1
2
4595.3
0
3663.0
9
3660.4
3
4129.6
7
1980.1
5
7509.2
6
3700.0
0
1717.2
3
3663.0
9

−49.63 −55.61 −83.36 −48.40 −66.06 −79.68 −83.83 −45.68 −59.64 −82.95 −81.86
−6.29 −7.90 −11.05 −6.00

-----------
-----------
-----------

Nantong
Wuda Lianchi
Qingdao Beijing Langfang Yuxi
Guangzhou
Jixian
Wuda Lianchi
Heihe
−9.36 −10.92 −11.58 −6.83 −8.31 −11.12 −10.57
Beidahuangguo Beidahuangguo

Baidaganquan Himalayans
Nêdong Himalayans
Lhasa glacier glacier glacier glacier glacier glacier glacier glacier glacier glacier glacier glacier
91.76 90.00 91.16 86.71 91.11 91.11 91.16 80.26 74.95 86.81 87.42 90.99
29.22 29.34 29.71 28.68 30.51 29.92 29.71 42.14 38.30 43.22 43.53 29.65

  • −135.7
  • −17.25

−19.39 −17.65 −21.63 −19.01 −19.76 −18.43 −10.09 −11.88 −9.22
------------
------------
2015.9.30 2015.7.10 2015.7.04 2015.6.10 2015.5.28 2015.7.13 2015.7.04 2014.9.14 2015.4.22 2015.4.12 2015.9.07 2014.7.24
−155.0
0
−134.8
2
Zangshenyuan Zhufengshenqu an
−165.9
Xigazê
4

−142.3
8
−155.0
5
Nyaínqêntangl ha
5100

  • Shendi
  • Goola pass

Lhasa
−136.5
7
Zangyushenshui Binchuanbindon g

  • Aksu
  • −64.05

−81.99 −59.92 −59.5
PaMIRS Gailaixue
Kashi Ürümqi Ürümqi Donggar
Xuebaizhen Lhasabinquan
−9.59
−137.8
1
−17.66
2014.7.25 2015.7.15 2015.7.30
-
3568.0
9

  • Klunshan (3)
  • Golmud
  • glacier
  • 94.68
  • 35.92
  • −66.96
  • −10.67
  • 0.11
  • 0.11

  • Jingdu
  • Qinghai

Xigazê glacier glacier
102.0 88.92
35.51 29.32
2535 3825
−79.60 −160.2
1
−11.39

--
--

  • Qomolangma
  • −20.91

-
7649.2
3

  • Kongur
  • Akto
  • glacier
  • 76.02
  • 39.25

39.73

  • −74.52
  • −10.81
  • -

-
--
2014.12.3
-
Watsonʹs
Distilled water

  • Beijing
  • distilled
  • 116.42
  • 29.41
  • −66.15
  • −8.93
  • -

2

Nongfu Spring Nongfu Spring
(2)

  • Hangzhou
  • lake

reservoi r
119.05 111.51
29.61 32.59
100.00 109.88

  • −41.78
  • −6.21
  • 1.06

-
0.17
-
2014.7.14 2015.3.08 2015.6.15 2015.5.24 2015.5.28 2015.6.03 2015.2.12 2015.4.03 2015.5.05 2015.4.12 2015.5.04 2015.6.10 2015.8.15 2015.3.29 2015.6.04 2015.3.17 2015.8.13 2015.8.21 2015.4.03 2015.8.02

  • Danjiangkou
  • −57.96
  • −8.52

  • Cestbon (3)
  • Suzhou
  • -
  • 120.54

119.10
31.33 33.55
5.49 8.26

  • −55.98
  • −7.82
  • 0.23

3.06
0.07

  • 0.37
  • Binlu (2)
  • Huaian
  • -
  • −35.37
  • −4.65

Cestbon Run Tian Pepsico Binlu Wahaha Xiyinli
Nanchanig Nanchanig Wuhan
----------
115.93 115.92 114.33 114.24 111.30 113.63 116.73 116.74 115.51 115.51
28.72 28.75 30.62 30.65 30.72 34.84 40.14 40.15 38.92 38.92
15.86 28.02 13.23 27.23 62.23 115.23 20.12 20.12 15.23 15.23
−43.43 −40.82 −52.98 −47.74 −45.73 −75.94 −64.85 −82.14 −71.30 −71.09
−6.74 −6.24 −7.82 −7.29 −7.21 −10.28 −8.62 −10.61 −9.84 −9.98
----------
----------
Wuhan Yichang Zhengzhou Shunyi Shunyi Baoding Baoding
Qinlin Huiyuan Bingtianxue
Yili

  • Wahah (2)
  • Gaobeidian
  • -
  • 115.87
  • 39.33
  • 34.21
  • −67.64
  • −9.61
  • 1.67
  • 0.24

Yangdao (Wahaha) Baixiang Cestbon

  • Baoding
  • -
  • 115.52
  • 38.91
  • 15.33
  • −24.53
  • −2.13
  • -
  • -
  • 2015.6.08

Baoding Taiyuan
--
115.5 2 112.62
38.91 37.91
15.33 800.23
−70.02 −63.79
−9.47 −8.85
--
--
2015.8.06 2015.4.16 2015.3.12 2015.5.22 2015.4.20 2015.5.04
Wahaha (2) Wahaha (2)
Zilali
Jingzhou Aksu
----
112.68 80.26
37.68 41.19 39.71 30.56

  • 780.29
  • −75.57

−76.60 −62.79 −64.82
−10.12 −11.51 −9.86
0.7 0.55
-
0.08 0.30
-
1107.5
4
1340.6
4

  • Artux
  • 76.10
  • 2015.10.7

2015.5.04 2015.9.10

  • Cestbon (2)
  • Chengdu
  • 103.98
  • 500.37
  • −9.51
  • 1.82
  • 0.57

1305.7
1
284.92
Wahaha Binlu
Guiyang Chongqing Harbin
---
106.62 106.57 126.86
26.71 29.63 45.24
−45.14 −62.89 −74.69
−6.41 −9.25 −9.93
-
18.5
-
-
2.39
-
2015.9.05 2015.6.11 2015.3.17 2015.5.18 2015.5.14 2015.5.26 2015.8.09

  • Binlu (2)
  • 169.68

  • Jingyue (2)
  • Quanzhou

Hefei
---
118.74 117.23 118.32
24.92 31.78 32.25
300.21 25.80 31.42
−53.89 −40.87 −38.21
−8.63 −5.85 −5.84
---
---
Binlu Master
Kong−Youyue

  • Chuzhou
  • 2015.7.16

2014.11.03 2015.4.04 2015.4.05 2015.4.20 2015.4.02 2015.5.13 2015.5.14 2015.6.01 2015.6.04 2015.6.13 2015.3.23 2015.5.07 2015.3.17 2015.7.24 2015.7.11 2015.3.14

  • Fuyang (3)
  • Hangzhou
  • -
  • 120.24
  • 30.31
  • 15.23
  • −32.74
  • −4.46
  • 1.65
  • 2.98

Yangdao
Tianranhunquan
Wuhan Shangqiu
--
114.34 115.65
30.62 34.41
13.21 50.14
−27.11 −74.45
−3.07 −10.06
--
--

Watsonʹs
Distilled water
(4)

  • Daxing
  • -
  • 116.42
  • 39.73
  • 29.23
  • −64.21
  • −8.71
  • 1.11
  • 0.26

Jinmailang Shangjianwang
Chunyue Master Kong
Binlu
Master Kong
Binlu
Beijing Tianjin Tianjin
-------
116.52 117.04 117.42 114.39 114.51 112.59 87.52
40.03 39.38 40.01 37.96 38.13 37.81 43.85
44.22 8.50 8.51 81.00 82.16 770.77 821.05 1107.5
4
−77.28 −78.17 −78.34 −60.02 −66.60 −63.96 −71.44
−10.24 −10.31 −10.49 −8.06 −9.17 −8.51
-----
-----
Shijiazhuang Shijiazhuang

  • Taiyuan
  • 0.19

0.54
0.05

  • 0.13
  • Ürümqi
  • −10.53

  • Master Kong
  • Aksu
  • -
  • 80.22
  • 41.22
  • −75.02
  • −11.33
  • -
  • -
  • 2015.8.01

2015.3.13 2015.5.23 2015.6.18
Nongfu Spring
(3)

  • Baoji
  • -
  • 107.92
  • 34.32
  • 513.02
  • −66.78
  • −9.72
  • 1.44
  • 0.47

3

1516.9
7
1535.5
5
2015.6.23 2015.7.20 2015.3.17 2015.5.20

  • Binlu (2)
  • Lanzhou

Lanzhou
--
103.89 103.72
36.07 36.14

  • −69.50
  • −10.02
  • 2.59

0.91
0.18

  • 0.20
  • Master Kong (2)
  • −70.13
  • −9.89

2403.2
3
529.45 1891.2
3
Yueyashenquan Master Kong Master Kong
Wuwei Chengdu Kunming
-----
103.12 104.14 102.82 121.81 127.81
37.01 30.72 24.92 39.08 42.52
−64.31 −82.35 −73.99 −94.28 −94.16
−10.4 −12.41 −10.06 −13.13 −13.27

  • -
  • -
  • 2015.6.22

2015.8.22 2015.6.23

  • -
  • -

--
-

  • -
  • Master Kong

Nongfu Spring
(2)
Dalian Changbai Shan

  • 71.76
  • 2015.10.18

2015.4.26 2015.5.10 2015.2.18 2015.6.12
738.13 304.12

  • 0.01
  • 0.02

Ganten (2) Lutuni
Yuyao Kashi
--
121.14 76.04
29.91 39.56

  • −40.27
  • −6.43
  • 0.6

-
0.02
-
1276.2
1

  • −86.96
  • −12.44
  • 2015.5.10

2015.4.23 2015.5.20 2015.6.04 2015.6.17
1535.2
3

  • Pepsico (3)
  • Lanzhou
  • -
  • 103.72
  • 36.13
  • −71.47
  • −10.05
  • 1.05
  • 0.10

Cestbon Master Kong JingYue
Harbin Guiyang Quanzhou Xuzhou
----
126.52 106.62 118.71 117.33
45.81 26.71 24.92 34.23
131.13 1299.1
2
−74.31 −46.27 −53.89 −57.06
−9.89 −6.86 −8.63 −7.92

  • -
  • -

--
--
2015.5.26

  • 300.56
  • 2015.6.16

2015.5.06 2015.7.14 2015.6.02 2015.6.20 2014.12.03 2015.3.20 2015.4.25 2015.6.20 2015.7.03 2015.7.10 2015.6.13 2015.5.23 2015.8.23 2015.6.03 2015.7.01 2015.7.20
-

  • Alkaqua (2)
  • 46.36

49.26 32.24

  • 0.02
  • 0.03

Master Kong (2)
Nestle (2)
Xuzhou Jixian
--
117.32 117.52
34.22 40.01

  • −53.27
  • −7.42
  • 0.65

0.48
0.13

  • 0.02
  • −60.09
  • −8.02

Nongfu Spring
Suyefon
Shiyan
ShiJiazhuang
--
111.51 114.52
32.52 38.02
130.14 75.26
−55.70 −70.99
−8.41 −10.16
--
--

Recommended publications
  • Landscape Analysis of Geographical Names in Hubei Province, China

    Landscape Analysis of Geographical Names in Hubei Province, China

    Entropy 2014, 16, 6313-6337; doi:10.3390/e16126313 OPEN ACCESS entropy ISSN 1099-4300 www.mdpi.com/journal/entropy Article Landscape Analysis of Geographical Names in Hubei Province, China Xixi Chen 1, Tao Hu 1, Fu Ren 1,2,*, Deng Chen 1, Lan Li 1 and Nan Gao 1 1 School of Resource and Environment Science, Wuhan University, Luoyu Road 129, Wuhan 430079, China; E-Mails: [email protected] (X.C.); [email protected] (T.H.); [email protected] (D.C.); [email protected] (L.L.); [email protected] (N.G.) 2 Key Laboratory of Geographical Information System, Ministry of Education, Wuhan University, Luoyu Road 129, Wuhan 430079, China * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel: +86-27-87664557; Fax: +86-27-68778893. External Editor: Hwa-Lung Yu Received: 20 July 2014; in revised form: 31 October 2014 / Accepted: 26 November 2014 / Published: 1 December 2014 Abstract: Hubei Province is the hub of communications in central China, which directly determines its strategic position in the country’s development. Additionally, Hubei Province is well-known for its diverse landforms, including mountains, hills, mounds and plains. This area is called “The Province of Thousand Lakes” due to the abundance of water resources. Geographical names are exclusive names given to physical or anthropogenic geographic entities at specific spatial locations and are important signs by which humans understand natural and human activities. In this study, geographic information systems (GIS) technology is adopted to establish a geodatabase of geographical names with particular characteristics in Hubei Province and extract certain geomorphologic and environmental factors.
  • Journal of Hydrology 569 (2019) 218–229

    Journal of Hydrology 569 (2019) 218–229

    Journal of Hydrology 569 (2019) 218–229 Contents lists available at ScienceDirect Journal of Hydrology journal homepage: www.elsevier.com/locate/jhydrol Water quality variability in the middle and down streams of Han River under the influence of the Middle Route of South-North Water diversion T project, China ⁎ Yi-Ming Kuoa,1, , Wen-wen Liua,1, Enmin Zhaoa, Ran Lia, Rafael Muñoz-Carpenab a School of Environmental Studies, China University of Geosciences, Wuhan 430074, China b Department of Agricultural and Biological Engineering-IFAS, University of Florida, Gainesville, FL, USA ARTICLE INFO ABSTRACT This manuscript was handled by Huaming Guo, The middle and down streams of Han River are complex river systems influenced by hydrologic variations, Editor-in-Chief, with the assistance of Chong- population distributions, and the engineering projects. The Middle Route of China’s South-to-North Water Yu Xu, Associate Editor Transfer (MSNW) project planned to transfer 95 billion m3 annually from Han River to north China. The op- Keywords: eration of the MSNW project may alter the flow rate and further influence the water quality of Han River. This Min/max autocorrelation factor analysis study used min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA) to analyze Dynamic factor analysis spatio-temporal variations of the water quality variables in three typical tributary-mainstream intersection zones Han River in Han River from June 2014 to April 2017. MAFA results showed that chlorophyll-a (Chl-a), chemical oxygen Flow rate − demand (COD), suspend solid (SS) and phosphate (PO 3 ) (represented as trophic dynamics) are main con- Water transfer 4 cerned water quality variables in densely populated zones (Zones 1 and 3), and total nitrogen (TN), nitrate Water quality variation − 3− nitrogen (NO3 ), COD, and PO4 (regarded as nutrient formations dynamics) represent the underlying water quality variations in agricultural cultivation zone (Zone 2).
  • Present Status, Driving Forces and Pattern Optimization of Territory in Hubei Province, China Tingke Wu, Man Yuan

    Present Status, Driving Forces and Pattern Optimization of Territory in Hubei Province, China Tingke Wu, Man Yuan

    World Academy of Science, Engineering and Technology International Journal of Environmental and Ecological Engineering Vol:13, No:5, 2019 Present Status, Driving Forces and Pattern Optimization of Territory in Hubei Province, China Tingke Wu, Man Yuan market failure [4]. In fact, spatial planning system of China is Abstract—“National Territorial Planning (2016-2030)” was not perfect. It is a crucial problem that land resources have been issued by the State Council of China in 2017. As an important unordered and decentralized developed and overexploited so initiative of putting it into effect, territorial planning at provincial level that ecological space and agricultural space are seriously makes overall arrangement of territorial development, resources and squeezed. In this regard, territorial planning makes crucial environment protection, comprehensive renovation and security system construction. Hubei province, as the pivot of the “Rise of attempt to realize the "Multi-Plan Integration" mode and Central China” national strategy, is now confronted with great contributes to spatial planning system reform. It is also opportunities and challenges in territorial development, protection, conducive to improving land use regulation and enhancing and renovation. Territorial spatial pattern experiences long time territorial spatial governance ability. evolution, influenced by multiple internal and external driving forces. Territorial spatial pattern is the result of land use conversion It is not clear what are the main causes of its formation and what are for a long period. Land use change, as the significant effective ways of optimizing it. By analyzing land use data in 2016, this paper reveals present status of territory in Hubei. Combined with manifestation of human activities’ impact on natural economic and social data and construction information, driving forces ecosystems, has always been a specific field of global climate of territorial spatial pattern are then analyzed.
  • This Article Appeared in a Journal Published by Elsevier. The

    This Article Appeared in a Journal Published by Elsevier. The

    This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright Author's personal copy Energy for Sustainable Development 14 (2010) 238–244 Contents lists available at ScienceDirect Energy for Sustainable Development Household level fuel switching in rural Hubei Wuyuan Peng a,⁎, Zerriffi Hisham b, Jiahua Pan c a School of Economic Management, China University of Geosciences (Wuhan Campus), 388 Lumo Road, Hongshan District, Wuhan, Zip code 430074, China b Liu Institute for Global Issues, University of British Columbia, Vancouver, Canada c Research Centre for Sustainable Development, Chinese Academy of Social Sciences, Beijing, China article info abstract Article history: The majority of rural residents in China are dependent on traditional fuels, but the quality and quantity of Received 3 July 2010 existing data on the process of fuel switching in rural China are insufficient to have a clear picture of current Accepted 3 July 2010 conditions and a well-grounded outlook for the future. Based on an analysis of a rural household survey data in Hubei province in 2004, we explore patterns of residential fuel use within the conceptual framework of Keywords: fuel switching using statistical approaches.
  • Preferred Hierarchical Control Strategy of Non-Point Source Pollution at Regional Scale

    Preferred Hierarchical Control Strategy of Non-Point Source Pollution at Regional Scale

    Preferred Hierarchical Control Strategy of Non-Point Source Pollution at Regional Scale Weijia Wen Chinese Academy of Sciences Innovation Academy for Precision Measurement Science and Technology Yanhua Zhuang ( [email protected] ) Institute of Geodesy and Geophysics Chinese Academy of Sciences https://orcid.org/0000-0002-7041- 1118 Liang Zhang Chinese Academy of Sciences Innovation Academy for Precision Measurement Science and Technology Sisi Li Chinese Academy of Sciences Innovation Academy for Precision Measurement Science and Technology Shuhe Ruan Chinese Academy of Sciences Innovation Academy for Precision Measurement Science and Technology Qinjing Zhang Wuhan University Research Article Keywords: Non-point source (NPS) pollution, Critical periods (CPs), Critical source areas (CSAs), Dual- structure export empirical model, Point density analysis (PDA), Management Posted Date: February 18th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-193825/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Preferred hierarchical control strategy of non-point source pollution at regional scale 2 3 Author names and affiliations 4 Weijia Wen1,2, Yanhua Zhuang1*, Liang Zhang1, Sisi Li1, Shuhe Ruan1,2, Qinjing Zhang3 5 1 Hubei Provincial Engineering Research Center of Non-Point Source Pollution Control, Innovation 6 Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 7 430077, Peoples R China 8 2 University of Chinese Academy of Sciences, Beijing
  • Adaptive Optimal Allocation of Water Resources Response to Future Water

    Adaptive Optimal Allocation of Water Resources Response to Future Water

    www.nature.com/scientificreports OPEN Adaptive optimal allocation of water resources response to future water availability and water demand in the Han River basin, China Jing Tian1, Shenglian Guo1*, Lele Deng1, Jiabo Yin1, Zhengke Pan2, Shaokun He1 & Qianxun Li1 Global warming and anthropogenic changes can result in the heterogeneity of water availability in the spatiotemporal scale, which will further afect the allocation of water resources. A lot of researches have been devoted to examining the responses of water availability to global warming while neglected future anthropogenic changes. What’s more, only a few studies have investigated the response of optimal allocation of water resources to the projected climate and anthropogenic changes. In this study, a cascade model chain is developed to evaluate the impacts of projected climate change and human activities on optimal allocation of water resources. Firstly, a large set of global climate models (GCMs) associated with the Daily Bias Correction (DBC) method are employed to project future climate scenarios, while the Cellular Automaton–Markov (CA–Markov) model is used to project future Land Use/Cover Change (LUCC) scenarios. Then the runof simulation is based on the Soil and Water Assessment Tool (SWAT) hydrological model with necessary inputs under the future conditions. Finally, the optimal water resources allocation model is established based on the evaluation of water supply and water demand. The Han River basin in China was selected as a case study. The results show that: (1) the annual runof indicates an increasing trend in the future in contrast with the base period, while the ascending rate of the basin under RCP 4.5 is 4.47%; (2) a nonlinear relationship has been identifed between the optimal allocation of water resources and water availability, while a linear association exists between the former and water demand; (3) increased water supply are needed in the water donor area, the middle and lower reaches should be supplemented with 4.495 billion m3 water in 2030.
  • Multitemporal Landsat Image Based Water Quality Analyses of Danjiangkou Reservoir

    Multitemporal Landsat Image Based Water Quality Analyses of Danjiangkou Reservoir

    Multitemporal Landsat Image Based Water Quality Analyses of Danjiangkou Reservoir Yinuo Zhang, Xin Huang, Wei Yin, and Dun Zhu Abstract Danjiangkou Reservoir (DJKR) is one of the largest artificial In addition, DJKR is one of the water sources of “NongFu freshwater lakes in Asia and a water source of the South: Spring”, which has been one of the most popular drinking wa- the North Water Transfer Project. However, few studies have ter brands of China since 1996 and produces over 0.6 million analyzed the spatio-temporal water quality distribution or tons of natural drinking water annually. The water quality of investigated the causative factors of the long-term water qual- DJKR directly affects the drinking water security of hundreds ity variation of DJKR. In this study, we used multi-temporal of millions of Chinese people and the implementation of the Landsat images combined with the multiple linear stepwise largest-ever water transfer project. Therefore, periodic and ef- regression (MLSR) method to retrieve long-term distributions of ficient water quality monitoring in DJKR is urgently needed. the main water quality parameters in DJKR, i.e., total nitrogen Traditional in-situ measurements are able to provide (TN), total phosphorus (TP), permanganate index (CODMn), details of the optical properties of water, and they provide and five-day biochemical oxygen demand (BOD5). Results accurate data at fixed sample sites inDJKR . Nevertheless, this indicated the heavily polluted regions and an alarming water approach is not only costly and time-consuming, but also quality deterioration trend between May 2006 and May 2014. restricted by natural conditions, e.g.
  • Hubei Province Overview

    Hubei Province Overview

    Mizuho Bank China Business Promotion Division Hubei Province Overview Abbreviated Name E Provincial Capital Wuhan Administrative 12 cities, 1 autonomous Divisions prefecture, and 64 counties Secretary of the Li Hongzhong; Provincial Party Wang Guosheng Committee; Mayor 2 Size 185,900 km Shaanxi Henan Annual Mean Hubei Anhui 15–17°C Chongqing Temperature Hunan Jiangxi Annual Precipitation 800–1,600 mm Official Government www.hubei.gov.cn URL Note: Personnel information as of September 2014 [Economic Scale] Unit 2012 2013 National Share (%) Ranking Gross Domestic Product (GDP) 100 Million RMB 22,250 24,668 9 4.3 Per Capita GDP RMB 38,572 42,613 14 - Value-added Industrial Output (enterprises above a designated 100 Million RMB 9,552 N.A. N.A. N.A. size) Agriculture, Forestry and Fishery 100 Million RMB 4,732 5,161 6 5.3 Output Total Investment in Fixed Assets 100 Million RMB 15,578 20,754 9 4.7 Fiscal Revenue 100 Million RMB 1,823 2,191 11 1.7 Fiscal Expenditure 100 Million RMB 3,760 4,372 11 3.1 Total Retail Sales of Consumer 100 Million RMB 9,563 10,886 6 4.6 Goods Foreign Currency Revenue from Million USD 1,203 1,219 15 2.4 Inbound Tourism Export Value Million USD 19,398 22,838 16 1.0 Import Value Million USD 12,565 13,552 18 0.7 Export Surplus Million USD 6,833 9,286 12 1.4 Total Import and Export Value Million USD 31,964 36,389 17 0.9 Foreign Direct Investment No.
  • Global Map of Irrigation Areas CHINA

    Global Map of Irrigation Areas CHINA

    Global Map of Irrigation Areas CHINA Area equipped for irrigation (ha) Area actually irrigated Province total with groundwater with surface water (ha) Anhui 3 369 860 337 346 3 032 514 2 309 259 Beijing 367 870 204 428 163 442 352 387 Chongqing 618 090 30 618 060 432 520 Fujian 1 005 000 16 021 988 979 938 174 Gansu 1 355 480 180 090 1 175 390 1 153 139 Guangdong 2 230 740 28 106 2 202 634 2 042 344 Guangxi 1 532 220 13 156 1 519 064 1 208 323 Guizhou 711 920 2 009 709 911 515 049 Hainan 250 600 2 349 248 251 189 232 Hebei 4 885 720 4 143 367 742 353 4 475 046 Heilongjiang 2 400 060 1 599 131 800 929 2 003 129 Henan 4 941 210 3 422 622 1 518 588 3 862 567 Hong Kong 2 000 0 2 000 800 Hubei 2 457 630 51 049 2 406 581 2 082 525 Hunan 2 761 660 0 2 761 660 2 598 439 Inner Mongolia 3 332 520 2 150 064 1 182 456 2 842 223 Jiangsu 4 020 100 119 982 3 900 118 3 487 628 Jiangxi 1 883 720 14 688 1 869 032 1 818 684 Jilin 1 636 370 751 990 884 380 1 066 337 Liaoning 1 715 390 783 750 931 640 1 385 872 Ningxia 497 220 33 538 463 682 497 220 Qinghai 371 170 5 212 365 958 301 560 Shaanxi 1 443 620 488 895 954 725 1 211 648 Shandong 5 360 090 2 581 448 2 778 642 4 485 538 Shanghai 308 340 0 308 340 308 340 Shanxi 1 283 460 611 084 672 376 1 017 422 Sichuan 2 607 420 13 291 2 594 129 2 140 680 Tianjin 393 010 134 743 258 267 321 932 Tibet 306 980 7 055 299 925 289 908 Xinjiang 4 776 980 924 366 3 852 614 4 629 141 Yunnan 1 561 190 11 635 1 549 555 1 328 186 Zhejiang 1 512 300 27 297 1 485 003 1 463 653 China total 61 899 940 18 658 742 43 241 198 52
  • Background Report on Low Carbon Strategies and Policies In

    Background Report on Low Carbon Strategies and Policies In

    Background Report on Low-Carbon Development Strategies and Policies in Wuhan City , China Submitted by Green Innovative Development Center (iGDP) 2017-12 Contents 1. Preface........................................................................................................ 3 2. City Profile ................................................................................................. 6 3. Key Driving Factors Behind Wuhan’s Carbon Dioxide Emissions ......... 11 3.1 Demography ................................................................................... 12 3.2 Economic Growth and Structure .................................................... 13 3.3 Energy Consumption ..................................................................... 15 4.4 Carbon Dioxide Emissions ............................................................ 17 4. Low-Carbon Development Strategies and Policy Practices .................... 17 4.1 Low-Carbon Management System ................................................ 19 4.2 Key Areas ....................................................................................... 20 4.2.1 The Energy System ............................................................. 20 4.2.2 Industry ............................................................................... 22 4.2.3 Buildings ............................................................................. 26 4.2.4 Transportation ..................................................................... 28 4.2.5 Environment and Land Use ...............................................
  • The Detection of Flood Characteristics Alteration Induced by the Danjiangkou Reservoir at Han River, China

    The Detection of Flood Characteristics Alteration Induced by the Danjiangkou Reservoir at Han River, China

    water Article The Detection of Flood Characteristics Alteration Induced by the Danjiangkou Reservoir at Han River, China Xiao Zhang *, Baofei Feng, Jun Zhang, Yinshan Xu, Jie Li, Wenjing Niu and Yanfei Yang Bureau of Hydrology, Changjiang Water Resources Commission, Wuhan 430010, China; [email protected] (B.F.); [email protected] (J.Z.); [email protected] (Y.X.); [email protected] (J.L.); [email protected] (W.N.); [email protected] (Y.Y.) * Correspondence: [email protected]; Tel.: +86-152-4023-3358 Abstract: As one of the most common natural phenomena, floods can bring both risks and benefits for human beings. They can pose a risk of inundation to a human habitat but can also be utilized as a resource with hydraulic engineering. Improving the knowledge of flood characteristics is the basis and premise of improving water resources management and ecological environmental protection. Presently, the quantitative evaluation of flood characteristics needs to gradually evolve from a single indicator to a systematic one. In this paper, by introducing the concepts of ecohydrology on magnitude, frequency, and duration, a set of flood characteristics indicators evaluation system is constructed based on the hydrological characteristics for the section where Danjiangkou Reservoir is located at the middle reach of the Yangtze in China. The results showed that the Danjiangkou Reservoir has changed the flood characteristics to a great extent both of seasonal or annual floods, and the mean degree of the flood characteristic alteration indicators is about 19%. The changing trend of the flood indicators upstream showed an increasing trend from the 1970s to 2010s, while downstream were divided into two periods by the year of about 1975.
  • “Stayers”? Examining China's Resettlement Induced By

    “Stayers”? Examining China's Resettlement Induced By

    land Article What about the “Stayers”? Examining China’s Resettlement Induced by Large Reservoir Projects Tianhe Jiang 1,2,3, Mark Wang 2,4 , Yingnan Zhang 2,5, Guoqing Shi 1,3,* and Dengcai Yan 1,3 1 School of Public Administration, Hohai University, Nanjing 211100, China; [email protected] (T.J.); [email protected] (D.Y.) 2 School of Geography, The University of Melbourne, Carlton, VIC 3053, Australia; [email protected] (M.W.); [email protected] (Y.Z.) 3 National Research Centre for Resettlement, Hohai University, Nanjing 210098, China 4 Centre for Contemporary Chinese Studies, Asia Institute, The University of Melbourne, Parkville, VIC 3010, Australia 5 Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China * Correspondence: [email protected] Abstract: Large reservoir projects typically occupy vast lots of rural land and trigger resettlement on a massive scale. In China’s reservoir context, increasing concerns have arisen regarding distant- resettlees (those who are resettled outside the reservoir area), while fewer studies have examined the nearby-resettlees (those who are resettled near the original area) and the non-movers (those who do not resettle). The significance of these two groups has been downplayed and their populations are in the millions (or more) in China. How and why they are impacted is under-researched and their relative position and intergroup nexus with the distant-resettlees remains unclear in the existing literature. To address this research gap, this paper incorporates the differences between nearby- resettlees and non-movers and collectively refers to them as the “stayers” as they are left behind in Citation: Jiang, T.; Wang, M.; Zhang, reservoir areas.