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SCIENCE CHINA Earth Sciences, Volume 60, Issue 11: 2051-2058(2017) https://doi.org/10.1007/s11430-016-9060-5

From solar terms to medical terms (Part II): Some implications for traditional Chinese Medicine

Jie SHI1, Ge CHEN2,3,*
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  • ReceivedMar 1, 2017
  • AcceptedMay 22, 2017
  • PublishedJul 19, 2017

Abstract

In the traditional Chinese Medicine (TCM), the timing of performing the treatment during the year is always an important factor for maximizing its effectiveness. Based on over half a century of observational and reanalysis data, a modified calendric system, named the Twenty-four Medical Terms (24-MTs), has been established for mainland China following a systematic calibration and geographical adjustment of the classic Twenty-four Solar Terms (24-STs). In view of “adapting the human body to the changing universe”, a core philosophy of the TCM, this improved medical calendar is expected to make a significant contribution to the development of precise Chinese Medicine in the big data era. Specifically, two maps of localized timings for the so-called Triple-Fu (TF) and Triple-Jiu (TF) defined using a joint heat index of air temperature and relative humidity are created as an alternative to the two nationwide unified timings representing the warmest and coldest periods of the year. These location-specific medical calendars, with a maximum regional time shift of one week for TJ and a systematic advancing of 3.6–28.2 days for TF in mainland China, are thought to be clinically useful for carrying out precise TCM such as “treating winter deceases in summer”. In addition, similar maps of localized timings for peak spring and peak autumn defined as the days of fastest warming and cooling around the years are generated for mainland China, so as to provide a helpful guidance for practicing season (ST/MT) related and geographically dependent precise health care in the context of “born in spring, grow in summer, harvest in autumn, and preserve in winter”, which is a key ideology in the TCM.


Funded by

Natural Science Foundation of China(61361136001)


Acknowledgment

The authors would like to thank WANG Xuan, REN Yibin and LIU Yingjie for their assistances in figure plotting and literature survey. This research was supported by the National Natural Science Foundation of China (Grant No. 61361136001).


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  • Figure 1

    The interrelationships of the 24-STs, the human organs and the meridians.

  • Figure 2

    Geographical distributions of the annual maximum and minimum SAT and SHU respectively derived from the BEST and NCEP datasets using EEMD. (a) Maximum SAT; (b) Minimum SAT; (c) Maximum SHU; (d) Minimum SHU. (e) is the geographical distribution of the correlation coefficient between the covariations of SAT and SHU for the period 1960–2013.

  • Figure 3

    Moving correlation between the covariations of SAT and SHU for the period 1960–2013. (a) Mainland China; (b) YRB; (c) CRB; (d) ZRB. The vertical dashed lines depict a zero time lag.

  • Figure 4

    Geographical distributions of the timings of MTs-based (a) mid-TJ and (b) mid-TF derived from the BEST and NCEP datasets using EEMD. (c) and (d) are the time shifts of (a) and (b) with respect to their traditional definitions for 2016 corresponding to sequential calendar days of 12.5 and 217.0, respectively.

  • Figure 5

    Geographical distributions of the timing of maximum SAT gradient during (a) warming period and (b) cooling period derived from the BEST dataset using EEMD.

  • Table 1   Timings of maximum and minimum specific humidity derived from the BEST dataset using EEMD for the YRB, CRB and ZRB

    SHU

    Timing (sequential day)

    YRB

    CRB

    ZRB

    Max.

    212.34

    205.18

    200.80

    Min.

    2.80

    8.67

    3.50

  • Table 2   Timings of the mid-TF and mid-TJ days of major cities in mainland China derived from the BEST dataset

    City

    Location (longitude, latitude)

    Mid-TF Day (sequential day)

    Mid-TJ Day (sequential day)

    Beijing

    116°28′E, 39°54′N

    202.62

    10.38

    Shanghai

    121°29′E, 31°14′N

    213.40

    20.46

    Tianjin

    117°11′E, 39°09′N

    203.79

    11.50

    Chongqing

    106°32′E, 29°32′N

    211.00

    14.00

    Harbin

    126°41′E, 45°45′N

    202.00

    11.52

    Changchun

    125°19′E, 43°52′N

    202.43

    11.51

    Shenyang

    123°24′E, 41°50′N

    205.39

    11.95

    Huhehot

    111°48′E, 40°49′N

    201.01

    10.28

    Shijiazhuang

    114°28′E, 38°02′N

    200.53

    9.99

    Taiyuan

    112°34′E, 37°52′N

    201.52

    9.06

    Jinan

    117°E, 36°38′N

    201.91

    12.45

    Zhengzhou

    113°42′E, 34°48′N

    201.00

    11.00

    Xi’an

    108°54′E, 34°16′N

    203.93

    9.89

    Lanzhou

    103°49′E, 36°03′N

    204.66

    8.49

    Yinchuan

    106°16′E, 38°20′N

    201.99

    9.25

    Xining

    101°45′E, 36°38′N

    208.00

    7.00

    Urumqi

    87°36′E, 43°48′N

    199.04

    14.80

    Hefei

    117°18′E, 31°51′N

    208.00

    14.50

    Nanjing

    118°50′E, 32°02′N

    209.71

    15.68

    Hangzhou

    120°09′E, 30°14′N

    210.34

    16.63

    Changsha

    113°E, 28°11′N

    208.77

    15.50

    Nanchang

    115°52′E, 28°41′N

    210.56

    15.13

    Wuhan

    114°21′E, 30°37′N

    209.00

    14.00

    Chengdu

    104°05′E, 30°39′N

    208.01

    13.17

    Guiyang

    106°42′E, 26°35′N

    208.00

    16.00

    Fuzhou

    119°18′E, 26°05′N

    211.54

    19.75

    Guangzhou

    113°15′E, 23°08′N

    206.72

    15.59

    Nanning

    108°20′E, 22°48′N

    206.70

    16.03

    Kunming

    102°41′E, 25°N

    188.83

    7.54

    Lasa

    90°08′E, 29°39′N

    193.03

    6.21

  • Table 3   Timings of maximum SAT gradient during warming and cooling periods of the year derived from the BEST dataset using EEMD for the YRB, CRB and ZRB

    SAT gradient

    Timing (sequential day)

    YRB

    CRB

    ZRB

    Max (warming)

    80.21

    86.14

    86.15

    Max (cooling)

    304.15

    301.66

    312.88

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