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 Table of Contents  
ORIGINAL ARTICLE
Year : 2021  |  Volume : 7  |  Issue : 3  |  Page : 75-81

Difference analysis of accumulated degree-day samples in different regions of China


Department of Forensic Pathology and Forensic Clinical Medicine, School of Forensic Medicine, Henan University of Science and Technology, Luoyang, China

Date of Submission11-Jan-2020
Date of Decision11-Mar-2021
Date of Acceptance17-Jul-2021
Date of Web Publication27-Sep-2021

Correspondence Address:
Yaonan Mo
School of Forensic Medicine, Henan University of Science and Technology, Luoyang 471003
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jfsm.jfsm_4_21

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  Abstract 


Background: Accumulated degree-days (ADD) refers to the temperature value and time within a certain period. More and more attention has been paid to the ADD in the study of postmortem interval (PMI) estimation. Aim and Objective: This study is to confirm whether ADD is applicable in China. Materials and Methods: We collected meteorological data of 10 different regions in China for 12 months, analyzed the distribution characteristics of ADD in different regions at different time periods, and tested the two ADD calculation methods (accumulated hourly temperature and accumulated daily average temperature), in an attempt to establish a more precise calculation method of ADD. Results: The results show that when the yearly or monthly effective temperature data is taken as the research object, the law of daily ADD mean value gradually decreases from south to north, and the average value of accumulated hourly temperature at each region is larger than the average value of accumulated daily average temperature (the difference was significant). In addition, in different periods of each day, ADD obtained at different regions is different, and the variation of its specific gravity also has a north-south difference. We take the daily average temperature as the independent variable and accumulated hourly temperature as the linear equation fitted by the dependent variable, showing a good linear relationship (0.992 ≤ R2 ≤ 0.999). At the same time, we also identify that extra caution should be exercised when using ADD in some special regions (such as Lhasa) or during the cold season of some regions. It may be unrealistic to attempt divide daily ADD into equal parts and accurately estimate PMI to a certain hour on the day of the crime. However, accurate estimation of PMI can be improved by dividing ADD on the day of the crime according to proportion of different periods and checking the time period of the final ADD value. Conclusion: At present, the study results on ADD need to be further developed. our study provides a preliminary research basis for the future establishment of an unified, simple, accurate, and suitable for the ADD model in China.

Keywords: Accumulated degree-day, China, distribution characteristics, postmortem interval


How to cite this article:
Zhu W, Zhai X, Yang M, Qian M, Zhang Z, Mo Y. Difference analysis of accumulated degree-day samples in different regions of China. J Forensic Sci Med 2021;7:75-81

How to cite this URL:
Zhu W, Zhai X, Yang M, Qian M, Zhang Z, Mo Y. Difference analysis of accumulated degree-day samples in different regions of China. J Forensic Sci Med [serial online] 2021 [cited 2021 Nov 27];7:75-81. Available from: https://www.jfsmonline.com/text.asp?2021/7/3/75/326802




  Introduction Top


The estimation of the late postmortem interval (PMI) has been hailed as cancer in the field of forensic pathology research. In recent years, with the transformation and application of new methods and technologies,[1],[2] the late PMI research has come a long way. But so far, regardless of what kind of technical methods, there are still great challenges to achieve accurate PMI estimation in decomposition corpses.[3] Corpse corruption is a complex process, which is influenced by various factors such as the corpse itself and the external environment. However, as far as the current research technology is concerned, it is unrealistic to encompass all factors. For this purpose, priority can be placed on the role of the main factors (ambient temperature and elapsed time) that affect the corruption process.[4],[5]

Accumulated degree-day (ADD) refers to the product of temperature and elapsed time during a certain period of time, and is typically used as an indicator to study the relationship between temperature and biological development rate. It can exploring the impact on biological growth and development from two aspects of temperature intensity and elapsed time.[6],[7],[8] In 1855, Bergeret established the link between entomology and PMI estimation, and the ADD began to be employed in forensic entomology research.[9],[10] In 1992, Vass et al.[11] applied ADD conversion to the study of cadaver decomposition, and modeled the data of volatile fatty acids and various anions and cations in the soil around the cadaver with ADD to help accurately estimate PMI. In 2005, Megyesi et al.[12] concluded that the best decomposition evaluation model depends on ADD, not just time. At the same time, total body score and ADD regression models established by it enable PMI to be quantified, which narrows the interference of subjective factors to a certain extent and greatly stimulates the application potential of ADD in forensic medicine.[12]

The ADD calculation method is mainly divided into accumulated hourly temperature and accumulated daily average temperature. Many previous studies[13],[14],[15] have shown that ADD calculated by hourly temperature data has the best prediction effect and was more suitable for PMI estimation. However, it is difficult to obtain hourly temperature (it needs to apply to the meteorological department and pay a certain amount of compensation), and when the late PMI was involved, the hourly temperature data are huge, so the accumulated hourly temperature calculation is cumbersome, so it is not applicable to the quick estimation of PMI at the murder scene.

China is in possession of complex terrain, large differences between east, west, south, and north, and with different climate zones. Therefore, it is especially important to analyze the distribution characteristics of ADD and explore the best ADD calculation method. However, at present, no such related research has been found in the field of forensic science. Therefore, we used data from ten regions collected over a 12-month period, analyzed the distribution characteristics of ADD in different regions from different time periods, and examined the difference between accumulated hourly temperature and accumulated daily average temperature, which try to establish a better ADD calculation model. We hope that it will be helpful for China to make better use of the ADD to estimate PMI in future.


  Materials and Methods Top


Location and temperature data

The distribution of ten regions in China for this study is presented in [Figure 1]. The selection of regions was based on the criteria of covering the entire mainland of China, including different terrains, distinct climatic regions, and taking into account the population density. The meteorological data of different regions were provided by the Chinese Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn). The meteorological data include variable information during the period from March 1, 2019, to February 29, 2020, such as hourly temperature, daily maximum temperature, daily minimum temperature, daily average temperature, humidity, and wind velocity. The temperature data are measured and calculated by WQG-11 dry and wet bulb thermometer (Tianjin Kailong Instrument Co., Ltd., China) and WQG-13 highest and lowest temperature table (Changzhou Ruiming Instrument Factory, China).
Figure 1: The location of ten monitoring points in China in this study. 1 – Urumchi (43.45N, 87.36E); 2 – Hohhot (40.48N, 111.41E); 3 – Harbin (45.44N, 126.36E); 4 – Lanzhou (36.04N, 103.51E); 5 – Zhengzhou (34.46N, 113.40E); 6 – Shanghai (31.14N, 121.29E); 7 – Lhasa (29.39N, 91.08E); 8 – Chongqing (29.35N, 106.33E); 9 – Changsha (28.12N, 112.59E); and 10 – Guangzhou (23.08N, 113.14E). This map is only a stick figure, and does not indicate areas such as the Chinese coastline and the Nansha Islands. It is strictly forbidden to use it as a map of China

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A total of 3660 days of hourly temperature and daily average temperature were collected from the ten regions, excluding the date of incomplete 24-h temperature data in 1 day, and the final effective number of days was 3232 days (a total of 80,800 data), among which the effective days of each region were 327 days in Urumchi, 326 days in Hohhot, 327 days in Harbin, 324 days in Lanzhou, 325 days in Zhengzhou, 331 days in Shanghai, 319 days in Lhasa, 333 days in Chongqing, 323 days in Changsha, and 297 days in Guangzhou. Refer to Megyesi's method[12] for calculating ADD, 0°C was taken as the starting point of temperature, which was also the temperature threshold of ADD that was commonly selected for decay of corpses and forensic burial studies.[16],[17],[18] Accordingly, all temperatures below 0°C in the meteorological data are recorded at 0°C.

Year

Taking all effective temperature data of each region as the research object, daily ADD was calculated by using hourly temperature and daily average temperature, respectively, and using paired sample t-tests in SPSS (v. 21.0; IBM, Armonk, NY, USA) to compare the results and test whether there was a significant difference between the two (inspection level α =0.01), the results were expressed by “ADD mean and P value.” To correct the calculation error of daily average temperature, we set up the linear fitting equation with daily average temperature as the independent variable (x) and accumulated hourly temperature as the dependent variable (y).

Monthly

In order to more carefully examine the differences between the two calculation methods of ADD in each region, we took the effective temperature data of each month as the research object and used the paired sample t-tests to compare whether there was a significant difference between the accumulated hourly temperature and accumulated daily average temperature of each month. The results were expressed as “ADD mean and P value.” In addition, accumulated hourly temperature was employed as an evaluation indicator to analyze the change of the daily ADD mean for each region with month.

Hourly temperature distribution and accumulated degree-day proportion

In order to check the daily temperature distribution characteristics of each region and the proportion of ADD in different periods, we took 24 h as the independent variable (x) and the hourly temperature as the dependent variable (y) to make a scatter plot (the hourly temperature here is the original temperature data, and the temperature below 0°C is not converted to 0°C), which was used to analyze the temperature distribution characteristics of each region 24 h a day. In addition, we divide the 24 h of each day into four periods (0–5 h, 6–11 h, 12–17 h, and 18–23 h), and calculate the proportion of ADD in each period. The result was expressed as “ADD proportion mean ± standard deviation.”

The data in this study come from an open shared network platform, and this article does not contain any studies with human participants or animals performed by any of the authors.


  Results Top


Year

When the effective temperature data for the whole year were taken as the research object, we found that the daily accumulated hourly temperature of ten regions was greater than the accumulated daily average temperature, and the two showed significant statistical differences in each region [Table 1]. The mean value of ADD at each region (accumulated hourly temperature was employed as the evaluation index) was: Guangzhou > Chongqing > Changsha > Shanghai > Zhengzhou > Urumchi > Lan zhou > Hohhot > Harbin > Lhasa. The difference between the results of the two ADD calculation methods (accumulated hourly temperature minus accumulated daily average temperature) was:Lhasa (17.529°C•d)> Lanzhou (13.793°C•d)> Shanghai (12.006°C•d)> Chongqing (11.829°C•d)> Zhengzhou (11.739°C•d) )> Guangzhou (11.589°C•d)> Changsha (10.631°C•d)> Hohhot (10.349°C•d)> Harbin (9.049°C•d)> Urumchi (7.758°C•d). The linear equation established by using the daily average temperature as the independent variable (x) and accumulated hourly temperature as the dependent variable (y) is shown in [Table 1]: the correlation between the daily average temperature and accumulated hourly temperature at all regions was high (0.992 ≤ R2 0.999). Among them, Urumchi, Zhengzhou, and Changsha had the highest correlation coefficients and the best linear fitting relationship.
Table 1: Differences in accumulated degree-day data and linear fitting equations at ten regions

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Monthly

Monthly ADD data paired sample t-test results [Table 2] showed that the P value of most of the ten regions was less than 0.05, and indicating that two calculation methods of the ADD within the month had a significant statistical difference, but in Urumchi and Harbin from December 2019 to February 2020, as well as the Hohhot from December 2019 to January 2020, during the two calculation methods of the ADD, there was no significant difference. Taking accumulated hourly temperature as the evaluation index, the changes of the ADD mean for each region with the month were discussed, as shown in [Figure 2]. The maximum ADD mean of each region was widely distributed from July 2019 to August 2019, and the minimum ADD mean was 2020 January. Among them, Lhasa area was special, with the largest ADD mean in June 2019 and the smallest ADD mean in December 2019. The magnitude of ADD mean change (accumulated hourly temperature range within 12 months) was:Zhengzhou (637.618°C•d) > Urumchi (602.154°C•d) > Changsha (600.381°C•d) > Harbin (559°C•d) > Lanzhou (525.967°C•d) > Hohhot (505.562°C•d) > Shanghai (501.234°C•d) > Chongqing (496.912°C•d) > Lhasa (423.095°C•d) > Guangzhou (286.943°C•d), and there was no obvious regional regularity of the changes.
Table 2: Monthly accumulated degree-day data paired sample t-test results of ten regions

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Figure 2: Accumulated degree-day mean of ten regions changes with the month

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Hourly temperature distribution and accumulated degree-day proportion

The hourly temperature changes of the ten regions were different [Figure 3]. The approximate daily occurrences of high and low temperatures were Urumchi (17 h, 8 h), Hohhot (15 h, 5 h), Harbin (15 h, 3 h), Lanzhou (16 h, 7 h), Zhengzhou (15 h, 5 h), Shanghai (14 h, 5 h), Lhasa (18 h, 7 h), Chongqing (16 h, 7 h), Changsha (15 h, 6 h), and Guangzhou (14–15 h, 6 h), and the average daily temperature differences (the highest temperature minus the lowest temperature) were Lhasa (12.947°C) > Lanzhou (12.077°C) > Hohhot (11.859°C) > Harbin (10.468°C) > Zhengzhou (9.698°C) > Urumchi (8.419°C) > Changsha (7.483°C) > Guangzhou (6.919°C) > Shanghai (6.045°C) > Chongqing (5.730°C). In addition, the ADD obtained in different periods was changed by analyzing the ADD proportion in different periods of the day [Table 3]. All regions earned the most ADD within 12–17 h and every day, and the least ADD within 0–5 h. Among them, Urumchi, Hohhot, Harbin, Lanzhou, Zhengzhou, and Lhasa had larger changes at the ADD proportion in different periods, while Shanghai, Chongqing, Changsha, and Guangzhou had smaller changes at the ADD proportion in different periods.
Figure 3: The hourly temperature change at ten regions (blue line indicates the time of daily minimum temperature and red line indicates the time of daily maximum temperature). A – Urumchi, B – Hohhot, C – Harbin, D – Lanzhou, E – Zhengzhou, F – Shanghai, G – Lhasa, H – Chongqing, I – Changsha, and J – Guangzhou

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Table 3: Distribution of accumulated degree-day proportion at ten regions in different periods of the day (X̄±S)

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  Discussion Top


Among the various factors that affect PMI, temperature was undoubtedly the main influencing factor,[4],[5],[19],[20],[21] but theory and practice have proved that the single consideration of temperature factors to estimate PMI has limitations. ADD is a key indicator of biological growth and development, which takes into account the two major variables of environmental temperature and elapsed time. Therefore, some authors have established a mathematical model of ADD and some evaluation indicators to estimate PMI, and the prediction effect is much better than a single time variable.[11],[12],[13],[22],[23] However, Forbes et al.[24] verified the accuracy of two different ADD models (Megyesi and Moffatt models) in the Cape Town area of South Africa, and found that neither was applicable in South Africa. This was agreement with the experimental results of Myburgh et al.[25] Afterward, the verification results in Australia,[26] Central Texas,[27] and other regions also indicate that it was unrealistic to apply the ADD model developed for a region or in a region to other regions, so it was necessary to establish suitable for different regional ADD equations to estimate PMI. In China, where the terrain is complex, the differences between east, west, south, and north, and with different climate zones, this should be paid more attention to.

At present, the research on the accumulated temperature distribution in different regions of China mainly focuses on the meteorology,[28],[29] insect development,[30],[31] and agricultural science,[32],[33] and the temperature starting point was popular selected at 5°C or 10°C, which was not suitable for direct conversion for PMI estimation. Therefore, taking 0°C as the starting point of temperature, we explored the distribution differences of ADD in different regions and at different time periods, and took ADD data calculated at hourly temperature as the standard value. Paired sample t-tests were used to compare the average difference between the standard value and the accumulated daily average temperature. When taking the effective annual temperature data as the research object, we found that the distribution of daily ADD mean value at different monitoring points was significantly different, but with certain rules. From south to north, ADD gradually decreased, which was consistent with the feature that the higher the latitude in China, the lower the temperature. Among them, Lhasa is located in the plateau climate region with a high altitude and is less affected by latitude, resulting in a small value of ADD mean (the temperature decreases with the increase of height, and the temperature decreases by about 6°C for every 1000 m of rise in the troposphere). In addition, accumulated hourly temperature of the ten regions was higher than the accumulated daily average temperature, and the difference was statistically significant. However, no obvious regional rule was found for ADD difference (accumulated hourly temperature minus accumulated daily average temperature). Taking the daily average temperature as the independent variable (x) and accumulated hourly temperature as the dependent variable (y), the linear fitting results show that the correlation between the daily average temperature and accumulated hourly temperature at each region is higher, and the linear fitting relationship is better (0.992 ≤ R2 0.999), indicating that the ADD calculation method is simple and accurate, and can try to replace poor accumulated daily average temperature calculation accuracy.

There were significant statistical differences between accumulated hourly temperature and accumulated daily average temperature in most months of the ten regions, but there were no significant differences between accumulated hourly temperature and accumulated daily average temperature in Urumchi and Harbin from December 2019 to February 2020, and in Hohhot from December 2019 to January 2020. The reason is that during this period from December 2019 to February 2020, the hourly temperatures in the three regions were all below 0°C, resulting in the accumulated hourly temperature and accumulated daily average temperature value were both 0, with no statistical difference. In addition, we also found that the maximum mean ADD of each region was generally distributed from July to August 2019, while the minimum daily mean of ADD was in January 2020. However, due to the special geographic location (average altitude above 3600 m), both the maximum and minimum ADD mean of Lhasa were brought forward by 1 month.

We assessed the daily temperature data in detail, and found that the diurnal temperature changes of ten regions were also different, but had obvious geographical rules. From east to west, daily occurrence time of high and low temperatures in each region is gradually delayed, which may be caused by the influence of light factor, because the occurrence time of light is slightly delayed from east to west. The daily average temperature difference of each region also has a meaningful difference between the north and the south. The temperature difference in the north is generally larger than that in the south, and is influenced by the difference in the thermal properties of land and sea. The temperature change in coastal areas is poor, while that in inland areas is large (Lhasa and Chongqing are exceptional). According to the analysis of the ADD proportion at different periods of the day reveals that the proportion of ADD in different periods is different. The daily ADD proportion is the largest in the 12–17 h, and the ADD proportion is the smallest in the 0–5 h. Moreover, the variation of the ADD ratio in different periods also has significant north–south differences. This warns that it may be unrealistic to attempt to divide daily ADD into equal parts[34],[35] and accurately estimate PMI to a certain hour on the day of the crime (especially in northern China). However, the accuracy of PMI estimation can be improved by dividing ADD on the day of the crime according to proportion and checking the time period of the final ADD value.

To sum up, there are obvious differences in the distribution characteristics of ADD in different regions of China, but there are also rules. People can establish the ADD model suitable for China to obtain the nuances of corruption decomposition in different regions, so as to accurately estimate PMI. In addition, only 12 months' meteorological data from ten regions were collected in this experiment, which some microscopic regions in China may not be considered. Furthermore, we mainly analyze and calculate based on the samples data of ADD in different regions of China, and did not explore the relationship between ADD and PMI. Establishing an ADD model that is uniform, simple, accurate, and suitable for all regions of China for PMI estimation will be our next research focus.


  Conclusion Top


The study collected 12 months of temperature data from different parts of China, analyzed the distribution characteristics of ADD in different time periods in each region, tested the difference between the two ADD calculation methods, and established a novel ADD calculation method. At present, our research results cannot be directly used for PMI estimation, mainly to provide a preliminary research basis for the future establishment of unified, simple, accurate, and suitable for the ADD model in China.

Acknowledgment

We wish to thank financial support provided by the Forensic Center of HAUST (Henan University of Science and Technology). We also thank the Chinese Air Quality Online Monitoring and Analysis Platform for granting access to its data repositories.

Financial support and sponsorship

Nil.

Conflicts of interest

Dr. Xiandun Zhai and Dr. Yaonan Mo are editorial board members of Journal of Forensic Science and Medicine.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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