Ashes to Ashes, and Dust to Dust: Is Scattering Garden the Sustainable Destination for Cremated Ashes?

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Sai-Leung Ng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1232548/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Cremation is commonly practiced around the world because it requires small space for the disposal of ashes and also causes less environmental impacts. Among various options for ash disposal, many people choose to scatter the ashes of their loved ones in a scattering garden. Is scattering garden a sustainable solution to the disposal of cremated ashes? No research has ever attempted to answer this question based on empirical data. This study aimed at filling the research gap by characterizing and evaluating the vegetation performance of a scattering garden after it had been open for ash scattering for six years. The results indicated that, overall, approximately half of vegetation was degraded to either unhealthy or bare soil. The area of bare soil in the lawns of high scattering level was larger than that of low scattering level. Furthermore, the belowground biomass of vegetation in the lawns of high scattering level was significantly lower than that of low scattering level. It is concluded that the current practice of ash scattering in Hong Kong was not sustainable and the intensity of impacts was dependent upon the level of ash scattering. The findings of this study may provide a reference for the policy and management of ash scattering in Hong Kong and other cities around the world. ash scattering cremated human ashes scattering garden vegetation biomass vegetation composition Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Everyone’s life has a beginning and an end. As death is an essential aspect of our existence that we cannot avoid, disposal of the dead is an inevitable activity of human society that is of personal, emotional, social, and environmental significance (Canning & Szmigin, 2010 ). Although different cultures and traditions have their own death rites and rituals, land burial had been the commonest practice since pre-history (Decker, Muniz, & Cruz, 2018 ). However, we see a dramatic increase in cremation in the last half-century (Davies & Mates, 2005 ). Take the United States as an example, the cremation rate increased from 5.7% in 1975 to 48.7% in 2015 and since then, cremation has taken over land burial to be the commonest way of handling the dead. The rate was forecasted to further increase to 79.1% in 2035 (Statista, 2020 ). Around the world, the cremation rate is often higher than 70% in those areas with a high density of population but limited land space (National Funeral Directors Association (NFDA), 2015 ). For example, Hong Kong sustains a population of approximately 7.5 million people on 1107 km² of land (Census and Statistics Department (CSD), 2019 ), approximately 90% of the deceased bodies are cremated (Food and Environmental Hygiene Department (FEHD), 2018 ). The popularity of cremation reflects people holding a more relaxed stance on cremation as the social power of religion and tradition is declining (Heessels, 2012 ). From the viewpoint of public administration, local authorities promote cremation in order to address the constraints of cemetery capacity and provide the public with a hygienic means of disposal (Canning & Szmigin, 2010 ). First, cremation turns the body into ashes that greatly saves space for disposal. The Cremation Society of Great Britain adopted the motto “Save the Land for the Living” (Jupp, 2005 ). The total shadow price of environmental impacts caused by burial was estimated at 67€/person and that of cremation was only 52€/person, respectively (Keijzer, 2017 ). Second, land burial is identified as a potential source of a wide range of environmental pollutants(Silva, 1995 )(Silva & Malagutti Filho, 2009) (Aruomero & Afolabi, 2014 ). Not only the decomposition of the corpse produces viscous leachate that may contaminate groundwater and potentially spread diseases to the population(Pacheco, Mendes, Martins, Hassuda, & Kimmelmann, 1991 )(Dent, 2000 ) (Zychowski, 2012 ), but also residues of chemicals, e.g., cosmetics, dyes, and stiffeners, used in corpse makeup and preparation may cause soil contamination for a reasonable time(Üçisik & Rushbrook, 1998 ) (Amuno, 2013 ). Cremation effectively helps improve sanitary conditions by killing all viruses, germs, and bacteria carried by the body through intense heat and evaporation (Decker, Muniz, & Cruz, 2018 ). However, cremation is not the final solution to the disposal of the dead. It is just a treatment that makes the disposal more flexible. There are many options for ash disposals, such as ash scattering (either on land or at sea), urn storage (either keeping at home, at a columbarium), and burying in a cemetery, etc. (Mathijssen, 2017 ). Among these options, many people choose to scatter the ashes of their loved ones in a scattering garden. For example, more than 70% of the total number of cremation cases in the Netherlands were disposed of by scattering (Dijk & Mengen, 2002 ). Although ash scattering in Asia is not as popular as in western countries, the number of ash scattering has greatly increased in recent years. For example, in 2017, there were 5,573 cases of scattering in scattering garden, respectively, accounted for about 12.1 % of the total number of 45,883 deats in Hong Kong (Legislative Council, 2020 ), increased from 4.6% in 2010 (Legislative Council, 2018 ). Despite the continuously increasing demands of scattering garden, no scientific studies had ever attempted to evaluate the environmental sustainability of ash scattering using empirical data. Because ashes are an alternative form of human body, research ethnics requires researchers to seek consent from the studied subjects before measurements or experiments are conducted. However, such consent is impossible to obtain, implying the existence of unsurmountable difficulty. Furthermore, researchers tended to avoid having physical contact with cremated ashes because of sensitive implications, such as cultural beliefs and issues linked to the phenomenon of death history (Decker, Muniz, & Cruz, 2018 ). Although a few studies had discussed the environmental implications of ash scattering, their views were diverse or even contradictory. For example, Niziolomski, Rickson, Marquez-Grant, & Pawlett ( 2016 ) indicated that cremated ashes would be toxic to plant because high sodium content of cremated ashes exceeded the tolerable limit of plants. On the other hand,(Strand, Shields, & Swiader, 2008 ) indicated the potential of cremation ashes as sources of phosphorous for soil additive or fertilizer, based on a laboratory experiment which used EDTA and citric acid to extract phosphorus from cremated ashes. With this in mind, this study aimed at filling the research gap by charactering and evaluating the vegetation performance of a scattering garden which had been open for ash scattering for six years. Specifically, the quality and quantity of vegetation of six lawns were estimated by analysing the aerial photos of the lawns; then grass samples were harvested and the weight of aboveground and belowground biomass were determined to examine the effects of ash scattering on the growth of the plant. The findings of this study may contribute to the body of literature by providing the portrait of the vegetation performance of a scattering garden after receiving cremated ashes. Furthermore, the findings provide reference to the policy and management of ash scattering. As the global landscape becomes increasingly populated and secularized, it is believed that ash scattering will become more and more popular, especially in Asia. There is an urgent need for scientific data, guidelines, and standards that inform the policy and plan for disposal of the dead (Basmajian & Coutts, 2010 ). 2. Materials And Methods 2.1 Studied Area This study selected the Garden of Remembrance (GoR) at Diamond Hill Crematorium and Columbarium as the studied area because it was the most popular venue of ash scattering in Hong Kong. In 2017, the GoR handled 2,365 cases of ash scattering, representing 42.45% of the total number of 5,573 scattering cases in Hong Kong (Legislative Council, 2020 ). There were six lawns (total area = 502.76 m²) in the GoR (Figures 1 ). Although these lawns were varied in size, they were identical in terms of design, construction, and management. The grass in the lawn was Zoysia grass ( Zoysia tenuifolia ) and the soil was a mixture of peaty soil and sand (1:1), respectively. These lawns received different loadings of cremated ashes that allowed a comparison of the conditions under different levels of ash scattering. Using the overall mean scattering rate (4.41 cases/m²) as the reference, these lawns were categorized into two groups: high level of scattering (>4.41 cases/m²yr), and low level of scattering (<4.41 cases/m²yr). The lawns were watered daily and clipped once a month. Table 1 provides the basic information of these lawns. <> <> 2.2 Vegetation Cover The performance of vegetation was assessed in two ways. First, the quality and quantity of vegetation cover, indicating plant abundance (Kent & Coker, 1992 ), was determined by analysing the aerial photos of the lawns acquired by unmanned aerial vehicle (UAV). The advantages of the remote sensing and image analysis are the non-destructive nature of these techniques and at the same time, allowing an accurate estimation of vegetation types and areas (Luna & Lobo, 2016 ). The UAV (Dji model: Mavic Pro) was equipped with a professional-grade camera with a 35 mm focal length and a sensor (78.8 mm X 26 mm) of 12.71 megapixels, that was mounted under the UAV. The red-green-blue (RGB) images were acquired at the heights of 25 m and 110 m above the GoR in December 2018. These images were taken around noon time to avoid the formation of shadow. ArcGIS ver. 10.5 was used to correct and provide the mosaic image with geo-reference using the images acquired from the flight at 110 m above ground. The simple first-degree polynomial transformation was used because the terrain was flat and a few pairs of ground control points were available to be the reference. Images acquired from the flight at 25 m above ground were used to produce a mosaic image with a pixel resolution of 0.83 cm. The resultant mosaic approximately had a geometric error of 5 cm estimated by the visual identification of the grasses and other features of the GoR. Envi ver. 5.3 was used to classify three classes of vegetation using the red-green-blue bands of the mosaic image. They were: (1) healthy grass, (2) unhealthy grass, and (3) bare soil (i.e., no grass). A sufficient number of representative pixels of each class were selected for the training set (Richards & Jia, 2005 ). To test the quality of training samples, spectral separability between classes was accessed by calculating the transformed divergence (TD) and Jeffries-Matusita (J-M) Distance of class pairs. The range of TD and J-M is 0 to 2. The value of 1.9 indicated good statistical separability. Training samples would be modified when the separability is low. Table 2 and Table 3 show the number of training pixels and spectral separability report, respectively. <> <> Maximum Likelihood Classifier (MLC) was used, with the pavements being masked out in the process. MLC is a parametric classifier making use of Bayes’ theorem of decision making, with the assumption of normally distributed classes in each band. It assigns the class to pixel based on the highest probability of membership to a class, derived from the mean vector and the covariance matrix (Zheng, Cai, & Qin, 2009 ). The discriminant function of each pixel was calculated using equation (1) (Richards & Jia, 2005 ). A scoring system was proposed for the calculation of vegetation index: healthy vegetation = 1, unhealthy vegetation = 0.5, and bare soil = 0. The index ranged from 0 (100% bare soil) to 1 (100% healthy vegetation). Based on the relative area of three levels of vegetation, the vegetation index could be calculated using the equation (2). Initially, the lawn was assumed to be covered by healthy vegetation (vegetation index = 100% ⋅ 1 = 1). Any changes of vegetation composition were assumed to be the consequence of ash scattering. When more than half of vegetation became unhealthy (vegetation index = (50% ⋅ 1) + (50% ⋅ 0.5) = 0.75), the vegetation performance was considered unsatisfactory, implying that the level of scattering exceeded the acceptable level. 2.3 Vegetation Biomass Second, vegetation biomass was measured because it reflected the amount of energy stored in the plant (Kent & Coker, 1992 ). In this study, 90 sampling points were systematically selected from the GoR for the determination of the biomass of vegetation, based on the following criteria. First, a random start was set and samples were collected in an interval of 1.5 - 2.0 m. Second, the distance between the sampling point and the edge was as far as possible not less than 1 m. Third, the inner part of the lawn was not sampled because stepping on the lawn was prohibited. There were 64 samples in lawns of high scattering level and 26 samples in lawns of low scattering level, respectively. At each sampling point, plant samples were harvested by a metal core sampler with a diameter of 4.8 cm. After the attached soils and ashes were carefully washed and returned to the lawn, the grass samples were oven-dried for 24 hours and the weight of aboveground and belowground biomass were determined. Data were input to SPSS 25.0 for statistical analyses. The normality of the data was checked, skewed data were log-transformed. The homoscedasticity of the data was tested by Levene’s Test of equality of error variances. One-way analysis of variance (ANOVA) and post hoc tests (LSD) were employed to evaluate the difference between high and low rates of use. Although biomass harvesting is a direct measurement of plant productivity, the method is destructive, labor-intensive, and time-consuming (Muukkonen, et al., 2006 ). Furthermore, because of sensitive nature of sampling human remains, a relatively small number of sample points were selected so the statistical basis of the sample size was compromised (Catchpole & Wheeler, 1992 ). 3. Results 3.1 Vegetation Cover The aerial photos taken at the height of 25 m above ground were integrated into a mosaic photo (Figure 2 a), then the photo was used for the classification of vegetation cover (Figure 2 b). All lawns showed different degree of degradation as unhealth vegetation and based ground were found in all lawns. Unhealthy grasses were usually found along the footpath but relatively healthy vegetation was established at inner locations away from the footpath. <> Figure 3 a summarizes the results of vegetation classification. Overall, approximately half of vegetation was degraded to either unhealthy (39.6%) or bare soil (13.9%). The area of bare soil in the lawns of high scattering level was larger than that of low scattering level. For the lawns of high scattering level, up to 26.8% of vegetation cover was eliminated. The results of vegetation composition were used to calculate the vegetation indexes using Equation 2 (Figure 3 b). Overall, the vegetation index was 0.68 for the whole GoR, lower than the acceptable value of 0.75. The index of the lawns of high scattering level was 0.60, and that of low scattering level was 0.69, respectively. <> 3.2 Vegetation Biomass 90 points were sampled for the determination of the biomass of vegetation. The results of the aboveground biomass and belowground biomass are shown in Figure 4 , respectively. Overall, the amount of aboveground biomass (0.5141 ± 0.2772 kg/m²) was similar to that of belowground biomass (0.5283 ± 0.2697 kg/m²). There was no significant difference (F= 0.067, p>0.05) of aboveground biomass between the lawns of high scattering level (0.5260 ± 0.3301 kg/m²) and low scattering level (0.5093 ± 0.2551 kg/m²). However, these figures might not truly reflect the aboveground productivity of the lawns because the lawns were clipped once a month and aboveground biomass was regularly removed. On the other hand, the results of belowground biomass were more reliable that deserved more attention (Fiala, 2010 ). The difference of belowground biomass between lawns under high use (0.4131 ± 0.1823 kg/m²) and low use (0.5752 ± 0.2861 kg/m²) was significant (F= 7.14, p<0.01). The results of belowground biomass were consistent with the vegetation performance index, both indicating that the level of ash scattering significantly affected the growth of grass. <> 4. Discussion 4.1 Environmental Sustainability of Ash Scattering The results indicated that the current practice of ash scattering was not sustainable in Hong Kong. Overall, more than half of the lawn area was degraded, indicating that the performance of the GoR was not satisfactory. The vegetation index was 0.68 for the whole GoR (4.41 cases/m²yr), which was lower than the acceptable value of 0.75. For the lawns under low use (3.50 cases/m²yr), the performance was still not satisfactory as the vegetation index was 0.69. The vegetation index of lawns under high use (7.47 cases/m²yr) was only 0.60, indicating severe degradation. As Hong Kong, like many Asian cities, sustains a large population in a relatively small area, the high population density inevitably translates into high demands of infrastructures for the disposal of dead. The overloading of the GoR reflects the inadequate provision of venue for ash scattering, it is recommended that the government should open more GoRs to meet the increasing demand of ash scattering. Of course, it is possible for the government to promote other forms of ash scattering, e.g., scattering at sea, that help reduce the demand of GoRs. Although the current practice of ash scattering was proved to be not sustainable, this study was not able to recommend standards for the rate of ash scattering. Ash scattering also resulted in the reduction of biomass, indicating the negative effects of cremated ashes on the plant production. The underground biomass of the lawns of high scattering level was significantly lower than that of low scattering level. Both the results of underground biomass and vegetation cover indicated that the level of ash scattering generated significant impacts on the growth of plant. 4.2 Spatial pattern of degradation There were a few factors explaining the spatial pattern of degradation. The first factor was the location. Whilst higher levels of use were found at the lawns near the entrance of the GoR, lower levels were at those lawns at the peripheral locations. For example, Lawns 1 and 2 enjoyed highest levels of scattering as they were located at the convenient locations, but Lawns 5 and 6 had low levels of scattering because of far distance to the entrance of the garden. The second factor was the lawn size. Second, surrounding features or landscape might also affect the level of scattering. For example, Lawn 4 was a relatively sensible choice for scattering because of the nearby bridge and stream; but Lawn 6 was not popular because of the adjacent power cable tower. The last factor was the accessibility, unhealthy grasses were usually found along the footpath but relative healthy vegetation was established at inner locations away from the footpath, indicating that impacts on the vegetation probably were associated with pedestrian traffic. Globally, the demand of venues for ash disposal is in increases. Many countries, e.g., Singapore (The National Environmental Agency, 2021 ), Taiwan (Atlas Obscura, 2021), Poland (Długozima & Ewa, 2020 ), and the United States (Detroit Free Press, 2021) have developed their scattering gardens for ash disposal. If there is proper management, scattering gardens are green urban spaces with cultural and natural values. They could provide a public service and be integrated into the green infrastructure planning system (Nordh & Evensen, 2018 ). These findings may cast light on the design of scattering gardens in the future. 4.3 Limitations This paper has three limitations that future studies should address. First, although this study demonstrated that ash scattering affected both vegetation cover and productivity, the mechanism is still unknown. Soil is believed to play an important role in mediating the effects because soil is the substrate for the growth of plants (Tan, 2014 ). Future studies should examine the relations between plants and soil by measuring some physically and chemically properties of the soil. Second, a few variables were not controlled in this study. For example, the amount of cremated ashes was varied in scattering cases, also the ashes were not evenly applied to the lawns but up to the preference of the persons who scattered the ashes. Considering cremated ashes are human bodies, it is impossible to conduct a controlled laboratory-typed experiment. Although the scope and depth of discussion in this study was compromised with the limitations, some findings are timely and meaningful to the policy and management of ash scattering in Hong Kong. Third, it is arbitrary to define the change of 50% healthy vegetation to unhealthy as unacceptable. Actually, different conclusions may be drawn if different benchmarks were set dependent upon the goals and practical needs. 5. Conclusions This study represented the first empirical study to evaluate the environmental sustainability of the practise of ash scattering in Hong Kong. Two complementary methods were employed to study the vegetation performance of a scattering garden which had been open for ash scattering for six years. The results indicated that, overall, approximately half of vegetation was degraded to either unhealthy or bare soil due to ash scattering. The vegetation performance index was 0.68 that is lower than the accepted level of 0.75, indicating that the current practise of ash scattering is not environmentally sustainable. The higher the use rate resulted in a larger area of bare soil and lower value of vegetation performance index. On the other hand, the belowground biomass of lawns under high use was significantly lower than those lawns under low use, indicating the negative effects of cremated ashes on plant biomass. However, the difference in the aboveground biomass between lawns was not significant. The findings of this study may provide a reference for the policy and management of ash scattering in Hong Kong and other cities around the world. Declarations Acknowledgments It will be provided later. Ethical approval Compliance with ethical standards and standards of research involving animals is not applicable to this study. Consent to Participate Compliance with ethical standards and standards of research involving humans is not applicable to this study. Consent to Publish This paper was written by one single author. Authors Contributions This paper was written by one single author. Funding This study was supported by a research fund (Ref: FEHD A&D SDT 31-138/8/1) from the Food and Environmental Hygiene Department (FEHD), the Hong Kong Special Administrative Region Government. Declaration of competing interest The author declares that I have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data Availability The ownership of the data belongs to the Food and Environmental Hygiene Department (FEHD), the Hong Kong Special Administrative Region Government. The data that support the findings of this study are available on request from the Food and Environmental Hygiene Department (FEHD), the Hong Kong Special Administrative Region Government. References Amuno S (2013) Potential ecological risk of heavy metal distribution in cemetery soils. 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Photogram Eng Remote Sens 9:1109–1117 Zychowski J (2012) Impact of cemeteries on groundwater chemistry: A Review. CATENA 1(93):29–37 Tables Table 1 Description of Lawns of the Garden of Remembrance at Diamond Hill Crematorium and Columbarium Lawn no. Area (m 2 ) Number of scattering cases a Scattering rate b (cases/m 2 yr) Level of scattering 2016 2017 2018 Average (2016 – 2018) 1 + 2 49.7 416 472 440 442.7 8.91 High c 3 284.1 938 1065 992 998.3 3.51 Low d 4 65.9 396 449 419 421.3 6.39 High c 5 72.8 250 284 264 266.0 3.65 Low d 6 30.2 83 95 88 88.7 2.94 Low d Overall 502.76 2,083 2,365 2,203 2217.0 4.41 -- (note: a Internal statistics of Food and Environmental Hygiene Department (FEHD); b Scattering rate = Average number of scattering cases (2016 - 2018) / Area; c High level of scattering if scattering rate > 4.41 cases/m 2 yr; d Low level of scattering if scattering rate < 4.41 cases/m 2 yr) Table 2 Number of selected training pixels Vegetation type Number of training pixels Healthy grass 2,201 Unhealthy grass 1,304 Bare soil 1,568 Table 3 Spectral separability report of the classification bare (ROI_final): healthy (ROI_final): (1.99009889 1.99967857) unhealthy (ROI_final): (1.92224458 1.99979507) healthy (ROI_final): bare (ROI_final): (1.99009889 1.99967857) unhealthy (ROI_final): (1.89900942 1.99088503) unhealthy (ROI_final): bare (ROI_final): (1.92224458 1.9979507) healthy (ROI_final): (1.89900942 1.99088503) Pair Separation (least to most): healthy (ROI_final) and unhealthy (ROI_final) - 1.89900942 bare soil (ROI_final) and unhealthy (ROI_final) - 1.92224458 bare soil (ROI_final) and healthy (ROI_final) - 1.99009889 Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 Feb, 2022 Reviewers invited by journal 07 Feb, 2022 Editor assigned by journal 13 Jan, 2022 First submitted to journal 05 Jan, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1232548","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":82117217,"identity":"f99001cf-bfe2-4dc3-945f-ad137f2fa4e8","order_by":0,"name":"Sai-Leung Ng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFAC5vYfHxgseJBEEghpYWyQnMEggablAAEt0jwMEsgiBLQYnD/YYGzbJiFjcLyB7cGPmsMM/Ow5Bswf2/BouZHYkJzbJsFjcOYAu2HPscMMkj1vDBgO4tFidoOx4TBIi+SMBDZpIBtoSA5QyzY8Ws4fbGy2BGmZ/wCixZ6glgOJzcyMQC38EgxQWyQIaLG/kdjG2HMOqIUnsR3ol3QeiTPPCg6c/Ydbi2T/4WMMP8ps7NnYDx8Dhpi1HH978sYHFWdwa0ECjOBQAsfpAaI0AAEbsQpHwSgYBaNghAEALZ9OB4gCHmYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9367-8539","institution":"Chinese Culture University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sai-Leung","middleName":"","lastName":"Ng","suffix":""}],"badges":[],"createdAt":"2022-01-05 15:38:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1232548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1232548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18071710,"identity":"a2fbf2cf-b6af-4610-91a0-915c59f3c849","added_by":"auto","created_at":"2022-02-09 16:59:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1028406,"visible":true,"origin":"","legend":"\u003cp\u003eLayout of the Garden of Remembrance at Diamond Hill Crematorium and Columbarium (Left, a sketch map; Right: an aerial photo taken at the height of 110m above ground)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1232548/v1/0411e10f6685afec54794652.png"},{"id":18071496,"identity":"b1600fb9-5e2a-47cc-a42b-b564024b7574","added_by":"auto","created_at":"2022-02-09 16:56:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159212,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation of six lawns. (Left, the mosaic image taken at the height of 25 m above ground; Right: vegetation classification)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1232548/v1/40874d5c75eb6ccba851bf21.png"},{"id":18071498,"identity":"0f72e86b-f807-4bb5-84cd-f378276d5d27","added_by":"auto","created_at":"2022-02-09 16:56:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79011,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation performance under different levels of scattering (Left: vegetation composition; Right: vegetation index)\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1232548/v1/5d41d3d324220215a3d364e7.png"},{"id":18071499,"identity":"db7045b3-4064-4c17-a010-5f2bfb22a50a","added_by":"auto","created_at":"2022-02-09 16:56:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77600,"visible":true,"origin":"","legend":"\u003cp\u003eBiomass of grass under different levels of scattering (Left: aboveground biomass; Right: belowground biomass)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1232548/v1/e8b6e5ea3bfb41243d782567.png"},{"id":18071711,"identity":"8283bd6a-9015-405e-a5fa-ddd2a32c2cc0","added_by":"auto","created_at":"2022-02-09 16:59:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":648381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1232548/v1/141ed971-9b94-42b8-8a2c-1b626557f28b.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAshes to Ashes, and Dust to Dust: Is Scattering Garden the Sustainable Destination for Cremated Ashes?\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEveryone\u0026rsquo;s life has a beginning and an end. As death is an essential aspect of our existence that we cannot avoid, disposal of the dead is an inevitable activity of human society that is of personal, emotional, social, and environmental significance (Canning \u0026amp; Szmigin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough different cultures and traditions have their own death rites and rituals, land burial had been the commonest practice since pre-history (Decker, Muniz, \u0026amp; Cruz, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, we see a dramatic increase in cremation in the last half-century (Davies \u0026amp; Mates, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Take the United States as an example, the cremation rate increased from 5.7% in 1975 to 48.7% in 2015 and since then, cremation has taken over land burial to be the commonest way of handling the dead. The rate was forecasted to further increase to 79.1% in 2035 (Statista, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAround the world, the cremation rate is often higher than 70% in those areas with a high density of population but limited land space (National Funeral Directors Association (NFDA), \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For example, Hong Kong sustains a population of approximately 7.5 million people on 1107 km\u0026sup2; of land (Census and Statistics Department (CSD), \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), approximately 90% of the deceased bodies are cremated (Food and Environmental Hygiene Department (FEHD), \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe popularity of cremation reflects people holding a more relaxed stance on cremation as the social power of religion and tradition is declining (Heessels, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). From the viewpoint of public administration, local authorities promote cremation in order to address the constraints of cemetery capacity and provide the public with a hygienic means of disposal (Canning \u0026amp; Szmigin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). First, cremation turns the body into ashes that greatly saves space for disposal. The Cremation Society of Great Britain adopted the motto \u0026ldquo;Save the Land for the Living\u0026rdquo; (Jupp, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The total shadow price of environmental impacts caused by burial was estimated at 67\u0026euro;/person and that of cremation was only 52\u0026euro;/person, respectively (Keijzer, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, land burial is identified as a potential source of a wide range of environmental pollutants(Silva, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1995\u003c/span\u003e)(Silva \u0026amp; Malagutti Filho, 2009) (Aruomero \u0026amp; Afolabi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Not only the decomposition of the corpse produces viscous leachate that may contaminate groundwater and potentially spread diseases to the population(Pacheco, Mendes, Martins, Hassuda, \u0026amp; Kimmelmann, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1991\u003c/span\u003e)(Dent, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) (Zychowski, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), but also residues of chemicals, e.g., cosmetics, dyes, and stiffeners, used in corpse makeup and preparation may cause soil contamination for a reasonable time(\u0026Uuml;\u0026ccedil;isik \u0026amp; Rushbrook, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) (Amuno, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Cremation effectively helps improve sanitary conditions by killing all viruses, germs, and bacteria carried by the body through intense heat and evaporation (Decker, Muniz, \u0026amp; Cruz, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, cremation is not the final solution to the disposal of the dead. It is just a treatment that makes the disposal more flexible. There are many options for ash disposals, such as ash scattering (either on land or at sea), urn storage (either keeping at home, at a columbarium), and burying in a cemetery, etc. (Mathijssen, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among these options, many people choose to scatter the ashes of their loved ones in a scattering garden. For example, more than 70% of the total number of cremation cases in the Netherlands were disposed of by scattering (Dijk \u0026amp; Mengen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Although ash scattering in Asia is not as popular as in western countries, the number of ash scattering has greatly increased in recent years. For example, in 2017, there were 5,573 cases of scattering in scattering garden, respectively, accounted for about 12.1 % of the total number of 45,883 deats in Hong Kong (Legislative Council, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), increased from 4.6% in 2010 (Legislative Council, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the continuously increasing demands of scattering garden, no scientific studies had ever attempted to evaluate the environmental sustainability of ash scattering using empirical data. Because ashes are an alternative form of human body, research ethnics requires researchers to seek consent from the studied subjects before measurements or experiments are conducted. However, such consent is impossible to obtain, implying the existence of unsurmountable difficulty. Furthermore, researchers tended to avoid having physical contact with cremated ashes because of sensitive implications, such as cultural beliefs and issues linked to the phenomenon of death history (Decker, Muniz, \u0026amp; Cruz, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although a few studies had discussed the environmental implications of ash scattering, their views were diverse or even contradictory. For example, Niziolomski, Rickson, Marquez-Grant, \u0026amp; Pawlett (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) indicated that cremated ashes would be toxic to plant because high sodium content of cremated ashes exceeded the tolerable limit of plants. On the other hand,(Strand, Shields, \u0026amp; Swiader, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) indicated the potential of cremation ashes as sources of phosphorous for soil additive or fertilizer, based on a laboratory experiment which used EDTA and citric acid to extract phosphorus from cremated ashes.\u003c/p\u003e \u003cp\u003eWith this in mind, this study aimed at filling the research gap by charactering and evaluating the vegetation performance of a scattering garden which had been open for ash scattering for six years. Specifically, the quality and quantity of vegetation of six lawns were estimated by analysing the aerial photos of the lawns; then grass samples were harvested and the weight of aboveground and belowground biomass were determined to examine the effects of ash scattering on the growth of the plant. The findings of this study may contribute to the body of literature by providing the portrait of the vegetation performance of a scattering garden after receiving cremated ashes. Furthermore, the findings provide reference to the policy and management of ash scattering. As the global landscape becomes increasingly populated and secularized, it is believed that ash scattering will become more and more popular, especially in Asia. There is an urgent need for scientific data, guidelines, and standards that inform the policy and plan for disposal of the dead (Basmajian \u0026amp; Coutts, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Studied Area\u003c/h2\u003e\n \u003cp\u003eThis study selected the Garden of Remembrance (GoR) at Diamond Hill Crematorium and Columbarium as the studied area because it was the most popular venue of ash scattering in Hong Kong. In 2017, the GoR handled 2,365 cases of ash scattering, representing 42.45% of the total number of 5,573 scattering cases in Hong Kong (Legislative Council, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThere were six lawns (total area = 502.76 m\u0026sup2;) in the GoR (Figures \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Although these lawns were varied in size, they were identical in terms of design, construction, and management. The grass in the lawn was Zoysia grass (\u003cem\u003eZoysia tenuifolia\u003c/em\u003e) and the soil was a mixture of peaty soil and sand (1:1), respectively. These lawns received different loadings of cremated ashes that allowed a comparison of the conditions under different levels of ash scattering. Using the overall mean scattering rate (4.41 cases/m\u0026sup2;) as the reference, these lawns were categorized into two groups: high level of scattering (\u0026gt;4.41 cases/m\u0026sup2;yr), and low level of scattering (\u0026lt;4.41 cases/m\u0026sup2;yr). The lawns were watered daily and clipped once a month. Table 1 provides the basic information of these lawns.\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026lt;Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e is here\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026lt;Table 1 is here\u0026gt;\u0026gt;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Vegetation Cover\u003c/h2\u003e\n \u003cp\u003eThe performance of vegetation was assessed in two ways. First, the quality and quantity of vegetation cover, indicating plant abundance (Kent \u0026amp; Coker, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e), was determined by analysing the aerial photos of the lawns acquired by unmanned aerial vehicle (UAV). The advantages of the remote sensing and image analysis are the non-destructive nature of these techniques and at the same time, allowing an accurate estimation of vegetation types and areas (Luna \u0026amp; Lobo, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe UAV (Dji model: Mavic Pro) was equipped with a professional-grade camera with a 35 mm focal length and a sensor (78.8 mm X 26 mm) of 12.71 megapixels, that was mounted under the UAV. The red-green-blue (RGB) images were acquired at the heights of 25 m and 110 m above the GoR in December 2018. These images were taken around noon time to avoid the formation of shadow.\u003c/p\u003e\n \u003cp\u003eArcGIS ver. 10.5 was used to correct and provide the mosaic image with geo-reference using the images acquired from the flight at 110 m above ground. The simple first-degree polynomial transformation was used because the terrain was flat and a few pairs of ground control points were available to be the reference. Images acquired from the flight at 25 m above ground were used to produce a mosaic image with a pixel resolution of 0.83 cm. The resultant mosaic approximately had a geometric error of 5 cm estimated by the visual identification of the grasses and other features of the GoR.\u003c/p\u003e\n \u003cp\u003eEnvi ver. 5.3 was used to classify three classes of vegetation using the red-green-blue bands of the mosaic image. They were: (1) healthy grass, (2) unhealthy grass, and (3) bare soil (i.e., no grass). A sufficient number of representative pixels of each class were selected for the training set (Richards \u0026amp; Jia, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). To test the quality of training samples, spectral separability between classes was accessed by calculating the transformed divergence (TD) and Jeffries-Matusita (J-M) Distance of class pairs. The range of TD and J-M is 0 to 2. The value of 1.9 indicated good statistical separability. Training samples would be modified when the separability is low. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e show the number of training pixels and spectral separability report, respectively.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026lt;Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e is here\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026lt;Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e is here\u0026gt;\u0026gt;\u003c/p\u003e\n \u003cp\u003eMaximum Likelihood Classifier (MLC) was used, with the pavements being masked out in the process. MLC is a parametric classifier making use of Bayes\u0026rsquo; theorem of decision making, with the assumption of normally distributed classes in each band. It assigns the class to pixel based on the highest probability of membership to a class, derived from the mean vector and the covariance matrix (Zheng, Cai, \u0026amp; Qin, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The discriminant function of each pixel was calculated using equation (1) (Richards \u0026amp; Jia, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eA scoring system was proposed for the calculation of vegetation index: healthy vegetation = 1, unhealthy vegetation = 0.5, and bare soil = 0. The index ranged from 0 (100% bare soil) to 1 (100% healthy vegetation). Based on the relative area of three levels of vegetation, the vegetation index could be calculated using the equation (2). Initially, the lawn was assumed to be covered by healthy vegetation (vegetation index = 100% \u0026sdot; 1\u0026thinsp;=\u0026thinsp;1). Any changes of vegetation composition were assumed to be the consequence of ash scattering. When more than half of vegetation became unhealthy (vegetation index = (50% \u0026sdot; 1) + (50% \u0026sdot; 0.5) = 0.75), the vegetation performance was considered unsatisfactory, implying that the level of scattering exceeded the acceptable level.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Vegetation Biomass\u003c/h2\u003e\n \u003cp\u003eSecond, vegetation biomass was measured because it reflected the amount of energy stored in the plant (Kent \u0026amp; Coker, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). In this study, 90 sampling points were systematically selected from the GoR for the determination of the biomass of vegetation, based on the following criteria. First, a random start was set and samples were collected in an interval of 1.5 - 2.0 m. Second, the distance between the sampling point and the edge was as far as possible not less than 1 m. Third, the inner part of the lawn was not sampled because stepping on the lawn was prohibited. There were 64 samples in lawns of high scattering level and 26 samples in lawns of low scattering level, respectively.\u003c/p\u003e\n \u003cp\u003eAt each sampling point, plant samples were harvested by a metal core sampler with a diameter of 4.8 cm. After the attached soils and ashes were carefully washed and returned to the lawn, the grass samples were oven-dried for 24 hours and the weight of aboveground and belowground biomass were determined.\u003c/p\u003e\n \u003cp\u003eData were input to SPSS 25.0 for statistical analyses. The normality of the data was checked, skewed data were log-transformed. The homoscedasticity of the data was tested by Levene\u0026rsquo;s Test of equality of error variances. One-way analysis of variance (ANOVA) and post hoc tests (LSD) were employed to evaluate the difference between high and low rates of use.\u003c/p\u003e\n \u003cp\u003eAlthough biomass harvesting is a direct measurement of plant productivity, the method is destructive, labor-intensive, and time-consuming (Muukkonen, et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). Furthermore, because of sensitive nature of sampling human remains, a relatively small number of sample points were selected so the statistical basis of the sample size was compromised (Catchpole \u0026amp; Wheeler, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Vegetation Cover\u003c/h2\u003e \u003cp\u003eThe aerial photos taken at the height of 25 m above ground were integrated into a mosaic photo (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), then the photo was used for the classification of vegetation cover (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). All lawns showed different degree of degradation as unhealth vegetation and based ground were found in all lawns. Unhealthy grasses were usually found along the footpath but relatively healthy vegetation was established at inner locations away from the footpath.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is here\u0026gt;\u0026gt;\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea summarizes the results of vegetation classification. Overall, approximately half of vegetation was degraded to either unhealthy (39.6%) or bare soil (13.9%). The area of bare soil in the lawns of high scattering level was larger than that of low scattering level. For the lawns of high scattering level, up to 26.8% of vegetation cover was eliminated. The results of vegetation composition were used to calculate the vegetation indexes using Equation 2 (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Overall, the vegetation index was 0.68 for the whole GoR, lower than the acceptable value of 0.75. The index of the lawns of high scattering level was 0.60, and that of low scattering level was 0.69, respectively.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is here\u0026gt;\u0026gt;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Vegetation Biomass\u003c/h2\u003e \u003cp\u003e90 points were sampled for the determination of the biomass of vegetation. The results of the aboveground biomass and belowground biomass are shown in Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively. Overall, the amount of aboveground biomass (0.5141 \u0026plusmn; 0.2772 kg/m\u0026sup2;) was similar to that of belowground biomass (0.5283 \u0026plusmn; 0.2697 kg/m\u0026sup2;). There was no significant difference (F= 0.067, p\u0026gt;0.05) of aboveground biomass between the lawns of high scattering level (0.5260 \u0026plusmn; 0.3301 kg/m\u0026sup2;) and low scattering level (0.5093 \u0026plusmn; 0.2551 kg/m\u0026sup2;). However, these figures might not truly reflect the aboveground productivity of the lawns because the lawns were clipped once a month and aboveground biomass was regularly removed. On the other hand, the results of belowground biomass were more reliable that deserved more attention (Fiala, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The difference of belowground biomass between lawns under high use (0.4131 \u0026plusmn; 0.1823 kg/m\u0026sup2;) and low use (0.5752 \u0026plusmn; 0.2861 kg/m\u0026sup2;) was significant (F= 7.14, p\u0026lt;0.01). The results of belowground biomass were consistent with the vegetation performance index, both indicating that the level of ash scattering significantly affected the growth of grass.\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026lt;Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is here\u0026gt;\u0026gt;\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Environmental Sustainability of Ash Scattering\u003c/h2\u003e \u003cp\u003eThe results indicated that the current practice of ash scattering was not sustainable in Hong Kong. Overall, more than half of the lawn area was degraded, indicating that the performance of the GoR was not satisfactory. The vegetation index was 0.68 for the whole GoR (4.41 cases/m\u0026sup2;yr), which was lower than the acceptable value of 0.75. For the lawns under low use (3.50 cases/m\u0026sup2;yr), the performance was still not satisfactory as the vegetation index was 0.69. The vegetation index of lawns under high use (7.47 cases/m\u0026sup2;yr) was only 0.60, indicating severe degradation. As Hong Kong, like many Asian cities, sustains a large population in a relatively small area, the high population density inevitably translates into high demands of infrastructures for the disposal of dead. The overloading of the GoR reflects the inadequate provision of venue for ash scattering, it is recommended that the government should open more GoRs to meet the increasing demand of ash scattering. Of course, it is possible for the government to promote other forms of ash scattering, e.g., scattering at sea, that help reduce the demand of GoRs. Although the current practice of ash scattering was proved to be not sustainable, this study was not able to recommend standards for the rate of ash scattering.\u003c/p\u003e \u003cp\u003eAsh scattering also resulted in the reduction of biomass, indicating the negative effects of cremated ashes on the plant production. The underground biomass of the lawns of high scattering level was significantly lower than that of low scattering level. Both the results of underground biomass and vegetation cover indicated that the level of ash scattering generated significant impacts on the growth of plant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Spatial pattern of degradation\u003c/h2\u003e \u003cp\u003eThere were a few factors explaining the spatial pattern of degradation. The first factor was the location. Whilst higher levels of use were found at the lawns near the entrance of the GoR, lower levels were at those lawns at the peripheral locations. For example, Lawns 1 and 2 enjoyed highest levels of scattering as they were located at the convenient locations, but Lawns 5 and 6 had low levels of scattering because of far distance to the entrance of the garden. The second factor was the lawn size. Second, surrounding features or landscape might also affect the level of scattering. For example, Lawn 4 was a relatively sensible choice for scattering because of the nearby bridge and stream; but Lawn 6 was not popular because of the adjacent power cable tower. The last factor was the accessibility, unhealthy grasses were usually found along the footpath but relative healthy vegetation was established at inner locations away from the footpath, indicating that impacts on the vegetation probably were associated with pedestrian traffic. Globally, the demand of venues for ash disposal is in increases. Many countries, e.g., Singapore (The National Environmental Agency, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Taiwan (Atlas Obscura, 2021), Poland (Długozima \u0026amp; Ewa, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the United States (Detroit Free Press, 2021) have developed their scattering gardens for ash disposal. If there is proper management, scattering gardens are green urban spaces with cultural and natural values. They could provide a public service and be integrated into the green infrastructure planning system (Nordh \u0026amp; Evensen, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings may cast light on the design of scattering gardens in the future.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations\u003c/h2\u003e \u003cp\u003eThis paper has three limitations that future studies should address. First, although this study demonstrated that ash scattering affected both vegetation cover and productivity, the mechanism is still unknown. Soil is believed to play an important role in mediating the effects because soil is the substrate for the growth of plants (Tan, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future studies should examine the relations between plants and soil by measuring some physically and chemically properties of the soil. Second, a few variables were not controlled in this study. For example, the amount of cremated ashes was varied in scattering cases, also the ashes were not evenly applied to the lawns but up to the preference of the persons who scattered the ashes. Considering cremated ashes are human bodies, it is impossible to conduct a controlled laboratory-typed experiment. Although the scope and depth of discussion in this study was compromised with the limitations, some findings are timely and meaningful to the policy and management of ash scattering in Hong Kong. Third, it is arbitrary to define the change of 50% healthy vegetation to unhealthy as unacceptable. Actually, different conclusions may be drawn if different benchmarks were set dependent upon the goals and practical needs.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study represented the first empirical study to evaluate the environmental sustainability of the practise of ash scattering in Hong Kong. Two complementary methods were employed to study the vegetation performance of a scattering garden which had been open for ash scattering for six years. The results indicated that, overall, approximately half of vegetation was degraded to either unhealthy or bare soil due to ash scattering. The vegetation performance index was 0.68 that is lower than the accepted level of 0.75, indicating that the current practise of ash scattering is not environmentally sustainable. The higher the use rate resulted in a larger area of bare soil and lower value of vegetation performance index. On the other hand, the belowground biomass of lawns under high use was significantly lower than those lawns under low use, indicating the negative effects of cremated ashes on plant biomass. However, the difference in the aboveground biomass between lawns was not significant. The findings of this study may provide a reference for the policy and management of ash scattering in Hong Kong and other cities around the world.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt will be provided later. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompliance with ethical standards and standards of research involving animals is not applicable to this study. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompliance with ethical standards and standards of research involving humans is not applicable to this study.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was written by one single author. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper was written by one single author. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by a research fund (Ref: FEHD A\u0026amp;D SDT 31-138/8/1) from the Food and Environmental Hygiene Department (FEHD), the Hong Kong Special Administrative Region Government. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that I have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ownership of the data belongs to the Food and Environmental Hygiene Department (FEHD), the Hong Kong Special Administrative Region Government. The data that support the findings of this study are available on request from the Food and Environmental Hygiene Department (FEHD), the Hong Kong Special Administrative Region Government. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmuno S (2013) Potential ecological risk of heavy metal distribution in cemetery soils. Water, Air and Soil Pollution 224:1435\u0026ndash;1447\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAruomero A, Afolabi O (2014) Comparative assessment of trace metals in soils associated with casket burials: Towards implementing green burials. Eurasian Journal of Soil Science 3(1):65\u0026ndash;76\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtlas Obscura (2020) Burying the Dead in Taipei\u0026rsquo;s Public Parks. 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Patent App. 11/634,612.\u003c/em\u003e Washington, DC: U.S. Patent and Trademark Office\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan K (2014) \u003cem\u003eEnvironmental Soil Science (3rd edition).\u003c/em\u003e Boca Raton: CRC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Uuml;\u0026ccedil;isik A, Rushbrook P (1998) The Impact of Cemeteries on the Environment and Public Health. WHO Regional Office for Europe, Copenhagen\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng M, Cai Q, Qin M (2009) The effect of prior probabilities in the maximum likelihood classification on individual classes. Photogram Eng Remote Sens 9:1109\u0026ndash;1117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZychowski J (2012) Impact of cemeteries on groundwater chemistry: A Review. CATENA 1(93):29\u0026ndash;37\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Description of Lawns of the Garden of Remembrance at Diamond Hill Crematorium and Columbarium\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003eLawn no.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003eArea (m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"46.976090014064695%\"\u003e\n \u003cp\u003eNumber of scattering cases\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003eScattering rate\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(cases/m\u003csup\u003e2\u003c/sup\u003eyr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"13.361462728551336%\"\u003e\n \u003cp\u003eLevel of scattering\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.856287425149702%\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.75449101796407%\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.75449101796407%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.634730538922156%\"\u003e\n \u003cp\u003eAverage (2016 \u0026ndash; 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003e1 + 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.267229254571026%\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.330520393811533%\"\u003e\n \u003cp\u003e442.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.361462728551336%\"\u003e\n \u003cp\u003eHigh\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003e284.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.267229254571026%\"\u003e\n \u003cp\u003e938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e1065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.330520393811533%\"\u003e\n \u003cp\u003e998.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.361462728551336%\"\u003e\n \u003cp\u003eLow\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003e65.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.267229254571026%\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.330520393811533%\"\u003e\n \u003cp\u003e421.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.361462728551336%\"\u003e\n \u003cp\u003eHigh\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003e72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.267229254571026%\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.330520393811533%\"\u003e\n \u003cp\u003e266.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.361462728551336%\"\u003e\n \u003cp\u003eLow\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003e30.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.267229254571026%\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.330520393811533%\"\u003e\n \u003cp\u003e88.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.361462728551336%\"\u003e\n \u003cp\u003eLow\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"11.673699015471167%\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.533052039381154%\"\u003e\n \u003cp\u003e502.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.267229254571026%\"\u003e\n \u003cp\u003e2,083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e2,365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"10.68917018284107%\"\u003e\n \u003cp\u003e2,203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"15.330520393811533%\"\u003e\n \u003cp\u003e2217.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"16.455696202531644%\"\u003e\n \u003cp\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.361462728551336%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(note: \u003csup\u003ea \u003c/sup\u003eInternal statistics of Food and Environmental Hygiene Department (FEHD);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Scattering rate = Average number of scattering cases (2016 - 2018) / Area;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec \u003c/sup\u003eHigh level of scattering if scattering rate \u0026gt; 4.41 cases/m\u003csup\u003e2\u003c/sup\u003eyr;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Low level of scattering if scattering rate \u0026lt; 4.41 cases/m\u003csup\u003e2\u003c/sup\u003eyr)\u003c/p\u003e\n\u003cp\u003eTable 2 Number of selected training pixels\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eVegetation type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eNumber of training pixels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eHealthy grass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e2,201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eUnhealthy grass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e1,304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003eBare soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"50%\"\u003e\n \u003cp\u003e1,568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 Spectral separability report of the classification\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"100%\"\u003ebare (ROI_final):\u003cp\u003ehealthy (ROI_final): (1.99009889 \u0026nbsp; \u0026nbsp;1.99967857)\u003c/p\u003e\n \u003cp\u003eunhealthy (ROI_final): (1.92224458 \u0026nbsp; 1.99979507)\u003c/p\u003e\n \u003cp\u003ehealthy (ROI_final):\u003c/p\u003e\n \u003cp\u003ebare (ROI_final): (1.99009889 \u0026nbsp; \u0026nbsp;1.99967857)\u003c/p\u003e\n \u003cp\u003eunhealthy (ROI_final): (1.89900942 \u0026nbsp; \u0026nbsp;1.99088503)\u003c/p\u003e\n \u003cp\u003eunhealthy (ROI_final):\u003c/p\u003e\n \u003cp\u003ebare (ROI_final): (1.92224458 \u0026nbsp; \u0026nbsp;1.9979507)\u003c/p\u003e\n \u003cp\u003ehealthy (ROI_final): (1.89900942 \u0026nbsp; \u0026nbsp;1.99088503) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePair Separation (least to most):\u003c/p\u003e\n \u003cp\u003ehealthy (ROI_final) and unhealthy (ROI_final) - 1.89900942\u003c/p\u003e\n \u003cp\u003ebare soil (ROI_final) and unhealthy (ROI_final) - 1.92224458\u003c/p\u003e\n \u003cp\u003ebare soil (ROI_final) and healthy (ROI_final) - 1.99009889\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ash scattering, cremated human ashes, scattering garden, vegetation biomass, vegetation composition","lastPublishedDoi":"10.21203/rs.3.rs-1232548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1232548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCremation is commonly practiced around the world because it requires small space for the disposal of ashes and also causes less environmental impacts. \u0026nbsp;Among various options for ash disposal, many people choose\u0026nbsp;to\u0026nbsp;scatter\u0026nbsp;the\u0026nbsp;ashes\u0026nbsp;of their loved ones in a\u0026nbsp;scattering garden.\u0026nbsp;Is scattering garden a sustainable solution to the disposal of cremated ashes? No research has ever attempted to answer this question based on empirical data.\u0026nbsp;This study aimed at filling the research gap by characterizing and evaluating the vegetation performance of a scattering garden after it had been open for ash scattering for six years.\u0026nbsp;The results indicated that, overall, approximately half of vegetation was degraded to either unhealthy or bare soil.\u0026nbsp;The area of bare soil in the lawns of high scattering level was larger than that of low scattering level.\u0026nbsp;Furthermore, the belowground biomass of vegetation in the lawns of high scattering level was significantly lower than that of low scattering level.\u0026nbsp;It is concluded that the current practice of ash scattering in Hong Kong was not sustainable and the intensity of impacts was dependent upon the level of ash scattering. \u0026nbsp;The findings of this study may provide a reference for the policy and management of ash scattering in Hong Kong and other cities around the world.\u003c/p\u003e","manuscriptTitle":"Ashes to Ashes, and Dust to Dust: Is Scattering Garden the Sustainable Destination for Cremated Ashes?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-09 16:56:39","doi":"10.21203/rs.3.rs-1232548/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-02-08T07:21:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-02-07T17:17:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-13T05:21:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2022-01-05T10:38:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7d070b23-b386-4102-b6e1-6b78a902d854","owner":[],"postedDate":"February 9th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-05-17T21:02:07+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-09 16:56:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1232548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1232548","identity":"rs-1232548","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}