STEM Other – 2021 UG Conference https://www.lancaster.ac.uk/2021ugconference Sun, 30 Jan 2022 22:18:44 +0000 en-US hourly 1 https://wordpress.org/?v=5.9 Samuel Ling https://www.lancaster.ac.uk/2021ugconference/samuel-ling/?utm_source=rss&utm_medium=rss&utm_campaign=samuel-ling https://www.lancaster.ac.uk/2021ugconference/samuel-ling/#respond Sun, 17 Jan 2021 22:53:24 +0000 https://www.lancaster.ac.uk/2021ugconference/pgjw42xb/
Samuel Ling

Samuel Ling

Psychology (Sunway) | Year 3 | Degree: BSc (Hons) Psychology
How to be psychologically sane: Predictors of psychological well-being

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A Little History… 

According to the Institute of Public Health in Malaysia, the prevalence of mental health issues among adults has increased from 10.7% to 29.2% between 1996 to 2015.  

Poor psychological well-being can have drastic effects on a person. Studies have shown that poor mental health can lead to impaired academic performance (Keyes et al., 2012), poorer physical health (Hernandez et al., 2012), suicidal thoughts and depression (Hawton et al., 2013; Tyson et al., 2010). With these in mind, the need for research on psychological well-being is imperative.     



Why these variables?

Studies have shown that autonomy, FoMO and social support do play a role in psychological well-being. For instance, high authorship i.e. a facet of autonomy (Paradnikė & Bandzevičienė, 2015) and social support especially from family (Ammar et al., 2013; Gülaçtı, 2010) significantly predicted psychological well-being. FoMO, on the other hand, was associated with decreased psychological well-being (Fabris et al., 2020). Findings from Farbis et al. (2020) suggest that the fear of missing out on events can trigger stress and anxiety in one’s mind, which therefore leads to decreased well-being.

 



How did we do it?

154 Malaysian students (98 females, 56 males) aged between 18 – 24 years old were recruited via social media (WhatsApp, Facebook, Instagram etc.) between October 2020 to December 2020. Online questionnaires were administered on Qualtrics, an online survey tool.  




What did we find?

A statistical analysis tool called IBM SPSS was used to analyse the data. Multiple regression analysis revealed that all 3 variables significantly predicted psychological well-being. This means that participants who had higher autonomy and higher social support from friends and family were found to have higher psychological well-being. On other hand, participants who had higher FoMO were found to have lower psychological well-being. 

Among all these 3 variables, FoMO was the strongest predictor of psychological well-being.




So what does this mean?

Having good social support from friends and family and having a greater sense of autonomy in life is beneficial to one’s psychological well-being.

But among all these variables FoMO has the greatest effect on psychological well-being.  The fear of missing out on social opportunities leading to the desire to stay connected with others can bring about feelings of fear, worry, and anxiety. This in turn affects one’s mental health. Although frequency and usage of social media was not investigated in this study, social media has been found to contribute to FoMO. 

Thus, taking a break from social media, and being contented with one’s own experiences can reduce the prevalence of FoMO, leading to a healthier well-being.






Some definitions…

Autonomy can be defined as a person’s ability to act in his/her own self-interests. 

FoMO is a relatively new social phenomenon and is defined as the belief that others are having more rewarding experiences than you. 

Social support was defined as the support received from friends and family. 





 

References

Ammar, D., Nauffal, D., & Sbeity, R. (2013). The role of perceived social support in predicting subjective wellbeing in Lebanese college students. Journal of Happiness and Well-Being, 1(2), 121–143.

Fabris, M., Marengo, D., Longobardi, C., & Settanni, M. (2020). Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: The role of stress associated with neglect and negative reactions on social media. Addictive Behaviors, 106, 106364. https://doi.org/10.1016/j.addbeh.2020.106364

Gülaçtı, F. (2010). The effect of perceived social support on subjective well-being. Procedia Social and Behavioural Sciences, 2, 3844-3849. https://doi.org/10.1016/j.sbspro.2010.03.602

Hawton, K., Casañas I Comabella, C., Haw, C., & Saunders, K. (2013). Risk factors for suicide in individuals with depression: a systematic review. Journal of affective disorders, 147(1-3), 17–28.  https://doi.org/10.1016/j.jad.2013.01.004

Hernandez, R., Bassett, S. M., Boughton, S. W., Schuette, S. A., Shiu, E. W., & Moskowitz, J. T. (2018). Psychological Well-Being and Physical Health: Associations, Mechanisms, and Future Directions. Emotion Review, 10(1), 18–29.  https://doi.org/10.1177/1754073917697824

Keyes, C. L., Eisenberg, D., Perry, G. S., Dube, S. R., Kroenke, K., & Dhingra, S. S. (2012). The relationship of level of positive mental health with current mental disorders in predicting suicidal behavior and academic impairment in college students. Journal of American college health : J of ACH, 60(2), 126–133. https://doi.org/10.1080/07448481.2011.608393

Paradnikė, K. & Bandzevičienė, R. (2015). Autonomy as a personal resource for students occupational well-being. International Journal of Psychology: A Biopsychosocial Approach, 17, 97 – 115. https://doi.org 10.7220/2345-024X.17.6

Tyson, P., Wilson, K., Crone, D., Brailsford, R., & Laws, K. (2010). Physical activity and mental health in a student population. Journal of mental health (Abingdon, England), 19(6), 492–499. https://doi.org/10.3109/09638230902968308


Acknowledgements

I would like to thank Dr Wu Shin Ling for providing guidance and supervision on this research project.



This graph shows the predicting effect of autonomy, FoMO and Social Support on Psychological well-being. FoMO is the strongest predictor, followed by social support and autonomy
This graph shows the predicting effect of autonomy, FoMO and Social Support on Psychological well-being. FoMO is the strongest predictor, followed by social support and autonomy

















Predicting Effect of Autonomy, FoMO and Social Support on Psychological Well-Being


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]]> https://www.lancaster.ac.uk/2021ugconference/samuel-ling/feed/ 0 Koh Qai-Ni https://www.lancaster.ac.uk/2021ugconference/koh-qai-ni/?utm_source=rss&utm_medium=rss&utm_campaign=koh-qai-ni https://www.lancaster.ac.uk/2021ugconference/koh-qai-ni/#respond Sun, 17 Jan 2021 22:53:24 +0000 https://www.lancaster.ac.uk/2021ugconference/d59vjyfu/  

Koh Qai-Ni

Koh Qai-Ni

Psychology (Sunway) | Year 3 | Degree: BSc (Hons) Psychology
Effect of Family Mealtimes and Interparental Conflict on Young Adults’ Self-Esteem

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]]> https://www.lancaster.ac.uk/2021ugconference/koh-qai-ni/feed/ 0 Calvin Yee Kim Guan Yee https://www.lancaster.ac.uk/2021ugconference/calvin-yee-kim-guan-yee/?utm_source=rss&utm_medium=rss&utm_campaign=calvin-yee-kim-guan-yee https://www.lancaster.ac.uk/2021ugconference/calvin-yee-kim-guan-yee/#respond Sun, 17 Jan 2021 22:53:23 +0000 https://www.lancaster.ac.uk/2021ugconference/k84cpz95/  

Calvin Yee Kim Guan Yee

Calvin Yee Kim Guan Yee

Biomedical and Life Sciences (Bailrigg) | Year 3 | Degree: Bsc Biological Sciences
Uracil DNA glycosylase: An important tool for DNA repair in all living organisms

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All living organisms contain genes that carry genetic information in the form of a molecule known as DNA.

These genes make you who you are! 


Unfortunately, the DNA is very vulnerable and there are many factors that can lead to the DNA being damaged. 

One common example:

UV light exposure can damage DNA, causing skin cancer. 


Our body has adapted unique mechanisms to repair DNA damage! 

Repair mechanisms include:
1) Base Excision Repair (BER)
2) Nucleotide Excision Repair (NER)
3) Mismatch-specific repair (MMR)
4) Repair of DNA double-stranded breaks





This project aims to investigate the structure of a UDG found in Giardia intestinalis (G.intestinalis), a causative agent of giardiasis. An understanding of the structure of UDG in G.lamblia (GiUDG) would provide a potential therapeutic target for the treatment of giardiasis. In short, we can develop a drug to target and kill this parasite! Below are two short introductory videos to give you an idea of the BER pathway and the parasite.



Introduction


The 3D structure of UDGs obtained from 6 different organisms
The 3D structure of UDGs obtained from 6 different organisms

Uracil DNA glycosylases of 6 different organisms.


DNA damage can come in many forms; one of which is the presence of a base called uracil on DNA. If uracil is present on the DNA, it has to be removed to avoid subsequent mutations. 



The Base Excision Repair (BER) pathway is the primary repair pathway used to remove non-bulky DNA damage such as uracil. 

Components of the BER pathway:
1) DNA glycosylases
2) AP endonuclease
3) DNA polymerase
4) DNA ligase



DNA glycosylases are enzymes that used to target and remove a specific base from DNA. Uracil DNA glycosylase (UDG) is a type of DNA glycosylase used to specifically eliminate uracil from DNA.


UDGs can be found in all living organisms. The image on the right shows the 3D structure of UDGs found in 6 different organisms. 

They are quite similar in their overall structure except for that of the mimivirus.


By comparing the two structures, a disoriented loop region (pointed by black arrow) can be observed in the GiUDG model but not in human UDG. This loop region may be a potential divergent feature of GiUDG and can potentially be targeted in an attempt to treat giardiasis.


What is nifuroxazide?

– a drug that treats colitis and diarrhoea

– a proven inhibitor of human UDG


How does the structure of GiUDG compare to that of human UDG?

To answer the question above, we used an inhibitory approach whereby nifuroxazide was used to see whether or not it could block the action of GiUDG. 


If nifuroxazide is able to inhibit GiUDG, it can be postulated that its structure would be fairly identical to that of human UDG due to a similar binding mechanism.


If nifuroxazide is unable to inhibit GiUDG, it can be postulated that its structure would be different from that of human UDG due to a different binding mechanism.


DNA inhibitory assay to check for activity of GiUDG and E.coli UDG in three different conditions
DNA inhibitory assay to check for activity of GiUDG and E.coli UDG in three different conditions

DNA repair assay showing the activity of GiUDG and E.coli UDG in 1) the absence of inhibitors, 2) the presence of nifuroxazide, 3) the presence of uracil.


Results


Methods


What did we find?

There was a cleaved band in the sample containing GiUDG + nifuroxazide, suggesting that GiUDG was still able to perform its function. Hence, nifuroxazide was unable to inhibit GiUDG.

Another interesting observation was that nifuroxazide had somehow enhanced the activity of GiUDG as the cleaved band was much more intense compared to the other GiUDG samples. 

 


Now that we know that nifuroxazide cannot inhibit GiUDG, we can postulate that the structure of GiUDG is different from that of human UDG. 

We then sought to predict the structure of GiUDG to allow comparison with human UDG.  


The 3D structure of human UDG
The 3D structure of human UDG

Structure of human UDG


The predicted 3D model of GiUDG
The predicted 3D model of GiUDG

Model of the structure of GiUDG using human UDG as a template


Summary of findings

The 3D structure of GiUDG is clearly different from that of human UDG. This is evidenced by the presence of a disoriented loop region found in GiUDG but not found in human UDG. This suggested that GiUDG can be used as a potential therapeutic target for the treatment of giardiasis. If we can develop a drug that targets the loop region and inhibits GiUDG, we can eliminate the parasite and treat giardiasis. 


Acknowledgements

I would like to thank my family and friends for their continuous support for me in completing my dissertation. I would also like to especially acknowledge my supervisor, Dr Sarah Allinson, for her tremendous support and guidance throughout my dissertation journey. 




Conclusion


Procedure:

1) Mix GiUDG with nifuroxazide
2) Add substrate (uracil-containing DNA)
3) Check for activity using DNA repair assay (presence of a cleaved band indicates activity)

 


Future directions

In this study, we have only managed to predict the structure of GiUDG by generating a model using human UDG as a template. Future studies should focus on investigating the crystal structure of GiUDG in order to fully determine its 3D structure. 

 


References

Evans, M. D., Dizdaroglu, M. & Cooke, M. S. (2004) Oxidative DNA damage and disease: induction, repair and significance. Mutation Research-Reviews in Mutation Research, 567(1), 1-61.

H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne.
(2000) The Protein Data Bank Nucleic Acids Research, 28: 235-242.

Krokan, H. E., Standal, R. & Slupphaug, G. (1997) DNA glycosylases in the base excision repair of DNA. Biochemical Journal, 325, 1-16.

Li, Guodong, Adam Henry, Stuart, Liu, Hao., Kang, Tian-Shu., Nao, Sang-Cuo., Zhao, Yichao., Wu, Chun., Jin, Jianwen., Zhang, Jia-Tong et al. (2020) “A Robust Photoluminescence Screening Assay Identifies Uracil-DNA Glycosylase Inhibitors against Prostate Cancer.” Chemical Science, vol. 11, no. 7, 2020, pp. 1750–1760.

Prorok, P., Alili, D., Saint-Pierre, C., Gasparutto, D., Zharkov, D. O., Ishchenko, A. A., Tudek, B. & Saparbaev, M. K. (2013) Uracil in duplex DNA is a substrate for the nucleotide incision repair pathway in human cells. Proceedings of the National Academy of Sciences of the United States of America, 110(39), E3695-E3703.

Sancar, A., Lindsey-Boltz, L. A., Unsal-Kacmaz, K. & Linn, S. (2004) Molecular mechanisms of mammalian DNA repair and the DNA damage checkpoints. Annual Review of Biochemistry, 73, 39-85.

Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F.T., de Beer, T.A.P., Rempfer, C., Bordoli, L., Lepore, R., Schwede, T. (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296-W303.

 










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]]> https://www.lancaster.ac.uk/2021ugconference/calvin-yee-kim-guan-yee/feed/ 0 Dominic James Thomas Hardy https://www.lancaster.ac.uk/2021ugconference/dominic-james-thomas-hardy/?utm_source=rss&utm_medium=rss&utm_campaign=dominic-james-thomas-hardy https://www.lancaster.ac.uk/2021ugconference/dominic-james-thomas-hardy/#respond Sun, 17 Jan 2021 22:53:23 +0000 https://www.lancaster.ac.uk/2021ugconference/m2835j6r/  

Dominic James Thomas Hardy

Dominic James Thomas Hardy

Lancaster Environment Centre (Bailrigg) | Year 2 | Degree: Physical Geography (BSc)
Application of Critical nodes to valley glaciers

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Google Earth Imagery of four Glacier Study Sites


Glaciers with Centre flowlines mapped and nodes assigned



Study sites

This study used 4 glaciers from the Northern Hemisphere. These are (a) Midtre Lovenbreen (Svalbard), (b) Peyto (Canada), (c) Hintereisferner (Austria) and (d) Wolverine (Alaska). These are WGMS reference glaciers as they have +30 years worth of mass balance data. This allows for long term analysis to be undertaken to establish whether our hypothesis is likely to be accepted or rejected.


Methodology

• Using Maussion et al, (2019) (OGGM) Glacial extents were established and centrelines drawn from Kienholz et al., (2014) algorithms. These were then graphically represented (Figure to right).

• The critical node was then identified by assessing the importance of each node within the whole ’nodal network’ by undertaking Sarker et al. (2019) method of identification. (see schematic below).

 

 

 

• Mass balance and ELA data between 1960 – 2018 was obtained from WGMS (WGMS, 2020).

• The Elevations of the identified critical nodes were established using DEMs and given ±10m buffer.

• The WGMS data was then split into two groups based on if the ELA was above or below the critical node. These groups were then plotted as box plots and statistically tested using Kruskal-Wallis tests.


Results

Figure above illustrates the identification of all the nodes on the glaciers. The critical nodes established where: (a) = 5, (b) = 5 &7, (c) 3 &5, (d) 7. The elevation of these were established and buffered ±10m and added to the figure to the left (red band) .

This figure suggests that when the ELA is above the critical node position the the rate of net balance changes considerably this is particularly exemplified by Wolverine (d) where the graphs suggest phases of drastic changes and stabilisation which coincides with the relative position of the ELA to the critical node.

Our results from kruskal-Wallis testing (Table 1) suggests that only Peyto failed to reach the 95% threshold and therefore, is inconclusive about establishing scale of exacerbated changes failing our hypothesis. The others appear to pass our early hypothesis suggesting that there is some link between the critical nodes and exacerbated changes.


Discussion

– Critical nodes appear to be linked or influenced by the hypsometric median value (Figure above (Left column, black band)). This is interesting as the hypsometric median is linked with glacial sensitivity. Confirming the idea that these critical node points are threshold values to changes in glacial dynamics and extent, thickness (geometry). The extent to which these are linked is still unclear.

– Reasoning behind Peyto failing to meet threshold significance is due to only 1 value being below the critical nodes calculated therefore providing insufficient data. Furthermore, arguably the wrong nodes were selected as critical. Peyto has a weird and unconventional spherical rather than linear shape. We suggest that node number 4 is more critical particularly long term. This new node does exhibits significance at the 95% threshold and suggests a scaling factor of 3.3 times. A very realistic outcome.

– The scaling factors we found had a large range. The range presnting some unrealistic outcomes notably Midtre Lovenbreen. Therefore, we should take these values with caution. However, the range may  represent the different environmental conditions at each site. Furthermore the shape of the glaciers may also be a factor. Reasoning behind Wolverines low scaling factor is due to it being a ‘stable’ glacier and consequently would expect little change unless considerablly affected by climatic forces.  More conclusive refined data would better conclude the excaerbated change from critical node disruption. Data specifically at the critical node margin would be useful. Despite this it does highlight the importance of these positions as reference points of glacial conditions/’health’ as significant changes do occur in years where the ELA is above these points.


References

Kienholz, C., Rich, J. L., Arendt, A. A., and Hock, R.(2014). A new method for deriving glacier centerlines applied to glaciers in Alaska and northwest Canada, The Cryosphere, 8, 503–519, https://doi.org/10.5194/tc-8-503-2014, 2014

Maussion, F., Butenko, A.,hampollion, N.,Dusch, M., Eis, J.,Fourteau, K., Gregor, P., Jarosch, A. H., Landmann, J., Oesterle, F., Recinos, B., Rothenpieler, T., Vlug, A., Wild, C. T. and Marzeion, B. (2019). The Open Global Glacier Model (OGGM) v1.1, Geoscientific Model Development. pp.909-931. Available at: https://www.geosci-model-dev.net/12/909/2019/ , DOI: 10.5194/gmd-12-909-2019

Sarker, S., Veremyev, A., Boginski, V. and Singh, A., 2019. Critical Nodes in River Networks. Scientific Reports, 9(1).


Conclusions

– This is only early investigations and methodology concepts. Closer observations need to be established with better more concise data. Particularly geodentic data of the mass balance at the margins of these critical nodes to be absolutely certain that cataytlic changes occur when these critical nodes are comprimised (‘disconnected’). However, the real success was using OGGM we can identify these critical node locations. Furthermore, our results do appear to sugest that the identified critical nodes are threshold point at which exacerbated changes in mass balance does occur. The scale to which varies between glaciers shape and complexity. Overall, this study does proivide evidence that Sarket et al., (2019) critical node theory can be applied to glaciers to assess glacial dynamics.

– Future Studies could try using surface velocities using InSAR to clearly confirm if during these ’disconnection’ events where the critical node is comprimised do excaerbated changes occur. This would be identified by sudden changes in surface velocity.


Acknowledgements

Kind thanks to Dr. Wynn and Dr. Folkard for helping me turn these abstract concepts into logical reasoned ideas.


Introduction

Sarker et al., (2019) established a method of identifying within a river channel network the point, which if lost or disconnected would lead to catastrophic system affects. Valley glaciers have similar simplified networks and if this method was applied would also indicate a critical point on the glacier. Characterisation of nodes are: either the starting point of a tributary or the point where two central flow lines meet (Point where branches start and join).

This study aimed to primarily establish whether the principle of Sarker et al., (2019) theory of critical nodes can be applied to glaciers by:

(i) establishing the critical node position on a glacier

(ii)Analysing the influence of this position in relation to the ELA and testing the hypothesis:’If the ELA is above the critical node the glacier is ’disconnected’ and exhibits exacerbated changes in the mass balance’.


Inspiration to Project

Whilst undertaking a module on Glacial and fluvial processes I was introduced to a theory by Sarker et al., Which identified critical parts of a river network and assessed if these critical points if disrupted caused drastic changes to the river processes. As a Glaciologist i thought how this system thinking could be applied to branched glaciers notably those in valley settings as a means to provide a new framework of thinking for glacial dynamics and monitoring of health/condition. Having utilised OGGM before i thought this tool would help visulaise my abstract concept. The research i present here is the primative ideas of my concept and since applying to present here my ideas have devloped. I provide insight into these developments in the conclusion.






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]]> https://www.lancaster.ac.uk/2021ugconference/dominic-james-thomas-hardy/feed/ 0 Samuel Payne https://www.lancaster.ac.uk/2021ugconference/samuel-payne/?utm_source=rss&utm_medium=rss&utm_campaign=samuel-payne https://www.lancaster.ac.uk/2021ugconference/samuel-payne/#respond Sun, 17 Jan 2021 22:53:23 +0000 https://www.lancaster.ac.uk/2021ugconference/g6s2x5p4/
Samuel Payne

Samuel Payne

Lancaster Environment Centre (Bailrigg) | Year 3 | Degree: BA (Hons) Geography (Study Abroad)
Analysis of the WASH facilities across the Kutupalong Refugee Camp


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Spatial analysis of the WASH facilities within two subcamps of the Kutupalong Rohingya refugee camp, Bangladesh


Setting the scene



Setting the scene


With a population of around 900,000 people in an area 13km2, the Kutupalong is the largest refugee camp (by population) in the world. Located on the Teknaf Peninsula in Eastern Bangladesh, the refugees living in the camp are predominantly Rohingya, having fled neighbouring Myanmar. The Rohingya have faced persecution and abuse in Myanmar since 1948, however a military crackdown from 2016-present has displaced the largest numbers of the population.

Water, Sanitation and Hygiene (WASH) implementations and facilities are essential in refugee camps, as if facilities are inadequate, disease and death can quickly spread through camps. Adequate WASH facilities though is attributed to an all-round improvement in quality of life. There are however important safety factors to be taken into consideration with WASH facilities such as :

  • Are there inside locking doors on the facilities?
  • Is there lighting in the facilities?
  • Are washrooms and latrines gender separated?

These three factors being present in WASH facilities has been proven to increase the safety of camps and reduce the level of Gender Related Violence.


Figure 1. The location of the Kutupalong Refugee Camp within Bangladesh, and the two study subcamps



The study



This short Vox video gives some more background to the Rohingya crisis, whilst the video below gives more detail on WASH implementations


The research carried out investigates the safety features of WASH facilities within the Kutupalong Refugee Camp, and how these features relate to the population distribution. Due to the size of the camp, to subcamps were chosen to study. These were the original, Kutupalong Refugee Camp (red) and the expansion Subcamp 17 (green).

Washrooms and latrines were ranked into ‘high’, ‘medium’ and ‘low’ quality based upon the number of the three safety features (locking doors, lighting, gender separation) they had. Facilities with 0 or 1 features were ranked low quality, 2 features were medium quality, and washrooms/latrines with all three were ranked high quality. 

WASH facilities were mapped aginst the buildings in the two subcamps and compared to the UN Post Emergency Targets for WASH facilities:

  • A maximum of 200m from households to safe and functional water collection points;
  • Less than 100 people per usable water collection point;
  • A maximum of 206 people per toilet/latrine and per bath shelter/shower;
  • No household should be further than 50m from a toilet or washroom.






Left: Buildings within the Kutupalong Refugee Camp (Amnesty International)

Right: An aerial view of a section of the Kutupalong Refugee Camp (Lowy Institute)



Results



The number of buildings within 50m of low/medium/high quality latrines is represented in the maps below and is colour coded. Brown buildings are not within 50m of a facility, red buildings are within 50m of a low quality facility, orange buildings within 50m of a medium quality facility, and green buildings within 50m of a high quality facility. 

Scrolling over the maps compares the difference between the Kutupalong Refugee Main Camp, and Subcamp 17.




Large disparities exist between the two camps in their connectivity to different WASH facilities. The high number of brown (hence unconnected) buildings in the Kutupalong Main Camp highlights this, compared to the high number of connected buildings in subcamp 17. However, across both camps, there is a distinct lack of high-quality latrines and washrooms, shown by the lack of green buildings in the first two maps.

When taking into account the camp populations, this results in 95.45% of the population being within a 50m distance of a washroom, latrine and water point in subcamp 17, compared to 23.45% of the population in the Kutupalong Main Camp. When planning for future WASH implementations by humanitarian workers ina the Kutupalong Refugee Camp, an analysis such as this could be used, to understand which areas of the camp require WASH facilities more urgently. Throughout all three of the above facilities, there appears to be a lack of connection to WASH facilities throughout the Northern area of the Main Camp, this therefore is an area that further implementations may be required.



References

 


Abdelkader, E. (2013). The Rohingya Muslims in Myanmar: Past, Present, and Future. Oregon Review of International Law (2014 Forthcoming). Available at SSRN: HTTPS://SSRN.COM/ABSTRACT=2277949

Baghri, S. and Reed, R. A. (1998). Emergency sanitation in refugee camps. IN: Pickford, J. (ed). Sanitation and water for all: Proceedings of the 24th WEDC International Conference, Islamabad, Pakistan, 31 August – 4th September 1998, pp.23-26.

Bhatia A., Mahmud A., Fuller A., Shin, R., Rahman, A., Shatil, T., Sultana, M., Morshed, K. A. M., Leaning, J., Balsari, S. (2018). The Rohingya in Cox’s Bazar: When the Stateless Seek Refuge. Health and Human Rights Journal, 20(2), pp. 105-122.

Emery, M., Shaw, M., Santosa, L, Bird, O. (2011). Recommendations to reduce sexual violence in Haitian internally displaced persons (IDP) camps. New Voices in Public Policy, George Mason University.

Johnston, R., Prüss-Ustün, A, Wolf, J. (2019). Safer water, better health. World Health Organization. Geneva, Switzerland, ISBN 978-92-4-151689-1

Milton, A. H., Rahman, M., Hussain, S., Jindal, C., Choudry, S., Akter, S., Ferdousi, S., Mouly, T. A., Hall, J., Efird, J. T. (2017). Trapped in Statelessness: Rohingya Refugees in Bangladesh. International Journal of Environmental Research and Public Health, 14(8), 942. HTTPS://DOI.ORG/10.3390/IJERPH14080942


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]]> https://www.lancaster.ac.uk/2021ugconference/samuel-payne/feed/ 0 Anna Tillin https://www.lancaster.ac.uk/2021ugconference/anna-tillin/?utm_source=rss&utm_medium=rss&utm_campaign=anna-tillin https://www.lancaster.ac.uk/2021ugconference/anna-tillin/#respond Sun, 17 Jan 2021 22:53:23 +0000 https://www.lancaster.ac.uk/2021ugconference/y5d3wekh/  

Anna Tillin

Anna Tillin

Lancaster Environment Centre (Bailrigg) | Year 3 | Degree: Physical Geography
Are wind turbines weather changers? A quantitative study of wind turbine effects on meteorology.

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How the data was processed before it could be analysed
How the data was processed before it could be analysed

Methodology


Background


Results




Data from December 2012 to May 2020 were used to investigate the effects of the wind turbine. This data needed checking for quality before it could be analysed. This flow chart shows the process followed. Matlab was used to analyse the data.



  1. Each 10 minute period was categorised by wind direction.
  2. The difference between temperature at sites A and B was calculated.
  3. Median difference and Median Absolute Deviation was calculated for each wind sector (16 sectors were used).
  4. The Kolmogorov-Smirnov test for distribution differences was used to test the significance of differences between temperature recorded at the two sites.

Data were also categorised by season, time of day, consistency of wind direction, and rotor speed. The same tests were carried out.


In most cases, site A was cooler than site B. This difference was significant for wind directions from SSW to WNW.

Time of day and season had an effect on the temperature difference between sites A and B.

Bigger differences were found in the daytime than the nighttime.

The biggest differences were in the Summer (JJA), followed by Autumn (SON). The smallest differences were in the Spring (MAM). The difference was less variable in the Winter (DJF).

The daytime results are to the left, and night-time results are below.


Differences were also found between the two sites before the turbine was present.

These differences could be due to

  • Natural spatial variation in weather patterns
  • An influence of the university campus
  • Other landscape effects

For most wind directions, the temperature differences between the sites are similar with and without the turbine. The exceptions are West, WNW and WSW, where differences between the medians are greater than Median Absolute Deviation.



What Does This Mean?


Commitments to limit global warming to 1.5°C compared to pre-industrial temperatures mean that many more wind turbines and wind farms are being built across the world. It is important to understand the effects these turbines have on their surroundings.

Models have predicted that surface air temperature in the turbine wake will decrease in the daytime and increase in the night-time (e.g. Baidya Roy et al., 2010).



Some studies of large wind farms have found these effects to be true (e.g. Zhou et al., 2012). However, others have found no effect or the opposite effect to predictions (e.g. Moravec et al., 2018; Harris et al., 2014). More research is needed to fully understand the effects of turbines on surface air temperature.


Lancaster University built a wind turbine in 2012. This turbine supplies up to 17% of the university’s electricity.

The turbine is built between the two measurement sites of Hazelrigg Meteorological Station. Site A is to the east of the turbine, and site B is to the north west.

This provides an great opportunity to investigate the effects of the wind turbine on surface air temperature, as there is data over quite a long period of time.



The Question: Does the LU wind turbine affect temperature at the weather station sites? How?


The turbine may have a cooling effect in its wake, during both daytime and night-time. This is different to the predictions of models. There is uncertainty about the results:

  • The effect is not seen at site B.
  • There is a limited amount of data from before the turbine is installed, so comparison is difficult.
  • It is likely that other factors have an influence, so some or all of the effect may be due to a different cause.

This investigation highlights the difficulty in assessing the effects of wind turbines on surface air temperature. The specific location of turbines may play a big role in controlling the size of their effects. Therefore, it is important to consider this when building wind farms.


References
     Baidya Roy, S., Traiteur, J. and Schneider, S. (2010) Impacts of wind turbines on air surface temperatures. Proceeding of the National Academy of Sciences, 107, 17899-17904. 10.1073/pnas.1000493107.
     Moravec, D., Bartak, V., Pus, V. and Wild, J. (2018) Wind turbine impact on near-ground air temperature. Renewable Energy, 123, 627-633. 10.1016/j.renene.2018.02.091.
     Zhou, L., Tian, Y., Baidya Roy, S., Thorncroft, C., Bosart, L. and Hu, Y. (2012) Impacts of wind farms on land surface temperature. Nature Climate Change, 2, 539-543. 10.1038/NCLIMATE1505.


Acknowledgements
Thank you to Dr Wlodek Tych for his assistance with this project.





Photo from stephenson-halliday.com





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]]> https://www.lancaster.ac.uk/2021ugconference/anna-tillin/feed/ 0 Amy Newell https://www.lancaster.ac.uk/2021ugconference/amy-newell/?utm_source=rss&utm_medium=rss&utm_campaign=amy-newell https://www.lancaster.ac.uk/2021ugconference/amy-newell/#respond Sun, 17 Jan 2021 22:53:23 +0000 https://www.lancaster.ac.uk/2021ugconference/cgq53m2h/
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