Juurikäävän virustorjunta kohti käytäntöä uuden tartutusmenetelmän avulla

Researcher Eeva Vainio, PhD, Principal Scientist, Forest health and biodiversity

Juurikääpä aiheuttaa vuosittain yli 50 miljoonan euron tappiot maamme metsätaloudelle ja on Suomessa ylivoimaisesti tuhoisin puiden sienitauti. Uusia juurikääpätartuntoja voidaan torjua mm. kantokäsittelyillä, puulajivalinnalla ja hakkuiden ajoittamisella talviaikaan, mutta nykyiset keinot eivät riitä torjumaan tautia, joka yleistyy jatkuvasti paikallisesti ja leviää maantieteellisesti ilmastonmuutoksen ja metsänhoitokäytäntöjen seurauksena. Luonnonvarakeskuksessa on useiden vuosien tutkimustyön seurauksena kehitetty juurikäävältä löydettyjen virusten hyödyntämiseen perustuva uusi torjuntamenetelmä, jolla voitaisiin täsmätorjua juurikäävän tautipesäkkeitä sellaisissa metsiköissä, joissa juurikääpää jo esiintyy ja joissa ennakoiva torjunta esim. perinteisin kantokäsittelymenetelmin ei ole enää mahdollista.

Aikaisemmissa tutkimuksissamme löydettyjen partitivirusten on todettu vähentävän juurikäävän kasvua huomattavasti sekä laboratorio- että maastoolosuhteissa. Hankkeessa selvitetetään, voisiko näitä juurikäävän kasvua heikentäviä viruksia tartuttaa valmiiksi juurikäävän vaivaamiin kantoihin muilla tavoin kuin alkuperäisen patogeenisen isäntäsienensä sisällä. Lopputuloksena olisi tärkeä askel kohti toimivaa juurikäävän biologista torjuntamenetelmää, jolla tuhoja voitaisiin hillitä tapauksissa, joissa muut torjuntamenetelmät eivät ole taloudellisesti kannattavia tai mahdollisia. Tämänkaltaisten tuhokohteiden oletetaan yleistyvän mm. jos kuusen kasvatuksessa siirrytään enenevässä määrin kohti jatkuvapeitteistä kasvatusmenetelmää, jossa juurikäävän juuristovälitteinen levintä uuteen puusukupolveen on tehokasta.

New engineering methods to improve the circularity and sustainability of battery raw materials value chains (CircuS)

Ms. Gulsah Tas, MSc, Doctoral candidate.

Background and Project Description: Circular Economy (CE) is considered as a way of achieving sustainability due to its potential to decrease the demand for natural resources and environmental impacts compared to the current traditional linear econo my models. This is due that materials are not disposed at the end of their product life but instead they rejoin the value chain by different CE strategies as described in Figure 1. Despite its potential to reduce the resources consumption, CE models still include material and energy losses which may cause additional hazardous environmental impacts threatening sustainability. Therefore, it is necessary to measure the performances of CE strategies to truly claim that they help moving towards sustainability by ensuring the efficient use of resources. However, there is still no established methodology for the evaluation of CE concerning different aspects including material, energy and monetary values together with their relation to environmental impacts.

CircuS aims to develop a new robust engineering methodology to evaluate the circularity and sustainability of transformative processes. This new engineering methodology includes Statistical Entropy Analysis (SEA) for materials circularity, Exergy Analysis (EX) for energy circularity, Techno-Economic Analysis (TEA) for economical viability and Life Cycle Assessment (LCA) for the environmental impacts’ evaluation. The main purpose of the project is to simultaneously improve the aforementioned dimensions by establishing their correlation through detailed analyses. As a case study, Lithium-ion battery (LIB) recycling processes are focused considering their importance in transitioning from fossil fuels to renewable energy especially for electrification. This work includes small scale experimentation campaigns, intense data collection and process simulations for the accurate implementation and combination of scientifically proven analyses. This project will help the decision-making processes for the establishment and optimization of the responsible use of the natural resources through the road on reaching sustainability.

Biodiversity and albedo of forests: from hyperspectral technologies to climate benefits

Dr. Jussi Juola

Researcher: Dr. Jussi Juola, postdoctoral researcher, Aalto University

Research team: Dr. Aarne Hovi, Prof. Miina Rautiainen, Aalto University

Description of the project

The aim of the project is to investigate the links between biodiversity, biomass, site fertility, and albedo of Finnish forests. Besides the carbon balance, albedo (i.e., ‘reflectivity’) of land surfaces has an important impact on climate, especially in the northern latitudes. However, albedo of boreal forests remains poorly quantified, which causes uncertainty in climate predictions. In particular, the interconnections between forest albedo, nutrient balance, and biodiversity actions (e.g., continuous cover forestry and preference for rare broadleaf tree species) are poorly known. Understanding these relationships is essential for planning optimal forest management that balances between economic, climatic, and biodiversity impact of forests.

The project offers novel insights into the effects of biodiversity, biomass, and nutrient balance on forests’ albedo. Spanning across a diverse range of Finnish forests, from southern boreal to the arctic zone, the project utilizes albedo estimates from satellite data products, and develops the parameterization of biodiversity-related variables in physically-based forest reflectance models. Additionally, we develop novel spectral measurement techniques to create open access spectral libraries that can be used as input in those models. The results of the project will help to improve the impact assessment of forests on climate and contribute to the development of tools that enable holistic evaluation of climate, biodiversity, and economic impact of forests.

Soda-Lights: producing highly luminescent sodalites for lighting using Finnish minerals

Researcher: Sami Vuori, Doctoral Researcher, University of Turku

Supervisor: Professor Mika Lastusaari, Intelligent Materials Chemistry Research Group,Department of Chemistry, University of Turku

Background of the project: The most commonly used material in common white LEDs is a yellow-emitting Y₃Al₅O₁₂ doped with cerium

Sami Vuori is checking if a newly-made sodalite sample is luminescent

(YAG:Ce), which combines with the blue emission from a semiconductor to create white light. The problem with this material is that it contains cerium and yttrium, which are rare earth elements with limited availability, and that is why non-endangered, sustainable elements are needed to be sought after in this field.

Aim: The goal of our research is to synthesize sodalite (Na₈Al₆Si₆O₂₄Cl₂), a mineral with unique optical properties, using various Finnish mineral sources including feldspars and spodumene. According to earlier research, sodalite’s visible light emission is dominant in the orange region, but the aim of this work is to shift the wavelength to yellow in order to obtain white light and eventually replace YAG:Ce as the material in LEDs. The utilization of Finnish mineral resources will not only contribute to the development of sustainable materials but also improve the availability of sodalite for various industrial applications in the country and worldwide. The main challenges of the project lie in the unpredictable purity of the natural materials, however in many cases impurities are the key to obtain certain optical properties.

We will first screen possible candidate minerals using X-ray powder diffraction, X-ray fluorescence spectroscopy, then develop and tune the synthesis procedure by utilizing thermal analysis techniques to investigate the optimal synthesis temperatures. In the last step, the light emission properties are tuned by different synthetic and doping methods, and the final aim is to develop an efficient and cost-efficient process to synthesize large batches. An example of the effect of different dopant elements are shown in Figure 1. The light emitted from these materials are measured using luminescence spectroscopy to gain information about their emission and excitation wavelengths and quantum yield.

SpeedBreed – Accelerated breeding for improved hardwood growth and quality

Dr. Juha Immanen, Postdoctoral Researcher, University of Helsinki and Natural Resources Institute Finland (Luke) joint project

Supervisor: Dr. Kaisa Nieminen, University of Helsinki and Natural Resources Institute Finland (Luke)

Description of the project:

It is difficult to exaggerate the role of trees and forests in the mitigation of climate change: trees are elementary

both as essential long-term carbon sink and as raw material for the bioeconomy. By accelerating tree breeding, the aim of my project is to both mitigate the effects of climate change and facilitate the adaptation to them.

By developing an accelerated speed-breeding program for silver birch, my project will facilitate the production of improved ha

rdwood genotypes for silviculture purposes. My project tests novel high-throughput techniques tree phenotyping: 3D laser scanning of forest tree trials and automated high-precision architectural analysis of greenhouse grown trees. To identify the genetic basis of the best speed-bred trees I will genotype them through genomic DNA sequencing and further identify wood development related genes through RNA-sequencing based expression analyses.

The best speed-bred genotypes can be directly incorporated into Luke’s ongoing traditional birch breeding program.

Regeneration of biobased adsorbents, utilization of regeneration solution and used adsorbents

Researcher: Minja Korhonen, Doctoral researcher, University of Oulu

Supervisors: Assoc. Prof. Sari Tuomikoski, D.Sc. Hanna Runtti and Prof. Ulla Lassi, Research Unit of Sustainable Chemistry, University of Oulu

Description of the project:

The aim of this research is to develop the usage of biobased material as alternative adsorbents. Biobased materials such as biochar, can replace widely used activated carbon material. Activated carbon consumes a lot of carbon dioxide emissions and costs while regeneration processes. Therefore, there is a need to develop cheap, easy and eco-friendly method for chemical regeneration. Based on the results, materials that support the circular economy and sustainability, can be used more widely in water purification. In addition, finding out the change of functional groups between adsorption-desorption cycles, gives excellent opportunities to target the regeneration more efficiently and selectively to the desired impurity. Also, different kind of materials such as industrial side-stream-based adsorbents are studied in this work. Together these research results can create a possibility for the cheap adsorbent production from waste material, but also the capability of recycling the material afterwards in sustainable way locally in Finland.

Machine learning approaches for responsible ore prospectivity modelling

Researcher: Fereshteh Khammar, Doctoral candidate, University of Helsinki

Supervisors: Prof. Christoph Beier, Department of Geosciences and Geography, University of Helsinki, and Research Prof. Vesa Nykänen at Geological Survey of Finland (GTK) Adjunct Professor at University of Helsinki

Description of the Project:

An increased demand for base metals, dependency on high-tech elements, and diversity in application of critical raw materials have motivated researchers to put priority on exploration of potential deposits. However, exploration of hidden and complex ore deposits without outcrops is a challenge that geologists are facing especially in Finland. Therefore, more efforts are rquired to apply new techniques to explore new prospects in areas with thick/ a vast vegetation and/or snow cover and/or to improve the circumstantial evidence of deposits, to reduce the cost of the exploration process and reduce the negative impacts of mining. The Exploration Information System (EIS) project, funded by European Onion and lead by the GTK, to which the present PhD study contributes, aims to develop new data analysis methods using artificial intelligence (AL), machine learning (ML), deep learning into mineral prospectivity mapping (MPM) according to mineral systems modelling.  MPM from regional to local scales has been developed to integrate various geoscience data to identify exploration target areas. In a mineral prospectivity model, a set of different data including geology, geochemistry, geophysics, etc. is considered as an input and mineralization potential areas as a desirable output from an integration function.

 Aims of the project:

The main objective of this research is to map favorable exploration target areas/mineral targeting maps using the mineral potential tool (MPM) with the application of machine learning (ML) algorithms and deep learning for three main target areas/IOCG style deposits: Hannukainen, Kuervitikko, and Cu-Rautuvaara in the Kolari region, Central Lapland Greenston Belt, north of Finland. To achieve the objective, the study has been divided in a geological and a mathematical framework. The geological framework focusses on ore-forming process and critical geological processes based on mineral system analysis. At the first stage, I will concentrate on defining a set of targeting criteria/mappable criteria (proxies) according to their spatial or genetic associations with the targeted mineral deposits. e.g., element (Fe-Au-Cu ± As, Bi, Co, K, Li, LREE, Mo, Se, Te, U), mineral (magnetite-chalcopyrite-pyrite-gold) association, and host rock type (e.g., skarn, monzonite) in Fennoscandian IOCG deposits. Key is to take into account geochronological, and geochemical data as indicators for the ore-formation processes to evaluate the main geological criteria as initial input. Moreover, artificial intelligence (AI) and machine learning (ML) methods have been recently used to enhance statistical methods applied in the Earth sciences. ML methods can potentially generate models of complex and nonlinear systems such as multistage geological events. Therefore, we will apply diverse knowledge- and data-based approaches and compared the results based on geomodels, representing how reliable the outcomes are as the second stage of the project. The reliable output would be able to demonstrate promising areas where they have obvious similarities with well-known IOCG-style deposits in the mentioned region.