Monitoring of GHS and sequestration of C02 in nature.

The effects of climate change (CC) are often discussed in terms of their impacts on biodiversity, the general population, and ecosystems. Lake Kivu, located north of Lake Tanganyika contains a very large amount of carbon dioxide (CO2), methane (CH4), and possibly sulfur dioxide (SO2) which can be oxidized in water droplets carried by the wind to form sulfuric acid (H2SO4). This sulfur dioxide, in combination with other molecules in the air, contributes to the formation of acid rain. On plants, SO2 causes effects such as reduced photosynthesis, the appearance of spots on the leaves, reduced growth, etc. In addition, it causes corrosion of metals and significant degradation of construction materials.

 As for carbon dioxide, it is partly responsible for the greenhouse effect in our area. As a result, temperatures rise and our planting seasons are disrupted as the Great Lakes region is heavily dependent on agriculture.

The mass of carbon dioxide concentrated in Lake Kivu and other greenhouse gases emitted by human activities have and are already causing a great deal of damage to the health of populations, animal and plant species; however, no study has ever been published to demonstrate all of its consequences in order to guide local populations to adopt other behaviors aimed at mitigating these dangers or adapting to the climate changes observed in recent times.

For methane (CH4), today, as indicated in the IPCC (Intergovernmental Panel on Climate Change) report, the influence of methane has been calculated as adding about 0.5 ° C to the warming that our planet is currently experiencing.

 The study reveals that 30 to 50% of the current temperature rise is due to this powerful but short-lived gas in the atmosphere. Unfortunately, this lake is full of billions.

And therefore, these greenhouse gases significantly affect the normal functioning of the area. Note that CO2 stays in the atmosphere for 100 years, CH4 only stays there for a dozen years and the last one can stay there for 120 years[1]. The effect of the diffusion of greenhouse gases in nature prompts us to make an evaluative study of the concentration of carbon dioxide, methane, sulfur dioxide and fine particles present in Lake Kivu, in the air and their impact on biodiversity through the collection of as much data as possible by artificial intelligence (AI) with a view to possibly proposing possible solutions consistent with the Paris Agreement. Biodiversity provides goods and services that are essential both for adapting to the effects of climate change (wetlands provide natural protection against flooding,vegetation allows local improvement in the quantity and quality of water, green spaces improve the microclimate and air quality in cities, etc.) and mitigate climate change, in particular through the absorption of CO2 by marine and terrestrial ecosystems.

Unfortunately, this biodiversity is directly threatened by climate change. In many parts of the world, species composition has changed and species have become extinct at rates 100 to 1000 times greater than normal.

In the DRC in general and in the Great Lakes region in particular, nobody or institution has yet clearly initiated a reflection that uses machine learning on the issues of these gases in relation to Climate Change. Everything is still in its infancy. It is in this context that the present study will be carried out with a view to producing indicative data on mitigation and adaptation to climatic conditions.

AI will serve as essential tools for predicting multiple observable changes in the climate, biodiversity, and ecosystems of the Great Lakes region. Artificial intelligence and machine learning will take into account, data on meteorology, on temperature variations, orientation movements of greenhouse gases by the wind in nature, will play a very important role in regarding the construction of data sets with indicators revealing climate change due to the presence of tons of CO2, CH4, SO2, and other fine particles during our research period. These processes include learning (acquiring information and the rules for using that information), reasoning (using rules to reach rough or definitive conclusions) that meet FAIR data principles (findable, accessible, and reusable Interoperable).

To help local communities understand our approach, an aggressive campaign to reduce emissions of methane, carbon dioxide, sulfur dioxide, and fine particles will be organized to allow them to save time in the fight against change climate.

[1] IPCC report (https://www.bbc.com/afrique/monde)

Our research project is a first in the Great Lakes sub-region. It is an innovation that will help to enlighten national, regional and international opinion on issues related to climate change, on issues related to the disruption of cropping seasons and decision-making issues in favor of new research and action of fight against global warming. The project consists of monitoring the increase in GHG emissions in the sub-region, building climate data sets to be integrated into artificial intelligence (AI) to generate new indicators that will help guide environmental protection actions.

Due to the complexity of the project, we will define algorithms to track climate change in the Great Lakes sub-region with R using ASUS X540L laptops with specific characteristics.

 R programming language:

R is a programming language for statistical computation and graphics that you can use to clean, analyze, and graph your data. It is widely used by researchers in various disciplines to estimate and display results and by teachers of statistics and research methods. [1]

Our methodology consists of exploring large databases acquired during the period 2012-2020. The existing database includes limnological and planktonic variables (diversity, biomass and production of phyto-and zooplankton), the abundances of aquatic species, and meteorological data. This database will be supplemented by sedimentary archives (biogeochemical and biological proxies) from Lake Kivu and by satellite data of phytoplankton biomass and surface hydrological characteristics, in order to take into account spatial and temporal heterogeneity.

New in situ studies will be carried out to extend the database and improve understanding of the impact of the release in nature of CO2, CH4 and other pollutants (sulfur dioxide and fine particles) on biodiversity and the functioning of the ecosystem. Laboratory studies will be undertaken to determine the quantities disseminated per day, per month and / or per year; and to identify the consequences recorded on species in the sub-region.

Our algorithm will reduce complex models using deep neural networks. We plan to study schematics to decompose a process model into PDE and statistical components. We will discuss a set of IT topics, in particular:

• Structured neural networks based on graphs,
• Learning and adaptation: this subject includes active learning/repetition/multitasking, transfer learning (TL), and domain adaptation
• Causality and explainable AI: This is a central concern in computer science at the present time. This is also an essential component of the challenge as we intend to use the models created to serve as a means of understanding the nature and sources of new theories.

The last step will be devoted to the processing and modeling of the data, in order to:

• demonstrate the negative and positive impacts of the process implemented by the Limnological firm since 2012 on atmospheric conditions, temperature, and the structure of the water column, in order to understand and simulate the variability of seasonal mixing processes and predict long-term changes,
• predict future changes in ecosystem processes and resources, depending on the management of fisheries, exploitation of carbon dioxide and/or methane in deep water, and climate changes observed on crops (establishing the link between global climate and regional climate).

We plan to study schematics to decompose a process model into PDE and statistical components.

The impact of research is multidimensional. Initially, the project will provide regular and updated data on the process of accumulation of methane, carbon dioxide, fine particles which have harmful effects on the populations of two countries (Rwanda and the Democratic Republic of Congo) in order to enlighten the authorities in decision-making and put in place mechanisms for adapting to and mitigating climate change. In a second step, the project will provide data on the abundance of species threatened with extinction as well as their dynamics following the barcoding of the DNA of the species by Artificial Intelligence. This will provide in-depth scientific knowledge to local, regional and international communities to initiate innovative activities for the conservation of the environment and biodiversity as well as other degrading ecosystems.Thirdly, the project will also have the impact of setting up a database, modules and tools developed in accordance with the FAIR principles (Easy to find, Accessible, Interoperable, Reusable) to be shared with other sub-regional scientists and international and why not with the Massachusetts Institute of Technology.

First, we are a multidisciplinary team that works to protect the environment, biodiversity and support local communities on issues that affect them. For more than three years we have been conducting environmental research focused on biodiversity conservation, artificial intelligence in relation to climate change, environmentally resilient agriculture, global warming. All these activities and problems encountered allow us to be at the side of the population in view of guiding them in the implementation of their socio-economic and environmental activities. Our participatory approach allows us to involve local authorities and populations to immerse themselves in everything we do, for example, publications, our research reports, our conferences, and workshops so that everyone can give their point of view on the results of our research.Because Gandi says: "All things which are done for me, without me, are done against me" And we respect this principle.

Our project is multidisciplinary. Beyond the financial means that we are looking for in order to implement the project and produce good results, ISM-Goma also wants the effective involvement of other Solve teams to help us accomplish our missions. In addition, our project is international. We are also asking Solve for artificial intelligence technicians, biologists, geographers, cartographers, and environmentalists to help us work throughout the implementation period of the project. We will contact Copernicus and ArcGIS to provide us with software for spatial mapping as well.

My solution is innovative in our area because it will help people to be up-date and know more about Biodiversity faces a large number of threats, including land-use change, habitat loss and fragmentation (e.g. due to agricultural expansion), overexploitation of natural resources (e.g. unsustainable logging, hunting, and fishing), pollution (for example, excessive use of fertilizers and litter), invasive alien species and climate change.

Artificial intelligence to the rescue of biodiversity: new applications of artificial intelligence could make it possible to better analyze the evolution of biodiversity, to better preserve it, and to predict possible changes with plausible indicators. - Climate change is here. It is global and its impacts are difficult to assess, especially when it comes to estimating the evolution and stability of biodiversity. Thanks to AI, computers are now able to make the most of databases, images or are accumulated around the world by researchers. It is becoming more interesting than ever to acquire, in situ, data on the observation of biodiversity, seasonal variation, temperature, and to use AI to extract useful information.

Using AI, this research project provides precise information on the distribution, diversity, and viability of species at more than 20 sites in the Great Lakes sub-region, in relation to climate change and fluctuations environmental. Use a learning machine to try to develop an accurate predictive model for use by researchers, governments, and states in their efforts to preserve ecosystems and fight climate change.

- Implementation of monitoring technology and a statistical database on concentrated and disseminated CO2 and CH4

- Establishment of a system capable of controlling the process of high precision analysis which determines the sources of pollution and the amount of gas generated, and also offers solutions to the problem,

- Spread a range of social, environmental, and health problems due to CO2 and CH4 to the public,

- Assess areas of “afforestation”, “reforestation” and “deforestation” in relation to carbon sequestration and reduction of CO2 emissions;

- Develop allometric models to estimate the aboveground biomass of some plants to establish a basis for calculating carbon stocks;

- Finally, propose possible solutions for the reduction of polluting emissions and of a few degrees Celcius on global warming according to the Paris agreement.

- The existence of machines for machine learning and analysis

- Installation of automatic CO2 and CH4 analysis devices

- The existence of an Artificial Intelligence (AI) equipped with data sets

- The existence of quantified lists of problems due to CO2, CH4, SO2, and fine particles by sector (social, environmental, and health)

- Number of cards maps produced

- Quantity and quality of remotely sensed vegetation

My solution will inevitably have an impact of course. The establishment of monitoring devices for methane, carbon dioxide, and fine particles and the existence of reference Big Data will, in the short term, allow multiple beneficiaries to acquire a general knowledge of the real existence of greenhouse gases on their territory, to know the negative effects they have on the population (local community, administrative and scientific authorities), on the environment and on biodiversity. On this, this general knowledge will generate in the medium term, a collective awareness that will encourage the design of local plans for adaptation and mitigation of the problems they face. Therefore,the solution will influence and direct all social strata towards environmental resilience activities or practices to adapt to new climate change situations and practices aimed at mitigating pressures on marine and/or terrestrial biodiversity in order to preserve the ecosystems for present and future generations. And in the long term, the solution will allow the existence of two or three technologies applicable in sustainable agriculture (with improved seeds) and the community protection of agricultural land, in the protection of forests and protected areas, reinforcement of reforestation. Finally, stop a technology based on Artificial Intelligence to inventory the quantities of carbon sequestered by the forests of the sub-region to reduce global warming. This is the contribution of my solution to all mankind with SMART indicators.

The project consists of monitoring the increase in GHG emissions in the sub-region, building climate data sets to be integrated into artificial intelligence (AI) to generate new indicators that will help guide environmental protection actions.

Due to the complexity of the project, we will define algorithms to track climate change in the Great Lakes sub-region with R using ASUS X540L laptops with specific characteristics.

 R programming language:

R is a programming language for statistical computation and graphics that you can use to clean, analyze, and graph your data. It is widely used by researchers in various disciplines to estimate and display results and by teachers of statistics and research methods. [1]

Our methodology consists of exploring large databases acquired during the period 2012-2020. The existing database includes limnological and planktonic variables (diversity, biomass, and production of phyto-and zooplankton), the abundances of aquatic species, and meteorological data. This database will be supplemented by sedimentary archives (biogeochemical and biological proxies) from Lake Kivu and by satellite data of phytoplankton biomass and surface hydrological characteristics, in order to take into account spatial and temporal heterogeneity.

New in situ studies will be carried out to extend the database and improve understanding of the impact of the release in nature of CO2, CH4, and other pollutants (sulfur dioxide and fine particles) on biodiversity and the functioning of the ecosystem. Laboratory studies will be undertaken to determine the quantities disseminated per day, per month, and/or per year; and to identify the consequences recorded on species in the sub-region.

Our algorithm will reduce complex models using deep neural networks. We plan to study schematics to decompose a process model into PDE and statistical components. We will discuss a set of IT topics, in particular:

• Structured neural networks based on graphs,
• Learning and adaptation: this subject includes active learning/repetition/multitasking, transfer learning (TL), and domain adaptation
• Causality and explainable AI: This is a central concern in computer science at the present time. This is also an essential component of the challenge as we intend to use the models created to serve as a means of understanding the nature and sources of new theories.

The last step will be devoted to the processing and modeling of the data, in order to:

• demonstrate the negative and positive impacts of the process implemented by the Limnological firm since 2012 on atmospheric conditions, temperature, and the structure of the water column, in order to understand and simulate the variability of seasonal mixing processes and predict long-term changes,
• predict future changes in ecosystem processes and resources, depending on the management of fisheries, exploitation of carbon dioxide and/or methane in deep water, and climate changes observed on crops (establishing the link between global climate and regional climate).

We plan to study schematics to decompose a process model into PDE and statistical components.

 My organization is a university. We work with other members from other organizations with whom we are in partnership with. 

Morocco

Co-researcher

HIBA  ASRI

PhD (Computer Science)

Produce a global AI, train in AI models, Make the AI dataset accessible to the public, Evaluate, refine, combine and continue to improve AI models on the impact on biodiversity.

DRC

Co-researcher

Dr BALAGIZI MUHIGIRWA

Charles

PhD Fluid Geochemistry, Novel Physics Methods for Environmental Research

Stable Isotopes Geochemistry,

Volcanic Gases, Volcano Monitoring,

Geo-Hazards

DRC

Co-researcher

Serge KIKOBYA

webmaster

Report and publish; The results will be shared among a community of users and made available for future use. The exports and publication of results will be prepared in this WP.

Rwanda

Co-researcher

HABAKARAMO MACUMU

patrick

PhD Student at University of Campania

Environmental impact of active volcanoes in the Virunga region: Health risks related to drinking water and food plants around Nyiragongo and Nyamulagira mountains

Extract and prepare (clean and pre-process as necessary)

Our approach is multidimensional, multicultural, and multidisciplinary. We work with everyone who agrees to accompany us in the search for solutions to our problems and to the problems of the planet. We respect gender equality in the exercise of our activities. We never tolerate racism, tribalism, or regionalism. In relation to the management of the funds allocated to our activities, we give priority to skills and respect for management rules. This, moreover, allows a good result of our activities and will achieve on these results:

Mastering the processes of accumulation of GHS
_ control the process of adaptation and mitigation to climate change.
Effects:
1) development of mechanisms (resilient agriculture, meteorological, environmental) for adaptation and mitigation of the adverse effects of climate change at the local, regional and international level,
2) Communities will have profound knowledge on the protection of biodiversity, forests, and human life for present and future generations.

the government will be equipped with CO2 sequestration methods and the establishment of CO2 sequestration statistics over a given period in the application of the polluter pays principle.

A training center is operational for 200 young people each year (empowerment of finalist boys and girls) on artificial intelligence, on the principles of adaptation and mitigation to climate change, on agriculture, and other environmental technologies.

Key Resources

• Human Resources :
• Materials resources for greenhouse gases monitoring
• Materials resources for carbon sequestration
• Financial resources :

• Technical and Administrative staff
• Software, Hardware, Computers, Cameras, Printers, Projectors, Office and furniture, Copernicus Software and tools, and ESRI Software and tools (ArcGIS Pro).
• Compass to measure location, Fiberglass measuring tapes, GPS to locate plots, Aluminum nails and numbered tags for marking trees, Feature Analyst Software, Tree diameter (dbh) tape for measuring trees, Clinometers (percentage scale), Colored rope and tokens, or digital measuring device (DME Haglöf),  100m line or two 50m lines to measure dead wood, Compass for measuring dead wood, Hand saw for collecting deadwood samples and cutting destructive samples, Spring scales, 1kg and 300g for weighing destructive samples, Soil sample probes for soil samples, Rubber hammer for driving in soil probes, Cloths (e.g. Tyvek) or paper bags for soil and undergrowth samples
• Salaries (gross salaries including social security charges and other related costs, local staff,
• Publications, Studies, research, Expenditure verification/Audit, Evaluation costs, Translation, interpreters, 6 Financial services (bank guarantee costs, Costs of conferences/seminars, Visibility actions.

 What resources will you need to run your activities? People, finance, access? 

Keys Activities

Monitoring of greenhouse gases and evaluation of the quantity and quality of the carbon sequestration by our forest.

• Identification of different elements of the climate system that can amplify or lessen climate change,
• Installation of automatic CO2 and CH4 analysis devices,
• Training of a technical team on machine learning, software and indicator monitoring,
• Monitoring of pollutants (CO2, CH4, SO2, and fine particles) in the air and at Lake Kivu,
• Organization of training, conferences, and workshops on the concentration and dissemination of these pollutants,
• Quantitative and qualitative assessment of the impact of CO2, CH4, SO2, and fine particles on species and the environment,
• Estimates of carbon stock changes (and associated CO2 emissions and absorptions) attributable to biomass are assessed
• Carbon Measurement and Monitoring in Virunga Forests
• Identification of carbon storage by species

 What program and non-program activities will your organization be carrying out?

 Type of Intervention (2)

Research, Training, conferences, publications,

 What is the format of your intervention? Is it a workshop? A service? A product?

 THESE WILL BE EXCLUSIVELY SERVICES

Segments (1)

 Who benefits from your intervention?

• Environmental activists,
• Climate change researchers,
• Students,
• Government,
• The local population of the subregion

 Beneficiary

* Environmental activists,

• Climate change researchers,
• Students,
• Government,
• The local population of the subregion
• Environmental activists,
• Climate change researchers,
• Students,
• Government,
• The local population of the subregion
• Massachusetts Institute Technology
• Government of DRC and Rwanda,
• Intergovernmental Panel on Climate Change (IPCC)
• UNDP and other international organizations with work in the environment
• Local community

 Value Proposition    (3)

 This proposal is of great value for me who designed it because it will open me a lot of horizons, it will allow me to improve my knowledge in the protection of the environment, in connection with artificial intelligence technology and the protection of biodiversity, it will increase my reputation in the world for contributing to provide a solution to global problems.

 User Value Proposition

Impact Measures

The project will build and inform the authorities in decision-making on anthropogenic activities that affect the planet. It will strengthen the knowledge of scientists and the local community on approaches to preserve the environment and biodiversity. Finally, the project will impact agriculture, energy, waste management, the conservation of marine and terrestrial species. 

How will you show that you are creating social impact?

 Partners + Key Stakeholders

 Who are the essential groups you will need to involve to deliver your program? Do you need special access or permissions?

Yes.

Channels   (6)

 How are you reaching your users and customers?

 We will organize punctual descents once on the ground to meet the different actors involved in the project.

 Customers  (4)

 Who are the people or organizations who will pay to address this issue?

 No payment will be asked for this solution because it is not a selling business.

Customers Value Proposition (5)

 What do your customers want to get out of this initiative?

My beneficiaries expect the solution to bring a significant change to the problems caused by the presence of greenhouse gases in their circles and they also expect to obtain concrete proposals that guide them on climate change and seasonal variations in the area in order to be adapted.

 Cost Structure

 The cost of our structure is 926406US$.

 What are your biggest expenditure areas?

How do they change as you scale up?

 Surplus

 Where do you plan to invest your profits?

 In community and scientists team

 Revenue

 Break down your revenue sources by %

 Nothing

I think I can't make a plan because its research solution is focused on people

Here too there is no financial viability plan has been successful because it is a research project. Its success will come when the regional and global population understands the benefits generated by our solution.