Environment | Insight Maker

This model uses simple functions (converters, cosine) to simulate the water balance inside a reservoir.

In Chile, 60% of its population are exposed to levels of Particulate Matter (PM) above international standards. Air Pollution is causing 4,000 premature deaths per year, including health costs over US$8 billion.

The System Dynamics Causal Loop Diagram developed herein shows an initial study of the dynamics among the variables that influences the accumulation of PM in the air, in particular the case of Temuco, in the South of Chile. In Temuco, 97% of the PM inventories comes from the combustion of low quality firewood, which in turns is being burned due to its low price and cultural habits/tradition.

Clone of Air Pollution Dynamics - Firewood Combustion

David Bonin

This model illustrates predator prey interactions using real-life data of wolf and moose populations on the Isle Royale.

We incorporate logistic growth into the moose dynamics, and we replace the death flow of the moose with a kill rate modeled from the kill rate data found on the Isle Royale website.

I start with these parameters:
Wolf Death Rate = 0.15
Wolf Birth Rate = 0.0187963
Moose Birth Rate = 0.4
Carrying Capacity = 2000
Initial Moose: 563
Initial Wolves: 20

I used RK-4 with step-size 0.1, from 1959 for 60 years.

The moose birth flow is logistic, MBR*M*(1-M/K)
Moose death flow is Kill Rate (in Moose/Year)
Wolf birth flow is WBR*Kill Rate (in Wolves/Year)
Wolf death flow is WDR*W

Clone of Midterm - Power Model

Maria E Ruwe

3

March 12, 2019

Population

Sakovia Thompson

A model of an infectious disease and control

Clone of Disease Dynamics (Agent Based Modeling)

Dav Fem

Very simple model demonstrating growth of phytoplankton using Steele's equation for potential production and Michaelis-Menten equation for nutrient limitation.

Both light and nutrients (e.g. nitrogen) are modelled as forcing functions, and the model is "over-calibrated" for stability.

The phytoplankton model approximately reproduces the spring-summer diatom bloom and the (smaller) late summer dinoflagellate bloom.

Oyster growth is modelled only as a throughput from algae. Further developments would include filtration as a function of oyster biomass, oyster mortality, and other adjustments.

Clone of Simple phytoplankton and oyster model

Pagandai V Pannir Selvam

In Chile, 60% of its population are exposed to levels of Particulate Matter (PM) above international standards. Air Pollution is causing 4,000 premature deaths per year, including health costs over US$8 billion.

The System Dynamics Causal Loop Diagram developed herein shows an initial study of the dynamics among the variables that influences the accumulation of PM in the air, in particular the case of Temuco, in the South of Chile. In Temuco, 97% of the PM inventories comes from the combustion of low quality firewood, which in turns is being burned due to its low price and cultural habits/tradition.

Clone of Air Pollution Dynamics - Firewood Combustion

dounia

This diagram provides a stylised description of important feedbacks within a shallow-lake system.

Mahinga Kai

Key feedbacks in a shallow lake relating to Koura

Mereana Wilson

Very simple model demonstrating growth of phytoplankton using Steele's equation for potential production and Michaelis-Menten equation for nutrient limitation.

Both light and nutrients (e.g. nitrogen) are modelled as forcing functions, and the model is "over-calibrated" for stability.

The phytoplankton model approximately reproduces the spring-summer diatom bloom and the (smaller) late summer dinoflagellate bloom.

Oyster growth is modelled only as a throughput from algae. Further developments would include filtration as a function of oyster biomass, oyster mortality, and other adjustments.

Clone of Simple phytoplankton and oyster model

niyi,wu

This model describes the flow of energy from generation to consumption for neighborhoods in the metro Atlanta area. It also calculates the cost of energy production and the number of years it will take to recover that cost.

Clone of Microgrid with storage

Jeb Eddy

This incomplete model represents a building that is heated by conduction from the hot outside air, solar gain through the windows, and internal heat from the people and machines inside. To complete the model, define the flow that represents the heat removed by mechanical cooling.

Simple Building Heat Flow

Daniel Everett

82

FISHERIES MANAGEMENT THROUGH PUBLIC POLICICES

Stefan

Clone of Water Pollution

Ashley Harris

General global ocean box model, including isotopes.

JM attempt 1 GEOSC416 isotope box model w/o mass transience

Jeff McGuckin

This diagram provides an accessible description of the key processes that influence the water quality within a lake.

Clone of Conceptual model of a lake

Caleb Jacob Coffey

Diagrams on generalized knowledge claims and workflow processes from Magliocca 2018 Global Environmental Change article

Closing global knowledge gaps

Geoff McDonnell

Overview:

This simulation will show the relationship between tree logging forestry and how this can affect mountain biking tourism in Derby Park Tasmania. The main goal of this simulation is to show these two industries can co-exist in the same environment, or increase in demand or production in one sector will affect the result of another.

Function of the model:

In comparison there are both pros and cons for both sectors working correspondently. Demand for derby park is caused by individual past experience when visiting the park or friends recommendation which increase in the number of demands. Increase in demands will increase in the number of visitors. When visitors visits the park they require make a purchase a bike and pay the park for using the park facilities. All this will adds up to bikers total spending when visiting Derby. When consumer spend it is booting the economy especially in the tourism sector. Similarly tree logging will also contribute financially towards the Tasmania economy. The regeneration stage is relatively low compare to the logging rate. The growth will not cover the loss which can cause some level of damage in the scenery of the park, affecting tourist to view when mountain biking. Visitors overall experience will have the impact towards the demand for mountain biking in derby park, if visitors experience is satisfied they will come back to visit again or visit with group of friends, even words of mouth recommendation will also increase the level of demand of visiting Derby.

Some key insights base on the simulation:

Based on the simulation of the two models we can see there are some key changes.

Tree logging increase will cause the disturbance of the natural scenery, thus change the overall experience of the visitors, decrease in the level of demand. Tree logging will also have negative impact towards the overall tourist experience thus affect the park facility and track. The natural scenery and the overall experience can affect their experience and if they would continue to recommend this area to friends to increase the demand.

Assignment 3 Derby mountain biking and the effect of logging - 586984

Kelly

Interplay between wolves eating sheep and farmers killing wolves.

Simple Sheep and Wolves

Davor Tomasic

Simple model to illustrate oyster growth based on primary production of Phytoplankton as a state variable, forced by light and nutrients, running for a yearly period.

Phytoplankton growth based on on Steele's and Michaelis-Menten equations), where:

Primary Production=(([Pmax]*[I]/[Iopt]*exp(1-[I]/[Iopt])*[S])/([Ks]+[S]))

Pmax: Maximum production (d-1)
I: Light energy at depth of interest (uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (uE m-2 s-1)
S: Nutrient concentration (umol N L-1)
Ks: Half saturation constant for nutrient (umol N L-1).

Further developments:
- Nutrients as state variable in cycle with detritus from phytoplankton and oyster biomass.
- Light limited by the concentration of phytoplankton.
- Temperature effect on phytoplankton and Oyster growth.

micro algae , biogas , bioelectrcidades

Pagandai V Pannir Selvam

22

M.Sc. in Environmental Engineering SIMA 2018
New University of Lisbon, Portugal

Model to represent oyster individual growth by simulating feeding and metabolism. Model (i) partitions metabolic costs into feeding and fasting catabolism; (ii) adds allometry to clearance rate; (iii) adds temperature dependence to clearance rate; (iv) illustrates how clearance rate per gram is used if we multiply by the oyster biomass

SIMA 2018 full

Joao G. Ferreira

5

Diagrams of theories of control of destiny at multiple scales as fundamental causes of social determinants of health from Whitehead 2016 article in Health and Place

Control over decisions and health inequalities at multiple scales of experience

Geoff McDonnell

Sagebrush ecosystem dynamics with intrusion of cheatgrass

Clone of Sagebrush Ecosystem

Josh Kruhm

Carbon Cycle

Liz

This model provides a dynamic simulation of the Sverdrup (1953) paper on the vernal blooming of phytoplankton.

The model simulates the dynamics of the mixed layer over the year, and illustrates how it's depth variation leads to conditions that trigger the spring bloom. In order for the bloom to occur, production of algae in the water column must exceed respiration.

This can only occur if vertical mixing cannot transport algae into deeper, darker water, for long periods, where they are unable to grow.

Sverdrup, H.U., 1953. On conditions for the vernal blooming of phytoplankton. J. Cons. Perm. Int. Exp. Mer, 18: 287-295

Blooming of phytoplankton & Oyster growth

Niels Van Rosmalen

Environment Models