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.

This model implements a very simple proxy for vertical dispersion of heat in a lake based on the equation:

dT/dt = 1/A d(EA)/dz (dT/dz)

where: T: temperature (oC); t: time (days); z: depth (m); A: cross-sectional area (m2); E: vertical dispersion coefficient (m2 d-1)

If we consider that E is constant (it is in this model), then the equation becomes dT/dt = (EA/A)(d^2T/dz^2) = E(d^2T/dz^2), the classic diffusion equation

The model is simplified by exchanging temperature as a state variable, rather than executing  the full heat balance. This would require a computation of fluxes of atmospheric longwave and shortwave radiation, water longwave radiation, water conduction and convection, and water evaporation and condensation.

The vertical dispersion coefficients are adjusted artificially so that mixing increases at lower temperatures, thus quickly homogenizing the water column in colder months of the year.

Simple model to illustrate Steele's equation for primary production of phytoplankton.

The equation is:

Ppot = Pmax I/Iopt exp(1-I/Iopt)

Where:

Ppot: Potential production (e.g. d-1, or mg C m-2 d-1)
Pmax: Maximum production (same units as Ppot)
I: Light energy at depth of interest (e.g. uE m-2 s-1)
Iopt: Light energy at which Pmax occurs (same units as I)

The model contains no state variables, just illustrates the rate of production, by making the value of I equal to the timestep (in days). Move the slider to the left for more pronounced photoinhibition, to the right for photosaturation.

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

This story presents a conceptual model of nitrogen cycling in a dune-lake system in the Northland region of New Zealand. It is based on the concept of a stock and flow diagram. Each orange ellipse represents an input, while each blue box represents a stock. Each arrow represents a flow. A flow involves a loss from the stock at which it starts and an addition to the stock at which it ends.

Working Draft of a model to simulate the effect on ecosystem service values of planting 10 billion oysters in the Chesapeake Bay by the year 2025.

Simple Model of the Food Chain

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.

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.

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.

DRAFT conceptual model of climate change connections in Yamuna river project.

FORCED GROWTH GROWTH GOES INTO TURBULENT CHAOTIC DESTRUCTION 
 BEWARE pushing increased growth blows the system!
(governments are trying to push growth on already unstable systems !)

The existing global capitalistic growth paradigm is totally flawed

The chaotic turbulence is the result of the concept and flawed strategy of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks

See Guy Lakeman Bubble Theory for more details on keeping systems within finite limited size working capacity containers (villages communities)

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

This model explains the mussel growth (Mytillus Edulis) based on primary production of phytoplankton biomass.

Light, nutrients and temperature were used as forcing functions over a two year period.

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.

The time-variable solution to a step-function change in inflow concentration for an ideal, completely mixed lake.