(Continued) Draw a scatter plot for the CO2 uptake rate against the concentration, with the two non-linear regressed lines. (Rubric: correct scatter points for 2 marks, correct axis labels for 2 marks, displaying the regressed lines for 4 marks, appropriate lagends for 2 marks)

I'm sorry, as an AI language model, I'm not able to generate visual content. However, I can provide you with instructions on how to create a scatter plot with two non-linear regressed lines using Python and Matplotlib library.

First, you need to import the necessary libraries:

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

Then, you need to define the non-linear functions for the two regressed lines. Let's assume they are exponential functions:

def f1(x, a, b, c):
    return a * np.exp(-b * x) + c

def f2(x, d, e, f):
    return d * np.exp(-e * x) + f

Next, you need to load your data into two arrays, one for concentration and one for CO2 uptake rate. Let's assume they are called `x` and `y`.

x = np.array([concentration data])
y = np.array([CO2 uptake rate data])

Now, you can use the `curve_fit` function to fit the non-linear functions to your data:

popt1, pcov1 = curve_fit(f1, x, y)
popt2, pcov2 = curve_fit(f2, x, y)

Here, `popt` is an array of optimized parameters for the non-linear function, and `pcov` is the covariance matrix for the fit.

Finally, you can create the scatter plot with the regressed lines and appropriate labels and legends:

plt.scatter(x, y, label='data', color='blue')
plt.plot(x, f1(x, *popt1), 'r-', label='regression 1')
plt.plot(x, f2(x, *popt2), 'g-', label='regression 2')
plt.xlabel('Concentration')
plt.ylabel('CO2 Uptake Rate')
plt.legend()
plt.show()

This will create a scatter plot with two non-linear regressed lines and appropriate axis labels and legends.