Monthly scale – refers to 5 weeks of data plotted 1-2-3-4…as days
Plot: CO2 continuous data from picarro, 5-min running mean
Analyses:
- regression line
o 8 oct à 27 oct, fitted to 2 weeks of data from window
o 28 oct à 14 nov, fitted to the other 3 weeks of data (from the roof)
o slope from regression line is the ‘quantifiable’ increase in [CO2]/time
- periodicity – interpolate onto graph to see how data of individual days relates to ‘average’ periodicity of the dataset
o are our hypothesized timescales of interest reflected in the periodicity of the data?
- Fourier Transform – see how noisy the data is, look at frequency domain
o Substantial differences between the roof and window data?
o Is data useful or just a mess (extremely noisy)?
- regression and correlation for each factor to [CO2] and see which is dominant (has the greatest ‘relationship’ to the pattern of CO2
o wind speed and (direction), ppn, T
§ do you have to transform CO2 to the same timescale to analyze or do you interpolate the factor data
- do Principle Component Analysis?
- regression between δ13C month-long plot & [CO2] (negative)
- Mass balance and keeling plot
Weekly scale (M, T, W, Th, F, Sa, Su stacked on top of each other – average)
x2, for 2 weeks of window measurements and 3 weeks of roof measurements
Analyses:
Just for traffic and CO2
- must test which days of the calendar week best represent our hypothesis for ‘weekday’ traffic pattern being different from ‘weekend’ traffic pattern
o i.e. how do we define a weekday and a weekend? (aside from solely based on the calendar)
1st test:
plot Mà F vs Sa, Su
2nd test:
plot T à Th vs F, Sa, Su, M
From these tests:
- Is there a significant different between the traffic and CO2 patterns on weekdays (defined by tests 1 & 2) and weekends?
Analyzing all factors:
Now, take out a ‘test week’ from roof and window datasets (picarro) and look at these two datasets in relation to averaged out weekly CO2
- again correlation, regression etc
o do the values change as compared to those calculated for monthly?
o If so, can we infer something about the degree of relationship (between each factor and CO2, or for traffic and biogenic - δ13C) changing over time or according to timescale being analysed
§ Is our method of looking at these relationships skewing the results or do they change over time
- for CO2, look at variance/std deviation etc in relation to averaged out week to see if there is some sort of representativeness in the average values
- pick weeks with 2+ days of traffic data from counting cars (‘real-time’ instead of averaged out google data)
Diurnal – Averaged out so that it represents more than a single day i.e. is representative of all days stacked into 1 24h period
- there are many options for how we construct this 24 hour period
o Based on weekly analysis of the CO2 pattern
1) 2 plots, one for a weekday (defined as Mà F or T à Th) and one for a weekend (Sa, Su or F, Sa, Su, M)
o x2 for the 2 weeks of window data and the 3 weeks of roof data
2) 1 plot using data from weekends and weekdays
o x2 for window and roof
3) 1 plot using data from weekends and weekdays for all 5 weeks
Analyses: will compare this ‘ideal’ or averaged out day to actual days where the influence of one factor is predominant (e.g. Oct 22, very heavy winds)
- see if some factors have a relatively constant value for correlation despite not being ‘dominant’ on that day
- will want to look at amplitude of peaks, [CO2] values, variation (fluctuations), etc
o basically, characterize or describe the CO2 pattern, δ13C as an ideal/representative pattern, and then see how it changes on a case-by-case basis
Case-by-case analysis – Has been expanded and will be explained in class and then added.
