Estimate NO2/NOX emission ratios from monitoring data

Given hourly NOX and NO2 from a roadside site and hourly NOX, NO2 and O3 from a background site the function will estimate the emissions ratio of NO2/NOX — the level of primary NO2

Usage

calcFno2(input, tau = 60, user.fno2, main = "", xlab = "year", ...)

Arguments

input

A data frame with the following fields. nox andno2 (roadside NOX and NO2 concentrations), back_nox, back_no2 and back_o3 (hourly background concentrations of each pollutant). In addition temp (temperature in degrees Celsius) and cl (cloud cover in Oktas). Note that if temp and cl are not available, typical means values of 11 deg. C and cloud = 3.5 will be used.

tau

Mixing time scale. It is unlikely the user will need to adjust this. See details below.

user.fno2

User-supplied f-NO2 fraction e.g. 0.1 is a NO2/NOX ratio of 10% by volume. user.no2 will be applied to the whole time series and is useful for testing "what if" questions.

main

Title of plot if required.

xlab

x-axis label.

...

Arguments passed on to scatterPlot

mydata

A data frame containing at least two numeric variables to plot.

x

Name of the x-variable to plot. Note that x can be a date field or a factor. For example, x can be one of the openair built in types such as "year" or "season".

y

Name of the numeric y-variable to plot.

z

Name of the numeric z-variable to plot for method = "scatter" or method = "level". Note that for method = "scatter" points will be coloured according to a continuous colour scale, whereas for method = "level" the surface is coloured.

method

Methods include “scatter” (conventional scatter plot), “hexbin” (hexagonal binning using the hexbin package). “level” for a binned or smooth surface plot and “density” (2D kernel density estimates).

group

The grouping variable to use, if any. Setting this to a variable in the data frame has the effect of plotting several series in the same panel using different symbols/colours etc. If set to a variable that is a character or factor, those categories or factor levels will be used directly. If set to a numeric variable, it will split that variable in to quantiles.

avg.time

This defines the time period to average to. Can be “sec”, “min”, “hour”, “day”, “DSTday”, “week”, “month”, “quarter” or “year”. For much increased flexibility a number can precede these options followed by a space. For example, a timeAverage of 2 months would be period = "2 month". See function timeAverage for further details on this. This option se useful as one method by which the number of points plotted is reduced i.e. by choosing a longer averaging time.

data.thresh

The data capture threshold to use (\ the data using avg.time. A value of zero means that all available data will be used in a particular period regardless if of the number of values available. Conversely, a value of 100 will mean that all data will need to be present for the average to be calculated, else it is recorded as NA. Not used if avg.time = "default".

statistic

The statistic to apply when aggregating the data; default is the mean. Can be one of "mean", "max", "min", "median", "frequency", "sd", "percentile". Note that "sd" is the standard deviation and "frequency" is the number (frequency) of valid records in the period. "percentile" is the percentile level (\ "percentile" option - see below. Not used if avg.time = "default".

percentile

The percentile level in percent used when statistic = "percentile" and when aggregating the data with avg.time. The default is 95. Not used if avg.time = "default".

type

type determines how the data are split i.e. conditioned, and then plotted. The default is will produce a single plot using the entire data. Type can be one of the built-in types as detailed in cutData e.g. “season”, “year”, “weekday” and so on. For example, type = "season" will produce four plots — one for each season.

It is also possible to choose type as another variable in the data frame. If that variable is numeric, then the data will be split into four quantiles (if possible) and labelled accordingly. If type is an existing character or factor variable, then those categories/levels will be used directly. This offers great flexibility for understanding the variation of different variables and how they depend on one another.

Type can be up length two e.g. type = c("season", "weekday") will produce a 2x2 plot split by season and day of the week. Note, when two types are provided the first forms the columns and the second the rows.

smooth

A smooth line is fitted to the data if TRUE; optionally with 95 percent confidence intervals shown. For method = "level" a smooth surface will be fitted to binned data.

spline

A smooth spline is fitted to the data if TRUE. This is particularly useful when there are fewer data points or when a connection line between a sequence of points is required.

linear

A linear model is fitted to the data if TRUE; optionally with 95 percent confidence intervals shown. The equation of the line and R2 value is also shown.

ci

Should the confidence intervals for the smooth/linear fit be shown?

mod.line

If TRUE three lines are added to the scatter plot to help inform model evaluation. The 1:1 line is solid and the 1:0.5 and 1:2 lines are dashed. Together these lines help show how close a group of points are to a 1:1 relationship and also show the points that are within a factor of two (FAC2). mod.line is appropriately transformed when x or y axes are on a log scale.

cols

Colours to be used for plotting. Options include “default”, “increment”, “heat”, “jet” and RColorBrewer colours — see the openair openColours function for more details. For user defined the user can supply a list of colour names recognised by R (type colours() to see the full list). An example would be cols = c("yellow", "green", "blue")

plot.type

lattice plot type. Can be “p” (points — default), “l” (lines) or “b” (lines and points).

key

Should a key be drawn? The default is TRUE.

key.title

The title of the key (if used).

key.columns

Number of columns to be used in the key. With many pollutants a single column can make to key too wide. The user can thus choose to use several columns by setting columns to be less than the number of pollutants.

key.position

Location where the scale key is to plotted. Allowed arguments currently include “top”, “right”, “bottom” and “left”.

strip

Should a strip be drawn? The default is TRUE.

log.x

Should the x-axis appear on a log scale? The default is FALSE. If TRUE a well-formatted log10 scale is used. This can be useful for checking linearity once logged.

log.y

Should the y-axis appear on a log scale? The default is FALSE. If TRUE a well-formatted log10 scale is used. This can be useful for checking linearity once logged.

x.inc

The x-interval to be used for binning data when method = "level".

y.inc

The y-interval to be used for binning data when method = "level".

limits

For method = "level" the function does its best to choose sensible limits automatically. However, there are circumstances when the user will wish to set different ones. The limits are set in the form c(lower, upper), so limits = c(0, 100) would force the plot limits to span 0-100.

windflow

This option allows a scatter plot to show the wind speed/direction shows as an arrow. The option is a list e.g. windflow = list(col = "grey", lwd = 2, scale = 0.1). This option requires wind speed (ws) and wind direction (wd) to be available.

The maximum length of the arrow plotted is a fraction of the plot dimension with the longest arrow being scale of the plot x-y dimension. Note, if the plot size is adjusted manually by the user it should be re-plotted to ensure the correct wind angle. The list may contain other options to panel.arrows in the lattice package. Other useful options include length, which controls the length of the arrow head and angle, which controls the angle of the arrow head.

This option works best where there are not too many data to ensure over-plotting does not become a problem.

y.relation

This determines how the y-axis scale is plotted. “same” ensures all panels use the same scale and “free” will use panel-specific scales. The latter is a useful setting when plotting data with very different values.

x.relation

This determines how the x-axis scale is plotted. “same” ensures all panels use the same scale and “free” will use panel-specific scales. The latter is a useful setting when plotting data with very different values.

ref.x

See ref.y for details.

ref.y

A list with details of the horizontal lines to be added representing reference line(s). For example, ref.y = list(h = 50, lty = 5) will add a dashed horizontal line at 50. Several lines can be plotted e.g. ref.y = list(h = c(50, 100), lty = c(1, 5), col = c("green", "blue")). See panel.abline in the lattice package for more details on adding/controlling lines.

k

Smoothing parameter supplied to gam for fitting a smooth surface when method = "level".

dist

When plotting smooth surfaces (method = "level" and smooth = TRUE, dist controls how far from the original data the predictions should be made. See exclude.too.far from the mgcv package. Data are first transformed to a unit square. Values should be between 0 and 1.

map

Should a base map be drawn? This option is under development.

auto.text

Either TRUE (default) or FALSE. If TRUE titles and axis labels will automatically try and format pollutant names and units properly e.g. by subscripting the ‘2’ in NO2.

plot

Should a plot be produced? FALSE can be useful when analysing data to extract plot components and plotting them in other ways.

Value

an openair object

Details

The principal purpose of this function is to estimate the level of primary (or direct) NO2 from road vehicles. When hourly data of NOX, NO2 and O3 are available, the total oxidant method of Clapp and Jenkin (2001) can be used. If roadside O3 measurements are available see linearRelation() for details of how to estimate the primary NO2 fraction.

In the absence of roadside O3 measurements, it is rather more problematic to calculate the fraction of primary NO2. Carslaw and Beevers (2005c) developed an approach based on linearRelation() the analysis of roadside and background measurements. The increment in roadside NO2 concentrations is primarily determined by direct emissions of NO2 and the availability of One to react with NO to form NO2. The method aims to quantify the amount of NO2 formed through these two processes by seeking the optimum level of primary NO2 that gives the least error.

Test data is provided at https://davidcarslaw.github.io/openair/.

References

Clapp, L.J., Jenkin, M.E., 2001. Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOX in the UK. Atmospheric Environment 35 (36), 6391-6405.

Carslaw, D.C. and N Carslaw (2007). Detecting and characterising small changes in urban nitrogen dioxide concentrations. Atmospheric Environment. Vol. 41, 4723-4733.

Carslaw, D.C., Beevers, S.D. and M.C. Bell (2007). Risks of exceeding the hourly EU limit value for nitrogen dioxide resulting from increased road transport emissions of primary nitrogen dioxide. Atmospheric Environment 41 2073-2082.

Carslaw, D.C. (2005a). Evidence of an increasing NO2/NOX emissions ratio from road traffic emissions. Atmospheric Environment, 39(26) 4793-4802.

Carslaw, D.C. and Beevers, S.D. (2005b). Development of an urban inventory for road transport emissions of NO2 and comparison with estimates derived from ambient measurements. Atmospheric Environment, (39): 2049-2059.

Carslaw, D.C. and Beevers, S.D. (2005c). Estimations of road vehicle primary NO2 exhaust emission fractions using monitoring data in London. Atmospheric Environment, 39(1): 167-177.

Carslaw, D. C. and S. D. Beevers (2004). Investigating the Potential Importance of Primary NO2 Emissions in a Street Canyon. Atmospheric Environment 38(22): 3585-3594.

Carslaw, D. C. and S. D. Beevers (2004). New Directions: Should road vehicle emissions legislation consider primary NO2? Atmospheric Environment 38(8): 1233-1234.

Author

David Carslaw