the Creative Commons Attribution 4.0 License.

the Creative Commons Attribution 4.0 License.

# Technical note: Effects of uncertainties and number of data points on line fitting – a case study on new particle formation

### Mikko R. A. Pitkänen

### Tuomo Nieminen

### Antti Lipponen

### Sini Isokääntä

### Antti Arola

### Kari E. J. Lehtinen

Fitting a line to two measured variables is considered one of the simplest statistical procedures researchers can carry out. However, this simplicity is deceptive as the line-fitting procedure is actually quite a complex problem. Atmospheric measurement data never come without some measurement error. Too often, these errors are neglected when researchers make inferences from their data.

To demonstrate the problem, we simulated datasets with different numbers of
data points and different amounts of error, mimicking the dependence of the atmospheric new particle
formation rate (*J*_{1.7}) on the sulfuric acid concentration
(H_{2}SO_{4}). Both variables have substantial measurement error and, thus, are good test variables for our study. We show that ordinary least
squares (OLS) regression results in strongly biased slope values compared
with six error-in-variables (EIV) regression methods (Deming regression, principal component analysis, orthogonal regression, Bayesian EIV and two different bivariate
regression methods) that are known to take errors in the variables into account.

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Atmospheric measurements always come with some measurement error. Too often, these errors are neglected when researchers make inferences based on their data. Describing the relationship between two variables typically involves making deductions in a more general context than that in which the variables were directly studied. If the relationship is not defined correctly, the inference is also not valid. In some cases, the bias in the analytical method is even given a physical meaning.

When analysing the dependencies of two or more measured variables, regression models are usually applied. Regression models can be linear or non-linear, depending on the relationship between the datasets that are analysed. Standard regression models assume that the independent variables of the model have been measured without error and that the model only accounts for errors in the dependent variables or responses. In cases where the measurements of the predictors contain error, estimating using standard methods (usually ordinary least squares, OLS) does not tend to provide the true parameter values, even when a very high number of data points is used. In linear models, the coefficients are underestimated (e.g. Carroll et al., 2006); however, in non-linear models, the bias is likely to be more complicated (e.g. Schennach, 2004). If predictor variables in regression analyses contain any measurement error, methods that account for errors should be applied – particularly when errors are large. Thus, test variables in this study were chosen such that they included significant uncertainties in both the independent and dependent variables.

The sulfuric acid concentration (H_{2}SO_{4}) is known to strongly affect the formation
rates (*J*) of aerosol particles
(Kirkby
et al., 2016; Kuang et al., 2008; Kulmala et al., 2006; Kürten et al.,
2016; Metzger et al., 2010; Riccobono et al., 2014; Riipinen et al., 2007;
Sihto et al., 2006; Spracklen et al., 2006). The relationship between *J*
(cm^{−3} s^{−1}) and H_{2}SO_{4} (molec cm^{−3}) is
typically assumed to be in the following form: log${}_{\mathrm{10}}\left(J\right)=\mathit{\beta}\times {\mathrm{log}}_{\mathrm{10}}$(H_{2}SO_{4})+*α* (Seinfeld and Pandis,
2016). In addition, parameterisations based on the results from these fits
have been implemented in global models (e.g. in
Dunne
et al., 2016, Metzger et al., 2010 and Spracklen et al., 2006) to estimate the
effects of new particle formation on global aerosol amounts and
characteristics. Theoretically, in homogeneous nucleation, the slope of this
relationship is related to the number of sulfuric acid molecules in the
nucleating critical cluster, based on the first nucleation theorem
(Vehkamäki, 2006).

Some published results have shown discrepancies in the expected *J* vs.
H_{2}SO_{4} dependence. Analysing data from Hyytiälä in 2003,
Kuang et al. (2008) used an unconstrained least squares method, which was
not specified in the paper, and obtained a *β* value of 1.99 for the slope,
whereas Sihto et al. (2006) reported a *β* value of 1.16 using OLS
from the same field campaign. The studies had some differences in pre-treatment of the
data and used different time windows, but a significant proportion of this
inconsistency is very likely due to the use of different fitting methods. The
problem regarding the relationship of H_{2}SO_{4} and *J* was previously acknowledged in Paasonen
et al. (2010), who noted that the bivariate fitting method, as presented in
York et al. (2004), should be applied; however, this method could not
be used due to the lack of proper error estimates for each quantity. They
were not aware of methods that did not require knowledge of the errors in
advance, and instead made use of estimated variances. Here, we present the
appropriate tools required for the above-mentioned approach.

Multiple attempts have been made to present methods that account for errors in the predictor variables for regression-type analyses, going back to Deming (1943). However, the traditional least squares fitting method is still the de facto line-fitting method due to its simplicity and common availability in frequently used software. In atmospheric sciences, Cantrell (2008) drew attention to the method introduced by York (1966) and York et al. (2004) and listed multiple other methodological papers utilising similar methodology. Pitkänen et al. (2016) raised awareness of the fact that errors are not accounted for in the predictor variables in the remote-sensing community, and this study partly follows their approach and introduces multiple methods to account for the errors in predictors. Cheng and Riu (2006) studied methods involving heteroscedastic errors, whereas Wu and Yu (2018) approached the problem with measurement errors via weighted regression and applied some techniques that are also used in our study.

Measurement errors in each variable must be taken into account using
approaches known as errors-in-variables (EIV) regression. EIV methods simply
mean that errors in both variables are accounted for. In this study, we
compared OLS regression results to six different regression methods (Deming
regression, principal component analysis regression, orthogonal regression,
Bayesian EIV regression and two different bivariate regression methods) that are known to be able to take errors in variables into account and provide (at
least asymptotically) unbiased estimates. In this study, we focus exclusively on linear EIV methods, but it is important to acknowledge that
non-linear methods also exist, e.g. ORDPACK introduced in
Boggs et al. (1987) and
implemented in Python SciPy and *R* (Boggs et
al., 1989; Spiess, 2015). ORDPACK is a somewhat improved version of
classical orthogonal regression, in that arbitrary covariance structures are
acceptable, and it is specifically set up so that a user can specify measurement
error variance and covariance point by point; some of the methods in
this study carry out the same process in linear analysis.

## 2.1 Data illustrating the phenomenon

Measurement data contain different types of errors. Usually, the errors are divided to two main class: systematic error and random error.

Systematic errors,
commonly referred as bias, in experimental observations usually come from
the measurement instruments. They may occur because something is wrong
with the instrument or its data handling system, or due to operator error. In line fitting, bias cannot be taken into
account and needs to be minimised by way of careful and regular instrument
calibrations and zeros or data preprocessing. The random error, in comparison, may
have different components; the two components discussed here are the natural error
and the measurement error. In addition, one should note the existence of
equation error (discussed in Carroll and Ruppert, 1996), which refers to using an inappropriate form of a fitting equation. Measurement error is more generally understood; it is where measured values
do not fully represent the true values of the variable being measured. This
also contains sampling error (e.g. in the case of H_{2}SO_{4}
measurement, the sampled air in the measurement instrument is not a
representative sample of the outside air due to losses of H_{2}SO_{4}
occurring in the sampling lines, among other factors). Natural error is the variability caused by
natural or physical phenomenon (e.g. a specific amount of H_{2}SO_{4} does
not always cause the same number of new particles to be formed).

In the analysis of the
measurement data, some amount of these errors are known or can be estimated,
but some of the error will usually remain unknown; this should be kept in mind
when interpreting fits. Even though the measurement error is taken into
account, the regression fit may be biased due to unknown natural error. In
this study, we assume that the errors of the different variables are
uncorrelated, but in some cases this has to be accounted for, as noted, for example, in Trefall and Nordö (1959) and
Mandel (1984). The correlation between the errors of two
variables, measured with separate instruments, independent of each other,
such as formation rate and H_{2}SO_{4}, may come from factors such as environmental
variables that affect both of the variables at the same time. Factors affecting the
formation of sulfuric acid have been studied in various papers, e.g. in
Weber et al. (1997) and
Mikkonen et al. (2011). New particle
formation rates, in turn, have been studied in works such as
Boy et al. (2008) and Hamed
et al. (2011) and similarities between the affecting factors can be seen. In
addition, factors like room temperature in the measurement space and
atmospheric pressure may affect the performance of instrumentation, thereby
causing additional error.

The data used in this study consist of simulated new particle formation
rates at 1.7 nm (*J*_{1.7}) and sulfuric acid (H_{2}SO_{4})
concentrations mimicking observations of pure sulfuric acid in nucleation
experiments from the CLOUD chamber at CERN
(Kürten et al. 2016; https://home.cern/about/experiments/cloud, last access: 16 August 2019), including the corresponding expected
values, their variances and covariance structures. The Proton Synchrotron at CERN
provides an artificial source of “cosmic rays” that simulates the natural
ionisation conditions between the ground level and the stratosphere. The core
is a large (volume 26 m^{3}) electro-polished stainless-steel chamber with
temperature control (temperature stability better than 0.1 K) at any
tropospheric temperature, precise delivery of selected gases (SO_{2},
O_{3}, NH_{3} and various organic compounds) and ultra-pure humidified
synthetic air, as well as very low gas-phase contaminant levels. The existing data
on new particle formation include what are believed to be the most important formation routes that
involve sulfuric acid, ammonia and water vapour
(Kirkby et al., 2011); sulfuric acid and amine (Almeida et al., 2013); and ion-induced organic
nucleation (Kirkby et al.,
2016). The actual nucleation of new particles occurs at a slightly smaller
size. After formation, particles grow by condensation to reach the detection
limit (1.7 nm) of the instrument; thus, *J*_{1.7} refers to the
formation rate of particles as the instrument detects them, accounting for the known particle losses due to coagulation and deposition on the
chamber walls. The relationships between precursor gas-phase concentrations
and particle formation rates were chosen because they are both known to have
considerable measurement errors and their relationship has been well-studied
using regression-based analyses
(Kirkby
et al., 2016; Kürten et al., 2016; Riccobono et al., 2014; Tröstl et
al., 2016). Additionally, many of the published papers on this topic do not
describe how they accounted for the uncertainties in the analysis,
which casts doubt on the fact that errors were treated properly. However, it should
be kept in mind that the data could be any set of numbers assumed to have a
linear relationship, but, in order to raise awareness in the aerosol research
community, in this study we relate our analysis to the important problem of
understanding new particle formation.

## 2.2 Regression methods

We carried out fits for the linear dependency of the logarithms of the two study variables, such that the equation for the fit was given by

where *y* represents log_{10}(*J*_{1.7}), *x* is log_{10}(H_{2}SO_{4}),
*β* values are the coefficients estimated from the data and *ε*
is the error term. In order to demonstrate the importance of taking the measurement errors into
account in the regression analysis, we tested seven
different line-fitting methods: ordinary least squares (OLS), not taking
the uncertainty in *x* variable into account; orthogonal regression (ODR;
Boggs et al., 1987);
Deming regression (DR; Deming, 1943); principal component
analysis (PCA; Hotelling, 1957) regression;
Bayesian EIV regression (Kaipio and Somersalo, 2005);
and two different bivariate least squares methods by
York et al. (2004) and
Francq and Govaerts (2014, BLS), respectively, which are known to be able to account for errors in variables and
provide (at least asymptotically) unbiased estimates. The differences
between the methods stem from the criterion they minimise when calculating
the coefficients and how they account for measurement errors. The minimising
criteria for all methods are given in Appendix A1, but in the following we give the principles of the methods.

OLS minimises the squared distance of the
observation and the fit line either in the *x* or *y* direction, but not both at the
same time, whereas ODR minimises the sum of the squared weighted orthogonal
distances between each point and the line. DR was originally an improved
version of orthogonal regression, accounting for the ratio of the error
variances, *λ*_{xy}, of the variables, (in classical non-weighted
ODR *λ*_{xy}=1), and it is the maximum likelihood estimate (MLE)
for the model (given in Eq. 1) when *λ*_{xy} is known. The PCA approach is
the same as in ODR, but the estimation procedure is somewhat different as can
be seen in Table S1 in the Supplement. The bivariate algorithm by York et al. (2004) provides a simple
set of equations for iterating the MLE of the slope and intercept with weighted
variables, which makes it similar to ODR in this case. However, using ODR
allows for a regression to be performed on a user-defined model, whereas the York (2004) solution only works on linear models. This, for instance, enables the use of linear-scale uncertainties in ODR in this study, whereas the York (2004)
approach could only use log-scale uncertainties. In Bayes EIV, statistical
models for the uncertainties in the observed quantities are used and probability
distributions for the line slope and intercept are computed according to
Bayes' theorem. In this study, we computed the Bayesian maximum a posteriori
(MAP) estimates for the slope and intercept that are the most probable
values given the likelihood and prior models (see Appendix A1 for more
details on models used in Bayes EIV). BLS takes errors and
heteroscedasticity into account, i.e. unequal variances, in both variables; thus, it is a
more advanced method than DR (under normality and equal variances, BLS is
exactly equivalent to DR). PCA only accounts for the observed variance in
data, whereas ODR, Bayes EIV and York bivariate regression require known
estimates for measurement errors, although for Bayes EIV the error can be
approximated with a distribution. DR and BLS can be applied with both errors
given by the user and measurement variance-based errors. In this study, we
applied measurement variance-based errors for these methods. The analyses for OLS and
PCA were calculated with the “lm” and “prcomp” *R* functions (R Core Team, 2018), respectively, DR was calculated with the
“deming” package (Therneau, 2018) and BLS was calculated with the “BivRegBLS” package
(Francq and Berger, 2017) in *R*. The ODR-based estimates were
obtained using the “scipy.odr” Python package (Jones et al.,
2001), while the “PyStan” Python package (Stan Development
Team, 2018) was used for calculating the Bayesian regression estimates.
Finally, the York bivariate estimates were produced with a custom Python
implementation of the algorithm presented by York et al. (2004).

## 3.1 Simulated data

In measured data, the variables that are observed are not *x* and *y*, but
(*x*+*e*_{x}) and (*y*+*e*_{y}), where *e*_{x} and *e*_{y} are the uncertainty in
the measurements, and the true *x* and *y* cannot be exactly known. Thus, we used
simulated data, where the true, i.e. noise-free, *x* and *y* were known to illustrate
how the different line-fitting methods perform in different situations.

We simulated a dataset mimicking the new particle formation rates (*J*_{1.7})
and sulfuric acid concentrations (H_{2}SO_{4}) reported from
CLOUD-chamber measurements at CERN. Both variables are known to have
substantial measurement error and, thus, they are good test variables for our
study. Additionally, the relationship of the logarithms of these variables is
quite often described with linear OLS regression and, thus, the inference may
be flawed.

We generated 1000 random noise-free H_{2}SO_{4} concentration
values assuming a log-normal distribution with a median of 2.0×10^{6} (molec cm^{−3}) and a
standard deviation of 2.4×10^{6} (molec cm^{−3}). The corresponding noise-free
*J*_{1.7} was calculated using model log_{10}(${J}_{\mathrm{1.7}})=\mathit{\beta}\times {\mathrm{log}}_{\mathrm{10}}$(H_{2}SO_{4})+*α* with the noise-free slope *β*=3.3
and $\mathit{\alpha}=-\mathrm{23}$, which are both realistic values presented by Kürten et al. (2016,
Table 2 in their paper, for the no added ammonia cases).

Simulated observations of the noise-free H_{2}SO_{4} concentrations were obtained by
adding random errors ${e}_{x}={e}_{\mathrm{rel},x}x+{\mathit{\sigma}}_{\mathrm{abs},x}$ that
have a random absolute component ${e}_{\mathrm{abs},x}\sim $ normal($\mathrm{0},{\mathit{\sigma}}_{\mathrm{abs},x}$) and a random component relative to the
observation *x* itself *e*_{rel,x}*x*, where ${e}_{\mathrm{rel},x}\sim $ normal($\mathrm{0},{\mathit{\sigma}}_{\mathrm{rel},x}$). Similar definitions apply for the noise-free
*J*_{1.7}, *e*_{y}, *σ*_{abs,y} and *σ*_{rel,y}. The
standard deviations of the measurement error components were chosen as ${\mathit{\sigma}}_{\mathrm{abs},x}=\mathrm{4}\times {\mathrm{10}}^{\mathrm{5}}$, ${\mathit{\sigma}}_{\mathrm{rel},x}=\mathrm{0.3}$, *σ*_{abs,y} = $\mathrm{3}\times {\mathrm{10}}^{-\mathrm{3}}$ and ${\mathit{\sigma}}_{\mathrm{rel},y}=\mathrm{0.5}$, which are subjective estimates
based on measurement data. The resulting total errors were occasionally
about as large as the data values themselves; however, they are not unusually
large error values with respect to corresponding real datasets, where overall
uncertainties may reach 150 % for H_{2}SO_{4} concentrations and 250 % for
nucleation rates (e.g. Dunne et al., 2016).

These choices regarding generating simulated data reflect what real dataset can often be like: the bulk of the data approximates a log-normal distribution with one of the tails possibly being thinned or cut close to a limit of detection of an instrument or close to a limit of the data filtering criterion. In our simulated data, each negative observation and each negative noise-free value was replaced with a new random simulated value, which only slightly offsets the final distribution from a perfectly symmetric log-normal shape.

Simulating the observations tends to generate infrequent extreme outlier observations from the infinite tails of the normal distribution. We discarded outliers with an absolute error larger than 3 times the combined standard uncertainty of the observation in order to remove the effect of outliers from the regression analysis. This represents the quality control procedure in data analysis and also improves the stability of our results between different simulations.

## 3.2 Case study on measured data

In order to show that the results gained with simulated data are also applicable
in real measurement data, we applied our methods to data measured in the
CLOUD chamber and published by Dunne et al. (2016). Fig. 1 in Dunne et al. (2016) shows nucleation rates (*J*) at a 1.7 nm mobility
diameter as a function of the sulfuric acid concentration. We used their
measurements with no added ammonia at two different temperatures, 278 and
292 K, as shown in their Fig. 1D and E and given in their Supplementary material.

## 4.1 Fits for simulated data

Differences between the regression methods are illustrated in four different ways: firstly, by showing line fits on a scatterplot of simulated data; secondly, by illustrating how the slopes change when the uncertainty in the measured variables increase; thirdly, by showing the sensitivity of the fits on number of observations; and finally, by showing how the fits are affected by adding outliers in the data. Regression fits using each of the respective methods are shown in Fig. 1.

As we know that the noise-free slope
*β*_{true} is 3.30, we can easily see how the methods perform. The
worst performing method was OLS, with a *β*_{ols} value of 1.55, which is
roughly half of the *β*_{true}. The best performing methods that displayed
equal accuracy, i.e. within 2 % range, were ODR (*β*_{ODR}=3.27),
Bayes EIV (*β*_{BEIV}=3.24) and BLS (*β*_{BLS}=3.22),
whereas York (*β*_{York}=3.15) was within a range of 5 %; Deming (*β*_{DR}=2.95) and PCA (*β*_{PCA}=2.92), in comparison, slightly
underestimated the slope.

The sensitivity of the methods was first tested by varying the uncertainty
in the H_{2}SO_{4} observations. We simulated six datasets with 1000
observations and with varying absolute and relative uncertainties (as listed in Table 1), and then performed fits with each method on all of these datasets. The
performance of the methods is shown in Fig. 2,
with the results corresponding to Fig. 1 marked in black. The results show that
when the uncertainty is small, the bias in the OLS fit is smaller, but when more
uncertainty is added to data, the bias increases significantly. A decrease in
performance can also be seen with ODR, which overestimates the slope, and
PCA, DR and Bayes EIV, which all underestimate the slope. The bivariate methods,
BLS and York, seem to be quite robust with increasing uncertainty, as the
slopes do not change significantly.

The sensitivity of the methods to the decreasing *n* (number of observations) was tested
by picking 100 random samples from the 1000-sample simulation dataset with *n* of 3,
5, 10, 20, 30, 50, 70, 100, 300 and 500 and carrying out fits for all samples using
all methods. The average slopes and their standard errors are shown in
Fig. 3. It is clear that when the *n*≤10, the variation in the estimated slopes can be
considerably high. When *n*≥30 the average slopes stabilised close to
their characteristic levels (within 5 %), except for Bayes EIV and York
bivariate, which needed more than 100 observations. The most sensitive
methods for a small *n* were Bayes EIV, ODR and PCA; thus, these methods should not be
applied for data with a small *n* and a similar type of uncertainty to that presented
here. However, the reader is reminded that number of points needed for a
good fit depends on the uncertainties in the data.

The sensitivity of the predictor variable H_{2}SO_{4} to outliers was
tested using two different scenarios. In the first scenario, outliers were randomly allowed to be
at either the high or low end of the distribution. In the second scenario,
outliers were only allowed to be large numbers, which is often the case in
H_{2}SO_{4} and aerosol concentration measurements as
numbers are removed from the data when they are smaller than the
detection limit of the measurement instrument. Five cases with an *n* of 1000 were
simulated with an increasing number of outliers (0, 5, 10, 20 and 100) and 10
repetitions of H_{2}SO_{4} values with a different set of outliers.
Outliers were defined such that ${x}_{\mathrm{obs}}-{x}_{\mathrm{true}}\mathit{>}\mathrm{3}\times $ the combined
standard uncertainty. The methods most sensitive to outliers in both
scenarios were OLS and Bayes EIV. A high number of outliers caused
underestimations in PCA and DR, especially when using the outliers with high values (second scenario mentioned above), and a slight
overestimation in BLS in the random outlier case (first scenario mentioned above). York bivariate and ODR were
not affected in either case, and BLS only showed small variation between the 10
replicates in the estimated slope. We did not explore how large a number of
outliers would needed to be to seriously disrupt the fits for the various
methods. We felt that it is likely not realistic to have situations that have
more than 10 % outliers.

We also applied an alternative method for simulating the data to different testing methods. The main difference compared with our method was that the
distribution of noise-free H_{2}SO_{4} followed a uniform distribution in
log-space. With this assumption, it could be seen that OLS works almost
as well as the EIV methods introduced here if the range of the data is
wide (H_{2}SO_{4} concentration in the range of 10^{6}–10^{9}). However,
when scaled to concentrations usually measured in the atmosphere
(10^{4}–10^{7}), the high uncertainties caused similar behaviour to the data
seen in our previous simulations. Details of these results can be seen
in Supplement S1.

## 4.2 Results of the case study

Figure 5 shows the fits of the data from Dunne et al. (2016). As expected,
the fit using OLS is underestimated at both temperatures: *β*_{ols}(278 K) was 2.4 and *β*_{ols}(292 K) was 3.0. The regression
equations for all methods are shown in Fig. 5. Dunne et al. (2016) did not use a
linear fit in their study and instead applied a non-linear Levenberg–Marquardt
algorithm (Moré, 1978) on function ${J}_{\mathrm{1.7}}=k\times [{\mathrm{H}}_{\mathrm{2}}{\mathrm{SO}}_{\mathrm{4}}{]}^{\mathit{\beta}},$ where *k* is a temperature-dependent rate coefficient
with a non-linear function including three estimable parameters (see Sect. 8
of their Supplement for details). Thus, the results are not directly
comparable as, for simplicity, we fit the data measured at
different temperatures separately. However, their *β* value for the fit
(*β*=3.95) is quite close to our results using EIV methods, especially as slopes from Bayes EIV at 292 K and BLS and PCA at both temperatures were
within a range of 5 %. We also carried out some tests on data measured at lower
temperatures (results not shown here). However, the slopes did not
vary drastically from those at *β*_{ols}(278 K) and *β*_{ols}(292 K) when the other conditions were similar, even though the
lower number of observations at lower temperatures increased uncertainty in
the data. Nevertheless, the intercepts *β*_{0}(*T*) varied between
temperatures.

Ordinary least squares regression can be used to answer some simple
questions regarding data, such as “How is *y* related to *x*?”. However, if we are
interested in the strength of the relationship and the predictor variable
*X* contains some error, then error-in-variables methods should be applied.
There is no single correct method to make the fit, as the methods
behave slightly differently with different types of error. The choice of
method should be based on the properties of data and the specific research
question. There are usually two types of error in the data: natural and
measurement error, where natural error refers to stochastic variation in the
environment. Even if the natural error in the data is not known, taking the measurement error into
account improves the fit significantly. Weighting the
data based on some factor, typically the inverse of the uncertainty, reduces
the effect of outliers and makes the regression more depend on the data that
are more certain (see e.g. Wu and Yu, 2018), but it does not solve the problem
completely.

As a test study, we simulated a dataset mimicking the dependence of the
atmospheric new particle formation rate on the sulfuric acid concentration. We
introduced three major sources of uncertainty when establishing inference from
scatterplot data: the increasing measurement error, the number of data points and
the number of outliers. In Fig. 1, we showed that for simulations where
errors are taken from real measurements of *J*_{1.7} and H_{2}SO_{4}
four of the methods gave slopes within 5 % of the known noise-free value:
BLS, York bivariate, Bayes EIV and ODR. Estimates from BLS and York
bivariate even remained stable when the uncertainty in the simulated
H_{2}SO_{4} concentration was increased drastically, as seen in Fig. 2. The main message to
take away from Fig. 3, in comparison, is that if the data contain some error, all fit methods are highly uncertain when small
numbers of observations are used. BLS was the
most accurate with the smallest sample sizes (10 or less), ODR stabilised with
20 observations, and York bivariate and Bayes EIV needed 100 or more data
points to become accurate. After that, these methods approached the noise-free value
asymptotically, whereas the OLS slope converged towards an
incorrect value. With an increasing number of outliers (Fig. 4), ODR and
York bivariate were the most stable methods, even when 10 % of observations were
classified as outliers in both test cases. BLS remained stable in the scenario with only high outlier values. Bayes EIV was the most sensitive to outliers after OLS.

From this, we can recommend that if the uncertainty in the predictor variable is known, York bivariate, or another method able to use known variances,
should be applied. If the errors are not known, and they are estimated from
data, BLS and ODR were found to be the most robust in cases with increasing
uncertainty (relative error, *r**E* > 30 % in Fig. 2) and with a high number of outliers. In our test data, BLS and ODR remained stable up to *r**E* > 80 % (Fig. 2), whereas DR and PCA began to become more
uncertain when *r**E* > 30 % and Bayes EIV when *r**E* > 50 %. If the number of observations is less than 10 and the uncertainties
are high, we would recommend considering if a regression fit is appropriate at
all. However, with the chosen uncertainties in our simulation tests, BLS was found to be the most robust with small numbers of data points. Bayes EIV displayed significant advantages if the number of observations was high enough and
there were not too many outliers, as it did not require an explicit definition
of the errors and could treat them as unknown parameters given their
probability distributions.

We also carried out a case study on data measured in the CLOUD chamber and published by
Dunne et al. (2016). In these analyses, we saw
that our above-mentioned recommended methods also performed best for these data. Our tests indicated that the slope *β*_{1} for the fit is not highly
sensitive to changes in temperature in the chamber but the intercept
*β*_{0} in linear fit is. This dependency was also seen, and taken into
account, in Dunne et al. (2016).

Python code for running the methods can be found on GitHub: https://gist.github.com/mikkopitkanen/da8c949571225e9c7093665c9803726e (Pitkänen, 2019).

Simulated datasets used in the example analysis are given in the Supplement.

In this appendix, we introduce the minimising criteria (*C*_{method}) for
all methods applied in the main text. We also give the equations for the
regression coefficients (${\widehat{\mathit{\alpha}}}_{\mathrm{method}}$ and ${\widehat{\mathit{\beta}}}_{\mathrm{method}}$) for the methods.

## A1 Ordinary least squares (OLS)

OLS minimises the sum of squares vertical distances (residuals) between each point and the fitted line. OLS regression minimises the following criterion:

where ${\widehat{\mathit{\alpha}}}_{\mathrm{OLS}}$ and ${\widehat{\mathit{\beta}}}_{\mathrm{OLS}}$ refer to estimators calculated from the data. These estimations are given by

where observed variances for $x{S}_{x}={\sum}_{i=\mathrm{1}}^{N}{\left({x}_{i}-\stackrel{\mathrm{\u203e}}{x}\right)}^{\mathrm{2}}$ and for $y{S}_{y}={\sum}_{i=\mathrm{1}}^{N}{\left({y}_{i}-\stackrel{\mathrm{\u203e}}{y}\right)}^{\mathrm{2}}$, and observed covariance for *x* and *y*
${S}_{xy}={\sum}_{i=\mathrm{1}}^{N}\left({x}_{i}-\stackrel{\mathrm{\u203e}}{x}\right)\left({y}_{i}-\stackrel{\mathrm{\u203e}}{y}\right)$.

## A2 Orthogonal regression (ODR)

ODR (https://docs.scipy.org/doc/external/odrpack_guide.pdf, last access: 16 August 2019, https://docs.scipy.org/doc/scipy/reference/odr.html, last access: 27 July 2018) minimises the sum of the square of the orthogonal distances between each point and the line. The criteria is given by

where

and

ODR accounts for the fact that errors exist in both axes but does not account for the exact
values of the variances of variables. Thus, only the ratio between the two
error variances (*λ*_{xy}) is needed to improve the
methodology. With notation of
Francq and Govaerts (2014), this ratio is given by the following:

where the numerator of the ratio is the error variance in the data in the *y* axis
and the denominator is the error variance in the data in the *x* axis.

## A3 Deming regression (DR)

DR is the ML (maximum likelihood) solution of Eq. 1 when *λ*_{xy} is known. In practice, *λ*_{xy} is unknown and it is
estimated from the variances of *x* and *y* calculated from the data.

The DR minimises the criterion *C*_{DR}, which is the sum of the square of (weighted)
oblique distances between each point to the line:

where

and

## A4 Bivariate least squares regression (BLS)

BLS is a generic name but here we refer to the formulation
described in Francq and Govaerts (2014) and references therein. BLS takes
errors and heteroscedasticity in both axes into account and is usually written in matrix notation. BLS minimises the criterion *C*_{BLS}, which is the sum
of weighted residuals *W*_{BLS} given by the following:

with

Estimators for the parameters are computed by iterations using the following formulas:

where known uncertainties ${\mathit{\sigma}}_{x}^{\mathrm{2}}$ and ${\mathit{\sigma}}_{y}^{\mathrm{2}}$ are replaced with estimated variances *S*_{x} and *S*_{y} in this
study.

A second bivariate regression method that was used in this study is an implementation of the regression method described by York et al. (2004, Section III). The minimisation criterion is described in York (1968):

where $w\left({x}_{i}\right)=\mathrm{1}/{\mathit{\sigma}}_{x}^{\mathrm{2}}$ and $w=\left({y}_{i}\right)\mathrm{1}/{\mathit{\sigma}}_{y}^{\mathrm{2}}$ are the weight coefficients for *x* and *y*, respectively, and *r* is
the correlation coefficient between *x* and *y*. *x*_{i,adj} and *y*_{i,adj} are
adjusted values of *x*_{i}, *y*_{i}, which fulfil the requirement

The solution for ${\widehat{\mathit{\alpha}}}_{\mathrm{york}}$ and ${\widehat{\mathit{\beta}}}_{\mathrm{york}}$ is found iteratively following the 10-step algorithm presented in York et al. (2004, Section III).

## A5 The principal component analysis-based regression (PCA)

PCA can be applied for bivariate and multivariate cases.

For one independent and one dependent variable, the regression line is

$y={\widehat{\mathit{\alpha}}}_{\mathrm{PCA}}+{\widehat{\mathit{\beta}}}_{\mathrm{PCA}}x$ where the error between the
observed value *y*_{i} and the estimated value *a*+*b**x*_{i} is minimum. For *n* data points, we compute
*a* and *b* using the method of least squares that minimises

This is a standard technique that gives the regression coefficients *α*
and *β*.

## A6 Bayesian error-in-variables regression (Bayes EIV)

Bayes EIV regression estimate applies Bayesian inference using the popular Stan software tool (http://mc-stan.org/users/documentation/, last access: 27 July 2018), which allows the use of prior information of the model parameters. We assumed

where *μ* and *σ* are the respective mean and standard deviation of
*x*_{true} and *y*_{true} and are treated as unknowns. The observations
*x*_{obs} and *y*_{obs} of *x*_{true} and *y*_{true}, respectively, were
defined as follows:

where *σ*_{rel} and *σ*_{abs} are the relative
and absolute components of standard uncertainties, respectively.

The Stan tool solved regression problems using 1000 iterations, and it
provided a posteriori distributions for the model parameters *β*_{BEIV} and *α*_{BEIV}. For the definitions of given
Student t, log-normal and normal probability distributions,
see Stan documentation. In our regression analysis, we used the maximum a
posteriori estimates for *β*_{BEIV} and *α*_{BEIV} provided
by the software tool.

The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-12531-2019-supplement.

SM prepared the paper with contributions from all co-authors. SM, MRAP and SI performed the formal analysis. MRAP simulated the data. SM, AA and KEJL formulated the original idea. SM, MRAP and AL developed and implemented the methodology. SM, MRAP, TN and AL were responsible for the investigation and validation of the data and methods.

The authors declare that they have no conflict of interest.

This research has been supported by the Nessling Foundation and the Academy of Finland (grant no. 307331).

This paper was edited by Fangqun Yu and reviewed by three anonymous referees.

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