Particle number size distribution (PNSD) and auxiliary variables measured at the SMEAR I Varrio forest station in Finland. The raw data were downloaded from https://smear.avaa.csc.fi/download and subset to two months (January and July 2020) for package size reasons.
dataA data frame with 7,640 observations on the following 65 variables.
datea POSIXct vector, sampling time (10-minute resolution).
tconca numeric vector, total number concentration.
TPNCa numeric vector, total particle number concentration.
monthyeara character vector, e.g. "January 2020".
1a numeric vector, particle diameter in nm (1 nm).
1.12a numeric vector, particle diameter in nm.
1.26a numeric vector, particle diameter in nm.
1.41a numeric vector, particle diameter in nm.
1.58a numeric vector, particle diameter in nm.
1.78a numeric vector, particle diameter in nm.
2a numeric vector, particle diameter in nm.
2.24a numeric vector, particle diameter in nm.
2.51a numeric vector, particle diameter in nm.
2.82a numeric vector, particle diameter in nm.
3.16a numeric vector, particle diameter in nm.
3.55a numeric vector, particle diameter in nm.
3.98a numeric vector, particle diameter in nm.
4.47a numeric vector, particle diameter in nm.
5.01a numeric vector, particle diameter in nm.
5.62a numeric vector, particle diameter in nm.
6.31a numeric vector, particle diameter in nm.
7.08a numeric vector, particle diameter in nm.
7.94a numeric vector, particle diameter in nm.
8.91a numeric vector, particle diameter in nm.
10a numeric vector, particle diameter in nm.
11.2a numeric vector, particle diameter in nm.
12.6a numeric vector, particle diameter in nm.
14.1a numeric vector, particle diameter in nm.
15.8a numeric vector, particle diameter in nm.
17.8a numeric vector, particle diameter in nm.
20a numeric vector, particle diameter in nm.
22.4a numeric vector, particle diameter in nm.
25.1a numeric vector, particle diameter in nm.
28.2a numeric vector, particle diameter in nm.
31.6a numeric vector, particle diameter in nm.
35.5a numeric vector, particle diameter in nm.
39.8a numeric vector, particle diameter in nm.
44.7a numeric vector, particle diameter in nm.
50.1a numeric vector, particle diameter in nm.
56.2a numeric vector, particle diameter in nm.
63.1a numeric vector, particle diameter in nm.
70.8a numeric vector, particle diameter in nm.
79.4a numeric vector, particle diameter in nm.
89.1a numeric vector, particle diameter in nm.
100a numeric vector, particle diameter in nm.
112a numeric vector, particle diameter in nm.
126a numeric vector, particle diameter in nm.
141a numeric vector, particle diameter in nm.
158a numeric vector, particle diameter in nm.
178a numeric vector, particle diameter in nm.
200a numeric vector, particle diameter in nm.
224a numeric vector, particle diameter in nm.
251a numeric vector, particle diameter in nm.
282a numeric vector, particle diameter in nm.
316a numeric vector, particle diameter in nm.
355a numeric vector, particle diameter in nm.
398a numeric vector, particle diameter in nm.
447a numeric vector, particle diameter in nm.
501a numeric vector, particle diameter in nm.
562a numeric vector, particle diameter in nm.
631a numeric vector, particle diameter in nm.
708a numeric vector, particle diameter in nm.
794a numeric vector, particle diameter in nm.
891a numeric vector, particle diameter in nm.
1000a numeric vector, particle diameter in nm.
The 61 numeric channels between 1 and 1000 are size
bins of the differential particle number size distribution,
approximately logarithmically spaced. Column names are
the geometric mean diameter in nanometres, written in the shortest
form that R accepts as a name (so 1 rather than 1.00,
and 10 rather than 10.0). Values are number
concentrations in cm\(^{-3}\). tconc and TPNC are
integrals of the size distribution; monthyear is the month
label used for group-wise cleaning.
The smallest seven size bins (1 through 2) and the
largest three (794, 891, 1000) are entirely
NA in this two-month subset, so varidele() will drop
them even at a permissive fraction.
The data are the raw input for the cleaning pipeline described in
vignette("dataprep-cleaning"). They contain:
size bins with substantial missing fractions (especially the smallest and largest bins),
observations with long consecutive missing runs,
extreme values that are not always genuine outliers.
After running dataprep on this table, a smaller,
cleaner table is returned. The data1 dataset is the
already-aggregated seven-column version; it has no long gaps and
no obvious outliers, and is not a useful input for the cleaning
pipeline.
Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020). Efficient data preprocessing, episode classification, and source apportionment of particle number concentrations. Science of the Total Environment, 741, 140923. tools:::Rd_expr_doi("10.1016/j.scitotenv.2020.140923")
data
dim(data)
names(data)[1:6]
# Per-column missing fraction, ordered by severity.
# The smallest and largest size bins have the most missing data.
na_frac <- sort(colMeans(is.na(data)), decreasing = TRUE)
head(na_frac, 10)
# The 61 size bins run from column 5 to column 65.
size_bins <- 5:65
summary(data[, size_bins[1:3]])
Run the code above in your browser using DataLab