Last updated: 2020-07-29

Checks: 7 0

Knit directory: Cant_eMLR/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200707)

The command set.seed(20200707) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: cb5074e

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version cb5074e. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    analysis/figure/
    Ignored:    data/GLODAPv2_2016b_MappedClimatologies/
    Ignored:    data/GLODAPv2_2020/
    Ignored:    data/World_Ocean_Atlas_2018/
    Ignored:    data/eMLR/
    Ignored:    data/pCO2_atmosphere/
    Ignored:    dump/
    Ignored:    output/figure/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/read_GLODAPv2_2020.Rmd) and HTML (docs/read_GLODAPv2_2020.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	cb5074e	jens-daniel-mueller	2020-07-29	subset Cant to GLODAP before merging
html	8d71a56	jens-daniel-mueller	2020-07-29	Build site.
Rmd	82db969	jens-daniel-mueller	2020-07-29	format and plots revised
html	2e2412d	jens-daniel-mueller	2020-07-29	Build site.
Rmd	a9fbf3a	jens-daniel-mueller	2020-07-29	formating
html	5274d0b	jens-daniel-mueller	2020-07-29	Build site.
Rmd	304aa74	jens-daniel-mueller	2020-07-29	plot improvements
html	75da185	jens-daniel-mueller	2020-07-29	Build site.
Rmd	26db597	jens-daniel-mueller	2020-07-29	cleaned code and output
html	2e08795	jens-daniel-mueller	2020-07-24	Build site.
html	c84424c	jens-daniel-mueller	2020-07-23	Build site.
Rmd	8d47b71	jens-daniel-mueller	2020-07-23	era names changed
html	2df2065	jens-daniel-mueller	2020-07-23	Build site.
Rmd	fa350cf	jens-daniel-mueller	2020-07-23	predictor correlation plots, bin2d map plots
html	9d1d67d	jens-daniel-mueller	2020-07-23	Build site.
Rmd	3b6658b	jens-daniel-mueller	2020-07-23	predictor correlation plots, bin2d map plots
html	2e3691a	jens-daniel-mueller	2020-07-23	Build site.
Rmd	26bdc0a	jens-daniel-mueller	2020-07-23	new era label, predictor correlation check started
html	556e6cc	jens-daniel-mueller	2020-07-23	Build site.
html	c1289a2	jens-daniel-mueller	2020-07-23	Build site.
html	2890e73	jens-daniel-mueller	2020-07-23	Build site.
html	fdfa7b9	jens-daniel-mueller	2020-07-22	Build site.
html	bb9c002	jens-daniel-mueller	2020-07-21	Build site.
Rmd	d2ed0f8	jens-daniel-mueller	2020-07-21	harmonied lat lon labeling
html	b47adc2	jens-daniel-mueller	2020-07-20	Build site.
Rmd	366d7d5	jens-daniel-mueller	2020-07-20	update plots
html	25812b9	jens-daniel-mueller	2020-07-20	Build site.
Rmd	e45a882	jens-daniel-mueller	2020-07-20	basins included, higher grid resolution for maps
html	6a6431e	jens-daniel-mueller	2020-07-20	Build site.
Rmd	50be8de	jens-daniel-mueller	2020-07-20	formating
html	b9769e8	jens-daniel-mueller	2020-07-20	Build site.
Rmd	82120fe	jens-daniel-mueller	2020-07-20	nitrate, gamma included, 65N removed
html	fe55909	jens-daniel-mueller	2020-07-19	Build site.
Rmd	66540a9	jens-daniel-mueller	2020-07-19	formating and color scale
html	61b3390	jens-daniel-mueller	2020-07-18	Build site.
Rmd	d3032c7	jens-daniel-mueller	2020-07-18	formating
html	22b588c	jens-daniel-mueller	2020-07-18	Build site.
html	3964fd7	jens-daniel-mueller	2020-07-17	Build site.
Rmd	4685ad7	jens-daniel-mueller	2020-07-17	150m depth limit implemented
html	56c3ed9	jens-daniel-mueller	2020-07-14	Build site.
Rmd	aed89af	jens-daniel-mueller	2020-07-14	added Rmd for MappedClimatologies
html	74d4abd	jens-daniel-mueller	2020-07-14	Build site.
html	1c511ce	jens-daniel-mueller	2020-07-14	Build site.
Rmd	e03016e	jens-daniel-mueller	2020-07-14	split read in per data set

library(tidyverse)
library(lubridate)
library(patchwork)

1 Read master file

Main data source for this project is GLODAPv2.2020_Merged_Master_File.csv downloaded from glodap.info in June 2020.

GLODAP <- read_csv(here::here("data/GLODAPv2_2020/Merged_data_product",
                              "GLODAPv2.2020_Merged_Master_File.csv"),
                   na = "-9999",
                   col_types = cols(.default = col_double()))

# relevant columns
GLODAP <- GLODAP %>% 
  select(cruise:talkqc)

GLODAP <- GLODAP %>% 
  mutate(date = ymd(paste(year, month, day))) %>% 
         #decade = as.factor(floor(year / 10) * 10)) %>% 
  relocate(date)

GLODAP <- GLODAP %>% 
  select(-c(month:minute, 
            maxsampdepth, pressure,
            theta, sigma0:sigma4,
            nitrite:nitritef))

2 Data preparation

2.1 Flags and missing data

flag_f <- 2
flag_qc <- 1

Only rows (samples) for which all relevant parameters are available were selected. In additions, following flagging criteria were applied:

flag f: 2
flag qc: 1

Summary statistics were calculated during cleaning process.

GLODAP_stats <- GLODAP %>% 
  summarise(total = n())

##

GLODAP <- GLODAP %>% 
  filter(!is.na(tco2))

GLODAP <- GLODAP %>% 
  filter(tco2f == flag_f) %>% 
  select(-tco2f)

GLODAP <- GLODAP %>% 
  filter(tco2qc == flag_qc) %>% 
  select(-tco2qc)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(tco2 = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP <- GLODAP %>% 
  filter(!is.na(talk))

GLODAP <- GLODAP %>% 
  filter(talkf == flag_f) %>% 
  select(-talkf)

GLODAP <- GLODAP %>% 
  filter(talkqc == flag_qc) %>% 
  select(-talkqc)

##

GLODAP <- GLODAP %>% 
  filter(!is.na(phosphate))

GLODAP <- GLODAP %>% 
  filter(phosphatef == flag_f) %>% 
  select(-phosphatef)

GLODAP <- GLODAP %>% 
  filter(phosphateqc == flag_qc) %>% 
  select(-phosphateqc)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(C_star_variables = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP <- GLODAP %>% 
  filter(!is.na(temperature))

##
  
GLODAP <- GLODAP %>% 
  filter(!is.na(salinity))

GLODAP <- GLODAP %>% 
  filter(salinityf == flag_f) %>% 
  select(-salinityf)

GLODAP <- GLODAP %>% 
  filter(salinityqc == flag_qc) %>% 
  select(-salinityqc)

##
  
GLODAP <- GLODAP %>% 
  filter(!is.na(silicate))

GLODAP <- GLODAP %>% 
  filter(silicatef == flag_f) %>% 
  select(-silicatef)

GLODAP <- GLODAP %>% 
  filter(silicateqc == flag_qc) %>% 
  select(-silicateqc)

##
  
GLODAP <- GLODAP %>% 
  filter(!is.na(oxygen))

GLODAP <- GLODAP %>% 
  filter(oxygenf == flag_f) %>% 
  select(-oxygenf)

GLODAP <- GLODAP %>% 
  filter(oxygenqc == flag_qc) %>% 
  select(-oxygenqc)

##

GLODAP <- GLODAP %>% 
  filter(!is.na(aou))

GLODAP <- GLODAP %>% 
  filter(aouf == flag_f) %>% 
  select(-aouf)

##
  
GLODAP <- GLODAP %>% 
  filter(!is.na(nitrate))

GLODAP <- GLODAP %>% 
  filter(nitratef == flag_f) %>% 
  select(-nitratef)

GLODAP <- GLODAP %>% 
  filter(nitrateqc == flag_qc) %>% 
  select(-nitrateqc)

##

GLODAP <- GLODAP %>% 
  filter(!is.na(depth))

GLODAP <- GLODAP %>% 
  filter(!is.na(gamma))

##

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)

rm(GLODAP_stats_temp)
rm(flag_f, flag_qc)

2.2 Spatial boundaries

min_depth <- 150
min_bottomdepth <- 500
max_lat <- 65

Only observations were taken into consideration with:

minimum sampling depth: 150m
minimum bottom depth: 500m
maximum latitude: 65°N

GLODAP <- GLODAP %>% 
  filter(depth >= min_depth)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables_150m = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)

##

GLODAP <- GLODAP %>% 
  filter(latitude <= max_lat)

GLODAP <- GLODAP %>% 
  filter(bottomdepth >= min_bottomdepth)

GLODAP_stats_temp <- GLODAP %>% 
  summarise(eMLR_variables_150m_65N_500m = n())

GLODAP_stats <- cbind(GLODAP_stats, GLODAP_stats_temp)
rm(GLODAP_stats_temp)


GLODAP_stats_long <- GLODAP_stats %>% 
  pivot_longer(1:length(GLODAP_stats), names_to = "parameter", values_to = "n")

GLODAP_stats_long  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                             "GLODAPv2.2020_stats.csv"))

rm(GLODAP_stats_long, GLODAP_stats)
rm(min_depth, max_lat, min_bottomdepth)

2.3 Reference eras

start <- 1981
JGOFS <- 1999
GOSHIP <- 2012

Samples were assigned to following eras:

JGOFS-WOCE: 1981 - 1999
GO-SHIP: 2000 - 2012
new-era: > 2013

GLODAP <- GLODAP %>%
  filter(year >= start) %>% 
  mutate(era = "JGOFS_WOCE",
         era = if_else(year > JGOFS, "GO_SHIP", era),
         era = if_else(year > GOSHIP, "new_era", era))

2.4 Horizontal gridding

For merging with other data sets, all observations were grouped into latitude intervals of:

1° x 1°

GLODAP <- GLODAP %>% 
  mutate(lat = cut(latitude, seq(-90, 90, 1), seq(-89.5, 89.5, 1)),
         lat = as.numeric(as.character(lat)),
         lon = cut(longitude, seq(-180, 180, 1), seq(-179.5, 179.5, 1)),
         lon = as.numeric(as.character(lon))) %>% 
  arrange(date) %>% 
  select(-c(latitude,longitude))

2.5 Basin mask

The basin mask from the World Ocean Atlas was used. For details consult the data base section for WOA18 data Link.

basinmask <- read_csv(here::here("data/World_Ocean_Atlas_2018/_summarized_files",
                                 "basin_mask_WOA18.csv"))

GLODAP_obs <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

mapWorld <- borders("world", col = "gray60", fill = "gray60") 

ggplot() +
  mapWorld +
  geom_raster(data = basinmask, aes(lon, lat, fill = basin)) +
  geom_raster(data = GLODAP_obs, aes(lon, lat)) +
  scale_fill_brewer(palette = "Dark2") +
  coord_quickmap(expand = 0) +
  theme(legend.position = "top")

rm(mapWorld, GLODAP_obs)

Please note that some GLODAP observations were made outside the WOA18 basin mask and will be removed for further analysis.

GLODAP <- inner_join(GLODAP, basinmask)
rm(basinmask)

2.6 Observations grid

GLODAP_obs_grid <- GLODAP %>% 
  group_by(lat, lon) %>% 
  summarise(n = n()) %>% 
  ungroup()

2.7 Write summary filea

GLODAP_obs_grid  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                           "GLODAPv2.2020_clean_obs_grid.csv"))

GLODAP  %>%  write_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                  "GLODAPv2.2020_clean.csv"))

rm(GLODAP, GLODAP_obs_grid)

3 Overview plots

3.1 Cleaning stats

GLODAP_stats_long <-  read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                                             "GLODAPv2.2020_stats.csv"))

GLODAP_stats_long <- GLODAP_stats_long %>%
  mutate(parameter = fct_reorder(parameter, n))

GLODAP_stats_long %>% 
  ggplot(aes(parameter, n/1000)) +
  geom_col() +
  coord_flip() +
  labs(y = "n (1000)") +
  theme(axis.title.y = element_blank())

rm(GLODAP_stats_long)

3.2 Assign coarse spatial grid

For the following plots, the cleaned data set was re-opened and observations were gridded spatially to intervals of:
- 5° x 5°

GLODAP <- read_csv(here::here("data/GLODAPv2_2020/_summarized_data_files",
                              "GLODAPv2.2020_clean.csv"))

GLODAP <- GLODAP %>% 
  mutate(lat_grid = cut(lat, seq(-90, 90, 5), seq(-87.5, 87.5, 5)),
         lat_grid = as.numeric(as.character(lat_grid)),
         lon_grid = cut(lon, seq(-180, 180, 5), seq(-177.5, 177.5, 5)),
         lon_grid = as.numeric(as.character(lon_grid))) %>% 
  arrange(date)

3.3 Histogram Zonal coverage

GLODAP_histogram_lat <- GLODAP %>% 
  group_by(era, lat_grid, basin) %>% 
  tally() %>% 
  ungroup()

GLODAP_histogram_lat %>% 
  ggplot(aes(lat_grid, n, fill = era)) +
  geom_col() +
  scale_x_continuous(breaks = seq(-87.5,90,10)) +
  scale_fill_brewer(palette = "Dark2") +
  facet_wrap(~basin) +
  coord_flip(expand = 0) +
  theme(legend.position = "top")

rm(GLODAP_histogram_lat)

3.4 Histogram temporal coverage

GLODAP_histogram_year <- GLODAP %>% 
  group_by(year, basin) %>% 
  tally() %>% 
  ungroup()

era_median_year <- GLODAP %>% 
  group_by(era) %>% 
  summarise(t_ref = median(year)) %>% 
  ungroup()

GLODAP_histogram_year %>% 
  ggplot() +
  geom_vline(xintercept = c(JGOFS + 0.5, GOSHIP + 0.5)) +
  geom_col(aes(year, n, fill = basin)) +
  geom_point(data = era_median_year, aes(t_ref,0, shape = "Median year"), size = 2, fill = "white") +
  scale_fill_brewer(palette = "Dark2") +
  scale_shape_manual(values = 24, name = "") +
  coord_cartesian(expand = 0) +
  theme(legend.position = "top",
        legend.direction = "vertical")

rm(GLODAP_histogram_year,
   era_median_year)

3.5 Zonal temporal coverage (Hovmoeller)

GLODAP_hovmoeller_year <- GLODAP %>% 
  group_by(year, lat_grid, basin) %>% 
  tally() %>% 
  ungroup()

GLODAP_hovmoeller_year %>% 
  ggplot(aes(year, lat_grid, fill = log10(n))) +
  geom_tile() +
  geom_vline(xintercept = c(1999.5, 2012.5)) +
  scale_fill_viridis_c(option = "magma", direction = -1) +
  facet_wrap(~basin, ncol = 1) +
  theme(legend.position = "top")

rm(GLODAP_hovmoeller_year)

3.6 Coverage maps by era

mapWorld <- borders("world", colour = "gray60", fill = "gray60")

GLODAP <- GLODAP %>% 
  mutate(era = factor(era, c("JGOFS_WOCE", "GO_SHIP", "new_era")))

GLODAP %>%
  ggplot(aes(lon, lat)) +
  mapWorld +
  geom_bin2d(binwidth = c(1,1)) +
  scale_fill_viridis_c(option = "magma", direction = -1, trans = "log10") +
  scale_x_continuous(breaks = seq(-180, 180, 30)) +
  scale_y_continuous(breaks = seq(-90, 90, 30)) +
  coord_quickmap(expand = FALSE) +
  facet_wrap(~era, ncol = 1) +
  theme(legend.position = "top")

rm(mapWorld)

4 Individual cruise sections

Zonal and meridional section plots are produce for each cruise individually and stored locally.

cruises <- GLODAP %>% 
  group_by(cruise) %>% 
  summarise(date_mean = mean(date, na.rm = TRUE),
            n = n()) %>% 
  ungroup() %>% 
  arrange(date_mean)

GLODAP <- full_join(GLODAP, cruises)

mapWorld <- borders("world", colour="gray60", fill="gray60")

n <- 0
for (i_cruise in unique(cruises$cruise)) {

#i_cruise <- unique(cruises$cruise)[1]
n <- n+1
print(n)  
  
GLODAP_cruise <- GLODAP %>%
  filter(cruise == i_cruise) %>% 
  arrange(date)

cruises_cruise <- cruises %>%
  filter(cruise == i_cruise)
  
map <- GLODAP_cruise %>%
  ggplot(aes(lon, lat)) +
  mapWorld+
  geom_point(aes(col=date)) +
  coord_quickmap(expand = FALSE) +
  scale_color_viridis_c(trans = "date") +
  labs(title = paste("Mean date:", cruises_cruise$date_mean,
                     "| cruise:", cruises_cruise$cruise,
                     "| n(samples):", cruises_cruise$n))


lon_section <- GLODAP_cruise %>%
  ggplot(aes(lon, depth)) +
  scale_y_reverse() +
  scale_color_viridis_c()

lon_tco2 <- lon_section+
  geom_point(aes(col=tco2))

lon_talk <- lon_section+
  geom_point(aes(col=talk))

lon_phosphate <- lon_section+
  geom_point(aes(col=phosphate))


lat_section <- GLODAP_cruise %>%
  ggplot(aes(lat, depth)) +
  scale_y_reverse() +
  scale_color_viridis_c()

lat_tco2 <- lat_section+
  geom_point(aes(col=tco2))

lat_talk <- lat_section+
  geom_point(aes(col=talk))

lat_phosphate <- lat_section+
  geom_point(aes(col=phosphate))

map /
((lat_tco2 / lat_talk / lat_phosphate) |
(lon_tco2 / lon_talk / lon_phosphate))

ggsave(here::here("output/figure/data/all_cruises_clean",
                  paste("GLODAP_cruise_date",
                        cruises_cruise$date_mean,
                        "n",
                        cruises_cruise$n,
                        "cruise",
                        cruises_cruise$cruise,
                        ".png",
                        sep = "_")),
                  width = 9, height = 9)

rm(map,
   lon_section, lat_section,
   lat_tco2, lat_talk, lat_phosphate, lon_tco2, lon_talk, lon_phosphate,
   GLODAP_cruise, cruises_cruise)

}

# library("rnaturalearth")
# library("rnaturalearthdata")
# library("sf")
# 
# world <- ne_countries(scale = "small", returnclass = "sf")
# class(world)
# 
# GLODAP_map <- GLODAP %>% 
#   group_by(lat_grid, lon_grid) %>% 
#   tally() %>% 
#   ungroup()
# 
# ggplot() +
#   geom_raster(data = GLODAP_map, aes(lon_grid, lat_grid)) +
#   geom_sf(data = world) +
#   coord_sf(crs = "+proj=robin +lat_0=0 +lon_0=0 +x0=0 +y0=0")


# https://gist.github.com/clauswilke/783e1a8ee3233775c9c3b8bfe531e28a
# https://twitter.com/clauswilke/status/1066024436208406529
# https://www.r-spatial.org/r/2018/10/25/ggplot2-sf.html

5 Open tasks

move A16 cruises in 2013 from new_era to GO_SHIP era

6 Questions

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

locale:
[1] LC_COLLATE=English_Germany.1252  LC_CTYPE=English_Germany.1252   
[3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] patchwork_1.0.1 lubridate_1.7.9 forcats_0.5.0   stringr_1.4.0  
 [5] dplyr_1.0.0     purrr_0.3.4     readr_1.3.1     tidyr_1.1.0    
 [9] tibble_3.0.3    ggplot2_3.3.2   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         here_0.1           assertthat_0.2.1   rprojroot_1.3-2   
 [5] digest_0.6.25      R6_2.4.1           cellranger_1.1.0   backports_1.1.8   
 [9] reprex_0.3.0       evaluate_0.14      httr_1.4.2         pillar_1.4.6      
[13] rlang_0.4.7        readxl_1.3.1       rstudioapi_0.11    whisker_0.4       
[17] blob_1.2.1         rmarkdown_2.3      labeling_0.3       munsell_0.5.0     
[21] broom_0.7.0        compiler_4.0.2     httpuv_1.5.4       modelr_0.1.8      
[25] xfun_0.16          pkgconfig_2.0.3    htmltools_0.5.0    tidyselect_1.1.0  
[29] viridisLite_0.3.0  fansi_0.4.1        crayon_1.3.4       dbplyr_1.4.4      
[33] withr_2.2.0        later_1.1.0.1      grid_4.0.2         jsonlite_1.7.0    
[37] gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0          git2r_0.27.1      
[41] magrittr_1.5       scales_1.1.1       cli_2.0.2          stringi_1.4.6     
[45] farver_2.0.3       fs_1.4.2           promises_1.1.1     xml2_1.3.2        
[49] ellipsis_0.3.1     generics_0.0.2     vctrs_0.3.2        RColorBrewer_1.1-2
[53] tools_4.0.2        glue_1.4.1         maps_3.3.0         hms_0.5.3         
[57] yaml_2.2.1         colorspace_1.4-1   rvest_0.3.6        knitr_1.29        
[61] haven_2.3.1

GLODAPv2_2020

Jens Daniel Müller

29 July, 2020