Initial commit with the latest version

.gitignore

+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+data/*.accdb
+data/*.laccdb
+data/Query_Results.csv
+data/Table*.csv
+docs/*.csv
+src/Paper_monitoring.R
+src/plot_sum_of_pfas.R
+graphs/*.png 
+graphs/*.jpg
+

CONTRIBUTING.md

+# Contributing to the project
+
+Thank you for your interests on our project. For further discussions on the packages and code we are using for environmental data analysis, you can contact me via email: meiqi.liu@tuwien.ac.at
+
+We welcome contributions on bug reports.
+
+## Report Bugs 
+
+If you find a bug, please create an issue and provide as much information as possible:
+
+- A clear and descriptive title.
+- Steps to reproduce the bug.
+- Expected and actual behavior.
+- Screenshots or error logs if applicable.
+
+You can open a new issue in the Issues tab on GitLab.
+
+## Code Improvements
+
+Although the code here is prepared for specific usage (mainly publicaton purpose), we welcome any suggestions for improvments, e.g. packages, visulisation, analysis process.
\ No newline at end of file

README.md

+# PROMISCES: Analysis for Case Study 2 Monitoring Data
+
+Welcome to the repository for the data analysis on monitoring data collected within the framework of the [EU project PROMISCES](https://promisces.eu/) Case Study 2.
+
+
+## Project Folder Structure
+
+This project follows the structure enforced by the *ProjectTemplate* R package, which simplifies origanizing and managing complex data science projects. Beflow is an overview of the folder structure: 
+
+- cache
+  - Contains pre-loaded files, including geodata and R data
+- config
+  - Contains configuration files for loading and managing data
+  - For list of loaded r-librarys, you need to change the global.dcf 
+- data
+  - Raw and processed data files
+     - subfolder /rawdaya_pfas: Raw data files (unprocessed)
+     - others: Processed and cleaned data
+- diagnostics
+  - Not used in this project
+- doc
+  - Output data and results
+- graphs
+  - Output visulisations
+- lib
+  - Custom R functions for the project
+- logs
+- munge
+  - Scripts for preprocessing and data transformation
+- profilling
+- reports
+- src
+- tests
+
+
+
+At the beginning of script, you should call `create.project()` to set up this project and all of its contents:
+
+	library('ProjectTemplate')
+	load.project()
+
+After you enter the second line of code, you'll see a series of automated messages as ProjectTemplate goes about doing its work. This work involves:
+* Reading in the global configuration file contained in `config`.
+* Loading any R packages you listed in the configuration file.
+* Reading in any datasets stored in `data` or `cache`.
+* Preprocessing your data using the files in the `munge` directory.
+
+For more details about ProjectTemplate, see http://projecttemplate.net
\ No newline at end of file

cache/README.md

+This folder stores any data sets that:
+
+ - (a) are generated during a preprocessing step
+ - (b) don't need to be regenerated every single time you analyze your data.
+
+
+You can use the `cache()` function to store data to this directory automatically. 
+Any data set found in both the `cache` and `data` directories will be drawn from `cache` instead of `data` based on ProjectTemplate's priority rules.

config/README.md

+Here you'll store any configurations settings for your project. Use the DCF format that the `read.dcf()` function parses.

config/global.dcf

+version: 0.11.0
+data_loading: TRUE
+data_loading_header: TRUE
+data_ignore: 
+cache_loading: TRUE
+recursive_loading: FALSE
+munging: TRUE
+logging: FALSE
+logging_level: INFO
+load_libraries: TRUE
+libraries: readxl, crayon, NADA, ggsci, ggrepel, 
+           tidyverse, odbc, here, rmarkdown, ggh4x, scales, FSA
+as_factors: FALSE
+tables_type: data.frame
+attach_internal_libraries: FALSE
+cache_loaded_data:  TRUE
+sticky_variables: NONE
+underscore_variables: TRUE
+cache_file_format: RData

data/README.md

+This folder stores raw data files. 
+
+If they are encoded in a supported file format, they'll automatically be loaded when calling `load.project()`.
+
+In case to reproduce the code for further data analysis, you need to download the data here:
+> Liu, M. (2024). Per- and Polyfluoroalkyl Substances (PFAS) Concentrations in the Upper Danube Catchment: Integrated Dataset from H2020 Project PROMISCES - Case Study 2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14027088
+
+put the .accdb file or "Query_Results.csv" under this folder. This file will be ignored when push to git (.gitignore) and only visible at local level.
\ No newline at end of file

data/info_ww_share.csv

+"sample","perc_wwtp"
+"Altarm Poigenau",3.55
+"Alz upstream",1.55
+"Inn, Wernstein am Inn",2
+"Inn, Winklham",1.55
+"Ipoly",1.49
+"Isar",5.49
+"Morava",7.64
+"Salzach",1.59
+"Svratka",10.09
+"Thaya",5.78
+"Enns",0.72
+"Inn, Neuötting",1.54
+"Inn, Ruderverein",1.54
+"Traun",2.06
+"Raab",12.14
+"DH-RIV-AYH",0.18
+"ATFW73200987",1.18
+"DH-RIV-AYL",2.04
+"DESM_CBZ029",5.8
+"DESM_CRI018",3.51
+"DESM_DEBY_2306",2.77
+"ATFW54110087",1.6
+"ATFW31100127",44.23
+"DESM_DEBY_2322",2.77
+"DESM_DEBY_10157",3.8
+"DESM_DEBY_102007",3.47
+"DESM_DEBY_102027",3.88
+"DESM_DEBY_10251",3.8
+"DESM_DEBY_105892",2.95
+"DESM_DEBY_105900",2.95
+"DESM_DEBY_10607",3.53
+"DESM_DEBY_106070",1.11
+"DESM_DEBY_10754",0.58
+"DESM_DEBY_10782",0.58
+"DESM_DEBY_10881",1.87
+"DESM_DEBY_102948",3.22
+"DESM_DEBY_103111",3.19
+"DESM_DEBY_103242",3.73
+"DESM_DEBY_103286",2.24
+"DESM_DEBY_103342",3.22
+"DESM_DEBY_103362",3.95
+"DESM_DEBY_103364",3.97
+"DESM_DEBY_103370",3.47
+"DESM_DEBY_103395",3.19
+"DESM_DEBY_103406",2.88
+"DESM_DEBY_103408",4.62
+"DESM_DEBY_103409",4.62
+"DESM_DEBY_103416",2.88
+"DESM_DEBY_10909",5.67
+"DESM_DEBY_10928",5.67
+"DESM_DEBY_10987",5.67
+"DESM_DEBY_10988",5.67
+"DESM_DEBY_11062",1.11
+"DESM_DEBY_11094",3.81
+"DESM_DEBY_11138",3.81
+"DESM_DEBY_111636",1.29
+"DESM_DEBY_111662",1.77
+"DESM_DEBY_111756",1.54
+"DESM_DEBY_111794",1.29
+"DESM_DEBY_2358",2.24
+"DESM_DEBY_2371",2.24
+"DESM_DEBY_2384",2.95
+"DESM_DEBY_2389",3.97
+"DESM_DEBY_103419",3.86
+"DESM_DEBY_10428",3.62
+"DESM_DEBY_10529",3.62
+"DESM_DEBY_105412",4.57
+"DESM_DEBY_105426",5.49
+"DESM_DEBY_105434",3.47
+"DESM_DEBY_105438",3.47
+"DESM_DEBY_10551",3.53
+"DESM_DEBY_105532",5.67
+"DESM_DEBY_105572",3.63
+"DESM_DEBY_105632",3.63
+"DESM_DEBY_105645",1.65
+"DESM_DEBY_105786",1.11
+"DESM_DEBY_2398",3.95
+"DESM_DEBY_2413",3.95
+"DESM_DEBY_2617",3.47
+"DESM_DEBY_105841",2.24
+"DESM_DEBY_105890",2.95
+"DESM_DEBY_111808",1.77
+"DESM_DEBY_111817",1.46
+"DESM_DEBY_11233",3.63
+"DESM_DEBY_11239",5.67
+"DESM_DEBY_11274",0.78
+"DESM_DEBY_11353",5.49
+"DESM_DEBY_11423",5.49
+"DESM_DEBY_114296",1.38
+"DESM_DEBY_11431",5.49
+"DESM_DEBY_11437",5.49
+"DESM_DEBY_11446",5.49
+"DESM_DEBY_2918",3.47
+"DESM_DEBY_2927",3.47
+"DESM_DEBY_2939",3.04
+"DESM_DEBY_3015",1.13
+"DESM_DEBY_3022",1.13
+"DESM_DEBY_3029",1.13
+"DESM_DEBY_114475",1.11
+"DESM_DEBY_3044",3.32
+"DESM_DEBY_3092",2.23
+"DESM_DEBY_3095",3.32
+"DESM_DEBY_3129",3.19
+"DESM_DEBY_3151",1.01
+"DESM_DEBY_114528",3.81
+"DESM_DEBY_11492",3.07
+"DESM_DEBY_11513",3.07
+"DESM_DEBY_115684",1.38
+"DESM_DEBY_115837",2.18
+"DESM_DEBY_11584",1.53
+"DESM_DEBY_115858",2.95
+"DESM_DEBY_11595",1.53
+"DESM_DEBY_11609",1.53
+"DESM_DEBY_11650",1.33
+"DESM_DEBY_31712",3.88
+"DESM_DEBY_3195",3.88
+"DESM_DEBY_11678",1.33
+"DESM_DEBY_11698",1.33
+"DESM_DEBY_3226",2.88
+"DESM_DEBY_3247",2.88
+"DESM_DEBY_3255",2.88
+"DESM_DEBY_3278",2.88
+"DESM_DEBY_3306",2.88
+"DESM_DEBY_3315",3.86
+"DESM_DEBY_3357",4.57
+"DESM_DEBY_3390",4.62
+"DESM_DEBY_11700",1.33
+"DESM_DEBY_11899",2.64
+"DESM_DEBY_11938",1.18
+"DESM_DEBY_12046",1.29
+"DESM_DEBY_12074",1.29
+"DESM_DEBY_12084",1.46
+"DESM_DEBY_121167",1.11
+"DESM_DEBY_12126",1.46
+"DESM_DEBY_3500",2.83
+"DESM_DEBY_12302",1.54
+"DESM_DEBY_3703",2.83
+"DESM_DEBY_3848",2.74
+"DESM_DEBY_3982",2.61
+"DESM_DEBY_40553",1.65
+"DESM_DEBY_4172",2.3
+"DESM_DEBY_42442",3.22
+"DESM_DEBY_4304",2.3
+"DESM_DEBY_4328",2.3
+"DESM_DEBY_4433",3.8
+"DESM_DEBY_4535",3.8
+"DESM_DEBY_4574",3.73
+"DESM_DEBY_5031",3.73
+"DESM_DEBY_12341",1.36
+"DESM_DEBY_125619",4.57
+"DESM_DEBY_125643",0.78
+"DESM_DEBY_12615",1.55
+"DESM_DEBY_126323",1.46
+"DESM_DEBY_12732",1.38
+"DESM_DEBY_12771",1.38
+"DESM_DEBY_12857",1.38
+"DESM_DEBY_5415",3.73
+"DESM_DEBY_5882",3.03
+"DESM_DEBY_12859",1.38
+"DESM_DEBY_128652",1.77
+"DESM_DEBY_5963",3.03
+"DESM_DEBY_6591",0.63
+"DESM_DEBY_66985",1.13
+"DESM_DEBY_6953",1.65
+"DESM_DEBY_7254",1.65
+"DESM_DEBY_7927",4.51
+"DESM_DEBY_8049",4.51
+"DESM_DEBY_8081",4.51
+"DESM_DEBY_8104",3.45
+"DESM_DEBY_128654",1.77
+"DESM_DEBY_12872",1.92
+"DESM_DEBY_12940",1.55
+"DESM_DEBY_129870",0.58
+"DESM_DEBY_12988",2.38
+"DESM_DEBY_13027",2.38
+"DESM_DEBY_130316",0.78
+"DESM_DEBY_13104",2.38
+"DESM_DEBY_131156",1.29
+"DESM_DEBY_131157",1.29
+"DESM_DEBY_8244",1.89
+"DESM_DEBY_131653",5.49
+"DESM_DEBY_13235",2.38
+"DESM_DEBY_8319",1.89
+"DESM_DEBY_8618",1.89
+"DESM_DEBY_8672",1.89
+"DESM_DEBY_9110",2.04
+"DESM_DEBY_95991",3.8
+"DESM_DEBY_96041",1.13
+"DESM_DEBY_96508",1.01
+"DESM_DEBY_96510",3.86
+"DESM_DEBY_96548",0.86
+"DESM_DEBY_96672",3.8
+"DESM_DEBY_96769",1.77
+"DESM_DEBY_96982",2.26
+"DESM_DEBY_9734",2.04
+"DESM_DEBY_9921",3.8
+"DESM_DEBY_132350",1.46
+"DESM_DEBY_13243",1.68
+"DESM_DEBY_132673",1.38
+"DESM_DEBY_13289",3.1
+"DESM_DEBY_136338",3.88
+"DESM_DEBY_137190",3.9
+"DESM_DEBY_139744",1.53
+"DESM_DEBY_140480",3.22
+"DESM_DEBY_140993",0.58
+"DESM_DEBY_141110",2.23
+"DESM_DEBY_143021",3.04
+"DESM_DEBY_143161",3.03
+"DESM_DEBY_176643",3.95
+"DESM_DEBY_176645",3.47
+"DESM_DEBY_176725",5.67
+"DESM_DEBY_176735",3.8
+"DESM_DEBY_193155",2.64
+"DESM_DEBY_195630",0.78
+"DESM_DEBY_196258",3.03
+"DESM_DEBY_196404",5.49
+"DESM_DEBY_198949",4.51
+"DESM_DEBY_198967",2.18
+"DESM_DEBY_199818",3.07
+"DESM_DEBY_2290",1.11
+"DESM_DEBY_206206",5.49
+"DESM_DEBY_206325",3.8
+"DESM_DEBY_206371",4.62
+"DESM_DEBY_206728",1.55
+"DESM_DEBY_2097",3.81
+"DESM_DEBY_213772",3.8
+"DESM_DEBY_213778",1.38
+"DESM_DEBY_213975",2.23
+"DESM_DEBY_2272",1.11
+"DESM_DEBY_2294",2.18
+"JDS4-11-R-SW-LS",7.41
+"JDS4-12-R-SW-LS",7.64
+"JDS4-13-L-SW-LS",7.59
+"JDS4-15-R-SW-LS",3.8
+"JDS4-20-R-SW-LS",3.36
+"JDS4-19-R-SW-LS",5.01
+"JDS4-21-R-SW-LS",1.49
+"JDS4-5-L-SW-LS",1.68
+"RW-UpperAustria-1",1.6
+"RW-UpperAustria-2",1.59
+"RW-UpperAustria-3",1.59
+"RW-UpperAustria-4",0
+"RW-UpperAustria-5",1.55
+"RW-UpperAustria-6",5.13
+"RW-UpperAustria-7",3.28
+"RW-UpperAustria-8",12.2
+"RW-UpperAustria-9",6.39
+"RW-UpperAustria-10",2
+"RW-UpperAustria-11",2.72
+"RW-UpperAustria-12",7.31
+"RW-UpperAustria-13",2.06
+"RW-UpperAustria-14",92.55
+"RW-UpperAustria-15",0
+"RW-UpperAustria-16",0
+"RW-UpperAustria-17",0.72
+"RW-UpperAustria-18",7.2
+"RW-UpperAustria-19",3.01
+"RW-UpperAustria-24",1.6
+"RW-UpperAustria-25",1.59
+"RW-UpperAustria-26",1.59
+"RW-UpperAustria-27",0
+"RW-UpperAustria-28",1.55
+"RW-UpperAustria-29",5.13
+"RW-UpperAustria-30",3.28
+"RW-UpperAustria-31",12.2
+"RW-UpperAustria-32",6.39
+"RW-UpperAustria-33",2
+"RW-UpperAustria-34",2.72
+"RW-UpperAustria-35",7.31
+"RW-UpperAustria-36",2.06
+"RW-UpperAustria-37",92.55
+"RW-UpperAustria-38",0
+"RW-UpperAustria-39",0
+"RW-UpperAustria-40",0.72
+"RW-UpperAustria-41",7.2
+"RW-UpperAustria-42",3.01
+"RW-UpperAustria-47",1.6
+"RW-UpperAustria-48",1.6
+"RW-UpperAustria-49",1.59
+"RW-UpperAustria-50",1.59
+"RW-UpperAustria-51",0
+"RW-UpperAustria-52",1.55
+"RW-UpperAustria-53",5.13
+"RW-UpperAustria-54",3.28
+"RW-UpperAustria-55",12.2
+"RW-UpperAustria-56",6.39
+"RW-UpperAustria-57",2
+"RW-UpperAustria-58",2.72
+"RW-UpperAustria-59",2.72
+"RW-UpperAustria-60",7.31
+"RW-UpperAustria-61",2.06
+"RW-UpperAustria-62",92.55
+"RW-UpperAustria-63",0
+"RW-UpperAustria-64",0
+"RW-UpperAustria-65",0.72
+"RW-UpperAustria-66",7.2
+"RW-UpperAustria-67",1.32
+"RW-UpperAustria-68",3.01
+"RW-UpperAustria-73",1.6
+"RW-UpperAustria-74",1.6
+"RW-UpperAustria-75",1.59
+"RW-UpperAustria-76",1.59
+"RW-UpperAustria-77",0
+"RW-UpperAustria-78",1.55
+"RW-UpperAustria-79",5.13
+"RW-UpperAustria-80",3.28
+"RW-UpperAustria-81",12.2
+"RW-UpperAustria-82",6.39
+"RW-UpperAustria-83",2
+"RW-UpperAustria-84",2.72
+"RW-UpperAustria-85",2.72
+"RW-UpperAustria-86",7.31
+"RW-UpperAustria-87",2.06
+"RW-UpperAustria-88",92.55
+"RW-UpperAustria-89",0
+"RW-UpperAustria-90",0
+"RW-UpperAustria-91",0.72
+"RW-UpperAustria-92",7.2
+"RW-UpperAustria-93",1.32
+"RW-UpperAustria-94",3.01
+"RW-UpperAustria-99",1.6
+"RW-UpperAustria-100",1.6
+"RW-UpperAustria-101",1.6
+"RW-UpperAustria-102",1.55
+"RW-UpperAustria-103",1.55
+"RW-UpperAustria-104",1.92
+"RW-UpperAustria-105",2
+"RW-UpperAustria-106",0
+"RW-UpperAustria-107",0
+"RW-UpperAustria-108",0.72
+"RW-UpperAustria-109",0
+"RW-UpperAustria-110",0
+"RW-UpperAustria-111",0
+"RW-UpperAustria-113",1.6
+"RW-UpperAustria-114",1.6
+"RW-UpperAustria-115",1.6
+"RW-UpperAustria-116",1.55
+"RW-UpperAustria-117",1.55
+"RW-UpperAustria-118",1.92
+"RW-UpperAustria-119",2
+"RW-UpperAustria-120",0
+"RW-UpperAustria-121",0
+"RW-UpperAustria-122",0.72
+"RW-UpperAustria-123",0
+"RW-UpperAustria-124",0
+"RW-UpperAustria-125",0
+"RW-UpperAustria-127",1.6
+"RW-UpperAustria-128",1.6
+"RW-UpperAustria-129",1.6
+"RW-UpperAustria-130",1.6
+"RW-UpperAustria-131",1.92
+"RW-UpperAustria-132",0
+"RW-UpperAustria-133",0
+"RW-UpperAustria-134",0
+"RW-UpperAustria-135",0
+"RW-UpperAustria-136",0
+"RW-UpperAustria-137",0
+"RW-UpperAustria-138",0
+"RW-UpperAustria-139",0
+"RW-UpperAustria-140",0
+"RW-UpperAustria-141",0
+"RW-UpperAustria-142",0
+"RW-UpperAustria-143",0
+"RW-UpperAustria-144",1.6
+"RW-UpperAustria-145",1.6
+"RW-UpperAustria-146",1.6
+"RW-UpperAustria-147",1.6
+"RW-UpperAustria-148",1.92
+"RW-UpperAustria-149",1.6
+"RW-UpperAustria-150",1.59
+"RW-UpperAustria-151",1.59
+"RW-UpperAustria-152",0
+"RW-UpperAustria-153",1.55
+"RW-UpperAustria-154",5.13
+"RW-UpperAustria-155",3.28
+"RW-UpperAustria-156",1.55
+"RW-UpperAustria-157",12.2
+"RW-UpperAustria-158",1.92
+"RW-UpperAustria-159",6.39
+"RW-UpperAustria-160",2
+"RW-UpperAustria-161",2.72
+"RW-UpperAustria-162",7.31
+"RW-UpperAustria-163",2.06
+"RW-UpperAustria-164",92.55
+"RW-UpperAustria-165",0
+"RW-UpperAustria-166",0.72
+"RW-UpperAustria-167",7.2
+"RW-UpperAustria-168",1.32
+"RW-UpperAustria-169",3.01
+"RW-UpperAustria-174",0
+"RW-UpperAustria-175",7.2
+"RW-UpperAustria-176",3.01
+"RW-UpperAustria-177",0
+"RW-UpperAustria-178",0
+"RW-UpperAustria-179",0
+"RW-UpperAustria-180",0
+"RW-UpperAustria-181",0
+"RW-UpperAustria-182",0
+"RW-UpperAustria-183",1.6
+"RW-UpperAustria-184",1.59
+"RW-UpperAustria-185",1.59
+"RW-UpperAustria-186",0
+"RW-UpperAustria-187",1.55
+"RW-UpperAustria-188",5.13
+"RW-UpperAustria-189",3.28
+"RW-UpperAustria-190",12.2
+"RW-UpperAustria-191",6.39
+"RW-UpperAustria-192",2
+"RW-UpperAustria-193",2.72
+"RW-UpperAustria-194",7.31
+"RW-UpperAustria-195",2.06
+"RW-UpperAustria-196",92.55
+"RW-UpperAustria-197",0
+"RW-UpperAustria-198",0.72
+"RW-UpperAustria-199",7.2
+"RW-UpperAustria-200",1.32
+"RW-UpperAustria-201",3.01
+"RW-UpperAustria-206",2.06
+"RW-UpperAustria-207",92.55
+"RW-UpperAustria-208",92.55
+"RW-UpperAustria-209",0
+"RW-UpperAustria-210",0
+"RW-UpperAustria-211",0
+"RW-UpperAustria-212",3.17
+"RW-UpperAustria-216",1.6
+"RW-UpperAustria-217",1.6
+"RW-UpperAustria-218",1.59
+"RW-UpperAustria-219",1.6
+"RW-UpperAustria-220",1.6
+"RW-UpperAustria-221",1.92
+"RW-UpperAustria-222",1.6
+"RW-UpperAustria-223",1.6
+"RW-UpperAustria-224",1.6
+"RW-UpperAustria-225",2.06
+"RW-UpperAustria-226",5.16
+"RW-UpperAustria-227",92.55
+"RW-UpperAustria-228",92.55
+"RW-UpperAustria-229",0
+"RW-UpperAustria-230",0
+"RW-UpperAustria-231",0
+"RW-UpperAustria-232",3.17
+"RW-UpperAustria-236",2.06
+"RW-UpperAustria-237",5.16
+"RW-UpperAustria-238",92.55
+"RW-UpperAustria-239",92.55
+"RW-UpperAustria-240",0
+"RW-UpperAustria-241",0
+"RW-UpperAustria-242",0
+"RW-UpperAustria-243",3.17
+"RW-UpperAustria-247",92.55
+"RW-UpperAustria-248",0
+"RW-UpperAustria-260",2.06
+"RW-UpperAustria-261",2.06
+"RW-UpperAustria-262",3.17
+"RW-UpperAustria-263",2.06
+"RW-UpperAustria-264",3.17
+"RW-UpperAustria-265",3.17
+"RW-UpperAustria-266",3.17
+"RW-UpperAustria-267",2.06
+"RW-UpperAustria-268",2.06

data/pfas_rpf.csv

+substance,cas_number,RPF_EQS,RPF_DWD
+PFBA,375-22-4,0.05,1
+PFPeA,2706-90-3,0.03,1
+PFHxA,307-24-4,0.01,1
+PFHpA,375-85-9,0.505,1
+PFOA,335-67-1,1,1
+PFNA,375-95-1,10,1
+PFDA,335-76-2,7,1
+PFUdA,2058-94-8,4,1
+PFDoDA,307-55-1,3,1
+PFTrDA,72629-94-8,1.7,1
+PFTeDA,376-06-7,0.3,0
+PFBS,375-73-5,0.001,1
+PFPeS,2706-91-4,0.3005,1
+PFHxS,355-46-4,0.6,1
+PFHpS,375-92-8,1.3,1
+PFOS,1763-23-1,2,1
+PFNS,68259-12-1,0,1
+PFDS,335-77-3,2,1
+GenX,13252-13-6,0.06,0
+ADONA,919005-14-4,0.03,0
+4:2 FTS,757124-72-4,0,0
+6:2 FTS,27619-97-2,0,0
+8:2 FTS,39108-34-4,0,0
+PFOSA,754-91-6,0,0
+N-MeFOSA,31506-32-8,0.02,0
+N-EtFOSA,4151-50-2,0.03,0
+MeFOSAA,2355-31-9,0,0
+EtFOSAA,2991-50-6,0,0
+9Cl-PF3ONS,73606-19-6,0.02,0
+11Cl-PF3OUdS,763051-92-9,0,0
+N-MeFOSE,24448-09-7,0,0
+N-EtFOSE,1691-99-2,0,0

diagnostics/README.md

+Here you can store any scripts you use to diagnose your data sets for corruption or problematic data points.

docs/1_data_overview.R

+library(ProjectTemplate)
+load.project()
+
+# original data structure
+glimpse(data_all)
+
+# amount of data for each type
+str_type <- data_all %>% 
+  group_by(type,substance) %>%
+  arrange(type,substance) %>%
+  summarise(n = n(), .groups = "drop") %>%
+  pivot_wider(names_from = substance, values_from = n)
+
+# sampling time period
+data_all %>%
+  filter(datasource == "EU-PROMISCES") %>%
+  summarise(min_date = min(sampling_time, na.rm = TRUE), 
+            max_date = max(sampling_time, na.rm = TRUE))
+
+min(data_all$sampling_time, na.rm = T)
+max(data_all$sampling_time, na.rm = T)
+table(data_all$datasource)
+
+data_all %>% 
+  filter(datasource == "EU-PROMISCES") %>%
+  summarise(min = min(sampling_time, na.rm = T), 
+            max = max(sampling_time, na.rm = T))

docs/2_catchment_analysis.R

+# check whether different wastewater load will affect the PFAS concentration
+library(ProjectTemplate)
+load.project()
+
+# -------- load data -------
+# define list of pfas to present
+detect_sw <- data_all %>%
+  filter(matrix == "Riverwater") %>%
+  group_by(substance) %>%
+  summarise(n = n(),
+            detection_rate = mean(!value_below_loq) * 100) %>%
+  arrange(desc(detection_rate)) 
+# top 10 detects - rate > 20%
+pfas_sw <- detect_sw$substance[1:10] %>% droplevels()
+
+# extact tributary data, join with ww share load info
+data_tributary <- data_ros %>%
+  filter(type == "Danube Tributary", substance %in% pfas_sw) %>%
+  droplevels() %>%
+  left_join(info_ww_share, by = "sample")
+
+colSums(is.na(data_tributary))
+str(data_tributary)
+
+# -------- visulisation ------
+data_tributary$perc_wwtp_1pct_group <- cut(data_tributary$perc_wwtp,
+  breaks = seq(0, max(data_tributary$perc_wwtp, na.rm = TRUE) + 1, by = 1),
+  labels = paste0(seq(0, max(data_tributary$perc_wwtp, na.rm = TRUE)), 
+  "-", seq(1, max(data_tributary$perc_wwtp, na.rm = TRUE) + 1, by = 1), "%"),
+  include.lowest = TRUE)
+
+# Calculate counts for each group
+group_counts <- data_tributary %>%
+  group_by(perc_wwtp_1pct_group) %>%
+  summarise(count = n())
+
+# Plot
+ggplot(data_tributary, aes(x = value)) +
+  geom_histogram(binwidth = 0.5, fill = "steelblue", color = "black", alpha = 0.7) +
+  facet_wrap(~ perc_wwtp_1pct_group, scales = "free_y") +
+  scale_x_log10() +
+  labs(title = "PFAS Concentration Distribution by Wastewater Contribution Group (1% Increment)",
+       x = "PFAS Concentration (ng/L)",
+       y = "Frequency") +
+  theme_minimal()
+
+# ------- classify groups --------
+data_tributary$perc_wwtp_group <- cut(data_tributary$perc_wwtp,
+  breaks = c(-Inf, 1, 3, Inf),
+  labels = c("<1%", "1-3%", ">3%"))
+
+table(data_tributary$perc_wwtp_group)
+
+# -------- summary statistics --------
+summary_stats <- aggregate(value ~ perc_wwtp_group, data = data_tributary, 
+  FUN = function(x) c(mean = mean(x), median = median(x), sd = sd(x)))
+print(summary_stats)
+
+# Perform one-way ANOVA without "Tributary Alz"
+anova_result <- aov(value ~ perc_wwtp_group, data = data_tributary)
+# View the summary of ANOVA
+summary(anova_result)
+
+# Perform Tukey HSD test
+tukey_result <- TukeyHSD(anova_result)
+print(tukey_result_tri)
+
+

docs/3_PFOS_EQS.R

+# test the compliance of PFOS EQS
+library(ProjectTemplate)
+load.project()
+
+# -------- check compliance -----
+# total entries of PFOS
+nrow(data_all[data_all$substance == "PFOS", ])
+
+# entries where value > 0.65
+nrow(data_all[data_all$substance == "PFOS" & data_all$value > 0.65, ])
+
+# percentage
+nrow(data_all[data_all$substance == "PFOS" & data_all$value > 0.65, ])/nrow(data_all[data_all$substance == "PFOS", ])
+
+# grouped percentages
+data_all %>%
+  filter(substance == "PFOS") %>%
+  group_by(matrix) %>%
+  summarise(
+    total = n(),
+    above_065 = sum(value > 0.65),
+    percentage_above_065 = (above_065 / total) * 100
+  ) %>%
+  arrange (desc(percentage_above_065))
+
+data_all %>%
+  filter(substance == "PFOS") %>%
+  group_by(type) %>%
+  summarise(
+    total = n(),
+    above_065 = sum(value > 0.65),
+    percentage_above_065 = (above_065 / total) * 100
+  ) %>%
+  arrange (desc(percentage_above_065))

docs/README.md

+This folder stores documentations briefly state the data analysis.
+
+Scripts here should not involve new coding, but only examing the structure and contents of R-objects.
\ No newline at end of file