About
The Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System (ATTAINS) is the U.S. Environmental Protection Agency (EPA) database used to track information provided by states about water quality assessments conducted under the Clean Water Act. The assessments are conducted every two years to evaluate if the nation’s water bodies meet water quality standards. States are required to take Actions (TMDLs or other efforts) on water bodies that do not meet standards. Public information in ATTAINS is made available through webservices and provided as JSON files. rATTAINS facilitates accessing this data with various functions that provide raw JSON or formatted “tidy” data for each of the ATTAINS webservice endpoints. More information about Clean Water Act assessment and reporting is available through the EPA. For alternative methods of accessing the same data, see “How’s My Waterway” webpage for interactive data exploration or the ArcGIS MapService for spatial data.
Functions
Summary Services
The EPA provides two summary service endpoint that provide summaries of assessed uses by the organization identifier or by hydrologic unit code (HUC). For example, to return a summary of assessed uses by the state of Tennessee the following function is used:
library(rATTAINS)
x <- state_summary(organization_id = "TDECWR",
reporting_cycle = "2016")
x
#> # A tibble: 71 × 24
#> organization_identifer organization_name organization_type_text
#> <chr> <chr> <chr>
#> 1 TDECWR Tennessee State
#> 2 TDECWR Tennessee State
#> 3 TDECWR Tennessee State
#> 4 TDECWR Tennessee State
#> 5 TDECWR Tennessee State
#> 6 TDECWR Tennessee State
#> 7 TDECWR Tennessee State
#> 8 TDECWR Tennessee State
#> 9 TDECWR Tennessee State
#> 10 TDECWR Tennessee State
#> # ℹ 61 more rows
#> # ℹ 21 more variables: reporting_cycle <chr>, water_type_code <chr>,
#> # units_code <chr>, use_name <chr>, fully_supporting <dbl>,
#> # fully_supporting_count <int>, use_insufficient_information <dbl>,
#> # use_insufficient_information_count <int>, not_assessed <dbl>,
#> # not_assessed_count <int>, not_supporting <dbl>, not_supporting_count <int>,
#> # parameter_group <chr>, parameter_insufficient_information <dbl>, …The HUC12 service operates similarly but provides data summarized by area, specifically HUC12 units. For example:
x <- huc12_summary("020700100204")
x
#> $huc_summary
#> # A tibble: 1 × 14
#> huc12 assessment_unit_count total_catchment_area…¹ total_huc_area_sq_mi
#> <chr> <int> <dbl> <dbl>
#> 1 020700100204 17 46.1 46.2
#> # ℹ abbreviated name: ¹total_catchment_area_sq_mi
#> # ℹ 10 more variables: assessed_catchment_area_sq_mi <dbl>,
#> # assessed_cathcment_area_percent <dbl>,
#> # assessed_good_catchment_area_sq_mi <dbl>,
#> # assessed_good_catchment_area_percent <dbl>,
#> # assessed_unknown_catchment_area_sq_mi <dbl>,
#> # assessed_unknown_catchment_area_percent <dbl>, …
#>
#> $au_summary
#> # A tibble: 17 × 1
#> assessment_unit_id
#> <chr>
#> 1 MD-02140205-Northwest_Branch
#> 2 MD-02140205
#> 3 DCTFD01R_00
#> 4 DCTNA01R_00
#> 5 DCTFS01R_00
#> 6 MD-ANATF
#> 7 DCTTX27R_00
#> 8 DCTFC01R_00
#> 9 MD-02140205-Mainstem
#> 10 DCTWB00R_02
#> 11 DCANA00E_02
#> 12 DCTHR01R_00
#> 13 DCTWB00R_01
#> 14 DCTPB01R_00
#> 15 DCTDU01R_00
#> 16 DCANA00E_01
#> 17 DCAKL00L_00
#>
#> $ir_summary
#> # A tibble: 2 × 4
#> EPA_IR_category_name catchment_size_sq_mi catchment_size_percent
#> <chr> <dbl> <dbl>
#> 1 4A 12.7 27.5
#> 2 5 25.8 56.0
#> # ℹ 1 more variable: assessment_unit_count <int>
#>
#> $status_summary
#> # A tibble: 1 × 4
#> overall_status catchment_size_sq_mi catchment_size_percent
#> <chr> <dbl> <dbl>
#> 1 Not Supporting 35.2 76.4
#> # ℹ 1 more variable: assessment_unit_count <int>
#>
#> $use_group_summary
#> # A tibble: 5 × 5
#> use_group_name use_attainment catchment_size_sq_mi catchment_size_percent
#> <chr> <chr> <dbl> <dbl>
#> 1 ECOLOGICAL_USE Not Supporting 10.9 23.7
#> 2 FISHCONSUMPTION_USE Insufficient … 15.9 34.4
#> 3 FISHCONSUMPTION_USE Not Supporting 15.9 34.4
#> 4 OTHER_USE Fully Support… 0.683 1.48
#> 5 RECREATION_USE Not Supporting 15.2 32.9
#> # ℹ 1 more variable: assessment_unit_count <int>
#>
#> $use_summary
#> # A tibble: 6 × 6
#> use_name use_group_name use_attainment catchment_size_sq_mi
#> <chr> <chr> <chr> <dbl>
#> 1 Navigation OTHER_USE Fully Support… 10.9
#> 2 Primary Contact Recreation RECREATION_USE Not Supporting 15.9
#> 3 Protection and Propagation… ECOLOGICAL_USE Not Supporting 15.9
#> 4 Protection of Human Health… FISHCONSUMPTI… Insufficient … 0.683
#> 5 Protection of Human Health… FISHCONSUMPTI… Not Supporting 15.2
#> 6 Secondary Contact Recreati… RECREATION_USE Not Supporting 15.9
#> # ℹ 2 more variables: catchment_size_percent <dbl>, assessment_unit_count <int>
#>
#> $param_summary
#> # A tibble: 16 × 4
#> parameter_group_name catchment_size_sq_mi catchment_size_percent
#> <chr> <dbl> <dbl>
#> 1 ALGAL GROWTH 9.21 20.0
#> 2 CHLORINE 1.73 3.75
#> 3 HABITAT ALTERATIONS 2.80 6.07
#> 4 HYDROLOGIC ALTERATION 5.98 13.0
#> 5 METALS (OTHER THAN MERCURY) 11.8 25.7
#> 6 NUTRIENTS 9.21 20.0
#> 7 OIL AND GREASE 10.9 23.7
#> 8 ORGANIC ENRICHMENT/OXYGEN DEPLET… 12.7 27.5
#> 9 PATHOGENS 15.9 34.4
#> 10 PESTICIDES 15.2 32.9
#> 11 PH/ACIDITY/CAUSTIC CONDITIONS 1.95 4.22
#> 12 POLYCHLORINATED BIPHENYLS (PCBS) 15.2 32.9
#> 13 SEDIMENT 1.90 4.11
#> 14 TOXIC ORGANICS 13.3 28.7
#> 15 TRASH 9.21 20.0
#> 16 TURBIDITY 15.9 34.4
#> # ℹ 1 more variable: assessment_unit_count <int>
#>
#> $res_plan_summary
#> # A tibble: 1 × 4
#> summary_type_name catchment_size_sq_mi catchment_size_percent
#> <chr> <dbl> <dbl>
#> 1 TMDL 15.9 34.4
#> # ℹ 1 more variable: assessment_unit_count <int>
#>
#> $vision_plan_summary
#> # A tibble: 1 × 4
#> summary_type_name catchment_size_sq_mi catchment_size_percent
#> <chr> <dbl> <dbl>
#> 1 TMDL 15.9 34.4
#> # ℹ 1 more variable: assessment_unit_count <int>huc12_summary() returns a list of tibbles with different
summaries of information. Using the above example: -
x$huc_summary provides a summary of HUC area, and the area
and percentage of catchment assessed as good, unknown, or impaired. -
x$au_summary provides a tibble with the unique identifiers
for the assessment units (or distinct sections of waterbodies) within
the queried HUC12. - x$ir_summary provides a simple summary
of the area of the catchment classified under different Integrated
Report Categories. - x$status_summary provides a summary of
the overall status within the HUC12. - x$use_group_summary
provides a summary of use attainment bu use group within the HUC12. -
x$use_summary breaks the use summary down further by the
use name. - x$param_summary provides the same information
for parameter groups. - x$res_plan_summary and
x$vision_plan_summary provides a summary of the amount of
the watershed covered by particular types of restoration plans or vision
plan, such as TMDLs.
Domains
Each function has a number of allowable arguments and associated
values. In order to explore what values you might be interested in
querying, the Domain Value service provides information about allowable
options. This is mapped to the domain_values() function.
When used without any arguments you get a full list of possible
“domains.” These are typically searchable parameters used in all the
functions in rATTAINS. Note that the domain names returned by these
service are not a one to one match with the argument names used in
rATTAINS. It is typically fairly easy to figure out which ones match up
to which arguments.
For example if I want to find out the possible organization identifiers to query by:
x <- domain_values(domain_name = "OrgStateCode")
x
#> # A tibble: 146 × 5
#> domain name code context context_2
#> <chr> <chr> <chr> <chr> <chr>
#> 1 OrgStateCode AK AK EPA NA
#> 2 OrgStateCode FL FL 21FL303D NA
#> 3 OrgStateCode PA PA EPA NA
#> 4 OrgStateCode CC CC TEST_ORG_C NA
#> 5 OrgStateCode AZ AZ TEST_TRIBE_B NA
#> 6 OrgStateCode MS MS 21MSWQ NA
#> 7 OrgStateCode CT CT CT_DEP01 NA
#> 8 OrgStateCode ND ND 21NDHDWQ NA
#> 9 OrgStateCode MN MN REDLAKE NA
#> 10 OrgStateCode NM NM PUEBLO_POJOAQUE NA
#> # ℹ 136 more rowsThe function returns a variable with the state codes and the possible parameter values as the context variable. Similarly if I want to look up possible Use Names that are utilized by the Texas Commission on Environmental Quality:
x <- domain_values(domain_name = "UseName", context = "TCEQMAIN")
x
#> # A tibble: 17 × 5
#> domain name code context context_2
#> <chr> <chr> <chr> <chr> <chr>
#> 1 UseName Recreation Use Recrea… TCEQMA… NA
#> 2 UseName Fish Consumption Use Fish C… TCEQMA… NA
#> 3 UseName INTERMEDIATE AQUATIC LIFE INTERM… TCEQMA… NA
#> 4 UseName OVERALL USE SUPPORT OVERAL… TCEQMA… NA
#> 5 UseName Aquatic Life Use Aquati… TCEQMA… NA
#> 6 UseName Oyster Waters Use Oyster… TCEQMA… NA
#> 7 UseName FISH CONSUMPTION FISH C… TCEQMA… NA
#> 8 UseName OYSTER AQUATIC LIFE OYSTER… TCEQMA… NA
#> 9 UseName NON-CONTACT RECREATION NON-CO… TCEQMA… NA
#> 10 UseName CONTACT RECREATION USE CONTAC… TCEQMA… NA
#> 11 UseName DOMESTIC WATER SUPPLY - PUBLIC WATER SUPPLY DOMEST… TCEQMA… NA
#> 12 UseName Public Water Supply Use Public… TCEQMA… NA
#> 13 UseName General Use Genera… TCEQMA… NA
#> 14 UseName PRIMARY RECREATION/SWIMMING PRIMAR… TCEQMA… NA
#> 15 UseName CONTACT RECREATION CONTAC… TCEQMA… NA
#> 16 UseName NONCONTACT RECREATION USE NONCON… TCEQMA… NA
#> 17 UseName Recreational Beaches Recrea… TCEQMA… NAOther Services
assessment_units(): provides information about assessment units by the specified argument parameters.assessments()provides information about assessment decisions by the specified argument parameters.actions()provides information about Actions (such as TMDLs, 4B Actions, or similar) that have been finalized by the specified argument parameters.plans()is similiar to actions but provides information about finalized Actions and assessment units by HUC8.surveys()provides information about organization conducted statistical surveys about water quality assessment results.
JSON Files
By default, all the functions rATTAINS return one or more “tidy”
dataframes. These dataframe are created by attempting to flatten the
nested JSON data returned by the webservice. This does require some
opinionated decisions on what constitutes flat data, and at what
variable data should be flattened to. We recognize that the dataframe
output might not meet user needs. There if you would prefer to parse the
JSON data yourself, use the tidy=FALSE argument to return
an unparsed JSON string. A number of R packages are available to parse
and flatten JSON data to prepare it for analysis.
Notes
The U.S. EPA is the data provider for this public information. rATTAINS and the author are not affiliated with the EPA. Questions about the package functionality should be directed to the package author. Questions about the webservice or underlying data should be directed to the U.S. EPA. Please do not abuse the webservice using this package.