README.md

In MPCA-air/mnrisks: MNRISKS

mnrisks

Helper functions and results from MNRISKS - Minnesota’s air modeling of the statewide emission inventory.

1. Install
2. Load model inputs
3. Read model results
4. Block group spatial averages
5. Find closed facilities
6. Map receptors and block groups
7. Add data to package

1. Install

install.packages("remotes")
remotes::install_github("mpca-air/mnrisks")

2. Load model inputs

library(mnrisks)
library(dplyr)

Load all modeling receptors

all_receptors <- get_receptors()

| lakes_rec_id | mpca_rec_id | county | mpca_bg_geoid | mpca_x_utm | mpca_y_utm | long | lat | |-------------:|------------:|:-------|--------------:|------------:|------------:|----------:|---------:| | 1 | 1 | Canada | 9.9e+11 | 356982 | 5473519 | -94.97113 | 49.39755 | | 2 | 2 | Canada | 9.9e+11 | 359799 | 5472979 | -94.93214 | 49.39335 | | 3 | 3 | Canada | 9.9e+11 | 348033 | 5473265 | -95.09428 | 49.39310 | | 4 | 4 | Canada | 9.9e+11 | 345878 | 5473259 | -95.12396 | 49.39250 | | 5 | 5 | Canada | 9.9e+11 | 342510 | 5473117 | -95.17028 | 49.39036 | | 6 | 6 | Canada | 9.9e+11 | 355346 | 5470752 | -94.99265 | 49.37229 |

Load a single block group’s receptors

points <- get_receptors(bg_id = c(270530269101))

plot(points$lat, points$long, pch = 16)

| lakes_rec_id | mpca_rec_id | county | mpca_bg_geoid | mpca_x_utm | mpca_y_utm | long | lat | |-------------:|------------:|:---------|--------------:|------------:|------------:|----------:|---------:| | 32425 | 32425 | Hennepin | 270530269101 | 459142 | 5010378 | -93.52063 | 45.24571 | | 32533 | 32533 | Hennepin | 270530269101 | 459632 | 5010014 | -93.51436 | 45.24246 | | 32547 | 32547 | Hennepin | 270530269101 | 459283 | 5009994 | -93.51880 | 45.24226 | | 32602 | 32602 | Hennepin | 270530269101 | 459813 | 5009756 | -93.51203 | 45.24015 | | 32703 | 32703 | Hennepin | 270530269101 | 459610 | 5009412 | -93.51459 | 45.23704 | | 32748 | 32748 | Hennepin | 270530269101 | 459391 | 5009351 | -93.51738 | 45.23648 |

Block group Census 2010 population data

bg_census <- get_census(year = 2010)

| geoid | area | metro | county | county_fips | population | population_moe | income_median | income_median_moe | pct_below_185pct_poverty | status_tribal_nation | status_poc | status_185x_poverty | |-------------:|----------:|------:|:----------------|------------:|-----------:|---------------:|--------------:|------------------:|-------------------------:|:---------------------|:-----------|:--------------------| | 271739701001 | 13913100 | 0 | YELLOW MEDICINE | 27173 | 680 | 153 | 70625 | 29412 | 29.41 | FALSE | FALSE | FALSE | | 271739701002 | 513000 | 0 | YELLOW MEDICINE | 27173 | 570 | 114 | 48214 | 18919 | 22.37 | FALSE | FALSE | FALSE | | 271739701003 | 4736700 | 0 | YELLOW MEDICINE | 27173 | 781 | 172 | 41786 | 8800 | 34.44 | FALSE | FALSE | FALSE | | 271739701004 | 187942000 | 0 | YELLOW MEDICINE | 27173 | 534 | 101 | 70000 | 9490 | 10.86 | FALSE | FALSE | FALSE | | 271739701005 | 176791000 | 0 | YELLOW MEDICINE | 27173 | 535 | 67 | 68077 | 5450 | 18.68 | TRUE | FALSE | FALSE | | 271739702001 | 3450600 | 0 | YELLOW MEDICINE | 27173 | 801 | 138 | 48750 | 8863 | 22.97 | FALSE | FALSE | FALSE |

3. Read model results

Read MNRISKS .rsk file output

Use filter_word to only return rows containing a given word

boilers <- read_rsk("area_results.rsk", year = 2017, filter_word = "Boilers")

4. Calculate Block group spatial averages using the area coverage of each receptor

View the block group areas for each receptor

rec_areas <- get_receptor_bg_areas()

| geoid | receptor | area_wt | |:-------------|---------:|----------:| | 270531260001 | 52508 | 0.0117515 | | 270531260001 | 52381 | 0.3542159 | | 270531260001 | 52225 | 0.0058149 | | 270531260001 | 52524 | 0.0395259 | | 270531260001 | 52279 | 0.1053743 | | 270531260001 | 52306 | 0.2695935 |

The spatial_bg_avg function calculates the spatial block group averages by joining the area fractions above to MNRISKS receptor results.

df <- get(data(onroad_inhalation_risk_2011))

# For a single pollutant
benzene <- filter(df, `COPC Name` == "Benzene")

bg_avg <- benzene %>% 
          spatial_bg_avg(result_cols   = c("Inhalation Cancer Risk", "Inhalation Hazard Index"),
                         receptor_col  = "Receptor",
                         pollutant_col = "COPC Name",
                         year          = 2011)

## Joining, by = "receptor"

## [1] "Calculating average [Inhalation Cancer Risk]"
## [1] "Benzene"
## [1] "Calculating average [Inhalation Hazard Index]"
## [1] "Benzene"

| geoid | pollutant | Inhalation Hazard Index_avg | Inhalation Cancer Risk_avg | |:-------------|:----------|----------------------------:|---------------------------:| | 270531054001 | Benzene | 0.046000 | 4.6e-06 | | 270531054002 | Benzene | 0.007880 | 8.0e-07 | | 270531044002 | Benzene | 0.001450 | 1.0e-07 | | 270530059012 | Benzene | 0.000881 | 1.0e-07 | | 270531261001 | Benzene | 0.000452 | 0.0e+00 | | 270530001011 | Benzene | 0.000000 | 0.0e+00 |

# For multiple pollutants
multi <- filter(df, `COPC Name` %in% c("Benzene", "Acrolein", "Lead", "Cobalt"))

bg_avg <- multi %>% 
          spatial_bg_avg(result_cols   = c("Inhalation Cancer Risk", "Inhalation Hazard Index"), 
                         receptor_col  = "Receptor",
                         pollutant_col = "COPC Name",
                         year = 2011)

## Joining, by = "receptor"

| geoid | pollutant | Inhalation Hazard Index_avg | Inhalation Cancer Risk_avg | |:-------------|:----------|----------------------------:|---------------------------:| | 270531054001 | Benzene | 0.046000 | 4.6e-06 | | 270531054002 | Benzene | 0.007880 | 8.0e-07 | | 270531054001 | Cobalt | 0.024100 | 6.0e-07 | | 270531044002 | Benzene | 0.001450 | 1.0e-07 | | 270531054002 | Cobalt | 0.004100 | 1.0e-07 | | 270530059012 | Benzene | 0.000881 | 1.0e-07 |

5. Find closed facilities

The find_closed_facilities function returns a table with CEDR Source IDs and Agency IDs of facilities with terminated air permits from between the calendar year 2013 and the entered year. The default year is 2022.

# For the default year
closed_facilities <- get_closed_facilities()

# For a future year
closed_facilities <- get_closed_facilities(year = 2023)

6. Maps

Receptor map

data(hennepin_onroad_inhalation_risk_2017)

# Receptor cancer risks from onroad sources in Hennepin county
map_receptors(hennepin_onroad_inhalation_risk_2017 %>% sample_n(7200),
              result_col   = "inh_cancer_risk",
              receptor_col = "receptor",
              colors       = "viridis")

## Assuming "long" and "lat" are longitude and latitude, respectively

## PhantomJS not found. You can install it with webshot::install_phantomjs(). If it is installed, please make sure the phantomjs executable can be found via the PATH variable.

## NULL

Block group average map

# Average block group cancer risks from onroad sources in Hennepin county
bg_avg <- hennepin_onroad_inhalation_risk_2017 %>% 
          spatial_bg_avg(result_cols = c("inh_cancer_risk", "inh_hazard_risk"), 
                         year = 2017)
# Leaflet map
map_bgs(bg_avg,
        result_col = "inh_cancer_risk_avg",
        colors     = "inferno")

## [1] "Calculating average [inh_cancer_risk]"
## [1] "All"
## [1] "Calculating average [inh_hazard_risk]"
## [1] "All"

## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE

## PhantomJS not found. You can install it with webshot::install_phantomjs(). If it is installed, please make sure the phantomjs executable can be found via the PATH variable.

## NULL

7. Add data

new_data

usethis::use_data(new_data)

devtools::document()

MPCA-air/mnrisks documentation built on March 21, 2023, 4:24 a.m.