Package 'airPb'

Title: Calculate Estimates of Airborne Lead Exposure at Point Locations in the Cincinnati, OH Area
Description: Easily and reproducibly assess exposure to airborne lead at specific locations in and around Cincinnati, Ohio. The package calculates predictions of air lead exposure from a land use random forest model developed by Dr. Cole Brokamp based on ambient air sampling in Cincinnati, OH between 2001 and 2005. Additionally scale estimates to account for temporal variation in air lead in the region.
Authors: Erika Rasnick [aut, cre], Cole Brokamp [aut], Pat Ryan [aut]
Maintainer: Erika Rasnick <[email protected]>
License: GPL-3
Version: 0.9
Built: 2024-11-14 06:02:36 UTC
Source: https://github.com/geomarker-io/airPb

Help Index

Calculate temporally scaled air lead exposure estimates at specific locations.

Description

calculate_scaled_airPb() is a wrapper function that estimates airborne lead exposures at provided locations by calling calculate_airPb(), then temporally scales those estimates using scaling factors computed by calling calculate_scaling_factors(). This function is particularly useful for calculating exposures at the same locations on different dates.

Usage

add_scaled_airPb(locations)

Arguments

locations

Data.frame with columns 'id', 'lat','lon', 'start_date', and 'end_date' at minimum.

Value

A numeric vector of air lead estimates (ug/m3).

References

Cole Brokamp, Roman Jandarov, MB Rao, Grace LeMasters, Patrick Ryan. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment. 151. 1-11. 2017. http://dx.doi.org/10.1016/j.atmosenv.2016.11.066

Examples

my_data <- data.frame(id = rep(1,3),
    lat = c(39.19674, 39.19674,	39.19674),
    lon = c(-84.58260, -84.58260, -84.58260),
    start_date = c(as.Date("2010-01-08"), as.Date("2012-06-08"), as.Date("2015-04-09")),
    end_date = c(as.Date("2010-02-08"), as.Date("2012-07-08"), as.Date("2015-05-09")))

airPb_scaled <- add_scaled_airPb(my_data)

Calculate air lead exposure estimates at specific locations.

Description

calculate_airPb() uses a land use random forest model developed by Dr. Cole Brokamp based on ambient air sampling in Cincinnati, OH between 2001 and 2005 to estimate exposure to airborne lead at point locations in the area specified by latitude and longitude. The model predictors include greenspace (NDVI) within 1000 meters, population density within 500 meters, length of bus routes within 900 meters, percent pasure within 800 meters, percent developed open land within 1100 meters, percent developed medium land within 400 meters, percent developed low land within 900 meters, and percent developed high land within 1500 meters.

Usage

calculate_airPb(locations, return.LU.vars = FALSE)

Arguments

locations

Data.frame with columns 'id', 'lat', and 'lon' at minimum.
return.LU.vars

When return.LU.vars = TRUE, the land use predictors used to generate the air lead values are also returned.

Value

If return.LU.vars = FALSE, a numeric vector of air lead estimates (ug/m3) ? is returned. If return.LU.vars = TRUE, the locations data.frame with additional columns for air lead values and the land use predictors used to generate the air lead values is returned.

References

Cole Brokamp, Roman Jandarov, MB Rao, Grace LeMasters, Patrick Ryan. Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment. 151. 1-11. 2017. http://dx.doi.org/10.1016/j.atmosenv.2016.11.066

Examples

my_data <- data.frame(id = 1:3,
    lat = c(39.19674, 39.12731,	39.28765),
    lon = c(-84.58260, -84.52700, -84.51017))

lead_est <- calculate_airPb(my_data, return.LU.vars = FALSE)
lead_est <- calculate_airPb(my_data, return.LU.vars = TRUE)

Calculate temporal scaling factors based on EPA measurements of airborne lead.

Description

calculate_scaling_factors() constructs temporal scaling factors based on measurements of airborne lead recorded by the EPA in the Cincinnati area. These scaling factors are the average lead measured over the time period specified by start_date and end_date, divided by the average lead recorded over the ambient air sampling period (2001 to 2005). Scaling factors can be multiplied by air lead estimates from calculate_airPb() to adjust for temporal variability in airborne lead in the Cincinnati area over time.

Usage

calculate_scaling_factors(dates)

Arguments

dates

A data.frame with 2 columns called 'start_date' and 'end_date' at minimum. Both columns must be of class Date. See as.Date for help converting a character vector to a Date vector.

Details

EPA data in this package is available from November 9, 2001 through November 28, 2018. Scaling factors that attempt to average over air lead measured on dates outside this range will not be calculated. In addition, it is important to be mindful of the frequency of air lead measurements recorded by the EPA. Note that air lead was measured every 6 days through the end of 2010, and every 3 days starting in 2011. If there are less than 4 measurements of air lead between the start_date and end_date, the scaling factor will not be calculated and NA will be returned.

Value

A numeric vector of temporal scaling factors.

Examples

my_dates <- data.frame(start_date = c("2010-01-08", "2012-06-08", "2010-01-09",
                                      "2015-04-09", "2010-01-10"),
                       end_date = c("2010-02-08", "2012-07-08", "2010-02-09",
                                    "2015-05-09", "2010-02-10"))

## Not run: 
class(my_dates$start_date)  # character vector
scaling1m <- calculate_scaling_factors(my_dates)

## End(Not run)

my_dates$start_date <- as.Date(my_dates$start_date)
my_dates$end_date <- as.Date(my_dates$end_date)
scaling1m <- calculate_scaling_factors(my_dates)