BlackCarbon (version 0.1.0)

filter_adjust_ONA: ONA adjusting filter change

Description

This function is an extended version of (ONA and filter_loading) which can only work at individual file level, similar to the ONA function. This function has an inbuilt function of filter loading correction as well as has capacity to adjust for filter change or a huge drop in ATN marked as mechanical error.

Usage

filter_adjust_ONA(data =data, ATN = "ATN", BC = "BC", dATN = 0.05,
threshold = 5, skip = 15, ignore = 10)

Arguments

data

a datafram containing the raw data of black carbon.

ATN

the coloumn having ATN coefficient.

BC

the column having raw black carbon data.

dATN

the difference in ATN coefficient which is used for defining the averaging window of raw black carbon concentration, by default it is 0.05.

threshold

the change in ATN value which indicates the filter change, it is 5 by default.

skip

number of observations before the filter change point that has to be skipped or deleted from analysis. When the AE51 device is about to indicate the need of filter change or when it<U+2019>s about encounters a sudden drop of ATN it either produces maximum negative or positive black carbon concentration which is a mechanical noise and averaging those values also introduces bias. Therefore certain observations before filter change or extreme drop down of ATN should be deleted depending on the data collection interval. By default it is 15 observation for 10s interval data.

ignore

number of observations after the filter change point that has to be ignored or deleted from analysis. After changing the filter or after a while of sudden drop of ATN value, AE51 device either produces maximum negative or positive black carbon concentration which is a mechanical noise and averaging those values also introduces bias. Therefore certain observations after filter change or sudden drop of ATN should be deleted depending on the data collection interval. By default it is 10 observation for 10s interval data.

Value

The default method returns the existing data frame with two additional columns 1) with ONA processed black carbon concentration and 2) filter loading corrected ONA processed black carbon concentration. If there is a filter change or a drop of ATN by more than 5 units within two adjoining observations some observations (25 rows), which is ignore + skip given in the function, will be deleted to correct for the mechanical error

Details

This method is only applicable when the raw files are not compiled

References

Hagler GSW, Yelverton TLB, Vedantham R, Hansen ADA, Turner JR. Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data. Aerosol Air Qual Res [Internet]. 2011 Oct [cited 2020 May 3];11(5):539<U+2013>46. Available from: http://www.aaqr.org/doi/10.4209/aaqr.2011.05.0055

Kirchstetter, T.W., Novakov, T., 2007. Controlled generation of black carbon particles from a diffusion flame and applications in evaluating black carbon measurement methods. Atmospheric Environment 41, 1874-1888 Available from: https://doi.org/10.1016/j.atmosenv.2006.10.067

See Also

ONA filter_loading

Examples

# NOT RUN {
data(data_ONA)#loading the example dataset
#saving a processed dataset with the same name
data_ONA<-filter_adjust_ONA(data =data_ONA, ATN = "ATN", BC = "BC", dATN = 0.05, threshold = 5,
                  skip = 15, ignore = 10)


## The function is currently defined as
function (data =data, ATN = "ATN", BC = "BC", dATN = 0.05, threshold = 5,
    skip = 15, ignore = 10)
{
    if (nrow(data) < (skip + ignore)) {
        print("not enough observations to correct for filter loading")
    }
    else {
        for (j in skip:(nrow(data) - ignore)) {
            if ((data[, ATN][j] - data[, ATN][j - 1]) < (-threshold)) {
                f <- j
                k1 <- data[1:(f - skip), ]
                k2 <- data[(f + ignore):nrow(data), ]
                k1[, "BC_ONA"] <- ONA(data = k1, ATN = "ATN",
                  BC = "BC", dATN = dATN)
                k1[, "BC_ONA_Cor"] <- k1[, "BC_ONA"] * (0.88 *
                  exp(-(k1[, ATN])/100) + 0.12)^(-1)
                k2[, "BC_ONA"] <- ONA(data = k2, ATN = "ATN",
                  BC = "BC", dATN = dATN)
                k2[, "BC_ONA_Cor"] <- k2[, "BC_ONA"] * (0.88 *
                  exp(-(k2[, ATN])/100) + 0.12)^(-1)
            }
        }
    }
    if (exists("k1") == T) {
        data <- rbind(k1, k2)
        rm(k1, k2)
    }
    else {
        data[, "BC_ONA"] <- ONA(data, ATN = "ATN", BC = "BC",
            dATN = 0.1)
        data[, "BC_ONA_Cor"] <- data[, "BC_ONA"] * (0.88 * exp(-(data[,
            ATN])/100) + 0.12)^(-1)
    }
    return(data)
  }
# }