Interpreting the Results of Airport Water Monitoring (2017) / Chapter Skim
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From page 65... ...
65 C h a p t e r 2 2.1 Introduction This chapter presents guidance on the interpretation of water monitoring data. Interpretation of the monitoring data is defined as the process of assessing the monitoring data set as a whole to provide quantitative descriptors of the data sets characteristics.
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66 Interpreting the results of airport Water Monitoring Bias – The systematic or persistent distortion of a measurement process, which causes errors in one direction (i.e., the expected sample measurement is higher or lower than the sample's true value)
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Interpreting Monitoring Data 67 2.3 Verifying the Accuracy and Representativeness of Raw Data Chapter 1 presented guidance that airport staff can use to acquire monitoring data. By consistently applying sound data acquisition techniques, users can improve accuracy and representativeness, while reducing error.
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68 Interpreting the results of airport Water Monitoring 2.3.1 Potential Measurement Errors Measurement errors can occur in the sample collection and analysis stages. Measurement error is influenced by the inherent variability of the sampled population over space and time, the sample collection design, and the number of samples collected.
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Interpreting Monitoring Data 69 4. Maintenance Issues – Loose monitoring probes – Frozen sample inlets – Fouled sample inlets – Sedimentation over sample inlet – Damage from debris to sampling equipment – Power failures during part of sampling cycle Sample collection procedures should follow approved quality assurance project plans and standard operation procedures that are part of the water monitoring plan discussed in Chapter 1, to minimize the potential for field sampling errors.
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70 Interpreting the results of airport Water Monitoring naturally occurring arsenic and arsenic from treated wood, pesticide applications, or smelting operations.
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Interpreting Monitoring Data 71 Dilutions Dilution is the act of adding distilled water and/or other preparation reagents to a sample to overcome an interferent or to bring the concentration of a target analyte back into the working calibration range of the instrument. The dilution factor is the total number of volumes, including the sample volume, in which the sample will be diluted with distilled water.
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72 Interpreting the results of airport Water Monitoring Flags The laboratory may assign qualifiers or flags to the data to identify potential data quality problems for the data user. If flags are being used, the data user should determine if their application was defined clearly in the data report, and whether the flags were appropriately assigned to sample results based on these definitions.
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Interpreting Monitoring Data 73 of the error in the calculated variable can be of a different order than the error associated with any one of the measurements, depending on their mathematical relationship. 2.3.4 Parameter Relationships The results for related analytical parameters should be reviewed as an accuracy check.
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74 Interpreting the results of airport Water Monitoring Flow monitoring of stormwater in open channels or partially full pipes, on the other hand, requires field calibration and is subject to many field variables that can affect accuracy. These kinds of flow monitors are often systems rather than single devices.
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Interpreting Monitoring Data 75 upstream from the flow-measuring device can cause secondary flow or large eddies, which tend to concentrate the flow in part of a cross section. Excessive turbulence will adversely affect the accuracy of any measuring device but is particularly objectionable when using current meters or propeller meters of any kind.
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76 Interpreting the results of airport Water Monitoring Table 7. (Continued)
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Interpreting Monitoring Data 77 Table 7. (Continued)
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78 Interpreting the results of airport Water Monitoring Key Takeaways Verifying the Accuracy and Representativeness of Raw Data • Understand the differences in key terms describing accuracy and representativeness such as accuracy, precision, bias, measurement error, random error, and systematic error • List and assess the potential sources of error in the field data acquisition process at the airport. • Establish a protocol for the information the airport will communicate to the analytical laboratory.
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Interpreting Monitoring Data 79 • Methods for utilizing censored (nondetect) data • Overview of error, uncertainty, and variability This section includes example applications of the described methods.
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80 Interpreting the results of airport Water Monitoring distribution in which the data is distributed symmetrically about the mean and median. Many distribution types are possible and how well the data fit a distribution (i.e., goodness of fit)
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Interpreting Monitoring Data 81 2. Non-parametric Statistical Methods • Data does not need to follow a known distribution to apply statistical analysis methods.
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82 Interpreting the results of airport Water Monitoring distribution)
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Interpreting Monitoring Data 83 pollutants, but the normal distribution was preferred for total dissolved solids, chlorides, sulfate and COD. Maestre et al.
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84 Interpreting the results of airport Water Monitoring However, the formulae should be applied with care. If the data set distribution significantly departs from lognormality, the use of these equations is inappropriate.
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Interpreting Monitoring Data 85 data. This command will also provide the standard deviation, confidence interval, and other parametric statistics.
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86 Interpreting the results of airport Water Monitoring Box Plots Box plots (or box and whisker plots) provide a schematic representation of the central tendency and spread of the data.
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Interpreting Monitoring Data 87 In the comparison of paired or matched data, the extent to which the confidence intervals for the distributions of event concentrations at the inflow and outflow overlap gives a good indication if the medians can be considered statistically different (i.e., the null hypothesis that the inflow and outflow medians are the same can be rejected)
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88 Interpreting the results of airport Water Monitoring Quantile Plots and Probability Plots Quantile plots are used to visually display data for three main reasons: (1) to compare the data distributions of two data sets (called a Q-Q plot)
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Interpreting Monitoring Data 89 in Figure 16 (note the log-scale on the y-axis)
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90 Interpreting the results of airport Water Monitoring Independent Data Sets Independent data sets can be compared using the Mann–Whitney–Wilcoxon rank sum test or the t-test. The rank sum is a non-parametric test of the assumption that two groups arise from the same population (called the null hypothesis)
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Interpreting Monitoring Data 91 concentrations. In some cases, industrial stormwater permits may only require monitoring outfalls from drainage areas that have distinctly different land uses, activities, and stormwater discharge concentrations.
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92 Interpreting the results of airport Water Monitoring the effects of extreme values, the non-parametric Kendall–Theil robust line approach, which computes the slope of the line as the median of all possible pairwise slopes between two data sets, can be used (Granato, 2006)
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Interpreting Monitoring Data 93 2.4.6 Hydrograph Analysis The analysis of flow through a surface water body is an important aspect of the understanding of concentration data. The concentration of a particular parameter is a function of the amount of mass present in a particular volume.
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94 Interpreting the results of airport Water Monitoring median may become biased high or low depending on the level of censoring and the substitution method employed. It is strongly recommended that simple substitution be avoided, especially when the level of censoring exceeds 5 to 10 percent of observed data.
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Interpreting Monitoring Data 95 extreme values in the data set (Helsel, 2005)
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96 Interpreting the results of airport Water Monitoring Key Takeaways Applying Statistical Methodologies • Statistical methods can be used to project the behavior of an entire population of water monitoring data from a subset of the total population. • Statistical methods are not valid under all conditions.
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