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Precision is a concept for which there is no universally accepted metric. Reports of precision vary depending on the formula and inclusion criteria used to calculate them. To properly interpret and utilize reported precisions, the user must understand exactly what the precision represents. This paper uses duplicate Interagency Monitoring of Protected Visual Environments (IMPROVE) measurements to illustrate distinctions among different approaches to reporting precision. Three different metrics are used to estimate the precision from the relative differences between the duplicate measurements: the root mean square (RMS), the mean absolute value, and a percentile spread. Precisions calculated using the RMS relative difference yield wide distributions that tend to overestimate most of the observed differences. Precisions calculated using percentiles of the relative differences yield narrower distributions that tend to fit the bulk of the observed differences very well. Precisions calculated using the mean absolute relative difference lie between the other two precision estimates. All three approaches underestimate the observed differences for a small percentage of outliers. INTRODUCTIONPrecision is defined by the International Standardization Organization (ISO) as "the closeness of agreement between quantity values obtained by replicate measurements of a quantity, under specified conditions…precision is usually expressed numerically by measures of imprecision, such as standard deviation, variance, or coefficient of variation under the specified conditions of measurement." 1 As noted by the ISO definition, there is no single prescribed formula for reporting precision. The quantitative expression of precision is subjectively chosen, and multiple issues must be considered when choosing a method, including whether to express the precision in dimensional or nondimensional terms, what range of concentrations to include in the precision calculations, and how to aggregate measurements made under differing conditions.

Particulate matter (PM)2.5 dust concentrations (mineral particles with aerodynamic diameters less than 2.5 µm) typically peak in spring and early summer at rural and remote sites across the southwestern United States. Trend analyses indicate that springtime regional mean PM2.5 dust concentrations have increased from 1995 to 2014, especially in March (5.4% yr−1, p < 0.01). This increase reflects an earlier onset of the spring dust season across the Southwest by 1 to 2 weeks over the 20 year time period. March dust concentrations were strongly correlated with the Pacific Decadal Oscillation index (r = −0.65, p < 0.01), which was mostly in its negative phase from 2007 to 2014, during which the region was drier, windier, and less vegetated. The positive spring trend and its association with large‐scale climate variability have several important implications for visibility, particulate matter, health effects, and the hydrologic cycle in the region.

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The IMPROVE (Interagency Monitoring of Protected Visual Environments) network has characterized fine particulate matter composition at locations throughout the United States since 1988. A main objective of the network is to evaluate long-term trends in aerosol concentrations. Measurements inevitably advance over time, but changes in measurement technique have the potential to confound the interpretation of long-term trends. Problems of interpretation typically arise from changing biases, and changes in bias can be difficult to identify without comparison data that are consistent throughout the measurement series, which rarely exist. We created a consistent measurement series for exactly this purpose by reanalyzing the 15-year archives (1995-2009) of aerosol samples from three sites - Great Smoky Mountains National Park, Mount Rainier National Park, and Point Reyes National Seashore-as single batches using consistent analytical methods. In most cases, trend estimates based on the original and reanalysis measurements are statistically different for elements that were not measured above the detection limit consistently over the years (e.g., Na, Cl, Si, Ti, V, Mn). The original trends are more reliable for elements consistently measured above the detection limit. All but one of the 23 site-element series with detection rates >80% had statistically indistinguishable original and reanalysis trends (overlapping 95% confidence intervals).