Mobile air monitoring data-processing strategies and effects on spatial air pollution trends

Abstract

Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data-processing approaches. The study being reported here utilized 40 h (> 140 000 observations) of mobile monitoring data collected on a roadway network in central North Carolina to explore common data-processing strategies including local emission plume detection, background estimation, and averaging techniques for spatial trend analyses. One-second time resolution measurements of ultrafine particles (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen dioxide (NO 2 ) were collected on 12 unique driving routes that were each sampled repeatedly. The route with the highest number of repetitions was used to compare local exhaust plume detection and averaging methods. Analyses demonstrate that the multiple local exhaust plume detection strategies reported produce generally similar results and that utilizing a median of measurements taken within a specified route segment (as opposed to a mean) may be sufficient to avoid bias in near-source spatial trends.A time-series-based method of estimating background concentrations was shown to produce similar but slightly lower estimates than a location-based method. For the complete data set the estimated contributions of the background to the mean pollutant concentrations were as follows: BC (15 %), UFPs (26 %), CO (41 %), PM 2.5−10 (45 %), NO 2 (57 %), PM 10 (60 %), PM 2.5 (68 %). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset

“…The median can be used as a more representative central tendency measure as opposed to the mean since air quality measurements are not normally distributed and skewed, and so the median tends to be more robust to bias caused by emission events Brantley et al, 2014). However, the results show that the median also leads to much lower BC concentrations than the average concentration.…”

“…The trimmed mean appears to be less sensitive to peaks than the arithmetic mean, but without the large underestimation of the average concentration compared to the median. The COV method as used in Hagler et al (2012) and Brantley et al (2014) to detect events also shows good results. However, this method has the risk that it can mask hotspots where peaks occur systematically.…”

“…For this method, a running 5 s standard deviation of the BC concentrations is calculated and divided by the mean concentration of the entire sampling period. The 99th percentile of the calculated COV is used as a threshold and all data points with a COV above this threshold are removed along with the data points 2 s before and after (Brantley et al, 2014).…”

“…Van Poppel et al (2013) applied background correction to reduce the number of repetitions required to obtain representative results of the spatial variability of pollutants at different micro-environments in a city. The results are also sensitive to the chosen data processing approaches, as stressed by Brantley et al (2014), who explored the influence of different techniques for local emission event detection, background estimation and averaging.…”

Developing High Spatial Resolution Concentration Maps Using Mobile Air Quality Measurements

“…An increasing number of air quality studies use mobile air pollution monitoring. Mobile measurements have several advantages over conventional stationary measurements, including the opportunity for better data coverage, efficient collection of data in close proximity to sources and logistical efficiency (Hagler et al, 2012;Hu et al, 2012;Birmili et al, 2013;Choi et al, 2013;Peters et al, 2013;Brantley et al, 2014;Lähde et al, 2014). Although mobile measurement data can be highly spatially resolved, they are not always presented as high spatial resolution concentration maps.…”