Long-term continuous measurement of near-road air pollution in Las Vegas: seasonal variability in traffic emissions impact on local air quality

Kimbrough, Sue; Baldauf, Richard; Hagler, Gayle; Shores, Richard C.; Mitchell, William J.; Whitaker, D. R.; Croghan, Carry; Vallero, Daniel A.

doi:10.1007/s11869-012-0171-x

Cited by 68 publications

(56 citation statements)

References 15 publications

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“…Kimbrough et al (2013), in a near-road study conducted in Las Vegas, Nevada, also found that the background contribution of NO 2 to the total concentration was higher than the background contribution of CO and BC, with measured upwind concentrations approximately 69, 63, and 44 % of downwind concentrations for NO 2 , CO, and BC, respectively. The background contributions measured by Kimbrough et al (2013) are higher than those calculated for the current study, likely because the downwind measurements made by Kimbrough et al (2013) were collected 20 m from the road, while many of the measurements in the current study were collected on the highway or on roads with high traffic volume, causing the total concentrations to be higher and the fraction attributable to regional background to be lower. Upwind concentrations of UFPs measured by Hagler et al (2009) were roughly 30 % of the nearest downwind site and about 50 % of the levels observed at 100 m from the road.…”

Section: Comparison Of Background Estimation Methodsmentioning

confidence: 85%

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

et al. 2014

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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.

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Section: Comparison Of Background Estimation Methodsmentioning

confidence: 85%

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

et al. 2014

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“…This agrees with the results of Peters et al (2014) that the distance to the traffic and the street topology were the dominant factors influencing the BC concentration. Kimbrough et al (2013) also reported that BC tended to be at least 50% higher within the first 50-100 m than sites further from the highway.…”

Section: Walking and Cyclingmentioning

confidence: 88%

Personal exposure to black carbon during commuting in peak and off-peak hours in Shanghai

Lei

Xiu

et al. 2015

Science of The Total Environment

109

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“…The wind flow patterns and the traffic counts confirm that the 12-hour frequency is unlikely due to the daily rush hour traffic (Fig. S2), although it should be noted that traffic patterns in Las Vegas do not have a typical bimodal nature (Kimbrough et al, 2013). Hourly wind direction with shaded area shows winds from the direction of the I-15 indicates that the evening traffic hour rush may be missed almost entirely as favorable winds reset only around 6 PM (Fig.…”

Section: Determination Of the Cut-off Frequencymentioning

confidence: 63%

“…In many other studies of metropolitan areas, the 12-hour timescale has been noted as a result of the dilution effects of the morning rush hour traffic due to the expansion of the Planetary Boundary Layer (PBL) over the rest of the day (Kimbrough et al, 2013). Prior near-road studies confirm that traffic-related ultrafine particle concentrations are generally asymmetrical comparing morning to afternoon periods, as the greater atmospheric mixing in the afternoon tends to decrease roadside pollution concentrations (Hagler et al, 2009, Janhall et al, 2004.…”

Section: Determination Of the Cut-off Frequencymentioning

confidence: 99%

Adaptive Decomposition of Highly Resolved Time Series into Local and Non-Local Components

Vedantham

Hagler

Holm

et al. 2015

Aerosol Air Qual. Res.

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Highly time-resolved air monitoring data are widely being collected over long time horizons in order to characterize ambient and near-source air quality trends. In many applications, it is desirable to split the time-resolved data into two or more components (e.g., local and regional) for apportionment and mitigation purposes. While there may be increased information content in highly time-resolved data, the temporal resolution may also increase entropic effects on the data, thereby dramatically clouding the very information sought in time-resolved data. Specialized methods such as filtering may be required to extract the underlying information content. Constrained and Adaptive Decomposition of Time Series (CADETS) is a new method that can help carve out components of time series based on the content of the frequencies present in the time series. CADETS is also a flexible approach that allows the user to choose the bifurcation point with minimal negative impacts. Using this algorithm, we demonstrate that a time series signal may be decomposed into two useful and interpretable signals that can help identify aspects that may otherwise be hidden or distorted. Using the output from the CADETS algorithm, we show that ultrafine particles (30-100 nm) collected near a major highway may be split into a 64:36 ratio of highly varying (local) and slowly varying (regional) components, meanwhile identical measurements at a background location were estimated to split into a 56:44 local versus regional ratio.

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Long-term continuous measurement of near-road air pollution in Las Vegas: seasonal variability in traffic emissions impact on local air quality

Cited by 68 publications

References 15 publications

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

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

Personal exposure to black carbon during commuting in peak and off-peak hours in Shanghai

Adaptive Decomposition of Highly Resolved Time Series into Local and Non-Local Components

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