Prevalence of wide area impacts downwind of freeways under pre-sunrise stable atmospheric conditions

Choi, Won-Sik; He, Meilu; Barbesant, Vincent; Kozawa, Kathleen; Mara, Steve; Winer, Arthur M.; Paulson, Suzanne E.

doi:10.1016/j.atmosenv.2012.07.084

Cited by 49 publications

(71 citation statements)

References 16 publications

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“…Mobile monitoring data were processed and displayed using MATLAB (2012), ArcGIS (ESRI, 2011), and R version 2.15.1 (R Core Team, 2012) along with the R packages ggplot2 (Wickham, 2009), openair (Carslaw and Ropkins, 2012), and mcgv (Wood, 2003). A noise-reduction algorithm was applied to black carbon concentrations to reduce the frequency of negative values (Hagler et al, 2011).…”

Section: Data-processing Methodsmentioning

confidence: 99%

“…To characterize this spatial variation, dense networks of stationary monitors can be deployed, but mobile monitoring is often preferred because of the increased spatial flexibility (Baldauf et al, 2008;Choi et al, 2012;Durant et al, 2010;Hagler et al, 2012;Kozawa et al, 2009;Zwack et al, 2011a;Rooney et al, 2012;Westerdahl et al, 2005;Drewnick et al, 2012;Massoli et al, 2012). Broader surveys of ambient air quality are also frequently conducted using mobile monitoring on a scale ranging from neighborhood to country in order to characterize regional concentrations or locate previously unknown hotspots (Hagler et al, , 2010Arku et al, 2008;Adams et al, 2012;Farrell et al, 2013;Drewnick et al, 2012;Van Poppel et al, 2013;Hu et al, 2012).…”

Section: Recreational Vehiclementioning

confidence: 99%

“…Because the pollutants are co-emitted, the best estimate of the difference in response times between the instruments can be assumed to correspond with the lag time that produces the maximum correlation coefficient (Choi et al, 2012). CO was chosen as the reference measurement because the quantum cascade laser was the most sensitive instrument with the fastest response time.…”

Section: Experimental Datamentioning

confidence: 99%

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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: Data-processing Methodsmentioning

confidence: 99%

Section: Recreational Vehiclementioning

confidence: 99%

Section: Experimental Datamentioning

confidence: 99%

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

et al. 2014

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“…Therefore, a passing car could distort the result heavily as they are less interested in the local emissions of that car. Hudda et al (2014) and Choi et al (2012) used a rolling percentile to obtain a baseline concentration to discard the local peaks, as they wanted to map the larger scale variations due to the impact of an airport and a highway, respectively. However, in an urban environment with busy traffic, peak concentrations are an integral part of the air quality that people are typically exposed to.…”

Section: Influence Of Events On Spatial Concentration Patternsmentioning

confidence: 99%

“…Other studies address the potential of using mobile measurements to construct air pollution maps at a high spatial resolution (e.g. Hagler et al, 2010;Choi et al, 2012;Hudda et al, 2014;Peters et al, 2014). Mobile monitoring techniques also receive an increasing attention for participatory sensing and crowd-sourcing methods.…”

Section: Introductionmentioning

confidence: 99%

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

Bossche

Peters

Verwaeren

et al. 2015

Atmospheric Environment

196

132

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Mobile monitoring is increasingly used as an additional tool to acquire air quality data at a high spatial resolution. However, given the high temporal variability of urban air quality, a limited number of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodology to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concentrations aggregated at a resolution of 20 m are observed. Mapping at such a high resolution is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalisation, depending on the location 24 to 94 repeated measurement runs (median of 41) are required to map the BC concentrations at a 50 m resolution with an uncertainty of 25 %. When relaxing the uncertainty to 50 %, these numbers reduce to 5 to 11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resolution, and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient number of repetitions in relation to the desired reliability and spatial resolution. Specific data processing methods such as background normalisation and event detection have to be applied.

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Ammonia and methane dairy emission plumes in the San Joaquin Valley of California from individual feedlot to regional scales

et al. 2015

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Agricultural ammonia (NH3) emissions are highly uncertain, with high spatiotemporal variability and a lack of widespread in situ measurements. Regional NH3 emission estimates using mass balance or emission ratio approaches are uncertain due to variable NH3 sources and sinks as well as unknown plume correlations with other dairy source tracers. We characterize the spatial distributions of NH3 and methane (CH4) dairy plumes using in situ surface and airborne measurements in the Tulare dairy feedlot region of the San Joaquin Valley, California, during the NASA Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality 2013 field campaign. Surface NH3 and CH4 mixing ratios exhibit large variability with maxima localized downwind of individual dairy feedlots. The geometric mean NH3:CH4 enhancement ratio derived from surface measurements is 0.15 ± 0.03 ppmv ppmv−1. Individual dairy feedlots with spatially distinct NH3 and CH4 source pathways led to statistically significant correlations between NH3 and CH4 in 68% of the 69 downwind plumes sampled. At longer sampling distances, the NH3:CH4 enhancement ratio decreases 20–30%, suggesting the potential for NH3 deposition as a loss term for plumes within a few kilometers downwind of feedlots. Aircraft boundary layer transect measurements directly above surface mobile measurements in the dairy region show comparable gradients and geometric mean enhancement ratios within measurement uncertainties, even when including NH3 partitioning to submicron particles. Individual NH3 and CH4 plumes sampled at close proximity where losses are minimal are not necessarily correlated due to lack of mixing and distinct source pathways. Our analyses have important implications for constraining NH3 sink and plume variability influences on regional NH3 emission estimates and for improving NH3 emission inventory spatial allocations.

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Prevalence of wide area impacts downwind of freeways under pre-sunrise stable atmospheric conditions

Cited by 49 publications

References 16 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

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

Ammonia and methane dairy emission plumes in the San Joaquin Valley of California from individual feedlot to regional scales

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