Characterizing local traffic contributions to particulate air pollution in street canyons using mobile monitoring techniques

Zwack, Leonard M.; Paciorek, Christopher J.; Spengler, John D.; Levy, Jonathan I.

doi:10.1016/j.atmosenv.2011.02.035

Cited by 72 publications

(32 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zwack et al (2011) investigated the contribution of local traffic to PM concentrations in street canyons of Manhattan. GPS receivers continuously tracked the volunteer's movements along designated walking routes at specific times.…”

Section: Gps Enabled Personal Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying human exposure to air pollution—Moving from static monitoring to spatio-temporally resolved personal exposure assessment

Steinle

Reis

Sabel³

2013

Science of The Total Environment

349

231

View full text Add to dashboard Cite

Eric. 2013. Quantifying human exposure to air pollution: moving from static monitoring to spatiotemporally resolved personal exposure assessment.Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. AbstractQuantifying human exposure to air pollutants is a challenging task. Ambient concentrations of air pollutants at potentially harmful levels are ubiquitous in urban areas and subject to high spatial and temporal variability. At the same time, every individual has unique activitypatterns. Exposure results from multifaceted relationships and interactions between environmental and human systems, adding complexity to the assessment process. Traditionally, approaches to quantify human exposure have relied on pollutant concentrations from fixed air quality network sites and static population distributions. New developments in sensor technology now enable us to monitor personal exposure to air pollutants directly while people are moving through their activity spaces and varying concentration fields. The literature review on which this paper is based on reflects recent developments in the assessment of human exposure to air pollution. This includes the discussion of methodologies and concepts, and the elaboration of approaches and study designs applied in the field. We identify shortcomings of current approaches and discuss future research needs. We close by proposing a novel conceptual model for the integrated assessment of human exposure to air pollutants taking into account latest technological capabilities and contextual information.

show abstract

Section: Gps Enabled Personal Monitoringmentioning

confidence: 99%

“…Study designs applying GPS technology (e.g. Greaves et al, 2008;Zwack et al, 2011) are similar to personal monitoring studies which do not apply GPS receivers -looking at designated routes and times in specific MEs (e.g. Gulliver and Briggs, 2004;Kaur et al, 2005).…”

Section: Personal Monitoring -Following Every Move?mentioning

confidence: 99%

Quantifying human exposure to air pollution—Moving from static monitoring to spatio-temporally resolved personal exposure assessment

Steinle

Reis

Sabel³

2013

Science of The Total Environment

349

231

View full text Add to dashboard Cite

show abstract

“…It can provide insight into the spatial variability that would not be possible with stationary monitors. Previous case studies already showed the potential of repeated mobile measurements for the assessment of spatial variability of pollutants at different micro-environments in a city at street level Peters et al, 2013) and within street-level (Zwack et al, 2011b;Peters et al, 2014).…”

Section: Mobile Monitoring To Assess Spatial Variationmentioning

confidence: 99%

“…Especially for traffic-related air pollutants such as NOx and fine particulate matter (PM), these differences can occur on a small scale (Seinfeld and Pandis, 2012;Kaur et al, 2007) and are important to take into account for mapping the pollution levels and accurate exposure assessment . In contrast to traditional stationary monitoring stations, mobile platforms are able to acquire air quality data at a high spatial resolution (Wallace et al, 2009;Zwack et al, 2011b). But at the same time, due to the high temporal variability of the urban air quality and the mobile nature of the measurements, the representativeness of the mobile measurements is a major issue.…”

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

177

136

View full text Add to dashboard Cite

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.

show abstract

“…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%

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

et al. 2014

View full text Add to dashboard Cite

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.

show abstract

Characterizing local traffic contributions to particulate air pollution in street canyons using mobile monitoring techniques

Cited by 72 publications

References 19 publications

Quantifying human exposure to air pollution—Moving from static monitoring to spatio-temporally resolved personal exposure assessment

Quantifying human exposure to air pollution—Moving from static monitoring to spatio-temporally resolved personal exposure assessment

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

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

Contact Info

Product

Resources

About