Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data

Hagler, Gayle; Vedantham, Ram; Turner, Jay R.

doi:10.4209/aaqr.2011.05.0055

Cited by 210 publications

(134 citation statements)

References 17 publications

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“…Measuring BC with the micro-aethalometer at one second time resolution is challenging due to the occurrence of signal noise, especially at low concentrations. Therefore, a noise reduction algorithm was used (Hagler et al, 2011) (see Section 2.3).…”

Section: Mobile Monitoring Campaignmentioning

confidence: 99%

“…The microaethalometer measurements were compared between the different instruments, and also to the measurements of the MAAP instrument at the monitoring station. Noise reduction was realised using the Optimized Noise-reduction Averaging algorithm (ONA algorithm from Hagler et al, 2011), with an attenuation threshold of 0.05. Rescaling parameters were derived from the comparison of the individual micro-aethalometers with the MAAP instrument from the monitoring station.…”

Section: Data Validation and Quality Controlmentioning

confidence: 99%

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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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Section: Mobile Monitoring Campaignmentioning

confidence: 99%

Section: Data Validation and Quality Controlmentioning

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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“…Carbonaceous matter has been found to be associated with human health and urban environmental problems (von Schneidemesser et al, 2010;Hagler et al, 2011;Pavese et al, 2012;Zhou et al, 2012). It (total carbon: TC) consists of two major components: elemental carbon (EC) and organic carbon (OC).…”

Section: Introductionmentioning

confidence: 99%

Seasonal Study of Primary and Secondary Sources of Carbonaceous Species in PM10 from Five Northern Chinese Cities

Tian

Xiao

Han

et al. 2013

Aerosol Air Qual. Res.

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PM 10 samples were collected from five northern Chinese cities (Tianjin, Anyang, Ji'nan, Kaifeng and Taiyuan) during the winter and summer months. Carbonaceous species concentrations in the PM 10 samples from these cities were then measured. The concentration values show that PM 10 , organic carbon (OC) and elemental carbon (EC) exhibited relatively high concentrations during winter months as opposed to the summer months for all five cities. The contributions from primary and secondary sources were estimated using the Chemical Mass Balance (CMB) and the CMB-Iteration models. The results of two models were comparable, and results showed that the coal combustion and crustal dust were the most important source categories for both seasons. Additionally, seasonal variations of species mass fractions in receptors were analyzed, as well as variations in the estimated source percentage contributions. Seasonal variations of species mass fractions agreed with the variations of source percentage contributions for the five cities. Finally, primary organic carbon (POC) and secondary organic carbon (SOC) concentrations, and their seasonal variations, were estimated. We found that SOC showed relatively high concentrations (μg/m 3 ) in the winter, while the SOC/TOC ratios in the summer were higher than those in the winter.

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“…Measuring BC with the micro-aethalometer at one second time resolution is challenging due to the occurrence of signal noise, especially at low concentrations. Therefore, a noise reduction algorithm, the Optimized Noise-reduction Averaging algorithm (ONA algorithm from Hagler et al, 2011), was used with an attenuation threshold of 0.05.…”

Section: Quality Control and Processing Of The Bc Datamentioning

confidence: 99%

Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp

Bossche

Theunis

Elen

et al. 2016

Atmospheric Environment

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The goal of this paper is to explore the potential of opportunistic mobile monitoring to map the exposure to air pollution in the urban environment at a high spatial resolution. Opportunistic mobile monitoring makes use of existing mobile infrastructure or people's common daily routines to move measurement devices around. Opportunistic mobile monitoring can also play a crucial role in participatory monitoring campaigns as a typical way to gather data.A case study to measure black carbon was set up in Antwerp, Belgium, with the collaboration of city employees (city wardens). The Antwerp city wardens are outdoors for a large part of the day on surveillance tours by bicycle or on foot, and gathered a total of 393 hours of measurements. The data collection is unstructured both in space and time, leading to sampling bias. A temporal adjustment can only partly counteract this bias. Although a high spatial coverage was obtained, there is still a rather large uncertainty on the average concentration levels at a spatial resolution of 50 m due to a limited number of measurements and sampling bias. Despite of this uncertainty, large spatial patterns within the city are clearly captured.This study illustrates the potential of campaigns with unstructured opportunistic mobile monitoring, including participatory monitoring campaigns. The results demonstrate that such an approach can indeed be used to identify broad spatial trends over a wider area, enabling applications including hotspot identification, personal exposure studies, regression mapping, etc. But, they also emphasize the need for repeated measurements and careful processing and interpretation of the data.

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Post-processing Method to Reduce Noise while Preserving High Time Resolution in Aethalometer Real-time Black Carbon Data

Cited by 210 publications

References 17 publications

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

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

Seasonal Study of Primary and Secondary Sources of Carbonaceous Species in PM10 from Five Northern Chinese Cities

Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp

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