Passive sampling of ambient, gaseous air pollutants: an assessment from an ecological perspective

Krupa, Sagar V.; Legge, A.H.

doi:10.1016/s0269-7491(99)00154-2

Cited by 190 publications

(115 citation statements)

References 71 publications

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“…Passive sampling techniques have several advantages such as ease of deployment, no power requirements and low maintenance, and have been used as an alternative to continuous monitors for monitoring temporal trends of air pollutants in remote areas (Krupa and Legge, 2000;Cox, 2003;Seethapathy et al, 2008;Bytnerowicz et al, 2010) and evaluation of air quality of large areas (Gerboles et al, 2006). Their disadvantages are low sensitivity, inability to resolve short duration 15 concentration peaks, and adverse effects of meteorological conditions on reported observations (Tang et al 1997(Tang et al , 1999Krupa and Legge, 2000;Tang, 2001;Kirby et al, 2001;Partyka et al, 2007;Fraczek et al, 2009;Salem et al, 2009;Zabiegala et al, 2010).…”

Section: Monitoring Datamentioning

confidence: 99%

Associativity Analysis of SO2 and NO2 for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Soares

Makar

Aklilu

et al. 2018

Preprint

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Abstract. Associativity analysis is a powerful tool to deal with large-scale datasets by clustering the data on the basis of (dis)similarity, and can be used to assess the efficacy and design of air-quality monitoring networks. We describe here our use of Kolmogorov-Zurbenko filtering and hierarchical clustering of NO 2 and SO 2 passive and continuous monitoring data, 10 to analyse and optimize air quality networks for these species in the province of Alberta, Canada. The methodology applied in this study assesses dissimilarity between monitoring station time series based on two metrics: 1-R, R being the Pearson correlation coefficient, and the Euclidean distance. We have combined the analytic power of hierarchical clustering with the spatial information provided by deterministic air quality model results, using the gridded time series of model output as potential station locations, as a proxy for assessing monitoring network design and for network optimization. We find that 15 both metrics should be used to evaluate the similarity between monitoring time series, since this allows a cross-comparison in terms of temporal variation and magnitude of concentrations to assess station potential redundancy. Here, the relative level of potential redundancy of an existing monitoring location was ranked according to each dissimilarity metric, with sites forming clusters at low values of both 1-R and Euclidean distance being the most redundant. We demonstrate clustering results depend on the air contaminant analyzed, reflecting the difference in the respective emission sources of SO 2 and NO 2 20 in the region under study. Our work shows that much of the signal identifying the sources of NO 2 and SO 2 emissions resides in shorter time scales (hourly to daily) due to short-term variation of concentrations. However, the methodology nevertheless identifies stations mainly influenced by seasonality, if larger time scales (weekly to monthly) are considered. We have found that data consisting of longer-term averages may lose the short-term variation needed to identify local sources, implying that long-term averaged observations are not suitable for source identification purposes. In addition to averaging time, round-off 25 levels in data reports, and the accuracy of instrumentation were also shown to have a negative influence on the clustering results. We have performed the first dissimilarity analysis based on gridded air-quality model output, and have shown that the methodology is capable of generating maps of sub-regions within which a single station will represent the entire subregion, to a given level of dissimilarity. Maps of this nature may be combined with other georeferenced data (e.g. road networks, power availability) to assist in monitoring network design. We have also shown that our methodology is capable 30 of identifying different sampling methodologies, as well as identifying outliers (stations' time series which are markedly different from all others in a given dataset).

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Section: Monitoring Datamentioning

confidence: 99%

Associativity Analysis of SO2 and NO2 for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Soares

Makar

Aklilu

et al. 2018

Preprint

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“…The detection limit of this method, for 1 h exposure at 25 C, is 25 mg/m 3 (corresponding to a mass, m, of 0.105 mg), whereas the uncertainty (2s) is ±8.7% (Fondazione Maugeri, 2007). However, recent studies (Koutrakis et al, 1993;Gair and Penkett, 1995;Brown, 2000;Krupa and Legge, 2000;Pennequin-Cardinal et al, 2005) pinpointed that weather conditions (e.g. humidity, temperature, wind speed and direction) can affect the efficiency of the Radiello ® traps, although a reliable evaluation of their effective influence on passive H 2 S measurements is not available.…”

Section: Radiello ® Trapsmentioning

confidence: 99%

“…The low cost and no power supply requirement of the passive traps allow their use at multiple locations, in order to easily obtain a general view about the spatial distribution of the air pollutant (Hangartner, 2000). However, this method implies a relatively long exposure time (up to several days), preventing the detection of short-term temporal variations (faster than the exposure time) of the pollutant concentrations (Krupa and Legge, 2000). In addition, the efficiency of the traps is strongly affected by several environmental factors, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the efficiency of the traps is strongly affected by several environmental factors, e.g. temperature, humidity and wind speed (Brown, 2000;Krupa and Legge, 2000;Delgado-Saborit and EsteveCano, 2006), whose effects are difficult to quantify. Analytical instrumentations equipped with high-sensitivity detectors have successfully been used to provide active (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Hydrogen sulfide measurements in air by passive/diffusive samplers and high-frequency analyzer: A critical comparison

Venturi

Cabassi

Tassi

et al. 2016

Applied Geochemistry

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“…Passive samplers are substantially less costly to construct and deploy than typical central monitoring equipment because they do not require a pump, flow control equipment, or a power source (Krupa and Legge 2000;Nothstein et al 2000). Briggs et al (2000) used passive samplers to measure nitrogen dioxide at 80 sites in four metropolitan areas to develop an air quality model.…”

Section: Introductionmentioning

confidence: 99%

A Shelter to Protect a Passive Sampler for Coarse Particulate Matter, PM_{10 − 2.5}

Ott¹,

Peters²

2008

Aerosol Science and Technology

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This work designed and tested a shelter to protect a passive sampler for measuring coarse particulate matter, PM 10−2.5 . The shelter protects the sampler from precipitation and reduces the effects of wind on the deposition of particles to its collection surface. Six shelters were tested in a wind tunnel at three wind speeds: 2, 8, and 24 km hr −1 . Shelter performance was expressed as the ratio of PM 10−2.5 measured with the passive samplers to that measured with a filter-based dichotomous sampler. For most shelters, the PM 10−2.5 ratio averaged across wind speeds was well above one (2.4 to 8.5) and was generally dependent on wind speed. However, the PM 10−2.5 ratio for one shelter, the Flat Plates shelter, was 1.04 with substantially less effect on particle deposition from wind speed.

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Passive sampling of ambient, gaseous air pollutants: an assessment from an ecological perspective

Cited by 190 publications

References 71 publications

Associativity Analysis of SO<sub>2</sub> and NO<sub>2</sub> for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Associativity Analysis of SO<sub>2</sub> and NO<sub>2</sub> for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Hydrogen sulfide measurements in air by passive/diffusive samplers and high-frequency analyzer: A critical comparison

A Shelter to Protect a Passive Sampler for Coarse Particulate Matter, PM_{10 − 2.5}

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Passive sampling of ambient, gaseous air pollutants: an assessment from an ecological perspective

Cited by 190 publications

References 71 publications

Associativity Analysis of SO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;2&lt;/sub&gt; for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Associativity Analysis of SO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;2&lt;/sub&gt; for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Hydrogen sulfide measurements in air by passive/diffusive samplers and high-frequency analyzer: A critical comparison

A Shelter to Protect a Passive Sampler for Coarse Particulate Matter, PM10 − 2.5

Contact Info

Product

Resources

About

Associativity Analysis of SO<sub>2</sub> and NO<sub>2</sub> for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

Associativity Analysis of SO<sub>2</sub> and NO<sub>2</sub> for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering

A Shelter to Protect a Passive Sampler for Coarse Particulate Matter, PM_{10 − 2.5}