A New All-Season Passive Sampling System for Monitoring H2S in Air

Tang, Hongmao; Sandeluk, James; Lin, Linda; Lown, J. William

doi:10.1100/tsw.2002.87

Cited by 14 publications

(24 citation statements)

References 22 publications

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“…The all-season SO 2 passive sampling system (SPSS) (Tang et al, 1997), all-season NO 2 passive sampling system (NPSS) (Tang et al, 1999) and O 3 passive sampling system (OPSS) (Tang and Lau, 2000) were used to measure SO 2 , NO 2 and O 3 . As shown in Fig.…”

Section: Sampling and Analysismentioning

confidence: 99%

Trends in Passively-Measured Ozone, Nitrogen Dioxide and Sulfur Dioxide Concentrations in the Athabasca Oil Sands Region of Alberta, Canada

Hsu

2013

Aerosol Air Qual. Res.

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The Athabasca Oil Sands Region (AOSR) in northeastern Alberta, Canada has attracted much international attention in recent years due to the increased level of oil sands operations. A passive sampling program was initiated in 1999 to monitor ozone (O 3 ), nitrogen dioxide (NO 2 ) and sulfur dioxide (SO 2 ) in the AOSR for the estimation of the exposure of the forest monitoring sites and the characterization of temporal trends. Since 1999, highest concentrations of O 3 and NO 2 occurred in April and winter, respectively. The observed spring O 3 maximum is common in the northern hemisphere. The higher winter-time NO 2 concentrations were due to low atmospheric mixing height, stable atmosphere, and higher emissions during winter.Sen-Theil trend analysis, a non-parametric analysis for temporal trending, determined that O 3 concentrations from 2000 to 2009 did not change. NO 2 concentrations increased slightly at three sites, and significantly increased at two sites closer to stationary and mobile sources. SO 2 concentration was increasing at JP107 and was decreasing at JP101. SO 2 concentrations did not increase at 4 other sites close to the major emissions. This suggests that SO 2 emissions were likely stable.Spatial analysis was conducted to characterize the concentration distribution in the region. The O 3 concentrations were low near the emission sources (9.4 km) likely due to local O 3 titration. Highest NO 2 and SO 2 concentrations were measured near the main source area. Generally, passively measured monthly average concentrations of O 3 , NO 2 and SO 2 stabilized at 20, 48 and 48 km from the main source area suggesting NO 2 and SO 2 emission influences were limited to < 50 km away from the major sources. However, one site (JP107) located near the Athabasca River Valley, 94 km north of the main source area, had higher SO 2 and NO 2 concentrations. This could be attributed to influence of valley flow, and/or to additional sources added in the region since 2007.

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Section: Sampling and Analysismentioning

confidence: 99%

Trends in Passively-Measured Ozone, Nitrogen Dioxide and Sulfur Dioxide Concentrations in the Athabasca Oil Sands Region of Alberta, Canada

Hsu

2013

Aerosol Air Qual. Res.

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“…Therefore, the key factor for using passive samplers is how to determine their sampling rates. Tang et al have reported using equations to address the problems [4][5][6][7]. Field applications demonstrate that Tang's approach was practically useful in many cases.…”

Section: Amec Multigas Passive Sampling System -A Patented Technologymentioning

confidence: 99%

Integrative air quality management airshed by using multigas passive sampling technology in Canada

Tang¹,

Apon²

2012

Environmental Impact

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Environmental impacts including air pollution have become more important as the world toward sustainable development. To properly assess a large area's impact of air pollution requires modeling prediction and/or real measurement by using active and/or passive methods. Real-time active methods can provide instant measurement results, which is an advantage that passive technology does not have. But passive technology is easy to use and very cost effective. Therefore, many air pollution monitoring networks normally combine active and passive methods together. In the past decades, a new strategy for air quality management has been developed in Canada. It is an integrative air quality management or airshed management zone (AMZ), which includes local stakeholders and therefore can extensively use local resources to address many local concerns. The application of a newly patented technology -Multigas Passive Sampling Technology in the AMZ will be described.

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“…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). Moreover, the passive monitors depend on monthly meteorological information, needed in order to calculate diffusion rates.…”

Section: Monitoring Datamentioning

confidence: 99%

“…In contrast, passive sampling is carried out in order to determine monthly average ambient air concentrations of atmospheric 5 compounds for determination of long-term trends, assessment of potential ecological exposure risks, and to understand the spatial distribution of the measured pollutant. The majority of the Alberta passive monitors for NO 2 and SO 2 were developed by Maxxam Analytics Inc. (Tang et al, 1997;Tang et al, 1999;Tang, 2001), with the exception of those employed by PAS (PAS, 2016). The detection limit for 30-day average NO 2 and SO 2 sampling periods with these samplers is 0.1 ppbv.…”

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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A New All-Season Passive Sampling System for Monitoring H₂S in Air

Cited by 14 publications

References 22 publications

Trends in Passively-Measured Ozone, Nitrogen Dioxide and Sulfur Dioxide Concentrations in the Athabasca Oil Sands Region of Alberta, Canada

Trends in Passively-Measured Ozone, Nitrogen Dioxide and Sulfur Dioxide Concentrations in the Athabasca Oil Sands Region of Alberta, Canada

Integrative air quality management airshed by using multigas passive sampling technology in Canada

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

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A New All-Season Passive Sampling System for Monitoring H2S in Air

Cited by 14 publications

References 22 publications

Trends in Passively-Measured Ozone, Nitrogen Dioxide and Sulfur Dioxide Concentrations in the Athabasca Oil Sands Region of Alberta, Canada

Trends in Passively-Measured Ozone, Nitrogen Dioxide and Sulfur Dioxide Concentrations in the Athabasca Oil Sands Region of Alberta, Canada

Integrative air quality management airshed by using multigas passive sampling technology in Canada

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

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A New All-Season Passive Sampling System for Monitoring H₂S in Air

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