Ion chromatography of nitrite at the ppb level with photometric measurement of iodine formed by post-column reaction of nitrite with iodide

Miura, Yasuyuki; Hamada, Hiroshi

doi:10.1016/s0021-9673(99)00352-0

Cited by 18 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such concentrations are far beyond the detection limit of currently used chemical snow analysis methods (e.g. [55][56][57]). Nevertheless, NO 2 − concentrations up to 1.8 × 10 −7 M in arctic snow samples were reported at depths of 25 cm, while the concentrations remained below 7 × 10 −8 M at the surface [58].…”

Section: Comparison With Field Data: No X Emissionsmentioning

confidence: 92%

A mechanism for the photochemical transformation of nitrate in snow

Jacobi

Hilker

2007

Journal of Photochemistry and Photobiology A: Chemistry

110

150

View full text Add to dashboard Cite

Photochemical reactions of trace compounds in snow have important implications for the composition of the atmospheric boundary layer in snow-covered regions and for the interpretation of concentration profiles in snow and ice regarding the composition of the past atmosphere. One of the prominent reactions is the photolysis of nitrate, which leads to the formation of OH radicals in the snow and to the release of reactive nitrogen compounds, like nitrogen oxides (NO and NO 2 ) and nitrous acid (HONO) to the atmosphere. We performed photolysis experiments using artificial snow, containing variable initial concentrations of nitrate and nitrite, to investigate the reaction mechanism responsible for the formation of the reactive nitrogen compounds. Increasing the initial nitrite concentrations resulted in the formation of significant amounts of nitrate in the snow. A possible precursor of nitrate is NO 2 , which can be transformed into nitrate either by the attack of a hydroxy radical or the hydrolysis of the dimer (N 2 O 4 ). A mechanism for the transformation of the nitrogen-containing compounds in snow was developed, assuming that all reactions took place in a quasi-liquid layer (QLL) at the surface of the ice crystals. The unknown photolysis rates of nitrate and nitrite and the rates of NO and NO 2 transfer from the snow to the gas phase, respectively, were adjusted to give an optimum fit of the calculated time series of nitrate, nitrite, and gas phase NO x with respect to the experimental data. Best agreement was obtained with a ∼25 times faster photolysis rate of nitrite compared to nitrate. The formation of NO 2 is probably the dominant channel for the nitrate photolysis. We used the reaction mechanism further to investigate the release of NO x and HONO under natural conditions. We found that NO x emissions are by far dominated by the release of NO 2 . The release of HONO to the gas phase depends on the pH of the snow and the HONO transfer rate to the gas phase. However, due to the small amounts of nitrite produced under natural conditions, the formation of HONO in the QLL is probably negligible. We suggest that observed emissions of HONO from the surface snow are dominated by the heterogeneous formation of HONO in the firn air. The reaction of NO 2 on the surfaces of the ice crystals is the most likely HONO source to the gas phase.

show abstract

Section: Comparison With Field Data: No X Emissionsmentioning

confidence: 92%

A mechanism for the photochemical transformation of nitrate in snow

Jacobi

Hilker

2007

Journal of Photochemistry and Photobiology A: Chemistry

110

150

View full text Add to dashboard Cite

show abstract

“…The SERS sensor showed lower LOD than the common techniques used for nitrite detection, such as chromatography and Griess test. The LODs are typically 1.6~75 ppb and 23~115 ppb for chromatography 42,43 and Griess test 44 , respectively. Such a low LOD for the SERS sensor resulted from the SERS enhancement factor of ~10 10 , demonstrating high sensitivity of the SERS sensor.…”

Section: Resultsmentioning

confidence: 99%

Detection of nitrite with a surface-enhanced Raman scattering sensor based on silver nanopyramid array

Zheng

Kasani

Shi

et al. 2018

Analytica Chimica Acta

View full text Add to dashboard Cite

Nutrient pollution is of worldwide environmental and health concerns due to extensive use of nitrogen fertilizers and release of livestock waste, which induces nitrite compounds in aquatic systems. Here-in a surface-enhanced Raman scattering (SERS) sensor is developed for nitrite detection based on coupling between the plasmonic gold nanostars and the silver nanopyramid array. When nitrite is present in the assay, an azo group is formed between the 1-naphthylamine-functionalized silver nanopyramids and the 4-aminothiophenol-functionalized gold nanostars. This not only generates the SERS spectral finger-print for selective detection, but also creates “hot spots” at the gap between the Au nanostars and the Ag nanopyramids where the azo group is located, amplifying SERS signals remarkably. Finite-difference time-domain (FDTD) simulation shows a SERS enhancement factor of 4×1010 at the “hot spots”. As a result, the SERS sensor achieves a limit of detection of 0.6 pg/mL toward nitrite in water, and enables nitrite detection in real-world river water samples. In addition, this sensor eliminates the use of any Raman reporter and any expensive molecular recognition probe such as antibody and aptamer. This highly sensitive, selective and inexpensive SERS sensor has unique advantages over colorimetric, electrochemical and fluorescent devices for small molecule detection.

show abstract

“…10,11 Numerous analytical techniques are used for the detection of NO 2 À and S 2 O 3 2À with good sensitivity. Some of the common hyphenated techniques include gas chromatography-mass spectrometry (GC-MS), 12,13 liquid chromatography-mass spectrometry (LC-MS), 14,15 HPLC, 16 uorescence detection, 17 and electrochemical methods. 18 Electrochemical techniques are ideal analytical and detection methods because of their sensitive response, rapid detection, simple operation, low cost, and portability.…”

Section: Introductionmentioning

confidence: 99%