A Simple Technique Based on Digital Images for Determination of Nitrogen Dioxide in Ambient Air

Abstract

The lockdown to prevent the coronavirus spread resulted in an immediate reduction in gas concentration worldwide. This fact shows the importance of nitrogen dioxide as a pollutant gas directly associated with human activities. For indoor exposure, NO 2 has been associated with effects on the respiratory system. In outdoor environments, ozone reaches a maximum after NO 2 peaks, and acid rain arises with NO 2 oxidation to forms nitric acid. Therefore, monitoring the NO 2 concentration in atmospheric air can help prevent respiratory diseases and lower the concentration of other atmospheric pollutants. The experiment proposed in this article uses a low-cost passive sampler for the NO 2 collection. An innovative and straightforward technique to determine the gas concentration through a gel-dyed formation and based on digital image analysis RGB colors channel are split by the software ImageJ. Results of digital image analysis and spectrophotometry were statistically agreed at a 95% confidence level. The advantages of the technique include low cost, the ready availability of components, ease of use, and sensitivity. The achievable resolution of nitrogen dioxide concentrations is 9 ppb for 24-h sampling. Supplementary Information The online version contains supplementary material available at 10.1007/s11270-021-05031-4.

Smartphone-Based Color Evaluation of Passive Samplers for Gases: A Review

“…(2) In a paper by Souza et al [73], a passive sampling method is described for the determination of nitrogen dioxide through the formation of a colored dye, followed by digital image analysis of the resulting color intensity. The passive samplers were purposemade from conical Falcon tubes used in chemical laboratories cut to the desired size.…”

Smartphone digital image colorimetry for quantification of serum proteins

“…27 Typically, one of the channels is more intense than the others; this channel is chosen for quantication by translating the pixel intensity of the image into a value that is correlated with the analyte concentration. 28 SDIC has opened colorimetric analysis to low-budget laboratories including a wider range of researchers and students 29 and has been applied in the analysis of a variety of samples such as food, 28,[30][31][32] environmental samples, 33 alloys, 34,35 drugs [36][37][38] and vitreous humor. 39 In this study, we expand the applicability of SDIC to the quantication of serum proteins.…”

“…SDIC has opened colorimetric analysis to low-budget laboratories including a wider range of researchers and students 29 and has been applied in the analysis of a variety of samples such as food, 28,30–32 environmental samples, 33 alloys, 34,35 drugs 36–38 and vitreous humor. 39 In this study, we expand the applicability of SDIC to the quantification of serum proteins.…”