Comparison of UV–VIS and FDOM sensors for in situ monitoring of stream DOC concentrations

Abstract

Abstract. Optical measurements using ultraviolet-visible (UV-VIS) spectrophotometric sensors and fluorescent dissolved organic matter (FDOM) sensors have recently been used as proxies of dissolved organic carbon (DOC) concentrations in streams and rivers at a high temporal resolution. Despite the merits of the sensors, temperature changes and particulate matter in water can interfere with the sensor readings, over-or underestimating DOC concentrations. However, little efforts have been made to compare responses of the two types of the sensors to critical interferences such as temperature and turbidity. The performance of a UV-VIS sensor and an FDOM sensor was compared in both laboratory experiments and in situ monitoring in a forest stream in Korea during three storm events. Although the UV-VIS sensor did not require temperature correction in laboratory experiments using the forest stream water, the deviations of its values from the DOC concentrations measured with a TOC analyzer increased linearly as turbidity increased. In contrast, the FDOM sensor outputs decreased significantly as temperature or turbidity increased, requiring temperature and turbidity correction for in situ monitoring of DOC concentrations. The results suggest that temperature correction is relatively straightforward but turbidity correction may not be simple because the attenuation of light by particles can significantly reduce the sensitivity of the sensors in highly turbid waters. Shifts in composition of fluorophores also need to be carefully tracked using periodically collected samples since light absorbance and fluorescence can vary as the concentrations of dominant fluorophores change.

Short-Term Dissolved Organic Carbon Dynamics Reflect Tidal, Water Management, and Precipitation Patterns in a Subtropical Estuary

“…Sequential removal of turbidity was achieved by siphoning 250 mL aliquots from the stirred vessel, filtering and then returning the filtrate to solution ( Figure S2). As previously observed, highly turbid waters attenuated FDOM fluorescence considerably (Downing et al, 2012;Lee et al, 2015). Less turbid conditions (values < 100 FNU) showed a strong linear relationship (r = 0.99, p < 0.0001, n = 216) to FDOM ( Figure S2, values <100 FNU).…”

“…To assess fluorescence interference due to suspended solids (measured as turbidity), a turbid solution containing 15 g of mangrove sediment collected at SRS-5 (dried, ground, and sieved through a 30-mesh screen) and ∼4 L of filtered low-absorbance (A 254 : 0.09, a 254 : 21 m −1 ) mangrove creek water was mixed (water was filtered using combusted 0.7 µm GF/F filters). The solution was equilibrated for 48 h to negate potential FDOM inputs leached from the sediments (Downing et al, 2012;Lee et al, 2015). The solution was then placed in a constantly stirred vessel connected to a YSI flow-cell containing the FDOM and turbidity sensors with previously acid-washed tubing and a peristaltic pump.…”

A systematic examination of the relationships between CDOM and DOC in inland waters in China

“…Comparing Tables S5 and S6, the regression model performances were degraded, but still acceptable. The large slope variations in regressions between DOC and a CDOM (275) in different types of waters are probably due to the aromaticity and colored fractions in the DOC component (Spencer et al, 2009(Spencer et al, , 2012Lee et al, 2015). As shown in Fig.…”

“…This condensed effect was particularly marked in these shallow water bodies where ice forming remarkably condensed the DOC in the underlying waters (Zhao et al, 2016a). It also should be noted that DOC concentration has a strong connection with hydrological condition and catchment landscape features (Neff et al, 2006;Ågren et al, 2007;Lee et al, 2015). It should be noted that large DOC variations were observed in saline lakes in different regions (Table 2).…”

“…The relationships between CDOM and DOC in river and stream waters are very variable due to the hydrological variability and catchment features (Ågren et al, 2010;Spencer et al, 2012;Ward et al, 2013;Lee et al, 2015;Zhao et al, 2017). As shown in Fig.…”