Effects of land use and nutrients on the characteristics of dissolved organic matter in the Nanchong Section of Jialing River, China in December 2019

Chromophoric dissolved organic matter (CDOM) in aquatic ecosystems can reflect the impacts of human activities on the carbon-cycling process. However, direct evidence of the combined effect of land use and anthropogenic nutrients on CDOM characteristics in river ecosystems is limited. Herein, we collected water samples from 18 sites in the Nanchong section of Jialing River in December 2019 to elucidate how the land use and nutrients affect the source and composition of CDOM through parallel factor (PARAFAC) analysis of excitation–emission matrices (EEMs). First, the absorption coefficient a254 (r2=0.29, p<0.01) and three fluorescence components (humic-like C1 and C2 and protein-like C3) (r2=0.31–0.37, p<0.01) significantly increased with increased urban area, and the four parameters were higher in the urban than in the suburb (p<0.05). The correlation between small CDOM molecule and cropland land was positive (p<0.01). Second, the increase in nutrient levels increased the a254 (r2=0.84 and 0.33, p<0.01) and three fluorescence components (r2=0.30–0.84, p<0.01 or p<0.05). Third, allochthonous CDOM were prevalent in the Nanchong Section of Jialing River, and the proportions of C1 and C2 were 42 and 41%, respectively. Our findings indicated that the variability of source and composition of CDOM significantly depended on urbanization and increased nutrients in the Nanchong Section of Jialing River.

Assessment of Empirical and Semi-Analytical Algorithms Using MODIS-Aqua for Representing In-Situ Chromophoric Dissolved Organic Matter (CDOM) in the Bering, Chukchi, and Western Beaufort Seas of the Pacific Arctic Region

We analyzed a variety of satellite-based ocean color products derived using MODIS-Aqua to investigate the most accurate empirical and semi-analytical algorithms for representing in-situ chromophoric dissolved organic matter (CDOM) across a large latitudinal transect in the Bering, Chukchi, and western Beaufort Seas of the Pacific Arctic region. In particular, we compared the performance of empirical (CDOM index) and several semi-analytical algorithms (quasi-analytical algorithm (QAA), Carder, Garver-Siegel-Maritorena (GSM), and GSM-A) with field measurements of CDOM absorption (aCDOM) at 412 nanometers (nm) and 443 nm. These algorithms were compared with in-situ CDOM measurements collected on cruises during July 2011, 2013, 2014, 2015, 2016, and 2017. Our findings show that the QAA a443 and GSM-A a443 algorithms are the most accurate and robust representation of in-situ conditions, and that the GSM-A a443 algorithm is the most accurate algorithm when considering all statistical metrics utilized here. Our further assessments indicate that geographic variables (distance to coast, latitude, and sampling transects) did not obviously relate to algorithm accuracy. In general, none of the algorithms investigated showed a statistically significant agreement with field measurements beyond an approximately ± 60 h offset, likely owing to the highly variable environmental conditions found across the Pacific Arctic region. As such, we suggest that satellite observations of CDOM in these Arctic regions should not be used to represent in-situ conditions beyond a ± 60 h timeframe.

Machine Learning Algorithms for Chromophoric Dissolved Organic Matter (CDOM) Estimation Based on Landsat 8 Images

Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m−1. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R2 = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R2 = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R2 = 0.71 and MRE = 24.4% for random forest regression (RFR) and R2 = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R2 less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

Binding of Calcium and Magnesium Ions to Terrestrial Chromophoric Dissolved Organic Matter (CDOM): A Combination of Steady-State and Time-Resolved Fluorescence Study

Revealing the binding properties of calcium ion (Ca2+) and magnesium ion (Mg2+) to terrestrial chromophoric dissolved organic matter (CDOM) facilities understanding the effect of natural water components on the photophysics of dissolved organic matter. Steady-state and time-resolved fluorescence spectrometry, and dynamic light scattering were applied to investigate the fluorescence quenching process of CDOM by Ca2+ and Mg2+. Due to a remarkable decrease of the steady-state fluorescence intensity and a slight decrease of fluorescence lifetime, the fluorescence quenching of CDOM by cations mainly occurred through a static process. The fluorescence quenching was profound under longer excitation and emission wavelengths. The binding constant (K, L/mol) for Ca2+ to CDOM ranged from 4.29 to 5.09 (lgK), which was approximately one order of magnitude higher than that of Mg2+ to CDOM (3.86 to 4.56). Nevertheless, the efficiency of CDOM fluorescence quenching by Ca2+, Mg2+ was much lower than that by Cu2+. Fluorescence decay became faster in the presence of a high concentration of Ca2+ (>20 mg/L) and Mg2+ (>50 mg/L). In the presence of these two metal ions, particularly for Ca2+, the lifetime of CDOM excited states shifted to the relatively small value side, indicating fluorescence quenching of CDOM mainly occurred through the interaction of Ca2+/Mg2+ with relatively long-lived fluorophores.