Organic carbon transport and C/N ratio variations in a large tropical river: Godavari as a case study, India

This study gives an insight into the source of organic carbon and nitrogen in the Godavari river and its tributaries, the yield of organic carbon from the catchment, seasonal variability in their concentration and the ultimate flux of organic and inorganic carbon into the Bay of Bengal. Particulate organic carbon/particulate organic nitrogen (POC/PON or C/N) ratios revealed that the dominant source of organic matter in the high season is from the soil (C/N = 8–14), while in the rest of the seasons, the river-derived (in situ) phytoplankton is the major source (C/N = l–8). Amount of organic materials carried from the lower catchment and flood plains to the oceans during the high season are 3 to 91 times higher than in the moderate and low seasons. Large-scale erosion and deforestation in the catchment has led to higher net yield of organic carbon in the Godavari catchment when compared to other major world rivers. The total flux of POC, and dissolved inorganic carbon (DIC) from the Godavari river to the Bay of Bengal is estimated as 756 · 109 and 2520 · 109 g yr1, respectively. About 22% of POC is lost in the main channel because of oxidation of labile organic matter, entrapment of organic material behind dams/sedimentation along flood plains and river channel; the DIC fluxes as a function of alkalinity are conservative throughout the river channel. Finally, the C/N ratios (12) of the ultimate fluxes of particulate organic carbon suggest the dominance of refractory/ stable soil organic matter that could eventually get buried in the coastal sediments on a geological time scale

Use of stable isotopes, organic and inorganic chemistry to identify pollution sources and weathering processes in two small tropical rivers in southwestern India

The two main objectives of this study were to assess pollution dynamic from organic and inorganic major ion chemistry and stable isotopes (δ15N and δ18O) and to determine the weathering processes using carbon isotopes in two tropical river basins, i.e. Nethravati and Swarna, along southwest coast of India. These short length river basins (around 100 km) are characterized by high annual rainfall, warm temperature, high runo" (~3300mm) draining Precambrian basement rocks composed of green-stones, granitic-gneiss, charnockite and meta sediments. Intense silicate weathering is induced by high runo" and warm temperature (Gurumurthy et al., 2012). In this study, stable isotopes (δ15N & δ18O)of organic molecules from sewage and agricultural effluents,and carbon isotopes (δ13C) of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were measured to trace agricultural and domestic pollution and to identify the sources of inorganic carbon and the nature of chemical weathering in these river basins. Carbon isotopes measured on DIC reveals sources of carbon into the river, such as carbonate/silicate weathering of rocks, mineralization of organic matter from C3/C4 plants, soil and atmospheric CO2. The nitrate and phosphate levels remain low, with values ranging from 5 to 9 μM, and 0 to 2 μM respectively. The δ13C DIC values range from =-9.03 +/- 0.99 for the Swarna basin to -8.08 +/-0.78 for the Nethravati basin. These values point to a mixing of carbonate and silicate weathering products with a dominance of C3 vegetation, prevalent in the Western Ghats. The DOC values for both river basins are very low and very close: 0.72 +/- 0.09 mg/L (Swarna river) and 0.62 +/-0.11 mg/L (Nethravati river). This indicates that the contributions of organic matter from the adjacent forests and the $ood plains are very low during the sampling period. The analysis of organic acids reveals low amount of Oxalate and Acetate, and trace of Malate and Tartaric acids. The dissolved and particulate organic carbon (DOC and POC) concentrations are very low in these two rivers. During the dry season, river discharge is mainly supplied by groundwater with generally low contents in dissolved and particulate fractions. Even if we observe low concentration, we measured higher DOC and POC in the Swarna river. These higher DOC concentrations are accompanied with lower SUVA value. This indicates that more labile DOC (less aromaticity) is exported within this basin during dry season. C/N values in POC also show that the organic carbon is “fresher” and is probably more autochtonous than in the Nethravati river. Indeed, C/N value are closer of an autochthonous production (C/N : 2-6) than allochthonous one (C/N: 8-20). These observations can be explained as the Svarna watershed land use is more agricultural than in Nethravati. Agricultural lands generally export signi%cant amount of nutrients to rivers and participate to enhance autochthonous productivity. Autochthonous organic carbon production is more labile and less aromatic

Mass loading and removal of pharmaceuticals and personal care products including psychoactives, antihypertensives, and antibiotics in two sewage treatment plants in Southern India

Environmental contamination by pharmaceuticals and personal care products (PPCPs) is barely studied in India despite being one of the largest global producers and consumers of pharmaceuticals. In this study, 29 pharmaceuticals and six metabolites were determined in sewage treatment plants (STPs) in Udupi (STPU: population served ~150,000) and Mangalore (STPM: population served ~450,000); the measured mean concentrations ranged from 12 to 61,000 ng/L and 5.0 to 31,000 ng/L, respectively. Atorvastatin (the most prescribed antihypercholesterolemic in India), mefenamic acid, and paraxanthine were found for the first time in wastewater in India at the mean concentrations of 395 ng/L, 1100 ng/L, and 13,000 ng/L, respectively. Select pharmaceutical metabolites (norverapamil and clopidogrel carboxylic acid) were found at concentrations of upto 7 times higher than their parent drugs in wastewater influent and effluent. This is the first study in India to report mass loading and emission of PPCPs and their select metabolites in STPs. The total mass load of all PPCPs analyzed in this study at STPU (4.97 g/d/1000 inhabitants) was 3.6 times higher than calculated for STPM. Select recalcitrant PPCPs (carbamazepine, diazepam, and clopidogrel) were found to have negative or no removal from STPU while additional treatment with upflow anaerobic sludge blanket reactor at STPM removed (up to 95%) these PPCPs from STPM. Overall, 5.1 kg of caffeine, 4.1 kg of atenolol, 2.7 kg of ibuprofen, and 1.9 kg of triclocarban were discharged annually from STPU. The PPCP contamination profile in the Indian STP was compared with a similar study in the USA

A review of the occurrence of pharmaceuticals and personal care products in Indian water bodies

Little information exists on the occurrence and the ultimate fate of pharmaceuticals in the water bodies in India despite being one of the world leaders in pharmaceutical production and consumption. This paper has reviewed 19 published reports of pharmaceutical occurrence in the aquatic environment in India [conventional activated sludge wastewater treatment plants (WTPs), hospital WTPs, rivers, and groundwater]. Carbamazepine (antipsychoactive), atenolol (antihypertensive), triclocarban and triclosan (antimicrobials), trimethoprim and sulfamethoxazole (antibacterials), ibuprofen and acetaminophen (analgesics), and caffeine (stimulant) are the most commonly detected at higher concentrations in Indian WTPs that treat predominantly the domestic sewage. The concentration of ciprofloxacin, sulfamethoxazole, amoxicillin, norfloxacin, and ofloxacin in Indian WTPs were up to 40 times higher than that in other countries in Europe, Australia, Asia, and North America. A very few studies in Indian rivers reported the presence of ciprofloxacin, enoxacin, ketoprofen, erythromycin, naproxen, ibuprofen, diclofenac and enrofloxacin. Similar compounds were reported in rivers in China, indicating a similar usage pattern in both of these developing countries. In a study reported from an open well in southern India, the groundwater showed the presence of cetirizine, ciprofloxacin, enoxacin, citalopram and terbinafine, which was close to a WTP receiving effluents from pharmaceutical production

Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods

Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. In the absence or lag of continuous field monitoring, remote sensing observations become vital for maintaining a constant supply of measurable data. However, remote satellite observations suffer from atmospheric effects, resolution disparity, and use of a multitude of mapping methods. Efficient image-processing routines are, hence, necessary to prepare and test the derivable data for mapping applications. The existing literature provides an application-centric view for selection of image processing schemes. This can create confusion, as it is not clear which method of atmospheric correction would be ideal for retrieving facies spectral reflectance, nor are the effects of pansharpening examined on facies. Moreover, with a variety of supervised classifiers and target detection methods now available, it is prudent to test the impact of variations in processing schemes on the resultant thematic classifications. In this context, the current study set its experimental goals. Using very-high-resolution (VHR) WorldView-2 data, we aimed to test the effects of three common atmospheric correction methods, viz. Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH); and two pansharpening methods, viz. Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on thematic classification of facies using 12 supervised classifiers. The conventional classifiers included: Mahalanobis Distance (MHD), Maximum Likelihood (MXL), Minimum Distance to Mean (MD), Spectral Angle Mapper (SAM), and Winner Takes All (WTA). The advanced/target detection classifiers consisted of: Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), Matched Filtering (MF), Mixture-Tuned Matched Filtering (MTMF), Mixture-Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF), Orthogonal Space Projection (OSP), and Target-Constrained Interference-Minimized Filter (TCIMF). This experiment was performed on glaciers at two test sites, Ny-Ålesund, Svalbard, Norway; and Chandra–Bhaga basin, Himalaya, India. The overall performance suggested that the FLAASH correction delivered realistic reflectance spectra, while DOS delivered the least realistic. Spectra derived from HCS sharpened subsets seemed to match the average reflectance trends, whereas GS reduced the overall reflectance. WTA classification of the DOS subsets achieved the highest overall accuracy (0.81). MTTCIMF classification of the FLAASH subsets yielded the lowest overall accuracy of 0.01. However, FLAASH consistently provided better performance (less variable and generally accurate) than DOS and QUAC, making it the more reliable and hence recommended algorithm. While HCS-pansharpened classification achieved a lower error rate (0.71) in comparison to GS pansharpening (0.76), neither significantly improved accuracy nor efficiency. The Ny-Ålesund glacier facies were best classified using MXL (error rate = 0.49) and WTA classifiers (error rate = 0.53), whereas the Himalayan glacier facies were best classified using MD (error rate = 0.61) and WTA (error rate = 0.45). The final comparative analysis of classifiers based on the total error rate across all atmospheric corrections and pansharpening methods yielded the following reliability order: MXL > WTA > MHD > ACE > MD > CEM = MF > SAM > MTMF = TCIMF > OSP > MTTCIMF. The findings of the current study suggested that for VHR visible near-infrared (VNIR) mapping of facies, FLAASH was the best atmospheric correction, while MXL may deliver reliable thematic classification. Moreover, an extensive account of the varying exertions of each processing scheme is discussed, and could be transferable when compared against other VHR VNIR mapping methods

Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines

Fundamental image processing methods, such as atmospheric corrections and pansharpening, influence the signal of the pixel. This morphs the spectral signature of target features causing a change in both the final spectra and the way different mapping methods may assign thematic classes. In the current study, we aim to identify the variations induced by popular image processing methods in the spectral reflectance and final thematic maps of facies. To this end, we have tested three different atmospheric corrections: (a) Quick Atmospheric Correction (QUAC), (b) Dark Object Subtraction (DOS), and (c) Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods: (a) Hyperspherical Color Sharpening (HCS) and (b) Gram–Schmidt (GS). WorldView-2 and WorldView-3 satellite images over Chandra-Bhaga Basin, Himalaya, and Ny-Ålesund, Svalbard are tested via spectral subsets in traditional (BGRN1), unconventional (CYRN2), visible to near-infrared (VNIR), and the complete available spectrum (VNIR_SWIR). Thematic mapping was comparatively performed using 12 pixel-based (PBIA) algorithms and 3 object-based (GEOBIA) rule sets. Thus, we test the impact of varying image processing routines, effectiveness of specific spectral bands, utility of PBIA, and versatility of GEOBIA for mapping facies. Our findings suggest that the image processing routines exert an extreme impact on the end spectral reflectance. DOS delivers the most reliable performance (overall accuracy = 0.64) averaged across all processing schemes. GEOBIA delivers much higher accuracy when the QUAC correction is employed and if the image is enhanced by GS pansharpening (overall accuracy = 0.79). SWIR bands have not enhanced the classification results and VNIR band combination yields superior performance (overall accuracy = 0.59). The maximum likelihood classifier (PBIA) delivers consistent and reliable performance (overall accuracy = 0.61) across all processing schemes and can be used after DOS correction without pansharpening, as it deteriorates spectral information. GEOBIA appears to be robust against modulations in atmospheric corrections but is enhanced by pansharpening. When utilizing GEOBIA, we find that a combination of spatial and spectral object features (rule set 3) delivers the best performance (overall accuracy = 0.86), rather than relying only on spectral (rule set 1) or spatial (rule set 2) object features. The multiresolution segmentation parameters used here may be transferable to other very high resolution (VHR) VNIR mapping of facies as it yielded consistent objects across all processing schemes

Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas

Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays consistent performance and is the most reliable, followed by QUAC and FLAASH. Pansharpening improved overall accuracy and GS performed better than HCS. The study reports robust segmentation parameters that may be transferable to other VHR-based glacier surface facies mapping applications. The rule sets are adjusted across the processing schemes to adjust to the change in spectral characteristics introduced by the varying routines. The results indicate that GEOBIA for glacier surface facies mapping may be less prone to the differences in spectral signatures introduced by different atmospheric corrections but may respond well to increasing spatial resolution. The study highlighted the role of spatial attributes for mapping fine features, and in combination with appropriate spectral features may enhance thematic classification

Use of stable isotopes, organic and inorganic chemistry to identify pollution sources and weathering processes in two small tropical rivers in southwestern India

International audienceThe two main objectives of this study were to assess pollution dynamic from organic and inorganic major ion chemistry and stable isotopes (δ15N and δ18O) and to determine the weathering processes using carbon isotopes in two tropical river basins, i.e. Nethravati and Swarna, along southwest coast of India. These short length river basins (around 100 km) are characterized by high annual rainfall, warm temperature, high runo" (~3300mm) draining Precambrian basement rocks composed of green-stones, granitic-gneiss, charnockite and meta sediments. Intense silicate weathering is induced by high runo" and warm temperature (Gurumurthy et al., 2012). In this study, stable isotopes (δ15N & δ18O)of organic molecules from sewage and agricultural effluents,and carbon isotopes (δ13C) of dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were measured to trace agricultural and domestic pollution and to identify the sources of inorganic carbon and the nature of chemical weathering in these river basins. Carbon isotopes measured on DIC reveals sources of carbon into the river, such as carbonate/silicate weathering of rocks, mineralization of organic matter from C3/C4 plants, soil and atmospheric CO2. The nitrate and phosphate levels remain low, with values ranging from 5 to 9 μM, and 0 to 2 μM respectively. The δ13C DIC values range from =-9.03 +/- 0.99 for the Swarna basin to -8.08 +/-0.78 for the Nethravati basin. These values point to a mixing of carbonate and silicate weathering products with a dominance of C3 vegetation, prevalent in the Western Ghats. The DOC values for both river basins are very low and very close: 0.72 +/- 0.09 mg/L (Swarna river) and 0.62 +/-0.11 mg/L (Nethravati river). This indicates that the contributions of organic matter from the adjacent forests and the $ood plains are very low during the sampling period. The analysis of organic acids reveals low amount of Oxalate and Acetate, and trace of Malate and Tartaric acids. The dissolved and particulate organic carbon (DOC and POC) concentrations are very low in these two rivers. During the dry season, river discharge is mainly supplied by groundwater with generally low contents in dissolved and particulate fractions. Even if we observe low concentration, we measured higher DOC and POC in the Swarna river. These higher DOC concentrations are accompanied with lower SUVA value. This indicates that more labile DOC (less aromaticity) is exported within this basin during dry season. C/N values in POC also show that the organic carbon is “fresher” and is probably more autochtonous than in the Nethravati river. Indeed, C/N value are closer of an autochthonous production (C/N : 2-6) than allochthonous one (C/N: 8-20). These observations can be explained as the Svarna watershed land use is more agricultural than in Nethravati. Agricultural lands generally export signi%cant amount of nutrients to rivers and participate to enhance autochthonous productivity. Autochthonous organic carbon production is more labile and less aromatic