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Plant litter plays an important role in affecting the water quality of wetland ecosystems. However, it is unknown whether litter decomposability and species traits might predict water quality changes during litter submergence. Here, we conducted a greenhouse experiment to examine the effects of four submerged plant species, together with two water sources (sampled from tourism and protected areas), and oxygen injection treatments on the changes of eight water quality parameters during litter submergence. Our results showed that litter submergence significantly affected water quality changes, and the observed effects changed through time and differed between two water sources, between oxygen injection and the control treatments, and among different litter species. Moreover, water electric conductivity (EC), total dissolved solids (TDS), water total nitrogen (TN), ammonium and nitrite nitrogen increased with increasing initial litter total carbon (TC), TN and total phosphorus (TP), but water dissolved oxygen (DO) decreased with increasing litter TC, TN and TP. Moreover, water EC, TDS and TN increased with the final mass losses after 10-week submergence. These results indicated that species traits (including decomposability) might be good predictors for the water quality changes during litter submergence, and such a trait-based approach might be a promising tool to link plant species diversity via plant functional traits to water quality or other wetland ecosystem services., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019. Published by Elsevier B.V.)

Plant litter is an indispensable component of constructed wetlands, but how the submergence of plant litter affects their ecosystem functions and services, such as water purification, is still unclear. Moreover, it is also unclear whether the effects of plant litter submergence depend on other factors such as the duration of litter submergence, water source or litter species identity. Here we conducted a greenhouse experiment by submerging the litter of 7 wetland plant species into three types of water substrates and monitoring changes in water nutrient concentrations. Litter submergence affected water quality positively via decreasing the concentration of nitrate nitrogen and negatively via increasing the concentrations of total nitrogen, ammonium nitrogen and total phosphorus. The effects of litter submergence depended on the duration of litter submergence, the water source, the litter species identity, and the plant life form. Different plant species had different effects on the water nutrient concentrations during litter submergence, and the effects of floating plants might be more negative than that of emergent plants. These results are novel evidence of how the submergence of different plant (life form) litter may affect the purification function of constructed wetlands. For water at low eutrophication levels, submerging a relative small amount of plant litter might improve water quality, via benefiting the denitrification process in water. These findings emphasized the management of floating plant litter (a potential removal) during the maintenance of human-controlled wetland ecosystems and provided a potential tool to improve the water quality of constructed wetlands via submerging plant litter of different types., Competing Interests: The authors have declared that no competing interests exist.

• Seasonal single-peak patterns of CH 4 and CO 2 emissions. • Watershed-scale spatial heterogeneity of CH 4 and CO 2 emissions was closely related to the proportion of built-up area. • TN, TP, DTC and conductivity directly affected riverine CH 4 and CO 2 emissions. • Land use and urbanization indirectly control riverine CH 4 and CO 2 emissions by altering riverine nutrients input. Urban rivers are recognized as significant sources of methane (CH 4) and carbon dioxide (CO 2) emissions. Despite this, the influence of land use and urbanization on carbon emissions across rural-urban rivers at the watershed scale has been insufficiently explored. This study utilized in-situ surveys of the Liao River in northern China to investigate the spatial and temporal variations of CH 4 and CO 2 emissions and their relationship with urbanization and its potential controlling factors. The findings revealed that CH 4 emissions peaked in fall, whereas CO 2 emissions were highest in summer. The average fluxes of CH 4 and CO 2 at the water-gas interface were 1387.22 ± 2474.98 µmol·m−2·d−1 and 52.78 ± 54.44 mmol·m−2·d−1, respectively. Water quality parameters accounted for 80.49 % of the total variation in CH 4 and CO 2 concentrations and fluxes. Structural equation modeling indicated that TN, TP, DTC, and conductivity had direct effects on riverine CH 4 and CO 2 emissions, with standardized direct effects of 0.50 and 0.49, respectively. Nutrient input emerged as the primary driver, increasing CH 4 and CO 2 concentrations and fluxes, particularly in urban-adjacent river sections likely receiving higher nutrient loads. This study underscores that land use and urbanization indirectly influence riverine CH 4 and CO 2 emissions by modifying nutrient inputs. Effective land use management and nutrient input control are recommended strategies to mitigate riverine CH 4 and CO 2 emissions. [Display omitted] [ABSTRACT FROM AUTHOR]

Suspended particulate matter (SPM) is a dominant water constituent of case-II waters, and SPM concentration (CSPM) is a key parameter describing water quality. This study, using Landsat 8 Operational Land Imager (OLI) images, aimed to develop theCSPMretrieval models and further to estimate theCSPMvalues of Dongting Lake. One Landsat 8 OLI image and 53CSPMmeasurements were employed to calibrate Landsat 8-basedCSPMretrieval models. TheCSPMvalues derived from coincident Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) images were compared to validate calibrated Landsat 8-basedCSPMmodels. After the best stable Landsat 8-basedCSPMretrieval model was further validated using an independent Landsat 8 OLI image and its coincidentCSPMmeasurements, it was applied to four Landsat 8 OLI images to retrieve theCSPMvalues in the South and East Dongting Lake. Model calibration results showed that two exponential models of the red band explained 61% (estimated standard error (SE) = 7.96 mg l–1) and 67% (SE = 6.79 mg l–1) of the variation ofCSPM; two exponential models of the red:panchromatic band ratio obtained 81% (SE = 5.48 mg l–1) and 77% (SE = 4.96 mg l–1) fitting accuracy; and four exponential and quadratic models of the infrared band explained 72–83% of the variation ofCSPM(SE = 5.18–5.52 mg l–1). By comparing the MODIS- and Landsat 8-basedCSPMvalues, an exponential model of the Landsat 8 OLI red band (CSPM = 1.1034 × exp(23.61 × R)) obtained the best consistentCSPMestimations with the MODIS-based model (r = 0.98,p < 0.01), and its further validation result using an independent Landsat 8 OLI image showed a significantly strong correlation between the measured and estimatedCSPMvalues at a significance level of 0.05 (r = 0.91,p < 0.05). TheCSPMspatiotemporal distribution derived from four Landsat 8 images revealed a clear spatial distribution pattern ofCSPMin the South and East Dongting Lake, which was caused by natural and anthropogenic factors together. This study confirmed the potential of Landsat 8 OLI images in retrievingCSPMand provided a foundation for retrieving the spatial distribution ofCSPMaccurately from this new data source in Dongting Lake. [ABSTRACT FROM PUBLISHER]

Abstract: It is challenging to develop Landsat-5 TM (TM5) image-based retrieval models for estimating the suspended particulate matter concentration (C SPM) in water when missing coincident ground C SPM measurements. This study, with the Poyang Lake in China as a case study, proposed an approach for developing TM5-based C SPM retrieval models with the assistance of moderate resolution imaging spectroradiometer (MODIS) images. After validation with an independent dataset, a cubic C SPM retrieval model of 250m MODIS red band was used to estimate the C SPM values at 100 sampling points from the MODIS images (MODIS-based C SPM) captured at three time periods. The MODIS-based C SPM values at the time period with the largest C SPM variation were combined with their coincident TM5 image reflectance for TM5-based model calibrations. The linear, quadratic, cubic, power and exponential models of MODIS-based C SPM against TM5 single bands and their combinations were calibrated, respectively. Four best-fitting TM5-based C SPM models were selected to retrieve the C SPM values at 100 sampling points from the TM5 images (TM5-based C SPM) at the other two time periods, and the coincident MODIS- and TM5-based C SPM values were compared to assess TM5-based model performances. Model calibration results showed that the cubic and exponential models of TM5 red band (band 3) and red subtracting mid-infrared band (band 5) obtained the best fitting for estimating C SPM from the TM5 image on 12 August 2005, and they explained 94–97% of the variation of MODIS-based C SPM values with an estimated standard error of 6.617–8.457mg/l. Model validations indicated that the exponential model of TM5 red band got the best result for estimating C SPM from TM5 images when the MODIS-based C SPM values were assumed as ground truths (correlation coefficient between MODIS- and TM5-based C SPM values=0.96, root mean square error=4.60mg/l). We concluded that the TM5-based C SPM retrieval models could be developed with the assistance of MODIS, and the approach proposed in this study will be helpful for other researchers who also want to retrieve C SPM from TM5 image archive but without coincident ground C SPM measurements. [Copyright &y& Elsevier]

Suspended particulate matter (SPM) is a key parameter describing water quality, and developing the retrieval model of SPM concentration ( C SPM ) is fundamental for obtaining the spatiotemporal information of C SPM and further for understanding, managing and protecting aquatic ecosystems. This study aimed to compare moderate resolution imaging spectroradiometer (MODIS)-based C SPM retrieval models in order to find the optimal model for improving the C SPM estimation in Poyang Lake. The C SPM measurements on 27 September 2007 and their coincident MODIS Terra image were used to calibrate retrieval models with the least-squares technique. The C SPM measurements on 31 August 2012 and the MODIS Terra image on 30 August 2012 were applied to validate the calibrated models, and the correlation coefficient ( r ) between the measured and estimated C SPM values, the root mean square error (RMSE) and relative root mean square error (RRMSE) of estimation as well as the model bias evaluation result were compared to determine the optimal model for estimating the C SPM values of Poyang Lake from MODIS images. Model calibration showed that, after two samples were removed, the exponential models of blue, green and red band, the linear model of infrared band, the cubic model of red band as well as the exponential model of red minus infrared band explained about 92%, 88%, 90%, 89%, 90% and 76% of the variation of C SPM , respectively; while model validation indicated that, after removing two samples, the exponential models of blue and green band got biased C SPM estimations, the agreement between the measured and estimated C SPM values was not very high ( r = <0.8) for the models with single red and infrared band, and the exponential model of red minus infrared band got the best result among all calibrated models ( r = 0.87, RMSE = 22.1 mg/l, RRMSE = 52.8%). We concluded that the exponential model of red minus infrared band obtained stable C SPM estimation and was the optimal model for C SPM estimation in this study, and more independent datasets should be obtained to further validate our finding for improving the C SPM estimation in Poyang Lake. [ABSTRACT FROM AUTHOR]

This study applied Moderate-Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2010 to obtain and analyze the spatiotemporal variation of suspended sediment concentration (SSC) and discussed factors affecting it in Poyang Lake, China. Results showed that (1) the mean SSC was lower in the south, higher in the north, and moderate in the central lake region; (2) the mean SSC in the south was lower than or close to 20 mg/L, with no clear annual trend; (3) the mean SSC in the north was slightly higher than 20 mg/L in 2000 and increased from 2001, with the highest value >60 mg/L in 2006; (4) the mean SSC in the central lake region, except for 2009, ranged from 20 to 40 mg/L and had an annual pattern similar to that in the southern lake region; (5) for the entire lake, the mean SSC declined from January to March, increased from September to December, and fluctuated from April to August; and (6) several higher SSC values were found in the central or southern lake regions. The spatiotemporal variation of SSC was controlled by natural and human factors, in which dredging was dominant. Limiting the area of dredging and reducing dredging intensity would decrease SSC and maintain sustainable development of Poyang Lake. Remote sensing can obtain the spatiotemporal information of some water quality parameters, which will help managers understand the lake dynamics and mechanisms to make better decisions for lake management. [ABSTRACT FROM AUTHOR]

Decomposition of emergent macrophytes is now recognized as an internal nutrient source for shallow lakes. Temperate lakes always experience seasonal ice cover in winter, but the influences of emergent macrophytes decomposition on water quality have rarely been examined under ice. Here, we conducted an incubation experiment to investigate winter decomposition of two common emergent macrophytes species (Typha orientalis and Phragmites australis) and its influences on water quality in the Hengshui Lake, North China. Mesocosms simulating a lake ice regime were incubated in the field for 120 days in winter and were treated with and without plant material addition. Water quality was monitored through dissolved oxygen (DO), dissolved organic carbon (DOC), total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH4-N), and nitrate nitrogen (NO3-N). We found that both species were significantly decomposed in winter and that the majority of mass loss occurred in the first 10 days of decomposition when the water surface of mesocosms were already frozen. The concentrations of DO rapidly dropped to values close to zero after plant material submergence. At the end of incubation, the concentrations of DOC, TN, and NO3-N in the mesocosms with plant material addition were significantly higher than initial concentrations. In contrast, the concentrations of DOC, TN, TP, NO3-N, and NH4-N in the mesocosms without plant material addition were equal to or less than initial concentrations. Our research suggests that winter decomposition of emergent macrophytes produces negative influences on water quality under ice that lasts for the whole winter. [ABSTRACT FROM AUTHOR]

Highlights: [•] Three configurations of bioremediation treatment systems for eutrophic water were tested. [•] Integrated water quality, economic and emergy evaluations were performed. [•] Disparate results from the environmental and economic analyses were integrated. [•] Environmental impacts, economic benefit and sustainability are spatial concepts. [ABSTRACT FROM AUTHOR]