Sustainability

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12 pages, 2128 KiB

Open AccessArticle

Development and Application of a Purification Method for the Determination of Three EDCs Isotopes in Sediments and Water

by Zewen Pan, Rui Wang, Jun Wei and Yingjie Cao

Sustainability 2023, 15(11), 8583; https://0-doi-org.brum.beds.ac.uk/10.3390/su15118583 - 25 May 2023

Viewed by 988

Abstract

Compound-specific stable isotope analysis (CSIA) is an efficient method for source apportionment and the identification of the transformation process in organic compounds. However, most studies of CSIA are still limited to laboratory experiments. Few studies used have CSIA in an in situ environment [...] Read more.

Compound-specific stable isotope analysis (CSIA) is an efficient method for source apportionment and the identification of the transformation process in organic compounds. However, most studies of CSIA are still limited to laboratory experiments. Few studies used have CSIA in an in situ environment due to the complexity of environmental samples. Therefore, a purification method for analyzing the carbon isotope ratios of three phenolic endocrine disrupting compounds (EDCs) (nonylphenols (NPs), octylphenol (OP), and bisphenol A(BPA)) in sediment and water samples was developed in this study. The silica gel column was used to isolate EDCs from complex matrices with multiple organic solvents. Gas chromatography/mass spectrometry was used to quantify the targeted EDCs and analyze the purity of the extracts in full-scan mode. The interfering peaks disappeared, the baseline was sharply reduced, and all the target compounds appeared as single peaks in the chromatogram after purification. Analyzing the standard samples with known isotope ratios showed that the purification treatment did not cause isotope fractionation. The isotopic difference before and after purification was less than 0.04. The method was successfully used to analyze the isotope composition of BPA, OP, and NPs in river water and sediments in the Guangzhou River, Pearl River Delta, South China. Sewage discharge significantly affected the carbon isotope values of BPA, OP and NPs in Guangzhou rivers, suggesting that sewage discharge is the main source of EDCs in the Guangzhou rivers. There is a significant correlation between the isotopic values and concentrations of OP and NPs in sediments, indicating that they may undergo chemical transformation. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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29 pages, 2433 KiB

Open AccessArticle

Techno-Economic Analysis of Photovoltaic Hydrogen Production Considering Technological Progress Uncertainty

by Xiang Huang, Yapan Qu, Zhentao Zhu and Qiuchi Wu

Sustainability 2023, 15(4), 3580; https://0-doi-org.brum.beds.ac.uk/10.3390/su15043580 - 15 Feb 2023

Cited by 8 | Viewed by 3210

Abstract

The application of photovoltaic (PV) power to split water and produce hydrogen not only reduces carbon emissions in the process of hydrogen production but also helps decarbonize the transportation, chemical, and metallurgical industries through P2X technology. A techno-economic model must be established to [...] Read more.

The application of photovoltaic (PV) power to split water and produce hydrogen not only reduces carbon emissions in the process of hydrogen production but also helps decarbonize the transportation, chemical, and metallurgical industries through P2X technology. A techno-economic model must be established to predict the economics of integrated PV–hydrogen technology at key time points in the future based on the characteristics, variability, and uncertainties of this technology. In this study, we extracted the comprehensive technical factors (including PV tracking system coefficient, PV conversion efficiency, electrolyzer efficiency, and electrolyzer degradation coefficient) of an integrated PV–hydrogen system. Then, we constructed a PV hydrogen production techno-economic (PVH2) model. We used the levelized cost of hydrogen production (LCOH) method to estimate the cost of each major equipment item during the project lifetime. We combined the PVH2 and learning curve models to determine the cost trend of integrated PV–hydrogen technology. We developed a two-dimensional Monte Carlo approach to predict the variation interval of LCOH for PV–hydrogen projects in 2030 and 2050, which described the current technology variability with variable parameters and the uncertainty in the technology advancement with uncertain parameters. The results showed that the most critical factors influencing LCOH are PV conversion efficiency and the capital cost of the electrolyzer. The LCOH of PV to hydrogen in China will drop to CNY 18–32/kg by 2030 and CNY 8–18/kg by 2050. The combination of a learning curve model and a Monte Carlo method is an effective tool to describe the current variability in hydrogen production technologies and the uncertainty in technological progress. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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15 pages, 1823 KiB

Open AccessArticle

Detection Framework of Abrupt Changes and Trends in Rainfall Erosivity in Three Gorges Reservoir, China

by Qian Feng, Linyao Dong, Jingjun Liu and Honghu Liu

Sustainability 2023, 15(3), 2062; https://0-doi-org.brum.beds.ac.uk/10.3390/su15032062 - 21 Jan 2023

Cited by 2 | Viewed by 1134

Abstract

Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this [...] Read more.

Rainfall erosivity is commonly used to estimate the probability of soil erosion caused by rainfall. The accurate detection of temporal changes in rainfall erosivity and the identification of abrupt changes and trends are important for understanding the physical causes of variation. In this study, a detection framework is introduced to identify temporal changes in rainfall erosivity time series as follows: (i) The significance of time series variation of rainfall erosivity is assessed based on the Hurst coefficient and divided into three levels: None, medium, and high. (ii) The detection of abrupt changes (Mann–Kendall, Moving T, and Bayesian tests) and trends (Spearman and Kendall rank correlation tests) of variate series and the correlation coefficient between the variation component and the original series is calculated. (iii) The modified series is obtained by preferentially eliminating the variation component (trend or change point) with larger correlation coefficients. (iv) We substituted the modified series into steps i to iii until the correlation coefficient was not significant. This framework is used to analyze the variation of rainfall erosivity in the Three Gorges Reservoir, China. The results showed that by using traditional methods, both an increasing trend and an upward change point were observed in Zigui station. However, after the upward change point was deducted from the annual rainfall erosivity series R(t), the resultant R_m(t) showed no statistically significant trend. Trend analysis should be performed considering the existence of an abrupt change to assess the long-term changes in rainfall erosivity series; otherwise, it would result in the wrong conclusion. In addition, the change points detected in the R_m(t) varied with the methods. Compared with the single-test method, the proposed framework can effectively reduce uncertainty. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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20 pages, 5896 KiB

Open AccessArticle

Simulation of Water Balance Components Using SWAT Model at Sub Catchment Level

by Dinagarapandi Pandi, Saravanan Kothandaraman and Mohan Kuppusamy

Sustainability 2023, 15(2), 1438; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021438 - 12 Jan 2023

Cited by 6 | Viewed by 3319

Abstract

Simulation of Water Balance Components (WBCs) is import for sustainable water resources development and management. The Soil Water and Assessment Tool (SWAT) is a semi-distributed hydrological model to estimate the WBCs by forcing the hydrological response unit (HRU) and meteorological variables. The developed [...] Read more.

Simulation of Water Balance Components (WBCs) is import for sustainable water resources development and management. The Soil Water and Assessment Tool (SWAT) is a semi-distributed hydrological model to estimate the WBCs by forcing the hydrological response unit (HRU) and meteorological variables. The developed model simulates five WBCs viz. surface runoff, lateral flow, percolation, actual evapotranspiration and soil water at sub catchment level. To demonstrate the model compatibility a case study taken over Chittar catchment, Tamilnadu, India. The catchment was divided in to 11 sub catchments. The ten year interval LULC (i.e., 2001 and 2011), twenty year daily meteorological data (i.e., 2001–2020) and time invariant soil and slope data were used in developing the water balance model. Developed model was calibrated and evaluated with river gauge monthly discharging using SUFI-2 algorithm in SWAT-CUP. The model calibration performed in two stage i.e., pre-calibration (2001–2003) and post-calibration (2004–2010). The model performance was evaluated with unseen river gauge discharging data (i.e., 2011–2015). Then, results of statistical outputs for the model were coefficient of determination (R²) is 0.75 in pre-calibration, 0.94 in post-calibration and 0.81 in validation. Further strengthen the model confidential level the sub catchments level monthly actual evapotranspiration were compared with gridded global data GLEAM v3.6a. Finally, the developed model was simulate the five WBCs whereas, surface runoff, lateral flow, percolation, actual evapotranspiration and soil water at sub catchment level during 2001–2020. The sub catchment level WBCs trend helps to make fast and accurate decision. At all 11 sub catchments a long drought was observed during 2016–2018 due to failure of northeast monsoon. The WBCs were directly reinforced by their north east monsoon which gives the major portion of rainfall i.e., September to December. Hence all the WBCs were directly correlated with rainfall with or without time lag. By understanding the sub catchment level of monthly WBCs over the Chittar catchment is useful for land and water resource management. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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17 pages, 8154 KiB

Open AccessArticle

The Hydrochemistry, Ionic Source, and Chemical Weathering of a Tributary in the Three Gorges Reservoir

by Qianzhu Zhang, Ke Jin, Linyao Dong, Ruiyi Zhao, Wenxiang Liu, Yang Lu, Xiaoqing Gan, Yue Hu and Cha Zhao

Sustainability 2022, 14(22), 15376; https://0-doi-org.brum.beds.ac.uk/10.3390/su142215376 - 18 Nov 2022

Viewed by 961

Abstract

Riverine dissolved matter reflects geochemical genesis information, which is vital to understand and manage the water environment in a basin. The Ganjing River located in the hinterland of the Three Gorges Reservoir was systematically investigated to analyze the composition and spatial variation of [...] Read more.

Riverine dissolved matter reflects geochemical genesis information, which is vital to understand and manage the water environment in a basin. The Ganjing River located in the hinterland of the Three Gorges Reservoir was systematically investigated to analyze the composition and spatial variation of riverine ions, probe the source and influencing factors, and assess the chemical weathering rates and CO₂ consumption. The results showed that the total dissolved solid value (473.31 ± 154.87 mg/L) with the type of “HCO₃⁻–Ca²⁺” was higher than that of the global rivers’ average. The hydrochemical parameters were relatively stable in the lower reservoir area of the Ganjing River, which was largely influenced by the backwater of Three Gorges Reservoir. The carbonate weathering source contributed 69.63% of TDS (Total dissolved solids), which generally dominated the hydrochemical characteristics. The contribution rates of atmospheric rainfall were relatively low and stable in the basin, with an average of 4.01 ± 1.28%. The average contribution rate of anthropogenic activities was 12.05% in the basin and even up to 27.80% in the lower reservoir area of the Ganjing River, which illustrated that the impoundment of Three Gorges Reservoir had brought great challenges to the water environment in the reservoir bay. The empirical power functions were tentatively proposed to eliminate the dilution effect caused by runoff discharge on the basis of previous studies. Accordingly, the rock weathering rate was calculated as 23.54 t/km² in the Ganjing River Basin, which consumed atmospheric CO₂ with a flux of 6.88 × 10⁵ mol/y/km². These results highlight the geochemical information of tributaries in the hinterland of the Three Gorges Reservoir, have significant implications for understanding the impact of impoundment, and provide data support for the integrated management of water resources in the Ganjing River Basin. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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18 pages, 3326 KiB

Open AccessArticle

Impact of Land System Changes and Extreme Precipitation on Peak Flood Discharge and Sediment Yield in the Upper Jhelum Basin, Kashmir Himalaya

by Aazim Yousuf and Shakil Ahmad Romshoo

Sustainability 2022, 14(20), 13602; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013602 - 20 Oct 2022

Cited by 3 | Viewed by 1677

Abstract

The Kashmir valley is prone to flooding due to its peculiar geomorphic setup compounded by the rapid anthropogenic land system changes and climate change. The scarcity of observations is one of the major challenges for understanding various land surface processes in the mountainous [...] Read more.

The Kashmir valley is prone to flooding due to its peculiar geomorphic setup compounded by the rapid anthropogenic land system changes and climate change. The scarcity of observations is one of the major challenges for understanding various land surface processes in the mountainous and mostly ungauged terrain. The study assesses the impact of land use and land cover (LULC) changes between 1980 and 2020 and extreme rainfall on peak discharge and sediment yield in the Upper Jhelum Basin (UJB), Kashmir Himalaya, India using KINEROS2 model. Analysis of LULC change revealed a notable shift from natural LULC to more intensive human-modified LULC, including a decrease in vegetative cover, deforestation, urbanization, and improper farming practices. The findings revealed a strong influence of the LULC changes on peak discharge, and sediment yield relative to the 2014 timeframe, which coincided with the catastrophic September 2014 flood event. The model predicted a peak discharge of 115,101 cubic feet per second (cfs) and a sediment yield of 56.59 tons/ha during the September 2014 flooding, which is very close to the observed peak discharge of 115,218 cfs indicating that the model is reliable for discharge prediction. The model predicted a peak discharge of 98,965 cfs and a sediment yield of 49.11 tons/ha in 1980, which increased to 118,366 cfs and, 58.92 tons/ha, respectively, in 2020, showing an increase in basin’s flood risk over time. In the future, it is anticipated that the ongoing LULC changes will make flood vulnerability worse, which could lead to another major flooding in the event of an extreme rainfall as predicted under climate change and, in turn, compromise achievement of sustainable development goals (SDG). Therefore, regulating LULC in order to modulate various hydrological and land surface processes would ensure stability of runoff and reduction in sediment yield in the UJB, which is critical for achieving many SDGs. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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16 pages, 2108 KiB

Open AccessArticle

Modeling Biomass for Natural Subtropical Secondary Forest Using Multi-Source Data and Different Regression Models in Huangfu Mountain, China

by Congfang Liu, Donghua Chen, Chen Zou, Saisai Liu, Hu Li, Zhihong Liu, Wutao Feng, Naiming Zhang and Lizao Ye

Sustainability 2022, 14(20), 13006; https://0-doi-org.brum.beds.ac.uk/10.3390/su142013006 - 11 Oct 2022

Cited by 4 | Viewed by 1271

Abstract

Forest biomass estimation is an important parameter for calculating forest carbon storage, which is of great significance for formulating carbon-neutral strategies and forest resource management measures. We aimed at solving the problems of low estimation accuracy of forest biomass with complex canopy structure [...] Read more.

Forest biomass estimation is an important parameter for calculating forest carbon storage, which is of great significance for formulating carbon-neutral strategies and forest resource management measures. We aimed at solving the problems of low estimation accuracy of forest biomass with complex canopy structure and high canopy density, and large differences in the estimation results of the same estimation model under complex forest conditions. The Huangfu Mountain Forest Farm in Chuzhou City was used as the research area. As predictors, we used Gaofen-1(GF-1) and Gaofen(GF-6) satellite high-resolution imaging satellite data, combined with digital elevation model (DEM) and forest resource data. Multiple stepwise regression, BP neural network and random forest estimation models were used to construct a natural subtropical secondary forest biomass estimation model with complex canopy structure and high canopy closure. We extracted image information as modeling factors, established multiple stepwise regression models of different tree types with a single data source and a comprehensive data source and determined the optimal modeling factors. On this basis, the BP neural network and random forest biomass estimation model were established for Pinus massoniana, Pinus elliottii, Quercus acutissima and mixed forests, with the coefficient of determination n (R²) and root mean square error (RMSE) as the judgment indices. The results show that the random forest model had the best biomass estimation effect among different forest types. The R² of Quercus acutissima was the highest, reaching 0.926, but the RMSE was 11.658 t/hm². The R² values of Pinus massoniana and mixed forest were 0.912 and 0.904, respectively. The RMSE reached 10.521 t/hm² and 6.765 t/hm², respectively; the worst result was the estimation result of Pinus elliottii, with an R² of 0.879 and an RMSE of 14.721 t/hm². The estimation result of the BP neural network was second only to that of the random forest model in the four forest types. From high precision to low precision, the order was Quercus acutissima, Pinus massoniana, mixed forest and Pinus elliottii, with R²s of 0.897, 0.877, 0.825 and 0.753 and RMSEs of 17.899 t/hm², 10.168 t/hm², 18.641 t/hm² and 20.419 t/hm², respectively. In this experiment, the worst biomass estimation performance was seen for multiple stepwise regression, which ranked the species in the order of Quercus acutissima, Pinus massoniana, mixed forest and Pinus elliottii, with R²s of 0.658, 0.622, 0.528 and 0.379 and RMSEs of 29.807 t/hm², 16.291 t/hm², 28.011 t/hm² and 23.101 t/hm², respectively. In conclusion, GF-1 and GF-6 combined with data and a random forest algorithm can obtain the most accurate results in estimating the forest biomass of complex tree species. The random forest estimation model had a good performance in biomass estimation of primary secondary forest. High-resolution satellite data have great application potential in the field of forest parameter inversion. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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17 pages, 5223 KiB

Open AccessArticle

Spatial-Temporal Changes of Carbon Source/Sink in Terrestrial Vegetation Ecosystem and Response to Meteorological Factors in Yangtze River Delta Region (China)

by Chen Zou, Hu Li, Donghua Chen, Jingwei Fan, Zhihong Liu, Xuelian Xu, Jiani Li and Zuo Wang

Sustainability 2022, 14(16), 10051; https://0-doi-org.brum.beds.ac.uk/10.3390/su141610051 - 13 Aug 2022

Cited by 4 | Viewed by 1914

Abstract

As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO₂ concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with [...] Read more.

As an important part and the core link of a terrestrial ecosystem, terrestrial vegetation is the main means for human to regulate climate and mitigate the increase in atmospheric CO₂ concentration. The Yangtze River Delta (YRD) region is an urban agglomeration with the strongest comprehensive strength among developing countries (China). In the context of global climate change, a rapid, comprehensive, and detailed understanding of the spatio-temporal characteristics and variation tendency of the net ecosystem productivity (NEP) of vegetation and its response to climate during the rapid development of the YRD region is important for protecting ecological land, strengthening land management, and optimizing urban planning. The monthly mean temperature and rainfall data from 63 meteorological stations, the MODIS net primary productivity product, and a land cover product in the YRD region were used to estimate the NEP from 2000 to 2019 based on the soil respiration model, and the correlation between NEP and meteorological factors (such as temperature and rainfall) was analyzed. The results showed that: (1) From 2000 to 2019, the carbon sink area was much larger than the carbon source area in terrestrial vegetation in the Yangtze River Delta, the mean NEP of the vegetation ecosystem in the past 20 years was 253.2 g C·m⁻²·a⁻¹, and the spatial distribution presented a trend that was higher in the south and lower in the north, higher in the east and lower in the west, and that gradually increased from northwest to southeast; moreover, the NEP of mountain areas was generally higher than that of river courses and urban surroundings. The interannual fluctuation of NEP was small, but presented a slightly increasing trend, and the interannual variation of NEP was significantly correlated with the maximum NEP in this region. (2) The carbon sink capacity of different vegetation cover types was (from strong to weak): forestlands > grasslands > wetlands ≈ croplands. (3) The area with the NEP change rate (θ_slope) > 0 accounted for 69.0%; however, there was certain spatial difference, the proportions of the areas with θ_slope < 0 were (from large to small) 14.50% (Zhejiang Province, China), 9.10% (Anhui Province, China), 6.65% (Jiangsu Province, China), and 0.79% (Shanghai, China). In terms of the individual changes of these provinces and municipalities, Shanghai > Zhejiang Province > Jiangsu Province ≈ Anhui Province. (4) There was a correlation between NEP and the annual mean temperature and annual precipitation in some regions. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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12 pages, 6254 KiB

Open AccessArticle

Geochemical Characteristics and Controlling Factors of Chemical Composition of Groundwater in a Part of the Nanchang Section of Ganfu Plain

by Qingshan Ma, Weiya Ge and Fujin Tian

Sustainability 2022, 14(13), 7976; https://0-doi-org.brum.beds.ac.uk/10.3390/su14137976 - 30 Jun 2022

Cited by 5 | Viewed by 1414

Abstract

This work aims to investigate the hydrochemical characteristics and formation mechanisms of shallow groundwater in a part of the Nanchang section of Ganfu plain. The hydrochemical data from 90 groundwater samples were interpreted by the methods of mathematical statistics, Piper diagrams, Gibbs plots, [...] Read more.

This work aims to investigate the hydrochemical characteristics and formation mechanisms of shallow groundwater in a part of the Nanchang section of Ganfu plain. The hydrochemical data from 90 groundwater samples were interpreted by the methods of mathematical statistics, Piper diagrams, Gibbs plots, ratio graphs of ions, and geochemical modeling. The results show that shallow groundwater is weakly acidic, the average concentration of cation in groundwater decrease in Ca²⁺ > Na⁺ > Mg²⁺ > K⁺, and the abundance is in the order HCO₃⁻ > NO₃⁻ > SO₄²⁻ > Cl⁻ for anions. The hydrochemical type of groundwater was dominated by HCO₃-Na·Ca·Mg, HCO₃·Cl-Na·Ca·Mg, and HCO₃-Na·Ca. Moreover, the main controlling factor of groundwater hydrochemistry is water-rock interactions. Na⁺ and K⁺ mainly originate from the dissolution of halite. Ca²⁺ and Mg²⁺ are mainly controlled by carbonate dissolution, while the main anions come from the dissolution of evaporite and carbonate. The groundwater chemical evolution is affected by the dissolution and precipitation of the mineral phase and cation exchange. Full article

(This article belongs to the Special Issue Advances in Sustainable and Environmental Hydrology, Hydrogeology and Water Resources)

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