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23 pages, 4526 KiB

Open AccessEditor’s ChoiceArticle

Insights from an Evaluation of Nitrate Load Estimation Methods in the Midwestern United States

by Daeryong Park, Myoung-Jin Um, Momcilo Markus, Kichul Jung, Laura Keefer and Siddhartha Verma

Sustainability 2021, 13(13), 7508; https://doi.org/10.3390/su13137508 - 05 Jul 2021

Viewed by 1937

This study investigated the accuracy and suitability of several methods commonly used to estimate riverine nitrate loads at eight watersheds located southwest of Lake Erie in the Midwestern United States. This study applied various regression methods, including a regression estimator with five, six, [...] Read more.

This study investigated the accuracy and suitability of several methods commonly used to estimate riverine nitrate loads at eight watersheds located southwest of Lake Erie in the Midwestern United States. This study applied various regression methods, including a regression estimator with five, six, and seven parameters, an estimator enhanced by composite, triangular, and rectangular error corrections with residual and proportional adjustment methods, the weighted regressions on time, discharge, and season (WRTDS) method, and a simple linear interpolation (SLI) method. Daily discharge and nitrate concentration data were collected by the National Center for Water Quality Research. The methods were compared with subsampling frequencies of 6, 12, and 24 times per year for daily concentrations, daily loads, and annual loads. The results indicate that combinations of the seven-parameter regression method with composite residual and rectangular residual adjustments provided the best estimates under most of the watershed and sampling frequency conditions. On average, WRTDS was more accurate than the regression models alone, but less accurate than those models enhanced by residual adjustments, except for the most urbanized watershed, Cuyahoga. SLI was the most accurate in the Vermilion and Maumee watersheds. The results also provide some information about the effects of rating curve shape and slope, land use, and record length on model performance. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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15 pages, 39704 KiB

Open AccessArticle

Identification of High-Priority Tributaries for Water Quality Management in Nakdong River Using Neural Networks and Grade Classification

by Kang-Young Jung, Sohyun Cho, Seong-Yun Hwang, Yeongjae Lee, Kyunghyun Kim and Eun Hye Na

Sustainability 2020, 12(21), 9149; https://doi.org/10.3390/su12219149 - 03 Nov 2020

Cited by 5 | Viewed by 1914

Abstract

To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the [...] Read more.

To determine the high-priority tributaries that require water quality improvement in the Nakdong River, which is an important drinking water resource for southeastern Korea, data collected at 28 tributaries between 2013 and 2017 were analyzed. To analyze the water quality characteristics of the tributary streams, principal component analysis and factor analysis were performed. COD (chemical oxygen demand), TOC (total organic carbon), TP (total phosphorus), SS (suspended solids), and BOD (biochemical oxygen demand) were classified as the primary factors. In the self-organizing maps analysis using the unsupervised learning neural network model, the first factor showed a highly relevant pattern. To perform the grade classification, 11 parameters were selected. Six parameters are concentrations of the main parameters for the water quality standard assessment in South Korea. We added the pollution load densities for the selected five primary factors. Joochungang showed the highest pollution load density despite its small watershed area. According to the results of the grade classification method, Joochungang, Topyeongcheon, Hwapocheon, Chacheon, Gwangyeocheon, and Geumhogang were selected as tributaries requiring high-priority water quality management measures. From this study, it was concluded that neural network models and grade classification methods could be utilized to identify the high-priority tributaries for more directed and effective water quality management. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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18 pages, 6667 KiB

Open AccessArticle

Improved Framework for Assessing Vulnerability to Different Types of Urban Floods

by Quntao Yang, Shuliang Zhang, Qiang Dai and Rui Yao

Sustainability 2020, 12(18), 7668; https://doi.org/10.3390/su12187668 - 17 Sep 2020

Cited by 11 | Viewed by 2921

Abstract

Vulnerability assessment is an essential tool in mitigating the impact of urban flooding. To date, most flood vulnerability research has focused on one type of flood, such as a pluvial or fluvial flood. However, cities can suffer from urban flooding for several reasons, [...] Read more.

Vulnerability assessment is an essential tool in mitigating the impact of urban flooding. To date, most flood vulnerability research has focused on one type of flood, such as a pluvial or fluvial flood. However, cities can suffer from urban flooding for several reasons, such as precipitation and river levee overtopping. Therefore, a vulnerability assessment considering different types of floods (pluvial floods, fluvial floods, and compound flooding induced by both rainfall and river overtopping) was conducted in this study. First, a coupled urban flood model, considering both overland and sewer network flow, was developed using the storm water management model (SWMM) and LISFLOOD-FP model to simulate the different types of flood and applied to Lishui, China. Then, the results of the flood modeling were combined with a vulnerability curve to obtain the potential impact of flooding on different land-use classes. The results indicated that different types of floods could have different influence areas and result in various degrees of flood vulnerability for different land-use classes. The results also suggest that urban flood vulnerability can be underestimated due to a lack of consideration of the full flood-induced factors. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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20 pages, 5533 KiB

Open AccessArticle

Environmental Risk Assessment in Agriculture: The Example of Pistacia vera L. Cultivation in Greece

by Georgios Bartzas and Konstantinos Komnitsas

Sustainability 2020, 12(14), 5735; https://doi.org/10.3390/su12145735 - 16 Jul 2020

Cited by 3 | Viewed by 5845

Abstract

In this study, an integrated environmental risk assessment (ERA) study involving frequent monitoring of both water and soil parameters (24 on total), was carried out to assess and compare the environmental risk quality of three pistachio (Pistacia vera L.) fields (two in [...] Read more.

In this study, an integrated environmental risk assessment (ERA) study involving frequent monitoring of both water and soil parameters (24 on total), was carried out to assess and compare the environmental risk quality of three pistachio (Pistacia vera L.) fields (two in Aegina island and one in Kilkis) based upon risk categories identified and assessed in terms of quality and quantity. In this context, vertical profiles and risk matrices were created for a 60-month period for the most important soil and water parameters i.e., soil pH, soil organic matter, soil salinity, heavy metals, and irrigation water quality. According to the obtained results, the two pistachio fields in Aegina exhibited reduced overall risk values, i.e., 17% and 27%, respectively after the adoption of sustainable cultivation practices, thus reflecting a transition from “medium to high risk” to “low to medium risk” environmental quality. On the other hand, overall risk values for the pistachio field in Kilkis were reduced by 34% and were lower compared to the ones obtained for the pistachio fields in Aegina. The better environmental profile identified for the entire period in Kilkis ranging from “medium risk” to “low risk” was the result of lower inherent risk associated with irrigation water quality and soil salinity. The proposed methodology can be easily applied in other agricultural areas and for similar cultivations in Greece and other Mediterranean countries. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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19 pages, 3220 KiB

Open AccessArticle

Pollution, Sources and Human Health Risk Assessment of Potentially Toxic Elements in Different Land Use Types under the Background of Industrial Cities

by Qing Xia, Jiquan Zhang, Yanan Chen, Qing Ma, Jingyao Peng, Guangzhi Rong, Zhijun Tong and Xingpeng Liu

Sustainability 2020, 12(5), 2121; https://doi.org/10.3390/su12052121 - 09 Mar 2020

Cited by 11 | Viewed by 3111

Abstract

Residents in industrial cities may be exposed to potentially toxic elements (PTEs) in soil that increase chronic disease risks. In this study, six types of PTEs (Zn, As, Cr, Ni, Cu, and Pb) in 112 surface soil samples from three land use types—industrial [...] Read more.

Residents in industrial cities may be exposed to potentially toxic elements (PTEs) in soil that increase chronic disease risks. In this study, six types of PTEs (Zn, As, Cr, Ni, Cu, and Pb) in 112 surface soil samples from three land use types—industrial land, residential land, and farmland—in Tonghua City, Jilin Province were measured. The geological accumulation index and pollution load index were calculated to assess the pollution level of metal. Meanwhile, the potential ecological risk index, hazard index, and carcinogenic risk were calculated to assess the environmental risks. The spatial distribution map was determined by the ordinary kriging method, and the sources of PTEs were identified by factor analysis and cluster analysis. The average concentrations of Zn, As, Cr, Ni, Cu, and Pb were 266.57, 15.72, 72.41, 15.04, 20.52, and 16.30 mg/kg, respectively. The results of the geological accumulation index demonstrated the following: Zn pollution was present in all three land use types, As pollution in industrial land cannot be neglected, Cr pollution in farmland was higher than that in the other two land use types. The pollution load index decreased in the order of industrial land > farmland > residential land. Multivariate statistical analysis divided the six PTEs into three groups by source: Zn and As both originated from industrial activities; vehicle emissions were the main source of Pb; and Ni and Cu were derived from natural parent materials. Meanwhile, Cr was found to come from a mixture of artificial and natural sources. The soil environment in the study area faced ecological risk from moderate pollution levels mainly contributed by As. PTEs did not pose a non-carcinogenic risk to humans; however, residents of the three land use types all faced estimated carcinogenic risks caused by Cr, and As in industrial land also posed high estimated carcinogenic risk to human health. The conclusion of this article provides corresponding data support to the government’s policy formulation of remediating different types of land and preventing exposure and related environmental risks. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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17 pages, 3116 KiB

Open AccessArticle

Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation

by Kichul Jung, Deg-Hyo Bae, Myoung-Jin Um, Siyeon Kim, Seol Jeon and Daeryong Park

Sustainability 2020, 12(1), 400; https://doi.org/10.3390/su12010400 - 03 Jan 2020

Cited by 30 | Viewed by 3334

Abstract

The present work aimed to examine the feasibility of using artificial neural network (ANN) based models to obtain accurate estimates of nitrate loads in river basins, which is an important parameter for water quality management. Both Single ANN (SANN) and Ensemble ANN (EANN) [...] Read more.

The present work aimed to examine the feasibility of using artificial neural network (ANN) based models to obtain accurate estimates of nitrate loads in river basins, which is an important parameter for water quality management. Both Single ANN (SANN) and Ensemble ANN (EANN) models were used to obtain the load estimations for five river basins in the Midwest United States. These basins included the Cuyahoga, Raisin, Sandusky, Muskingum, and Vermilion basins in Michigan and Ohio. Further, canonical correlation analysis (CCA) was applied to the ANN models to improve the performance. The k-fold cross-validation method was then utilized to evaluate the proposed models based on two statistical indices, namely, the rRMSE and rBAIS, and the estimates were compared for four different k values (k = 3, 5, 7, and 10). According to the results, the EANN model seemed to produce better load estimations than the SANN model, and the CCA based EANN model tended to produce the best estimates among all of the proposed models in this study. The box plot data for the rRMSE index were also investigated, and the plot results indicated that increasing values of k tended to generate better estimates. Thus, the use of k = 10 is recommended for load estimations since this value was associated with better performances and less biased estimates. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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25 pages, 944 KiB

Open AccessArticle

Layout Planning of Highway Transportation Environment Monitoring Network: The Case of Xinjiang, China

by Na Zhang, Xianghui Zhao, Tao Liu, Ming Lei, Cui Wang and Yikun Wang

Sustainability 2020, 12(1), 290; https://doi.org/10.3390/su12010290 - 30 Dec 2019

Cited by 7 | Viewed by 2378

Abstract

Environmental monitoring is an important tool for environmental protection supervision and management. Environmental monitoring can help us effectively understand and master the degree of environmental pollution, and provide data support for putting forward environmental protection measures. Scientific layout and reasonable level of environmental [...] Read more.

Environmental monitoring is an important tool for environmental protection supervision and management. Environmental monitoring can help us effectively understand and master the degree of environmental pollution, and provide data support for putting forward environmental protection measures. Scientific layout and reasonable level of environmental monitoring network design is an essential cornerstone for environmental monitoring, and a significant measure to promote the industry and green sustainable development. This paper systematically analyzed its requirements of monitoring stations in the highway traffic environment monitoring network. First of all, the paper analyzed the influencing factors of regional monitoring stations in the Xinjiang transportation environment monitoring network by referring to the idea of planning the distribution points of the national transportation environment monitoring network, and determines the weight of them by using the analytic hierarchy process (AHP), which lays a foundation for the subsequent selection and determination of environmental monitoring stations. Secondly, the advantage order of ecological monitoring objects’ importance degree was synthetically sorted by the fuzzy comprehensive evaluation method. Finally, the ranking results of the environmental monitoring objects were integrated to determine the number of traffic environmental monitoring stations that need to be built, and the layout of the highway traffic environment monitoring network in Xinjiang was proposed. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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22 pages, 17650 KiB

Open AccessArticle

Pollution and Health Risk Assessment of Carcinogenic Elements As, Cd, and Cr in Multiple Media—A Case of a Sustainable Farming Area in China

by Kui Cai, Chang Li, Zefeng Song, Xin Gao and Moxin Wu

Sustainability 2019, 11(19), 5208; https://doi.org/10.3390/su11195208 - 23 Sep 2019

Cited by 15 | Viewed by 2595

Abstract

The high concentrations of trace elements in the environment, especially the carcinogenic elements Cr, Cd, and As, in populated areas can lead to an increased non-carcinogenic risk and carcinogenic risk in humans via the effective exposure pathways (inhalation and dermal contact). In this [...] Read more.

The high concentrations of trace elements in the environment, especially the carcinogenic elements Cr, Cd, and As, in populated areas can lead to an increased non-carcinogenic risk and carcinogenic risk in humans via the effective exposure pathways (inhalation and dermal contact). In this study, the concentrations of the trace elements Cd, Cr, and As in four media were comprehensively evaluated by collecting samples from atmospheric precipitates (A), wheat (W), soil (S), and groundwater (G) in the agricultural plain. This study not only considers the health risk level, but also focuses on the relationship between soil properties and the soil–wheat system. First, according to the results of the analysis, the concentration of carcinogenic elements in atmospheric precipitates was higher than that in other media. The sequence follows the order A > S > W > G. Moreover, the input flux of A was at a relatively higher level (determined via an input flux calculation) than other farming areas. Second, the pollution of Cr, Cd, and As in A and S were analyzed using the geoaccumulation method, and the level of Cd reached mild to moderate pollution. In addition, it was found that the bioaccumulation factors (BAFs) of Cd were much higher than those of As and Cr in the soil–wheat system. Furthermore, it was found that the negative relationship between BAFs and pH, CEC (cation exchange capacity), Corg (soil organic carbon), and clay was significant. Lastly, the hazard quotient (HQ) of the non-carcinogenic risk and carcinogenic risk (CR) of the three elements in multiple media were calculated using the health risk model. The HQ results showed that the total non-carcinogenic risk index (HI) of Cd, As, and Cr in the multiple-media did not exceed the risk limit (1.00), and there was no significant risk to the locally exposed population. However, the total carcinogenic risk index (TCR) indicated that the risk index of Cr, As and Cd in multiple media exceeded the safety index range (≈10⁻⁶–10⁻⁴), and the three elements posed a significant carcinogenic risk to local residents via the main pathways. In terms of individual elements, the risk of cancer was highest via the ingestion of the carcinogenic element Cd in G and W. Full article

(This article belongs to the Special Issue Sustainability through Environmental Monitoring, Modelling and Risk Assessment)

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