Rationalization of water quality monitoring locations under spatiotemporal heterogeneity of diffuse pollution using seasonal export coefficient

Varekar, Vikas; Yadav, Vinay; Karmakar, Subhankar

doi:10.1016/j.jenvman.2020.111342

Cited by 21 publications

(10 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6a–c . However, this linear relationship can be disturbed in part by the temporal distribution of precipitation (Qiu et al 2018 ; Varekar et al 2021 ). Thus, it is essential to additionally notice that the impact of precipitation unevenness on NPS is event-based.…”

Section: Discussionmentioning

confidence: 99%

“…(3) Soil moisture, which adjusts land-atmosphere interactions and cycles by redistributing near-surface energy and water, may also affect extreme precipitation (Li et al 2019a ). Recent NPS studies regard precipitation unevenness as an influencing factor of NPS (Nobre et al 2020 ; Varekar et al 2021 ). Therefore, distinguishing the contribution of HIP/LIP to precipitation unevenness may reduce the uncertainty of the export load, considering that precipitation with sufficient energy is the main driving force of pollutant migration.…”

Section: Discussionmentioning

confidence: 99%

“…Precipitation plays an important role in the processing of NPS, as it is not only the key driving force of NPS (in the way of precipitation runoff and/or soil erosion), but it can also affect the physicochemical properties of soils (e.g., moisture content, pH, C/N ratio, temperature), reshaping the surface water flux, groundwater flux, and ecohydrological flux accordingly (Ouyang et al 2019 ; Shortridge 2019 ). Precipitation is also the dominant input of both physics-based and statistics-based NPS models, indicating the importance of precipitation characteristics to NPS (Miralha et al 2020 ; Varekar et al 2021 ). Although the precipitation amount is considered a preferred feature of precipitation, precipitation unevenness, which describes the extent to which falling precipitation is asymmetrical over time, can also affect NPS (Nobre et al 2020 ; Qiu et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-16066-9.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…However, there are always practical limitations, such as budget constraints, to maintaining sampling sites [9]. Therefore, assessing the overall water quality status of a river network via a minimum number of sampling sites presents an important engineering problem [10].…”

Section: Introductionmentioning

confidence: 99%

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Goudarzi

Hedayatiaghmashhadi

Kazemi

et al. 2022

Water

View full text Add to dashboard Cite

The economic development, livelihood and drinking water of millions of people in the central plateau of Iran depend on the Qarah-Chay River, but due to a lack of inappropriate monitoring, it has been exposed to destruction and pollution. Consequently, an assessment of the river’s water quality is of utmost importance for both the management of human health and the maintenance of a safe environment, which can be achieved by determining the best locations for pollution monitoring stations along rivers. In this study, artificial neural networks (ANNs) has been used to optimize the locations for Qarah-Chay River monitoring stations in Markazi province, Iran. The data are collected based on the Iranian Water Quality Index (IRWQI), the US National Sanitation Foundation Water Quality Index (NSFWQI) and the Oregon Water Quality Index (OWQI). The database is given to a multilayer perceptron (MLP) neural network along with a geographic information system (GIS). The output of this study identified six pollution monitoring stations on the river, which are mainly downstream due to the accumulation of land uses and the concentration of pollution. The gradient of the MLP network training courses model from the proposed monitoring stations is 0.062299. In addition, the performance evaluation criteria of the proposed MLP model for F1-score, recall, precision and accuracy were 0.85, 0.84, 0.88 and 0.88, respectively. The results obtained help managers to properly monitor the river’s water resources with accuracy, efficiency and lower cost; furthermore, the findings were able to provide scientific references for river water quality monitoring and river ecosystem protection.

show abstract

“…The residents around the mine vicinity are using this contaminated water regularly for irrigation and household purposes. On drinking this dirty water, the residents suffer from severe physical and mental illness due to water-borne diseases like typhoid, cholera, paratyphoid fever, dysentery, jaundice and malaria (Choubey, 1991; Choudhury et al , 2017; Varekar et al , 2021; Merovich et al , 2021). The use of contaminated water for irrigation purposes also reduces soil fertility and converts the fertile land into barren land (Howladar et al , 2017; Kumar and Singh, 2016).…”

Section: Introductionmentioning

confidence: 99%

Environmental analysis of coal mining using Bradley–Terry model

Hasanuzzaman

Bhar

2021

MEQ

View full text Add to dashboard Cite

PurposeEnvironmental pollution and corresponding adverse health impacts have now become a significant concern for the entire planet. In this regard, analysts and experts are continually formulating policies to reduce environmental pollution and improve natural ecological conditions. To aid in coping with the ecological predicament, a framework has been developed in the present study to inspect the adverse environmental impacts and related health issues of coal mining.Design/methodology/approachThe parameters for this study have been identified through a review of the literature and finalized 23 critical parameters of air, water, land and soil, and noise related to coal mining by consultation with experts from industry and academia. Finally, the parameters have been categorized in accordance with the level of threat they pose to the environment by assigning weight using the Bradley–Terry model considering attitudinal data acquired by a questionnaire survey.FindingsIt is found that coal mining has a relatively higher impact on four attributes of “air pollution” (suspended particulate matter [SPM], respiratory particulate matter [RPM], sulfur dioxide [SO2] and oxides of nitrogen [NOx]), followed by “land and soil pollution” (deforestation and surface structure diversion), “noise pollution” (vehicle movement) and “water pollution” (water hardness, total solids (TSS/TDS) and iron content). It is also found that raising the air concentration of SPM and RPM results in increased respiratory and cardiopulmonary mortality. Therefore, reducing dust concentrations into the air generated during coal mining is recommended to reduce air pollution caused by coal mining, which will reduce contamination of water and land and soil.Research limitations/implicationsThe model built in this study is a hypothesized model that relied on the experts' opinions considering the parameters of coal production only. However, the parameters related to the usage of coal and its consequences have been excluded. Further, only industrial and academic experts were considered for this study; however, they excluded local people, coal mining personnel, policy authorities, etc. Therefore, the study findings might differ in real circumstances. The research can further be reproduced by considering the parameters related to the use of coal and its consequences, considering the opinions of the local people, coal mining personnel and policy authorities.Practical implicationsCategorizing the parameters according to the threat they pose to the environment due to coal mining can help the decision-maker develop an effective policy to reduce environmental pollution due to coal mining by considering the parameters on a priority basis. In addition, the results further help the decision-makers to assess the environmental impact of coal mining and take necessary action.Originality/valueThe study has developed a framework using the Bradley–Terry model to categorize the environmental parameters of coal mining to develop effective environmental policies, which are original and unique in nature.

show abstract

Rationalization of water quality monitoring locations under spatiotemporal heterogeneity of diffuse pollution using seasonal export coefficient

Cited by 21 publications

References 27 publications

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran

Environmental analysis of coal mining using Bradley–Terry model

Contact Info

Product

Resources

About