Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants

Wang, Puze; Yao, Jiping; Wang, Guoqiang; Hao, Fang; Shrestha, Sangam; Xue, Baolin; Xie, Gang; Peng, Yanbo

doi:10.1016/j.scitotenv.2019.07.246

Cited by 129 publications

(38 citation statements)

References 33 publications

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“…Numerical modeling of water quality at river basin scale must find convenient solutions by simulating potential scenarios in the catchment area that can improve the action plans to be taken in case of risk situations [42]. Such risks include prolonged periods with low discharge rates of the river and high loads of pollutants, floods, industrial accidents, accidental discharges of effluents with high loads of pollutants, polluting agricultural technologies in the river catchment area etc.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Water Quality in Ialomita River Basin in Relationship with Land Cover Patterns

Dunea

Brețcan

Tanislav

et al. 2020

Water

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The paper reviews the state of water quality in Ialomita River Basin (IRB), Romania, between 2007 and 2018 using the land use/land cover and basin-specific conditions effects on sediments and nutrients load. On-site monitoring was performed in two control sections of the Ialomita River, one in the upper part of the basin (near Targoviste city) and the second near the discharge into the Danube (downstream of Tandarei town). The statistical averages of water parameters for 10 years' monitoring in the control section that is close to the Ialomita River discharge in Danube were pH = 7.60 (range: 6.41-8.40), NH 4 -N = 1.20 mg/L (0.02-14.87), alkalinity = 4.12 mmol/L (1.34-6.27), NO 3 -N = 2.60 mg/L (0.08-17.30), PO 4 -P = 0.09 mg/L (0-0,31), dissolved oxygen (DO) = 8.87 mg/L (2.72-15.96), BOD 5 = 5.50 mg/L (0.01-74.71), suspended solids (TSS) = 508.32 mg/L (15.2-4457), total dissolved salts (TDS) = 733.69 mg/L (455.2-1053), and river discharge = 38.60 m 3 /s (8. . Expected mean concentration and soil and water assessment tool (SWAT) modeling have been employed in the GIS environment to extend the approach to large spatial patterns within the basin. The estimated average specific emission on the total area for nitrogen was 3.2 kg N/ha, and 0.3 kg P/ha for phosphorus highly influenced by the agricultural activities. The results are useful to raise awareness regarding water-quality degradation and the need to stop and even reverse such trends for local and national sustainable development.Water 2020, 12, 735 2 of 19 water [2]. The discharge of toxic substances, the contamination of groundwater, and the atmospheric transport of nutrients over long distances leading to eutrophication of surface waters, are some of the important processes responsible for the degradation of water quality [3].Massive pollution with organic compounds leads to increased biological oxygen demand and hypoxia, which is often accompanied by severe pathogenic contamination [4]. Eutrophication resulting from the enrichment with nutrients originating from various sources and in particular from agriculture and agro-industrial environment strongly influences the biosphere [5,6]. The over-application of fertilizers damages the ecosystems on large spaces, including soil, water and underground aquifer layers [7]. The main problems associated with intensive agriculture are salinization, nitrification, pesticide contamination and excessive erosion that produce high concentrations of colloids in flowing waters [8].Significant emissions of atmospheric pollutants and long-range transport have affected large areas, while droplets formed in the atmosphere through the combination of water with the gases produced by burning fossil fuels cause acid rain that leads to the acidification of surface waters, especially of lakes [9].Society uses natural resources in a steadily increasing rate. Human influence on nature may cause adverse consequences leading to the disturbance of the natural balance [10,11]. At the same time, technologies and approaches are being developed...

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Section: Discussionmentioning

confidence: 99%

Evaluation of Water Quality in Ialomita River Basin in Relationship with Land Cover Patterns

Dunea

Brețcan

Tanislav

et al. 2020

Water

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“…When faced with complex environmental issues and large quantities of data, AI systems have the potential to make knowledge-based decisions that balance the environmental outcomes of the city against the social and economic wellbeing of its residents [63,136]. AI systems can be used to monitor changes in the environment including, noise, temperature, humidity, emissions [90], water pollutants [133], fish stock, and other environmental indicators [136]. AI systems can respond to these changes, and quickly implement solutions for dealing with any issues [156].…”

Section: Ai In the Environment Dimension Of Smart Citiesmentioning

confidence: 99%

“…Environment PM ML PP NN n/a n/a Wang et al [133] Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants…”

Section: Renewable and Sustainable Energy Reviewsmentioning

confidence: 99%

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

et al. 2020

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Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban locations that are enabled by community, technology, and policy to deliver productivity, innovation, livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a comprehensive review on the topic. This paper generates insights into how AI can contribute to the development of smarter cities. A systematic review of the literature is selected as the methodologic approach. Results are categorized under the main smart city development dimensions, i.e., economy, society, environment, and governance. The findings of the systematic review containing 93 articles disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The central focus of the literature is on AI technologies, algorithms, and their current and prospective applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency, data analytics, education, energy, environmental sustainability, health, land use, security, transport, and urban management areas. (d) There is limited scholarly research investigating the risks of wider AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately examined. Current and potential contributions of AI to the development of smarter cities are outlined in this paper to inform scholars of prospective areas for further research.

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“…In recent years, applying AI in solving actual problems has been an increasing need because of its ability to generate diversify range of the mathematical functions 38,39 . The ability of AI methods in automatic SI and model reduction for nonlinear system makes AI methods for example, artificial neural network (ANN), 40 machine learning, 41 deep learning, 42 long short‐term memory network (LSTM) 43 popular in system modelling and parameter analysis in many researches 37 . GP is an evolutionary algorithm similar to GA, which is based on Darwin's evolutionary theory of survival of the fittest to optimize individual model 44 .…”

Section: Model Selection Criteria Approximated Genetic Programmingmentioning

confidence: 99%

A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

Jishnu

Garg

et al. 2020

Energy Storage

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Air‐cooling‐based battery thermal management system (BTMS) is a research hotspot for electric vehicles because of lower cost and simpler design. Past research works have immensely concentrated on the enhancement of heat removal from Li‐ion battery, but the minimum consideration has been given to minimize the parasitic power. In this paper, a novel procedure is proposed to predict the operating parameters (inlet velocity, working time of fan, and range of heat generation rates) of air‐cooling design for minimizing parasitic power and ensuring the battery temperature do not exceed the upper threshold limit simultaneously. Based on findings via computational fluid dynamics analysis, an empirical model of air‐cooling BTMS is further developed using an evolutionary approach of model selection criteria approximated genetic programming (MSC‐GP). The model is then optimized to determine operating parameters of air cooling which causes minimum parasitic power while keeping the average temperature of battery cells within the limit. Further, sensitivity analysis and parameter interaction (2D and 3D) analysis is also performed to study the effect and identify the contribution of various operating parameters on parasitic power. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only. Finally, the optimal values of operating parameters for various heat generation rate per cell (function of discharge rate) are obtained. The optimized parasitic power was observed to be nonlinearly increasing with heat generation rate. Operating time of fan has 49% influence on final temperature while inlet velocity has 36% influence only. However, parasitic power is more sensitive to inlet velocity (77%) while operating time has 23% influence only.

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Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants

Cited by 129 publications

References 33 publications

Evaluation of Water Quality in Ialomita River Basin in Relationship with Land Cover Patterns

Evaluation of Water Quality in Ialomita River Basin in Relationship with Land Cover Patterns

Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

A novel procedure combining computational fluid dynamics and evolutionary approach to minimize parasitic power loss in air cooling of Li‐ion battery for thermal management system design

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