Water quality monitoring, control and management (WQMCM) framework using collaborative wireless sensor networks

Improving water quality is of global concern, with agricultural practices being the major contributors to reduced water quality. The reuse of nutrient-rich drainage water can be a valuable strategy to gain economic-environmental benefits. However, currently the tools and techniques to allow this do not exist. Therefore, we have proposed a framework, WQMCM, which utilises increasingly common local farm-scale networks across a catchment, adding provision for collaborative information sharing. Using this framework, individual sub-networks can learn their environment and predict the impact of catchment events on their locality, allowing dynamic decision making for local irrigation strategies. Since resource constraints of network nodes (e.g. power consumption, computing power etc.) require a simplified predictive model for discharges, therefore low-dimensional model parameters are derived from the existing National Resource Conservation Method (NRCS), utilising real-time field values. Evaluation of the predictive models, developed using M5 decision trees, demonstrates accuracy of 84-94% compared with the traditional NRCS curve number model. The discharge volume and response time model was tested to perform with 6% relative root mean square error (RRMSE), even for a small training set of around 100 samples; however the discharge response time model required a minimum of 300 training samples to show reasonable performance with 16% RRMS

Water Sustainability through Drainage Reuse in Agriculture – A Case for Collaborative Wireless Sensor Networks

With increasing prevalence of wireless sensor networks (WSNs) and internet of things (IoT) in agriculture and hydrology, there exists an opportunity for providing a technologically viable solution for the conservation of already scarce freshwater resources. In this chapter, a novel framework is proposed for enabling a proactive management of agricultural drainage and nutrient losses at farm scale where complex models are replaced by in situ sensing, communication, and low complexity predictive models suited to an autonomous operation. This is achieved through the development of the proposed Water Quality Management using Collaborative Monitoring (WQMCM) framework that combines local farm-scale WSNs through an information sharing mechanism. In this chapter, we present the design of a framework for facilitating real-time utilization or disposal of agricultural drainage among farms using collaboration among prevalent farm networks. The basic system architecture comprises modules for environmental learning, prediction of the impact of neighboring events in terms of drainage and nutrients losses, and a local decision support mechanism. The overall functionality of the framework is explored in terms of stages of learning, training, and testing. A network learning model is required to identify flow links of a network with neighboring networks

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Catchment-Scale Water Quality Monitoring, Control And Management Framework Using Collaborative Wireless Sensor Networks

Improving water quality is a global concern, with agricultural practices being the major contributors to reduced water quality. The reuse of nutrient-rich drainage water can be a valuable strategy to maximise water resources and gain economic-environmental benefits. Transmitting event information across a catchment, as the event occurs upstream, allows prediction of the outflow dynamics of the expected discharges downstream. Here, we propose a framework architecture which utilises increasingly common local farm-scale networks and other water-quality monitoring networks across a catchment, adding provision for collaborative information sharing. The key part is that individual networks learn their environment, predicting the impact of events elsewhere in the catchment on their own zone of influence. The predicted events can then be classified to influence management practice. Resource constraints on the distributed network nodes (eg power consumption, battery life) require a predictive model executing a simplified form of the underlying physical model. Data-driven machine-learning techniques are becoming popular in hydrological modelling. Therefore, by using the information sharing framework, statistics gleaned from the shared parameters of the event and the observed data in response to that event, can lead to the development of a low-dimension learning model, thus allowing the generation of hydrograph and pollutograph dynamics. To develop the proposed framework, a Matlab-based modelling environment is developed. These models use a low-dimensional feature set, utilising real-time field values for accurate discharge prediction. Learning algorithms for different feature sets and training data are tested. Evaluation of the predictive models for discharge dynamics, using M5 decision trees, demonstrates accuracy of 85-94% compared with traditional NRCS curve number models. The discharge volume model was tested to perform well (94% accuracy) even for a small training set of 65 samples; however the discharge travel and response time models require a minimum of 300 training samples to show reasonable performance

A Study of the Organizational Stress among Public Sector Secondary School Teachers in Punjab

The aim of the study was to find out the factors which create stress among public sector secondary school teachers to determine the stressors being faced by the secondary school teachers. The study was related to the secondary schools of public sector in Punjab. Punjab province is comprised of 9 divisions. Due to limited time and resources, the study was delimited to public sector secondary schools of Lahore division. The study adopted descriptive survey design using a sample of 1000 teachers drawn from 100 secondary schools from public sector in Lahore division. From each school 10 teachers were randomly selected. One rating scale was developed to collect data for the study. The percentage, mean, standard deviation and t-test were applied as descriptive and inferential statistics to analyze the collected data. In the light of the results and conclusions of the study, it may be recommended that unwanted sounds and noise may be minimized in the school environment, trainings be imparted to the staff, workload should be equally distributed and necessary facilities should be provided at schools

A Study of the Organizational Stress among Public Sector Secondary School Teachers in Punjab

The aim of the study was to find out the factors which create stress among public sector secondary school teachers to determine the stressors being faced by the secondary school teachers. The study was related to the secondary schools of public sector in Punjab. Punjab province is comprised of 9 divisions. Due to limited time and resources, the study was delimited to public sector secondary schools of Lahore division. The study adopted descriptive survey design using a sample of 1000 teachers drawn from 100 secondary schools from public sector in Lahore division. From each school 10 teachers were randomly selected. One rating scale was developed to collect data for the study. The percentage, mean, standard deviation and t-test were applied as descriptive and inferential statistics to analyze the collected data. In the light of the results and conclusions of the study, it may be recommended that unwanted sounds and noise may be minimized in the school environment, trainings be imparted to the staff, workload should be equally distributed and necessary facilities should be provided at schools

Water, boundaries and borders, the great intangibles in water quality management: can new technologies enable more effective compliance?

The challenge of improving water quality has been a longstanding global concern. There has also been a general acceptance that the main drivers of poor water quality are economics, poor water management, agricultural practices, and urban development. Development, implementation, and compliance with transboundary water quality agreements, whether they be across basin, across water bodies or across national or international boundaries, remains constrained by our ability to monitor their effectiveness in real time. Despite significant advances in sensor and communication technologies, water quality monitoring (WQM) is primarily undertaken through small-scale and single-application sampling and testing that is limited by the available techniques, requires expensive highly technical instrumentation, and only provides selective data for decision support tools. The effects of diffuse pollutants and their distribution within water bodies and transboundary rivers systems are, therefore, difficult to capture, as is determination of the exact point and timing of their release into a defined “water system”.Improved data capture and timely analysis, enabled by innovative sensor technologies and communication networks, is an important aspect of compliance monitoring. This is particularly important for international and trans-border agreements where changes in water distribution, quality, and availability associated with regional climate variability are already creating challenges for future water, energy, and food security. Therefore, it is argued that by including all the multi-level impacts of various stakeholders in a water catchment, on water resources, and by removing the long lead times between when the sample was taken to when sample testing and data analysis has been completed, it is possible to develop and implement an effective water quality monitoring and management framework.This paper examines the prospect of improved sensor technologies and assessment frameworks that have the potential to be linked with water quality governance, polices and compliance requirements. By employing, a real time integrated and targeted monitoring system, which allows for the assessment of both the catchment functions and modifications to those functions or (eco) services by the various stakeholders, improvements in water quality is possible

Assessment of awareness of orthodontic emergencies and psychosocial wellbeing of patients during novel coronavirus pandemic through teledentistry

Introduction: At the end of December 2019, the novel coronavirus began to spread in central China and soon became a pandemic. Unfortunately, all elective dental treatments including orthodontic visits were postponed and patients could not be counselled on how to manage orthodontic emergencies that they could encounter at home. Teledentistry can play a major role in providing instructions to patients during quarantine. Objective: : The study aimed to assess the awareness of orthodontic patients regarding the management of orthodontic emergencies and their psychosocial well-being during the novel coronavirus pandemic through Teledentistry. Materials and Methods: A questionnaire-based study was conducted on two groups during the lockdown period in which their anxiety, psychological status, and their ability to manage orthodontic emergencies at home during the COVID-19 pandemic lockdown were assessed through teledentistry. Results: A total of 170 patients participated in our study, Independent sample t-test was used to compare the means of the group's control and experimental. Statistically, a significant difference was determined between the two groups regarding their psychosocial well-being (social media embarrassment p=0.049, awareness of how to manage orthodontic emergencies p=0.00). The participants displayed a better understanding of how to deal with orthodontic emergencies at home after having instructions. 48.2% of the patients selected voice call as a preferred mode of instruction for managing orthodontic emergencies at home. Conclusion: Experimental group of patients was more aware of how to manage orthodontic emergencies and they were less anxious. Teledentistry has proven to be an important tool for providing instructions to anxious patients as well as reducing the spread of coronavirus due to lack of contact. Voice call was the preferred mode of instruction. Keywords: Anxiety, COVID-19, Orthodontic emergencies, Tele-dentistry