scholarly journals Real-time monitoring system for weather and air pollutant measurement with HTML-based UI application

2021 ◽  
Vol 10 (3) ◽  
pp. 1669-1677
Author(s):  
Prisma Megantoro ◽  
Brahmantya Aji Pramudita ◽  
P. Vigneshwaran ◽  
Abdufattah Yurianta ◽  
Hendra Ari Winarno
Keyword(s):  
Real Time ◽  
Web Application ◽  
Barometric Pressure ◽  
Air Pollutant ◽  
Sensor Data ◽  
Time Data ◽  
Web Based ◽  
Uv Index ◽  
Weather Parameters ◽  
Real Time Database

This article discusses devising an IoT system to monitor weather parameters and gas pollutants in the air along with anHTML web-based application. Weather parameters measured include; speed and direction of the wind, rainfall, air temperature and humidity, barometric pressure, and UV index. On the other side, the gases measured are; ammonia, hydrogen, methane, ozone, carbon monoxide, and carbon dioxide. This article is introducing a technique to send all parameter data. All parameters read by each sensor are converted into a string then joined into a string dataset, where this dataset is sent to the server periodically. On the UI side, the dataset that has been downloaded from the server-parsed for processing and then displayed. This system uses Google Firebase as a real-time database server for sensor data. Also, using the GitHub platform as a web hosting. The web application uses the HTML programming platform. The results of this study indicate that the device operates successfully to provide information about the weather and gases condition as real-time data.

Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

2021 ◽  
Author(s):  
Goedele Verreydt ◽  
Niels Van Putte ◽  
Timothy De Kleyn ◽  
Joris Cool ◽  
Bino Maiheu
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Real Time ◽  
Flow Direction ◽  
Flow Dynamics ◽  
Sensor Data ◽  
Time Data ◽  
Mass Fluxes ◽  
First Case

<p>Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.</p><p> </p><p>This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.</p><p> </p><p>A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal ‘De Lieve’ in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.  </p>

Basin-Scale, Real-Time Salinity Management Using Telemetered Sensor Networks and Model-Based Salt Assimilative Capacity Forecasts

2016 ◽  
pp. 193-220
Author(s):  
Nigel W.T. Quinn ◽  
Roberta Tassey ◽  
Jun Wang
Keyword(s):  
Real Time ◽  
Software Tool ◽  
Sensor Data ◽  
Quality Data ◽  
Monitoring Site ◽  
Data Quality Management ◽  
Web Based ◽  
Web Access ◽  
State Water ◽  
Salinity Management

This chapter describes a new approach to environmental decision support for salinity management in the San Joaquin Basin that focuses on Web-based data sharing using tools such as YSI Econet and continuous data quality management using an enterprise-level software tool WISKI. These tools offer real-time Web-access to sensor data as well as providing the owner full control over the way the data is visualized. The same websites use GIS to superimpose the monitoring site locations on maps of local hydrography and allow point and click access to the data collected at each environmental monitoring site. This information technology suite of software and hardware work together with a watershed simulation model WARMF-SJR to provide timely, reliable, and high quality data and forecasts of river salinity that can used by stakeholder decision makers to ensure compliance with state water quality objectives.

Basin-Scale, Real-Time Salinity Management Using Telemetered Sensor Networks and Model-Based Salt Assimilative Capacity Forecasts

2015 ◽  
pp. 89-117
Author(s):  
Nigel W.T. Quinn ◽  
Roberta Tassey ◽  
Jun Wang
Keyword(s):  
Real Time ◽  
Software Tool ◽  
Sensor Data ◽  
Quality Data ◽  
Monitoring Site ◽  
Data Quality Management ◽  
Web Based ◽  
Web Access ◽  
State Water ◽  
Salinity Management

This chapter describes a new approach to environmental decision support for salinity management in the San Joaquin Basin that focuses on Web-based data sharing using tools such as YSI Econet and continuous data quality management using an enterprise-level software tool WISKI. These tools offer real-time Web-access to sensor data as well as providing the owner full control over the way the data is visualized. The same websites use GIS to superimpose the monitoring site locations on maps of local hydrography and allow point and click access to the data collected at each environmental monitoring site. This information technology suite of software and hardware work together with a watershed simulation model WARMF-SJR to provide timely, reliable, and high quality data and forecasts of river salinity that can used by stakeholder decision makers to ensure compliance with state water quality objectives.

scholarly journals Fossel: Efficient Latency Reduction in Approximating Streaming Sensor Data

2020 ◽  
Vol 12 (23) ◽  
pp. 10175
Author(s):  
Fatima Abdullah ◽  
Limei Peng ◽  
Byungchul Tak
Keyword(s):  
Real Time ◽  
Data Analytics ◽  
Streaming Data ◽  
Sensor Data ◽  
Simple Type ◽  
Data Sampling ◽  
Time Data ◽  
Latency Reduction ◽  
Network Delays ◽  
Real Time Data

The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.

Potential use of real‐time data capture and job‐tracking technology in the field

Facilities ◽  
2005 ◽  
Vol 23 (1/2) ◽  
pp. 31-46 ◽  
Author(s):  
Seán T. McAndrew ◽  
Chimay J. Anumba ◽  
Tarek M. Hassan ◽  
Alistair K. Duke
Keyword(s):  
Real Time ◽  
Case Studies ◽  
New Technology ◽  
Facilities Management ◽  
Application Service Provider ◽  
Time Data ◽  
Web Based ◽  
Content Type ◽  
Application Service ◽  
Current State

PurposeThe purpose of the paper is to discuss the scope for improving the delivery of FM services through the use of wireless web‐based communications infrastructure, delivered via an application service provider (ASP) business model. This paper discusses the findings from case studies of three organisations and their approach to the management of facilities.Design/methodology/approachAn investigation was undertaken to ascertain the current state of play in terms of managing and tracking processes within the facilities management department of three different organisations. These case studies were chosen from distinct sectors, namely health care, higher education, and banking. Emphasis is placed on analysing how the organisations currently operate with their existing FM systems and the degree of influence technology has on existing processes. This was considered mainly in terms of computer‐aided facilities management (CAFM) and computer‐integrated facilities management (CIFM).FindingsThe study found that a new wireless web‐based service for FM systems would be considered useful. Although notoriously slow adopters of new technology, there was an acceptance by the facilities managers interviewed that a wireless web‐based approach would improve current practice, especially with respect to real‐time job reporting and tracking and in the determination of FM operative working time utilisation.Practical implicationsFurther work by the author is focusing on the development of a suitable demonstrator to illustrate the key concepts of a wireless web‐based FM service which will then be tested and evaluated. For further information, visit the research project web site at www.wirelessfm.org Originality/value – The paper hopefully stimulates discussion in the area of emerging wireless technologies that have the potential to streamline and improve current practices for the management of facilities, in particular that of real‐time job reporting and tracking.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

On the initial-state assimilation for limited-area air-quality forecasts

2020 ◽  
Author(s):  
Rostislav Kouznetsov ◽  
Mikhail Sofiev
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Air Pollutant ◽  
Optimal Interpolation ◽  
Limited Area ◽  
Time Data ◽  
Initial State ◽  
Skill Scores ◽  
Forecast Lead Time ◽  
Set Up

<p>An ensemble of 9 regional Air Quality models is being run operationally within CAMS-50 project providing the 3D fields of air-pollutant distribution over Europe. The models are initialized from their previous-day's forecasts for 00Z and run for 4 days forward. The same models are used for near-real-time reanalysis of the previous day involving the air-quality observations to adjust the modelled  fields via data assimilation methods, such as 3D-var or optimal-interpolation procedures.  In this set-up the observed near-real-time data do not affect the forecasts.  Development of a method to improve the forecast quality by using the assimilated fields from the previous-day analysis is one of the goals for the CAMS-61 project.</p><p>As a prototype evaluation for this study, we made several tests with SILAM model (http://silam.fmi.fi) initializing the simulations from the assimilated or non-assimilated states and evaluated the evolution of the model skill scores along the forecast lead time. The tests were made for summer and winter seasons and for initialization time of 00Z vs 12Z.  In order to generalize the results, and make them independent on particular implementation of 3D-VAR in SILAM, the tests were made also with initialization from the analyses made with other CAMS-50 models.  That experiment utilized the list of species and vertical available in the CAMS-50 product catalog. </p><p>The results of the simulation corroborated with our earlier studies that showed a quite quick relaxation of the scores for runs initialized from analyses to the free-run state: with certain variability between the species, the runs converged to the free-run trajectory generally within several hours.  We also investigated the issues connected with initialization from the incomplete set of species and sparse vertical, which might make the scores of the forecast initialized from the incomplete assimilated model state being worse than the ones from the free-run model.</p><p> </p>

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