scholarly journals A low cost solution to monitor environmental parameters in industrial area perimeters

2020 ◽  
Vol 305 ◽  
pp. 00003
Author(s):  
Nicolae Patrascoiu
Keyword(s):  
Low Cost ◽  
Monitoring Network ◽  
Environmental Parameters ◽  
Industrial Area ◽  
Point Of View ◽  
Hierarchical Level ◽  
Air Quality Monitoring ◽  
Radio Communications ◽  
Environmental Accidents ◽  
Sources Of Pollution

The monitoring of environmental parameters in active industrial areas where there exist potential sources of pollution, and even more so in the area of decommissioned or closure mining activities, is very important from the point of view of prevention of environmental accidents. In this paper, we propose a solution for the monitoring of the environmental parameters with the local acquisition and processing of the data and the transmission of alarm signals to a higher hierarchical level through the use of radio communications. A flexible hardware structure and software development concept are presented to be integrated into the national air quality monitoring network.

scholarly journals Industrial area environmental monitoring based on transducers with MODBUS communication

2022 ◽  
Vol 354 ◽  
pp. 00069
Author(s):  
Nicolae Patrascoiu ◽  
Cosmin Rus
Keyword(s):  
Data Transmission ◽  
Monitoring Network ◽  
Environmental Parameters ◽  
Industrial Area ◽  
Point Of View ◽  
Hierarchical Level ◽  
Environmental Accidents ◽  
Sources Of Pollution ◽  
Flexible Hardware ◽  
Movement Sensors

The monitoring of environmental parameters in industrial areas where potential sources of pollution exist is very important from the point of view of prevention of environmental accidents. In this paper, we propose a solution for the monitoring of the environmental parameters with the local acquisition through specific environmental and movement sensors and data transmission to a higher hierarchical level through the use of MODBUS communications. A flexible hardware structure and software development concept are presented to offer local information and to be integrated into an environmental quality monitoring network.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network

2013 ◽  
Vol 24 (6) ◽  
pp. 065803 ◽  
Author(s):  
David E Williams ◽  
Geoff S Henshaw ◽  
Mark Bart ◽  
Greer Laing ◽  
John Wagner ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Measurement Instruments ◽  
Ozone Measurement

scholarly journals Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2219 ◽  
Author(s):  
Florentin Michel Jacques Bulot ◽  
Hugo Savill Russell ◽  
Mohsen Rezaei ◽  
Matthew Stanley Johnson ◽  
Steven James Johnston Ossont ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Optical Sensors ◽  
Limit Of Detection ◽  
Low Cost ◽  
Airborne Particulate Matter ◽  
Environmental Parameters ◽  
Additional Information ◽  
Sources Of Pollution ◽  
Premature Deaths ◽  
The Impact

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min , and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.

Development of Low-Cost Ozone and Nitrogen Dioxide Measurement Instruments Suitable for Use in An Air Quality Monitoring Network

2019 ◽  
Vol 19 (6) ◽  
pp. 251-254 ◽  
Author(s):  
David E. Williams ◽  
Geoff Henshaw ◽  
D. B. Wells ◽  
George Ding ◽  
John Wagner ◽  
...  
Keyword(s):  
Air Quality ◽  
Nitrogen Dioxide ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Measurement Instruments

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

scholarly journals The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6198
Author(s):  
Georgi Tancev ◽  
Céline Pascale
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Data Science ◽  
Professional Competence ◽  
Low Cost ◽  
Environmental Parameters ◽  
Sampling Bias ◽  
Quality Monitoring ◽  
Sensor Systems ◽  
Air Quality Monitoring

This publication revises the deteriorated performance of field calibrated low-cost sensor systems after spatial and temporal relocation, which is often reported for air quality monitoring devices that use machine learning models as part of their software to compensate for cross-sensitivities or interferences with environmental parameters. The cause of this relocation problem and its relationship to the chosen algorithm is elucidated using published experimental data in combination with techniques from data science. Thus, the origin is traced back to insufficient sampling of data that is used for calibration followed by the incorporation of bias into models. Biases often stem from non-representative data and are a common problem in machine learning, and more generally in artificial intelligence, and as such a rising concern. Finally, bias is believed to be partly reducible in this specific application by using balanced data sets generated in well-controlled laboratory experiments, although not trivial due to the need for infrastructure and professional competence.

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