Dust Sensor Research Articles

Smart Air Quality Monitoring System Using Arduino Mega

Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. The automatic air quality monitoring system using Arduino can save cost and easy to access. The project that we have undertaken has helped us gain a better perspective on various aspects related to our course of study as well as practical knowledge of electronic equipment and communication. Moreover, the project of air quality monitoring system has achieved to monitor and prevent the effect of high temperature and dusty air to the child and human just like the objective of the project needed. The project can detect the temperature and dust density and besides that it will send alert to the user when detecting the air quality is unhealthy. Furthermore, a project is design and develop air quality monitoring system using Arduino Mega which is to enhance safety affect such that even the minor eye irritation can make the user to lose his concentration and lead to serious accidents. Where the dust sensor and temperature able to detect hazy day that triggers buzzer to alert user to decrease the outdoor activities. Air quality is monitor for whole day at Ulu Pauh Putra by using this device and the data is recorded and make into a graph. The average dust density is 78.22ug/m3 and the range of dust density levels are about 68.07ug/m3 to 84.87ug/m3. By comparing the result to API reading show that air quality is moderate pollution that does not pose any bad effect on health. The testing of project was done for whole day is to verify the accuracy of the data. Moreover, it can help to avoid errors occur in the project. Focusing on the device produced, it detects high temperature and sense the dusty air immediately. Once the sensitivity is high, the buzzer module will active to alert user.

Estimation of particulate matter exposure to construction workers using low-cost dust sensors

The construction industry is one of the significant sources which pollutes the environment. As 66 % of the world’s population will move towards urban areas within 2050, the rate of construction work happening in the cities will be enormous. Thus, construction practitioners should be aware of Particulate matter (PM) exposure and associated health impacts on society living in urban areas and focus on executing targeted countermeasures. Low-cost sensors with higher sensitivity and higher saturation point can be used for PM measurements during the construction works. This study measures PM produced from a construction activity using low-cost sensors. A total of 4635 exposure samples were collected during the experiment from which the PM concentrations for PM10, PM2.5, and PM1 were recorded as 7731.32, 532.14, and 113.68 μg/m3 with a standard deviation (σ) of 1990.39, 64.97, and 12.06 μg/m3. The two-stage Monte Carlo simulation (MCS) method supports the conclusion that even after implementing efficient control measures, the concentration of PM10 and PM2.5 remain 30 and 8 fold higher than their respective 24-hs exposure standards. Thus, future research work should focus on creating targeted control measures, further research exploring the distribution of particles produced from different construction activities should be monitored.

Design and analysis of air quality monitoring system PM10 and PM2.5 integrated with weather parameters (a case study on Margonda Raya street Depok)

An observation of air quality is very important related to the increasing pollution in Indonesia resulted from the use of motor vehicles, industrial activities, wildfires, and others. It triggers the increase of PM10 and PM2.5 particulate pollutants in the atmosphere. Meteorological factors influence the fluctuation of PM10 and PM2.5 concentration. The solution to overcome the problem is the design of IoT-based PM10 and PM2.5 digital monitoring system that is integrated with parameters of temperature, humidity, wind speed and direction. The design system consists of sensor hardware ZH03A laser dust sensor, SHT11 temperature and humidity sensor, and JL-FS2 wind speed and direction. Data were processed on Arduino ATMega2560-based microcontroller and then sent to the server using internet so that they could be displayed on website. The information system on the website was developed using PHP and HTML. The device had been tested with calibration and comparison methods. As the results of the research, the device could be operated properly and the measuring results were displayed on web in real time to provide air quality information. The correlation of PM source concentrations with meteorological parameters is that temperature (positive correlation), wind speed (positive correlation), relative humidity (negative correlation) and based on the direction of wind, the area is dominated by west winds.

A Model of Cube-Sat

Weather satellite is a type of satellite which is mainly used for the monitoring of the weather and climate of the Earth. This project purposes the creation of a working model of weather cube-sat, a weather Pico-satellite to monitor the condition of a small geographical area and determine its weather conditions and pollution level. Various sensors like temperature, barometric, humidity, dust and lux intensity sensors as well as a rain detector is used to measure the corresponding physical quantities and situations for this purpose. This system uses nRF transceivers for the transmission of data from the cube-sat to the ground station. Even though it is not included in our project, it is also possible to forecast the weather for the area based on previous values measured from the system. For the validation of the project, the measured data were compared with each other as well as the corresponding values of the physical quantities measured from other devices to check for its consistency and accuracy.

Design and Power Management of a Secured Wireless Sensor System for Salton Sea Environmental Monitoring

An embedded system composed of commercial off the shelf (COTS) peripherals and microcontroller. The system will collect environmental data for Salton Sea, Imperial Valley, California in order to understand the development of environmental and health hazards. Power analysis of each system features (i.e. Central Processing Unit (CPU) core, Input/Output (I/O) buses, and peripheral (temperature, humidity, and optical dust sensor) are studied. Software-based power optimization utilizes the power information with hardware-assisted power gating to control system features. The control of these features extends system uptime in a field deployed finite energy scenario. The proposed power optimization algorithm can collect more data by increasing system up time when compared to a Low Power Energy Aware Processing (LEAP) approach. Lastly, the 128 bit Advanced Encryption Standard (AES) algorithm is applied on the collected data using various parameters. A hidden peripheral requirement that must be considered during design are also noted to impact the efficacy of this method.

Effect of Atmospheric Humidity on Fine Dust Measurement Using the Light Scattering Method

The interest in fine dust has greatly increased. Therefore, sensors and devices for measuring the concentration of fine dust have been developed and are being used. For example, the distribution of light scattering measuring sensors has increased the use of self-measurement to manufacture and measure fine dust detectors. However, fine dust detectors that use light scattering are extremely sensitive to humidity; there is a highly sensitive component that recognizes water vapor in the air as fine dust. Therefore, the purpose of this study is to quantitatively understand the influence of atmospheric humidity on the concentration of fine dust and to use this understanding to correct measured values. In this study, we investigated the correlation between fine dust concentration and relative humidity measured by a fine dust sensor using the light scattering method. As a result, relative humidity and fine dust concentration exhibited a high positive correlation. The same trend was also observed when the humidity either increased or decreased.

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

Pollution is adding contaminants into the nature that causes an adverse change in the environment. Air pollution is one of the highest mortality risk factors globally. The sources of air pollution in the industries are power plants, chamber process, cleaning, burning of materials, etc. A variety of pollutants emitted into the air such as sulfur dioxide, carbon monoxide, carbon dioxide, ammonia and volatile organic compounds. Particulate Matter (PM) is an air pollutant that is the mixture of solid dust or pollen and liquid droplets with air. Air pollution in industrial workplaces is a major concern and monitoring and management of it to be addressed to protect the industrial workers health from the air pollution effects. The people are suffering from several respiratory and heart issues along with cancer due to increasing air pollution. This device is composed of ESP32 MCU, MQ135 gas sensor, SDS011 optical dust particle sensor, and BME280 humidity and temperature sensor for monitoring the air quality. The gas sensor MQ135 senses the harmful gases present in the environment. SDS011 optical dust sensor senses the PM levels (PM10 and PM2.5) in the atmosphere. The sensor values are evaluated for the Air Quality Index (AQI) and display it on the ThingSpeak IoT platform. Vrituino app has used for a virtual screen with widgets on the mobile phone to monitor the system using the web. In order to improve the real-time performance of the system, an IoT and a cloud computing technology are being used. The ESP32 turns on the fan units to maintain the pollutants within the safe limit when the presence of harmful gases and PM levels exceeds a certain threshold level. This system is essential for industrial work places to adopt measures and control air pollution which increase industrial workers safety.

IoT based Real-time Air Quality Monitoring and Control System to Improve the Health and Safety of Industrial Workers

Pollution is adding contaminants into the nature that causes an adverse change in the environment. Air pollution is one of the highest mortality risk factors globally. The sources of air pollution in the industries are power plants, chamber process, cleaning, burning of materials, etc. A variety of pollutants emitted into the air such as sulfur dioxide, carbon monoxide, carbon dioxide, ammonia and volatile organic compounds. Particulate Matter (PM) is an air pollutant that is the mixture of solid dust or pollen and liquid droplets with air. Air pollution in industrial workplaces is a major concern and monitoring and management of it to be addressed to protect the industrial workers health from the air pollution effects. The people are suffering from several respiratory and heart issues along with cancer due to increasing air pollution. This device is composed of ESP32 MCU, MQ135 gas sensor, SDS011 optical dust particle sensor, and BME280 humidity and temperature sensor for monitoring the air quality. The gas sensor MQ135 senses the harmful gases present in the environment. SDS011 optical dust sensor senses the PM levels (PM10 and PM2.5) in the atmosphere. The sensor values are evaluated for the Air Quality Index (AQI) and display it on the ThingSpeak IoT platform. Vrituino app has used for a virtual screen with widgets on the mobile phone to monitor the system using the web. In order to improve the real-time performance of the system, an IoT and a cloud computing technology are being used. The ESP32 turns on the fan units to maintain the pollutants within the safe limit when the presence of harmful gases and PM levels exceeds a certain threshold level. This system is essential for industrial work places to adopt measures and control air pollution which increase industrial workers safety.

Design of PM2.5 Air Quality Detection System based on Median Value Average Filtering Algorithm and OLED Screen

Environmental pollution problems are becoming more and more serious, air pollution and air quality problems, has become a hot spot of special concern. PM2.5 is an important indicator of air quality testing .This design uses GP2Y1010AUOF dust sensor,SHT20 temperature and humidity sensor and so on, transmit the signal to ATMEGA328PAUM SCM. The system detects the detection through the analog to digital conversion and median value average filtering algorithm. The PM2.5 Concentration values are displayed to OLED screen, alert if the detection value exceeds the standard. Experiments show that the system is running well, with a measurement accuracy of 4.8%, and has a certain stability and practicality.

Personal Exposure Estimates via Portable and Wireless Sensing and Reporting of Particulate Pollution.

Low-cost, portable particle sensors (n = 3) were designed, constructed, and used to monitor human exposure to particle pollution at various locations and times in Lubbock, TX. The air sensors consisted of a Sharp GP2Y1010AU0F dust sensor interfaced to an Arduino Uno R3, and a FONA808 3G communications module. The Arduino Uno was used to receive the signal from calibrated dust sensors to provide a concentration (µg/m3) of suspended particulate matter and coordinate wireless transmission of data via the 3G cellular network. Prior to use for monitoring, dust sensors were calibrated against a reference aerosol monitor (RAM-1) operating independently. Sodium chloride particles were generated inside of a 3.6 m3 mixing chamber while the RAM-1 and each dust sensor recorded signals and calibration was achieved for each dust sensor independently of others by direct comparison with the RAM-1 reading. In an effort to improve the quality of the data stream, the effect of averaging replicate individual pulses of the Sharp sensor when analyzing zero air has been studied. Averaging data points exponentially reduces standard deviation for all sensors with n < 2000 averages but averaging produced diminishing returns after approx. 2000 averages. The sensors exhibited standard deviations for replicate measurements of 3–6 µg/m3 and corresponding 3σ detection limits of 9–18 µg/m3 when 2000 pulses of the dust sensor LED were averaged over an approx. 2 min data collection/transmission cycle. To demonstrate portable monitoring, concentration values from the dust sensors were sent wirelessly in real time to a ThingSpeak channel, while tracking the sensor’s latitude and longitude using an on-board Global Positioning System (GPS) sensor. Outdoor and indoor air quality measurements were made at different places and times while human volunteers carried sensors. The measurements indicated walking by restaurants and cooking at home increased the exposure to particulate matter. The construction of the dust sensors and data collected from this research enhance the current research by describing an open-source concept and providing initial measurements. In principle, sensors can be massively multiplexed and used to generate real-time maps of particulate matter around a given location.

Stationary and mobile particulate matter concentration measurement in Miskolc

A stationary and mobile device based on a low-cost dust sensor (Plantower PMS7003) was used to study particulate matter (PM) concentration in Miskolc. The stationary device was placed at the automatic monitoring station of the National Air Quality Measurement Network (OLM) in Martintelep, Miskolc. The mobile device was used to walk 4 streets along a square route with a total length of 800 m. Measurements were made on two days (September 9 and 11) between 7am and 9am, which according to preliminary experience coincided with the morning rush hour. The access route includes the OLM monitoring station, a busy road, two bypasses, a parking lot, and a smaller roadway blocked by traffic. The measured PM1, PM2.5, PM10 concentrations of the mobile device were interpolated on a circuit-by-circuit basis in ArcGIS, and conclusions were drawn for the area. The concentration of stationary and mobile devices was compared.

Extensive evaluation and classification of low-cost dust sensors in laboratory using a newly developed test method.

An extensive evaluation of low‐cost dust sensors was performed using an exponentially decaying particle concentration. A total of 264 sensors including 27 sensors with light‐emitting diodes (LEDs) and 237 sensors with laser lighting sources were tested. Those tested sensors were classified into 4 groups based on the deviation from the reference data obtained by a reference instrument. The response linearities of all the tested samples for PM1, PM2.5, and PM10 were in excellent agreement with the reference instrument, except a few samples. For the measurements of PM1 and PM2.5, the lighting source, that is, LED or laser, did not show any significant difference in overall sensor performance. However, LED‐based sensors did not perform well for PM10 measurements. The 32, 24, and 16% of all the tested sensors for PM1, PM2.5, and PM10 measurement, respectively, are in the category of Class 1 (reference instrument reading ± 20%) requirement. The performance of the low‐cost dust sensors for PM10 measurement was relatively less satisfactory.

Air Quality Evaluation with a Low-cost Dust Sensor for a Hencoop

A hygenic working environment is essential for human health. It includes environmental conditions like light, air flow, gasses, sound, and rubble. It became more important for agricultural indoor and outdoor applications. For example; the dust intensity in a hencoop sometimes negatively affects human and animal health as an environmental component. On account of this ground, dust concentration measurement is crucial for agricultural indoor and outdoor applications. Because of this reason, the intensity of dust have been evaluated and analyzed in this research. For this purpose, a microcontroller based dust intensity measurement unit was developed. The dust intensity measurement unit contains an Arduino based development board and Sharp dust sensor. Additionally, an SD card module and a Real-Time Clock (RTC) was utilized for a data logging operation. The addition of air stream, air filtering technique and dust removing machines can be qualified regarding the dust intensity or dust balance

Intelligent Pattern Recognition Algorithms based on Dust, Vision and Activity Sensors for User Unusual Event Detection

Intelligent Pattern Recognition Algorithms based on Dust, Vision and Activity Sensors for User Unusual Event Detection

Toward Open-Source Hardware and Software for the Mining Industry: a Case Study of Low-Cost Environmental Monitoring System for Non-Metallic Underground Mines

Open-source technology for supporting the Industry 4.0 has become the target of academic research in many sectors. This study reviewed the trends of open-source technology, its forms, and some industrial applications. As a case study using open-source hardware and software for the mining industry, this study proposed a low-cost environmental monitoring system for non-metallic underground mines to support mine safety and occupational health issues. The system was developed using open-source hardware, Arduino and 3D printer, to design the monitoring device. An open-source software, MIT App Inventor, was used for developing an Android application for smartphones to enable remote communication with the system. Field experiments were conducted at an underground tunnel and a non-metallic underground mining site to assess the performance of the system in both mobile and static modes, respectively. Although a few limitations related to the precision of the low-cost dust and gas sensors still exist, the findings of the experiments show that the mining industry can benefit from open-source technology deployment when considering cost factors.

Fine particulate matter monitoring via a visible light communication in DCT-based optical OFDM.

An electromagnetic interference (EMI)-free wide-range indoor dust monitoring system that employs the optical orthogonal frequency-division multiplexing (OFDM)-based visible-light communication (VLC) is proposed. For the long-term transmission of dust information, VLC can be utilized even in EMI-restricted areas, such as medical centers, emergency rooms, and nursing homes. Discrete cosine transform-based optical OFDM is adopted to transmit a large amount of dust information. For robust light detection from eliminate ambient light and low-frequency noise, an average voltage-tracking technique is utilized and as a result LED illumination is detected over 18 m distance with reliable error rate. Wide-range dust information from multiple dust sensors are clearly displayed through the designed user interface. Users can then monitor the air quality in real-time, improving the environmental awareness of individuals.

Tailings Storage Facilities (TSFs) Dust Control Using Biocompatible Polymers

Mine tailings storage facilities (TSFs) can be a significant source of fine, respirable dust. This dust poses concerns, especially for nearby communities, because of its detrimental impact on respiratory health. To address these concerns, this study examined the effectiveness of commercially available polymers, known for their biocompatible and environmentally friendly features, to prevent dust generation from upstream-type TSFs in active mining operations. It focused on two areas in TSFs: the tailings beach and slope/dike surfaces. Two types of polymers—polyethylene glycol and poloxamer—were selected, and their effectiveness was evaluated in a series of phased laboratory and field tests. The lab tests investigated the moisture-retention capacity of the tailings after treatment with the polymer solutions as well as the ability of these solutions to reduce dust emissions. Polymers were applied to a tailings bed surface in the lab and dust was generated from this surface using a wind tunnel. PM10 and PM2.5 concentrations were measured using laser-diffraction dust sensors, and the polymers were found to have reduced dust emissions up to 95%. Small-scale wind-blowing tests were conducted in the field (specifically, in a controlled environment at a TSF) to evaluate polymer effectiveness on the tailings beach and slope/dike areas. The tests revealed that the AP could efficiently suppress approximately 80% of PM10 and PM2.5 dust emissions at both types of areas. Finally, 1000 gallons of AP solution were discharged at the top of tailings beach and slope/dike areas, respectively, and found to be effective. The results of these tests showed the high potential of the biocompatible polymers to be a feasible solution for reducing the respirable dust emissions from upstream TSFs in arid and semi-arid regions.

ANALISIS KINERJA SENSOR PADA ROBOT PENDETEKSI KOTORAN DEBU DAN AIR

Sensors are electronic components that have capabilities that are quite reliable and consist of many types, such as ultrasonic sensors, humidity sensors, and dust sensors. At present almost every electronic equipment is equipped with sensor components, such as the robot designed, equipped with humidity sensors and dust sensors. The purpose of this study is to determine the performance of the humidity sensor and dust sensor in detecting dirt and the sensor sends a signal to the microcontroller, Arduino Mega 2560. From the results of sensor performance research on Arduino-based floor dust and water dust detector robots the proximity sensor response time detects a barrier in a room worth 2 seconds, the dust sensor response time detects dust that is 2 minutes due to improper installation of dust sensors so that slow detecting dust. While the humidity sensor response time in detecting water is 3 seconds. For the performance of the robot on the humidity sensor clean the water on the floor as much as 1 ml for 10 to 17 seconds and to show the robot's performance on the humidity sensor to clean the water as much as 2 ml for 20 to 30 seconds.

AIRTOR: A Portable Air Quality Monitor

Objectives: Detecting air pollutants is a key factor in combating the Air Pollution Phenomenon. This study focused on creating an innovative air quality monitor device with a mobile application that can track personal exposure to harmful air contaminants and harmful gases. Method: A specific System Development Life Cycle (SDLC) model was utilized in developing the system called Prototyping. This model iterates some phases before jumping to next phase. Furthermore, the system was developed through the integration of LCD display, dust sensor, gas sensor, temperature and humidity sensor, Bluetooth module, Arduino microcontroller and a mobile application. The device can display data readings independently that has a built-in Bluetooth sharing capability. This built-in sharing feature enables the device to share data to the android application the researcher developed independently. Result: To measure the over-all usability of the system, alpha and beta testing was conducted using International Business Machines (IBM) Computer Usability Scale. Results indicated a grand weighted mean of 4.93 and 4.81 respectively and both can be interpreted as highly usable. This results means the system adhered to the standard set by IBM in system development. Recommendation: Utilization of this device is highly recommended. Keywords: Air Monitor, Air Pollution Sensor, Air Quality, Air Quality Monitor, Pollution Detector

Electro-optical dust sensors calibration by aerosol image analysis

The paper describes the process of data obtaining for designing electro-optical sensors using a television aerosol particles image analyzer. Using simulation based on Mie theory, the process of calibration of simple electro-optical aerosol (dust) sensor is carried out. In the paper the main dependences for sensor calibration are shown. The proper calibration allows power saving by selection certain laser beam power level.