Air Quality Monitoring Sensors Research Articles

Air quality monitoring using drones (UAV)

Abstract Air pollution causes many diseases and is a major environmental threat, therefore, it is essential to monitor and improve the air quality. With this work, we aim to assess atmospheric pollution using drone-mounted air sensors, with specific applications in Vietnam. We aim to measure Green-House Gas (GHG) emissions and other pollutants in urban and non-urban areas of interest including, but not limited to landfills and airports, to evaluate the effects of pollution on climate and health. The project will use a novel and creative approach to collect three-dimensional atmospheric data using smart sensors mounted on Unmanned Aerial Vehicles (UAVs). UAVs have been extensively applied in recent years, both in Vietnam and on a global scale, to multiple fields and with different scopes. Surveying, crop monitoring, irrigation and fertilization, surveillance and rescue support, and 2D and 3D mapping, are just a few examples of how UAVs can be used for agriculture, archaeology, forestry, urban planning, and architecture. In this project, we develop an integrated system of UAVs and smart sensors for air quality monitoring. To develop mitigation and adaptation strategies for reducing the environmental impact of transport, it is imperative to assess the emission sources and trends. Therefore, we are developing a system able to collect comprehensive atmospheric data using remote aerosol sampling, and chemical speciation. Furthermore, the potential of using smart sensors on other flying devices is explored.

Morphology-dependent gas-sensing sensitivity of γ-Al2O3 for methacrolein

Methacrolein (MACR) is a key precursor in the formation of secondary organic aerosols and a significant occupational chemical hazard, posing risks to both the environment and health. In this work, three types of γ-Al2O3 were synthesized via hydrothermal methods, including nanorods, nanosheets, and a mixed morphology. Characterization results revealed that rod-like γ-Al2O3 has the largest specific surface area and the highest oxygen vacancy content. The possible formation mechanism of γ-Al2O3 samples with different morphologies was discussed. Further study indicated that the morphology of γ-Al2O3 significantly affects its sensitivity in the cataluminescence (CTL) sensing of MACR, with rod-like γ-Al2O3 exhibiting the highest sensitivity, whereas sheet-like γ-Al2O3 showed the lowest sensitivity. Thus, a CTL sensor based on rod-like γ-Al2O3 for MACR detection was developed, demonstrating outstanding performance in terms of sensitivity, selectivity, reproducibility, and stability. This sensor was applied to the analysis of real samples, and the results were compared with those obtained by gas chromatography. The results obtained by the two methods were consistent, with relative error ranged from −4.0 % to −4.9 %. The sensing mechanism was further elucidated via density functional theory calculations, in situ Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry. This work broadens the horizons for applying material science principles to enhance the performance of gas sensors, thereby contributing to design high-performance CTL sensors for air quality and occupational safety monitoring.

Development of a Smartwatch with Gas and Environmental Sensors for Air Quality Monitoring.

In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is a focus for ensuring compliance with safety standards and is an indispensable component in safeguarding human welfare. Air quality measurement is conducted by public institutions with high precision but costly equipment, which requires constant calibration and maintenance by highly qualified personnel for its proper operation. Such devices, used as reference stations, have a low spatial resolution since, due to their high cost, they are usually located in a few fixed places in the city or region to be studied. However, they also have a low temporal resolution, providing few samples per hour. To overcome these drawbacks and to provide people with personalized and up-to-date air quality information, a personal device (smartwatch) based on MEMS gas sensors has been developed. The methodology followed to validate the performance of the prototype was as follows: firstly, the detection capability was tested by measuring carbon dioxide and methane at different concentrations, resulting in low detection limits; secondly, several experiments were performed to test the discrimination capability against gases such as toluene, xylene, and ethylbenzene. principal component analysis of the data showed good separation and discrimination between the gases measured.

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring.

This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management.

Sensorsync auto: Integrating IoT sensors for smart vehicle air quality monitoring

This project introduces an innovative Air Quality Monitoring System based on IoT technology, leveraging components such as NodeMCU, DHT11, MQ135, a buzzer, an LCD, and the Blynk 2.0 app. The primary objective is to provide real-time monitoring of environmental conditions, encompassing temperature, humidity, and air quality, while also offering audible alerts for immediate user notification. The NodeMCU functions as the central microcontroller, interfacing with the DHT11 sensor for temperature and humidity readings and the MQ135 sensor for air quality measurements. The collected data is visually displayed on a 16x2 LCD screen. Additionally, the system enables remote monitoring and control through the Blynk 2.0 app. The app displays comprehensive information using gauge widgets for temperature, humidity, and air quality, along with detailed statistics through a display widget. To enhance user awareness, an integrated buzzer delivers audible alerts when air quality exceeds predefined thresholds. The Arduino sketch incorporates essential libraries for sensor interfacing, data processing, and seamless integration with the Blynk app. The app is configured with widgets corresponding to virtual pins in the sketch, ensuring smooth communication between the hardware and the app. This IoT-based Air Quality Monitoring System serves as a valuable tool for individuals and communities concerned about indoor or outdoor air quality. The system's features, including real-time data visualization, remote monitoring capabilities, and an alert system, contribute to creating a more informed and healthier living environment.

Chemical sensors for outdoor air quality monitoring

Air pollution is a global threat leading to large impacts on human health and ecosystems. Emissions and concentrations have increased in many areas and worldwide. In some areas of Europe, air quality remains very poor, despite reduced emissions and concentrations in the environment. Currently the most important environmental risk to human health is air pollution, and Europeans are considered the second biggest concern for the environment, right after climate change. Major problems related to air quality have an impact on human health, and in particular on respiratory diseases. In response to the problems of poor air quality, there is political, media and public interest in air quality issues. The growing public influence over air pollution challenges, including ongoing civic scientific initiatives involved in supporting air quality monitoring and targeted initiatives to raise public awareness and change behavior, has led to increasing support and demand for measures to improve air quality. Due to increasing air pollution, great efforts are being made to develop various chemical methods and chemical sensors used to measure air quality. Cheaper gas-based sensors based on the Arduino system are available today. Given their simplicity and given that they are more cost-effective, these sensors have proven to be very useful in some situations where it is necessary to detect certain gases in the air in a very short time. Several chemical methods have been developed that can also detect certain gases in the air. In this paper, only some methods by which gases and particles can be detected will be mentioned.

Designing and testing of low-power air quality monitoring sensor node using LoRa technology

Designing and testing of low-power air quality monitoring sensor node using LoRa technology

REVIEW OF AIR POLLUTION RESEARCH RESULTS ON THE EXAMPLE OF TBILISI

Background: Air pollution is one of the significant environmental challenges facing modern humanity. Atmospheric air is polluted with harmful substances emitted from anthropogenic and natural sources. Aim: The presented paper provides an overview of monitoring results conducted in the Capital of Georgia for 2023. Results from air quality monitoring sensors show that particulate matter, sulfur, and nitrogen dioxide represent problematic pollutants in different city areas. Methods: The research was carried out through sensors installed in different districts of Tbilisi. The investigation was carried out within the NATO SPS program project REACT. Results: Atmospheric air pollution was monitored at four stationary sensor sets on K. Cholokashvili, Nutsubidze Street, Chavchavadze Avenues, and the Old Tbilisi area. The results are presented in the article in the form of tables. Discussion: Average indexes of sulfur dioxide and PM particles were observed in all areas. Nitrogen dioxide indices were both low and medium. Conclusions: Based on the data obtained, it can be assumed that the air in most districts of Tbilisi is moderately polluted, and in some cases, the data exceeds the maximum allowable norm. There may be several reasons for this.: 1. Unauthorized slashing of Green islands and massive construction of high-rise residential buildings on their place in Tbilisi. 2. Malfunctioning vehicles, the technical inspection of which has recently been made stringent. However, some vehicles still pollute the air. 3. Emissions produced by mini factories and enterprises.

Optical fiber sensors for water and air quality monitoring: a review

Optical fiber sensors for water and air quality monitoring: a review

Monitoring of Cloud-based Air Quality using NodeMCU

Abstract: Air pollution has received a lot of attention in the modern world and has subsequently grown to pose a serious hazard to society. This research focuses on a device that can monitor air quality, ground vibration levels, and a form of fire safety alarm system for employees working in an industrial environment due to the urgency and quantity of air pollution. A WIFI-enabled ESP8266 NodeMCU microcontroller is used to implement this, and it is connected to air quality monitoring sensors MQ135 and DHT11 (Humidity and Temperature), as well as a geophone sensor to measure vibration levels in industrial settings and a flame sensor to protect workers from fire accidents. An alarm module is used with the buzzer. The sensor data is stored in NodeMCU and the data is analyzed and stored in the cloud platform and monitored on the BLYNK (an IoT platform) by interfacing the app with NodeMCU

Understanding the effect of temperature and relative humidity on sensor sensitivities in field environments and improving the calibration models of multiple electrochemical carbon monoxide (CO) sensors in a tropical environment

The data quality of low-cost air quality monitoring sensor systems in field environments needs to be improved through calibrations to compare with regulatory data. This research study focused on improving linear regression calibration models of multiple Alphasense CO-B4 sensors compared to the factory calibration, evaluating the sensor sensitivity and calibration model variations in different temperature and relative humidity (RH) ranges, evaluating the unit-to-unit variability of sensor performances and the use of averaged calibration models instead of individual calibration models. In this study, changes were observed in field sensor sensitivities and manufacturer-given sensor sensitivities, and the inclusion of field sensor sensitivities improved the calibration model performances significantly. Sensor calibration models performed best in the temperature range of 20–24 0C and the RH range of 75–90%, with averaged r and RMSE values around 0.98 and 50 ppb. When compared with raw sensor data (RMSE = 172.1 ppb), the averaged RMSE values of the calibrated concentration values were 97.4 ppb or below for different calibration models. Additionally, results clearly illustrated that the use of averaged calibration model was a reliable alternative for individual calibrations. Furthermore, it was observed that the field calibrations significantly reduce the effect of temperature and RH on sensor outputs and improve the factory calibrations.

Assessment of Air Quality Around Heavy Traffic Zone in Hyderabad

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city. Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB .The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 5 stations based heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 3 PM2.5,range 183.7 – 163.26µg/m3 is very poor as per AQI, PM10 range 151.11 to 137.76 µg/m3 is moderately polluted as per AQI, NO2 range 61.03 to 76.4 µg/m3 is satisfied as per AQI, NO3 range 1.196 to 7.96 is good, SO2 is 6.34 to 55.83 µg/m3 is satisfied ,CO 70.35 to 71.97 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Air Quality Monitoring at Heavy Traffic Zone in Hyderabad

Recently the intensity and magnitude of air pollutant concentration has grown up rapidly in the troposphere. The air pollution is primarily associated with automobiles and industrial sources Hyderabad, capital of Telangana, is a center of southern part of India for both industrial and commercial activity. It is one of the most populous urban areas with approximately 6.8 million inhabitants and over 73,000 small, medium and major industrial establishments sharing the greater city.Air pollution in India is monitored by the Central Pollution Control Board (CPCB) together with the State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with 7 sensor stations, 248 towns and cities have the air quality network of 591 Air Quality Monitoring Stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing their number to 883 to achieve the target of 1,500 by 2024. For our study due to practical difficulties to collect, analysis the air quality, pollutants data has been collected from CPCB.The dataset contains City, Date, PM2.5, PM10, NO, NO2, NOx, NH3, CO, SO2, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into 6 zones, air quality monitoring sensors is located at 14 places in and around six zones. For our study we selected 2 stations Newmalakpet and Sanathnagar based on heavy traffic area to analyse the maximum concentration of pollutants such as PM2.5, PM10, NO2, NO3, SO2, CO, for the period of three months ( Dec 2022,Jan, Feb 2023).It was observed at site 1 to site 52PM2.5,range 120 to 249 µg/m3 is very poor as per AQI, PM10 range 101.2 to 171 µg/m3 is moderately polluted as per AQI, NO2 range 7.46 to 32.9 µg/m3 is good as per AQI, NH3 range3.06 to 5.44 is good as per AQI, SO2 is 12.97 to 8.53 µg/m3 is good ,CO 61.83 to 71.9 is severe as per AQI, continuous air quality monitoring is necessary to protect environment and human health

Analysis of Water and Air Quality in and Around HIMSWM Treatment Plant- Hyderabad

In recent days the emission of air pollutants in to the environment has grown very quickly. The air pollution is caused due to the smoke emitted by vehicles, industries and other sources. Hyderabad is the largest and capital city of Telangana. It occupies 650km2 on the Deccan Plateau along the banks of the Musiriver, in the northern part of south India. According to the 2011 Census of India, Hyderabad is the fourth-most populous city in India with a population of 6.9 million residents within the city limits, and has a population of 9.7 million residents in the metropolitan region, making it the sixth-most populous metropolitan area in India. With an output of US$74 billion, Hyderabad has the fifth-largest urban economy in India. In India air pollution is monitored by Central Pollution Control Board(CPCB) along with State Pollution Control Boards (SPCBs) and the National Environmental Engineering Research Institute (NEERI) in Nagpur. The National Air Quality Monitoring Programme (NAMP) was started in 1984 with seven sensor stations 248 towns and cities have the air quality network of 591 air quality monitoring stations upto 2015, it is reported that in India 2022 added 180 manual air quality monitoring stations, increasing the number to 883 to achieve the goal of 1500 by 2024. For our study because of some practical difficulties and analysis of the air quality. Air Pollutants has been collected from the CPCB. The dataset contains City, Date, Time, PM2.5,PM10, NO2, SO2, CO, O3, Benzene, Toulene, Xylene, Air Quality Index (AQI). Hyderabad was divided into six zones, air quality monitoring sensors are located at 14 places in and around six zones. For our study we selected one sensor that is Secunderabad, which covers the surrounding areas including Jawahar Nagar treatment plant. To analysis the maximum concentration of air pollutants such as PM2.5,PM10,SO2,NO2,CO,O3over the time period of three months (Jan to Mar 2023).Most physio-chemical properties, including as pH, EC, Alkalinity, Chlorides, Cu, Mn, Pb, and Cr, exceeded their acceptable limits at more than three sample sites, according to this study. The water sources were determined to be unsafe for drinking, agricultural, and irrigation uses due to elevated toxicity levels.It was observed that PM2.5,ranges 218 - 403µg/m3 is very poor causes respiratory illness, PM10 range 112 - 360µg/m3 is moderately polluted causes breathing discomfort ( Asthma, lungs), NO2 range 10 – 12ppm is satisfied causes minor breathing discomfort to sensitive people ,O3 range 20 – 35ppm is good causes minor effect, SO2 is 12 – 22ppm is satisfied causes minor breathing discomfort to sensitive people ,CO 109 – 119ppm is severe effects on healthy people and serious impact for those with existing diseases, so continuous air quality monitoring is necessary to protect environment and human health

An Investigation on the Possible Application Areas of Low-Cost PM Sensors for Air Quality Monitoring.

In recent years, the availability on the market of low-cost sensors (LCSs) and low-cost monitors (LCMs) for air quality monitoring has attracted the interest of scientists, communities, and professionals. Although the scientific community has raised concerns about their data quality, they are still considered a possible alternative to regulatory monitoring stations due to their cheapness, compactness, and lack of maintenance costs. Several studies have performed independent evaluations to investigate their performance, but a comparison of the results is difficult due to the different test conditions and metrics adopted. The U.S. Environmental Protection Agency (EPA) tried to provide a tool for assessing the possible uses of LCSs or LCMs by publishing guidelines to assign suitable application areas for each of them on the basis of the mean normalized bias (MNB) and coefficient of variance (CV) indicators. Until today, very few studies have analyzed LCS performance by referring to the EPA guidelines. This research aimed to understand the performance and the possible application areas of two PM sensor models (PMS5003 and SPS30) on the basis of the EPA guidelines. We computed the R2, RMSE, MAE, MNB, CV, and other performance indicators and found that the coefficient of determination (R2) ranged from 0.55 to 0.61, while the root mean squared error (RMSE) ranged from 11.02 µg/m3 to 12.09 µg/m3. Moreover, the application of a correction factor to include the humidity effect produced an improvement in the performance of the PMS5003 sensor models. We also found that, based on the MNB and CV values, the EPA guidelines assigned the SPS30 sensors to the "informal information about the presence of the pollutant" application area (Tier I), while PMS5003 sensors were assigned to the "supplemental monitoring of regulatory networks" area (Tier III). Although the usefulness of the EPA guidelines is acknowledged, it appears that improvements are necessary to increase their effectiveness.

Modular Air Quality Calibration and Forecasting Method for Low-Cost Sensor Nodes

The climatic challenges are rising across the globe in general and in worst hit under-developed countries in particular. The need for accurate measurements and forecasting of pollutants with low-cost deployment is more pertinent today than ever before. Low-cost air quality monitoring sensors are prone to erroneous measurements, frequent downtimes, and uncertain operational conditions. Such a situation demands a prudent approach to ensure an effective and flexible calibration scheme. We propose a modular air quality calibration, and forecasting (MAQ-CaF) methodology, that side-steps the challenges of unreliability through its modular machine learning-based design which leverages the potential of IoT framework. It stores the calibrated data both locally and remotely with an added feature of future predictions. Our specially designed validation process and the discussion of the results help to establish the proposed solution’s applicability and flexibility. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {CO}, \text {SO}_{{2}}, \text {NO}_{{2}}, \text {O}_{{3}}, \text {PM}_{{1.0}}, \text {PM}_{{2.5}}~ \text {and}~ \text {PM}_{{10}}$ </tex-math></inline-formula> were calibrated and monitored with reasonable accuracy. Such an attempt is a step toward addressing climate change’s global challenge through appropriate monitoring and air quality tracking across a wider geographical region via affordable monitoring.

Field Calibration of a Low-Cost Air Quality Monitoring Device in an Urban Background Site Using Machine Learning Models

Field calibration of low-cost air quality (AQ) monitoring sensors is essential for their successful operation. Low-cost sensors often exhibit non-linear responses to air pollutants and their signals may be affected by the presence of multiple compounds making their calibration challenging. We investigate different approaches for the field calibration of an AQ monitoring device named ENSENSIA, developed in the Institute of Chemical Engineering Sciences in Greece. The present study focuses on the measurements of two of the most important pollutants measured by ENSENSIA: NO2 and O3. The measurement site is located in the center of Patras, the third biggest city in Greece. Reference instrumentation used for regulatory purposes by the Region of Western Greece was used as the evaluation standard. The sensors were installed for two years at the same locations. Measurements from the first year (2021) from seven ENSENSIA sensors (NO2, NO, O3, CO, PM2.5, temperature and relative humidity) were used to train several Machine Learning (ML) and Deep Learning (DL) algorithms. The resulting calibration algorithms were assessed using data from the second year (2022). The Random Forest algorithm exhibited the best performance in correcting O3 and NO2. For NO2 the mean error was reduced from 9.4 ppb to 3 ppb, whilst R2 improved from 0.22 to 0.86. Similar results were obtained for O3, wherein the mean error was reduced from 13 to 4.3 ppb and R2 increased from 0.52 to 0.69. The Long-Short Term Memory Network (LSTM) also showed good performance in correcting the measurements of the two pollutants.

Can Air Quality Gas Sensors Be Used for Emission Monitoring of Small-Scale Local Air Pollution Sources? Pilot Test Evaluation

In recent years we have seen a growing number of applications that use various sensory measurements of physicochemical features. Within the research project “Research on the identification of combustion of unsuitable fuels and systems of self-diagnostics of boilers combustion solid fuels for domestic heating”, the authors tested and evaluated the possible use of an air quality monitoring sensor unit for the measurement of operating parameters of solid fuel burning boilers and stoves. In the Czech Republic, programs to support citizens in the replacement of domestic boilers of poor combustion quality have been subsidized for several years. Unfortunately, no assessment of the impact of subsidies on air quality has been carried out. However, the increased pollutant emissions due to improper use of domestic boilers are supposed to be one of the greatest problems with domestic boilers. Hence, providing users with real-time feedback may lead to changes in combustion conditions and consequently to a reduction in air pollution. We focus on sensory measurements of CO, CO2, NO, and VOCs as compounds that correspond to the operating conditions of the combustion process. The research included sampling, construction of the flue gas dilution duct, and the influence of direct measurement on the service life of the sensors.

Anomaly Detection and Repairing for Improving Air Quality Monitoring.

Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants' concentrations can support the identification of urban areas with poor air quality and raise citizens' awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms' performances.

Cloud-based Air Quality Monitoring through Wireless Sensor Network using NodeMCU

Abstract: In the present-day world, air pollution has gained huge attention and eventually become a major threat to society. Considering the exigency and magnitude of air pollution, this paper focus mainly on the device which is capable of monitoring the air quality, vibration levels of the ground, and a type of fire safety alert system for the employees working in the industrial environment. This is implemented using a WIFI-enabled ESP8266 NodeMCU microcontroller which is interfaced with air quality monitoring sensors MQ135, DHT11 (Humidity and Temperature) sensor, Geophone sensor to detect measurements of vibration levels in industries, and a flame sensor to prevent the workers from fire accidents. The buzzer is used as an alert module. The sensor data is stored in NodeMCU and the data is analyzed and stored in the cloud platform and monitored on the BLYNK (an IoT platform) by interfacing the app with NodeMCU.