Low-cost Air Quality Monitoring Research Articles

Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA

Air quality assessments often require source apportioning of the air pollutants observed at the receptor site. Conventional source apportionment models are subject to high uncertainties due to the lack of accurate emission profiles of all the contributing sources and a limited number of measurements at the receptor sites. Recent advances in the development and application of low-cost PM2.5 sensors have facilitated the formation of a more robust database with greater numbers of measurements per location and time. The main objective of this study is to combine a large database of PM2.5 concentration records to records from low-cost sensors in Denver, Colorado, during January 2021. Using wind speed and wind direction at the receptors, we developed a visualization tool for source tracing of PM2.5 with resulting statistical analyses and back-trajectory modeling. For this purpose, a combination of in-house and existing packages of R scripts along with National Oceanic and Atmospheric Administration (NOAA)’s trajectory model and climate and weather toolkits were used. In general, the results show that the PM2.5 measurements obtained from such a network of PM2.5 sensors incorporated with hourly wind field data, which are publicly available, can provide a powerful screening tool to discover the transport pathways of PM2.5 before requiring costly source apportionment approaches. The fraction of PM2.5 concentration detected by each sensor in regard to wind direction and speed bins were quantified using this method. The results of cluster analysis identified the area groups in respect to wind speed and wind direction bins, which shines a light on how far and in which direction polluting sources are. Finally, the back-trajectory modeling outputs illustrated the exact travel path of the PM2.5-laden air parcels of each day to each sensor.

Towards practical learning using air quality monitors

In this study, we explore the use of a low-cost air quality monitor as an experiment within a first year undergraduate statistics setting. The aim is to enhance student engagement and to provide a basis for both individual and group assessments. A pilot, during the summer months of June-September 2023, involved 52 volunteer students who collected indoor and outdoor air quality data. The students shared their data and analytical insights. “Fun/enjoyment” was frequently mentioned in student feedback, suggesting this practical approach may improve student engagement.

Seasonal analysis of particulate matter and its exposure on urban bikers in Nashik city, India

Introduction: Exposure to Particulate Matter (PM) can cause ill health effects such as coughing, allergies, decreased lung function, chest discomfort and pain. The current study aims to monitor particulate matter concentrations on the highways in Nashik, India and to estimate its exposure to the bikers in the form of Respiratory Deposition Doses (RDDs) with its seasonal variation.
 Materials and methods: Low-cost air quality monitor was mounted on the bike to measure Particulate Matter (PM1, PM2.5 and PM10) concentrations at breathing level. Extensive mobile monitoring was performed on seven highway stretches passing through city limits at morning and evening peak hours for all the weekdays for three seasons.
 Results: The PM concentrations differed on each route seasonally as well as at peak hours in morning and evening. The maximum PM1, PM 2.5 and PM10 concentrations monitored were 119.84 μg/m3, 218.85 μg/m3 and 239.25 μg/m3 respectively on Route R3 in Winter morning. The maximum RDDHD exposure on R5 and R3 in Winter mornings was due to PM10. While maximum RDDTB and RDDAL exposure on R5 in Winter morning and evening was due to PM2.5. Also, the seasonal and particle size effect on RDD has been studied which exhibits higher rise in exposure in Winter mornings due to PM2.5.
 Conclusion: This study reveals that maximum exposure was observed during Winter mornings. The results recommend that seasons have a substantial effect on PM concentrations and their exposure. The minimum exposure was observed in monsoon, followed by summer and the maximum exposure was seen in winters.
 

Research of low-cost air quality monitoring models with different machine learning algorithms

Abstract. To improve the performance of the calibration model for the air quality monitoring, a low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM2.5 and PM10) and gas pollutants (SO2, NO2, CO and O3) simultaneously. The multi-input multi-output (MIMO) prediction model is developed based on the original signals of the sensors, ambient temperature (T) and relative humidity (RH), and the measurements of the reference instrumentations. The performance of the different algorithms (RF, MLR, KNN, BP, GA–BP) with parameters such as determination coefficient R2, root mean square error (RMSE), and mean absolute error (MAE) are compared and discussed. Using these methods, the R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the PM is in the range 0.68–0.99; the RMSE values of PM2.5 and PM10 are within 2.36–18.68 and 4.55–45.05 µg m−3, respectively; the MAE values of PM2.5 and PM10 are within 1.44–12.80 and 3.21–23.20 µg m−3, respectively. The R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the gas pollutants (O3, CO and NO2) is within 0.70–0.99; the RMSE values for these pollutants are 4.05–17.79 µg m−3, 0.02–0.18 mg m−3, 2.88–14.54 µg m−3, respectively; the MAE values for these pollutants are 2.76–13.46 µg m−3, 0.02–0.19 mg m−3, 1.84–11.08 µg m−3, respectively. The R2 of the algorithms (RF, KNN, BP, GA–BP, except for MLR) for SO2 is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 µg m−3, and the MAE value is in the range 0.39–4.24 µg m−3. These measurements are consistent with the national environmental protection standard requirement of China, and the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring.

The use of low-cost sensors (LCSs) for the mobile monitoring of oil and gas emissions is an understudied application of low-cost air quality monitoring devices. To assess the efficacy of low-cost sensors as a screening tool for the mobile monitoring of fugitive methane emissions stemming from well sites in eastern Colorado, we colocated an array of low-cost sensors (XPOD) with a reference grade methane monitor (Aeris Ultra) on a mobile monitoring vehicle from 15 August through 27 September 2023. Fitting our low-cost sensor data with a bootstrap and aggregated random forest model, we found a high correlation between the reference and XPOD CH4 concentrations (r = 0.719) and a low experimental error (RMSD = 0.3673 ppm). Other calibration models, including multilinear regression and artificial neural networks (ANN), were either unable to distinguish individual methane spikes above baseline or had a significantly elevated error (RMSDANN = 0.4669 ppm) when compared to the random forest model. Using out-of-bag predictor permutations, we found that sensors that showed the highest correlation with methane displayed the greatest significance in our random forest model. As we reduced the percentage of colocation data employed in the random forest model, errors did not significantly increase until a specific threshold (50 percent of total calibration data). Using a peakfinding algorithm, we found that our model was able to predict 80 percent of methane spikes above 2.5 ppm throughout the duration of our field campaign, with a false response rate of 35 percent.

Identification of the Safe Variation Limits for the Optimization of the Measurements in Low-Cost Electrochemical Air Quality Sensors

Nowadays, the study of air quality has become an increasingly prominent field of research, particularly in large urban centers, given its significant impact on human health. In many countries, government departments and research centers use official high-cost scientific instruments to monitor air quality in their regions. Meanwhile, concerned citizens interested in studying the air quality of their local areas often employ low-cost air quality sensors for monitoring purposes. The optimization and evaluation of low-cost sensors have been a field of research by many research groups. This paper presents an extensive study to identify the safe percentage change limits that low-cost electrochemical air quality sensors can have, in order to optimize their measurements. For this work, three low-cost air quality monitoring stations were used, which include an electrochemical sensor for nitrogen dioxide (NO2) (Alphasense NO2-B43F) and an electrochemical sensor for ozone (O3) (Alphasense OX-B431). The aim of this work is to explore the variance of the aforementioned sensors and how this variability can be used to optimize the measurements of low-cost electrochemical sensors, closer to real ones. The analysis is conducted by employing diagrams, boxplot and violin curves of the groups of sensors used, with satisfactory results.

Validation of low-cost air quality monitoring platforms using model-based control charts

The SARS COVID-19 pandemic highlighted the importance of routine indoor air quality (IAQ) monitoring. Recent advances in IAQ sensors and remote logging technologies offer opportunities to use low-cost platforms to monitor indoor air. The sensor's accuracy and stability are critical for reliable monitoring and health protection. Data from our low-cost IAQ platform (SKOMOBO) was validated against a commercial platform for carbon dioxide, temperature, and relative humidity measurements to test the reliability of the low-cost instrument. The traditional statistical method to test the variability between two data sets is the coefficient of determination method. We identified that this traditional method did not detect drifts in measurements, when comparing data from two platforms, in a controlled and uncontrolled environment. In our paper, we propose two complementary methods to detect potential drifts in measurements (a modified Shewhart method and a cumulative sum control chart method). The traditional coefficient of determination method indicated strong consistency (between 0.70 and 0.99) in the measurements between SKOMOBO and the reference platforms for both tested environments. Our more sensitive methods detected 100 % data matching for the controlled environment between the SKOMOBO and the reference platform but detected some drifts for the uncontrolled environment (between 81 % and 100 % data matching). It was expected that the uncontrolled environment would create more drifts in measurements than the controlled environment. Our new statistical methods achieved two important results; namely it advanced the validation process and proved the reliability of our low-cost platform for IAQ monitoring and assurance.

TinyML Models for a Low-Cost Air Quality Monitoring Device

TinyML Models for a Low-Cost Air Quality Monitoring Device

ECO4RUPA: 5G-IoT Inclusive and Intelligent Routing Ecosystem with Low-Cost Air Quality Monitoring

The increase and diversity of low-cost air quality (AQ) sensors, as well as their flexibility and low power consumption, offers us the opportunity to integrate them into broad AQ wireless sensor networks, with the aim of enabling real-time monitoring and higher spatial sampling density of pollution in all parts of cities. Considering that the vast majority of the population lives in cities and the increase in respiratory/allergic problems in a large part of the population, it is of great interest to offer services and applications to improve their quality of life by avoiding pollution exposure in their movements in the open air. In the ECO4RUPA project, we focus on this kind of service, proposing an inclusive and intelligent routing ecosystem carried out using a network of low-cost AQ sensors with the support of 5G communications along with official AQ monitoring stations, using spatial interpolation techniques to enhance its spatial resolution. The goal of this service is to calculate healthy walking and/or cycling routes according to the particular citizen’s profile and needs. We provide and analyse the results of the proposed route planner under different scenarios (different timetables, congestion road traffic, and routes) and different user profiles, with a special interest in citizens with asthma and pregnant women, since both have special needs. In summary, our approach can lead to an approximately average reduction in pollution exposure of 17.82% while experiencing an approximately average increase in distance travelled of 9.8%.

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.

Air Pollution Monitoring via Wireless Sensor Networks: The Investigation and Correction of the Aging Behavior of Electrochemical Gaseous Pollutant Sensors

The continuously growing human activity in large and densely populated cities pollutes air and consequently puts public health in danger. This is why air quality monitoring is necessary in all urban environments. However, the creation of dense air monitoring networks is extremely costly because it requires the usage of a great number of air monitoring stations that are quite expensive. Instead, the usage of wireless sensor networks (WSNs) that incorporate low-cost electrochemical gas sensors provides an excellent alternative. Actually, sensors of this kind that are recommended for low-cost air quality monitoring applications may provide relatively precise measurements. However, the reliability of such sensors during their operational life is questionable. The research work presented in this article not only experimentally examined the correlation that exists between the validity of the measurements obtained from low-cost gas sensors and their aging, but also proposes novel corrective formulae for gas sensors of two different types (i.e., NO2, O3), which are aimed at alleviating the impact of aging on the accuracy of measurements. The following steps were conducted in order to both study and lessen the aging of electrochemical sensors: (i) a sensor network was developed to measure air quality at a place near official instruments that perform corresponding measurements; (ii) the collected data were compared to the corresponding recordings of the official instruments; (iii) calibration and compensation were performed using the electrochemical sensor vendor instructions; (iv) the divergence between the datasets was studied for various periods of time and the impact of aging was studied; (v) the compensation process was re-evaluated and new compensation coefficients were produced for all periods; (vi) the new compensation coefficients were used to shape formulae that automatically calculate the new coefficients with respect to the sensors’ aging; and (vii) the performance of the overall procedure was evaluated through the comparison of the final outcomes with real data.

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.

Enhancing the Applicability of Satellite Remote Sensing for PM 2.5 Estimation Using Machine Learning Models in China

Numerous studies and monitoring data indicate that fine particle ( PM 2.5 ) pollution in China is still comparatively severe. Given the sparse and uneven distribution of air quality monitoring base stations established in China and the limitation of geographical conditions, inversion of aerosol optical depth by satellite remote sensing can achieve low-cost air quality monitoring in global areas. In this study, we use the machine learning algorithm XGBoost to build a prediction model to achieve nationwide average PM 2.5 concentration prediction. Meanwhile, we used aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS) in a specific band, combined with a land use regression (LUR) model as predictors of surface PM 2.5 concentrations in China, for the period Dec. 2019-Nov. 2021. In order to provide more accurate PM 2.5 concentration prediction, the correspondence between PM 2.5 and aerosol optical depth (AOD) under different seasons was studied. The coefficients of determination (R2) for different seasons are 0.86 (spring), 0.80 (summer), 0.90 (autumn), and 0.88 (winter), indicating that the fit is best for autumn and worse for summer. The study shows the potential usefulness of using the LUR model with the XGBoost algorithm for predictive assessment of PM 2.5 spatial distribution.

A low-cost air quality monitoring system based on Internet of Things for smart homes

Global climate change and COVID-19 have changed our social and business life. People spend most of their daily lives indoors. Low-cost devices can monitor indoor air quality (IAQ) and reduce health problems caused by air pollutants. This study proposes a real-time and low-cost air quality monitoring system for smart homes based on Internet of Things (IoT). The developed IoT-based monitoring system is portable and provides users with real-time data transfer about IAQ. During the COVID-19 period, air quality data were collected from the kitchen, bedroom and balcony of their home, where a family of 5 spend most of their time. As a result of the analyzes, it has been determined that indoor particulate matter is mainly caused by outdoor infiltration and cooking emissions, and the CO2 value can rise well above the permissible health limits in case of insufficient ventilation due to night sleep activity. The obtained results show that the developed measuring devices may be suitable for measurement-based indoor air quality management. In addition, the proposed low-cost measurement system compared to existing systems; It has advantages such as modularity, scalability, low cost, portability, easy installation and open-source technologies.

Assessment of the Performance of a Low-Cost Air Quality Monitor in an Indoor Environment through Different Calibration Models

Air pollution significantly affects public health in many countries. In particular, indoor air quality can be equally, if not more, concerning than outdoor emissions of pollutant gases. However, monitoring the air quality in homes and apartments using chemical analyzers may be not affordable for households due to their high costs and logistical issues. Therefore, a new alternative is represented by low-cost air quality monitors (AQMs) based on low-cost gas sensors (LCSs), but scientific literature reports some limitations and issues concerning the quality of the measurements performed by these devices. It is proven that AQM performance is significantly affected by the calibration model used for calibrating LCSs in outdoor environments, but similar investigations in homes or apartments are quite rare. In this work, the assessment of an AQM based on electrochemical sensors for CO, NO2, and O3 has been performed through an experiment carried out in an apartment occupied by a family of four during their everyday life. The state-of-the-art of the LCS calibration is featured by the use of multivariate linear regression (MLR), random forest regression (RF), support vector machines (SVM), and artificial neural networks (ANN). In this study, we have conducted a comparison of these calibration models by using different sets of predictors through reference measurements to investigate possible differences in AQM performance. We have found a good agreement between measurements performed by AQM and data reported by the reference in the case of CO and NO2 calibrated using MLR (R2 = 0.918 for CO, and R2 = 0.890 for NO2), RF (R2 = 0.912 for CO, and R2 = 0.697 for NO2), and ANN (R2 = 0.924 for CO, and R2 = 0.809 for NO2).

Internet of Things (IoT) Enabled Air Quality Monitoring System for Conventional and UAV Application

The purpose of this research is to make a system to read the air quality by detecting the various gases existing in the air by using Arduino UNO and node MCU module for conventional as well as UAV applications. The emergence of the Internet of Things (IoT) has made it easier to read the various gases present in the environment using smartphones from the workplace. The use of an Arduino processor with a Node MCU to construct an air quality monitoring system is discussed in this paper. The Node MCU is used to transmit live data for CO, CO2, and PM2.5 concentrations that are sensed by sensors. This data can be monitored continuously by the user via the mobile phone. The calibration of sensors is highly important while reviewing and grasping the large literature on the subject of IoT-based air quality monitoring. The proposed low-cost live air quality monitoring system uses commercially available gas sensors to detect environmental gases such as CO, CO2 and PM2.5 to monitor air quality in an outdoor area. The proposed system is used to correctly evaluate the experimental outcomes. This proposed prototype model incorporates an open-source cloud facility with Arduino for air quality monitoring, confirming low cost, comfort, and convenience for a customizable air quality monitoring system. As a result, the suggested system can simply be converted to use in a UAV for monitoring air quality in the outdoors at various altitudes, and it can be scaled up in the future.

Design of a Low-Cost Air Quality Monitoring System Using Arduino and ThingSpeak

The impact of daily emissions of gaseous and particulate pollutants of machines and industries on human health and the environment has attracted increasing concerns. This impact has significantly led to a notable increase in mortality in the highly industrialized zones. Therefore, monitoring air quality and creating public awareness are important for a safer future, which led the governments globally to invest multi-billion in policymaking and solution stratification to address the problem. This study aims to design a real-time Internet of Things low-cost air quality monitoring system. The system utilizes air quality and carbon monoxide sensors for monitoring gaseous pollutants. Moreover, the system utilizes an Arduino Nano development board equipped with a WiFi module to effectively send readings to a ThingSpeak online channel platform for instantaneous and real-time display of air quality. The ThingSpeak uses HTTP protocols to send emails in raising awareness of poor air quality. The level of concentration is monitored graphically through channels with the help of ThingSpeak to aid remote communication. A threshold value is set. Thus, when pollutants have become unhealthy and harmful, the system trips off an alarm, and e-mail notifications are sent to the officials. The results have shown that the work was successfully implemented a design of a low-cost air quality monitoring system using Arduino and ThingSpeak, showing that an air quality system can be implemented using a low-cost technology, Arduino and ThingSpeak.

Air Quality Monitoring in Chittagong by a Low Cost IoT Based Air Quality Monitoring System

Air is an essential element of the environment we live in. Unfortunately, this air is polluted incessantly in many ways. With the advancement of technology, to make life easier, to live with full comfort, this essential part of the environment is being polluted without any interruption. This constant pollution poses a significant threat to the ecosystem as well as to all the living beings on this planet. To make this planet a suitable place for living, for the betterment of all the living beings of this nature, this pollution should be checked. To create awareness, at first, it is essential to show the current condition of the atmosphere, so that people will see the extent to which their activities affect the environment. In this work, we developed an Internet-of-Things (IoT) based low-cost air quality monitoring system to monitor the air quality of Chittagong, a district located in the south-eastern region of Bangladesh. Our proposed system is not only cost-efficient, it also monitors the air quality in real-time. The air quality measurement was taken based on Parts per Million (PPM) metrics and then analyzed. The proposed method provides accurate results.

Low-cost air quality monitoring system design and comparative analysis with a conventional method

Low-cost air quality monitoring system design and comparative analysis with a conventional method