Air Quality Sensors Research Articles

An intelligent Health Monitoring System for Patients with Chronic Obstructive Pulmonary Disease based on Internet of Things

Abstract: Chronic obstructive pulmonary disease is an important cause of global mortality. These patients are in dire need of treatments to maintain lung function and reduce consecutive visits and hospital stays. IoT acts as a specialized assistant in healthcare, providing a wide range of potential applications such as remote monitoring. Therefore, this study has been compiled with the aim of designing a care system based on the Internet of Things for patients with chronic obstructive pulmonary disease. In this project, we designed a system for monitoring chronic obstructive pulmonary disease patients using an Arduino board and embedding three air quality sensors, heart rate and pulse oximetry, temperature sensor and Bluetooth module, and then implemented it on a patient with obstructive pulmonary disease. . The information obtained from the sensors is sent via Bluetooth to the mobile application that has the ability to share and store information and informs the doctor and the person about the changes. By using the pulse oximetry sensor, the patient increases the monitoring and part of his pulmonary rehabilitation, and by using the temperature and air quality sensor, he identifies the undesirable and harmful air and by informing the doctor and the person, he prevents the exacerbation of the symptoms of chronic obstructive pulmonary disease. This system increases the rehabilitation and self-care of patients, and by providing suitable environmental conditions, prevents unnecessary medical interventions and consecutive visits, and increases the quality of life of patients and reduces costs.

Designing a Socially Assistive Robot to Assist Older Patients with Chronic Obstructive Pulmonary Disease in Managing Indoor Air Quality

Chronic obstructive pulmonary disease (COPD) stems from airflow blockage and lung damage, and indoor air pollution exacerbates COPD, underscoring the necessity for proactive management. Older COPD patients, prone to respiratory and heat-related issues, require crucial assistance, yet their reduced awareness necessitates ongoing education to identify and enhance indoor air quality. To tackle this challenge, we developed a socially assistive robot (SAR) integrating IoT air quality sensors to guide patients in improving indoor air quality (IAQ). This study evaluated IAQ enhancement among older COPD patients using this technology, uncovering a significant reduction in ‘poor air quality alerts’ with a clear linear trend. Although ‘good alerts’ remained consistent, machine learning models predicted improved air quality post-alerts. Consistent alerts serve as a motivating factor for patients to maintain IAQ standards. However, barriers to SAR utilization, such as psychological and operational hurdles, need to be addressed in future research endeavors.

Unveiling the Nexus: Sulphur Dioxide Exposure, Proximity to Mining, and Respiratory Illnesses in Kankoyo: A Mixed-Methods Investigation.

The emission of sulphur dioxide (SO 2) from mining activities presents significant health hazards, particularly to communities near industrial zones. This mixed-methods study investigates the nexus between (SO 2) exposure and respiratory health in Kankoyo Township, Zambia. Employing community engagement, expert interviews, spatial analysis, and a retrospective examination of 15 years of health and (SO 2) data, the research identified a troubling correlation between (SO 2) exposure and adverse respiratory health effects among the local population. Expert interviews highlighted that respiratory issues constituted approximately 75% of health complications, with a notable reduction in asthma cases following the installation of a monitoring station and upgrades to smelter operations. Spatial analysis demonstrated that (SO 2) levels in Kankoyo exceeded the Zambian Environmental Management Agency (ZEMA) limits by 1713% identifying it as a significant pollution hotspot. Additionally, wind profile analysis indicated frequent low-speed winds from the east-northeast (ENE), contributing to pollutant accumulation. Based on these insights, the study recommends implementing real-time pollution data sharing, affordable air quality sensors, addressing medication shortages, establishing specialized respiratory clinics, launching IT-driven awareness campaigns, and further research into additional pollutants and confounding factors.

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany.

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods' black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m3 to 3.6 µg/m3 and the root mean squared error (RMSE) from 9.86 µg/m3 to 4.23 µg/m3 when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM2.5 concentrations with an MAE of less than 5 µg/m3 for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R2 from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study

Abstract. In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often “black boxes”, which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution.

Exploring public perceptions of indoor air quality under different quasi-experimental conditions: a UK case study

ABSTRACT The fast-growing sensing technology means there is an extensive range of low-cost indoor air quality (IAQ) devices or sensor modules readily available on the commercial market. However, less attention has been paid to exploring people’s perceptions and responses to indoor air pollution. Such knowledge is deemed important for improving the design of future IAQ sensors, enhancing public awareness and providing guidance on ways to improve indoor environmental conditions. This study aimed to explore people’s perceptions and responses to IAQ under different quasi-experimental environmental conditions and levels of information. The study is reported from the perspective of 16 staff and students at a UK university, held within a prototype smart and modular home built on campus. It compares questionnaire survey responses and focus group interview discussions with actual IAQ concentrations and other influential indoor parameters (indoor air temperature and relative humidity). Study outcomes revealed the importance of understanding contextual factors when developing feedback and communication strategies to best improve people’s awareness, acceptance, and behavioural responses to IAQ. Findings highlight the usefulness of focus group discussions in the design of future IAQ sensors.

Using a smart city framework to advance justice: applying an equity lens to compare air quality in neighbourhoods of different socioeconomic profiles

ABSTRACT The smart city framework, while initially received with enthusiasm, has been critiqued for reproducing urban inequalities rather than reducing marginalisation. This paper demonstrates how application of an equity lens within a smart city framework can advance justice by addressing air quality inequities in a diverse coastal city. We conducted a pilot study in which integrated air quality sensors are deployed for criteria pollutants in two contrasting neighbourhoods, to demonstrate that the smart city framework can be used with an equity lens for baseline data collection and analysis to support communities of colour in advocating on their own behalf. Communities of colour are disproportionately impacted by poor air quality, pollution exposure, and health burdens. Research shows that marginalised communities often lack air quality sensors installed by traditional regulatory measures. Thus, smart city technology using targeted deployment of air quality sensors can aid neighbourhood-level sampling, and data can be leveraged by marginalised communities for policy development to address inequities in pollution exposure and health impacts. In this study, air quality sensors were deployed and integrated with a web-based interactive platform called Community Resiliency Information Systems (CRIS) to compare a white, affluent neighbourhood with a marginalised, predominantly Black neighbourhood in Saint Petersburg, Florida. Data collected indicates significant differences in concentrations of air pollutants in the study neighbourhoods; exposure levels are higher in the poor, minority-dominated neighbourhood. CRIS facilitates two-way communication between residents and policy makers, demonstrating that careful application of technology can produce data to support community advocacy and address environmental inequalities. Key policy highlights The smart city framework integrated with air quality sensors has the potential to advance justice when applied through an equity lens. This pilot project demonstrates air quality inequities at neighbourhood level comparisons (affluent vs marginalised) in a coastal city – exposure levels are higher in the poor, minority-dominated neighbourhood. The CRIS platform facilitates two-way communication between residents and policy makers, demonstrating that careful application of technology can produce data to support community advocacy and address environmental inequalities.

The present state of low-cost air quality sensors in Japan and their accuracy

ABSTRACT The Sustainable Development Goals (SDGs) emphasise the importance of clean air (SDG 3 and SDG 11.6). Japan faces challenges in air quality prediction, especially in rural areas, because of the absence of a climate-specific calibration model and limited deployment of low-cost sensors. Our study compared three sensor types: low-cost pre-assembled and pre-calibrated sensors (PurpleAir); self-assembled low-cost sensors (Citizen Science Project); and highly accurate weather stations, focusing on PM2.5 values. Linear and generalised linear mixed models show potential for low-cost sensor calibration.

Screening and early detection of communicable diseases on board cruise ships: An assessment of passengers’ preferences on technical solutions

BackgroundImplementing technological solutions to screen for and detect early the most prevalent communicable diseases on cruise ships is contingent on, among others, willingness of passengers to accept use of such solutions. MethodWe surveyed passenger preferences to record their willingness to accept technological solutions for screening and early detection of communicable diseases on cruise ships. Self-reported sociodemographic characteristics, use of technology and acceptance of solutions were recorded anonymously in paper format. Multiple logistic regression analyses investigated the association of demographic and other characteristics with willingness and barriers/concerns of passengers to endorse proposed solutions. ResultsOf a total of 1344 passengers on two successive cruises on board CELESTYAL OLYMPIA, 336 (1 every 4) participated in the survey. The vast majority of passengers (92.3 %, n = 310) agreed with at least one solution. Passengers showed lower levels of acceptance for more personalized solutions, such as use of wearable devices (45.5 %) and monitoring with cameras (64.0 %), whereas they were more receptive to less personally invasive solutions, such as integration of cabins with air purifiers (89.6 %) and air quality sensors (80.4 %). Age, self-employment status, educational level, and fear of contacting a communicable disease were significantly correlated with passengers’ willingness to adopt proposed solutions. ConclusionsTo successfully integrate screening and early detection technological solutions in cruise ships, it is imperative that targeted awareness and education interventions are implemented on passengers to strengthen understanding and acceptance of such solutions and assuage concerns around monitoring and handling of personal health data.

A Place-Based Teacher Engagement Program Using Low-Cost Air Quality and Weather Sensors in Hawaiʻi After the 2018 Kīlauea Eruption

A Place-Based Teacher Engagement Program Using Low-Cost Air Quality and Weather Sensors in Hawaiʻi After the 2018 Kīlauea Eruption

Mobile air quality monitoring and comparison to fixed monitoring sites for instrument performance assessment.

Air pollution monitoring using mobile ground-based measurement platforms can provide high quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller scale air quality sensors, it is important to address the need for assessing these measurements in a scalable manner. We explore collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches - a standard collocation assessment technique where the mobile platform is parked near the fixed regulatory site for a period of time and an expanded approach using measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed reference site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, Colorado, USA and in the San Francisco Bay Area, California, USA. Our one-month Denver dataset includes both parked collocation periods near the fixed regulatory sites as well as general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, as the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (year-long) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40-hour running median converges to within ±4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be used during large-scale monitoring campaigns using small sensors to identify potential measurement biases.

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Montana Statewide Google Earth Engine-Based Wildfire Hazardous Particulate (PM2.5) Concentration Estimation

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R2 value of 0.572 and RMSE of 9.98 µg/m3. The corresponding R2 and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m3. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure.

Pollution Based Ventilation Control in Automobiles

Abstract: Growing worries about air pollution have driven new approaches to confined spaces, such autos. The proposed system includes air quality sensors, ventilation control mechanisms, and data processing units inside the car. It continuously monitors the amount of CO2 and other air pollutants. It emits warning sounds and turns on automated window ventilation when thresholds are passed. Furthermore, the system proposes closing windows in high-pollution areas and detects foul odors to put on air fresheners accordingly. Alerts are sent by push notifications, sirens that sound, or dashboard lights. This innovation combines sophisticated ventilation control, real-time monitoring, and alerts to make driving safer while prioritizing both environmental sustainability and passenger safety

IoT Based Air Pollution Monitoring System

Abstract: In light of the growing concern for air quality and its impact on public health, there is an increasing demand for air pollution monitoring systems that are both effective and efficient. This article introduces an IoT-based Air Pollution Detection System that utilizes the power of Internet of Things (IoT) technology to provide real-time monitoring and analysis of air quality parameters. The system incorporates a network of advanced air quality sensors that can measure particulate matter (PM) concentrations, as well as various gas levels such as carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). These sensors are integrated with a microcontroller that enables data processing and communication capabilities.

A Control-Theoretic Spatio-Temporal Model for Wildfire Smoke Propagation Using UAV-Based Air Pollutant Measurements

Wildfires have the potential to cause severe damage to vegetation, property and most importantly, human life. In order to minimize these negative impacts, it is crucial that wildfires are detected at the earliest possible stages. A potential solution for early wildfire detection is to utilize unmanned aerial vehicles (UAVs) that are capable of tracking the chemical concentration gradient of smoke emitted by wildfires. A spatiotemporal model of wildfire smoke plume dynamics can allow for efficient tracking of the chemicals by utilizing both real-time information from sensors as well as future information from the model predictions. This study investigates a spatiotemporal modeling approach based on subspace identification (SID) to develop a data-driven smoke plume dynamics model for the purposes of early wildfire detection. The model was learned using CO2 concentration data which were collected using an air quality sensor package onboard a UAV during two prescribed burn experiments. Our model was evaluated by comparing the predicted values to the measured values at random locations and showed mean errors of 6.782 ppm and 30.01 ppm from the two experiments. Additionally, our model was shown to outperform the commonly used Gaussian puff model (GPM) which showed mean errors of 25.799 ppm and 104.492 ppm, respectively.

Targeting disparate spaces: new technology and old tools.

A growing number of inexpensive, publicly available, validated air quality monitors are currently generating granular and longitudinal data on air quality. The expansion of interconnected networks of these monitors providing open access to longitudinal data represents a valuable data source for health researchers, citizen scientists, and community members; however, the distribution of these data collection systems will determine the groups that will benefit from them. Expansion of these and other exposure measurement networks represents a unique opportunity to address persistent inequities across racial, ethnic, and class lines, if the distribution of these devices is equitable. We present a lean template for local implementation, centered on groups known to experience excess burden of pulmonary disease, leveraging five resources, (a) publicly available, inexpensive air quality monitors connected via Wi-Fi to a centralized system, (b) discharge data from a state hospital repository (c) the U.S. Census, (d) monitoring locations generously donated by community organizations and (e) NIH grant funds. We describe our novel approach to targeting air-quality mediated pulmonary health disparities, review logistical and analytic challenges encountered, and present preliminary data that aligns with a growing body of research: in a high-burden zip code in Durham North Carolina, the census tract with the highest proportions of African Americans experienced worse air quality than a majority European-American census tract in the same zip code. These results, while not appropriate for use in causal inference, demonstrate the potential of equitably distributed, interconnected air quality sensors.

Drone-Assisted Particulate Matter Measurement in Air Monitoring: A Patent Review

Air pollution is caused by the presence of polluting elements. Ozone (O3), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) are the most controlled gasses because they can be released into the atmosphere naturally or as a result of human activity, which affects air quality and causes disease and premature death in exposed people. Depending on the substance being measured, ambient air monitors have different types of air quality sensors. In recent years, there has been a growing interest in designing drones as mobile sensors for monitoring air pollution. Therefore, the objective of this paper is to provide a comprehensive patent review to gain insight into the proprietary technologies currently used in drones used to monitor outdoor air pollution. Patent searches were conducted using three different patent search engines: Google Patents, WIPO’s Patentscope, and the United States Patent and Trademark Office (USPTO). The analysis of each patent consists of extracting data that supply information regarding the type of drone, sensor, or equipment for measuring PM, the lack or presence of a cyclone separator, and the ability to process the turbulence generated by the drone’s propellers. A total of 1473 patent documents were retrieved using the search engine. However, only 13 met the inclusion criteria, including patent documents reporting drone designs for outdoor air pollution monitoring. Therefore, was found that most patents fall under class G01N (measurement; testing) according to the International Patents Classification, where the most common sensors and devices are infrared or visible light cameras, cleaning devices, and GPS tracking devices. The most common tasks performed by drones are air pollution monitoring, assessment, and control. These categories cover different aspects of the air pollution management cycle and are essential to effectively address this environmental problem.

Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.