Performance assessment and deployment of a low-cost device for urban air quality monitoring in a developing country

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM2.5) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM2.5 concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM2.5 concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1 μg/m3. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.

The rapid increase in urban population has on one handled to a remarkable increase in demand for dwelling spaces, and on the other hand has led to unprecedented resource competition. With better medical, educational and employment opportunities in urban areas, there is a population exodus from nearby towns and villages towards larger urban centres. This migration trend is more noticeable in developing countries like India, where there is a shift from agriculture to the service sector as a significant employer and GDP contributor. This leads to the growth of cities and metropolis, both in a planned and unplanned manner, leading to a rapid change in Land Use/Land Cover. However, all this is not without cost. A cost which the urban dwellers are paying in terms of environmental and urban hazards like—urban subsidence, land use/land cover change, urban heat island development, urban flooding and increased air and water pollution. Proper monitoring of these hazards is needed for timely response in case of disaster and control and mitigation of any tragic event. This would be highly beneficial in terms of planning for future civic facilities, rescue provisions and emergency services, in addition to taking timely precautionary measures. Physical monitoring of urban hazards and its causes requires lots of expertise and sophisticated equipment, which is both complex and time-consuming. Remote sensing being a non-evasive tool is highly beneficial for urban and environmental hazard monitoring. With a multitude of remote sensors operating in various active–passive and regions of electromagnetic spectra, space-borne remote sensing has proved to be highly beneficial for urban land use/land cover change, urban air quality and pollution monitoring, urban flood modelling and urban heat island mapping, besides many more other potential hazards and their causes. This chapter focusses on some of the applications of space-borne remote sensing from optical, SAR and thermal sensors. The various applications discussed are a part of real-time research conducted in areas of urban land subsidence monitoring and mapping, urban land use/land cover mapping, urban heat island mapping, Urban flood run-off estimation and urban air pollution monitoring, using various satellite data. The chapter covers different regions and urban centres spread across various regions of India hence shows the geographical diversity of application of remote sensing technology.

In order to enhance the organization and management efficiency of multi-source heterogeneous data in the collection process for urban ambient air quality monitoring, according to the analysis of the limitations, the existing methods and the features of data collected, a new kind of multi-sensor and multi-level information fusion approach based on vague sets is proposed. The approach takes full advantage of the redundancy and complementarities from inter-level information to achieve the purpose of information integration. The mathematical description of vague sets based on the multi-sensor information fusion is defined and the corresponding model is developed in which the data organization and the monitoring method and the implementation of the hierarchical algorithm are discussed. Finally, the proposed approach is applied to a computing system of the ambient air quality monitoring. The study of this approach can supply scientific accordance for comprehensive monitoring of urban ambient air quality.

Air pollution is a contributor to approximately one in every nine deaths annually. To counteract health issues resulting from air pollution, air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality monitoring stations are expensive to maintain, resulting in sparse coverage. In this paper, we introduce the design and development of the MegaSense Cyber-Physical System (CPS) for spatially distributed IoT-based monitoring of urban air quality. MegaSense is able to produce aggregated, privacy-aware maps and history graphs of collected pollution data. It provides a feedback loop in the form of personal outdoor and indoor air pollution exposure information, allowing citizens to take measures to avoid future exposure. We present a battery-powered, portable low-cost air quality sensor design for sampling PM 2.5 and air pollutant gases in different micro-environments. We validate the approach with a use case in Helsinki, deploying MegaSense with citizens carrying low-maintenance portable sensors, and using smart phone exposure apps. We demonstrate daily air pollution exposure profiles and the air pollution hot-spot profile of a district. Our contributions have applications in policy intervention management mechanisms and design of clean air routing and healthier navigation applications to reduce pollution exposure.

Air pollution constitutes a significant environmental justice challenge, particularly affecting vulnerable communities in low- and middle-income countries (LMICs). Urban mobility plays a substantial role in personal exposure to pollutants, notably PM2.5, exacerbating health disparities among transport users. Effective monitoring of these exposures is essential for understanding and addressing these inequities; however, traditional air quality measurement infrastructures are often inadequate in LMIC contexts. The growing availability of low-cost sensors (LCS) presents a promising avenue for bridging data gaps in urban air quality monitoring. Nonetheless, the reliability and applicability of these sensors in dynamic urban transport environments require thorough evaluation. This study, executed as part of an interdisciplinary collaboration among experts in urban policy, air quality, and transport studies, investigates the deployment of an LCS for PM2.5 monitoring in Soledad, Colombia. The research aims to assess the potential of LCS to capture exposure disparities among various modes of transport while addressing associated technical and logistical challenges. The primary focus is to evaluate the feasibility of utilizing LCS to measure personal exposure to PM2.5 in urban transport microenvironments, emphasizing calibration accuracy, adaptability to local conditions, and the potential to inform equitable transport policies. The AirBeam3 sensor was employed across motorized three-wheelers, buses, and private cars during predetermined urban routes. A rigorous 15-day calibration process against a reference-grade station was conducted to ensure data accuracy, achieving a correlation coefficient of R² = 0.87. Data collection strategies were tailored to account for transport-specific dynamics, including variations in ventilation, proximity to emission sources, and traffic conditions. The study encountered several challenges, including adaptation to high humidity, protection of equipment in high-risk environments, and correction of measurement biases. Notably, the sensor identified significant PM2.5 exposure disparities among transport modes, with motorized three-wheeler users exhibiting the highest exposure levels. Adjusted data indicated that environmental conditions, traffic density, and vehicle type emerged as critical determinants of exposure. Despite certain limitations, the LCS provided robust, high-resolution exposure data, demonstrating its suitability for capturing real-world variability in LMIC contexts. This research underscores the challenges and opportunities presented by the deployment of LCS for air quality monitoring in resource-constrained urban settings. While technical hurdles, such as calibration and environmental sensitivity, persist, the affordability and accessibility of LCS render them invaluable tools for addressing environmental justice issues. The findings emphasize the potential of LCS to enhance local air quality initiatives, inform sustainable transport policies, and promote equitable health outcomes through data-driven interventions.Keywords: low-cost sensors, air quality, PM2.5, urban mobility, personal exposure, environmental justice, LMICs.

Spatial aspects of urban air quality management: Estimating the impact of micro-scale urban form on pollution dispersion

Multi-sensing paradigm based urban air quality monitoring and hazardous gas source analyzing: a review

In the urban areas, regional and local air quality monitoring networks (AQMN) provide the concentrations of regulated air quality parameters. However, there is a rising concern of aerosol particle number concentrations, lung deposited surface area and black carbon (BC) as novel health indicators that connect closely to the well-being of the citizens. The capacities of the AQMNs need to be improved to be able to respond to the need of novel air quality data.Aerosols, Clouds and TRace gases Research InfraStructure (ACTRIS) provides harmonized high-quality data on the variability of aerosols, aerosol precursors and their complex interactions through remote-sensing and in-situ measurement techniques. More specifically, ACTRIS has observations on surface aerosol levels, including nanoparticle-size distribution, PM size distributions, nanoparticles, online (aerosol mass spectrometers, MS and aerosol chemical speciation monitors ACSM) and offline (filter-based chemistry) chemical composition, BC, Volatile Organic Compounds as precursors of PM, nanoparticles and O3, radiative properties of aerosols, and 4D (3- dimensions and online in time) atmospheric measurements. There is a need to connect the ACTRIS expertise and that of the air quality monitoring networks. This provides the starting point of Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS (RI-URBANS), a European Commission funded project in the Horizon 2020 Call H2020-LC-GD-2020 (Building a low-carbon, climate resilient future: Research and innovation in support of the European Green Deal).The challenge of RI-URBANS is therefore to develop innovative urban AQ service tools, in clear complementarity with the AQMNs, and provide innovative tools to better quantify the impact of atmospheric species most deleterious to human health. Under the complex and changing AQ situation of urban pollution as described above, obtaining monitoring data on PM composition, source contributions to PM, nanoparticles, and gaseous precursors, as well as spatially resolved exposure maps of urban pollutants, will contribute to enhanced AQ policy assessment and evaluation of health effects in Europe. For such assessment both urban scale modelling (for nanoparticles, and other pollutants such as exhaust and non-exhaust vehicles PM emissions, and BC) and regional ones (for SOA and Secondary Inorganic Aerosols (SIA) and for the background levels of all the other pollutants) are also needed. RI-URBANS is based on the premise that advanced monitoring and modelling tools developed by RIs and science teams can be used to supplement current AQMNs of regulated pollutants.On one hand, the overarching objective of RI-URBANS is to demonstrate how Service Tools (STs from atmospheric Research Infrastructures (RIs) can be adapted and enhanced in a RIs-AQ Monitoring Networks (AQMNs) interoperable and sustainable way to better address the challenges and societal needs related to AQ in European cities (and industrial, harbour, airport and traffic hotspots) as areas with especially significant levels of air pollution and associated health effects. On the other hand, ACTRIS has then the opportunity provide harmonized pan-European observation capacity of urban air quality and therefore to contribute to the well-being of the population.In this work we will summarize the key developments of the RI-URBANS project acquired during the first year of the project.

In this paper, we report on extensive experiments conducted to evaluate Internet of Things (IoT) sensor performance in monitoring urban air quality. As certified sensors showed a considerably reduced air quality measurement error of 4.3% compared to uncalibrated sensors at 8.5%, our results highlight the crucial function of sensor calibration. The performance of sensors was impacted by environmental factors; higher temperatures produced better accuracy (3.6%), while high humidity levels caused sensors to react more quickly (2.3 seconds). The average air quality index (AQI) recorded by inside sensors was 45, but outside sensors reported an AQI of 60. This indicates that the positioning of the sensors had a substantial influence on the air quality data. Additionally, the methods of data transmission were examined, and it was found that Wi-Fi-transmitting sensors had lower latency (0.6 seconds) and data loss (1.8%) than cellular-transmitting sensors. These results emphasize the significance of environmental factors, sensor placement strategy, sensor calibration, and suitable data transmission techniques in maximizing IoT sensor performance for urban air quality monitoring, ultimately leading to more accurate and dependable air quality assessment.

As part of the European research project ICOS-Cities, the Laboratoire des Sciences du Climat et de l'Environnement (LSCE) and Origins.earth are seeking to extend the Greenhouse Gases (GHG) measurement network in Paris and its immediate suburbs by installing new sensors on the roofs of tall buildings. Each sensor will measure CO2 concentration in real time, which will then be used in an inversion model to determine CO2 emissions at the scale of a district. Several sensors will be upgraded in a second phase with the addition of an Air Quality (AQ) measurement cell.We present a new stand-alone sensor box design (called AtmoBox) that allows to connect GHG and AQ sensors in a single box, as well as the implementation of these Atmoboxes in a ground-based atmospheric monitoring network. In addition to 9 long-term stations equipped with high precision CRDS spectrometers, about 30 Mid Cost instruments are deployed within Paris and its near suburb, measuring CO2 through NDIR sensors. We will detail the steps from the search for suitable sites for the urban measurements, the characterisation of the sensors in relation to the environmental parameters in the laboratory, the related calibration and quality control strategy to meet the performance objective and the performance assessment of the sensors.

Co-benefit potential of urban CO2 and air quality monitoring: A study on the first mobile campaign and building monitoring experiments in Seoul during the winter

A sensors network based on 11 nodes (10 stationary and 1 mobile mounted on public bus) distributed in Bari (Italy) has been deployed for urban air quality (AQ) monitoring. The low-cost sensor-systems have been installed in specific sites (buildings, offices, schools, streets, port, airport) to enhance environmental awareness of the citizens and to supplement the expensive official air monitoring stations with cost-effective sensor-nodes at high spatial and temporal resolution. Continuous measurements were performed by low-cost electrochemical gas sensors (CO, NO2, O3, SO2), optical particle counter (PM1.0, PM2.5, PM10), NDIR infrared sensor (CO2), photo-ionisation detector (total VOCs), including microsensors for temperature and relative humidity. The sensors are running to assess the performance during a campaign (June 2015–December 2017) of several months for citizen science in sustainable smart cities. The air quality index (AQI) for a given pollutant has been measured and compared to the public reference environmental data. The results of the AQ monitoring long-term campaign for selected sensor-nodes are presented.

A high-resolution index suitable for multi-pollutant monitoring in urban areas

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges.

Air quality monitoring (AQM) is key to maintaining healthy air in cities. This is crucial in low- and middle-income countries due to increasing evidence of poor air quality but lack of monitors to consistently collect evaluate air quality data and effect policy changes, mainly because of the costs of monitoring devices. In participating in a challenge for the development of low-cost AQM devices in low-resource regions, an Arduino-based device with sensors for particulate matter size, temperature, and humidity data acquisition was developed for deployment in Port Harcourt, a city in Nigeria’s Niger Delta region, exposed to poor air quality partly due to gas and oil production activities. During the project, challenges to AQM were encountered, including inadequate awareness of air quality issues, lack of necessary AQM device components, unavailability of trained manpower and partnerships, and lack of funding. However, lack of a means of calibrating the device was a major hindrance, as no reference AQM instrument was available, rendering the data acquired largely qualitative, educational, and useless for regulatory purposes. There is an urgent need for AQM in such cities. However, a robust AQM strategy must be designed and used to address these constraints, especially whilst using low-cost devices, for significant progress in acquiring robust air quality data in such low-resource regions to be made.