Evolving trends in application of low-cost air quality sensor networks: challenges and future directions – Nature

Report on Evolving Trends in Low-Cost Air Quality Sensor Applications

Executive Summary

This report provides a scoping review of current topics, methods, and technologies in the application of low-cost air quality sensor (LCS) networks. The increasing use of LCS complements traditional monitoring, offering a pathway to achieve key Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). This review focuses on evolving trends in LCS network operation, mobile and drone-based monitoring, data fusion, and community engagement. Despite significant momentum, several challenges persist that hinder the full realization of these technologies for sustainable development. A primary limitation is the lack of unified performance metrics for comparing calibration and data imputation techniques. Furthermore, the absence of common protocols impedes data sharing and interpretation, which is critical for building partnerships (SDG 17). Key operational and technical challenges are identified across various applications:

• LCS Networks (LCSN): Difficulties in data sharing and interpretation due to a lack of standardized protocols.
• Mobile Monitoring: Inconsistencies in data collection arising from sensor siting, orientation, and platform speed.
• Drone-Based Monitoring: Complications in measurement accuracy due to rotor downwash, sensor placement, and environmental variability.
• Data Integration: Obstacles in realistic uncertainty quantification when integrating LCS data with air quality models.
• Citizen Science: Motivational, technological, economic, and regulatory barriers that limit the scalability and long-term impact of community-led initiatives, affecting inclusivity (SDG 10) and partnerships (SDG 17).

1.0 Introduction: Aligning Air Quality Monitoring with Sustainable Development Goals

Air pollution represents a significant global environmental challenge, directly undermining progress toward SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). Traditional air quality monitoring, which relies on sparse networks of high-cost reference stations, is often insufficient for providing the granular data needed for targeted urban interventions. The emergence of Low-Cost Sensors (LCS) marks a paradigm shift, introducing an innovative technology (SDG 9: Industry, Innovation, and Infrastructure) that democratizes data collection. The affordability and portability of LCS empower a diverse range of actors, including citizens and researchers, to actively participate in monitoring efforts. This is particularly crucial for advancing environmental equity in low- and middle-income countries, contributing to SDG 10 (Reduced Inequalities).

LCS deployments facilitate the collection of high-resolution, real-time data on key pollutants such as particulate matter (PM), Ozone (O₃), and Nitrogen dioxide (NO₂), thereby improving the understanding of local air pollution patterns. This data can inform evidence-based decision-making and policy formulation to improve public health outcomes. However, concerns regarding data accuracy, sensor drift, and standardization remain significant obstacles. This report reviews emergent applications of LCS and identifies key challenges to guide future research, focusing on:

1. LCS operational data quality evaluations.
2. Mobile air quality monitoring (ground and aerial).
3. Integration of LCS data with air quality models and remote sensing.
4. The role of LCS in citizen science and achieving SDG 17 (Partnerships for the Goals).

2.0 Data Integration and Calibration for Sustainable Urban Management

Ensuring data accuracy from LCS networks is fundamental for their effective use in urban planning and policy-making aligned with SDG 11. Field calibration is a critical step, and several methods have been developed to enhance the reliability of LCS data.

2.1 Direct Field Calibration

This foundational approach involves co-locating LCS units with high-precision reference instruments to develop site-specific calibration functions. While essential for establishing a baseline of accuracy, this method is limited by its vulnerability to sensor drift and changing local conditions, necessitating integration with other strategies for large-scale or mobile networks.

2.2 Proxy-Based Field Calibration

As an alternative when reference stations are sparse, this semi-blind approach uses a calibrated mobile LCS as a temporary proxy to transfer calibration functions to uncalibrated units. This method enhances the scalability of monitoring networks, supporting data collection in diverse urban and rural settings. However, it is sensitive to cumulative error propagation and is best used in combination with periodic direct calibration to maintain network-wide accuracy.

2.3 Transfer-Based Field Calibration

This strategy applies a calibration function developed at one site to other sensors of the same type under comparable environmental conditions. It reduces operational costs and efforts, making it a practical solution for expanding monitoring infrastructure (SDG 9). Its success depends on the similarity between training and deployment environments. A more advanced variant, global or multi-site transfer calibration, develops robust models from diverse datasets to enhance performance across different geographic contexts.

2.4 Connectivity-Based Field Calibration

Utilizing graph theory, this approach models the spatial and temporal relationships between sensor nodes to propagate corrections and detect anomalies. By representing the LCS network as a graph, it leverages the physical dispersion of pollutants to improve calibration, which is particularly effective in dense urban deployments and contributes to building smarter, more resilient city infrastructure (SDG 11).

2.5 Deep Learning as a Cross-Cutting Layer

Deep learning methods are increasingly used to complement traditional calibration techniques by capturing complex, non-linear relationships in sensor data. Architectures such as recurrent neural networks (RNNs) and graph neural networks (GNNs) improve the accuracy, stability, and transferability of calibration models, representing a key innovation (SDG 9) for enhancing data quality.

3.0 Mobile Monitoring Applications for Hyperlocal Insights and SDG Attainment

Mobile monitoring complements stationary networks by capturing hyperlocal variations in air pollution, which is essential for accurate personal exposure assessments (SDG 3) and for identifying pollution hotspots to promote environmental justice (SDG 10). This approach supports inclusive and sustainable urbanization as outlined in SDG 11.3.

3.1 Ground-Based Mobile Measurements

The portability of LCS allows for their deployment on non-specialized platforms such as bicycles, public transit, and waste collection vehicles. This opportunistic sampling strategy increases the spatial and temporal coverage of monitoring in a cost-effective manner, creating a dynamic data infrastructure (SDG 9) for smart cities. Data from these platforms can be integrated into high-resolution exposure models to inform urban planning and public health interventions.

3.2 Aerial-Based Mobile Measurements

The use of LCS on-board drones (Unmanned Aerial Vehicles) has emerged as a promising technology for monitoring the three-dimensional distribution of air pollutants. This provides critical data on vertical pollution gradients that are unobtainable from ground-based networks. Such information is vital for validating atmospheric models and understanding pollution dynamics in complex urban environments, thereby supporting more effective urban design and planning for sustainable cities (SDG 11).

4.0 Advanced Data Fusion for Comprehensive Environmental Assessment

Integrating LCS data with air quality models and satellite observations through data fusion and assimilation techniques represents a significant step toward creating comprehensive and robust environmental monitoring systems. This synergy embodies the principles of SDG 9 (Innovation) and SDG 17 (Partnerships) by combining different technologies and data sources to produce a result greater than the sum of its parts.

4.1 Data Fusion and Assimilation Techniques

These techniques merge data from various sources to improve the accuracy and resolution of air quality maps. This enhanced mapping capability provides policymakers with reliable information for developing strategies to protect public health (SDG 3) and create healthier urban environments (SDG 11). Common approaches include:

• Data Fusion: Methods such as geostatistics, land-use regression (LUR), and machine learning are applied as post-processing steps to combine observations with model outputs.
• Data Assimilation: Techniques like Optimal Interpolation (OI) and the Ensemble Kalman Filter (EnKF) actively update the model state during runtime by integrating real-world observations, thereby improving forecast accuracy.

4.2 Integration with Satellite Observations

Earth observation satellites provide global coverage of atmospheric pollutants but lack surface-level detail. Integrating satellite-derived columnar measurements with point-based LCS data allows for the generation of spatially continuous surface concentration maps. This fusion of ground and space-based data is a powerful tool for monitoring air quality from local to global scales, supporting international efforts to track progress on environmental SDGs.

5.0 Community Engagement and Citizen Science: Fostering Partnerships for the Goals (SDG 17)

Citizen science projects leveraging LCS technology have become a powerful tool for increasing public engagement and environmental awareness. These initiatives directly contribute to SDG 17 (Partnerships for the Goals) by fostering collaboration between the public, scientists, and policymakers. By involving citizens in data collection, these projects enhance the spatial density of monitoring networks and empower communities to advocate for cleaner air. This participatory approach is crucial for promoting inclusive and sustainable urbanization (SDG 11.3) and addressing issues of environmental justice (SDG 10).

The benefits of using LCS in citizen science projects for achieving SDGs include:

• Enhanced Data Coverage: Crowd-sourced data fills gaps left by official monitoring networks, enabling the identification of localized pollution hotspots.
• Increased Public Awareness: Direct involvement in monitoring fosters a deeper understanding of air pollution and its health impacts, motivating behavioral changes that support SDG 3 and SDG 11.
• Community Empowerment: Access to localized data empowers communities to participate in environmental decision-making and hold authorities accountable.
• Knowledge Exchange: These projects bridge the gap between scientists and the public, facilitating the co-creation of solutions for local environmental challenges.

6.0 Report on Key Challenges and Strategic Directions

Despite their potential, the widespread application of LCS for achieving the SDGs is constrained by several challenges. Addressing these issues is critical for ensuring the reliability and impact of LCS-based monitoring systems.

6.1 Enhancing Operational Efficiency and Data Quality

The lack of standardized performance metrics and cross-validation methods makes it difficult to compare different calibration models and assess their reliability for policy-making. This ambiguity hinders the ability to track progress toward SDG targets consistently.

6.2 Challenges in Mobile and Drone-Based Monitoring

Data consistency in mobile applications is compromised by variables such as sensor placement, platform speed, and rapid changes in environmental conditions. For drone-based monitoring, factors like rotor downwash and limited flight endurance restrict the scope and accuracy of data collection, limiting their utility for comprehensive urban planning (SDG 11).

6.3 Obstacles in Data Fusion and Assimilation

A major barrier to the effective integration of LCS data with models is the difficulty in quantifying measurement uncertainty accurately. Without realistic uncertainty estimates, the reliability of fused data products for informing public health advisories (SDG 3) and urban policy is diminished.

6.4 Barriers to Citizen Science and Community Engagement

The scalability and long-term impact of citizen science initiatives are often limited by a range of barriers:

1. Motivational: Lack of clear objectives or alignment with local needs can reduce long-term participant engagement.
2. Technological and Economic: Disparities in access to technology and financial resources can exclude individuals from lower-income backgrounds, undermining efforts for inclusive participation (SDG 10).
3. Societal: Citizen science projects are predominantly established in developed nations, often excluding marginalized and Indigenous communities and reinforcing existing inequalities.
4. Regulatory: Issues related to data privacy, ownership, and quality standards can hinder the acceptance of citizen-generated data in official decision-making processes, limiting the potential for collaborative governance (SDG 17).

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on low-cost air quality sensors (LCS) addresses and connects to the following Sustainable Development Goals (SDGs):

• SDG 3: Good Health and Well-being: The article’s central theme is the monitoring of air pollution, which it explicitly states has “negative impacts on human health.” By discussing methods to improve the accuracy and coverage of air quality data, the article directly relates to efforts to understand and mitigate the health risks associated with pollutants like particulate matter (PM), NO₂, and O₃.
• SDG 9: Industry, Innovation and Infrastructure: The article focuses on technological innovation through the development and application of low-cost sensors, drones, mobile monitoring platforms, and advanced data processing techniques like machine learning and data assimilation. It explores the challenges and advancements in creating reliable and resilient monitoring infrastructures (LCS networks), which aligns with enhancing scientific research and upgrading technological capabilities.
• SDG 11: Sustainable Cities and Communities: The primary context for the application of LCS in the article is urban environments. It discusses monitoring air quality “in cities,” “at local scales,” and integrating LCS into “smart city initiatives.” The goal is to provide high-resolution data to manage urban air pollution, identify hotspots, and inform policies, thereby contributing to making cities safer, more resilient, and sustainable by reducing their adverse environmental impact.
• SDG 17: Partnerships for the Goals: The article highlights the role of “citizen science and community engagement projects” in air quality monitoring. This involves multi-stakeholder partnerships between “citizens, researchers, and policy-makers.” The discussion on making data accessible and empowering communities to participate in data collection and decision-making exemplifies the collaborative approach needed to achieve sustainable development. The mention of deploying LCS in “low- and middle-income countries” also points to global partnerships for technology and capacity-building.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. SDG 3: Good Health and Well-being

   • Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination.
     
     Explanation: The article directly supports this target by focusing on improving the monitoring of air pollution, which is a leading cause of illness and death. The introduction states that “Air pollution is a global environmental challenge with negative impacts on human health.” The development of accurate LCS networks allows for better “public health systems” and “personal exposure assessments,” which are essential for identifying risks and implementing interventions to reduce pollution-related health problems.

2. SDG 9: Industry, Innovation and Infrastructure

   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending.
     
     Explanation: The article is a comprehensive review of “evolving trends” and “rapid advancements” in LCS technology. It details innovations in “drone-based and mobile monitoring,” “data fusion/assimilation,” and the use of “advanced computing technologies (Industry 4.0).” These discussions on improving sensor calibration, data accuracy, and integration with models represent a direct contribution to enhancing scientific research and upgrading technological capabilities in the environmental monitoring sector.

3. SDG 11: Sustainable Cities and Communities

   • Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
     
     Explanation: The article’s focus on using LCS to “fill in the gaps” of traditional monitoring provides cities with tools to pay “special attention to air quality.” It describes how LCS networks enable the collection of “real-time-high-resolution data” in “urban areas,” which can “inform evidence-based decision-making, policy formulation, and the development of targeted interventions to improve air quality.” This directly addresses the need to manage and reduce the environmental impact of urban air pollution.

4. SDG 17: Partnerships for the Goals

   • Target 17.17: Encourage and promote effective public, public-private and civil society partnerships, building on the experience and resourcing strategies of partnerships.
     
     Explanation: The section on “Low-cost sensors application in citizen science and community engagement projects” is a clear illustration of this target. The article states that LCS have “empowered a range of actors, including citizens, researchers, and policy-makers, to actively engage in data collection and analysis.” This describes a civil society partnership that leverages technology to achieve a common goal.
   • Target 17.18: By 2020, enhance capacity-building support to developing countries, including for least developed countries and small island developing States, to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.
     
     Explanation: The article notes that the adoption of LCS has empowered actors “even in low- and middle-income countries.” By providing an affordable alternative to expensive reference stations, LCS technology increases the availability of “high-quality, timely and reliable data” on air quality in regions that previously lacked such capacity.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. Indicator for Target 11.6

   • Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g. PM2.5 and PM10) in cities (population weighted).
     
     Explanation: The article is replete with references to the measurement of specific air pollutants that constitute this indicator. It explicitly mentions monitoring “particulate matter (PM),” including “PM₁, PM₂,₅, PM₁₀,” as well as other pollutants like “NO₂, CO, and O₃.” The entire purpose of the technologies discussed—from stationary LCS networks to mobile and drone-based sensors—is to measure the concentration of these pollutants in urban air, which is the fundamental data required for this indicator.

2. Indicator for Target 3.9

   • Implied Indicator: Availability and resolution of air pollutant concentration data (e.g., PM2.5, NO₂, O₃).
     
     Explanation: While the official indicator (3.9.1) is the “Mortality rate attributed to… ambient air pollution,” the article provides the means to measure the primary risk factor. The text implies that progress can be measured by the ability to generate “real-time-high-resolution data” and create “fine-grained air quality maps.” The increased spatial and temporal coverage of monitoring, as discussed throughout the article, serves as a direct measure of a country’s or city’s capacity to assess and manage the health risks associated with air pollution.

3. Indicator for Target 9.5

   • Implied Indicator: Development and adoption of new environmental monitoring technologies and methodologies.
     
     Explanation: The article does not provide quantitative metrics like R&D spending, but it qualitatively documents progress through its review of “evolving trends.” The adoption of “drone-based measurements,” “graph-based modeling of LCSN,” and “integration with satellite observations” are all concrete examples of technological upgrades and innovation in the field of scientific research for air quality, serving as indicators of progress toward this target.

4. Indicator for Target 17.17 & 17.18

   • Implied Indicator: Number and scale of citizen science projects and the volume of publicly accessible, real-time environmental data.
     
     Explanation: The article points to the proliferation of “citizen science projects using LCS technology… globally.” It mentions specific large-scale networks like “PurpleAir, Sensor.Community, and SmartCitizen” (Figure 4) as evidence of this trend. The growth of these networks and the amount of data they generate for public use (e.g., through “interactive maps”) can be used as a tangible indicator of successful multi-stakeholder partnerships and increased data availability.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: nature.com