Innovative Monitoring of Traffic-Related Air Pollution

TRAP poses significant health risks, impacting mortality and morbidity across multi-causes diseases. Short-term exposure is associated with cardiovascular dysfunction, respiratory disorders, and adverse birth outcomes, while long-term exposure increases the risks of cardiovascular disease, lung cancer, neurodevelopmental disorders, and metabolic diseases.

Link with cardiovascular conditions

Short-term TRAP exposure has been linked to an array of detrimental effects on health, particularly affecting the cardiovascular system, such as elevated blood pressure, increased resting heart rate, and reduced heart rate variability.^{[1] [2]} In particular, short-term exposure to black carbon and carbon monoxide are associated with increased all-cause, cardiovascular, and respiratory mortality, while long-term effects include ischemic heart disease and cancer, especially lung cancer. ^{[2] [3]}

Link with respiratory and neurodegenerative conditions

Strong evidence links long-term TRAP exposure to respiratory diseases and asthma in children and adults. It has been associated with increased lower respiratory diseases in children and exacerbates chronic obstructive pulmonary conditions in older persons. Multiple TRAPs (PM_2.5, black carbon) have also been implicated in the development of neurodegenerative conditions, including dementia and Parkinson’s diseases.

Fixed monitoring station. Installation costs typically range from $50,000 to $250,000 per station, with annual maintenance, calibration, and personnel expenses adding tens of thousands of U.S. dollars. As a result, the economic burden and technical complexity of high-grade stations still limit their deployment density, particularly in low- and middle-income countries.

Mobile monitoring. Air quality sensors on vehicles, drones, or handheld devices are deployed. Depending on the type and specifications of the monitoring equipment, unit costs typically range from several hundred to several thousand U.S. dollars. This estimate excludes additional expenditures associated with operation, maintenance, and data management infrastructure. The cost of equipping various mobile platforms, such as vehicles or drones, with high-precision sensors and managing their deployment can vary considerably.

Remote sensing and satellite-based monitoring. Operational costs vary depending on whether proprietary or open-access data are used. While data acquisition is often covered by space agencies, processing and validation require specialized software, expertise, and computational resources. Thus, satellite monitoring is cost-effective for large-scale assessments but may not fully replace ground-based systems, particularly for real-time, high-accuracy data. However, limitations in temporal resolution and accuracy, especially under cloudy conditions or in urban canyons, affect reliability (WHO, 2023).

Biological monitoring. Air pollution levels are assessed by utilizing the absorption or response of living organisms to pollutants. It offers a cost-effective and sustainable approach and is particularly suitable for long-term exposure assessment in areas where electronic monitoring infrastructure is lacking. Although the setup and maintenance costs are minimal, the method's limitations lie in its lower precision and susceptibility to confounding environmental variables such as weather, seasonality, and species variability. Additionally, biological monitoring is unable to provide real-time data, rendering it less appropriate for dynamic or near-real-time applications, particularly in high-traffic urban corridors.

Low-cost sensors. Requiring only a few hundred US dollars to set up, these are far more accessible than traditional monitors. However, operational costs, including maintenance, calibration, and data management, can vary significantly and may range into the thousands of US dollars. While they offer certain cost-effectiveness, these sensors generally provide lower accuracy, are sensitive to environmental factors like humidity and temperature, and require frequent calibration. As a result, they are not yet suitable for regulatory use without validation against reference-grade instruments.

A new method integrates deep learning with traffic camera data in air pollution monitoring. Traffic camera images are processed using a deep learning model to estimate air pollution concentrations—including particulate matter, wildfire smoke, and potentially other pollutants. The model has been validated and calibrated for accuracy.

Figure 1: Visual Representation of Pollution Assessment Using Traffic Camera

Source: Prepared by the Author team based on Liu et al. 2024.

While cost information is currently unavailable due to the technology not yet being commercialized, implementation is expected to be very low-cost, as it relies solely on publicly available traffic camera feeds and cloud-based data processing platforms. The system leverages existing traffic surveillance infrastructure, requiring no additional hardware and minimal operational cost, making it a highly scalable and cost-efficient monitoring solution for both urban and rural settings.

The novel approach has been piloted in Melbourne, Australia to predict PM_2.5 concentrations using traffic camera images combined with deep learning techniques, highlighting the potential of leveraging existing traffic camera for real-time air quality monitoring. Results indicate that air pollution estimates derived from traffic camera images closely match the measurements from standard air quality monitoring stations. Since traffic cameras are widely deployed and readily accessible, this solution can be further explored as a cost-effective and scalable solution for real-time pollution assessment for low- and middle-income countries.

Traffic-related air pollution (TRAP) carries significant health risks, impacting mortality and morbidity across multi-causes diseases. Despite limited data in certain health outcomes, the overall body of research underscores the substantial public health burden of TRAP. Thus, it is imperative to explore underlying mechanisms and develop effective mitigation strategies to reduce TRAP-related health risks. To address environmental and health policies, a robust scientific foundation is needed for innovative approaches to enhance TRAP's monitoring and management and to provide potential low-cost alternatives for developing countries.

Note: The findings in this article were presented by Professor Yuming Guo during the event “Health and Transport: Road Safety and Respiratory Health from Reduced Transport Emissions” held on 9 December 2024 at the Asian Development Bank.

^[1] X. Zou et al. 2022. Maternal Exposure to Traffic-Related Ambient Particles and Risk of Gestational Diabetes Mellitus with Isolated Fasting Hyperglycaemia: A Retrospective Cohort Study in Beijing, [People’s Republic of] China. International Journal of Hygiene and Environmental Health. 242: 113973.

^[2] H. Boogaard et al. 2022. Long-Term Exposure to Traffic-Related Air Pollution and Selected Health Outcomes: A Systematic Review and Meta-Analysis. Environment International. 164: 107262.

^[3] X. Zhu et al. 2023. Short and Long-Term Association of Exposure to Ambient Black Carbon with All-Cause and Cause-Specific Mortality: A Systematic Review and Meta-Analysis. Environmental Pollution. 324: 121086.

Y. Liu et al. 2024. Applying Traffic Camera and Deep Learning-Based Image Analysis to Predict PM_2.5 Concentrations. Science of the Total Environment. 912.