The Canadian wildfires of June 2023 led to significant smoke exposure in the Northeastern United States, prompting Penn State scientists to develop a model that combines wildfire smoke forecasts and ground-based sensor data to help public health officials plan targeted interventions in high-risk areas. The study, published in the journal Science of the Total Environment, suggests that long-distance smoke travel events, like the Canadian wildfires, could become more common. The research aims to assist officials in identifying communities most susceptible to harmful air pollution during such events and implementing appropriate interventions to mitigate negative health effects.
Analyzing data from June 6-8 and June 28-30, 2023, when smoke from Canada spread into the Northeastern United States due to weather conditions and a coastal storm, the researchers utilized ground-based sensors and deep learning techniques to refine a weather forecasting model called WRF-Chem. This model provides hourly data on fine particulate matter (PM 2.5), which can cause health problems when inhaled. The team also examined anonymized mobility data from devices like smartphones to track changes in travel patterns during smoke events, as well as conducted an environmental justice assessment to identify vulnerable communities based on environmental and demographic factors.
The refined forecasting model generated more accurate estimates of PM 2.5 levels across the study area compared to the current model, aligning closely with ground sensor measurements. They found that urban and rural communities already burdened by environmental pollution experienced higher air pollution levels during the unexpected smoke events. While the public tends to reduce mobility during smoke events, urban areas like New York City and Philadelphia showed high activity levels. The researchers suggest implementing targeted interventions in urban areas to reduce exposure to unhealthy air, while recognizing the specific needs of rural communities burdened by pollution from sources like power plants and mines.
The study highlights the importance of understanding existing vulnerabilities in rural areas to better serve these communities and protect public health. Individuals can take proactive measures to safeguard their health during the upcoming wildfire season, such as using air filters and indoor pollution monitors, enhancing insulation, working from home when possible, and wearing high-quality masks when outdoors. In Pennsylvania, the researchers recommend developing standards for organizations to respond to smoke events, such as working from home, taking a day off, or dismissing early, to protect public health. The research was supported by Penn State’s Miller Faculty Fellow Award from the College of Earth and Mineral Sciences.
In addition to lead author Manzhu Yu, other contributors from Penn State included Zhenlong Li, Shiyan Zhang, and Huan Ning, as well as Kai Zhang from the University at Albany’s School of Public Health. By combining wildfire smoke forecasting, ground-based sensor data, and deep learning techniques, the researchers hope to provide valuable insights for public health officials and communities to better prepare for and respond to smoke events and air pollution. The research emphasizes the importance of targeted interventions in both urban and rural areas to mitigate the health impacts of unexpected smoke events and protect vulnerable populations from harmful air pollution.
