Qiao Zhu, Yang Liu, and Sina Hasheminassab. Long-Term Source Apportionment of PM2. 5 across the contiguous United States (2000-2019) Using a Multilinear Engine Model. Journal of Hazardous Materials (2024): 134550.

Read Online at https://www.sciencedirect.com/science/article/pii/S0304389424011294

Identifying PM2.5 sources is crucial for effective air quality management and public health. This research used the Multilinear Engine (ME-2) model to analyze PM2.5 from 515 EPA Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE) sites across the U.S. from 2000 to 2019. The U.S. was divided into nine regions for detailed analysis. A total of seven source types (tracers) were resolved across the country: (1) Soil/Dust (Si, Al, Ca and Fe); (2) Vehicle emissions (EC, OC, Cu and Zn); (3) Biomass/wood burning (K); (4) Heavy oil/coal combustion (Ni, V, Cl and As); (5) Secondary sulfate (SO42-); (6) Secondary nitrate (NO3-) and (7) Sea salt (Mg, Na, Cl and SO42-). Furthermore, we extracted and calculated secondary organic aerosols (SOA) based on the secondary sulfate and nitrate factors. Notably, significant reductions in secondary sulfate, nitrate, and heavy oil/coal combustion emissions reflect recent cuts in fossil-fueled power sector emissions. A decline in SOA suggests effective mitigation of their formation conditions or precursors. Despite these improvements, vehicle emissions and biomass burning show no significant decrease, highlighting the need for focused control on these persistent pollution sources for future air quality management.

Qingyang Zhu, Danlu Zhang, Wenhao Wang, Rohan Richard D’Souza, Haisu Zhang, Binyu Yang, Kyle Steenland, Noah Scovronick, Stefanie Ebelt, Howard H Chang, and Yang Liu. Wildfires are associated with increased emergency department visits for anxiety disorders in the western United States. Nature Mental Health  2 (2024), 379–387.

Read the full paper online here.

Read the press release by Emory University here.

Check out the story by American Psychiatric Association here.

As wildfires increasingly impact the global economy and public health, understanding their effects is crucial. Particularly, the relationship between wildfires and anxiety disorders remains unclear. In this study, we explore this association by analyzing 1,897,865 emergency department visits for anxiety disorders in the western United States. We examined records from 2007 to 2018, using a case-crossover design and conditional logistic regression to assess the impact of wildfire-related exposures on these visits. Here we show that exposure to wildfire smoke PM_2.5 is positively linked with emergency department visits for anxiety disorders. This effect is more pronounced in women and girls and in older adults, highlighting their vulnerability. Notably, major smoke events (smoke PM_2.5 contributed ≥75% of the total PM_2.5) significantly amplify this risk. These findings underscore the psychological impacts of wildfires and their smoke, suggesting a need for targeted disaster risk reduction and climate risk management strategies, especially for vulnerable groups such as older adults and women. Our results call for increased climate awareness and tailored risk communication to mitigate these emerging health challenges.

Jin, Zhihao, Qiang Pu, Nathan Janechek, Huanxin Zhang, Jun Wang, Howard Chang, and Yang Liu. A MAIA-like modeling framework to estimate PM2.5 mass and speciation concentrations with uncertainty. Remote Sensing of Environment 303 (2024): 113995. 

Read Online at https://authors.elsevier.com/a/1iSUj7qzT3DS-

Ambient fine particulate matter (PM2.5) is strongly associated with various adverse health outcomes. However, the lack of extensive PM2.5 measurements, and especially of its components, hinders the assessment of negative health effects caused by PM2.5 in many parts of the world. To address this issue, a new satellite instrument, the Multi-angle Imager for Aerosols (MAIA), with improved design for providing aerosol optical depth (AOD) of high quality, will be helpful in determining concentrations of total and speciated PM2.5. According to the retrieval algorithm of MAIA particulate matter (PM) products, level 2 (L2) PM products are generated based on MAIA AOD on days of observation. Bias-corrected chemical transport model (CTM) outputs are then merged with the L2 PM products to fill their gaps using a Bayesian Model Averaging (BMA) ensemble framework. This process creates the MAIA Level 4 (L4) gap-filled PM products. In this study, we aim to implement the MAIA framework and validate its feasibility after the launch of the MAIA satellite instrument. We used both Bayesian hierarchical model (BHM) and a Bayesian additive regression tree (BART) to predict L2 and CTM-based daily 1 × 1 km2 PM2.5 mass and speciation concentrations, along with prediction uncertainties, over the MAIA Primary Target Area in the Northeastern US in 2018. We then employed the BMA ensemble model to combine the L2 and CTM-based PM2.5 mass predictions to fill gaps in L2 PM2.5 mass and produce Level 4 (L4) gap-filled PM2.5 mass. Our cross-validation experiments showed that both the BHM (R2 ranging from 0.60 to 0.82) and BART (R2 ranging from 0.59 to 0.79) models performed well in predicting CTM-based PM2.5 speciation, with better results for sulfate, organic carbon, and elemental carbon. At the stage of L4 PM2.5 mass predictions, both BHM-based and BART-based BMA ensemble models demonstrated improved performance with their traditional R2 of 0.81 and 0.73, surpassing the input L2 and CTM-based PM2.5 mass. Additionally, our models showed excellent prediction uncertainty control with the coverage rates of 95% posterior prediction confidence interval associated of concentration estimates to be 95% for BHM and 75% for BART across PM2.5 mass and speciation. Results from the proposed modeling techniques contribute to a deeper understanding of the health effects of PM2.5 for future epidemiological studies and provide insights into the MAIA mission for producing improved PM products for health research.

Figure. Framework of the MAIA-like Level 4 gap-filled PM products