Grantee Research Project Results
2023 Progress Report: Partnering for Resilient Opportunities To Eliminate Cumulative Toxic (PROTECT) Health Effects from Wildfire PM2.5 in Environmental Justice Communities
EPA Grant Number: R840481Title: Partnering for Resilient Opportunities To Eliminate Cumulative Toxic (PROTECT) Health Effects from Wildfire PM2.5 in Environmental Justice Communities
Investigators: Thakur, Neeta , Balmes, John R. , de la Rosa, Rosemarie Michelle , Holm, Stephanie , Chow, Fontini , Noth, Betsey M , Chan, Wanyu , Kirchstetter, Thomas W , Basu, Rupa
Institution: University of California - San Fransisco , California Office of Environmental Health Hazard Assessment , Lawrence Berkeley National Laboratory , University of California - Berkeley
EPA Project Officer: Hahn, Intaek
Project Period: December 1, 2022 through November 30, 2025
Project Period Covered by this Report: December 1, 2022 through November 30,2023
Project Amount: $1,330,536
RFA: Cumulative Health Impacts at the Intersection of Climate Change, Environmental Justice, and Vulnerable Populations/Lifestages: Community-Based Research for Solutions (2021) RFA Text | Recipients Lists
Research Category: Environmental Justice , Human Health
Objective:
This proposal seeks to 1) estimate the health effects of sub-daily exposure to wildfire-specific PM2.5 in California, including across social vulnerability factors, with particular focus on effects within EJ communities; 2) understand community recovery from short-term health effects following exposure; 3) understand indoor infiltration of wildfire smoke and the mitigating effect of housing quality and behaviors on health effects; and, 4) identify acceptable community-relevant mitigation interventions.
Progress Summary:
We have conducted a series of test cases to inform the overall analysis. For objective 1, to ensure our data assimilation methods are robust for the entire time period, 2016–2022, we have pilot tested methods using the November 2018 (which includes the Northern CA Camp Fire) HRRR-Smoke archived forecasts and observational data from the EPA AQS network. During these tests, we have varied the length scale factors to optimize the assimilated results to improve agreement with ground observations. The figure below shows uncorrected HRRR-Smoke wildfire-specific PM2.5 estimates (Figure 1, panel A), the corrected estimates with the addition of EPA air quality measures
(Figure 1, panel B), and, lastly, the magnitude and direction of the PM2.5 concentration correction (Figure 1, panel C). Next steps are to use the PurpleAir data in the assimilation process and test additional time periods.
Figure 1. Corrected HRRR-Smoke model with EPA air quality system data and estimated change in estimates.
Additionally, we have procured PurpleAir data from outdoor and indoor sensors operating in CA from 2016–2023. A thorough QAQC procedure has been conducted to ensure accurate measurements. First, standard measurement corrections were conducted using the EPA PurpleAir correction for outdoor data and using the pm_atm correction for indoor (Barkjohn et al. 2020 and 2022). Then, indoor and outdoor sensors were separated based on user-assigned location. However, due to potential error, outdoor data was evaluated for sensors with potential mislabels (i.e., an outdoor sensor that was placed indoors) by comparing sensor measurements to nearby PurpleAir and regulatory PM2.5 measurements. Outliers were removed if their correlations to nearby outdoor data were low. This has produced a robust data set that will be used for data assimilation in Objective 1 and the infiltration factor calculation in Objective 2.
Under Objective 2, we used Purple Air sensor data collected from an indoor air quality study of high density multiunit housing. Our analytical sample included 1501 sensor-days that included dates spanning August 17, 2022 to December 14, 2022 across 17 PA-II monitors and their matched outdoor monitors. Indoor concentration differed across the three SRO hotels in our study, but all three hotels followed similar diurnal trends in indoor concentration. To calculate infiltration factors (Finf), we identified and removed spikes in indoor PM2.5 measurements incongruous to outdoor readings to separate episodic indoor-generated sources of PM2.5 and baseline indoor PM2.5 (Figure 2, Panel A). This removal of episodic indoor-generated PM2.5 events was done by a Joint Baseline Correction and Denoising (JBCD) algorithm (Figure 2, Panel B). We then performed modified random component supercomposition (modified RCS) (Lunderberg et al., 2023) on the denoised indoor concentration PM2.5 (Figure 3). We are now expanding this approach to all California PurpleAir sensor data collected spanning 2016-2023 years.
Figure 2. Output from peak identification (left) and baseline algorithms (right) for an individual sensor observed in pilot study.
Figure 3. Estimated infiltration factors (indicated with points) and 95% confidence intervals (indicated with error bars) from pilot study. Dotted horizontal line indicates average infiltration factor across all units.
Under objective 4, using ED visit and hospitalization data from Benioff Children’s Hospital Oakland, we found that wildfire smoke exposure was associated with increased risk of asthma-related hospital and ED visits on lag days 0, 3, 4, and 5 (Relative Risk (RR): 1.06, 1.02, 1.05, 1.03;
95% CI: (1.01, 1.11), (1.00, 1.04), (1.02, 1.08), (1.00, 1.06), respectively. The risk of smoke resulted in a peak cumulative RR of 1.15 (95% CI: 1.09, 1.21) for lag-days 0–5 (Figure 4). These results suggest an immediate increase in risk to the pediatric population during a smoke event and a delayed effect with highest risk occurring on the 4th day post-exposure.
Figure 4. Relative risk of asthma-related emergency room vist or hospitilization for each 10ug/m3 increase in wildfire PM2.5 exposure among children seeking care and Benioff Children's Hospital Oakland.
Future Activities:
During the next performance period we will continue progress on all objectives. Specifically, under objective 1, we will integrate observations from the outdoor PurpleAir network of sensors into the data assimilation of HRRR-Smoke forecasts and EPA AQS observational data. We will assimilate across the entire time period 2016–2022 to produce a 3km x 3km hourly gridded model of PM2.5 concentrations. These results will be compared and validated against the EPA IMPROVE network to quantify accuracy and precision. For objective 2, we will process newly procured PurpleAir and classify monitors with indoor/outdoor placements as a function of diurnal patterns and variability. These classified monitors will be linked to buildings found in the National Structure Inventory to determine building type. Monitors identified to be located in residential buildings will be used in infiltration factor estimations with modified RCS. Estimates will be aggregated to the census tract and ZIP code level. For the health analyses proposed under objective 3, we anticipate receipt of the state-wide Health Care Access Information (HCAI) data on ED visits and hospitalizations for respiratory, cardiovascular, and neurovascular conditions spanning the study period in late Summer 2024. This data will undergo a series of data checks and cleaning as proposed in our QAPP. We will then conduct a series of pilots, modeling health outcomes using single wildfire smoke events (e.g., 2018 Camp Fire) or a single region using multiple years of data. This will inform the final model selection, which will be done in year 3 of the grant period. For objective 4, we will continue our key informant interviews to understand barriers to accessing and implementing weatherization services in San Francisco. We will also form our community stakeholder groups in Richmond and Fresno, CA. Over the second half of the performance period, we anticipate distributing our community survey in these communities.
Journal Articles:
No journal articles submitted with this report: View all 1 publications for this projectSupplemental Keywords:
wildfire smoke, environmental justice, health effects, community engagement, cumulative risk, infiltrationThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.