Makoto Kelp

All Publications

• Managing Smoke Risk from Wildland Fires: Northern California as a Case Study. Environmental science & technology Chung, K. E., Liu, T., Kelp, M. M., Vohra, K., Skelly, D., Carroll, M. C., Schwartz, J., Mickley, L. J. 2025

  Abstract

  Smoke fine particulate matter (PM2.5) from increasing wildfires in the western United States threatens public health. While land managers often prioritize reducing wildfire risk in the wildland-urban interface, the impact on regional air quality from mitigating wildfire spread has been less explored. We developed a framework to quantify wildfire contributions to smoke exposure and assess targeted land management strategies. This data-driven approach integrates fire emissions and smoke transport to generate a smoke risk index at 0.25° × 0.25° resolution. We deploy the smoke risk index in an online tool, enabling stakeholders to analyze smoke risk under various scenarios of burned area, fuel consumption, and land management. Using Northern California as a case study, we estimate that in 2020, targeted land management in the 15 highest risk areas (∼3.5% of the total) could have reduced smoke exposure by 17.6%. However, most prescribed burns conducted from 2017 to 2020 did not overlap with these high-risk zones. Our framework also estimates excess deaths from smoke PM2.5 exposure, attributing ∼36,400 (95% CI: 25,400-47,200) deaths nationally to western US fires in the year following the 2020 fire season. Our adaptable tool can incorporate higher-resolution data sets and help stakeholders prioritize fuel treatment and fire suppression to mitigate smoke exposure risks.

  View details for DOI 10.1021/acs.est.5c01914

  View details for PubMedID 40586815

• Effect of Recent Prescribed Burning and Land Management on Wildfire Burn Severity and Smoke Emissions in the Western United States AGU ADVANCES Kelp, M., Burke, M., Qiu, M., Higuera-Mendieta, I., Liu, T., Diffenbaugh, N. S. 2025; 6 (3)

  View details for DOI 10.1029/2025AV001682

  View details for Web of Science ID 001517258200001

• The rising threats of wildland-urban interface fires in the era of climate change: The Los Angeles 2025 fires. Innovation (Cambridge (Mass.)) Qiu, M., Chen, D., Kelp, M., Li, J., Huang, G., Yazdi, M. D. 2025; 6 (5): 100835

  View details for DOI 10.1016/j.xinn.2025.100835

  View details for PubMedID 40432781

  View details for PubMedCentralID PMC12105517

• The rising threats of wildland-urban interface fires in the era of climate change: The Los Angeles 2025 fires INNOVATION Qiu, M., Chen, D., Kelp, M., Li, J., Huang, G., Yazdi, M. 2025; 6 (5)

  View details for DOI 10.1016/j.xinn.2025.100835

  View details for Web of Science ID 001489343500001

• Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality. Science advances Kawano, A., Kelp, M., Qiu, M., Singh, K., Chaturvedi, E., Dahiya, S., Azevedo, I., Burke, M. 2025; 11 (4): eadq1071

  Abstract

  Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations. Here, we develop open-source daily fine particulate matter (PM2.5) datasets at a 10-kilometer resolution for India from 2005 to 2023 using a two-stage machine learning model validated on held-out monitor data. Analyzing long-term air quality trends, we find that PM2.5 concentrations increased across most of the country until around 2016 and then declined partly due to favorable meteorology in southern India. Recent reductions in PM2.5 were substantially larger in wealthier areas, highlighting the urgency of air quality control policies addressing all socioeconomic communities. To advance equitable air quality monitoring, we propose additional monitor locations in India and examine the adaptability of our method to other countries with scarce monitoring data.

  View details for DOI 10.1126/sciadv.adq1071

  View details for PubMedID 39854471

• Evaluating Chemical Transport and Machine Learning Models for Wildfire Smoke PM2.5: Implications for Assessment of Health Impacts. Environmental science & technology Qiu, M., Kelp, M., Heft-Neal, S., Jin, X., Gould, C. F., Tong, D. Q., Burke, M. 2024

  Abstract

  Growing wildfire smoke represents a substantial threat to air quality and human health. However, the impact of wildfire smoke on human health remains imprecisely understood due to uncertainties in both the measurement of exposure of population to wildfire smoke and dose-response functions linking exposure to health. Here, we compare daily wildfire smoke-related surface fine particulate matter (PM2.5) concentrations estimated using three approaches, including two chemical transport models (CTMs): GEOS-Chem and the Community Multiscale Air Quality (CMAQ) and one machine learning (ML) model over the contiguous US in 2020, a historically active fire year. In the western US, compared against surface PM2.5 measurements from the US Environmental Protection Agency (EPA) and PurpleAir sensors, we find that CTMs overestimate PM2.5 concentrations during extreme smoke episodes by up to 3-5 fold, while ML estimates are largely consistent with surface measurements. However, in the eastern US, where smoke levels were much lower in 2020, CTMs show modestly better agreement with surface measurements. We develop a calibration framework that integrates CTM- and ML-based approaches to yield estimates of smoke PM2.5 concentrations that outperform individual approach. When combining the estimated smoke PM2.5 concentrations with county-level mortality rates, we find consistent effects of low-level smoke on mortality but large discrepancies in effects of high-level smoke exposure across different methods. Our research highlights the differences across estimation methods for understanding the health impacts of wildfire smoke and demonstrates the importance of bench-marking estimates with available surface measurements.

  View details for DOI 10.1021/acs.est.4c05922

  View details for PubMedID 39694472

• Intercomparison of GEOS-Chem and CAM-chem tropospheric oxidant chemistry within the Community Earth System Model version 2 (CESM2) ATMOSPHERIC CHEMISTRY AND PHYSICS Lin, H., Emmons, L. K., Lundgren, E. W., Yang, L., Feng, X., Dang, R., Zhai, S., Tang, Y., Kelp, M. M., Colombi, N. K., Eastham, S. D., Fritz, T. M., Jacob, D. J. 2024; 24 (15): 8607-8624

  View details for DOI 10.5194/acp-24-8607-2024

  View details for Web of Science ID 001282371800001