Ayako Kawano

All Publications

• The Influence of Wildfire Smoke on Ambient PM2.5 Chemical Species Concentrations in the Contiguous US. Environmental science & technology Krasovich Southworth, E., Qiu, M., Gould, C. F., Kawano, A., Wen, J., Heft-Neal, S., Kilpatrick Voss, K., Lopez, A., Fendorf, S., Burney, J. A., Burke, M. 2025

  Abstract

  Wildfires significantly contribute to ambient air pollution, yet our understanding of how wildfire smoke influences specific chemicals and their resulting concentration in smoke remains incomplete. We combine 15 years of daily species-specific PM2.5 concentrations from 700 air pollution monitors with satellite-derived ambient wildfire smoke PM2.5, and use a panel regression to estimate wildfire smoke's contribution to the concentrations of 27 different chemical species in PM2.5. Wildfire smoke drives detectable increases in the concentration of 25 out of the 27 species with the largest increases observed for organic carbon, elemental carbon, and potassium. We find that smoke originating from wildfires that burned structures had higher concentrations of copper, lead, zinc, and nickel relative to smoke from fires that did not burn structures. Wildfire smoke is responsible for an increasing share of ambient concentrations of multiple species, some of which are particularly harmful to health. Using a risk assessment approach, we find that wildfire-induced enhancement of carcinogenic species concentrations could cause increases in population cancer risk, but these increases are very small relative to other environmental risks. We demonstrate how combining ground-monitored and satellite-derived data can be used to measure wildfire smoke's influence on chemical concentrations and estimate population exposures at large scales.

  View details for DOI 10.1021/acs.est.4c09011

  View details for PubMedID 39899563

• 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

• Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality Kawano, A., Kelp, M., Qiu, M., Singh, K., Chaturvedi, E., Azevedo, I., Burke, M. EarthArXiv. 2024

  Abstract

  Poor ambient air quality represents a substantial threat to public health globally. However, accurate and comprehensive measurement of air quality remains challenging in many parts of the world, including in populous countries like India, where ground monitors are scarce yet exposure and health burdens are expected to be high. This lack of precise measurement impedes understanding of how pollution exposure changes over time and varies across different populations, and inhibits monitoring of progress of interventions to improve air quality. Here we develop open-source daily fine particulate matter (PM2.5) datasets at a 10 km resolution for India from 2005 to 2023, using a region-specific two-stage machine learning model carefully validated on held-out monitor data that it was not trained on. Our model demonstrates robust out-of-sample performance, substantially outperforming existing publicly-available monthly PM2.5 datasets. We use model output to analyze long-term air quality trends, finding that PM2.5 increased across most of the country until around 2016 and then began to decline thereafter, partially driven by favorable meteorology in southern India. Importantly, recent PM2.5 reductions were substantially larger in wealthier areas, albeit from a higher initial level, but we find no evidence that the recently-adopted National Clean Air Program has improved air quality in targeted urban areas to date. Our results highlight the urgency of air quality control policies that effectively target both lower and higher socioeconomic groups. To further enhance air quality monitoring across populations in India and other countries, we use model output to propose locations where new ground monitors should be installed in India, and examine the adaptability of our method to other settings with scarce ground monitoring data.

  DOI
• Association between satellite-detected tropospheric nitrogen dioxide and acute respiratory infections in children under age five in Senegal: spatio-temporal analysis. BMC public health Kawano, A., Kim, Y., Meas, M., Sokal-Gutierrez, K. 2022; 22 (1): 178

  Abstract

  There is growing evidence to suggest that exposure to a high concentration of nitrogen dioxide (NO2) can lead to a higher incidence of Acute Respiratory Infections (ARIs) in children; however, such an association remains understudied in Sub-Saharan Africa due to the limited availability of exposure data. This study explored this association by using the satellite-detected tropospheric NO2 concentrations measured by Sentinel-5 Precursor and ARI symptoms in children under age five collected in the Demographic and Health Survey (DHS) in Senegal.We matched the daily tropospheric NO2 exposure with the individual ARI symptoms according to the DHS survey clusters spatially and temporally and conducted a logistic regression analysis to estimate the association of exposure to NO2 with ARI symptoms in two preceding weeks.We observed a positive association between exposure to continuous levels of NO2 and ARI symptoms after adjusting for confounders (OR 1.27 per 10 mol/m2, 95% CI: 1.06 - 1.52). When the association was further examined by quartile exposure categories, the 4th quartile category was positively associated with symptoms of ARI after adjusting for confounders (OR 1.71, 95% CI: 1.08-2.69). This suggests that exposure to certain high levels of NO2 is associated with the increased risk of children having symptoms of ARI in Senegal.This study highlights the need for increased research on the effects of ambient NO2 exposure in Africa as well as the need for more robust, ground-based air monitoring in the region. For a country like Senegal, where more than 90% of the population lives in areas that do not meet the national air quality standards, it is urgently required to implement air pollution prevention efforts to protect children from the health hazards of air pollution.

  View details for DOI 10.1186/s12889-022-12577-3

  View details for PubMedID 35081933

  View details for PubMedCentralID PMC8790943