Ayako Kawano is a Ph.D. student at Stanford University. Her research interests include the impact analysis of air pollution on population health and climate change in low- and middle-income countries using remote sensing data and machine learning methods. Before coming to Stanford, she worked as a Data Scientist at UN Global Pulse and as a Program Manager at the United Nations Industrial Development Organization (UNIDO).

Education & Certifications

  • Master of Public Health (MPH), University of California, Berkeley (2021)
  • Cert. in Applied Data Science, University of California, Berkeley (2021)
  • Master of Development Studies, Graduate Institute, Geneva (IHEID) (2013)

Lab Affiliations

Work Experience

  • Data Scientist, UN Global Pulse (10/1/2021 - 6/30/2022)


    New York, USA (remote)

  • Project Manager, UNIDO (4/23/2014 - 3/31/2020)


    Vienna, Austria / Tokyo, Japan

  • Management Consultant, Accenture (8/1/2006 - 8/31/2011)


    Tokyo, Japan

All Publications

  • 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


    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.

  • 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


    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