Thousands of people are injured every year from explosive remnants of war which include unexploded ordnance (UXO) and abandoned ordnance. UXO has negative long-term impacts on livelihoods and ecosystems in contaminated areas. Exact locations of remaining UXO are often unknown as survey and clearance activities can be dangerous, expensive and time-consuming. In Vietnam, Lao PDR and Cambodia, about 20% of the land remains contaminated by UXO from the Vietnam War. Recently declassified historical KH-9 satellite imagery, taken during and immediately after the Vietnam War, now provides an opportunity to map this remaining contamination. KH-9 imagery was acquired and orthorectified for two study areas in Southeast Asia. Bomb craters were manually labeled in a subset of the imagery to train convolutional neural networks (CNNs) for automated crater detection. The CNNs achieved a F1-Score of 0.61 and identified more than 500,000 bomb craters across the two study areas. The detected craters provided more precise information on the impact locations of bombs than target locations available from declassified U.S. bombing records. This could allow for a more precise localization of suspected hazardous areas during non-technical surveys as well as a more fine-grained determination of residual risk of UXO. The method is directly transferable to other areas in Southeast Asia and is cost-effective due to the low cost of the KH-9 imagery and the use of open-source software. The results also show the potential of integrating crater detection into data-driven decision making in mine action across more recent conflicts.

The Indo-Gangetic Plain (IGP) is one of the dominant sources of air pollution worldwide. During winter, the variations in planetary boundary layer (PBL) height, driven by a strong radiative thermal inversion, affect the regional air pollution dispersion. To date, measurements of aerosol-water vapour interactions, especially cloud condensation nuclei (CCN) activity, are limited in the Indian sub-continent, causing large uncertainties in the radiative forcing estimates of aerosol-cloud interactions. We present the results of a one-month field campaign (February-March 2018) in the megacity, Delhi, a significant polluter in the IGP. We measured the composition of fine particulate matter (PM1) and size-resolved CCN properties over a wide range of water vapour supersaturations. The analysis includes PBL modelling, backward trajectories, and fire spots to elucidate the influence of PBL and air mass origins on the aerosols. The aerosol properties depended strongly on the PBL height, and a simple power-law fit could parameterize the observed correlations of PM1 mass, aerosol particle number, and CCN number with PBL height, indicating PBL induced changes in aerosol accumulation. The low inorganic mass fractions, low aerosol hygroscopicity and high externally mixed weakly CCN-active particles under low PBL height (<100 m) indicated the influence of the PBL on aerosol aging processes. In contrast, aerosol properties did not depend strongly on air mass origins or wind direction, implying that the observed aerosol and CCN are from local emissions. An error function could parameterize the relationship between CCN number and supersaturation throughout the campaign.