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Air pollution due to haphazard industrialization has become a major concern in developing countries. Yet, enforcement of related norms remains problematic because violators cannot easily be pinpointed among closely situated industrial units. Accordingly, it has become imperative to equip regulatory authorities with an economical yet accurate tool that quickly locates emission sources and estimates emission rates. Against this backdrop, we propose RESILIENT, a method for Robust Estimation of Source Information from LImited field measuremENTs, which exhibits significant statistical robustness and accuracy even when the data are collected using a low-cost error-prone sensor. In our field experiment, where ground truth was unavailable, the sources estimated to be inactive based on the complete set of measurements were found inactive (up to three decimal places of accuracy) at least 72% of the time even when estimated using just 54% of random measurements. In that setting, rate estimates of active sources were also found to be statistically robust. For direct validation of RESILIENT, we considered a separate public dataset involving 10 tracer experiments, and obtained a significant correlation coefficient of 0.89 between estimated and recorded emission rates, and that of 0.99 between predicted and measured concentration levels at sensor locations.

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