Smith KR, Pillarisetti A, Hill LD, Charron D, Delapena S, Garland C, Pennise D. 2015. Quantification of a saleable health product (aDALYs) from household cooking interventions. World Bank.
University of California, Berkeley; Berkeley Air Monitoring Group, and SNV Netherlands Development Organisation. 2015. Quantifying the Health Impacts of ACE-1 Biomass and Biogas Stoves in Cambodia.
Hong Y-C, Hicks K, Malley C, Kuylenstierna J, Emberson L, Balakrishnan K, Pillarisetti A, Sunwoo Y, et al. (2018). Air Pollution in Asia and the Pacific: Science-based solutions. United Nations Environment Programme (UNEP) , Bangkok, Thailand. pure.iiasa.ac.at/15561. peer reviewed.
HEI Household Air Pollution Working Group. 2018. Household Air Pollution and Noncommunicable Disease. Communication 18. Boston, MA: Health Effects Institute. peer reviewed.
TRAINSET is a graphical tool for labeling time series data. Labeling is typically used to record interesting points in time series data. For example, if you had temperature data from a sensor mounted to a stove, you could label points that constitute cooking events. Labels could be used as-is or as a training set for machine learning algorithms. For example, TRAINSET could be used to build a training set for an algorithm that detects cooking events in temperature time series data.
Access WHO HOMES Model.
The WHO HOMES model is an online implementation of a single compartment boxmodel appropriate for estimating PM or CO concentrations resulting from the combustion of solid fuels in homes. It contains a number of easy to manipulate parameters, like air changes per hour, cooking time, etc, that are used to recreate distributions from which Monte Carlo analyses can be performed. It can estimate exposures using a number of methods.
Access WHO Performance Targets Model.
The WHO PT model is an online implementation of a single compartment boxmodel appropriate for estimating PM or CO concentrations resulting from the combustion of solid fuels in homes. It contains a number of easy to manipulate parameters, like air changes per hour, cooking time, etc, that are used to recreate distributions from which Monte Carlo analyses can be performed.
HAPIT estimates health changes due to interventions designed to lower exposures to household air pollution (HAP) of household members currently using unclean fuels (wood, dung, coal, kerosene, and others). These interventions could be due to cleaner burning stoves, cleaner fuels, other ventilation changes, motivating changes in behavior, etc. HAPIT currently uses background disease rates and relationships between exposure to PM2.5 and health outcomes described as part of the Institute for Health Metrics and Evaluation’s 2013 Global Burden of Disease and Comparative Risk Assessment efforts.
Pillarisetti A*, Ma R, Buyan M, Nanzad B, Argo Y, Yang X, Smith KR. 2019. Advanced household heat pumps for air pollution control: A pilot field study in Ulaanbaatar, the coldest capital city in the world. In press Environmental Research. doi.org/10.1016/j.envres.2019.03.019