NPR, reporting on a recent EPA Meeting:
At a public meeting Thursday that ran nearly two hours long, multiple members of that committee, including Chair Tony Cox and Steven Packham of the Utah Division of Air Quality, said they do not agree that breathing air polluted with soot can lead to an early death.
“[Committee] members have varying opinions on the adequacy of the evidence supporting the EPA’s conclusion that there is a causal relationship between [particulate matter] exposure and mortality,” said Cox, reading from the committee’s draft recommendations before explaining that he is “actually appalled” at the lack of scientific evidence connecting particulate pollution to premature death.
A quarter of a century of research has shown that breathing in fine airborne particles emitted by cars, power plants and other sources shortens people’s lifespans. But that scientific consensus is now under attack from a top advisor to the US Environmental Protection Agency (EPA), just as the agency is rushing to revise the national air-quality standard for such pollution before the end of President Donald Trump’s first term. Scientists fear that the result could be weaker rules on air pollution that are bad for public health — and based on politics, not science.
The case has been made — repeatedly — that the health and economic benefits of the Clean Air Act and subsequent regulatory processes are clear. This is summarized succinctly in the figure below, which shows energy consumption, vehicle miles traveled, GDP, and total emissions of EPA criteria pollutants.
Figure from The Lancet Commission on Pollution and Health
Let’s put aside the economic argument and focus on the principles that undergird the Clean Air Act - protection of public health, with standards set to protect the most vulnerable. This critical prerogative is undermined by these (and other) recent efforts to roll back regulations that have clear and demonstrable health benefits. This is yet another example of the Trump administration’s abnegation of responsibility to the health and welfare of the US population.
For more information, see Gretchen T. Goldman and Francesca Dominici’s discussion in Science and their claim-by-claim evidence base. See also a nice summary of the issue at NRDC and a letter from Professor John Samet to the EPA that comprehensively outlines issues with changes to the evidence review process for the National Ambient Air Quality Standards.
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