Key Areas of Air Pollution Research

There are many efforts in air pollution research. I guess that most efforts focus on measuring and identifying problems, while fewer efforts try to solve key problems and explain fundamental knowledge. These guesses are probably wrong and influenced by my familiarities, the difficulty of topics, publishing incentives, funding difficulties, and are not reflections of work undertaken. Even so, trying to solve these key problems would likely be impactful research. Criticisms are welcome.

Measuring problems (useful and common)

Take a problem and measure it, often using case studies. For example, what are the concentrations of pollutant X in some place and time? What are the health impacts of a change in exposure X on a disease outcome Y? These are useful when they are for a new location, time, pollutant, or outcome. They can be measured with increased accuracy, increased precision, and reduced uncertainty. These observations can be useful for refuting theories. Some issues are that they are repetitive, often overly simplified, quickly out of date, lack generalisability, highly uncertain, and are based on large assumptions (e.g., that associations can be causally extrapolated).

Identifying problems (useful and common)

Find a new problem. For example, what is the association between an exposure X and a disease outcome Y? These epidemiological studies are useful to discover new effects. Recent progress in causal inference has helped address confounding. Issues remain with the use of induction to extrapolate repeated observations into a theory, with a likelihood proportional to the number of observations (e.g., meta analyses). It is common to continually search for more and more associations. Consider smoking, is the research priority to find more associated diseases?

Fundamental knowledge (key and rare)

Create a good explanation. This maybe by removing something parochial? For example, remove the 2.5 in PM2.5 without replacing it with something else (e.g., 1.0, 0.1, etc.). These explanations might not use reductionism. For example, an explanation of how exposure causes disease might not be via its constituent chemicals, parts, and sizes. Toxicology has made progress identifying some of these mechanisms.

Solving problems (key and rare)

Resolve a current problem by correcting errors. This leads to new, better problems. For example, how can model parameterisations be removed to solve the numerical equations analytically? Maybe by using computational creativity, such as non-CPU or imprecise supercomputers? How can impacts be reduced without reducing the ability to solve other/future problems (especially for the most vulnerable and least resilient)? Maybe by technologies, externalities, knowledge, wealth, or game theory? How can people in poverty avoid using solid fuels? Maybe through innovative clean fuel programmes?

Ideas will likely need to embrace solutions, innovation, and progress and reject problem avoidance, prophesies of future knowledge, and parochial proposals.