Air Pollution, The Research – Part 6

Takeaways

These multipliers are sometimes confusing and contradictory. It seems like focusing on Africa is better due to its population and emissions growth, yet policy advocacy is less feasible. Despite this complexity, we can observe five key takeaways:

  1. Prioritize urban areas, and areas where pollution is most deadly.
    • This suggests cities, particularly in South Asia
  2. Focus where populations are growing and air pollution is worsening.
    • This points toward Southeast Asia
  3. Prioritize the largest, most affectable sources of PM2.5.
    • This means tackling the largest sources first
  4. Focus on the most neglected regions and sources, and where interventions are most politically feasible.
    • This suggests a focus on Sub-Saharan Africa, where there is relatively less climate funding
    • This means prioritizing areas where civil society influence is strong, and opposition is weak
  5. Prioritize policy, especially at the stage most appropriate for a country’s development.
    • This suggests targeting government regulations, via better monitoring and advocacy

Conclusion

In summary, air pollution remains a neglected issue, relative to its importance. South East Asia, the Middle East, Sub-Saharan Africa, and South America are particularly neglected regions – yet these areas are set to see their emissions grow.

Donors and foundations looking to maximize impact should focus their attention on policy change within these regions, via better air quality monitoring and advocacy.

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Appendix: Health burden of PM2.5

There are two challenges to estimating the health impact of PM2.5:

  1. The shape of the association between PM2.5 and mortality.
  2. Drawing causal inference from this association.

We conclude that the exposure-response curve described by Vodonos et al most accurately describes the shape of the PM2.5-mortality association and that this association is causal.

1. Exposure-response function shape

The exposure-response function plots levels of exposure to PM2.5 against different levels of relative risk (RR) of contracting a disease. Relative risk levels can be converted into population-attributable fractions (PAFs), which can attribute deaths and DALYs from various diseases to air pollution. Estimating the exposure-response function is difficult, particularly for high levels of PM2.5, where fewer studies have been conducted. It is unlikely that such a function is linear since this would lead to implausibly high relative risk levels (RR >>2) at high levels of PM2.5.

To fill the gaps in data at high ambient PM2.5 concentrations, Burnett et al use an integrated exposure model (IER), which combines various PM2.5 estimates from ambient pollution, indoor pollution, smoking, and other sources, to give a fuller range of estimates.

Using the corresponding relative risk levels, they construct population-attributable fractions (and hence death and DALY estimates) for all countries. This underpins the data used by the WHO. The IER is considered a superior fit to seven other functions, as explained in the paper.

However, more recent studies in high PM2.5 countries have been conducted. Subsequently, Vodonos et al published a meta-analysis using over 135 estimates of the PM2.5-mortality relationship, from over 50 studies. They provide both a parametric and non-parametric (penalized spline) model. For simplicity, the parametric model was used.

Table 5 shows Vodons et al’s meta-regression from the parametric model:

From this we can generate the following function:

We can interpret the following from this: At 10 μg/m3, a 1μg/m3 increase in PM2.5 leads to a 1.29% increase in mortality, as shown in the diagram below

Meta-regression analysis of long-term PM2.5 exposure and percent change in mortality.

This function tells us the mortality rate from a 1 unit change in PM2.5. To convert this to the mortality rate for any change in PM2.5, we use the function

This is a logarithmic relationship between PM2.5 and mortality at low pollution levels but tends to a linear relationship for very high PM2.5 levels – as illustrated below

Change in mortality rate from a given change in PM2.5.

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