Guest essay by Dr. Indur M. Goklany
Periodically we are flooded with reports of air pollution episodes in various developing countries, and claims of their staggering death toll, and consequent reductions in life spans. The Economic Times (India), for example, recently claimed:
If you are in NCR [National Capital Region, i.e. Delhi] right now, you may have a shorter lifespan
Nov 09, 2017, 05.11 PM IST
Can’t breathe
With pollution levels in NCR 40 times the World Health Organization’s safe limit, your life expectancy could be cut short…
Killing you softly
The US embassy website said levels of the fine pollutants known as PM2.5 that are most harmful to health reached 703 — well over double the threshold of 300 that authorities class as hazardous. PM2.5 are particles with a diameter of 2.5 micrometers or less and a study recently found that a 10 µg/m3 (per cubic meter of air) increase leads to a 1.03 year reduction in life expectancy…
What happens if something is done about it
In Delhi, people could live as much as nine years longer if India met WHO standards, and six years longer if it met its own standards. Similarly, people in other metros like Kolkata and Mumbai could live for around 3.5 years longer if India complied with WHO standards.
Not to be outdone, the BBC has a video online, Bosnia’s silent killer — air pollution:
How credible are these reports? Are aggregate data on life expectancy consistent with such claims? Here I will focus on air pollution from PM2.5, which are generally regarded to be the deadliest form of air pollution (and the subject of the above reports).
Consider that Delhi might have the worst respirable suspended particulate matter (RSPM) air quality among India’s major cities (Figure 1). But — surprise — it also has the second highest life expectancy among India’s states (73.2 years for 2010-2014)! [1] By contrast, average life expectancy for India is 67.9 years.
Figure 1: Concentration of respirable suspended particulate matter (RSPM) in residential and industrial areas in major Indian cities. Source: Hosamane SN, Desai GP. 2013. Urban air pollution trend in India-present scenario. International Journal of Innovative Research in Science, Engineering and Technology 2: 3738-47.
Similarly, Beijing, hardly the cleanest place in China, has its 2nd highest life expectancy, 82.0 yrs, behind Shanghai (83.2 yrs in 2016). The national average was 76.3 yrs in 2015.[2] By contrast, Hawaii, the state with the highest life expectancy in the U.S., had a life expectancy of 81.2 yrs in 2014,[3] while the U.S. average was 78.8 yrs in 2015!
[But can Chinese data be trusted? Yes (at least in Beijing), according to a report in the New York Times[4] … but can the NYT be trusted? ].
Trends in Population Exposure to PM2.5 and Life Expectancy
CHINA & INDIA
Figures 2 and 3 show trends in estimates of mean population exposure to ambient PM2.5, life expectancy, CO2 emissions (a surrogate for industrial activity and fossil fuel use), and GDP per capita (a surrogate for both income and economic well-being) for China and India, respectively. [Although I would not rely on these PM2.5 exposure estimates for any quantitative purposes, I will assume that they are good enough to identify broad qualitative trends.]
Figure 2: China — Trends in (1) GDP per capita (in constant PPP adjusted 2011 international dollars); (2) CO2 emissions (in million metric tons of carbon), (3) population weighted annual ambient exposure to PM2.5 based on Brauer, M. et al. 2016, for the Global Burden of Disease Study 2015; (4) life expectancy (in years). Sources: CO2 data from CDIAC; all other data from the World Bank’s World Development Indicators.
Figure 3: India — Trends in (1) GDP per capita (in constant PPP adjusted 2011 international dollars); (2) CO2 emissions (in million metric tons of carbon), (3) population weighted annual ambient exposure to PM2.5 based on Brauer, M. et al. 2016, for the Global Burden of Disease Study 2015; (4) life expectancy (in years). Sources: CO2 data from CDIAC; all other data from the World Bank’s World Development Indicators.
These figures show:
· Life expectancies increase even as both GDP per capita and CO2 emissions increase. [Note that China’s life expectancy declined toward the beginning and end of the 1960s. This was probably because China still had not conquered hunger and food supplies per capita were consequently low.]
- Life expectancies have gone up despite increases in ambient PM2.5 exposure.
- GDP per capita tracks fairly well with CO2 emissions.
BOSNIA-HERCEGOVINA (BOSNIA for short)
Bosnia-Hercegovina is an interesting case, as can be seen by Figure 4. To put this figure in context, consider that Bosnia underwent drastic social, political and economic turmoil during the 1980s and through the early 1990s. Following Tito’s death in 1980, Yugoslavia disintegrated into its constituent pieces, and Bosnia-Hercegovina emerged as an independent state in 1992. Shortly thereafter, the Bosnian War got underway. It ended formally in 1995.
During this period of upheaval, GDP per capita and fuel use declined temporarily, as did CO2 emissions and mean population exposure to PM2.5. Not surprisingly, because of war casualties and, possibly, declines in GDP per capita and fossil fuel use, life expectancy declined somewhat. By 1994, GDP per capita had started to climb again, and so did fossil fuel use and life expectancy. However, PM2.5 exposure stayed more or less constant through the early 2000s, after which it increased (i.e., deteriorated), yet life expectancy has continued to increase.
Figure 4: Bosnia — Trends in (1) GDP per capita (in constant PPP adjusted 2011 international dollars); (2) CO2 emissions (in million metric tons of carbon), (3) population weighted annual ambient exposure to PM2.5 based on Brauer, M. et al. (2016), for the Global Burden of Disease Study 2015; (4) life expectancy (in years). Sources: CO2 data from CDIAC; all other data from the World Bank’s World Development Indicators.
The above figures (2–4) also indicate that for each of the three countries, it’s not evident that PM2.5 shortens lifespans or, if it does, its effects are more than overwhelmed by increases in life expectancy enabled directly or indirectly by economic growth (which is underpinned by fossil fuel use).
[The figures also show that CO2 emissions rose much more rapidly in each country than PM2.5 — see the following table. This suggests that each society had determined formally or informally that they would rather first obtain the benefits associated with fossil fuel use before turning their attention to reducing PM2.5 exposure or, for that matter, foregoing the benefits of fossil fuel use. This should be kept in mind when one develops estimates of the willingness to pay for co-benefits from PM2.5 reductions as part of any analysis of the costs and benefits of CO2 reductions, or in the calculations of the social cost of carbon. In other words, real world data does not support the notion that people are willing to pay for reductions in PM2.5 (or other pollutants) at the expense of foregoing fossil fuel use except in extraordinary circumstances. This notion might have been valid once upon a time for colorless and odorless gases, and had the effects of air pollution been unknown, but it can no longer be considered true today given the wide coverage of pollution matters in the local and international media and the internet, and the emphasis on renewable sources and pollution controls.]
| China | India | Bosnia & Hercegovina | |
| Increase in PM2.5 exposure from 1995 to 2014 = ΔPM2.5 | 17% | 18% | 17% |
| Increase in CO2 emissions from 1995 to 2014 = ΔCO2 | 210% | 176% | 648% |
| Ratio of growth from 1995–2014
= ΔCO2/ΔPM2.5 |
12.5 | 9.7 | 32.3 |
UNITED STATES
Let’s now look at data from the United States.
Figure 5: United States — life expectancy (yrs), GDP per capita (1990 International, PPP-adjusted $), SO2 and PM10 emissions (million short tons), PM2.5 (mean annual exposure, μg/m3). Sources: Updated from Goklany. The improving state of the world: why we’re living longer, healthier, more comfortable lives on a cleaner planet, Cato Institute (2007) using: Haines, Michael R. , “ Expectation of life at birth, by sex and race: 1850–1998 ”; Historical statistics of the United States, colonial times to 1970, US Department of Commerce, Bureau of the Census (1975); CDC (2016); CDIAC (2017); World Bank Data Bank (2017).
In addition to trends in estimates of mean exposure to ambient PM2.5, life expectancy, CO2 emissions, and GDP per capita, Figure 5 also shows trends in sulfur dioxide (SO2) and PM10. [SO2 is a proxy for sulfate aerosols, which would be a component of PM2.5 and also PM10. Note the dramatic reductions in PM2.5 since 1990.]
This figure shows that life expectancy has been increasing, with occasional setbacks, since at least 1850. These improvements got steadier after 1880, a period during which fossil fuel use began to take off, but with occasional relapses, e.g. 1916–1918 during World War I and the Spanish flu epidemic, the Depression era years, and World War II years). But since World War II, the more or less steady increase in life expectancy has been punctuated by fewer and smaller relapses.
Over the entire period, 1850–2015, air pollution levels would have first increased as society’s reliance on fossil fuels and industrialization increased. Then, one by one, the various pollutants were reduced. As noted elsewhere, the order in which these pollutants peaked seem to more or less follow the order in which society perceived (or became aware) of their negative impacts.[5]
Setting aside the steep decline in life expectancy from 1916–1918, Figure 5 reinforces the observations made from Figures 2 through 4, namely: (a) life expectancy is better correlated (and improves) with GDP per capita and CO2 emissions, than pollution levels, and (b) and it has continued to improve, notwithstanding trends in pollution from fossil fuel combustion. The information on Figure 5 is broadly consistent with data on deposition from various forms of airborne particulate pollution from the Arctic from 1788–2003, shown in Figure 6. [6] The latter probably reflects a composite of industrial and forest fire activities in both the U.S. and Canada. [Significantly, because of the steady increase in urbanization since the late 18th century, mean exposure to PM would have risen more steeply than indicated by emissions alone; this casts further doubt on the purported detrimental effect of PM on life expectancy.]
Figure 6: Air pollution deposition in the Arctic, 1788–2003. (A) Annual average concentrations of black carbon (BC) and vanillic acid (VA). VA is an indicator of boreal forest fires. The gray shaded region represents the portion of black carbon (BC) attributed to industrial emissions, not boreal forest fires. (B) Annual average concentrations of BC and non-sea-salt sulfur (nss-S). Spikes in nss-S are from explosive volcanic eruptions (e.g., Tambora, 1816; Krakatoa, 1883; and Katmai, 1912). Source: McConnell et al. (2007)
An examination of historical trends for other countries indicates that for the most part each country follows the same general script outlined above, namely, industrialization, stoked by increases in fossil fuel use, has increased GDP per capita and is associated with increases in life expectancy, regardless of whether air pollution levels went up or down. The exceptions to this pattern would be countries that have easy access to alternative energy sources, e.g., hydropower, and/or nuclear. Also, there might be discontinuities in the general increases in GDP per capita, fossil fuel use and life expectancy for some countries during periods of major economic, social and political disruptions such as the collapse and disintegration of the Soviet Union and the restructuring of its satellite states.
To recap, the death toll from air pollution caused by fossil fuel combustion — and the resulting decline in life expectancy — are, to quote Mark Twain, greatly exaggerated. In fact, for whatever reason, life expectancy increases in association with fossil fuel use.
Finally, some may argue that while PM2.5 may not reduce life expectancy, it may actually make the population sicker. But this argument fails scrutiny.
The following table compares (unadjusted) life expectancy at birth in 1950 against “health-adjusted life expectancy” (HALE) in 2000 and 2015 for the U.S., the world’s two-most populous countries — India and China, and the world. [HALE is a measure that tries to combine the quantity of life (i.e., its length) with the quality of health experienced over that lifespan. The World Health Organization defines HALE as the “average number of years that a person can expect to live in full health by taking into account years lived in less than full health due to disease and/or injury.”][7] The table shows that HALE today substantially exceeds the unadjusted life expectancy in 1950. [1950 is shortly before the China and India began to industrialize in earnest. It is also at the start of a fresh burst of industrialization in the U.S. — see Figure 5). In other words, we are not only living longer, we are staying healthier for a longer period of time.
| Life expectancy in
1950 (unadjusted) (yrs) |
Health-adjusted life expectancy in 2000
(yrs) |
Health-adjusted life expectancy in 2015
(yrs) |
|
| China | 41 | 64.6 | 68.5 |
| India | 32 | 54.2 | 59.6 |
| USA | 68 | 67.2 | 69.1 |
| World | 49 | 63.1 | |
| Atmospheric CO2 level (ppm) | 311 | 370 | 401 |
Unadjusted and health-adjusted life expectancy (HALE) for China, India, U.S., and the World. Health-adjusted life expectancy adjusts unadjusted life expectancy downward to account for the amount of time spent in an unhealthy condition and the severity of that condition. Sources: Maddisson (2001), p.30; ESRL Mauna Loa data, ftp://aftp.cmdl.noaa.gov/products/trends/CO2/CO2_annmean_mlo.txt WHO (2016), http://gamapserver.who.int/gho/interactive_charts/mbd/hale_1/atlas.html
So why the discrepancy between claims that PM2.5 (or air pollution more generally) reduces life expectancy, and the reality that life expectancy has actually increased, and continues to increase in some of the most polluted cities of the world despite increases in PM2.5?
A couple of reasons, which are not mutually exclusive, come to mind:
- The cumulative direct and indirect effects of economic development (and fossil fuel use) on life expectancy not only outweigh the effects of PM2.5, they also enable populations to reduce PM2.5, once more significant health threats are reduced.[8]
- Life expectancy is based on data on real births and real deaths, whereas the mortality effects of PM2.5 are based on “statistical” deaths or, to use a term currently in vogue, “fake” deaths.[9] As Steve Milloy is fond of asking, “Where are the bodies?”[10]
In today’s world, claims of air pollution shortening life expectancy are fake news premised on fake deaths.
[1]http://www.censusindia.gov.in/Vital_Statistics/SRS_Life_Table/2.Analysis_2010-14.pdf.
[2] https://gbtimes.com/beijings-average-life-expectancy-hits-record-high.
[3] http://www.businessinsider.com/us-states-with-the-highest-and-lowest-life-expectancy-2017-5.
[4] https://www.nytimes.com/2016/03/31/world/asia/china-air-pollution-beijing-shanghai-guangzhou.html?_r=0.
[5] Goklany IM. Have increases in population, affluence and technology worsened human and environmental well-being. The Electronic Journal of Sustainable Development. 2009;1(3):1; p. 15.
[6] McConnell JR, Edwards R, Kok GL, Flanner MG, Zender CS, Saltzman ES, Banta JR, Pasteris DR, Carter MM, Kahl JD. “20th-century industrial black carbon emissions altered arctic climate forcing,” Science. 2007 Sep 7;317(5843):1381-4.
[7] http://www.who.int/healthinfo/statistics/indhale/en/
[8] Goklany IM. Have increases in population, affluence and technology worsened human and environmental well-being. The Electronic Journal of Sustainable Development. 2009;1(3):1
[9] Estimates of deaths from air pollutants are based on epidemiological studies. However, these studies have several shortcomings. They include the fact that it is inappropriate to use outdoor monitors fixed in space to represent population exposure to PM2.5 (because most people move around, spend a substantial period of time indoors, and indoor and outdoor air quality may not always be the same for a variety of reasons). Steve Milloy has an extensive critique of a recent epidemiological air pollution study that illustrates many of these shortcomings.
[10] Milloy S. Claim: PM2.5 killed 1.22 million Chinese in 2013 — So where are the bodies?
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While I agree that modern medicine almost certainly lengthens average lifespan and that the statistical treatment of air pollution is,at best, dubious, I’d submit that there is fairly solid evidence that sufficiently bad air can be lethal. Denora, PA 1948 https://en.wikipedia.org/wiki/1948_Donora_smog, London 1952 https://en.wikipedia.org/wiki/Great_Smog_of_London. Moreover, I think it’s reasonable to assume that there is a ramp up toward the fortunately rather rare headline grabbing conditions that cause substantial numbers of people to sicken and even die when the smog turns lethal.
Just because you can’t quantify something, doesn’t mean that it doesn’t exist.
Rich countries clean up their air (and water). IMHO, California, crazed though their current energy policies might be, deserves a lot of credit for cleaning up what passed for air in the LA Basin in the 1960s. With precious little support from the rest of America I might add. For developing countries it’s a trade off — industrialization now vs pollution that will have to be cleaned up later. It’s their choice how much pollution they allow.
It is upsetting when science descends into trivial ‘marketing’ speak so we now hear people being told they ‘do not believe in climate’. This article, and as far as I can tell almost all the comments, have fallen into the same trap laid by people who do not wish their work to be replicated and tested, as science is meant to do.
We are told repeatedly in posts above that PM2.5 are the problem or, from the lead contribution:
Indeed the EPA once claimed that PM2.5 were fatal.
Both statements are deliberately imprecise. Note none of the claims state what the particulate matter is merely that particles of that size are dangerous and reduce life expectancy.
Go to Engineering Toolbox: https://www.engineeringtoolbox.com/particle-sizes-d_934.html
Tabulated there are particle sizes in microns. PM stands for Particulate Matter and the number is the size of the particles in microns.
In the table you will find that face powder has particle sizes including “PM2.5” as does talcum powder. I suggest that along with diesel cars the Mayor of London should ban cosmetics that include face powder as he is obviously unconcerned about the lives of those that use cosmetics and similarly unconcerned with the health of babies and people using talcum powder. The same applies to all the regulatory authorities who appear to choose which industry they will attack.
These false claims dressed up in pretend ‘science’ like ‘climate change’ are being used by people with other agendas. Science would be precise and detail the particular constituents of particulates that are dangerous, in what way they are dangerous and provide a mechanism by which they are dangerous together with experiments set out to falsify the hypothesis that they are dangerous. Scientists would then repeat their own tests and attempt to replicate the falsifications.
Instead we get ambiguities such as PM2.5 are dangerous which is equivalent to do you believe in climate?; together with a set of bandwagons for politicians and marketing to leap on to get power and money.
nice job, Ian W
+1
You would add that the mayor of London would begin to mind his own business, that is, subway PM level
Generally, why do most husbands die before their wife?
Because they want to.
an old joke:
her :” what awful man you are. If I were your wife, i would brew you poison”
him: “If I were your husband, i’ll drink it on the spot”
A short recent review, only the abstract is available:
“Air quality environmental epidemiology studies are unreliable”
http://dx.doi.org/10.1016/j.yrtph.2017.03.009
thanks. another instance of “most research finding are false”
http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124
“a recent study showed” is the usual sign that BS lies ahead.
Life expectancy and air pollution doesn’t seem to have any correlation in the UK….
https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpectancies/bulletins/lifeexpectancyatbirthandatage65bylocalareasintheunitedkingdom/2014-04-16#local-area-life-expectancy
and yet supposedly 9500 extra deaths in London alone from air pollution….
https://www.theguardian.com/environment/2015/jul/15/nearly-9500-people-die-each-year-in-london-because-of-air-pollution-study
Life expectancy isn’t only related to air quality. Socioeconomic factors are more important overall. London is largely rich for example.
2.5pm may be considered the most dangerous pollutant, however the evidence that actually shows this to be the case is weak at best.
The general problem with epidemilogy that wants to establish a causation relation between pollution and mortality is the fact that nearly none of the studies can effectively measure the real pollution the group of people is exposed. So what they are doing is taking the measurement from a central measuring station in the relating town or area. Then they are calculating the pollution of every single person in the study by a computer model (LUR, land use regression). You can imagine the uncertainties in the range of several 100% of these results, depending on weather, temperature or just buildings/trees or whatever standing there. Still, this only means that the calculated pollution is supposed to happen at the front door of a single house. It does not mean that the man who lives in the house is exposed to it. He can drive a car every day to his office 50 miles away from his house and work there. So add another 100% or 200% uncertainty.
At the other side of the calculation the epidemiologists use another computer model to estimate the expected normal death rate of a specific area by taking age, sex, education, income and other social factors into account. This isn’t always done individually but estimated over large groups. So also add another huge uncertainty there.
Then they divide the two death rates, the real one and the expected one and plot it over the calculated pollution to arrive at the relative risk. Doing this for many areas with thousands/millions of people gives you a nice plot, especially when you have different computer models to chose between to achieve the intended result. At the end of the day they arrive with a conclusion that e.g. each 2 µg / m³ more pollution results in 5% more deaths. With uncertianties in the 1000% range
Go to William Briggs and see what this all about:
http://wmbriggs.com/post/8108/
http://wmbriggs.com/post/4353/
http://wmbriggs.com/post/16719/
http://wmbriggs.com/post/8720/
http://wmbriggs.com/post/4587/
http://wmbriggs.com/post/4857/
http://wmbriggs.com/post/3702/
or see the report of Panullo et al. who demonstrated that the result of a scottish study regarding NO2 could change from 5% to nearly zero by just changing the LUR computer model.
http://www.sciencedirect.com/science/article/pii/S1877584515300289
I think that this article mostly indicates that PM2.5 is not the largest influencer in life-expectancy.
I always remind people who are depressed about our non-organic foods, and toxin infused prepared food, that our life expectancy is higher than it has ever been.
“In Delhi, people could live as much as nine years longer if India met WHO standards”
If they live up to WHO standards, I’m moving to Delhi!
In Delhi, for that matter in northern parts of India, weather plays the havoc in winter. During this period cold waves frequency and direction changes the impact of pollution. In 70s I prepared a pollution potential index based on temperature inversion layer height and wind roses [direction, speed].
Dr. S. Jeevananda Reddy
“The best evidence for PM2,5 mortality comes from studies of ‘high pollution’ events – usually weather related – and increased hospitalisations/deaths over the next few days. Not entirely clear what that means for overall life expectancy, ….”
Or it is just the weather events.
We were on vacation sailing and sleeping on our boat. There was a heat advisory. I decided we should check into a hotel and enjoy A/C because of my wife’s chronic heart condition. My wife has a history of not telling me about how she is feeling. I asked her if I needed to take her to the emergency room. NO!
Thirty minutes later I am running traffic lights to get her to the ER. The first of her siblings to have stints put in.
The air quality was perfect.
I have looked at the Harvard studies. Her ER visit would be ‘evidence’ if the air quality was bad. However, ‘premature death’ was the very old with chronic health issues.
If you want to prevent a problem from reoccurring you find the root cause. For example, does a fall cause a broken hip or does a broken hip cause a fall?
My wife’s grandmother never recovered from a broken hip in her 90s. My wife’s mother never recovered from the heart attack following hip replacement surgery in her late 80s.
The point here is that each of us is an individual. Statistics are sometimes useful in making individual choices.
Statistics are also useful to manipulate society. I am waiting for the EPA to look at the statistics and tell congress that they have a done a good job and now their budget can be reduced by 90%.
Now imagine we would live to be 200 years. We would at least have to work until the age of 150 (or more) to pay for our retirement not counting inflation.
Beijing and Shanghai are NOWHERE near the worst air polluted cities in China, but they are the richest.
However as the author well knows, the difference in life outcome living in Beijing compared to say Auckland isn’t life expectancy per se, but life in good health, which is decades less than western cities.
https://www.bloomberg.com/view/articles/2014-06-19/why-living-in-beijing-could-ruin-your-life.
An article from Bloomberg? That’s just another arm of the marxist NYTimes. LMAO
With any numbers or stats other than sports scores? LMAO
This article is also being discussed over at stoves@bioenergylists.org where Nikhil writes:
“He is on the wrong track asking, “Are aggregate data on life expectancy consistent with such claims?”
There are only aggregate ESTIMATE of life expectancy at various ages by cohort (a hugely political issue) and in turn developed from something called Life Tables.
If you ask WHO and public health folks how Life Tables are developed, there are stories about that too. Read up WHO report on Causes of Death, Jan 2017.
Much of his analysis after that is the same problem as with the claims he is trying to dissect – associations are associations, attributions depend on methods and allocation, and above all, these are population (or cohort) level indicators (life expectancy or pollution in the way he chooses to describe). His claims don’t stand up to scrutiny either; he simply does not understand.
Then he writes “Finally, some may argue that while PM2.5 may not reduce life expectancy, it may actually make the population sicker. But this argument fails scrutiny.”
He fails there too – mixing apples and oranges. Yes, he finally recognizes his error in part – “Life expectancy is based on data on real births and real deaths, whereas the mortality effects of PM2.5 are based on “statistical” deaths or, to use a term currently in vogue, “fake” deaths.[9] ”
This is outrageous. “Statistical deaths” are not “fake deaths”. The man is simply ignorant, buying the rightwing ideology that seeks to attack the leftwing environmentalism.”
Nikhil continues at bionergylists.org
“All aggregate statistics have to be considered in their context of i) computation method and ii) the interpretation assigned to the definitions of underlying data.
That, in a way, is my general complaint against many such estimates and reporting of estimates as “data”.
It is true of things like “markets” and “prices” as of “global mean surface temperature” as of “greenhouse gas emissions” as here “premature deaths”, “HAP exposures”, and the Integrated Exposure Response. It is only when I dug into the original sources and debates surrounding the concepts in this last instance that even as I railed against them, I was able to forgive those who abuse the concept and derive politically convenient claims from deliberately ignoring what the definitions and the methods are.
Steve Milloy was an advisor to Scott Pruitt in re-organizing the EPA. Milloy is seen as a rightwing hack because of his attacks on the PM2.5 theology. I hope there is a thorough re-assessment of how PM2.5 – which is after all, only an indicator of pollution, NOT pollution itself – under Pruitt’s EPA and the newly reconstituted Science Advisory Board. Tony Cox, Jr. whose review concluded that there is no basis for finding causality between PM2.5 exposures and premature mortality, is one of the new members.
Milloy’s “Where are the bodies?” is tendentious, but he is correct. Nobody can find ONE premature death caused by HAP, and nobody can prove that a switch to LPG from TSF conferred a single DALY. That is the deceit of the Gold Standard Foundation and Sumi Mehta at GACC.
Did you have a chance to look at the WHO document I gave a link to a couple of days ago on HAP estimates. WHO has a spreadsheet on HAP attributable deaths by country and sex for under-5 and 25+ age groups for 2012. The numbers are given as ranges, which may give an impression that these reflect uncertainty in the otherwise legitimate estimates. That would be an incorrect impression. There is no there there, as the WHO document would itself confirm!
Where are the bodies dead? Or where are the lives saved from “truly health protective” ISO Tier 4 stoves like gas and electricity?
Conundrums, conundrums. Life is full of manufactured surprises. Not everybody in public health field takes IHME – Chris Murray, Alan Lopez – and the “pioneers” of PM2.5 theology such as C. Arden Pope (there are some other key figures) seriously.
But that is off-topic. To me, the constituents of PM2.5 are hugely important, beginning with SO2 and PAHs. EPA chose equitoxicity assumption so it can cook up justification for every new rule after it no longer had “acid rain” monster to market.”
Of course lifespan goes up as wealth goes up. (At least it has until the current obesity problem.) It goes up more than the small average reduction in lifespan due to pollution, though some people (like me) may be more adversely affected than the average person.
Wealth is healthy. Poverty is unhealthy.