Two New Papers: Atmospheric Ammonia Modeling

Both of these are in review at “peer review” journals, because as we all know, peer review guarantees truth.

Lot of people use what’s known as the Ryden & McNeil Ammonia Flux Model, which is a semi-physical semi-statistical model of ammonia flux. The physics are simple, and a bit too simple. The statistics in it aren’t bad for wind, which behaves nicely in the boundary layer, but it’s not so good for the ammonia itself.

What we did was

Fixes to the Ryden & McNeil Ammonia Flux Model

We propose two simple fixes to the Ryden and McNeil ammonia flux model. These are necessary to prevent estimates from becoming unphysical, which very often happens and which has not yet been noted in the literature. The first fix is to constrain the limits of certain of the model’s parameters; without this limit, estimates from the model are seen to produce absurd values. The second is to estimate a point at which additional contributions of atmospheric ammonia are not part of a planned expert but are the result of natural background levels. These two fixes produce results that are everywhere physical. Some experiment types, such as surface broadcast, are not well cast in the Ryden and McNeil scheme, and lead to over-estimates of atmospheric ammonia.

Uncertainty in the MAN Data Calibration & Trend Estimates

We investigate trend identification in the LML and MAN atmospheric ammonia data. The signals are mixed in the LML data, with just as many positive, negative, and no trends found. The start date for trend identification is crucial, with the trends claimed changing sign and significance depending on the start date. The MAN data is calibrated to the LML data. This calibration introduces uncertainty never heretofore accounted for in any downstream analysis, such as identifying trends. We introduce a method to do this, and find that the number of trends identified in the MAN data drop by about 50%. The missing data at MAN stations is also imputed; we show that this imputation again changes the number of trends identified, with more positive and fewer significant trends claimed. The sign and significance of the trends identified in the MAN data change with the introduction of the calibration and then again with the imputation. The conclusion is that great over-certainty exists in current methods of trend identification.

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7 Thoughts

  1. Briggs, in the abstract, what is meant by “not part of a planned expert”? Should that be “not part of a planned experiment”?

  2. Many years ago I was asked to review the stats used in a study of NH3 levels in lichens collected in Hells Canyon of the Snake River along the Idaho-Oregon border. The study found higher levels in the westernmost segments of the Canyon, and the researchers concluded that this was caused by air pollution from Portland, Oregon — 400 miles to the west.

    Setting aside the PC alarmist absurdity of the conclusion, the data actually showed that NH3 levels were correlated with the distance from the lichen collection points to the water’s edge. The correlation was not “significant” because there was a huge range in the lichen chemical assays. However, the (weak) implication was that the NH3 came from the river and didn’t travel very far (a few feet rather than hundreds of miles).

    Ammonia is invisible. If it wasn’t, if NH3 was a visible green fog, this “problem” would be a lot simpler. But since it is invisible, NH3 is a big mystery, a kind of ghost or hidden boogie man.

    Just a comment from my past. Your papers are very good, although they didn’t translate well from Dutch to English, or else the spell/grammar checker was broken. My takehome is that many (most) enviro boogie man studies would benefit from debunking by a star-approved real statistician.

  3. NH4 is an element while NH3 is a gas. NH4 is ammonium, it contains one more hydrogen atom than ammonia which is NH3. Ammonia is a weak base that is un-ionized (has not formed additional ions) and has a strong smell but ammonium is a strong yet odorless base that is ionized.

    And then I looked up ions…

    On my way, bit by bit! Thank you, Uncle Mike!

    God bless, C-Marie

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