There are at least four reasons to pay attention to aerosol components. The key components are sulfate ions [SO42-], nitrate ions [NO3-], light-absorbing carbon [LAC], sea salt [SS], organic carbon [OC], and soil content [Soil]. Here they are:
To know the extinction coefficient of a particle means predicting its light scattering. The IMPROVE network estimates aerosol Mie scattering of airborne particulates (also some Rayleigh scattering from air and nano-scale particulates). This is calculated via measuring the extinction coefficient b (a conglomerate of extinction values) where
and L is the (fixed) path length and I/I0 is the fraction of light scattered by the particles. The reference light intensity I0 requires some assumptions, and setting can vary from one protocol to another. The wavelength used is 545 nm, say for a single-wavelength integrating nephelometer (M903, Radiance Research, Seattle, USA). 450nm, 550, and 700 nm light are used in the TSI 3563.
Scattering as measured by the nephelometer is the sum of all aerosol components and their respective masses:
From Finlayson-Pitts and Pitts' Chemistry of the Upper and Lower Atmosphere, some typical net extinction values are b = 10-3 m-1 (for polluted regions) and 10-7 m-1 (for remote locations). The equation to obtain b is found here, as part of the IMPROVE background material:
The pre factor f(RH) is known as the wet to dry scattering ratio f(RH) and accounts for the effects of water content for certain species. It turns out some aerosol constituents scatter more light in wetter conditions. For the IMPROVE network's purposes, which are empirically driven, the change in organic scattering is only weakly dependent on RH, hence they choose to set forg(RH) as unity. The extinction of nitrates and sulfates, however, cannot be ignored; for these species f(RH) is required. The formula is an empirically derived value, and is also known as the relative humidity adjustment factor:
I/I0 = e-bL
and L is the (fixed) path length and I/I0 is the fraction of light scattered by the particles. The reference light intensity I0 requires some assumptions, and setting can vary from one protocol to another. The wavelength used is 545 nm, say for a single-wavelength integrating nephelometer (M903, Radiance Research, Seattle, USA). 450nm, 550, and 700 nm light are used in the TSI 3563.
Scattering as measured by the nephelometer is the sum of all aerosol components and their respective masses:
b = a1m1 + a2m2 + a3m3 +...
From Finlayson-Pitts and Pitts' Chemistry of the Upper and Lower Atmosphere, some typical net extinction values are b = 10-3 m-1 (for polluted regions) and 10-7 m-1 (for remote locations). The equation to obtain b is found here, as part of the IMPROVE background material:
b = 3f(RH)[SO4] + 3f(RH)[SO4] + 4forg(RH)[Organic carbon] + 1[Soil] + 0.6[Coarse mass]
The pre factor f(RH) is known as the wet to dry scattering ratio f(RH) and accounts for the effects of water content for certain species. It turns out some aerosol constituents scatter more light in wetter conditions. For the IMPROVE network's purposes, which are empirically driven, the change in organic scattering is only weakly dependent on RH, hence they choose to set forg(RH) as unity. The extinction of nitrates and sulfates, however, cannot be ignored; for these species f(RH) is required. The formula is an empirically derived value, and is also known as the relative humidity adjustment factor:
f(RH) = −0.18614 + 0.99211(1/(1 − RH))
It reminds me of the fugacity fudge factor used for high-pressure gas thermodynamic calculations. The adjustment is small for low-humidity scenarios, but rises rapidly above 95% RH, where f > 7. This means that for the IMPROVE data "errors in reconstructed scattering coefficients (associated with RH measurements) will increase together with RH". This is because, as they put it, "water uptake was responsible for about one third on average of the calculated reconstructed ambient light scattering coefficient."
Recap: For high RH the scattering coefficients are difficult to determine. Data in very wet conditions is often ignored (i.e. not reported in the final tally), as the scattering values are not considered sufficiently reliable. This can lead gaps in data.
Recap: For high RH the scattering coefficients are difficult to determine. Data in very wet conditions is often ignored (i.e. not reported in the final tally), as the scattering values are not considered sufficiently reliable. This can lead gaps in data.
Here is the light scattering formula used by Environment Canada's NAPS team:
bext ≈ 2.2 fs (RH) [ASO4]S + 4.8 fL (RH) [ASO4]L
+ 2.4 fs (RH) [ANO3]S + 5.1 fL (RH) [ANO3]L
+ 2.8 [OM]S + 6.1 [OM]L
+ 10 [EC] + [Fine Soil] + 0.6 [CM] + 1.7 fss (RH) [Sea Salt]
+ 0.33 [NO2 (ppb)] + Rayleigh Scattering (site specific)
I thought it was interesting that NAPS uses a more complex equation despite being a smaller network. Or does that make sense?
After all that work, we need to assume the total scattering is correlated to total mass. Hard to deduce that simple fact based on what I wrote. In case you need some convincing:
After all that work, we need to assume the total scattering is correlated to total mass. Hard to deduce that simple fact based on what I wrote. In case you need some convincing:
2) Reconstructing total mass
The IMPROVE network collects particles using three separate filters: Nylon, quartz, and Teflon (also Nucleopore filters made of polycarbonate). Together these yield the PM2.5 reconstructed fine mass (RCFM), which are summed in the following manner
Each of these categories is 'representative' of other species, as many are not measured for practical reasons (too many species, species are below detection limits, etc). Why reconstruct mass this way? Besides having a gross tally with which to compare species' aerosol contribution, the values can be compared to nephelometer measures. It has been said by Sciare et al. that "chemical mass closure experiments are more and more required as they will serve to better constrain the optical properties of aerosols or the formation of cloud condensation nuclei". In other words we cannot rely completely on the simple scattering of aerosols by lasers because these measurements themselves are derived from reconstructed masses. As network arrays grow in number and geographic diversity, the challenges intensify.
RCFM = a[SO4-2] + b[NH4+] + c[NO4-] + d[POM] + e[LAC] + f[Soil] + g[SS].
Or in other words
aerosol components by weight => empirical summation => total aerosol mass
Each of these categories is 'representative' of other species, as many are not measured for practical reasons (too many species, species are below detection limits, etc). Why reconstruct mass this way? Besides having a gross tally with which to compare species' aerosol contribution, the values can be compared to nephelometer measures. It has been said by Sciare et al. that "chemical mass closure experiments are more and more required as they will serve to better constrain the optical properties of aerosols or the formation of cloud condensation nuclei". In other words we cannot rely completely on the simple scattering of aerosols by lasers because these measurements themselves are derived from reconstructed masses. As network arrays grow in number and geographic diversity, the challenges intensify.
3) Predicting change in aerosol size under variable relative humidity (RH)
This section is the key to my project. I want to know aerosols will change in size hourly knowing only the daily (24h) mass/speciations totals in section 2 and the hourly light scattering and RH values reported in section 1. A lot can happen in 24 hours, as Jack Bauer will tell you.
How will I do this in practice? Knowing the components of aerosols combined with ab initio thermodynamics (via ISORROPIA II and/or AIM) will tell us how much water is retained in an aerosol at a given relative humidity, called a reverse problem (reverse-engineering an aerosol)
We then re-insert this value into the program to obtain new masses using variable RH and temperature T values. This is solving the forward problem, i.e. finding a new aerosol weight with known gas+aerosol conditions (as opposed to knowing only the aerosol conditions alone)
As the hierarchy goes, a computationally reported aerosol mass ranks slightly below reconstructed mass: Both rely on empirical estimates but computational methods require more assumptions and ignore more data (less attention paid to organics in the computational methods). So why do it this way? A lot of work goes into reconstructing aerosol mass using (see previous section), but there's no guarantee the specific water content was correctly accounted for: The upside is that an hourly resolution is now available. More critically, the SPARTA network may only provide weekly, or even just 21 day sampling periods. Hence computational estimates might be a way to interpolate aerosol values. Not sure yet if that's the best way to go about it. I'm thinking of collecting PM2.5 data from around the world to calibrate initial estimates. There's always the chance that a 100% purely empirical approach is a better avenue. It's my job to find this out.
How will I do this in practice? Knowing the components of aerosols combined with ab initio thermodynamics (via ISORROPIA II and/or AIM) will tell us how much water is retained in an aerosol at a given relative humidity, called a reverse problem (reverse-engineering an aerosol)
{aerosol components by weight} + {RH, T} => computational calc => total aerosol mass
We then re-insert this value into the program to obtain new masses using variable RH and temperature T values. This is solving the forward problem, i.e. finding a new aerosol weight with known gas+aerosol conditions (as opposed to knowing only the aerosol conditions alone)
starting aerosol mass + {new RH, new T} => computational calc => new aerosol mass
4) Health impacts
Health impacts is the ultimate reason many are interested in sub 2.5 micron sized aerosols. But we don't have enough worldwide dispersed data sets. Notably it has been stated in a recent global aerosol health assessment that
surface measurement data (for PM and even more so for ozone) are still far too sparse in most of the high concentration regions for direct use in exposure assessment throughout the world.
To estimate the health impacts of aerosols, we need their total mass: adverse health effects (reparatory and cardiac) are related best to total mass. Knowing aerosol components helps to distinguish acceptable PM2.5 from 'bad' PM2.5. Most PM2.5 is bad since things like dust and salt don't usually get that small. But ignoring these differences in composition could lead to erroneous health advisories. Knowing details of each aerosol type is important, especially since most of the ground networks now are located only in Europe and North America. That leaves a lot of earth left to cover.
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