SPARTAN update

At the moment it seems that the SPARTAN instrument will be designed as a stacked filter unit. I found the following image on an Australian Government website with a very similar design scheme. This is good news, as I wasn't sure if our instrument had any precedent (i.e. for using two stacked filters preceded by a impaction plate) Another group has been in charge of the the design stage hence why I did not know more about this general approach.

Stacked filter unit with a very similar design to our own (except that we will have eight columns instead of just one). 

Regarding the coarse and fine filters (separating the "PM2.5" and PM10-PM2.5 mass fractions), we ordered some filters from SPI to do the job: a 47 mm diameter grease-coated membrane filter (part# E4708G-MB with 8um holes, $550 per 100-pack) and a 25mm polycarbonate membrane filter (part # E0425-MB with 0.4 um holes, $70 per 100 pack).

These filters are Nuclepore in spirit but without the eponymous brand name. They are both made of polycarbonate with etched holes to trap coarse and fine particulates, respectively. Notice how much more the 47 mm filters cost. This is due to the grease coating used to prevent bounce-back. I don't know how bad the bounce back is in a straight-shot filter yet it has been argued enough to necessitate the extra cost of the greased filters. I don't know if grease would benefit the smaller size fraction as well. Also the coated disks come in 47 mm diameters only, so we needed to cut them down to size. There were many email discussions about cutting filters in a replicable way, i.e. we need to pre weigh filters accurate to 1 microgram so piece can't be later falling off. The plan is proceed using a sharp steel punch (made courtesy of the workshop in the Dunn basement) sandwiched between two filter papers.

Otherwise the filters look good; the 25 mm filters allows an air flow rate up to 62.5 litres per minute and stable until about 140 degrees Celsius and the coarse filter is much the same. As we are operating these at 4.0 lpm and a temperature between 5-40 degrees, there should be no issue with these parameters.

Measuring PM2.5 around the world

If you want an amazingly well-funded study, you'd be hard pressed to outdo sponsorship from the US department of defence (DOD). In that spirit I present the DODs Enhanced Particulate Matter Surveillance Program (EPMSP), which in their words collected

Aerosol and bulk soil samples ... during a period of approximately one year at 15 military sites—including Djibouti, Afghanistan (Bagram, Khowst), Qatar, United Arab Emirates, Iraq (Balad, Baghdad, Tallil, Tikrit, Taji, Al Asad), and Kuwait (Northern, Central, Coastal, and Southern regions). 

That's what SPARTAN equals in terms of proposed site counts: up to 20 depending on future funding.

The DOD sampled each site for 24 hours at 6-day intervals from 2006 to 2007. Each site was measured for PM10, PM2.5, and total suspended particulates (TSP).

Their sampling equipment came from Airmetrics, which certainly give great credit to the company's instrument reliability (SPARTAN's sampling instruments are still in the early stages of design, but we should (or ought to) have something comparable in the near future. The hard deadline to start field testing is March 2013. My focus is to avoid having things come down to the wire).

EPMSP collected samples on Teflon, Quartz, and Nuclepore filters (I keep wanting to spell it Nucleopore!) in the three size frames. The most impressive statistic is the following account of their total assembled data:

The number of laboratory analyses performed in the course of the EPMSP is summarized in Table 2-2, and includes 66,462 analyses on Teflon® membrane, 23,807 on quartz fiber, and several million single particle analyses on Nuclepore®  

Several million samples! I cannot imagine how long that would take SPARTAN to measure, but not certainly not within my stay at Dal. To be fair most of these were samplings of the Nuclepore filters for scanning electron microscopy (SEM) analysis. The Teflon and quartz filters consisted of 'only' a few tenns of thousands of chemical analyses.

The data itself is very useful for our network, such as the PM2.5 levels we might expect to encounter. In Baghdad and Tikrit, Iraq annual levels exceed 100 ug/m3; Recall that acceptably 'clean' air must have fewer than 10 ug/m3 PM2.5 particulates. The TSP in all 15 sites exceeded 100 ug/m3, while up to 600 ug/m3 in Tikrit. Considering trace metal levels, "The USACHPPM 1-year interim Air-MEG of 3.42 μg/m3 for aluminum (Al) was on average exceeded at all 15 sites". These are not a healthy places to breathe.

The study points out that PM2.5/PM10 ratios vary considerably from place to place. In the middle east they apparently lie between 0.21 and 0.60. More anthropogenic combustion usually means the ratio is higher. This large variance supports the need for separate PM2.5 monitoring; PM10 is clearly only weakly related with fine aerosols.

In terms of sand-based composition, the middle east contains similar amounts of aluminosilicates as compared to the United States' more arid desert regions.

The EPMSP study used Nuclepore filters specifically for SEM analysis, as this surface type is good for very size-selective trapping of particulates on a smooth surface. Background signals are small, and the particulates look 'cleaned', like rock species isolated in a museum case. Their methodology has convinced me we should probably be using only Teflon for chemical analysis and gravimmetric weighing.

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Aside: I really admire the openness of the data presented here. As a typical example, they write: "Gravimetric results from both Teflon® and quartz fiber filters were processed and are available on the DOEHRS Portal". Compare this to Canada's NAPS raw data access. Military-funded science is surprisingly more open than some other types. Food for thought.

Sampling flow chart

Here's the sampling procedure for our SPARTAN filter network:

Filters will be placed inside a regulated flow apparatus. The prototype design calls for 4 Lpm over a 25 mm diameter filter. The filter is going to be teflon or nucleopore. The former is good because it's easy to clean, the latter is good because volatile nitrates escape less easily. There is no obvious choice, but likely we will end up with teflon.

The steps in the above chart aren't individually complex, but extreme care is needed in order to ensure quality data. If anything goes wrong at any stage (weighing, ion dissolution in water, etc), you can bet the whole goal of the network might be compromised.

Last month I was focused on comparing total PM2.5 aerosol mass with nephelometer bscat measurements. It was important to know as much about what affects the light scattering of aerosols as possible, since the ultimate goal of the network is quantitative PM2.5 mass data (measured in micrograms per cubic meter). One should back spectroscopy data if at all possible (astrophysicists are, for example, limited to spectral data). Even ground nephelometer measurements must be supported by actual weighed filters. Somehow this gives me satisfaction known that practical chemistry has a place among all this modern satellite equipment.  

Splitting aerosols

How much more surface area do you obtain from splitting aerosols in half? That is to say a constant volume holds while splitting the aerosol volume like so:

I want to compare surface S₁ with surface S₂. Since surface is related to volume like so,

then the ration S₂/S₁ becomes (and accounting for the two spheres in S₂),

Hence there is 26% more surface area available if all sphere of volume V divide into spheres of volume V/2.

If the aerosol is split unevenly into fractions x and 1- x (where 0 < x < 1), then

which looks like this:

The equation is if course symmetric about the point x = 0.5.

Climate change is real

Well, duh. Sad, however, that professionals are now reduced to explaining things in terms of gambling. I don't like these odds...

Mars rover

Ok, this Mars rover landing trumps the olympics in my opinion. Sounds pretty awesome what they're trying to do:

UPDATE: ...and they landed! Pretty cool.

Updates on aerosol sampling methods

Since last last post I've made significant strides in improving the hourly aerosol prediction methods. That has been, in a nutshell, my job for the past two weeks: interpret aerosol scattering measures. Since my collected data has been thoroughly digested, I thought now might be an opportune time to regurgitate what I've done.

Airport location

First, the equation given below is currently how I'm assessing changes in PM2.5 levels:

M_PM2.5(t) = <PM2.5>*Rel bscat,p

where  Rel bscat,p = [b_sp/<b_sp>]/[b_sp,calc/<b_sp,calc>]


and b_sp,calc is determined using two separate methods, either empirically via a line of best fit reported by the IMPROVE team or from semi ab initio calculations done by a fortran program called ISORROPIA.


This light scattering-based  equation is also my guiding light. The first term <PM2.5> is the averaged BAM (beta attenuation monitor/mass) filter results. These filters come from BC's Abbotsford airport, as measured via Environment Canada's Met-One BAM 1020. EC's methodology can be found here.


The second part of the equation, Rel bscat,p, compares the amount of predicted nephelometer light scattering of PM2.5 aerosols with a reported value. If only the relative humidity changes though out the day (i.e. PM2.5 dry mass remains constant) then both numerator and denominator (b_sp and b_sp,calc, respectively) should fluctuate in tandem, assuming I got the equation right. If dry mass changes over the course of the day, then those changes should be reflected in the numerator changing more than the denominator causing an inflection with respect to the mean PM2.5.

I normalized both sets of data with their 24-hour means so that a systematic over prediction of bscat would cancel out.

The relative changes in bscat due to relative humidity changes are accounted for using empirical hygroscopic factors, i.e.

f_inorg(RH) = −0.18614 + 0.99211(1/(1 − RH))
f_org(RH) = 1 + 0.1/(1-RH)

and b_sp,calc is calculated as

b_{sp, IMP calc} = 3*f_inorg(RH)*[ASO4 + ANO3] + 4*f_org(RH)*[POM] + 10[LAC] + [Soil]

Or, if using the ISORROPIA approach (which we are also doing), we have 

b_{sp,ISO calc} = 3*[total calculated inorganic and water mass] + 4*f_org(RH)*[POM] + 10[LAC]

Clearly both methods rely on a semi-empirical organic equation; the only way of incorporating organics into ISORROPIA would be to effectively include this equation in the code, but unless we assume chemical interaction between organics and inorganics (thus opening a fresh can of worms), then it's better to just use f_org(RH) as given above.

Both scattering equations rely on similar -but not identical- inputs. To calculate b_{sp, IMP calc} we require aerosol sulfate and (Teflon-captured) nitrate concentrations SO4 and NO3 and total aerosol ammonium [NH4]. Ammonium nitrate (ANO3) and ammonium sulfate (ASO4) are then obtained implicitly via

[ANO3] = [NO3] + 18/62*[NO3]

[ASO4] = [SO4] + [NH4] - 18/62*[NO3]

Total organic carbon is measured and converted to total organic matter. LAC is identical to elemental carbon and soil is given in EC's data sheets (see list of assumptions below)

To calculate b_{sp, IMP calc} we require total nitrate (gaseous as well as aerosol) and total ammonia/ammonium. We need total sulfate as well, but assume this is identical with aerosol sulfate. The ISORROPIA model then requires knowledge of the ions Cl, K, Ca, and Mg (these may be high near coastal regions).

A few other assumptions made in both systems (ISORROPIA and IMPROVE)

1. No change in particle density. This isn't a perfect assumption since wet particles tend to weight more than dry ones (pure water has a density of 1.0 and sulphuric acid is 1.8; real aerosols lie between these values and approach unity as RH increases).

2. Particle count remains constant. In other words the number density does not change as particles grow in size (which leads to more surface area). This is a reasonable assumption over short time scales, however in time particles can conglomerate.

3. No change in index of refraction. I had mentioned before that b_ext  ~ [(m² - 1)/(m² + 2)]² so that changes in m (complex index of refraction) could have a strong effect on the total scattering. On short time scales we expect these changes to be slight enough to ignore.

4. Other forms of scattering are relatively minor and can be tentatively ignored. That is, b_sp,calc is only based on organics and inorganic material,  light-absorbing carbon LAC (aka EC) and soil.  We neglect changes in gaseous NO2 as it affects aerosol composition.


5. As recommended by Environment Canada, total particulate organic matter (POM) is obtained by multiplying the measured organic matter (OM) by 1.9.


6. Also recommended by EC, we assume aerosol soil remains constant at 0.45 ug/m3 (based on their speciation measurements). Soil could alternatively be estimated from four major soil components

[Soil] = 2.2[Al]+2.49[Si]+1.63[Ca]+2.42[Fe]+1.94[Ti]

Finally, some results:

A single day of replicated nephelometer scattering. The red line is an attempt to mimmic the blue line. The relative differences are taken to be changes in actual PM2.5 levels (overlaid in this plot).

This is how well the hourly PM2.5 is mimicked on this particular sampling day in Abbotsford (May 31, 2009)

Hourly PM2.5 sampling error averaged for matched hours from 35 sampling days spread over one year. No special hours of day in which errors increase or decrease

From the graph above you can tell there is little difference between either the purely empirical or semi-empirical approach (which is actually a good sign). The only issue is that Abbotsford isn't much of a stress test for our simulations; the weather is fairly mild and temperatures vary little year-round. As can be seen below, errors remain under control for any humidity below 96% (empirical bscat is not reported for 96-100% RH, hence variation above this threshold is unknown). 

The SPARTAN program is designed for coastal environments, so that means very hot/cold weather swings are not a significant concern. Yet I'm still hoping to see what happens in dry, dusty places like Saharan landscapes or with wild temperature swings in Siberian-type landlocked zones. 

Instruments and procedures

I was reading through the paper A review of atmospheric aerosol measurements and found it quite useful, especially since aerosol collection is the core of the SPARTAN project.  It was authored by Peter H. McMurry, a specialist in both theoretical and experimental aerosol science (my kind of guy!). Even better is that he focuses on small aerosols, i.e. PM2.5 and below.

SPARTAN is not about aerosol nucleation theory, which is of course an active field of research but too far off topic from our direct interests: below a few hundred nanometers particles cease to scatter much light or have much mass, hence do not change nephelometer light scattering readouts. Actually I should qualify that: there is one pesky light-scattering gas: NO₂. Nitrogen dioxide is the reason polluted cities look polluted. That brownish colour in the sky is NO₂. At more than a few parts per billion, visible wavelengths get noticeably scattered.

NO2 cross section from here. Note the overlap with the visible spectrum, i.e. above 420 nm, which creates a brownish colour  

Back to instrumentation measurements. Although Dr. McMurry is principally interested in the theory of sulphuric acid nucleation (his definition of small is smaller than ours), his review is extensive. Some highlights:

Size selection methods

Diffusion of particles down the length of a tube is a convenient way to size-sort parties. But only particles below 100 nm can be practically size-sorted by diffusion. Particles are sorted into histogram-like bins from 10-100 nm. Since differences in diffusion are proportional to the square root of the mass then larger particles do not separate well. Sorting heavier aerosols is akin to the problem of sorting 235UF₆ and 238UF₆, i.e. requiring many times more path length. For heavier particles, i.e. 0.1-2.5 um, one would use a multi-stage impactor.

Interesting thing about impactors is that wet particles 'bounce' less than dry ones, so that >75% RH is a good way to capture these particles. Downside is that means we'll need to be extra careful about size correcting these hydroscopic particles.

Ammonium nitrate losses on filters

When it comes to capturing ammonium nitrate, which is volatile, it turns out impactor plates are better than Teflon filters:

Evaporative losses of particulate nitrates have been investigated in laboratory and field experiments with filters and impactors. The laboratory studies involved parallel sampling of ammonium nitrate particles with a Berner impactor and a Teflon filter. Both samplers were followed by nylon filters to collect evaporated nitric acid. Losses from the impactor were 3-7% at 35C and 18% relative humidity, and losses from the filter were 81-95% under the same conditions. This result (that evaporative losses from the filter exceeded those from the impactor) is consistent with theoretical predictions

In other words Teflon is not very good for capturing nitrates. Huge losses, which means total aerosol mass is under predicted, chemical speciation is mis represented and so on.

Here is another great review, but slightly older.

I want to play with one of these H-DTMA instruments (H-DTMA stands for hygroscopic tandem differential mobility analyzer).

Also reading this paper linking hygroscopic aerosol particle growth with organic composition, which is right up my alley. Here's a handy tabulation of aerosol species' properties:

Including organics

In my previous post I had an outline for predicting changes in aerosol scattering, b_ext, versus one hour time intervals.

b_scat = α_ASf(RH){[ASO₄] + [ANO₃]} + α_POM[POM] + others (to be determined)

The key was computing an aerosol hygroscopic growth factor which used the ISORROPIA model and the standard 24h composition measurements (which are taken every third day in situ).

f(RH) = {(calc inorg. mass, wet) + [POM]}/{(calc inorg. mass, dry) + [POM]}

After talking to the developers of the ISORROPIA model I'm tweaking this plan a little. You see originally I was going to ignore the water retention of particulate organic matter (POM) but I will instead heed some advice and include a separate growth factor for organics and inorganic material. First let's reset the above equation to include only inorganics:

f_inorg(RH) = (calc inorg. mass, wet)/(calc inorg. mass, dry)

Then I will use a theoretical calculation for the organic fraction. Starting with the an equation and accompanying theory from this paper,

1/a_w = 1 + kV_s/V_w

where k is the number of soluble moles of organics matter per unit volume dry particle (and k = 0.1), V_s and V_w are the volumes of organic solids and liquid organic-associated water, respectively, and a_w is the water activity of the solution. Assuming all aerosol particles are in equilibrium with humid air, then a_w ~ RH. k can vary from 0.01 to 0.5, however professor Athanasios Nenes recommended to us k = 0.1  for the most up-to-date studies of typical mixed aerosol organics. If k = 0 it means the species is completely insoluble. For a given density p of aerosol-bound organic solids, the organic hygroscopic ratio (water mass/dry mass) becomes 

f_org(RH) = 1+ k/p[RH/(1-RH)]

(density is unit-less, where p_water = 1)


For most values of RH the factor f(RH) is near unity, as expected, but grows rapidly for RH > 0.9. 

Notice the resemblance of f_org(RH) with the empirically-fitted IMPROVE equation:

f(RH) = −0.18614 + 0.99211(1/(1 − RH))  

Enough discussion surrounds hygroscopic growth factors that it's easy to forget their practical use: parametrizing and predicting the water content in aerosols. More water means a greater nephelometer b_ext signal but we don't want to be fooled into thinking there's more PM2.5 dry mass than there truly is. Hence we'll need to relate f(RH) back to actual scattering values.

One serious problem lies in deciding what constitutes a 'dry' aerosol (as a reference point for the denominator in f(RH)). Normally it would be a dry mass anywhere from a theoretical 0% RH value to something below 40%. As long as the particle solidifies (effloresces) it's usually the same mass. But the problem is deciding what to use in day-to-day real-world scattering.

One idea I had was to normalize for (real) relative values of b_ext, divided into 24 1h segments

b_relmeas = b_ext/[24*<b_ext,24h>] 

Then obtain a similar formula for the calculated b_ext,calc

b_relcalc = bcalc/[24*<b_calc,24h>]

where

b_ext,calc = 2.66*f_inorg(RH)*{[ASO₄] + [ANO₃]} + 4.19*f_org(RH)[POM] + C

<b_ext,calc> = 2.66*f_inorg(<RH>)*{[ASO₄] + [ANO₃]} + 4.19*f_org(<RH>)[POM] + C

where C is a constant based on other scattering and absorbing airborne species and the coefficients 2.66 and 4.19 -units of m²/g- are borrowed from Sciare et al's paper. Compare these to the IMPROVE values, which are 3 and 4 m²/g, respectively.

Now to introduce something new from last time: taking the difference in mass values. That is, measuring changes in mass with time subtracting the changes due to RH:

delta M(t) = <M>{b_ext,rel - b_relcalc}

If RH remains constant for the day and PM2.5 mass changes, then only the measured b value should change. But if RH changes and PM2.5 stays constant, both will change hopefully to the same degree and the difference will be zero. In reality both PM2.5 and RH will change, so that's why we need this formula. My hope now is to calibrate these delta M(t) measurements from hourly BAM filters. 

Linking aerosol scattering to mass

It's time to start hunkering down at plotting some scattering coefficients. There's a lot to describe, and much will be omitted in this post. I'll start with how we plan to predict PM_2.5 mass with time.

From my previous entry we know that total aerosol scattering is proportional with total aerosol mass at any given period in time. For our purposes t = 1 hour, which is not 'instant' but a relatively short time (the shortest normally reported in routine satellite, temperature, etc observations)

b_scat(t) ~ PM_2.5(t)

Because these two quantitive are proportional, the re-arrangement means the ratio PM_2.5/b_scat is a constant:

PM_2.5(t)/b_scat(t) = C

and the average, too, is constant

<PM_2.5(t)/b_scat(t)> = <C> = C

The 'average' is an integration over 24 hours; we are summing over twenty four one-hour intervals (if you want, let <b_scat> = b_scat(24h)). Also we can split the average like so

<PM_2.5/b_scat> = <PM_2.5>/<b_scat>  

Compare ratios (1-hour and 24-hour) and we find they are of course equal

PM_2.5(t)/b_scat(t) = <PM_2.5>/<b_scat> 

We now have the setup for a prediction of PM_2.5 levels at one-hour time intervals

PM_2.5(t) = b_scat(t)*<PM_2.5>/<b_scat> 

It's worth considering this formula in detail since each parameter comes from a different source of data.

The b_scat(t) and <b_scat(24h)> values could be obtained by a nephelometer coupled to a BAM (beta attenuation monitor), or just a BAM. The nephelometer gives us immediate feedback as to the current particle mass in the air, while the BAM gives us integrated one or 24-hour mass totals.

At first glance it seems as though our problem is finished: simply monitor particle mass changes over time from variable b_scat(t). But here's the catch: the b_scat(t) proportionality with mass holds true only under constant humidity and temperature.

Changing the relative humidity (RH) with time will affect b_scat(t) (either increase or decrease it) even if total PM_2.5 mass remains constant. At any time during a 24h run b_scat is changing because of humidity, temperature, and mass: 

b_scat = b_scat(t, [water], T, m)

or as a function of RH

b_scat= b_scat(t, RH([water, T], T))

For simplicity we might compare b_scat at dry (RH < 40%) and humid conditions. This is defined as the hygroscopic growth factor, f(RH):

f(RH) = b_scat(RH > 60%)/b_scat(RH < 40%) 

This is an empirically-based function. Many studies have measured the scattering of 'dry' particles (often solid) versus 'humid' ones. We will return to this formula. Let's take a step back.

How might b_scat depend on changes in humidity? From Hegg (1993):

It is convenient to consider [changes of particulate scattering] as taking place through three proximate agents: the change in geometry cross section of the individual particles the change in the index of refraction of the particles and the shift of the particle size distribution into (or out of) the size range of more efficient light scattering as the individual particles grow in size.

The above figure indicates there is clearly more scattering for small (but not too small) particles in humid conditions. Total PM_2.5 mass in the atmosphere increases with RH, but significantly (and not obviously) most of these particles do not move to a larger size fraction. The water-based weight increases, but not enough to move into the coarse (= PM₁₀ - PM_2.5) size range (which is comparatively less dangerous to breathe). The number density of effective light scattering particles remains more or less unchanged. Hence an increase in a b_scat signal does not necessarily mean there is more PM_2.5 in the air, only a 'heavier' PM_2.5 (though water-logged particles are also less dense than dry weight, something else to consider).

Therefore the challenge is to find out whether an increase in the b_scat signal is from a water-logged increase or a legitimate boost in aerosol number density.  To avoid being fooled we must dissect the b_scat signal as a function of RH.

We start with a the full IMPROVE-derived formula for the light extinction coefficient. In theory this accounts for every major gas/aerosol component that would scatter or absorb light: 

b_ext ≈ 2.2 f_s (RH) [ASO₄]_S + 4.8 f_L (RH) [ASO₄]_L

+ 2.4 f_s (RH) [ANO₃]_S + 5.1 f_L (RH) [ANO₃]_L

+ 2.8 [OM]_S + 6.1 [OM]_L

+ 10 [EC] + [Fine Soil] + 0.6 [CM] + 1.7 f_ss (RH) [Sea Salt]

+ 0.33 [NO₂ (ppb)] + Rayleigh Scattering (site specific)

Here b_ext includes the scattering term b_scat, among others (i.e. absorption from EC & coarse mass, and Rayleigh gas scattering). The S and L subscripts refer to solid and liquid aerosol components, respectively. You can see the term f(RH) has been used; it accounts for both increase in particle size due to swelling and change in refractive index. Because most of these particles remain in the PM2.5 size fraction, concentrations of species [X] do not themselves alter with RH. We have the formula, but we'll need to simplify it a little. Our two paths to reduction come in the form of 1) ignoring trace species 2) grouping hygroscopic species together. 

For instance Sciare et al make the following assumptions:

We will consider here only ammonium sulfate and POM as the main chemical components of aerosols in the fine size fraction, since nitrate, potassium, sea salt and dust aerosols only account for 0.4, 2.4, 1.2% and 3.3% of the total mass respectively. Based on these assumptions  

 σ_SP [which is our b_scat] = α_ASf(RH)[ASO₄] + α_POM[POM]

where α_AS and α_POM stand for the specific scattering coefficient of [dry] ammonium sulfate and POM in the fine fraction, respectively [where α_AS = 2.66 m²/g, α_POM = 4.19 m²/g].

They used an empirical equation for f(RH):

f(RH) = −0.18614 + 0.99211(1/(1 − RH)) 

which stays close to unity until above 80%, then rises quickly. As you can see, using this method Sciare's scattering values are comparable to the nephelometer's. This is because, as they state, most of an aerosol consists of (liquid) ammonium sulfate and organics; aerosol sulfate composition in the United States is 40-60% by weight while POM is 40-75%. Fortunately a species' concentration is roughly proportional to scattering.

But we'd like to do better than use an empirical f(RH) (or at least try something different): We want to create our own computationally tailored version of f(RH) that takes advantage of known, but variable, aerosol composition. At out disposal is the aerosol modelling tool ISORROPIA II that can predict just how water logged an aerosol ought to be given a reported RH, temperature, and ion concentration (ions NH4, Ca, Na, K, Mg, anions Cl, NO3, SO4). Here is what I'm thinking:

f(RH) = (calc PM_2.5 mass @ RH > 60%)/(calc PM_2.5 mass @ RH = 0%)  

What's nice about this formula is that f(RH) contains the sea salt component f_ss(RH) (important for coastal areas). What's not so nice is that organics are ignored, which is a huge component to PM2.5 aerosols. Hence we'll need to re-include them to create a semi-empirical formula. To revise,

f(RH)' = {(calc PM_2.5 mass/m³ @ RH > 60%) + [POM]}/{(calc PM_2.5 mass/m³ @ RH = 0%) + [POM]}

Dividing through by POM creates a unit less ratio of POM to inorganic components (if that turns out to be useful, I don't yet know)

f(RH)' = {(calc PM_2.5 @ RH > 60%)/[POM] + 1}/{(calc PM_2.5 @ RH = 0%)/[POM] + 1}

(Aside: Another other issue is accounting for the change in the particles refractive index, m, due to dilution. In theory I'd calculate m as a function of individual particle's ASO₄, NaCl, and ANO₃ concentrations (which become more dilute with RH, unlike total ion concentration per cubic meter), then apply the relation

b_ext  ~ [(m² - 1)/(m² + 2)]²

For now I will just ignore it. End of aside). 


I will then use this calculated f(RH) in a formula like the following (an expanded version of Sciare's):

 b_scat = α_ASf(RH){[ASO₄] + [ANO₃]} + α_POM[POM] + others (to be determined)

As a reminder (to myself, mostly), the goal of this project is to measure total PM_2.5 mass on short time scales. Since many of these measurements will be done in hot and muggy urban locations we must account for humidity. The scattering coefficient will give us a reasonable mass estimate if we can account for the RH (and temperature) dependence.  Another self-reminder: PM_2.5 mass is best measured using gravity filtration weighing; this yields an actual mass value. But this method only gives -at best- 24h-averaged samplings, and usually spaced every third day or more (for SPARTAN perhaps just once every 10-14 days). In doing this work we might unleash some interesting possibilities...

Aerosol measurements: real and calculated

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:

1) Reconstructing light extinction from aerosol measurements. 

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

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] + 4f_org(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 f_org(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.

Here is the light scattering formula used by Environment Canada's NAPS team:

b_ext ≈ 2.2 f_s (RH) [ASO₄]_S + 4.8 f_L (RH) [ASO₄]_L

+ 2.4 f_s (RH) [ANO₃]_S + 5.1 f_L (RH) [ANO₃]_L

+ 2.8 [OM]_S + 6.1 [OM]_L

+ 10 [EC] + [Fine Soil] + 0.6 [CM] + 1.7 f_ss (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:

   

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 PM_2.5 reconstructed fine mass (RCFM), which are summed in the following manner 

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)

{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 

 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.

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.  

Some fresh aerosol info

Aerosols are to atmospheric scientists what living cells are to biologists. Both are impossibly complex, variable in time, space, size, and composition. Neither can be solved numerically. Aerosols are, however, not alive. They can be better approximated by computers, but many mysteries of their inner workings remain hidden.

I'm currently looking into how aerosols change diameter with time. Specifically how they change during the diurnal cycle. That is, humidity changes with time, naturally, and can be rather unpredictable if we're speaking about rain storms and sudden weather events. Humidity levels constantly change, and not always in a simple pattern. I came across this collection of Canadian weather stats.

The cycling is not simple. But this is not a problem because RH values are merely input parameters, whatever they may be. Given the RH numbers, we then use these values to predict aerosol diameter. The diameter of aerosols matters since this may affect whether or not they are trapped in a PM2.5 filter.

Here is a website that models aerosol thermodynamics, called E-AIM (Extended Inorganic Aerosol Model), which provides a simple input/output scheme for aerosols. The calculations are of course complex. I'll post more on the calculations when I understand them better.

PM2.5 monitoring network

Continuing my exploration into aerosol studies I now explore the American IMPROVE initiative. IMPROVE stands for Interagency Monitoring of Protected Visual Environments. Their website defines the program as

a cooperative measurement effort between the U. S. Environmental Protection Agency (EPA), federal land management agencies, and state agencies. The network is designed to

1. Establish current visibility and aerosol conditions in 156 mandatory Class I areas (CIAs)  

2. Identify chemical species and emission sources responsible for existing anthropogenic
visibility impairment 

3. Document long-term trends for assessing progress towards the national visibility goal  

4. With the enactment of the Regional Haze Rule, provide regional haze monitoring
representing all visibility-protected federal CIAs where practical. 

They link to a cool feature VIEWS, which displays aerosol data for every type of particulate composition in the country. Below is IMPROVE aerosol PM2.5 levels from NYC from 2004-2010

As you can see there's a lot more 'dirty' aerosols in a big city than, say, Rocky Mountain, Colorado:

Sophisticated stuff. I really like how full-disclosure these websites are. Excel files, readable plots, publications galore. There is little to hide. Here's the geography of the IMPROVE network:

Previous aerosol monitoring in the United States

Looking over a few aerosol papers now:

Here's a paper that measures the content of PM_2.5 from JGR: Seasonal composition of remote and urban fine particulate matter in the United States

So what did they find? Well, for starters just about anything you can imagine is in a aerosol, including salts, various forms of carbon, heavy metal (i.e. this paper), and acids (SO2, NO2, HCl, etc). Here the authors narrowed their radar to "ammonium sulfate, ammonium nitrate, particulate organic matter, light-absorbing carbon, mineral soil, and sea salt".

A problem one is faced when sampling is "do I choose a weighted average based on population density or maintain a uniform geographic scattering?" The answer here would appear to be geared to the former; there was a roughly 50/50 split between urban and rural analysis (176 vs 168) since there is a even divide of urban and rural populations (but increasingly urban).

The first two aerosols listed ((NH₄)₂SO₄ and (NH₄NO₃) are specific chemicals. The four thereafter (POM, LAC, MS, SS) are categories, not individual species. What is inside them (which are, in turn, inside aerosols)?

Sulfate and Nitrate Anions 

Determined "from ion chromatography using a nylon filter... preceded with a sodium carbonate coated denuder". The anions sulfate and nitrate are assumed to be neutralized by the ammonium cation (but not necessarily true: other cations could associate with these species eg Na or CaCO3). Relative to the total aerosol mass, sulfate accounted for 40%, up to 60%. Nitrates were smaller, at 10-20%.

Mineral Soil (MS)

For mineral soil we have as components Al2O3, SiO2, CaO, K2O, FeO, Fe2O3, and TiO2. These are found via X-ray fluorescence (XRF).

Light-absorbing carbon (LAC)

For LAC we have a combination of 'black' carbon (aka BC or 'soot') and 'brown' carbon.  Black carbon exists as an aggregate of 10-50 nm carbon particles. These structures are skeletally seen as elemental carbon, much of it graphite (plus other trappings found in the structure: aromatic hydrocarbons and some other aliphatics). Since carbon is the backbone of soot, elemental carbon (EC) is another means of defining these particles. This alternate classification is a functional one rather than chemical or physical. The paper states that "We use “LAC” instead of “EC” based on the recommendation of Bond and Bergstrom [2006] [proposing an absorption cross section of α(550 nm) = 7.5(1.2) m² g⁻¹] and to avoid possible improper classification". But 'improper' is subjective. From the 2006 paper by Andreae et al Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols

Both “BC” and “EC” can only be regarded as “proxies” for the concentration of soot carbon, whose accuracies depend on the similarity between atmospheric soot and the species used for calibration. If atmospheric soot were pure graphite and all the methods were calibrated against graphite, “BC” and “EC” readings would give exactly the mass concentration of soot carbon as intended. Since, however, graphite is only a trace component of atmospheric soot, “BC” and “EC” measurements usually give different results, which have possibly little in common with the “true” mass concentrations of atmospheric soot par ticles. However, in the literature these discrepancies are usually disregarded and the terms “BC” and “EC” are used interchangeably as synonyms for soot carbon.

Not to go off-topic but some of the contention could stem from debates in application of BC/EC to climate change models. Climate change is something everyone wants to throw in their two cents, and debating exact light-scattering parameters lays open some targets. Ultimately the definition hovers close to the notion of that carbon which absorbs light.

Brown carbon absorbs shorter wavelengths than black carbon (less than 550 nm, into the UV spectrum), and appears brown in color. It is composed of "light-absorbing organic matter in atmospheric aerosols of various origins, e.g., soil humics, HULIS (humic-like substances), tarry materials from combustion, bioaerosols"

How is LAC measured? Thermo-analysis is one way, whereby the sample is progressively heated and all carbon is converted to CO₂ and quantified. The original paper discussed here uses thermal desorption from quartz fibres. But since the point of defining LAC is to obtain the light-absorbing component, cross-sectional visible light absorption is an intuitive alternate method of measure. The problem is, however, that light absorption methods have higher measurement errors/lower detection limits. Andreae et al discuss this problem comparing various absorption instruments.

Relative mass to total PM2.5 weight is small, about 3-15% (errors are large due to the small amounts of LAC present in aerosols)

Particulate organic matter (POM):

Particulate organic matter was assumed to related to total molecular weight of 1.8 per unit mass total carbon (changed from 1.4 from an earlier publication). This value is empirical and can vary widely (e.g. 1.2 to 2.6). The 'additional' mass comes from O, N, and H and varies with aerosol age/location. Carbon in POM is assumed to be equivalent to organic carbon (OC) and is determined by thermal desorption techniques. In other words the difference between POM and LAC in this study is merely the temperature at which the carbon is oxidized to CO2; a threshold temperature must be established and therefore it is somewhat an arbitrary division (cutoff of about 650 Celsius).

POM constitutes a relatively large percentage of total PM2.5 mass (40-75%).

Sea Salt (SS)

Sea salt concentrations were determined by measuring chlorine ion concentrations and multiplying by the factor 1.8. Since only one ion represents the total salt ion concentration, there is a chance errors are present if chlorine escapes from reaction with gaseous nitric acid.

Sea salt constitutes a medium percentage of total PM2.5 mass: 10-20% in coastal and <5% in continental (land-locked areas).

Results/Conclusions

At this stage I'm as interested in the results as the methods. Nevertheless the rural/urban divide piques my interest  as this is my project's direction, i.e. to compare and contrast 'clean' country air and 'dirty' city air. I use quotation marks because sometimes a city can be cleaner than the country, especially if near a farm, waste depot, factory, etc. And cities can be very clean if electrically-driven public transportation dominates (such as the solar-powered city Freiburg, Germany). Here are the comparisons

Ammonium sulfate: no strong difference in city/country divide, but east had much more sulfate than western US. Likely this is because of the higher coal power plant density on the eastern half.

Ammonium nitrate: Urban levels were much higher than rural (4-5 times) and increased in the mid-western US, reflecting significant fertilizer use. High urban levels are not easily explained. The paper says maybe it's due to elevation differences (between rural/urban measurement methods?)

POM: Very much higher in urban environments (2-5 times), likely due to fuel combustion and outdoor cooking (my guess since maxima is in summer).

LAC: Urban LAC was much higher that rural levels. LAC is a combustion product, hence emissions are concentrated with human population.

Soil: Higher levels in 'dustier' areas, i.e. varies on the region of US (i.e. Death Valley is high in aerosol soil content). No strong differences were seen in city vs. country, within a given landscape.

Sea salt: Tied to proximity with oceans, not cities


Aside: Another publication (this one by Vandereli Martins et al) shows what PM2.5 light absorption looks like (for 250 to 2500 nm light):