Mass Reconstruction

Mass reconstruction reconstructs PM2.5 mass from chemical composition to verify the completeness and closure of chemical analysis.

Major Components

Secondary Inorganic Aerosol (SIA)

Ammonium Sulfate (AS)

\[(NH_4)_2SO_4 = 1.375 \times SO_4^{2-}\]

Ammonium Nitrate (AN)

\[NH_4NO_3 = 1.29 \times NO_3^{-}\]

Ammonium Status Determination

Calculate ion balance:

\[R = \frac{[NH_4^+]}{[SO_4^{2-}]/48 \times 2 + [NO_3^-]/62}\]

R Value	Status	Description
>1.1	Excess	NH4+ excess, possibly NH4Cl present
0.9-1.1	Balance	Near neutral
<0.9	Deficiency	NH4+ deficient, acidic aerosol

Organic Matter (OM)

\[OM = OC \times f_{OM/OC}\]

Environment Type	\(f_{OM/OC}\)	Description
Urban fresh	1.4-1.6	Primary organic matter dominated
Suburban aged	1.6-1.8	Mixed sources
Background	1.8-2.2	Secondary organic matter dominated

Elemental Carbon (EC)

Use measured value directly:

\[EC = EC_{measured}\]

Soil Dust

Estimated from crustal elements:

\[Soil = 2.20 \times Al + 2.49 \times Si + 1.63 \times Ca + 2.42 \times Fe + 1.94 \times Ti\]

If Si data is unavailable:

\[Soil = 2.20 \times Al + 1.63 \times Ca + 2.42 \times Fe + 1.94 \times Ti\]

Multiply by correction factor (~1.89).

Sea Salt (SS)

\[SS = 2.54 \times Na^+\]

Or considering chloride depletion:

\[SS = Na^+ + Cl^- + 0.038 \times Na^+\]

Total Mass Reconstruction

\[PM_{2.5,reconstructed} = AS + AN + OM + EC + Soil + SS\]

Closure Evaluation

\[Closure = \frac{PM_{2.5,reconstructed}}{PM_{2.5,measured}} \times 100\%\]

Closure	Assessment
80-120%	Good
70-80% or 120-130%	Acceptable
<70% or >130%	Needs review

Unidentified Mass

\[Unidentified = PM_{2.5,measured} - PM_{2.5,reconstructed}\]

Possible sources: - Bound water - Metal oxides - Analytical errors - Unmeasured components

AeroViz Implementation

from AeroViz.dataProcess import DataProcess
from pathlib import Path

dp = DataProcess('Chemistry', Path('./output'))

# Basic mass reconstruction
result = dp.reconstruction_basic(df_chem)

# Output
result['mass']        # Reconstructed mass DataFrame
#   AS, AN, OM, EC, Soil, SS, PM25_rc

result['NH4_status']  # Ammonium status
#   Excess / Balance / Deficiency

# Full reconstruction (with ite)
result_full = dp.reconstruction_full(df_chem)

Input Format

required_columns = [
    'SO42-', 'NO3-', 'NH4+',      # Ions
    'OC', 'EC',                    # Carbon components
    'Na+', 'Cl-',                  # Sea salt
    'Al', 'Fe', 'Ti', 'Ca',        # Crustal elements
    'PM25'                         # Total mass
]

References

Malm, W. C., et al. (1994). Spatial and monthly trends in speciated fine particle concentration in the United States. JGR, 99(D1), 1347-1370.
Turpin, B. J., & Lim, H. J. (2001). Species contributions to PM2.5 mass concentrations: Revisiting common assumptions for estimating organic mass. Aerosol Sci. Technol., 35(1), 602-610.