Dtypes, PM plots and exposure

This example demonstrates how to:

  • Load size-resolved ELPI data (Aerosol2D)

  • Inspect dtype, units, and particle density

  • Convert between different distribution types via dtype_converter

  • Compute cumulative Pₓ (e.g. PM₂.₅, PM₄.₂) with PM_calc

  • Plot PMₓ time series with plot_pm_timeseries

  • Adjust assumed particle density with set_density

  • Use summarize_activities and summarize_exposure for PM-based metrics

[ ]:

import matplotlib.pyplot as plt
import aerosoltools as at

Load ELPI sample data

Initially load an ELPI test dataset.

[4]:

filename = r"..\..\tests\data\Sample_ELPI.txt"
elpi = at.Load_ELPI_file(filename)

################# Warning! #################
          Density ≠ 1.0 assumed
  Bin edges estimated via geometric means
###########################################

A warning is given, seeing that the density in the given raw data is not set equal to 1.0. The density of the raw data is read, loaded and stored within the generated elpi variable, but the user is still alerted.

Check dtype, unit, and density

The loader sets the initial distribution type (e.g. dN) and unit. Assumed particle density is stored in elpi.density (g/cm³).

[5]:

print("Initial dtype:", elpi.dtype)
print("Initial unit:", elpi.unit)
print("Initial density (g/cm³):", elpi.density)

Initial dtype: dN
Initial unit: cm⁻³
Initial density (g/cm³): 0.14

Change density and convert to mass distribution

We can update the assumed particle density with set_density() and then convert to a mass-based size distribution using dtype_converter("dM").

[7]:

# For illustration, set density to 1.5 g/cm³
elpi.set_density(1.5)
elpi.dtype_converter("dM")
print("Updated dtype:", elpi.dtype)
print("Updated density (g/cm³):", elpi.density)
print("Updated unit:", elpi.unit)

Updated dtype: dM
Updated density (g/cm³): 1.5
Updated unit: ug/m³

Compute PM fractions with PM_calc

We now calculate mass-based Pₓ metrics such as PM₂.₅ and PM₄.₂. The results are stored in elpi.extra_data as additional columns.

[8]:

# Compute cumulative PM2.5 and PM4.2 (µm cut points)
elpi.PM_calc(dtype="dM", PM=2.5)
elpi.PM_calc(dtype="dM", PM=4.2)
print([c for c in elpi.extra_data.columns if c.startswith("PM")])

['PM2.5', 'PM4.2']

Plot PM time series

Use plot_pm_timeseries to visualize the evolution of PMₓ over time.

[12]:

elpi.plot_PM_timeseries(PM_values=[2.5, 4.2, 10])

[12]:

(<Figure size 1900x1000 with 1 Axes>, <Axes: ylabel='PM, ug/m³'>)
../_images/examples_5_-_Dtypes%2C_PM_plots_and_exposure_12_1.png

Define activities and summarize

As with Aerosol1D, we can mark time segments and compute activity-based statistics using summarize_activities().

[13]:

activity_periods = {
    "Background": [("2023/09/07 09:06:50", "2023/09/07 09:07:50")],
    "Emission":   [("2023/09/07 09:07:55", "2023/09/07 09:08:30")],
    "Decay":      [("2023/09/07 09:09:00", "2023/09/07 09:10:50")],
}
elpi.mark_activities(activity_periods)
summary = elpi.summarize_activities()
summary


Summary of aerosol properties (transposed):

+------------------------+----------+------------+----------+---------+
|                        | All data | Background | Emission |  Decay  |
+------------------------+----------+------------+----------+---------+
|    Duration (HH:MM)    |  00:04   |   00:01    |  00:00   |  00:01  |
|       PNC [cm⁻³]       |  197.44  |   222.84   |  203.63  | 179.39  |
|     PNC [cm⁻³] std     |   48.9   |   61.26    |  42.67   |  39.3   |
|      PM1 [µg/m³]       |  13.79   |   14.97    |  14.06   |  12.85  |
|    PM1 [µg/m³] std     |   1.15   |    0.89    |   0.26   |  0.38   |
|     PM2.5 [µg/m³]      |  36.51   |   37.94    |  37.83   |  34.95  |
|   PM2.5 [µg/m³] std    |   1.95   |    1.6     |   1.09   |  1.13   |
|      PM4 [µg/m³]       |  61.78   |   60.51    |  66.44   |  61.04  |
|    PM4 [µg/m³] std     |   6.28   |   10.08    |   2.77   |  3.12   |
|      PM10 [µg/m³]      |  325.0   |   320.29   |  374.75  | 318.69  |
|    PM10 [µg/m³] std    |  99.82   |   169.28   |  22.18   |  42.5   |
|   Total Mass [µg/m³]   | 1335.13  |  1421.95   | 1615.86  | 1225.18 |
| Total Mass [µg/m³] std |  549.75  |   896.41   |  216.46  | 248.88  |
|      Mode Dp [nm]      |  125.2   |   113.6    |  125.4   |  132.9  |
|    Mode Dp [nm] std    |  131.1   |   125.6    |  128.9   |  135.4  |
|     Median Dp [nm]     |  224.2   |   210.6    |  226.2   |  228.1  |
|   Median Dp [nm] std   |   68.6   |    76.9    |   66.2   |  67.3   |
|        GMD [nm]        |  203.3   |   193.0    |  201.4   |  212.6  |
|      GMD [nm] std      |   79.5   |    79.2    |   77.8   |  84.1   |
+------------------------+----------+------------+----------+---------+

[13]:
Segment Duration (HH:MM) PNC [cm⁻³] PNC [cm⁻³] std PM1 [µg/m³] PM1 [µg/m³] std PM2.5 [µg/m³] PM2.5 [µg/m³] std PM4 [µg/m³] PM4 [µg/m³] std PM10 [µg/m³] PM10 [µg/m³] std Total Mass [µg/m³] Total Mass [µg/m³] std Mode Dp [nm] Mode Dp [nm] std Median Dp [nm] Median Dp [nm] std GMD [nm] GMD [nm] std
0 All data 00:04 197.44 48.90 13.79 1.15 36.51 1.95 61.78 6.28 325.00 99.82 1335.13 549.75 125.2 131.1 224.2 68.6 203.3 79.5
1 Background 00:01 222.84 61.26 14.97 0.89 37.94 1.60 60.51 10.08 320.29 169.28 1421.95 896.41 113.6 125.6 210.6 76.9 193.0 79.2
2 Emission 00:00 203.63 42.67 14.06 0.26 37.83 1.09 66.44 2.77 374.75 22.18 1615.86 216.46 125.4 128.9 226.2 66.2 201.4 77.8
3 Decay 00:01 179.39 39.30 12.85 0.38 34.95 1.13 61.04 3.12 318.69 42.50 1225.18 248.88 132.9 135.4 228.1 67.3 212.6 84.1

Exposure summary for respirable dust (PM4.2)

Finally, we use summarize_exposure on the Aerosol2D object to obtain exposure metrics for PM₄.₂ during the Emission task, using Background as the background activity.

[15]:

exp = elpi.summarize_exposure(
    metric="PM4.2",
    activity="Emission",
    background="Background",  # use mean Background as background level
    exposure_hours=None,       # assume exposure equals task duration
    short_limit=1.0,
    long_limit=1.0,
    short_window="15min",
    twa_window="8h",
)
exp


Exposure summary for segment 'Emission' and metric 'PM4.2' (µg/m³):

+--------------------------------------------+----------+
|                   Metric                   | Emission |
+--------------------------------------------+----------+
|                   Metric                   |  PM4.2   |
|                    Unit                    |  µg/m³   |
|                Duration_min                |   0.6    |
|               Duration_HH:MM               |  00:00   |
|                    Mean                    |  71.797  |
|                    Std                     |  3.127   |
|                   Median                   |  71.21   |
|                    Max                     |  78.57   |
|                  Seg_TWA                   |  71.797  |
|                 Window_TWA                 |  64.657  |
|             Window_TWA_window              |    8h    |
|                Short_limit                 |   1.0    |
|                Short_window                |  15min   |
|         Short_limit_exceedance_min         |   0.6    |
|      Short_limit_exceedance_fraction       |   1.0    |
|   Short_limit_fullwindow_exceedance_min    |   0.02   |
| Short_limit_fullwindow_exceedance_episodes |    1     |
|                 Long_limit                 |   1.0    |
|            Long_limit_exceeded             |   True   |
|         Long_limit_exceedance_min          |   0.6    |
|                    C95                     |  76.709  |
|                    C99                     |  78.018  |
|                    IQR                     |  4.235   |
|                   Peaks                    |    0     |
+--------------------------------------------+----------+

[15]:
Segment Metric Unit Duration_min Duration_HH:MM Mean Std Median Max Seg_TWA ... Short_limit_exceedance_fraction Short_limit_fullwindow_exceedance_min Short_limit_fullwindow_exceedance_episodes Long_limit Long_limit_exceeded Long_limit_exceedance_min C95 C99 IQR Peaks
0 Emission PM4.2 µg/m³ 0.6 00:00 71.797 3.127 71.21 78.57 71.797 ... 1.0 0.02 1 1.0 True 0.6 76.709 78.018 4.235 0

1 rows × 25 columns