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Condensed Particle Counter (CPC) Investigation

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Condensed Particle Counter (CPC) Investigation

Load in necessary libraries

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os 
from datetime import datetime, timedelta
import fnmatch 
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import pytz 
import math
import time
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib.pyplot as plt
import xarray
import xarray as xr 
import numpy as np
import glob
import getpass
import act 

# Scikit-learn imports
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Dask imports
from distributed import Client, LocalCluster, progress

Download data from ARM directly using workflow below

# Note - recommended ARM Live token to be set as an environmental variable

# Check for env variables, otherwise set your username and token here!
if "ARM_USERNAME" in os.environ:
    arm_username = os.getenv("ARM_USERNAME")
else:
    arm_username = input("Enter ARM username: ").strip()
if "ARM_TOKEN" in os.environ:
    arm_token = os.getenv("ARM_TOKEN")
else:
    arm_token = getpass.getpass("Enter ARM token (hidden): ").strip()

# Set the datastream and start/enddates
#datastream = 'sgpaoscpcufE13.b1'
datastream = 'sgpaoscpcfE13.b1'

startdate = '2022-12-30'
enddate = '2024-01-02'

# Use ACT to easily download the data.  Watch for the data citation!  Show some support
# for ARM's instrument experts and cite their data if you use it in a publication
arm_files = act.discovery.download_arm_data(arm_username, arm_token, datastream, startdate, enddate)
Enter ARM username:  gcarrillocardenas
Enter ARM token (hidden):  ········

[DOWNLOADING] sgpaoscpcfE13.b1.20221230.000000.nc
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[DOWNLOADING] sgpaoscpcfE13.b1.20231025.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231026.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231027.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231028.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231029.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231030.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231031.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231101.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231102.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231103.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231104.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231105.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231106.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231107.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231108.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231109.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231110.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231111.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231112.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231113.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231114.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231115.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231116.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231117.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231118.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231119.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231120.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231121.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231122.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231123.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231124.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231125.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231126.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231127.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231128.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231129.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231130.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231201.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231202.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231203.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231204.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231205.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231206.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231207.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231208.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231209.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231210.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231211.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231212.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231213.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231214.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231215.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231216.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231217.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231218.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231219.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231220.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231221.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231222.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231223.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231224.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231225.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231226.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231227.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231228.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231229.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231230.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20231231.000000.nc
[DOWNLOADING] sgpaoscpcfE13.b1.20240101.000000.nc

If you use these data to prepare a publication, please cite:

Koontz, A., Kuang, C., Andrews, E., Hayes, C., Singh, A., & Salwen, C.
Condensation Particle Counter (AOSCPCF), 2022-12-30 to 2024-01-02, Southern
Great Plains (SGP), Lamont, OK (Extended and Co-located with C1) (E13).
Atmospheric Radiation Measurement (ARM) User Facility.
https://doi.org/10.5439/1352536


path = os.path.expanduser(
    '~/DOE_ARM_SummerSchool/sgpaoscpcufE13.b1/sgpaoscpcufE13.b1*.nc'
)
cpcuf_data_files = sorted(glob.glob(path))
len(cpcuf_data_files)
368
first_file = cpcuf_data_files[0]

ds = xr.open_dataset(first_file)
variables = list(ds.variables)

print(variables)

['base_time', 'time_offset', 'time', 'concentration', 'qc_concentration', 'saturation_temp', 'condensation_temp', 'optics_temp', 'cabinet_temp', 'ambient_pressure', 'orifice_pressure', 'nozzle_pressure', 'laser_current', 'liquid_level', 'fraction_full', 'corrected_flow', 'lat', 'lon', 'alt']

variable_time = ds["time"]

print(variable_time)
print("Name:", variable_time.name)
print("Dimensions:", variable_time.dims)
print("Shape:", variable_time.shape)
print("Data type:", variable_time.dtype)

print("\nAttributes:")

<xarray.DataArray 'time' (time: 86399)>
array(['2022-12-30T00:00:00.950000000', '2022-12-30T00:00:01.950000000',
       '2022-12-30T00:00:02.940000000', ..., '2022-12-30T23:59:57.810000000',
       '2022-12-30T23:59:58.810000000', '2022-12-30T23:59:59.810000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2022-12-30T00:00:00.950000 ... 2022-12-30T...
Attributes:
    long_name:      Time offset from midnight
    standard_name:  time
Name: time
Dimensions: ('time',)
Shape: (86399,)
Data type: datetime64[ns]

Attributes:

variable_concentration = ds["concentration"]

print(variable_concentration)
print("Name:", variable_concentration.name)
print("Dimensions:", variable_concentration.dims)
print("Shape:", variable_concentration.shape)
print("Data type:", variable_concentration.dtype)

print("\nAttributes:")

<xarray.DataArray 'concentration' (time: 86399)>
array([2722.374 , 2703.4688, 2779.0903, ..., 2476.6042, 2542.773 , 2476.6042],
      dtype=float32)
Coordinates:
  * time     (time) datetime64[ns] 2022-12-30T00:00:00.950000 ... 2022-12-30T...
Attributes:
    long_name:                    Particle concentration
    units:                        1/cm^3
    valid_min:                    5.0
    valid_max:                    50000.0
    corrections_applied:          flow_calibration
    flow_calibration_definition:  The concentration value has been multiplied...
    ancillary_variables:          qc_concentration
Name: concentration
Dimensions: ('time',)
Shape: (86399,)
Data type: float32

Attributes:

cpcuf_ds = xr.open_mfdataset(
    cpcuf_data_files,
    combine="by_coords"
)

# Keep concentration only where qc_concentration == 0
# Usually qc == 0 means good data
# Keep original concentration
# Add QC-fixed concentration as a new xarray variable
cpcuf_ds["concentration_qc_fixed"] = cpcuf_ds["concentration"].where(
    cpcuf_ds["qc_concentration"] == 0
)

# Optional: keep only the variables you care about
cpcuf_ds = cpcuf_ds[
    ["concentration", "qc_concentration", "concentration_qc_fixed"]
]

print(cpcuf_ds)
<xarray.Dataset>
Dimensions:                 (time: 31768745)
Coordinates:
  * time                    (time) datetime64[ns] 2022-12-30T00:00:00.950000 ...
Data variables:
    concentration           (time) float32 dask.array<chunksize=(86399,), meta=np.ndarray>
    qc_concentration        (time) int32 dask.array<chunksize=(86399,), meta=np.ndarray>
    concentration_qc_fixed  (time) float32 dask.array<chunksize=(86399,), meta=np.ndarray>
Attributes: (12/21)
    command_line:                aoscpcufcorr -s sgp -f E13
    Conventions:                 ARM-1.2
    process_version:             ingest-aoscpcufcorr-1.5-0.el7
    dod_version:                 aoscpcuf-b1-4.0
    site_id:                     sgp
    platform_id:                 aoscpcuf
    ...                          ...
    mentor_affiliation:          BNL
    input_datastreams:           sgpaoscpcufE13.a1 : 1.2 : 20221230.000000
    configuration_file:          sgpaoscpcufE13.20190328.160000.config
    AOS_DAC_type:                D
    doi:                         10.5439/1378933
    history:                     created by user dsmgr on machine prod-proc3....

# Select one month
month_ds = cpcuf_ds.sel(
    time=slice("2023-01-01", "2023-02-01")
)

print(month_ds)
<xarray.Dataset>
Dimensions:                 (time: 2764587)
Coordinates:
  * time                    (time) datetime64[ns] 2023-01-01T00:00:00.6799999...
Data variables:
    concentration           (time) float32 dask.array<chunksize=(86400,), meta=np.ndarray>
    qc_concentration        (time) int32 dask.array<chunksize=(86400,), meta=np.ndarray>
    concentration_qc_fixed  (time) float32 dask.array<chunksize=(86400,), meta=np.ndarray>
Attributes: (12/21)
    command_line:                aoscpcufcorr -s sgp -f E13
    Conventions:                 ARM-1.2
    process_version:             ingest-aoscpcufcorr-1.5-0.el7
    dod_version:                 aoscpcuf-b1-4.0
    site_id:                     sgp
    platform_id:                 aoscpcuf
    ...                          ...
    mentor_affiliation:          BNL
    input_datastreams:           sgpaoscpcufE13.a1 : 1.2 : 20221230.000000
    configuration_file:          sgpaoscpcufE13.20190328.160000.config
    AOS_DAC_type:                D
    doi:                         10.5439/1378933
    history:                     created by user dsmgr on machine prod-proc3....

fig, axes = plt.subplots(
    nrows=3,
    ncols=1,
    figsize=(16, 10),
    sharex=True
)

month_ds["concentration"].plot(ax=axes[0])
axes[0].set_title("Original Concentration")
axes[0].set_ylabel("Concentration")

month_ds["qc_concentration"].plot(ax=axes[1])
axes[1].set_title("QC Concentration")
axes[1].set_ylabel("QC")

month_ds["concentration_qc_fixed"].plot(ax=axes[2])
axes[2].set_title("QC-fixed Concentration")
axes[2].set_ylabel("QC-fixed Concentration")
axes[2].set_xlabel("Time")

for ax in axes:
    ax.grid(True)

plt.tight_layout()
plt.show()
<Figure size 1600x1000 with 3 Axes>
day_ds = cpcuf_ds.sel(
    time=slice("2023-01-05", "2023-01-07")
)

print(day_ds)
<xarray.Dataset>
Dimensions:                 (time: 259186)
Coordinates:
  * time                    (time) datetime64[ns] 2023-01-05T00:00:00.190000 ...
Data variables:
    concentration           (time) float32 dask.array<chunksize=(86399,), meta=np.ndarray>
    qc_concentration        (time) int32 dask.array<chunksize=(86399,), meta=np.ndarray>
    concentration_qc_fixed  (time) float32 dask.array<chunksize=(86399,), meta=np.ndarray>
Attributes: (12/21)
    command_line:                aoscpcufcorr -s sgp -f E13
    Conventions:                 ARM-1.2
    process_version:             ingest-aoscpcufcorr-1.5-0.el7
    dod_version:                 aoscpcuf-b1-4.0
    site_id:                     sgp
    platform_id:                 aoscpcuf
    ...                          ...
    mentor_affiliation:          BNL
    input_datastreams:           sgpaoscpcufE13.a1 : 1.2 : 20221230.000000
    configuration_file:          sgpaoscpcufE13.20190328.160000.config
    AOS_DAC_type:                D
    doi:                         10.5439/1378933
    history:                     created by user dsmgr on machine prod-proc3....

fig, axes = plt.subplots(
    nrows=3,
    ncols=1,
    figsize=(16, 10),
    sharex=True
)

day_ds["concentration"].plot(ax=axes[0])
axes[0].set_title("Original Concentration")
axes[0].set_ylabel("Concentration")

day_ds["qc_concentration"].plot(ax=axes[1])
axes[1].set_title("QC Concentration")
axes[1].set_ylabel("QC")

day_ds["concentration_qc_fixed"].plot(ax=axes[2])
axes[2].set_title("QC-fixed Concentration")
axes[2].set_ylabel("QC-fixed Concentration")
axes[2].set_xlabel("Time")

for ax in axes:
    ax.grid(True)

plt.tight_layout()
plt.show()
<Figure size 1600x1000 with 3 Axes>
cpcuf_ds["time_Local"] = cpcuf_ds["time"] - pd.Timedelta(hours=6)
cpcuf_ds["time_Local"].attrs["note"] = "UTC time minus 6 hours"
print(cpcuf_ds["time"].isel(time=slice(0, 5)).values)
print(cpcuf_ds["time_Local"].isel(time=slice(0, 5)).values)
['2022-12-30T00:00:00.950000000' '2022-12-30T00:00:01.950000000'
 '2022-12-30T00:00:02.940000000' '2022-12-30T00:00:03.950000000'
 '2022-12-30T00:00:04.950000000']
['2022-12-29T18:00:00.950000000' '2022-12-29T18:00:01.950000000'
 '2022-12-29T18:00:02.940000000' '2022-12-29T18:00:03.950000000'
 '2022-12-29T18:00:04.950000000']

print(cpcuf_ds["time_Local"].attrs)

{'note': 'UTC time minus 6 hours'}

path = os.path.expanduser(
    '~/DOE_ARM_SummerSchool/sgpaoscpcfE13.b1/sgpaoscpcfE13.b1*.nc'
)
cpcFINE_data_files = sorted(glob.glob(path))  

cpcFINE_ds = xr.open_mfdataset(
    cpcFINE_data_files,
    combine="by_coords"
)

# Keep concentration only where qc_concentration == 0
# Usually qc == 0 means good data
# Keep original concentration
# Add QC-fixed concentration as a new xarray variable
cpcFINE_ds["concentration_qc_fixed"] = cpcFINE_ds["concentration"].where(
    cpcFINE_ds["qc_concentration"] == 0
)

# Optional: keep only the variables you care about
cpcFINE_ds = cpcFINE_ds[
    ["concentration", "qc_concentration", "concentration_qc_fixed"]
]

print(cpcFINE_ds)
<xarray.Dataset>
Dimensions:                 (time: 31764571)
Coordinates:
  * time                    (time) datetime64[ns] 2022-12-30T00:00:00.950000 ...
Data variables:
    concentration           (time) float32 dask.array<chunksize=(86399,), meta=np.ndarray>
    qc_concentration        (time) int32 dask.array<chunksize=(86399,), meta=np.ndarray>
    concentration_qc_fixed  (time) float32 dask.array<chunksize=(86399,), meta=np.ndarray>
Attributes: (12/21)
    command_line:                aoscpcfcorr -s sgp -f E13 -b 20221205 -e 202...
    Conventions:                 ARM-1.2
    process_version:             ingest-aoscpcfcorr-2.7-0.el7
    dod_version:                 aoscpcf-b1-4.0
    site_id:                     sgp
    platform_id:                 aoscpcf
    ...                          ...
    mentor_affiliation:          BNL
    input_datastreams:           sgpaoscpcfE13.a1 : 1.1 : 20221230.000000
    configuration_file:          sgpaoscpcfE13.20190328.160000.config
    AOS_DAC_type:                D
    doi:                         10.5439/1352536
    history:                     created by user yahmad on machine prod-proc5...

cpcFINE_ds["time_Local"] = cpcFINE_ds["time"] - pd.Timedelta(hours=6)
cpcFINE_ds["time_Local"].attrs["note"] = "UTC time minus 6 hours"
print(cpcFINE_ds["time"].isel(time=slice(0, 5)).values)
print(cpcFINE_ds["time_Local"].isel(time=slice(0, 5)).values)
['2022-12-30T00:00:00.950000000' '2022-12-30T00:00:01.960000000'
 '2022-12-30T00:00:02.950000000' '2022-12-30T00:00:03.950000000'
 '2022-12-30T00:00:04.950000000']
['2022-12-29T18:00:00.950000000' '2022-12-29T18:00:01.960000000'
 '2022-12-29T18:00:02.950000000' '2022-12-29T18:00:03.950000000'
 '2022-12-29T18:00:04.950000000']

cpcFINE_local = cpcFINE_ds.swap_dims({"time": "time_Local"})
cpcuf_local = cpcuf_ds.swap_dims({"time": "time_Local"})
cpcFINE_local = cpcFINE_local.rename({
    "concentration": "concentration_FINE",
    "concentration_qc_fixed": "concentration_FINE_qc_fixed",
    "qc_concentration": "qc_concentration_FINE"
})
cpcFINE_local_clean = (
    cpcFINE_local[["concentration_FINE_qc_fixed"]]
    .drop_vars(["time"], errors="ignore")
)

cpcuf_local = cpcuf_local.rename({
    "concentration": "concentration_UF",
    "qc_concentration": "qc_concentration_UF",
    "concentration_qc_fixed": "concentration_UF_qc_fixed"
})

cpcuf_local_clean = (
    cpcuf_local[["concentration_UF_qc_fixed"]]
    .drop_vars(["time"], errors="ignore")
)


# 5-minute averages for each dataset
cpcFINE_merge = cpcFINE_local_clean.resample(time_Local="5min").mean()
cpcuf_merge = cpcuf_local_clean.resample(time_Local="5min").mean()

print("all done")
all done

fine_times = cpcFINE_merge["time_Local"].isel(time_Local=slice(0, 5)).values
uf_times = cpcuf_merge["time_Local"].isel(time_Local=slice(0, 5)).values

for i, (fine_t, uf_t) in enumerate(zip(fine_times, uf_times)):
    print(i, "FINE:", fine_t, "| UF:", uf_t)
0 FINE: 2022-12-29T18:00:00.000000000 | UF: 2022-12-29T18:00:00.000000000
1 FINE: 2022-12-29T18:05:00.000000000 | UF: 2022-12-29T18:05:00.000000000
2 FINE: 2022-12-29T18:10:00.000000000 | UF: 2022-12-29T18:10:00.000000000
3 FINE: 2022-12-29T18:15:00.000000000 | UF: 2022-12-29T18:15:00.000000000
4 FINE: 2022-12-29T18:20:00.000000000 | UF: 2022-12-29T18:20:00.000000000

#day_merge
Loading...
#plt.figure(figsize=(14, 5))

#day_merge["concentration_UF_qc_fixed"].plot()

#plt.xlabel("Local Time")
#plt.ylabel("UF QC-fixed Concentration")
#plt.title("UF QC-fixed Concentration, 5-minute average")
#plt.grid(True)
#plt.tight_layout()
#plt.show()
<Figure size 1400x500 with 1 Axes>
merged_cpc_5min = xr.merge(
    [cpcFINE_merge, cpcuf_merge],
    join="inner"
)

print(merged_cpc_5min)
<xarray.Dataset>
Dimensions:                      (time_Local: 105984)
Coordinates:
  * time_Local                   (time_Local) datetime64[ns] 2022-12-29T18:00...
Data variables:
    concentration_FINE_qc_fixed  (time_Local) float32 dask.array<chunksize=(1,), meta=np.ndarray>
    concentration_UF_qc_fixed    (time_Local) float32 dask.array<chunksize=(1,), meta=np.ndarray>

merged_cpc_5min["UF_minus_FINE"] = (
    merged_cpc_5min["concentration_UF_qc_fixed"]
    - merged_cpc_5min["concentration_FINE_qc_fixed"]
)

merged_cpc_2023 = merged_cpc_5min.sel(
    time_Local=slice("2023-01-01", "2024-01-01")
)

print(merged_cpc_2023)
<xarray.Dataset>
Dimensions:                      (time_Local: 105336)
Coordinates:
  * time_Local                   (time_Local) datetime64[ns] 2023-01-01 ... 2...
Data variables:
    concentration_FINE_qc_fixed  (time_Local) float32 dask.array<chunksize=(1,), meta=np.ndarray>
    concentration_UF_qc_fixed    (time_Local) float32 dask.array<chunksize=(1,), meta=np.ndarray>
    UF_minus_FINE                (time_Local) float32 dask.array<chunksize=(1,), meta=np.ndarray>


merged_cpc_2023_df = (
    merged_cpc_2023[["UF_minus_FINE"]]
    .chunk({"time_Local": -1})
    .compute()
    .to_dataframe()
    .reset_index()
)
merged_cpc_2023_df.to_csv("merged_cpc_2023_UF_minus_FINE5min.csv", index=False)
N-Dimensional Auto-Clustering of Lidar Measurements to define Defacto Aerosol Types
N-Dimensional Auto-Clustering of Lidar Measurements to define Defacto Aerosol Types
Project Development
SMPS Investigation of Nanometer-sized Particles