Unexpected Results after Integration into PM2.5, PMcoarse, etc. from APS data

[9kgli]

Hello, I have used the GenericParticleSizer function to integrate across data taken from an APS. I believe that the bin sizes were inputted correctly, and I'm okay with any weirdness from density assumptions for the moment. I am getting very high numbers, even though I specified "mass" as the weight.

What unit is the end result supposed to be? I have applied a natural log to the end result but that doesn't seem right either.

APS_original = pd.read_csv('/content/drive/MyDrive/APS Data/APS_Merged_Data.csv')
APS_original['Start_Hour'] = pd.to_datetime(APS_original['Start_Hour'])
APS_original = APS_original.set_index('Start_Hour')


non_bin_original_names = ['Median(µm)', 'Mean(µm)', 'Geo. Mean(µm)', 'Mode(µm)', 'Geo. Std. Dev.', 'Date', 'Aerodynamic Diameter', 'Side Scatter Channel', 'Status Flags', 'Aerodynamic Diameter Raw Data']


original_bin_names = [col for col in APS_original.columns if col not in non_bin_original_names and col != APS_original.index.name]



rename_dict = {}
numerical_right_edges_list = []
for col in original_bin_names:
    if col == '<0.523':
        
        rename_dict[col] = 0.523
        numerical_right_edges_list.append(0.523)
    else:
       
            numerical_value = float(col)
            rename_dict[col] = numerical_value
            numerical_right_edges_list.append(numerical_value)
        except ValueError:

            print(f"Warning: Could not convert original column name '{col}' to float. Excluding from bins.")
            pass


numerical_right_edges = np.array(sorted(numerical_right_edges_list))


left_edges = np.insert(numerical_right_edges[:-1], 0, 0.0)


midpoints = np.sqrt(left_edges * numerical_right_edges)
if left_edges[0] == 0.0:
    midpoints[0] = numerical_right_edges[0] / 2.0 # Use half of the right edge for the first bin



smps_bins = np.vstack([left_edges, midpoints, numerical_right_edges]).T



reverse_rename_dict = {v: k for k, v in rename_dict.items()}


original_bin_names_ordered = [reverse_rename_dict[edge] for edge in numerical_right_edges]


APS_data_for_smps = APS_original[original_bin_names_ordered]


bin_column_names = [f'bin{i}' for i in range(len(original_bin_names_ordered))]
APS_data_for_smps.columns = bin_column_names



obj_APS = smps.models.GenericParticleSizer(data=APS_data_for_smps, bins=smps_bins)
def custom_density(dp):
    """Calculate density as a function of particle diameter in µm.

    Parameters
    ----------
    dp : float
        Particle diameter in microns.
    """
    return 1.65 if dp < 3 else 2.5

PM25 = obj_APS.integrate(weight="mass", dmin=0., dmax=2.5, rho=custom_density)
PMc = obj_APS.integrate(weight="mass", dmin=2.5, dmax=10, rho=custom_density)
PM10 = obj_APS.integrate(weight="mass", dmin=0, dmax=10, rho=custom_density)
PMsc = obj_APS.integrate(weight="mass", dmin=10, dmax=30, rho=custom_density)

display(PM25.head())
display(PMc.head())
display(PM10.head())
display(PMsc.head())

APS_Merged_Data.csv