Printing Ink Study Analysis

---

author: Havala O.T. Pye (pye.havala@epa.gov)
date: created 2024-02-16
      last updated 2025-04-25

---

This notebook reads in the data from assembled across HPLC (Xiaoyu Liu), VOC (Ingrid George), and SVOC (Joe Martin) techniques. It provides visualization of the data and propagation to SOA, ozone, and risk endpoints.

Inputs

• Final experimental data axcerpt of 20240109_print_VCP Data Joe Update 20250407_corrected.xlsx (Final_print_VCP_data.xlsx)
• Representative structures list manually created by Havala (20240222_representativestructures.xlsx)
• Comptox dashboard data for representative structures (output from https://comptox.epa.gov/dashboard/batch-search) (CCD-Batch-Search*.xlsx)
• Utilities for SOA, O3, and CRACMM mapping from https://github.com/USEPA/CRACMM
• "HEM Toxicity Value files" from https://www.epa.gov/fera/download-human-exposure-model-hem: "Dose_Response_Library.xlsx" (last update 4/2023)
• SPECIATE printing ink profiles from Karl Seltzer (printing_ink_profiles_seltzer.xlsx)
• Ink sales/usage from Karl Seltzer (inksales.xlsx)

Note: Test T2, T3, T4 renamed ink C, B, A for presentation in manuscript

Setup

%matplotlib inline

!python -m pip install --user rdkit

1h=
Currently Loaded Modules:
  1) geos/3.11.2         5) udunits/2.2.28   9) intelpython/3.9.15
  2) pandoc/3.1.4        6) gdal/3.6.4      10) ncview/2.1.9
  3) jupyterlab/3.0.18   7) proj/9.2.1      11) nco/5.1.4
  4) intel/21.4          8) intelmpi/21.4   12) netcdf/4.9.2

 

>Happy coding!
Requirement already satisfied: rdkit in /home/phavala/.local/lib/python3.9/site-packages (2023.9.5)
Requirement already satisfied: Pillow in /home/phavala/.local/lib/python3.9/site-packages (from rdkit) (10.1.0)
Requirement already satisfied: numpy in /home/local-rhel8/apps/oneapi/intelpython/python3.9/lib/python3.9/site-packages (from rdkit) (1.22.3)

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta, date
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
import matplotlib.ticker as ticker
import matplotlib.patches as mpatches

# directories
inputdir   = './inputs/'      # inputs
outputdir  = './output/'      # outputs
utildir    = './utilities/'   # directory where utilities from CRACMM github are downloaded

# date for stamping output files
today = str(date.today())
today = today.replace('-','')

# import utilities from https://github.com/USEPA/CRACMM/
import sys
sys.path.append(utildir)

# Import the python utilities
from mir_estimates import *  # includes: get_mir(smiles,koh)
from soa_yields import *     # includes: get_soayield(smiles,pvap,molwght)
import cracmm2_mapper as cracmm2    # includes: get_cracmm_roc(smiles,koh,log10cstar) (Version 2)
import cracmm1_mapper  as cracmm1   # includes: get_cracmm_roc(smiles,koh,log10cstar) (Version 1)

# Constants
Treference = 298.15   # K
RJmolK     = 8.314    # J/molK

Read experimental data for T2, T3, and T4

# Read excel spreadsheet -- T2 and T3
measfilename = inputdir + 'Final_print_VCP_data.xlsx' #'20240109_print_VCP Data Joe Update 20250407_corrected.xlsx' # Joe's updated

dft23 = pd.read_excel( measfilename, sheet_name = 'Data Analysis', skiprows = 64, nrows = 8, header =1, usecols= 'D:F')
dft23 = dft23.rename(columns={'Unnamed: 3':'species'})
dft23 = dft23.rename(columns={'T2':'T2_emiss_g','T3-VOC Data':'T3_emiss_g'}) # add units and emission label
dft23 

	species	T2_emiss_g	T3_emiss_g
0	Formaldehyde	0.000017	0.000019
1	Acetaldehyde	0.000062	0.000552
2	Unknown as Acetone	0.000921	0.00106
3	Butanal	ND	0.000005
4	Hexaldehyde	0.000015	0.000018
5	Methanol	0.03633	0.00797
6	Isopropyl_Alcohol	0.00059	NR
7	Total SVOCs	0.017974	0.003953

# Read spreadsheet T4 data (drop T1)
#filename = inputdir + '20240109_print_VCP Data-For Sharing.xlsx'
dft4 = pd.read_excel( measfilename, sheet_name = 'Data Analysis', skiprows = 79, nrows = 15, header =1, usecols= 'D:G')
dft4 = dft4.rename(columns={'Unnamed: 3':'species'})
dft4 = dft4.rename(columns={'T4-VOC data':'T4_emiss_g'}) # add units and emission label
dft4.drop(columns='T1-No VOC Data', inplace = True)
dft4.drop(columns='T4- HPLC Data', inplace = True)
dft4

	species	T4_emiss_g
0	Acetaldehyde	0.000672
1	Acetone	0.002566
2	Propionaldehyde	0.000004
3	2-Butanone (MEK)	0.008596
4	3-Pentanone	1.295795
5	Benzaldehyde	0.000003
6	Methanol	0.003933
7	Ethanol	1.181273
8	Isopropyl_Alcohol	0.000861
9	Ethyl_Acetate	0.018519
10	Toluene	0.000013
11	Alkane	0.000327
12	PAH	0.000649
13	Glycol	0.034017
14	SubBenzene	0.006565

# get list of unique species, uncomment to produce list for preparing input to dashboard, manually assign representative compounds
#list(pd.concat( [dft23, dft4] ).species.unique())

# combine all test data after dropping rows that aren't species and dropping technique
dfmeasured = pd.merge(dft23,dft4, how='outer',on='species')
dfmeasured

	species	T2_emiss_g	T3_emiss_g	T4_emiss_g
0	Formaldehyde	0.000017	0.000019	NaN
1	Acetaldehyde	0.000062	0.000552	0.000672
2	Unknown as Acetone	0.000921	0.00106	NaN
3	Butanal	ND	0.000005	NaN
4	Hexaldehyde	0.000015	0.000018	NaN
5	Methanol	0.03633	0.00797	0.003933
6	Isopropyl_Alcohol	0.00059	NR	0.000861
7	Total SVOCs	0.017974	0.003953	NaN
8	Acetone	NaN	NaN	0.002566
9	Propionaldehyde	NaN	NaN	0.000004
10	2-Butanone (MEK)	NaN	NaN	0.008596
11	3-Pentanone	NaN	NaN	1.295795
12	Benzaldehyde	NaN	NaN	0.000003
13	Ethanol	NaN	NaN	1.181273
14	Ethyl_Acetate	NaN	NaN	0.018519
15	Toluene	NaN	NaN	0.000013
16	Alkane	NaN	NaN	0.000327
17	PAH	NaN	NaN	0.000649
18	Glycol	NaN	NaN	0.034017
19	SubBenzene	NaN	NaN	0.006565

Add metadata to measurements

Representative structures

# Read table of representative structures manually assigned by Havala
filename = inputdir + '20240222_representativestructures.xlsx'
repstruc = pd.read_excel( filename ) # representative names are input to dashboard
repstruc

	species	representative
0	Formaldehyde	Formaldehyde
1	Acetaldehyde	Acetaldehyde
2	Acetone	Acetone
3	Butanal	Butanal
4	Hexaldehyde	Hexaldehyde
5	Methanol	Methanol
6	Isopropyl_Alcohol	Isopropyl Alcohol
7	Total SVOCs	diethylene glycol
8	Propionaldehyde	Propionaldehyde
9	Benzaldehyde	Benzaldehyde
10	2-Butanone (MEK)	2-Butanone
11	3-Pentanone	3-Pentanone
12	Ethanol	Ethanol
13	Ethyl_Acetate	Ethyl Acetate
14	Toluene	Toluene
15	Alkane	octane
16	PAH	1H-Indene-3-carboxylic acid
17	Glycol	Glycol
18	SubBenzene	1,2,3-trimethylbenzene
19	Unknown as Acetone	acetone

# Append representative and print (review by team)
dfmeasured=pd.merge(dfmeasured,repstruc,on="species") 
nspc = dfmeasured.shape[0]
dfmeasured

	species	T2_emiss_g	T3_emiss_g	T4_emiss_g	representative
0	Formaldehyde	0.000017	0.000019	NaN	Formaldehyde
1	Acetaldehyde	0.000062	0.000552	0.000672	Acetaldehyde
2	Unknown as Acetone	0.000921	0.00106	NaN	acetone
3	Butanal	ND	0.000005	NaN	Butanal
4	Hexaldehyde	0.000015	0.000018	NaN	Hexaldehyde
5	Methanol	0.03633	0.00797	0.003933	Methanol
6	Isopropyl_Alcohol	0.00059	NR	0.000861	Isopropyl Alcohol
7	Total SVOCs	0.017974	0.003953	NaN	diethylene glycol
8	Acetone	NaN	NaN	0.002566	Acetone
9	Propionaldehyde	NaN	NaN	0.000004	Propionaldehyde
10	2-Butanone (MEK)	NaN	NaN	0.008596	2-Butanone
11	3-Pentanone	NaN	NaN	1.295795	3-Pentanone
12	Benzaldehyde	NaN	NaN	0.000003	Benzaldehyde
13	Ethanol	NaN	NaN	1.181273	Ethanol
14	Ethyl_Acetate	NaN	NaN	0.018519	Ethyl Acetate
15	Toluene	NaN	NaN	0.000013	Toluene
16	Alkane	NaN	NaN	0.000327	octane
17	PAH	NaN	NaN	0.000649	1H-Indene-3-carboxylic acid
18	Glycol	NaN	NaN	0.034017	Glycol
19	SubBenzene	NaN	NaN	0.006565	1,2,3-trimethylbenzene

Dashboad information

# Match with output from https://comptox.epa.gov/dashboard/batch-search
filename = inputdir + 'CCD-Batch-Search_2024-02-22_04_46_48.xlsx'
ccd = pd.read_excel( filename, sheet_name = 'Main Data')
ccd.drop_duplicates( subset = 'INPUT', inplace = True) # remove any duplicates

dfmeasured = pd.merge(dfmeasured,ccd,left_on='representative',right_on='INPUT')
if( dfmeasured.shape[0] != nspc ): 
    print('Number of species before mapping '+ str(nspc) +' does not match after reading comptox output. Correction needed.')
else:
    print(str(nspc)+' species mapped to properties')

20 species mapped to properties

/home/phavala/.local/lib/python3.9/site-packages/openpyxl/styles/stylesheet.py:226: UserWarning: Workbook contains no default style, apply openpyxl's default
  warn("Workbook contains no default style, apply openpyxl's default")

# display combined measurements and properties
pd.set_option('display.max_columns', None)
dfmeasured # display

	species	T2_emiss_g	T3_emiss_g	T4_emiss_g	representative	INPUT	FOUND_BY	DTXSID	PREFERRED_NAME	DTXCID	CASRN	SMILES	MOLECULAR_FORMULA	AVERAGE_MASS	BIOCONCENTRATION_FACTOR_TEST_PRED	BOILING_POINT_DEGC_TEST_PRED	48HR_DAPHNIA_LC50_MOL/L_TEST_PRED	DENSITY_G/CM^3_TEST_PRED	DEVTOX_TEST_PRED	96HR_FATHEAD_MINNOW_MOL/L_TEST_PRED	FLASH_POINT_DEGC_TEST_PRED	MELTING_POINT_DEGC_TEST_PRED	AMES_MUTAGENICITY_TEST_PRED	ORAL_RAT_LD50_MOL/KG_TEST_PRED	SURFACE_TENSION_DYN/CM_TEST_PRED	THERMAL_CONDUCTIVITY_MW/(M*K)_TEST_PRED	TETRAHYMENA_PYRIFORMIS_IGC50_MOL/L_TEST_PRED	VISCOSITY_CP_CP_TEST_PRED	VAPOR_PRESSURE_MMHG_TEST_PRED	WATER_SOLUBILITY_MOL/L_TEST_PRED	ATMOSPHERIC_HYDROXYLATION_RATE_(AOH)_CM3/MOLECULE*SEC_OPERA_PRED	BIOCONCENTRATION_FACTOR_OPERA_PRED	BIODEGRADATION_HALF_LIFE_DAYS_DAYS_OPERA_PRED	BOILING_POINT_DEGC_OPERA_PRED	HENRYS_LAW_ATM-M3/MOLE_OPERA_PRED	OPERA_KM_DAYS_OPERA_PRED	OCTANOL_AIR_PARTITION_COEFF_LOGKOA_OPERA_PRED	SOIL_ADSORPTION_COEFFICIENT_KOC_L/KG_OPERA_PRED	OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED	MELTING_POINT_DEGC_OPERA_PRED	OPERA_PKAA_OPERA_PRED	OPERA_PKAB_OPERA_PRED	VAPOR_PRESSURE_MMHG_OPERA_PRED	WATER_SOLUBILITY_MOL/L_OPERA_PRED	LOGD5.5	LOGD7.4	READY_BIO_DEG
0	Formaldehyde	0.000017	0.000019	NaN	Formaldehyde	Formaldehyde	Approved Name	DTXSID7020637	Formaldehyde	DTXCID30637	50-00-0	C=O	CH2O	30.026		-16.012		0.861			-18.267	-79.361	0.362		22.056	164.885		0.254097	1221.800000	9.571940	9.342880e-12	1.72988	8.20857	-19.0454	3.494710e-07	0.224902	1.20597	2.72646	0.349206	-105.2860		1.15	824.979000	14.662500	0.35	0.35	1
1	Acetaldehyde	0.000062	0.000552	0.000672	Acetaldehyde	Acetaldehyde	Approved Name	DTXSID5039224	Acetaldehyde	DTXCID202	75-07-0	CC=O	C2H4O	44.053		31.585	0.010765	0.906	0.491	0.005012	-0.414	-45.573	0.107	0.018197	23.046	149.31	0.014158	0.285759	420.727000	4.698940	1.579550e-11	2.45935	7.56818	20.1228	6.609010e-05	0.169684	1.79446	2.57283	-0.338130	-122.8790	16.76	-0.57	898.259000	16.051800	-0.34	-0.34	1
2	Unknown as Acetone	0.000921	0.00106	NaN	acetone	acetone	Approved Name	DTXSID8021482	Acetone	DTXCID101482	67-64-1	CC(C)=O	C3H6O	58.080	1.91426	41.374	0.021928	0.837	0.462	0.036308	9.637	-63.606	-0.029	0.062661	23.159	147.469	0.068391	0.319154	250.035000	5.623410	2.201470e-13	1.99247	7.56577	55.6196	3.532240e-05	0.160491	2.32758	3.65480	-0.237520	-96.2441		19.13	232.690000	17.063100	-0.24	-0.24	1
3	Butanal	ND	0.000005	NaN	Butanal	Butanal	Expert Validated Synonym	DTXSID8021513	Butyraldehyde	DTXCID601513	123-72-8	CCCC=O	C4H8O	72.107		96.408	0.001439	0.852	0.582	0.000214	9.308	-53.045	0.489	0.01574	23.573	145.26	0.001365	0.466659	100.231000	0.603949	2.351290e-11	5.29042	3.67368	74.7617	1.143670e-04	0.138848	3.18740	10.84970	0.879054	-98.4607			111.140000	0.978317	0.88	0.88	1
4	Hexaldehyde	0.000015	0.000018	NaN	Hexaldehyde	Hexaldehyde	Expert Validated Synonym	DTXSID2021604	Hexanal	DTXCID901604	66-25-1	CCCCCC=O	C6H12O	100.161		126.339	0.000484	0.843	0.586	0.000192	38.107	-39.764	0.682	0.012823	24.456	140.023	0.00075	0.739605	4.591980	0.040926	3.167590e-11	11.05860	4.44523	130.8260	2.120130e-04	0.352448	4.15557	18.40280	1.778440	-56.0859			11.356000	0.052127	1.78	1.78	1
5	Methanol	0.03633	0.00797	0.003933	Methanol	Methanol	Approved Name	DTXSID2021731	Methanol	DTXCID801731	67-56-1	CO	CH4O	32.042	0.530884	17.821	0.009484	0.897		0.035156	-6.582	-75.959	0.191		21.674	165.053	0.183654	0.722770	425.598000	2.437810	9.471120e-13	1.54130	7.57468	64.7295	4.557950e-06	0.120694	2.86273	2.75443	-0.768744	-97.5702	10.29		126.324000	32.643400	-0.77	-0.77	1
6	Isopropyl_Alcohol	0.00059	NR	0.000861	Isopropyl Alcohol	Isopropyl Alcohol	Expert Validated Synonym	DTXSID7020762	Isopropanol	DTXCID50762	67-63-0	CC(C)O	C3H8O	60.096	2.12814	83.075	0.013428	0.863	0.492	0.035892	6.163	-45.893	0.001	0.053703	26.22	157.829	0.059156	2.023020	15.958800	4.786300	5.077690e-12	4.59819	7.53284	82.3951	8.088620e-06	0.079930	3.47701	3.43640	0.051948	-89.6414	11.68		44.944600	9.005230	0.05	0.05	1
7	Total SVOCs	0.017974	0.003953	NaN	diethylene glycol	diethylene glycol	Approved Name	DTXSID8020462	Diethylene glycol	DTXCID20462	111-46-6	OCCOCCO	C4H10O3	106.121	0.656145	199.851	0.053951	1.076	0.215	0.119124	85.176	-6.591	0.263	0.038282	37.899	191.896	0.119674	18.879900	0.007447	5.395110	2.990660e-11	3.43715	6.13275	245.6130	2.960770e-09	0.071864	7.13012	4.08583	-1.093890	-10.4231		7.06	0.005701	9.417610	-2.66	-1.25	1
8	Acetone	NaN	NaN	0.002566	Acetone	Acetone	Approved Name	DTXSID8021482	Acetone	DTXCID101482	67-64-1	CC(C)=O	C3H6O	58.080	1.91426	41.374	0.021928	0.837	0.462	0.036308	9.637	-63.606	-0.029	0.062661	23.159	147.469	0.068391	0.319154	250.035000	5.623410	2.201470e-13	1.99247	7.56577	55.6196	3.532240e-05	0.160491	2.32758	3.65480	-0.237520	-96.2441		19.13	232.690000	17.063100	-0.24	-0.24	1
9	Propionaldehyde	NaN	NaN	0.000004	Propionaldehyde	Propionaldehyde	Expert Validated Synonym	DTXSID2021658	Propanal	DTXCID001658	123-38-6	CCC=O	C3H6O	58.080		58.534	0.001959	0.843	0.629	0.000536	2.396	-56.789	0.26	0.005875	22.728	149.586	0.003273	0.437522	221.820000	1.566750	1.961160e-11	5.49222	3.67945	48.0513	7.334760e-05	0.163907	2.90224	5.16901	0.589061	-80.2497			314.891000	4.453230	0.59	0.59	1
10	2-Butanone (MEK)	NaN	NaN	0.008596	2-Butanone	2-Butanone	Expert Validated Synonym	DTXSID3021516	Methyl ethyl ketone	DTXCID801516	78-93-3	CCC(C)=O	C4H8O	72.107	2.66686	85.913	0.004624	0.877	0.481	0.010351	11.812	-54.197	0.089	0.014723	24.679	144.036	0.022856	0.356451	106.170000	1.137630	1.153900e-12	4.09711	3.66957	79.3395	5.705120e-05	0.136632	2.72588	15.89840	0.291427	-86.3934			90.624500	3.183170	0.29	0.29	1
11	3-Pentanone	NaN	NaN	1.295795	3-Pentanone	3-Pentanone	Approved Name	DTXSID6021820	3-Pentanone	DTXCID301820	96-22-0	CCC(=O)CC	C5H10O	86.134	3.75837	104.735	0.001614	0.833	0.497	0.004775	19.233	-59.074	0.198	0.016033	24.846	142.618	0.0139	0.432514	32.885200	0.381944	2.004050e-12	3.96080	5.24613	102.0200	5.053810e-05	0.179811	3.20144	19.53570	0.906191	-42.0728			37.488700	0.546379	0.91	0.91	1
12	Benzaldehyde	NaN	NaN	0.000003	Benzaldehyde	Benzaldehyde	Approved Name	DTXSID8039241	Benzaldehyde	DTXCID90134	100-52-7	O=CC1=CC=CC=C1	C7H6O	106.124	6.16	185.010	0.000151	1.090	0.19	0.000158	73.440	6.480	0.04	0.012023	41.15	143.85	0.001288	1.412540	0.281838	0.035481	1.291460e-11	12.52050	5.92924	179.1020	2.673770e-05	0.202421	3.88581	32.50930	1.474340	-25.5658			1.263430	0.062603	1.47	1.47	1
13	Ethanol	NaN	NaN	1.181273	Ethanol	Ethanol	Approved Name	DTXSID9020584	Ethanol	DTXCID30584	64-17-5	CCO	C2H6O	46.069	1.25893	68.054	0.028249	0.912	0.455	0.017418	10.828	-69.464	-0.006	0.01714	24.095	182.077	0.103753	1.531090	31.550000	6.516280	3.277880e-12	1.81310	4.59705	78.2185	5.013510e-06	0.105204	3.24479	1.59008	-0.309717	-114.0380	12.31		58.854800	18.103000	-0.31	-0.31	1
14	Ethyl_Acetate	NaN	NaN	0.018519	Ethyl Acetate	Ethyl Acetate	Approved Name	DTXSID1022001	Ethyl acetate	DTXCID602001	141-78-6	CCOC(C)=O	C4H8O2	88.106	2.61216	81.196	0.002249	0.926	0.587	0.002004	14.040	-70.237	0.141	0.06223	23.931	142.058	0.018197	0.465586	75.162300	0.475335	1.603440e-12	3.23156	4.12611	78.4376	1.348000e-04	0.180171	2.69862	18.17440	0.731494	-83.9003			93.156800	0.911820	0.73	0.73	1
15	Toluene	NaN	NaN	0.000013	Toluene	Toluene	Expert Validated Synonym	DTXSID7021360	1-Methylbenzene	DTXCID501360	108-88-3	CC1=CC=CC=C1	C7H8	92.141	66.55	128.360	0.000229	0.900	0.28	0.000339	28.260	-33.640	-0.02	0.016982	29.18	132.42	0.001122	0.676083	23.988300	0.004074	5.973360e-12	8.33841	2.01480	110.6700	6.626450e-03	0.237095	3.49442	117.11500	2.728790	-93.2830			28.316500	0.005918	2.73	2.73	1
16	Alkane	NaN	NaN	0.000327	octane	octane	Approved Name	DTXSID0026882	Octane	DTXCID406882	111-65-9	CCCCCCCC	C8H18	114.232	580.764	130.843	0.00003	0.743	0.487	0.000016	24.976	-71.701		0.039628	21.263	124.078	0.000105	0.500035	9.246980	0.000038	8.662630e-12	312.32300	8.98873	125.4960	3.655730e-01	4.112610	3.39317	191.49500	5.178630	-57.4803			14.005100	0.000006	5.18	5.18	1
17	PAH	NaN	NaN	0.000649	1H-Indene-3-carboxylic acid	1H-Indene-3-carboxylic acid	Approved Name	DTXSID20161940	1H-Indene-3-carboxylic acid	DTXCID5084431	14209-41-7	OC(=O)C1=CCC2=CC=CC=C12	C10H8O2	160.172	6.223	298.031	0.000202	1.261	0.606	0.000052	150.849	110.977	0.43					15.703600	0.000062	0.001832	3.070700e-11	4.01565	3.10224	302.6460	1.252300e-08	0.110657	8.38386	77.89030	2.150060	104.0450	5.29		0.000060	0.001932	1.73	0.04	1
18	Glycol	NaN	NaN	0.034017	Glycol	Glycol	Synonym	DTXSID0021206	1,2-Propylene glycol	DTXCID901206	57-55-6	CC(O)CO	C3H8O2	76.095	1.07647	168.555	0.026915	1.010	0.545	0.095061	70.374	-21.625	0.196	0.053456	33.094	184.634	0.152055	12.618300	0.295121	4.315190	1.201600e-11	3.38004	4.26800	187.6690	6.054780e-08	0.059302	6.73741	2.30480	-0.918877	-59.8063	11.47		0.127532	13.154500	-0.92	-0.92	1
19	SubBenzene	NaN	NaN	0.006565	1,2,3-trimethylbenzene	1,2,3-trimethylbenzene	Approved Name	DTXSID8047769	1,2,3-Trimethylbenzene	DTXCID7027750	526-73-8	CC1=CC=CC(C)=C1C	C9H12	120.195	173.38	172.792	0.000047	0.877	0.408	0.000111	48.824	-17.530		0.043351	29.197	126.731	0.000335	1.119440	1.428890	0.001422	3.273340e-11	191.42700	4.01295	170.0170	4.442570e-03	0.604281	4.53535	631.60200	3.625410	-38.3499			1.687560	0.000619	3.63	3.63	0

# Metadata available: list column names
dfmeasured.columns

Index(['species', 'T2_emiss_g', 'T3_emiss_g', 'T4_emiss_g', 'representative',
       'INPUT', 'FOUND_BY', 'DTXSID', 'PREFERRED_NAME', 'DTXCID', 'CASRN',
       'SMILES', 'MOLECULAR_FORMULA', 'AVERAGE_MASS',
       'BIOCONCENTRATION_FACTOR_TEST_PRED', 'BOILING_POINT_DEGC_TEST_PRED',
       '48HR_DAPHNIA_LC50_MOL/L_TEST_PRED', 'DENSITY_G/CM^3_TEST_PRED',
       'DEVTOX_TEST_PRED', '96HR_FATHEAD_MINNOW_MOL/L_TEST_PRED',
       'FLASH_POINT_DEGC_TEST_PRED', 'MELTING_POINT_DEGC_TEST_PRED',
       'AMES_MUTAGENICITY_TEST_PRED', 'ORAL_RAT_LD50_MOL/KG_TEST_PRED',
       'SURFACE_TENSION_DYN/CM_TEST_PRED',
       'THERMAL_CONDUCTIVITY_MW/(M*K)_TEST_PRED',
       'TETRAHYMENA_PYRIFORMIS_IGC50_MOL/L_TEST_PRED',
       'VISCOSITY_CP_CP_TEST_PRED', 'VAPOR_PRESSURE_MMHG_TEST_PRED',
       'WATER_SOLUBILITY_MOL/L_TEST_PRED',
       'ATMOSPHERIC_HYDROXYLATION_RATE_(AOH)_CM3/MOLECULE*SEC_OPERA_PRED',
       'BIOCONCENTRATION_FACTOR_OPERA_PRED',
       'BIODEGRADATION_HALF_LIFE_DAYS_DAYS_OPERA_PRED',
       'BOILING_POINT_DEGC_OPERA_PRED', 'HENRYS_LAW_ATM-M3/MOLE_OPERA_PRED',
       'OPERA_KM_DAYS_OPERA_PRED',
       'OCTANOL_AIR_PARTITION_COEFF_LOGKOA_OPERA_PRED',
       'SOIL_ADSORPTION_COEFFICIENT_KOC_L/KG_OPERA_PRED',
       'OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED',
       'MELTING_POINT_DEGC_OPERA_PRED', 'OPERA_PKAA_OPERA_PRED',
       'OPERA_PKAB_OPERA_PRED', 'VAPOR_PRESSURE_MMHG_OPERA_PRED',
       'WATER_SOLUBILITY_MOL/L_OPERA_PRED', 'LOGD5.5', 'LOGD7.4',
       'READY_BIO_DEG'],
      dtype='object')

Risk estimates

# data file last updated 4/23 ("HEM Toxicity Value files" https://www.epa.gov/fera/download-human-exposure-model-hem)
dfdose = pd.read_excel(inputdir + "Dose_Response_Library.xlsx",usecols="A:P") # cols after P are shrunk/hidden so don't use ,converters={'CAS No.':str}; from website

# some hyphens retained, some not, get rid of all
dfdose=dfdose.dropna(subset=['CAS No.'])
dfdose['CAS Nohyp']=dfdose['CAS No.'].astype(str).str.replace('-','')
dfdose=dfdose.dropna(subset=['CAS Nohyp']) # drop species without CAS--its just a few mixtures

dfdose.rename(columns={"Pollutant": "HAP Pollutant"},inplace=True)
dfdose.rename(columns={"Pollutant Group": "HAP Group"},inplace=True)
dfdose

	HAP Pollutant	HAP Group	CAS No.	URE\n1/(ug/m3)	RFC\n(mg/m3)	AEGL-1 (1-hr)\n(mg/m3)	AEGL-1 (8-hr)\n(mg/m3)	AEGL-2 (1-hr)\n(mg/m3)	AEGL-2 (8-hr)\n(mg/m3)	ERPG-1\n(mg/m3)	ERPG-2\n(mg/m3)	MRL\n(mg/m3)	REL\n(mg/m3)	IDLH-10\n(mg/m3)	TEEL-0\n(mg/m3)	TEEL-1\n(mg/m3)	CAS Nohyp
0	1,1,1-Trichloroethane	1,1,1-Trichloroethane	71556	0.000000	5.0	1300.0	1300.0	3300	1700.00	1900.0	3800.0	11.00	68.0	NaN	NaN	NaN	71556
1	1,1,2,2-Tetrachloroethane	1,1,2,2-Tetrachloroethane	79345	NaN	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	79345
2	1,1,2-Trichloroethane	1,1,2-Trichloroethane	79005	0.000016	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.16	NaN	NaN	NaN	NaN	79005
3	1,1-Dimethylhydrazine	1,1-Dimethylhydrazine	57147	0.000000	0.0	NaN	NaN	7.4	0.93	NaN	NaN	NaN	NaN	NaN	NaN	NaN	57147
4	alpha-Hexachlorocyclohexane (a-HCH)	1,2,3,4,5,6-Hexachlorocyclohexane (All Stereo ...	319846	0.001800	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	319846
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
474	3-Methoxy-1-propanol	Glycol Ethers	1589497	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1589497
475	Di(ethylene glycol monobutyl ether) phthalate	Glycol Ethers	16672392	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	16672392
476	Diethylene glycol dibenzoate	Glycol Ethers	120558	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	120558
477	Ethylene glycol 2-ethylhexyl ether	Glycol Ethers	1559-35-9	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1559359
478	Ethylene glycol monovinyl ether	Glycol Ethers	764-48-7	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	764487

468 rows × 17 columns

# Format the dashboard CAS numbers to have not hyphens
dfmeasured['CAS Nohyp']=dfmeasured['CASRN'].astype(str).str.replace('-','')

# add URE and RFC onto measured data
dfmeasured = pd.merge(dfmeasured, dfdose, how="left", on = "CAS Nohyp", suffixes = ('','_dose' ) )

dfmeasured

	species	T2_emiss_g	T3_emiss_g	T4_emiss_g	representative	INPUT	FOUND_BY	DTXSID	PREFERRED_NAME	DTXCID	CASRN	SMILES	MOLECULAR_FORMULA	AVERAGE_MASS	BIOCONCENTRATION_FACTOR_TEST_PRED	BOILING_POINT_DEGC_TEST_PRED	48HR_DAPHNIA_LC50_MOL/L_TEST_PRED	DENSITY_G/CM^3_TEST_PRED	DEVTOX_TEST_PRED	96HR_FATHEAD_MINNOW_MOL/L_TEST_PRED	FLASH_POINT_DEGC_TEST_PRED	MELTING_POINT_DEGC_TEST_PRED	AMES_MUTAGENICITY_TEST_PRED	ORAL_RAT_LD50_MOL/KG_TEST_PRED	SURFACE_TENSION_DYN/CM_TEST_PRED	THERMAL_CONDUCTIVITY_MW/(M*K)_TEST_PRED	TETRAHYMENA_PYRIFORMIS_IGC50_MOL/L_TEST_PRED	VISCOSITY_CP_CP_TEST_PRED	VAPOR_PRESSURE_MMHG_TEST_PRED	WATER_SOLUBILITY_MOL/L_TEST_PRED	ATMOSPHERIC_HYDROXYLATION_RATE_(AOH)_CM3/MOLECULE*SEC_OPERA_PRED	BIOCONCENTRATION_FACTOR_OPERA_PRED	BIODEGRADATION_HALF_LIFE_DAYS_DAYS_OPERA_PRED	BOILING_POINT_DEGC_OPERA_PRED	HENRYS_LAW_ATM-M3/MOLE_OPERA_PRED	OPERA_KM_DAYS_OPERA_PRED	OCTANOL_AIR_PARTITION_COEFF_LOGKOA_OPERA_PRED	SOIL_ADSORPTION_COEFFICIENT_KOC_L/KG_OPERA_PRED	OCTANOL_WATER_PARTITION_LOGP_OPERA_PRED	MELTING_POINT_DEGC_OPERA_PRED	OPERA_PKAA_OPERA_PRED	OPERA_PKAB_OPERA_PRED	VAPOR_PRESSURE_MMHG_OPERA_PRED	WATER_SOLUBILITY_MOL/L_OPERA_PRED	LOGD5.5	LOGD7.4	READY_BIO_DEG	CAS Nohyp	HAP Pollutant	HAP Group	CAS No.	URE\n1/(ug/m3)	RFC\n(mg/m3)	AEGL-1 (1-hr)\n(mg/m3)	AEGL-1 (8-hr)\n(mg/m3)	AEGL-2 (1-hr)\n(mg/m3)	AEGL-2 (8-hr)\n(mg/m3)	ERPG-1\n(mg/m3)	ERPG-2\n(mg/m3)	MRL\n(mg/m3)	REL\n(mg/m3)	IDLH-10\n(mg/m3)	TEEL-0\n(mg/m3)	TEEL-1\n(mg/m3)
0	Formaldehyde	0.000017	0.000019	NaN	Formaldehyde	Formaldehyde	Approved Name	DTXSID7020637	Formaldehyde	DTXCID30637	50-00-0	C=O	CH2O	30.026		-16.012		0.861			-18.267	-79.361	0.362		22.056	164.885		0.254097	1221.800000	9.571940	9.342880e-12	1.72988	8.20857	-19.0454	3.494710e-07	0.224902	1.20597	2.72646	0.349206	-105.2860		1.15	824.979000	14.662500	0.35	0.35	1	50000	Formaldehyde	Formaldehyde	50000	0.000013	0.0098	1.1	1.1	17	17.0	1.2	12.0	0.049	0.055	NaN	NaN	NaN
1	Acetaldehyde	0.000062	0.000552	0.000672	Acetaldehyde	Acetaldehyde	Approved Name	DTXSID5039224	Acetaldehyde	DTXCID202	75-07-0	CC=O	C2H4O	44.053		31.585	0.010765	0.906	0.491	0.005012	-0.414	-45.573	0.107	0.018197	23.046	149.31	0.014158	0.285759	420.727000	4.698940	1.579550e-11	2.45935	7.56818	20.1228	6.609010e-05	0.169684	1.79446	2.57283	-0.338130	-122.8790	16.76	-0.57	898.259000	16.051800	-0.34	-0.34	1	75070	Acetaldehyde	Acetaldehyde	75070	0.000002	0.0090	81.0	81.0	490	200.0	18.0	360.0	NaN	0.470	NaN	NaN	NaN
2	Unknown as Acetone	0.000921	0.00106	NaN	acetone	acetone	Approved Name	DTXSID8021482	Acetone	DTXCID101482	67-64-1	CC(C)=O	C3H6O	58.080	1.91426	41.374	0.021928	0.837	0.462	0.036308	9.637	-63.606	-0.029	0.062661	23.159	147.469	0.068391	0.319154	250.035000	5.623410	2.201470e-13	1.99247	7.56577	55.6196	3.532240e-05	0.160491	2.32758	3.65480	-0.237520	-96.2441		19.13	232.690000	17.063100	-0.24	-0.24	1	67641	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Butanal	ND	0.000005	NaN	Butanal	Butanal	Expert Validated Synonym	DTXSID8021513	Butyraldehyde	DTXCID601513	123-72-8	CCCC=O	C4H8O	72.107		96.408	0.001439	0.852	0.582	0.000214	9.308	-53.045	0.489	0.01574	23.573	145.26	0.001365	0.466659	100.231000	0.603949	2.351290e-11	5.29042	3.67368	74.7617	1.143670e-04	0.138848	3.18740	10.84970	0.879054	-98.4607			111.140000	0.978317	0.88	0.88	1	123728	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	Hexaldehyde	0.000015	0.000018	NaN	Hexaldehyde	Hexaldehyde	Expert Validated Synonym	DTXSID2021604	Hexanal	DTXCID901604	66-25-1	CCCCCC=O	C6H12O	100.161		126.339	0.000484	0.843	0.586	0.000192	38.107	-39.764	0.682	0.012823	24.456	140.023	0.00075	0.739605	4.591980	0.040926	3.167590e-11	11.05860	4.44523	130.8260	2.120130e-04	0.352448	4.15557	18.40280	1.778440	-56.0859			11.356000	0.052127	1.78	1.78	1	66251	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
5	Methanol	0.03633	0.00797	0.003933	Methanol	Methanol	Approved Name	DTXSID2021731	Methanol	DTXCID801731	67-56-1	CO	CH4O	32.042	0.530884	17.821	0.009484	0.897		0.035156	-6.582	-75.959	0.191		21.674	165.053	0.183654	0.722770	425.598000	2.437810	9.471120e-13	1.54130	7.57468	64.7295	4.557950e-06	0.120694	2.86273	2.75443	-0.768744	-97.5702	10.29		126.324000	32.643400	-0.77	-0.77	1	67561	Methanol	Methanol	67561	0.000000	20.0000	690.0	350.0	2700	680.0	260.0	1300.0	NaN	28.000	NaN	NaN	NaN
6	Isopropyl_Alcohol	0.00059	NR	0.000861	Isopropyl Alcohol	Isopropyl Alcohol	Expert Validated Synonym	DTXSID7020762	Isopropanol	DTXCID50762	67-63-0	CC(C)O	C3H8O	60.096	2.12814	83.075	0.013428	0.863	0.492	0.035892	6.163	-45.893	0.001	0.053703	26.22	157.829	0.059156	2.023020	15.958800	4.786300	5.077690e-12	4.59819	7.53284	82.3951	8.088620e-06	0.079930	3.47701	3.43640	0.051948	-89.6414	11.68		44.944600	9.005230	0.05	0.05	1	67630	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
7	Total SVOCs	0.017974	0.003953	NaN	diethylene glycol	diethylene glycol	Approved Name	DTXSID8020462	Diethylene glycol	DTXCID20462	111-46-6	OCCOCCO	C4H10O3	106.121	0.656145	199.851	0.053951	1.076	0.215	0.119124	85.176	-6.591	0.263	0.038282	37.899	191.896	0.119674	18.879900	0.007447	5.395110	2.990660e-11	3.43715	6.13275	245.6130	2.960770e-09	0.071864	7.13012	4.08583	-1.093890	-10.4231		7.06	0.005701	9.417610	-2.66	-1.25	1	111466	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
8	Acetone	NaN	NaN	0.002566	Acetone	Acetone	Approved Name	DTXSID8021482	Acetone	DTXCID101482	67-64-1	CC(C)=O	C3H6O	58.080	1.91426	41.374	0.021928	0.837	0.462	0.036308	9.637	-63.606	-0.029	0.062661	23.159	147.469	0.068391	0.319154	250.035000	5.623410	2.201470e-13	1.99247	7.56577	55.6196	3.532240e-05	0.160491	2.32758	3.65480	-0.237520	-96.2441		19.13	232.690000	17.063100	-0.24	-0.24	1	67641	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
9	Propionaldehyde	NaN	NaN	0.000004	Propionaldehyde	Propionaldehyde	Expert Validated Synonym	DTXSID2021658	Propanal	DTXCID001658	123-38-6	CCC=O	C3H6O	58.080		58.534	0.001959	0.843	0.629	0.000536	2.396	-56.789	0.26	0.005875	22.728	149.586	0.003273	0.437522	221.820000	1.566750	1.961160e-11	5.49222	3.67945	48.0513	7.334760e-05	0.163907	2.90224	5.16901	0.589061	-80.2497			314.891000	4.453230	0.59	0.59	1	123386	Propionaldehyde	Propionaldehyde	123386	0.000000	0.0080	110.0	110.0	620	260.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
10	2-Butanone (MEK)	NaN	NaN	0.008596	2-Butanone	2-Butanone	Expert Validated Synonym	DTXSID3021516	Methyl ethyl ketone	DTXCID801516	78-93-3	CCC(C)=O	C4H8O	72.107	2.66686	85.913	0.004624	0.877	0.481	0.010351	11.812	-54.197	0.089	0.014723	24.679	144.036	0.022856	0.356451	106.170000	1.137630	1.153900e-12	4.09711	3.66957	79.3395	5.705120e-05	0.136632	2.72588	15.89840	0.291427	-86.3934			90.624500	3.183170	0.29	0.29	1	78933	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11	3-Pentanone	NaN	NaN	1.295795	3-Pentanone	3-Pentanone	Approved Name	DTXSID6021820	3-Pentanone	DTXCID301820	96-22-0	CCC(=O)CC	C5H10O	86.134	3.75837	104.735	0.001614	0.833	0.497	0.004775	19.233	-59.074	0.198	0.016033	24.846	142.618	0.0139	0.432514	32.885200	0.381944	2.004050e-12	3.96080	5.24613	102.0200	5.053810e-05	0.179811	3.20144	19.53570	0.906191	-42.0728			37.488700	0.546379	0.91	0.91	1	96220	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
12	Benzaldehyde	NaN	NaN	0.000003	Benzaldehyde	Benzaldehyde	Approved Name	DTXSID8039241	Benzaldehyde	DTXCID90134	100-52-7	O=CC1=CC=CC=C1	C7H6O	106.124	6.16	185.010	0.000151	1.090	0.19	0.000158	73.440	6.480	0.04	0.012023	41.15	143.85	0.001288	1.412540	0.281838	0.035481	1.291460e-11	12.52050	5.92924	179.1020	2.673770e-05	0.202421	3.88581	32.50930	1.474340	-25.5658			1.263430	0.062603	1.47	1.47	1	100527	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
13	Ethanol	NaN	NaN	1.181273	Ethanol	Ethanol	Approved Name	DTXSID9020584	Ethanol	DTXCID30584	64-17-5	CCO	C2H6O	46.069	1.25893	68.054	0.028249	0.912	0.455	0.017418	10.828	-69.464	-0.006	0.01714	24.095	182.077	0.103753	1.531090	31.550000	6.516280	3.277880e-12	1.81310	4.59705	78.2185	5.013510e-06	0.105204	3.24479	1.59008	-0.309717	-114.0380	12.31		58.854800	18.103000	-0.31	-0.31	1	64175	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
14	Ethyl_Acetate	NaN	NaN	0.018519	Ethyl Acetate	Ethyl Acetate	Approved Name	DTXSID1022001	Ethyl acetate	DTXCID602001	141-78-6	CCOC(C)=O	C4H8O2	88.106	2.61216	81.196	0.002249	0.926	0.587	0.002004	14.040	-70.237	0.141	0.06223	23.931	142.058	0.018197	0.465586	75.162300	0.475335	1.603440e-12	3.23156	4.12611	78.4376	1.348000e-04	0.180171	2.69862	18.17440	0.731494	-83.9003			93.156800	0.911820	0.73	0.73	1	141786	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
15	Toluene	NaN	NaN	0.000013	Toluene	Toluene	Expert Validated Synonym	DTXSID7021360	1-Methylbenzene	DTXCID501360	108-88-3	CC1=CC=CC=C1	C7H8	92.141	66.55	128.360	0.000229	0.900	0.28	0.000339	28.260	-33.640	-0.02	0.016982	29.18	132.42	0.001122	0.676083	23.988300	0.004074	5.973360e-12	8.33841	2.01480	110.6700	6.626450e-03	0.237095	3.49442	117.11500	2.728790	-93.2830			28.316500	0.005918	2.73	2.73	1	108883	Toluene	Toluene	108883	0.000000	5.0000	250.0	250.0	2100	940.0	190.0	1100.0	7.500	NaN	NaN	NaN	NaN
16	Alkane	NaN	NaN	0.000327	octane	octane	Approved Name	DTXSID0026882	Octane	DTXCID406882	111-65-9	CCCCCCCC	C8H18	114.232	580.764	130.843	0.00003	0.743	0.487	0.000016	24.976	-71.701		0.039628	21.263	124.078	0.000105	0.500035	9.246980	0.000038	8.662630e-12	312.32300	8.98873	125.4960	3.655730e-01	4.112610	3.39317	191.49500	5.178630	-57.4803			14.005100	0.000006	5.18	5.18	1	111659	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17	PAH	NaN	NaN	0.000649	1H-Indene-3-carboxylic acid	1H-Indene-3-carboxylic acid	Approved Name	DTXSID20161940	1H-Indene-3-carboxylic acid	DTXCID5084431	14209-41-7	OC(=O)C1=CCC2=CC=CC=C12	C10H8O2	160.172	6.223	298.031	0.000202	1.261	0.606	0.000052	150.849	110.977	0.43					15.703600	0.000062	0.001832	3.070700e-11	4.01565	3.10224	302.6460	1.252300e-08	0.110657	8.38386	77.89030	2.150060	104.0450	5.29		0.000060	0.001932	1.73	0.04	1	14209417	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18	Glycol	NaN	NaN	0.034017	Glycol	Glycol	Synonym	DTXSID0021206	1,2-Propylene glycol	DTXCID901206	57-55-6	CC(O)CO	C3H8O2	76.095	1.07647	168.555	0.026915	1.010	0.545	0.095061	70.374	-21.625	0.196	0.053456	33.094	184.634	0.152055	12.618300	0.295121	4.315190	1.201600e-11	3.38004	4.26800	187.6690	6.054780e-08	0.059302	6.73741	2.30480	-0.918877	-59.8063	11.47		0.127532	13.154500	-0.92	-0.92	1	57556	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
19	SubBenzene	NaN	NaN	0.006565	1,2,3-trimethylbenzene	1,2,3-trimethylbenzene	Approved Name	DTXSID8047769	1,2,3-Trimethylbenzene	DTXCID7027750	526-73-8	CC1=CC=CC(C)=C1C	C9H12	120.195	173.38	172.792	0.000047	0.877	0.408	0.000111	48.824	-17.530		0.043351	29.197	126.731	0.000335	1.119440	1.428890	0.001422	3.273340e-11	191.42700	4.01295	170.0170	4.442570e-03	0.604281	4.53535	631.60200	3.625410	-38.3499			1.687560	0.000619	3.63	3.63	0	526738	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Calculations

Calculate potential SOA and O3 estimates, map to CRACMM

dfmeasured['est_soa_yield'] = np.nan # SOA potential in g of SOA per g precursor
dfmeasured['est_mir'] = np.nan       # O3 potential as MIR in g O3 per g precursor
dfmeasured['CRACMM1'] = np.nan       # CRACMM version 1 species mapping (cite: Pye et al. 2023 ACP, https://doi.org/10.5194/acp-23-5043-2023)
dfmeasured['CRACMM2'] = np.nan       # CRACMM version 2 species mapping (cite: Skipper et al. 2024 EGUSphere)
dfmeasured['log10Cstar_ugm3']=np.nan
for idx, row in dfmeasured.iterrows(): # Model predictions
    smiles      = row['SMILES']
    pvap        = row['VAPOR_PRESSURE_MMHG_OPERA_PRED']
    molwght     = row['AVERAGE_MASS']
    dfmeasured.at[idx,'est_soa_yield'] = get_soayield(smiles,pvap,molwght)

    koh         = row['ATMOSPHERIC_HYDROXYLATION_RATE_(AOH)_CM3/MOLECULE*SEC_OPERA_PRED']
    dfmeasured.at[idx,'est_mir'] = get_mir(smiles,koh)
    
    log10cstar = np.log10( pvap * 101325.0/760 * molwght / RJmolK / Treference * 1e6 ) # log10Cstar where Cstar in micro-gram/m3
    dfmeasured.at[idx,'log10Cstar_ugm3'] = log10cstar
    dfmeasured.at[idx,'CRACMM2'] = cracmm2.get_cracmm_roc(smiles,koh,log10cstar)
    dfmeasured.at[idx,'CRACMM1'] = cracmm1.get_cracmm_roc(smiles,koh,log10cstar)
dfmeasured[['species','representative','CRACMM2','CRACMM1','est_soa_yield','log10Cstar_ugm3']]

	species	representative	CRACMM2	CRACMM1	est_soa_yield	log10Cstar_ugm3
0	Formaldehyde	Formaldehyde	HCHO	HCHO	0.000000	9.124599
1	Acetaldehyde	Acetaldehyde	ACD	ACD	0.000000	9.328035
2	Unknown as Acetone	acetone	ACT	ACT	0.000000	8.861463
3	Butanal	Butanal	ALD	ALD	0.000000	8.634506
4	Hexaldehyde	Hexaldehyde	ALD	ALD	0.016002	7.786582
5	Methanol	Methanol	MOH	MOH	0.000000	8.337864
6	Isopropyl_Alcohol	Isopropyl Alcohol	ROH	ROH	0.000000	8.162181
7	Total SVOCs	diethylene glycol	VROCIOXY	VROCIOXY	0.000000	4.512393
8	Acetone	Acetone	ACT	ACT	0.000000	8.861463
9	Propionaldehyde	Propionaldehyde	ALD	ALD	0.000000	8.992845
10	2-Butanone (MEK)	2-Butanone	MEK	MEK	0.000000	8.545881
11	3-Pentanone	3-Pentanone	KET	KET	0.021689	8.239733
12	Benzaldehyde	Benzaldehyde	BALD	BALD	0.021413	6.858023
13	Ethanol	Ethanol	EOH	EOH	0.000000	8.163849
14	Ethyl_Acetate	Ethyl Acetate	HC3	HC3	0.000000	8.644878
15	Toluene	Toluene	TOL	TOL	0.332008	8.147151
16	Alkane	octane	HC10	HC10	0.015400	7.934732
17	PAH	1H-Indene-3-carboxylic acid	VROCP5ARO	ROCP5ARO	0.324822	2.716479
18	Glycol	Glycol	PROG	PROG	0.000000	5.717633
19	SubBenzene	1,2,3-trimethylbenzene	XYL	XYM	0.067903	7.037804

filename = outputdir + '/' + str(today) + '_SuppTable_SpeciesProps.csv'
dfmeasured.to_csv(filename, index=False)

# Print results (lowest to highest yield)--SOA yields are SOA in g per g of that species emitted
dfmeasured[['species','representative','CRACMM2','est_soa_yield']].sort_values(by='est_soa_yield')

	species	representative	CRACMM2	est_soa_yield
0	Formaldehyde	Formaldehyde	HCHO	0.000000
14	Ethyl_Acetate	Ethyl Acetate	HC3	0.000000
13	Ethanol	Ethanol	EOH	0.000000
10	2-Butanone (MEK)	2-Butanone	MEK	0.000000
18	Glycol	Glycol	PROG	0.000000
8	Acetone	Acetone	ACT	0.000000
7	Total SVOCs	diethylene glycol	VROCIOXY	0.000000
9	Propionaldehyde	Propionaldehyde	ALD	0.000000
5	Methanol	Methanol	MOH	0.000000
3	Butanal	Butanal	ALD	0.000000
2	Unknown as Acetone	acetone	ACT	0.000000
1	Acetaldehyde	Acetaldehyde	ACD	0.000000
6	Isopropyl_Alcohol	Isopropyl Alcohol	ROH	0.000000
16	Alkane	octane	HC10	0.015400
4	Hexaldehyde	Hexaldehyde	ALD	0.016002
12	Benzaldehyde	Benzaldehyde	BALD	0.021413
11	3-Pentanone	3-Pentanone	KET	0.021689
19	SubBenzene	1,2,3-trimethylbenzene	XYL	0.067903
17	PAH	1H-Indene-3-carboxylic acid	VROCP5ARO	0.324822
15	Toluene	Toluene	TOL	0.332008

# print results (lowest to highest MIR)--MIR values are O3 formation in g per g of that species emitted
dfmeasured[['species','representative','CRACMM2','est_mir']].sort_values(by='est_mir')

	species	representative	CRACMM2	est_mir
16	Alkane	octane	HC10	1.216821
8	Acetone	Acetone	ACT	1.537857
2	Unknown as Acetone	acetone	ACT	1.537857
11	3-Pentanone	3-Pentanone	KET	1.703805
12	Benzaldehyde	Benzaldehyde	BALD	1.720558
10	2-Butanone (MEK)	2-Butanone	MEK	1.902075
14	Ethyl_Acetate	Ethyl Acetate	HC3	1.903262
6	Isopropyl_Alcohol	Isopropyl Alcohol	ROH	2.618392
13	Ethanol	Ethanol	EOH	3.235079
17	PAH	1H-Indene-3-carboxylic acid	VROCP5ARO	3.542421
18	Glycol	Glycol	PROG	3.609181
5	Methanol	Methanol	MOH	3.692454
15	Toluene	Toluene	TOL	4.004700
7	Total SVOCs	diethylene glycol	VROCIOXY	4.107045
4	Hexaldehyde	Hexaldehyde	ALD	5.358271
3	Butanal	Butanal	ALD	6.145126
9	Propionaldehyde	Propionaldehyde	ALD	6.704816
1	Acetaldehyde	Acetaldehyde	ACD	7.484428
0	Formaldehyde	Formaldehyde	HCHO	8.355971
19	SubBenzene	1,2,3-trimethylbenzene	XYL	10.463000

# Total emission and SDS sheet info
dftotemiss23 = pd.read_excel( measfilename, sheet_name = 'Data Analysis', skiprows = 15, nrows = 5, header=None, usecols= 'B,D,E')
dftotemiss23.rename(columns={1:'variable'},inplace=True)
dftotemiss23.rename(columns={3:'T2'},inplace=True)
dftotemiss23.rename(columns={4:'T3'},inplace=True)
dftotemiss4 = pd.read_excel( measfilename, sheet_name = 'Data Analysis', skiprows = 26, nrows = 5, header=None,  usecols= 'G,J')
dftotemiss4.rename(columns={9:'T4'},inplace=True)
dftotemiss4.rename(columns={6:'variable'},inplace=True)
dftotemiss = pd.merge(dftotemiss23,dftotemiss4,on='variable',how='outer') # slight renameing of variable
dftotemiss

	variable	T2	T3	T4
0	Total mass measured, g (VOC data)	NaN	0.013576	2.553791
1	Total mass measured, g (HPLC data)	0.055908	0.013128	1.264008
2	Ink mass lost during test, g	4.078460	4.538230	3.212960
3	10% Emittable mass bsed on SDS, g	0.407846	0.453823	NaN
4	% Measured/emittable	13.708183	2.991548	79.484047
5	Emittable mass bsed on SDS, g	NaN	NaN	3.212960

dftotused = pd.read_excel( measfilename, sheet_name = 'Data Analysis', skiprows = 36, nrows = 1, header=None, usecols= 'B,D,F,I')
dftotused.rename(columns={1:'variable'},inplace=True)
dftotused.rename(columns={3:'T2'},inplace=True)
dftotused.rename(columns={5:'T3'},inplace=True)
dftotused.rename(columns={8:'T4'},inplace=True)
dftotemiss = pd.concat([dftotemiss,dftotused],axis=0) # slight renaming of variable
dftotemiss.set_index('variable',inplace=True)
dftotemiss

	T2	T3	T4
variable
Total mass measured, g (VOC data)	NaN	0.013576	2.553791
Total mass measured, g (HPLC data)	0.055908	0.013128	1.264008
Ink mass lost during test, g	4.078460	4.538230	3.212960
10% Emittable mass bsed on SDS, g	0.407846	0.453823	NaN
% Measured/emittable	13.708183	2.991548	79.484047
Emittable mass bsed on SDS, g	NaN	NaN	3.212960
Ink mass for testing, g	5.349070	6.177660	4.064830

Analysis

Plots by Test

# T2 (water-based): composition, SOA, O3
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=[12,5])

plotdata = dfmeasured[['species','T2_emiss_g','est_soa_yield','est_mir']].copy()
plotdata.replace('ND',np.nan,inplace=True)
plotdata.replace('NR',np.nan,inplace=True)
plotdata['SOA_mg']=plotdata['T2_emiss_g']*plotdata['est_soa_yield']*1000
plotdata['O3_g']=plotdata['T2_emiss_g']*plotdata['est_mir']
plotdata.set_index(plotdata['species'],inplace=True)
plotdata=plotdata.drop(columns=['est_soa_yield','est_mir','species'])
#plotdata.dropna(inplace=True)
print(plotdata)

# effective yields
t2ysoaperemiss = plotdata['SOA_mg'].sum()/1000.0/plotdata['T2_emiss_g'].sum()
t2yo3peremiss = plotdata['O3_g'].sum()/plotdata['T2_emiss_g'].sum()
print('-->Potential SOA mass per ink mass emitted (g/g ROC): '+str(round(t2ysoaperemiss,6)))
print('-->Potential  O3 mass per ink mass emitted (g/g ROC): '+str(round(t2yo3peremiss,6)))

# absolute value panel
Tplotdata=plotdata.T # transpose
Tplotdata.plot.bar( ax=axes[0], stacked=True, ylabel='mass in grams', title='T2 emiss, SOA, and O3 (mass)', colormap='tab20')

# relative value panel
plotdata['T2_emiss_g']=plotdata['T2_emiss_g']/plotdata['T2_emiss_g'].sum()
plotdata['SOA_mg']=plotdata['SOA_mg']/plotdata['SOA_mg'].sum()
plotdata['O3_g']=plotdata['O3_g']/plotdata['O3_g'].sum()
Tplotdata=plotdata.T
Tplotdata.plot.bar( ax=axes[1], stacked=True, ylabel='fraction by mass', title='T2 emiss, SOA, and O3 (fraction)',colormap='tab20')

# legend for all
axes[0].get_legend().remove()
axes[1].get_legend().remove()
handles, labels = axes[0].get_legend_handles_labels()
axes[2].legend(handles[::-1], labels[::-1], loc='upper center') # reverse order of labels so they match stacking

                    T2_emiss_g    SOA_mg      O3_g
species                                           
Formaldehyde          0.000017  0.000000  0.000144
Acetaldehyde          0.000062  0.000000  0.000463
Unknown as Acetone    0.000921  0.000000  0.001416
Butanal                    NaN       NaN       NaN
Hexaldehyde           0.000015  0.000233  0.000078
Methanol              0.036330  0.000000  0.134148
Isopropyl_Alcohol     0.000590  0.000000  0.001544
Total SVOCs           0.017974  0.000000  0.073820
Acetone                    NaN       NaN       NaN
Propionaldehyde            NaN       NaN       NaN
2-Butanone (MEK)           NaN       NaN       NaN
3-Pentanone                NaN       NaN       NaN
Benzaldehyde               NaN       NaN       NaN
Ethanol                    NaN       NaN       NaN
Ethyl_Acetate              NaN       NaN       NaN
Toluene                    NaN       NaN       NaN
Alkane                     NaN       NaN       NaN
PAH                        NaN       NaN       NaN
Glycol                     NaN       NaN       NaN
SubBenzene                 NaN       NaN       NaN
-->Potential SOA mass per ink mass emitted (g/g ROC): 4e-06
-->Potential  O3 mass per ink mass emitted (g/g ROC): 3.784998

<matplotlib.legend.Legend at 0x14c66c59b9d0>

# T3 (water-based): composition, SOA, O3
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=[12,5])

plotdata = dfmeasured[['species','T3_emiss_g','est_soa_yield','est_mir']].copy()
plotdata.replace('NR',np.nan,inplace=True)
plotdata.replace('ND',np.nan,inplace=True)
plotdata['SOA_mg']=plotdata['T3_emiss_g']*plotdata['est_soa_yield']*1000
plotdata['O3_g']=plotdata['T3_emiss_g']*plotdata['est_mir']
plotdata.set_index(plotdata['species'],inplace=True)
plotdata=plotdata.drop(columns=['est_soa_yield','est_mir','species'])
#plotdata.dropna(inplace=True)
print(plotdata)

# effective yields
t3ysoaperemiss = plotdata['SOA_mg'].sum()/1000.0/plotdata['T3_emiss_g'].sum()
t3yo3peremiss = plotdata['O3_g'].sum()/plotdata['T3_emiss_g'].sum()
print('-->Potential SOA mass per ink mass emitted (g/g ROC): '+str(round(t3ysoaperemiss,6)))
print('-->Potential  O3 mass per ink mass emitted (g/g ROC): '+str(round(t3yo3peremiss,6)))

# abs value panel
Tplotdata=plotdata.T
Tplotdata.plot.bar( ax=axes[0], stacked=True, ylabel='grams', title='T3 emiss, SOA, and O3 (mass)', colormap='tab20')

# relative value panel
plotdata['T3_emiss_g']=plotdata['T3_emiss_g']/plotdata['T3_emiss_g'].sum()
plotdata['SOA_mg']=plotdata['SOA_mg']/plotdata['SOA_mg'].sum()
plotdata['O3_g']=plotdata['O3_g']/plotdata['O3_g'].sum()
Tplotdata=plotdata.T
Tplotdata.plot.bar( ax=axes[1], stacked=True, ylabel='fraction by mass', title='T3 emiss, SOA, and O3 (fraction)',colormap='tab20')

# legend for all
axes[0].get_legend().remove()
axes[1].get_legend().remove()
handles, labels = axes[0].get_legend_handles_labels()
axes[2].legend(handles[::-1], labels[::-1], loc='upper center') # reverse order of labels so they match stacking

                    T3_emiss_g    SOA_mg      O3_g
species                                           
Formaldehyde          0.000019  0.000000  0.000156
Acetaldehyde          0.000552  0.000000  0.004132
Unknown as Acetone    0.001060  0.000000  0.001631
Butanal               0.000005  0.000000  0.000029
Hexaldehyde           0.000018  0.000289  0.000097
Methanol              0.007970  0.000000  0.029427
Isopropyl_Alcohol          NaN       NaN       NaN
Total SVOCs           0.003953  0.000000  0.016234
Acetone                    NaN       NaN       NaN
Propionaldehyde            NaN       NaN       NaN
2-Butanone (MEK)           NaN       NaN       NaN
3-Pentanone                NaN       NaN       NaN
Benzaldehyde               NaN       NaN       NaN
Ethanol                    NaN       NaN       NaN
Ethyl_Acetate              NaN       NaN       NaN
Toluene                    NaN       NaN       NaN
Alkane                     NaN       NaN       NaN
PAH                        NaN       NaN       NaN
Glycol                     NaN       NaN       NaN
SubBenzene                 NaN       NaN       NaN
-->Potential SOA mass per ink mass emitted (g/g ROC): 2.1e-05
-->Potential  O3 mass per ink mass emitted (g/g ROC): 3.808533

<matplotlib.legend.Legend at 0x14c66715ba90>

# T4 (organic): composition, SOA, O3
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=[12,5])

plotdata = dfmeasured[['species','T4_emiss_g','est_soa_yield','est_mir']].copy()
plotdata.replace('NR',np.nan,inplace=True)
plotdata.replace('ND',np.nan,inplace=True)
plotdata['SOA_mg']=plotdata['T4_emiss_g']*plotdata['est_soa_yield']*1000
plotdata['O3_g']=plotdata['T4_emiss_g']*plotdata['est_mir']
plotdata.set_index(plotdata['species'],inplace=True)
plotdata=plotdata.drop(columns=['est_soa_yield','est_mir','species'])
#plotdata.dropna(inplace=True)
print(plotdata)

# effective yields
t4ysoaperemiss = plotdata['SOA_mg'].sum()/1000.0/plotdata['T4_emiss_g'].sum()
t4yo3peremiss = plotdata['O3_g'].sum()/plotdata['T4_emiss_g'].sum()
print('-->Potential SOA mass per ink mass emitted (g/g ROC): '+str(round(t4ysoaperemiss,6)))
print('-->Potential  O3 mass per ink mass emitted (g/g ROC): '+str(round(t4yo3peremiss,6)))

# absolute values
Tplotdata=plotdata.T
Tplotdata.plot.bar( ax=axes[0], stacked=True, ylabel='grams', title='T4 emiss, SOA, and O3 (mass)',colormap='tab20')
#plt.legend(loc='upper left')

# relative value panel
plotdata['T4_emiss_g']=plotdata['T4_emiss_g']/plotdata['T4_emiss_g'].sum()
plotdata['SOA_mg']=plotdata['SOA_mg']/plotdata['SOA_mg'].sum()
plotdata['O3_g']=plotdata['O3_g']/plotdata['O3_g'].sum()
Tplotdata=plotdata.T
Tplotdata.plot.bar( ax=axes[1], stacked=True, ylabel='fraction by mass', title='T4 emiss, SOA, and O3 (fraction)',colormap='tab20')


# legend for all
axes[0].get_legend().remove()
axes[1].get_legend().remove()
handles, labels = axes[0].get_legend_handles_labels()
axes[2].legend(handles[::-1], labels[::-1], loc='upper center') # reverse order of labels so they match stacking

                    T4_emiss_g     SOA_mg      O3_g
species                                            
Formaldehyde               NaN        NaN       NaN
Acetaldehyde          0.000672   0.000000  0.005026
Unknown as Acetone         NaN        NaN       NaN
Butanal                    NaN        NaN       NaN
Hexaldehyde                NaN        NaN       NaN
Methanol              0.003933   0.000000  0.014522
Isopropyl_Alcohol     0.000861   0.000000  0.002253
Total SVOCs                NaN        NaN       NaN
Acetone               0.002566   0.000000  0.003945
Propionaldehyde       0.000004   0.000000  0.000028
2-Butanone (MEK)      0.008596   0.000000  0.016351
3-Pentanone           1.295795  28.103985  2.207782
Benzaldehyde          0.000003   0.000054  0.000004
Ethanol               1.181273   0.000000  3.821511
Ethyl_Acetate         0.018519   0.000000  0.035247
Toluene               0.000013   0.004175  0.000050
Alkane                0.000327   0.005043  0.000398
PAH                   0.000649   0.210695  0.002298
Glycol                0.034017   0.000000  0.122772
SubBenzene            0.006565   0.445763  0.068686
-->Potential SOA mass per ink mass emitted (g/g ROC): 0.011265
-->Potential  O3 mass per ink mass emitted (g/g ROC): 2.467264

<matplotlib.legend.Legend at 0x14c66c0383a0>

Figure 3 Plots across all tests for SOA/O3 potential

# Add data from previous SPECIATE profiles
# From Karl: The 2570 profile is the one used in the original VCPY work, including the ES&T paper. 
# The second profile, 95827, is also quite old but from CARB and the current operational profile. 
# As you can see in the calculations, the estimated SOA yield of 95827 is about 1/3 smaller than 2570 and the estimated MIR of the two are about the same. 
filename = inputdir + 'printing_ink_profiles_seltzer.xlsx' 

dfspeciate = pd.read_excel( filename )
# yields per g emitted
profile2570soayield = np.sum(dfspeciate[dfspeciate.PROFILE_CODE == 2570].est_soa_yield * dfspeciate[dfspeciate.PROFILE_CODE == 2570].WEIGHT_PERCENT/100 )#g # = dfspeciate.rename(columns={'Unnamed: 3':'species'})
profile2570o3yield = np.sum(dfspeciate[dfspeciate.PROFILE_CODE == 2570].est_mir * dfspeciate[dfspeciate.PROFILE_CODE == 2570].WEIGHT_PERCENT/100 )
profile95837soayield = np.sum(dfspeciate[dfspeciate.PROFILE_CODE == 95837].est_soa_yield * dfspeciate[dfspeciate.PROFILE_CODE == 95837].WEIGHT_PERCENT/100 )
profile95837o3yield = np.sum(dfspeciate[dfspeciate.PROFILE_CODE == 95837].est_mir * dfspeciate[dfspeciate.PROFILE_CODE == 95837].WEIGHT_PERCENT/100 )
print('SOA yields g/g:')
print(profile2570soayield,profile2570o3yield,profile95837soayield,profile95837o3yield)

SOA yields g/g:
0.19688604033964424 2.1759332857585454 0.06363066799073001 2.2901000977542845

fig, axes = plt.subplots(nrows=4, ncols=1, figsize=[2.5,6], height_ratios=[6,8.5,12.5,2],gridspec_kw=dict(
    wspace=0.0, hspace=0.8, left=0.13,right=0.95,top=0.95,bottom=0.15), dpi=300)

cmap = plt.colormaps['tab10']
# colors
Acol = 'tab:orange'
Bcol = 'tab:blue'
Ccol = 'tab:green'
col1989 = 'tab:brown'
colCARB = 'tab:grey'
vcpycol = 'tab:olive'

ft2=dfmeasured.replace('ND',np.nan).replace('NR',np.nan).sum(numeric_only=True)['T2_emiss_g']/dftotemiss.loc['Ink mass for testing, g']['T2']
ft3=dfmeasured.replace('ND',np.nan).replace('NR',np.nan).sum(numeric_only=True)['T3_emiss_g']/dftotemiss.loc['Ink mass for testing, g']['T3']
ft4=dfmeasured.replace('ND',np.nan).replace('NR',np.nan).sum(numeric_only=True)['T4_emiss_g']/dftotemiss.loc['Ink mass for testing, g']['T4']

ax = axes[0]
# Mass emitted per mass used
data = { 'ROC': [0.2498,ft3,ft2,ft4] } #0.248 from Karl
#df = pd.DataFrame(data,index=['T2','T3','T4']).T
df = pd.DataFrame(data,index=['VCPy','C','B','A',]).T
color = [vcpycol,Ccol,Bcol,Acol]
df.plot.barh( ax=ax, stacked=False, ylabel=' ', width = 0.9, color=color,fontsize=8,log=True,linewidth=0.5,edgecolor='black')
ax.set_xlabel('g/g of ink',fontsize=8,labelpad=1)
ax.set_title('(a) Emissions per product mass',fontsize=8)
ax.xaxis.set_tick_params(which='major',labelsize=7,pad=1,length=2.5)
ax.xaxis.set_tick_params(which='minor',labelsize=7,pad=1,length=1.5)
ax.set_xlim(0.001,1)
ax.yaxis.set_tick_params(labelsize=7,pad=2,length=0)

# print to screen
print('----Emissions per mass used (g/g ink)')
print(df)
dfa = df.copy()

ax.get_legend().remove()

ax = axes[1]
# SOA and O3 per usage
data = { 'SOA': [t3ysoaperemiss*ft3,t2ysoaperemiss*ft2,t4ysoaperemiss*ft4],
         'O$_3$' : [t3yo3peremiss*ft3,t2yo3peremiss*ft2,t4yo3peremiss*ft4]}
#df = pd.DataFrame(data,index=['T2','T3','T4']).T
df = pd.DataFrame(data,index=['C','B','A']).T
color = [Ccol,Bcol,Acol]
df.plot.barh( ax=ax, stacked=False, ylabel=' ', width = 0.9, color=color,fontsize=8,log=True,linewidth=0.5,edgecolor='black')
ax.set_xlabel('g/g of ink',fontsize=8)
ax.set_title('(b) Potential per product mass',fontsize=8)
maj_loc = ticker.LogLocator(numticks=5)
ax.xaxis.set_major_locator(maj_loc)
ax.xaxis.set_tick_params(which='major',labelsize=7,pad=1,length=2.5)
ax.xaxis.set_tick_params(which='minor',labelsize=7,pad=1,length=1.5)
ax.yaxis.set_tick_params(labelsize=7,pad=2,length=0)
ax.set_xlim(1e-8,10)

# print to screen
print('----SOA or O3 potential per mass used (g/g ink)')
print(df)
dfb = df.copy()

ax.get_legend().remove()

ax = axes[2]
# SOA and O3 per emission
data = { 'SOA': [profile2570soayield,profile95837soayield,t3ysoaperemiss,t2ysoaperemiss,t4ysoaperemiss],
         'O$_3$' : [profile2570o3yield,profile95837o3yield,t3yo3peremiss,t2yo3peremiss,t4yo3peremiss]}
#df = pd.DataFrame(data,index=['T2','T3','T4']).T
df = pd.DataFrame(data,index=['1989 Ink','CARB Ink','C','B','A',]).T
color = [col1989,colCARB,Ccol,Bcol,Acol]
df.plot.barh( ax=ax, stacked=False, width = 0.9, color=color,fontsize=8,log=True,linewidth=0.5,edgecolor='black')
ax.set_xlabel('g/g ROC emitted',fontsize=8,labelpad=1)
handles, labels = ax.get_legend_handles_labels()
ax.set_title('(c) Potential per mass emitted',fontsize=8)
ax.xaxis.set_tick_params(which='major',labelsize=6,pad=1,length=2.5)
ax.xaxis.set_tick_params(which='minor',labelsize=7,pad=1,length=1.5)
ax.yaxis.set_tick_params(labelsize=7,pad=2,length=0)
ax.set_xlim(1e-6,10)

ax.get_legend().remove()

# print to screen
print('----SOA or O3 per speciated mass emitted (g/g ROC emitted)')
print(df)
dfc = df.copy()

# legend only
ax = axes[3]
apatch = mpatches.Patch( facecolor = Acol, linewidth=0.5,edgecolor='black', label='A')
bpatch = mpatches.Patch ( facecolor = Bcol, linewidth=0.5,edgecolor='black', label='B')
cpatch = mpatches.Patch (facecolor = Ccol , linewidth=0.5,edgecolor='black',label = 'C')
patch1989 = mpatches.Patch (facecolor = col1989, linewidth=0.5,edgecolor='black',label = '1989')
patchCARB = mpatches.Patch( facecolor= colCARB, linewidth=0.5,edgecolor='black',label = 'CARB')
vcpypatch = mpatches.Patch( facecolor = vcpycol, linewidth=0.5,edgecolor='black',label = 'VCPy')

ax.legend(handles=[apatch,bpatch,cpatch,vcpypatch,patch1989,patchCARB],loc='center',bbox_to_anchor=(0.45,-1),
          fontsize=8,labelspacing=0.22, title='Ink Types' )# 
ax.axis("off")

fig.savefig(outputdir + '/'+str(today)+'_Figure3_massyield.png', facecolor='w', transparent=False)

----Emissions per mass used (g/g ink)
       VCPy         C         B         A
ROC  0.2498  0.002198  0.010452  0.628265
----SOA or O3 potential per mass used (g/g ink)
                  C             B         A
SOA    4.672043e-08  4.347300e-08  0.007078
O$_3$  8.369820e-03  3.956065e-02  1.550096
----SOA or O3 per speciated mass emitted (g/g ROC emitted)
       1989 Ink  CARB Ink         C         B         A
SOA    0.196886  0.063631  0.000021  0.000004  0.011265
O$_3$  2.175933  2.290100  3.808533  3.784998  2.467264

# Fraction of ink place in test that is emitted and identified by a species
dfmeasured.replace('ND',np.nan).replace('NR',np.nan).sum(numeric_only=True)['T2_emiss_g']/dftotemiss.loc['Ink mass for testing, g']['T2']

0.010451961908468676

dfmeasured.replace('ND',np.nan).replace('NR',np.nan).sum(numeric_only=True)['T3_emiss_g']/dftotemiss.loc['Ink mass for testing, g']['T3']

0.0021976496105312548

dfmeasured.replace('ND',np.nan).replace('NR',np.nan).sum(numeric_only=True)['T4_emiss_g']/dftotemiss.loc['Ink mass for testing, g']['T4']

0.6282650525191288

Figure 4 Trends over time

# Total emission and SDS sheet info
filename = inputdir + 'inksales.xlsx' 
dftrends = pd.read_excel( filename )
dftrends.set_index('Unnamed: 0',inplace=True)
dftrends=dftrends.T
dftrends.index.rename('year',inplace=True)
dftrends

Unnamed: 0	Printing Ink Usage [kg/person/yr]	Population	Printing Ink Usage [Gg/yr]
year
2002	4.33	287625193.0	1246.13
2003	4.13	290107933.0	1199.54
2004	4.76	292805298.0	1392.59
2005	4.69	295516599.0	1385.92
2006	4.27	298379912.0	1275.45
2007	4.23	301231207.0	1275.02
2008	3.66	304093966.0	1113.38
2009	3.09	306771529.0	948.26
2010	3.58	309321666.0	1107.28
2011	3.58	311556874.0	1116.93
2012	3.41	313830990.0	1071.50
2013	3.56	315993715.0	1124.80
2014	3.26	318301008.0	1036.63
2015	3.12	320635163.0	999.19
2016	2.87	322941311.0	925.76
2017	2.66	324985539.0	864.06
2018	2.81	326687501.0	919.17
2019	2.58	328239523.0	846.14
2020	2.38	331511512.0	789.52
2021	2.27	332031554.0	752.65

# SOA and O3 potential (mass in Gg)
### IF DATA IS UPDATED, DOUBLE CHECK THIS--VALUES ARE FROM PREVIOUS PLOT

# Figure 3b, ink A--calculate SOA and O3 if all ink was A
SOAyieldpermassusedA = 7.078/1000 #g/kg
O3yieldpermassusedA = 1.55
dftrends['soaforallA'] = dftrends['Printing Ink Usage [Gg/yr]']*SOAyieldpermassusedA  #Gg*mg/g *1g/1000mg
dftrends['o3forallA'] = dftrends['Printing Ink Usage [Gg/yr]']*O3yieldpermassusedA 

# Figure 3b, ink B--calculate SOA and O3 if all ink was B
SOAyieldpermassusedB = 4.3e-8 #g/kg
O3yieldpermassusedB = 3.96e-2
dftrends['soaforallB'] = dftrends['Printing Ink Usage [Gg/yr]']*SOAyieldpermassusedB
dftrends['o3forallB'] =dftrends['Printing Ink Usage [Gg/yr]']*O3yieldpermassusedB

# Emission factors g/g from Figure 3a
ROCEFA = 0.628265
ROCEFB = 0.010452

dftrends

Unnamed: 0	Printing Ink Usage [kg/person/yr]	Population	Printing Ink Usage [Gg/yr]	soaforallA	o3forallA	soaforallB	o3forallB
year
2002	4.33	287625193.0	1246.13	8.820108	1931.5015	0.000054	49.346748
2003	4.13	290107933.0	1199.54	8.490344	1859.2870	0.000052	47.501784
2004	4.76	292805298.0	1392.59	9.856752	2158.5145	0.000060	55.146564
2005	4.69	295516599.0	1385.92	9.809542	2148.1760	0.000060	54.882432
2006	4.27	298379912.0	1275.45	9.027635	1976.9475	0.000055	50.507820
2007	4.23	301231207.0	1275.02	9.024592	1976.2810	0.000055	50.490792
2008	3.66	304093966.0	1113.38	7.880504	1725.7390	0.000048	44.089848
2009	3.09	306771529.0	948.26	6.711784	1469.8030	0.000041	37.551096
2010	3.58	309321666.0	1107.28	7.837328	1716.2840	0.000048	43.848288
2011	3.58	311556874.0	1116.93	7.905631	1731.2415	0.000048	44.230428
2012	3.41	313830990.0	1071.50	7.584077	1660.8250	0.000046	42.431400
2013	3.56	315993715.0	1124.80	7.961334	1743.4400	0.000048	44.542080
2014	3.26	318301008.0	1036.63	7.337267	1606.7765	0.000045	41.050548
2015	3.12	320635163.0	999.19	7.072267	1548.7445	0.000043	39.567924
2016	2.87	322941311.0	925.76	6.552529	1434.9280	0.000040	36.660096
2017	2.66	324985539.0	864.06	6.115817	1339.2930	0.000037	34.216776
2018	2.81	326687501.0	919.17	6.505885	1424.7135	0.000040	36.399132
2019	2.58	328239523.0	846.14	5.988979	1311.5170	0.000036	33.507144
2020	2.38	331511512.0	789.52	5.588223	1223.7560	0.000034	31.264992
2021	2.27	332031554.0	752.65	5.327257	1166.6075	0.000032	29.804940

#dftrends['soaforallA']/dftrends['soaforallB']
#dftrends['o3forallA']/dftrends['o3forallB']

# Calculate water-based ink usage by year
#waterfrac = 0.187 * (0.031 + 1 )**(dftrends.index-2022) # cagr formula
# data was for 2022, assume 2021 and 2022 same total sales and 18.7% was 2022 year data (thus 2021 is a bit lower)

finalusage =  0.187 * dftrends[dftrends.index==2021]['Printing Ink Usage [Gg/yr]'].loc[2021]
print(finalusage)
waterusagebyyear= finalusage  / (0.031 + 1 )**(2022-dftrends.index)
waterusagebyyear

140.74555

Float64Index([ 76.42956131046317,  78.79887771108753,  81.24164292013123,
                83.7601338506553,   86.3566980000256,  89.03375563802638,
                91.7938020628052,  94.63940992675215,  97.57323163448146,
              100.59800181515037, 103.71653987142003, 106.93175260743405,
              110.24663693826449, 113.66428268335069, 117.18787544653455,
              120.82069958537711, 124.56614127252377,   128.427691651972,
              132.40895009318314,  136.5136275460718],
             dtype='float64', name='year')

(259.18/140.74)**(1/20)-1 # check this is 3.1%, this is initial to final year

0.03100126213924015

dftrends['SOAyieldforscenario [g/g ink]']= ( waterusagebyyear * SOAyieldpermassusedB + ( dftrends['Printing Ink Usage [Gg/yr]'] - waterusagebyyear ) * SOAyieldpermassusedA )/ dftrends['Printing Ink Usage [Gg/yr]'] 

dftrends['O3yieldforscenario [g/g ink]']= ( waterusagebyyear * O3yieldpermassusedB + ( dftrends['Printing Ink Usage [Gg/yr]'] - waterusagebyyear ) * O3yieldpermassusedA )/ dftrends['Printing Ink Usage [Gg/yr]'] 
dftrends['ROC Emissions [Gg/yr]']= ( waterusagebyyear * ROCEFB + ( dftrends['Printing Ink Usage [Gg/yr]'] - waterusagebyyear ) * ROCEFA )

dftrends['Water-based Ink Usage [Gg/yr]']= ( waterusagebyyear )
dftrends

Unnamed: 0	Printing Ink Usage [kg/person/yr]	Population	Printing Ink Usage [Gg/yr]	soaforallA	o3forallA	soaforallB	o3forallB	SOAyieldforscenario [g/g ink]	O3yieldforscenario [g/g ink]	ROC Emissions [Gg/yr]	Water-based Ink Usage [Gg/yr]
year
2002	4.33	287625193.0	1246.13	8.820108	1931.5015	0.000054	49.346748	0.006644	1.457362	735.680688	76.429561
2003	4.13	290107933.0	1199.54	8.490344	1859.2870	0.000052	47.501784	0.006613	1.450780	704.946027	78.798878
2004	4.76	292805298.0	1392.59	9.856752	2158.5145	0.000060	55.146564	0.006665	1.461885	824.723413	81.241643
2005	4.69	295516599.0	1385.92	9.809542	2148.1760	0.000060	54.882432	0.006650	1.458717	818.976929	83.760134
2006	4.27	298379912.0	1275.45	9.027635	1976.9475	0.000055	50.507820	0.006599	1.447736	747.968304	86.356698
2007	4.23	301231207.0	1275.02	9.024592	1976.2810	0.000055	50.490792	0.006584	1.444530	746.044229	89.033756
2008	3.66	304093966.0	1113.38	7.880504	1725.7390	0.000048	44.089848	0.006494	1.425473	642.786281	91.793802
2009	3.09	306771529.0	948.26	6.711784	1469.8030	0.000041	37.551096	0.006372	1.399257	537.289111	94.639410
2010	3.58	309321666.0	1107.28	7.837328	1716.2840	0.000048	43.848288	0.006454	1.416904	635.383258	97.573232
2011	3.58	311556874.0	1116.93	7.905631	1731.2415	0.000048	44.230428	0.006441	1.413964	639.577273	100.598002
2012	3.41	313830990.0	1071.50	7.584077	1660.8250	0.000046	42.431400	0.006393	1.403800	609.108521	103.716540
2013	3.56	315993715.0	1124.80	7.961334	1743.4400	0.000048	44.542080	0.006405	1.406410	640.608645	106.931753
2014	3.26	318301008.0	1036.63	7.337267	1606.7765	0.000045	41.050548	0.006325	1.389367	583.166541	110.246637
2015	3.12	320635163.0	999.19	7.072267	1548.7445	0.000043	39.567924	0.006273	1.378182	557.532834	113.664283
2016	2.87	322941311.0	925.76	6.552529	1434.9280	0.000040	36.660096	0.006182	1.358805	509.222414	117.187875
2017	2.66	324985539.0	864.06	6.115817	1339.2930	0.000037	34.216776	0.006088	1.338802	468.214057	120.820700
2018	2.81	326687501.0	919.17	6.505885	1424.7135	0.000040	36.399132	0.006119	1.345310	500.523759	124.566141
2019	2.58	328239523.0	846.14	5.988979	1311.5170	0.000036	33.507144	0.006004	1.320750	452.255850	128.427692
2020	2.38	331511512.0	789.52	5.588223	1223.7560	0.000034	31.264992	0.005891	1.296694	414.223812	132.408950
2021	2.27	332031554.0	752.65	5.327257	1166.6075	0.000032	29.804940	0.005794	1.276048	388.523758	136.513628

# print Figure 4 data to file
dftrends.drop(columns=['soaforallA','soaforallB','o3forallA','o3forallB']).to_csv(outputdir+'Figure4.csv')

# Figure 4
dftrends['Printing Ink Usage [Gg/yr]']
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=[3.3,6], height_ratios=[1,1,1],gridspec_kw=dict(
    wspace=0.0, hspace=0.5, left=0.2,right=0.75,top=0.97,bottom=0.07), dpi=300)

ax=axes[0]
ax.plot(dftrends.index,dftrends['Printing Ink Usage [Gg/yr]'],color='black',label='Total usage')
ax.set_ylabel('Total [Gg/yr]',fontsize=8,labelpad=1)
ax.set_title('(a) U.S. Printing Ink Usage & Emissions',fontsize=8)
ax.set_xlabel('year',fontsize=8,labelpad=1)
ax.set_xticks(np.arange(2002,2021,2))
ax.tick_params(axis='x', rotation=55)
ax.xaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1)
ax.plot(dftrends.index,dftrends['ROC Emissions [Gg/yr]'],color='black',label='Total emission',linestyle='dashed')
ax.tick_params(axis='y')
ax.yaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1)
ax.legend(loc='lower right',fontsize=7)
ax.set_ylim(-300,1500)

ax2 = ax.twinx()
ax2.plot(dftrends.index,dftrends['Water-based Ink Usage [Gg/yr]'],color='tab:blue',label='Water-based usage',linestyle='dotted')
ax2.set_ylabel('Water-based Usage [Gg/yr]',fontsize=8,color='tab:blue',labelpad=1)
ax2.set_ylim(60,140)
ax2.tick_params(axis='y')
ax2.yaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1,labelcolor='tab:blue')

ax.set_zorder(ax2.get_zorder()+1)
ax.set_frame_on(False)

ax=axes[1]
# secondary as yield
ax2 = ax.twinx()
ax2.bar(dftrends.index,dftrends['SOAyieldforscenario [g/g ink]']*1000,color='tab:blue')
ax2.set_ylabel('SOA Yield [g/kg ink]',color='tab:blue',fontsize=8,labelpad=5)
ax2.set_title('(b) SOA Formation Potential',fontsize=8)
ax2.set_ylim(0.00,10)
ax2.tick_params(axis='y')
ax2.yaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1,labelcolor='tab:blue')

ax.plot(dftrends.index,dftrends['SOAyieldforscenario [g/g ink]']*dftrends['Printing Ink Usage [Gg/yr]'],color='black')
ax.set_ylabel('SOA [Gg/yr]',fontsize=8,labelpad=10)
ax.set_xlabel('year',fontsize=8,labelpad=3)
ax.set_ylim(0,10)
ax.set_xticks(np.arange(2002,2021,2))
ax.tick_params(axis='x', rotation=55)
ax.xaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1,)
ax.set_zorder(ax2.get_zorder()+1)
ax.set_frame_on(False)
ax.tick_params(axis='y')
ax.yaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1)

ax=axes[2]
ax2 = ax.twinx()
ax2.bar(dftrends.index,dftrends['O3yieldforscenario [g/g ink]'])
ax2.set_ylabel('O$_3$ [g/g ink]',fontsize=8,color='tab:blue',labelpad=1)
ax.set_title('(c) O$_3$ formation potential',fontsize=8)
ax.set_xlabel('year',fontsize=8)
ax2.set_ylim(0,2)
ax2.tick_params(axis='y')
ax2.yaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1,labelcolor='tab:blue')

ax.plot(dftrends.index,dftrends['O3yieldforscenario [g/g ink]']*dftrends['Printing Ink Usage [Gg/yr]'],color='black',zorder=99)
ax.set_ylabel('O$_3$ [Gg/yr]',fontsize=8,labelpad=1)
ax.set_xlabel('year',fontsize=8,labelpad=1)
ax.set_ylim(0,2200)
ax.set_xticks(np.arange(2002,2021,2))
ax.tick_params(axis='x', rotation=55)
ax.xaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1,)
ax.set_zorder(ax2.get_zorder()+1)
ax.set_frame_on(False)
ax.tick_params(axis='y')
ax.yaxis.set_tick_params(which='major',labelsize=7,pad=0.5,length=1)


fig.savefig(outputdir + '/'+str(today)+'_Figure4_trends.png', facecolor='w', transparent=False)

# secondary as yield
# stacked bar of change in SOA and O3 due to change in Usage, emission, yield
initialusage = (dftrends[dftrends.index==2002]['Printing Ink Usage [Gg/yr]'].loc[2002] +
               dftrends[dftrends.index==2003]['Printing Ink Usage [Gg/yr]'].loc[2003] +
               dftrends[dftrends.index==2004]['Printing Ink Usage [Gg/yr]'].loc[2004] +
               dftrends[dftrends.index==2005]['Printing Ink Usage [Gg/yr]'].loc[2005] +
               dftrends[dftrends.index==2006]['Printing Ink Usage [Gg/yr]'].loc[2006])/5
finalusage = (dftrends[dftrends.index==2017]['Printing Ink Usage [Gg/yr]'].loc[2017] +
               dftrends[dftrends.index==2018]['Printing Ink Usage [Gg/yr]'].loc[2018] +
               dftrends[dftrends.index==2019]['Printing Ink Usage [Gg/yr]'].loc[2019] +
               dftrends[dftrends.index==2020]['Printing Ink Usage [Gg/yr]'].loc[2020] +
               dftrends[dftrends.index==2021]['Printing Ink Usage [Gg/yr]'].loc[2021])/5

initialemission = (dftrends[dftrends.index==2002]['ROC Emissions [Gg/yr]'].loc[2002] +
               dftrends[dftrends.index==2003]['ROC Emissions [Gg/yr]'].loc[2003] +
               dftrends[dftrends.index==2004]['ROC Emissions [Gg/yr]'].loc[2004] +
               dftrends[dftrends.index==2005]['ROC Emissions [Gg/yr]'].loc[2005] +
               dftrends[dftrends.index==2006]['ROC Emissions [Gg/yr]'].loc[2006])/5
finalemission = (dftrends[dftrends.index==2017]['ROC Emissions [Gg/yr]'].loc[2017] +
               dftrends[dftrends.index==2018]['ROC Emissions [Gg/yr]'].loc[2018] +
               dftrends[dftrends.index==2019]['ROC Emissions [Gg/yr]'].loc[2019] +
               dftrends[dftrends.index==2020]['ROC Emissions [Gg/yr]'].loc[2020] +
               dftrends[dftrends.index==2021]['ROC Emissions [Gg/yr]'].loc[2021])/5

initialSOAyieldperusage = (dftrends[dftrends.index==2002]['SOAyieldforscenario [g/g ink]'].loc[2002] +
               dftrends[dftrends.index==2003]['SOAyieldforscenario [g/g ink]'].loc[2003] +
               dftrends[dftrends.index==2004]['SOAyieldforscenario [g/g ink]'].loc[2004] +
               dftrends[dftrends.index==2005]['SOAyieldforscenario [g/g ink]'].loc[2005] +
               dftrends[dftrends.index==2006]['SOAyieldforscenario [g/g ink]'].loc[2006])/5

finalSOAyieldperusage = (dftrends[dftrends.index==2017]['SOAyieldforscenario [g/g ink]'].loc[2017] +
               dftrends[dftrends.index==2018]['SOAyieldforscenario [g/g ink]'].loc[2018] +
               dftrends[dftrends.index==2019]['SOAyieldforscenario [g/g ink]'].loc[2019] +
               dftrends[dftrends.index==2020]['SOAyieldforscenario [g/g ink]'].loc[2020] +
               dftrends[dftrends.index==2021]['SOAyieldforscenario [g/g ink]'].loc[2021])/5

initialo3yieldperusage = (dftrends[dftrends.index==2002]['O3yieldforscenario [g/g ink]'].loc[2002] +
               dftrends[dftrends.index==2003]['O3yieldforscenario [g/g ink]'].loc[2003] +
               dftrends[dftrends.index==2004]['O3yieldforscenario [g/g ink]'].loc[2004] +
               dftrends[dftrends.index==2005]['O3yieldforscenario [g/g ink]'].loc[2005] +
               dftrends[dftrends.index==2006]['O3yieldforscenario [g/g ink]'].loc[2006])/5

finalo3yieldperusage = (dftrends[dftrends.index==2017]['O3yieldforscenario [g/g ink]'].loc[2017] +
               dftrends[dftrends.index==2018]['O3yieldforscenario [g/g ink]'].loc[2018] +
               dftrends[dftrends.index==2019]['O3yieldforscenario [g/g ink]'].loc[2019] +
               dftrends[dftrends.index==2020]['O3yieldforscenario [g/g ink]'].loc[2020] +
               dftrends[dftrends.index==2021]['O3yieldforscenario [g/g ink]'].loc[2021])/5

# Gg/yr changes
SOAchangeduetousage = ( finalusage - initialusage ) * initialSOAyieldperusage
SOAchangeduetoemiss = ( finalemission - initialemission ) * initialSOAyieldperusage * initialusage/initialemission - SOAchangeduetousage
SOAchangeduetoyield = ( finalusage * finalSOAyieldperusage ) - (initialusage * initialSOAyieldperusage ) - SOAchangeduetousage - SOAchangeduetoemiss
print('Change in usage: ',initialusage,',',finalusage)
print('Change in emission: ',initialemission,',',finalemission)
print('Change in SOA yield: ',initialSOAyieldperusage,',',finalSOAyieldperusage)
print('Change in SOA due to usage:', SOAchangeduetousage,',',SOAchangeduetoemiss,',',SOAchangeduetoyield)

o3changeduetousage = ( finalusage - initialusage ) * initialo3yieldperusage
o3changeduetoemiss = ( finalemission - initialemission ) * initialo3yieldperusage * initialusage/initialemission - o3changeduetousage
o3changeduetoyield = ( finalusage * finalo3yieldperusage ) - (initialusage * initialo3yieldperusage ) - o3changeduetousage - o3changeduetoemiss
print('Change in O3 yield per usage: ',initialo3yieldperusage,',',finalo3yieldperusage)
print('Change in O3 due to usgae: ',o3changeduetousage,',',o3changeduetoemiss,',',o3changeduetoyield)

Change in usage:  1299.926 , 834.308
Change in emission:  766.4590721958398 , 444.7482471734873
Change in SOA yield:  0.0066342031701869305 , 0.00597919610187837
Change in SOA due to usage: -3.0890044116960977 , -0.530791648704239 , -0.015685988442139642
Change in O3 yield per usage:  1.45529601525558 , 1.315520840586781
Change in O3 due to usgae:  -677.6120200312726 , -116.43583283091618 , -0.17971359666023545

Figure 2 on relative composition, SOA, O3, risk

# Relative composition, SOA, O3
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=[6.5,3], gridspec_kw=dict(
    wspace=0.0, hspace=0.0, left=0.08,right=0.74,top=0.91,bottom=0.27), dpi=300)

plotdata = dfmeasured[['species','T2_emiss_g','T3_emiss_g','T4_emiss_g','est_soa_yield','est_mir','URE\n1/(ug/m3)','RFC\n(mg/m3)']].copy()
plotdata.rename(columns={"T4_emiss_g": "A_emiss_g"},inplace=True)
plotdata.rename(columns={"T2_emiss_g": "B_emiss_g"},inplace=True)
plotdata.rename(columns={"T3_emiss_g": "C_emiss_g"},inplace=True)

plotdata.replace('NR',np.nan,inplace=True)
plotdata.replace('ND',np.nan,inplace=True)

axid = 0

# Calculate SOA, O3, cancer risk, noncancer risk by test, absolute values
for test in ['A','B','C']:
    var = test + '_emiss_g'
    print('-------'+var+' by fraction-------')
    plotdata['Emissions']=plotdata[var]
    plotdata['SOA']=plotdata[var]*plotdata['est_soa_yield']
    plotdata['O$_3$']=plotdata[var]*plotdata['est_mir']
    plotdata['Cancer']=plotdata[var]*plotdata['URE\n1/(ug/m3)']
    plotdata['Noncancer']=plotdata[var]/plotdata['RFC\n(mg/m3)']
    
    # normalize to total
    plotdata['Emissions']=plotdata['Emissions']/plotdata['Emissions'].sum()
    plotdata['SOA']=plotdata['SOA']/plotdata['SOA'].sum()
    plotdata['O$_3$']=plotdata['O$_3$']/plotdata['O$_3$'].sum()
    plotdata['Cancer']=plotdata['Cancer']/plotdata['Cancer'].sum()
    plotdata['Noncancer']=plotdata['Noncancer']/plotdata['Noncancer'].sum()
    
    pplotdata=plotdata[['Emissions','SOA','O$_3$','Cancer','Noncancer','species']]
    pplotdata.set_index('species',inplace=True)
    print(pplotdata)
    Tplotdata=pplotdata.T
    Tplotdata.plot.bar( ax=axes[axid], stacked=True, ylabel='species fractional contribution', width = 0.9, colormap='tab20',fontsize=8)
    axes[axid].set_title('Ink '+test,fontsize=8)
    axes[axid].set_ylim(0,1)
    if axid>0: 
        axes[axid].yaxis.set_tick_params(labelleft=False)
        axes[axid].set_yticks([])
        axes[axid].set_ylabel('')
    axid = axid+1

axes[0].get_legend().remove()
axes[1].get_legend().remove()
axes[2].get_legend().remove()
handles, labels = axes[0].get_legend_handles_labels()

axes[2].legend(handles[::-1], labels[::-1],loc='center right', bbox_to_anchor=(2.145, -0.05,0.02,0.8),fontsize=8,labelspacing=0.22 )# reverse order of labels so they match stacking # x,y,width, height bbox

fig.savefig(outputdir + '/'+str(today)+'_Figure2_relative_potential.png', facecolor='w', transparent=False)

-------A_emiss_g by fraction-------
                       Emissions       SOA         O$_3$  Cancer  Noncancer
species                                                                    
Formaldehyde                 NaN       NaN           NaN     NaN        NaN
Acetaldehyde        2.629520e-04  0.000000  7.976629e-04     1.0   0.990321
Unknown as Acetone           NaN       NaN           NaN     NaN        NaN
Butanal                      NaN       NaN           NaN     NaN        NaN
Hexaldehyde                  NaN       NaN           NaN     NaN        NaN
Methanol            1.540007e-03  0.000000  2.304740e-03     0.0   0.002610
Isopropyl_Alcohol   3.369600e-04  0.000000  3.575999e-04     NaN        NaN
Total SVOCs                  NaN       NaN           NaN     NaN        NaN
Acetone             1.004600e-03  0.000000  6.261716e-04     NaN        NaN
Propionaldehyde     1.660578e-06  0.000000  4.512638e-06     0.0   0.007036
2-Butanone (MEK)    3.366071e-03  0.000000  2.594987e-03     NaN        NaN
3-Pentanone         5.074006e-01  0.976860  3.503929e-01     NaN        NaN
Benzaldehyde        9.844195e-07  0.000002  6.864893e-07     NaN        NaN
Ethanol             4.625566e-01  0.000000  6.065047e-01     NaN        NaN
Ethyl_Acetate       7.251737e-03  0.000000  5.594033e-03     NaN        NaN
Toluene             4.923655e-06  0.000145  7.991752e-06     0.0   0.000033
Alkane              1.282262e-04  0.000175  6.323938e-05     NaN        NaN
PAH                 2.539939e-04  0.007323  3.646765e-04     NaN        NaN
Glycol              1.332006e-02  0.000000  1.948494e-02     NaN        NaN
SubBenzene          2.570572e-03  0.015494  1.090110e-02     NaN        NaN
-------B_emiss_g by fraction-------
                    Emissions  SOA     O$_3$    Cancer  Noncancer
species                                                          
Formaldehyde         0.000307  0.0  0.000679  0.621322   0.167866
Acetaldehyde         0.001107  0.0  0.002190  0.378678   0.658295
Unknown as Acetone   0.016468  0.0  0.006691       NaN        NaN
Butanal                   NaN  NaN       NaN       NaN        NaN
Hexaldehyde          0.000260  1.0  0.000368       NaN        NaN
Methanol             0.649822  0.0  0.633933  0.000000   0.173838
Isopropyl_Alcohol    0.010545  0.0  0.007295       NaN        NaN
Total SVOCs          0.321490  0.0  0.348844       NaN        NaN
Acetone                   NaN  NaN       NaN       NaN        NaN
Propionaldehyde           NaN  NaN       NaN       NaN        NaN
2-Butanone (MEK)          NaN  NaN       NaN       NaN        NaN
3-Pentanone               NaN  NaN       NaN       NaN        NaN
Benzaldehyde              NaN  NaN       NaN       NaN        NaN
Ethanol                   NaN  NaN       NaN       NaN        NaN
Ethyl_Acetate             NaN  NaN       NaN       NaN        NaN
Toluene                   NaN  NaN       NaN       NaN        NaN
Alkane                    NaN  NaN       NaN       NaN        NaN
PAH                       NaN  NaN       NaN       NaN        NaN
Glycol                    NaN  NaN       NaN       NaN        NaN
SubBenzene                NaN  NaN       NaN       NaN        NaN
-------C_emiss_g by fraction-------
                    Emissions  SOA     O$_3$    Cancer  Noncancer
species                                                          
Formaldehyde         0.001371  0.0  0.003008  0.166142   0.029848
Acetaldehyde         0.040667  0.0  0.079917  0.833858   0.963891
Unknown as Acetone   0.078113  0.0  0.031542       NaN        NaN
Butanal              0.000349  0.0  0.000563       NaN        NaN
Hexaldehyde          0.001329  1.0  0.001869       NaN        NaN
Methanol             0.587017  0.0  0.569125  0.000000   0.006261
Isopropyl_Alcohol         NaN  NaN       NaN       NaN        NaN
Total SVOCs          0.291154  0.0  0.313975       NaN        NaN
Acetone                   NaN  NaN       NaN       NaN        NaN
Propionaldehyde           NaN  NaN       NaN       NaN        NaN
2-Butanone (MEK)          NaN  NaN       NaN       NaN        NaN
3-Pentanone               NaN  NaN       NaN       NaN        NaN
Benzaldehyde              NaN  NaN       NaN       NaN        NaN
Ethanol                   NaN  NaN       NaN       NaN        NaN
Ethyl_Acetate             NaN  NaN       NaN       NaN        NaN
Toluene                   NaN  NaN       NaN       NaN        NaN
Alkane                    NaN  NaN       NaN       NaN        NaN
PAH                       NaN  NaN       NaN       NaN        NaN
Glycol                    NaN  NaN       NaN       NaN        NaN
SubBenzene                NaN  NaN       NaN       NaN        NaN