Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry
Due to the complex nature of ambient aerosols arising from the presence of myriads of organic compounds, the chemical reactivity of a particular compound with oxidant/s are studied through chamber experiments under controlled laboratory conditions. Several confounders (RH, T, light intensity, in cha...
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doaj-ea3be9fa28ef455889acfbfabd9f40262020-12-21T05:49:15ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2020-12-01810.3389/fenvs.2020.566136566136Instrumental Variable Analysis in Atmospheric and Aerosol ChemistryPrashant Rajput0Tarun Gupta1Centre for Environmental Health (CEH), Public Health Foundation of Indian, Gurugram, IndiaDepartment of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur, IndiaDue to the complex nature of ambient aerosols arising from the presence of myriads of organic compounds, the chemical reactivity of a particular compound with oxidant/s are studied through chamber experiments under controlled laboratory conditions. Several confounders (RH, T, light intensity, in chamber retention time) are controlled in chamber experiments to study their effect on the chemical transformation of a reactant (exposure variable) and the outcome [kinetic rate constant determination, new product/s formation e.g., secondary organic aerosol (SOA), product/s yield, etc.]. However, under ambient atmospheric conditions, it is not possible to control for these confounders which poses a challenge in assessing the outcome/s accurately. The approach of data interpretation must include randomization for an accurate prediction of the real-world scenario. One of the ways to achieve randomization is possible by the instrumental variable analysis (IVA). In this study, the IVA analysis revealed that the average ratio of fSOC/O3 (ppb−1) was 0.0032 (95% CI: 0.0009, 0.0055) and 0.0033 (95% CI: 0.0001, 0.0065) during daytime of Diwali and Post-Diwali period. However, during rest of the study period the relationship between O3 and fSOC was found to be insignificant. Based on IVA in conjunction with the concentration-weighted trajectory (CWT), cluster analysis, and fire count imageries, causal effect of O3 on SOA formation has been inferred for the daytime when emissions from long-range transport of biomass burning influenced the receptor site. To the best of our knowledge, the IVA has been applied for the first time in this study in the field of atmospheric and aerosol chemistry.https://www.frontiersin.org/articles/10.3389/fenvs.2020.566136/fullcausal inferencemachine learningair pollutionatmospheric chemistryaerosols |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Prashant Rajput Tarun Gupta |
spellingShingle |
Prashant Rajput Tarun Gupta Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry Frontiers in Environmental Science causal inference machine learning air pollution atmospheric chemistry aerosols |
author_facet |
Prashant Rajput Tarun Gupta |
author_sort |
Prashant Rajput |
title |
Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry |
title_short |
Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry |
title_full |
Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry |
title_fullStr |
Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry |
title_full_unstemmed |
Instrumental Variable Analysis in Atmospheric and Aerosol Chemistry |
title_sort |
instrumental variable analysis in atmospheric and aerosol chemistry |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Environmental Science |
issn |
2296-665X |
publishDate |
2020-12-01 |
description |
Due to the complex nature of ambient aerosols arising from the presence of myriads of organic compounds, the chemical reactivity of a particular compound with oxidant/s are studied through chamber experiments under controlled laboratory conditions. Several confounders (RH, T, light intensity, in chamber retention time) are controlled in chamber experiments to study their effect on the chemical transformation of a reactant (exposure variable) and the outcome [kinetic rate constant determination, new product/s formation e.g., secondary organic aerosol (SOA), product/s yield, etc.]. However, under ambient atmospheric conditions, it is not possible to control for these confounders which poses a challenge in assessing the outcome/s accurately. The approach of data interpretation must include randomization for an accurate prediction of the real-world scenario. One of the ways to achieve randomization is possible by the instrumental variable analysis (IVA). In this study, the IVA analysis revealed that the average ratio of fSOC/O3 (ppb−1) was 0.0032 (95% CI: 0.0009, 0.0055) and 0.0033 (95% CI: 0.0001, 0.0065) during daytime of Diwali and Post-Diwali period. However, during rest of the study period the relationship between O3 and fSOC was found to be insignificant. Based on IVA in conjunction with the concentration-weighted trajectory (CWT), cluster analysis, and fire count imageries, causal effect of O3 on SOA formation has been inferred for the daytime when emissions from long-range transport of biomass burning influenced the receptor site. To the best of our knowledge, the IVA has been applied for the first time in this study in the field of atmospheric and aerosol chemistry. |
topic |
causal inference machine learning air pollution atmospheric chemistry aerosols |
url |
https://www.frontiersin.org/articles/10.3389/fenvs.2020.566136/full |
work_keys_str_mv |
AT prashantrajput instrumentalvariableanalysisinatmosphericandaerosolchemistry AT tarungupta instrumentalvariableanalysisinatmosphericandaerosolchemistry |
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