The impact of secondhand smoking to health of general population, and specifically children, is a well-known phenomenon that researchers have studied for years. In this dissertation, I extend work done within a secondhand smoke intervention to understand and quantity the impact of intervention on flow of smoke air particle concentration from the main room where smoking generates air particle contamination to a room where a child living in the home sleeps. The paper also explores potential modelling techniques to proactively identify and the impact of the smoke air particles with the intent to discourage adults from smoking in the home and thus potentially minimizing the impact to children’s health. The data was analyzed using hierarchical linear models to quantify the impact of intervention. The analysis finds that smoke air particles attenuated, on average, by 31.6% from the main room to the child’s room. Using hierarchical linear models, I also quantified the effect of intervention where the relationship between the main room and child’s room concentrations decreased once the intervention became active (-0.146 to -0.034 based on random slope versus random intercept). I also developed an LSTM model that can proactively identify whether a smoking event would be an impact children’s health. The results of the model are very encouraging, with an accuracy of approximately 80% when using less than 4 minutes of main room data. The two key outcomes from this study are 1) I can quantify the impact of intervention on the flow of air particle concentration between the main room and child’s room and 2) I am able to develop a modelling approach that can proactively identify the potential impact of SHS to health of the child. The study open doors for several possibilities including use of the findings by practitioners in counselling sessions to provide metrics to smoking adults and advice on the potential impact of smoking to the health of the child. The modelling approach also lays a foundation for future research to implement proactive, real time monitoring and notification in smart homes.
","abstract_html":"<p>The impact of secondhand smoking to health of general population, and specifically children, is a well-known phenomenon that researchers have studied for years. In this dissertation, I extend work done within a secondhand smoke intervention to understand and quantity the impact of intervention on flow of smoke air particle concentration from the main room where smoking generates air particle contamination to a room where a child living in the home sleeps. The paper also explores potential modelling techniques to proactively identify and the impact of the smoke air particles with the intent to discourage adults from smoking in the home and thus potentially minimizing the impact to children’s health. The data was analyzed using hierarchical linear models to quantify the impact of intervention. The analysis finds that smoke air particles attenuated, on average, by 31.6% from the main room to the child’s room. Using hierarchical linear models, I also quantified the effect of intervention where the relationship between the main room and child’s room concentrations decreased once the intervention became active (-0.146 to -0.034 based on random slope versus random intercept). I also developed an LSTM model that can proactively identify whether a smoking event would be an impact children’s health. The results of the model are very encouraging, with an accuracy of approximately 80% when using less than 4 minutes of main room data. The two key outcomes from this study are 1) I can quantify the impact of intervention on the flow of air particle concentration between the main room and child’s room and 2) I am able to develop a modelling approach that can proactively identify the potential impact of SHS to health of the child. The study open doors for several possibilities including use of the findings by practitioners in counselling sessions to provide metrics to smoking adults and advice on the potential impact of smoking to the health of the child. The modelling approach also lays a foundation for future research to implement proactive, real time monitoring and notification in smart homes.</p>","abstract_has_math":false,"creators":["Ramchandani, Sunil"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Dr. Vincent Berardi Ph.D","Dr. Melbourne Hovell Ph.D","Dr. Cyril Rakowski Ph.D"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12-01T08:00:00Z","date_published":"2022-12-01T08:00:00Z","updated_at":"2026-07-24T01:38:24Z","subjects":["Computational Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_dissertations/29","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Vincent Berardi Ph.D","Dr. Melbourne Hovell Ph.D","Dr. Cyril Rakowski Ph.D"]},{"key":"dc:creator","label":"Author","values":["Ramchandani, Sunil"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computational Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_dissertations/29"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The impact of secondhand smoking to health of general population, and specifically children, is a well-known phenomenon that researchers have studied for years. In this dissertation, I extend work done within a secondhand smoke intervention to understand and quantity the impact of intervention on flow of smoke air particle concentration from the main room where smoking generates air particle contamination to a room where a child living in the home sleeps. The paper also explores potential modelling techniques to proactively identify and the impact of the smoke air particles with the intent to discourage adults from smoking in the home and thus potentially minimizing the impact to children’s health. The data was analyzed using hierarchical linear models to quantify the impact of intervention. The analysis finds that smoke air particles attenuated, on average, by 31.6% from the main room to the child’s room. Using hierarchical linear models, I also quantified the effect of intervention where the relationship between the main room and child’s room concentrations decreased once the intervention became active (-0.146 to -0.034 based on random slope versus random intercept). I also developed an LSTM model that can proactively identify whether a smoking event would be an impact children’s health. The results of the model are very encouraging, with an accuracy of approximately 80% when using less than 4 minutes of main room data. The two key outcomes from this study are 1) I can quantify the impact of intervention on the flow of air particle concentration between the main room and child’s room and 2) I am able to develop a modelling approach that can proactively identify the potential impact of SHS to health of the child. The study open doors for several possibilities including use of the findings by practitioners in counselling sessions to provide metrics to smoking adults and advice on the potential impact of smoking to the health of the child. The modelling approach also lays a foundation for future research to implement proactive, real time monitoring and notification in smart homes.
"]},{"key":"dc:source","label":"Dc Source","values":["S. Ramchandani, \"Novel techniques for quantifying secondhand smoke diffusion into children's bedroom,\" Ph.D. dissertation, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000411"]},{"key":"dc:title","label":"Title","values":["Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom"]}]}],"canonical_facts":{"dc:contributor":["Dr. Vincent Berardi Ph.D","Dr. Melbourne Hovell Ph.D","Dr. Cyril Rakowski Ph.D"],"dc:creator":["Ramchandani, Sunil"],"dc:description.abstract":["The impact of secondhand smoking to health of general population, and specifically children, is a well-known phenomenon that researchers have studied for years. In this dissertation, I extend work done within a secondhand smoke intervention to understand and quantity the impact of intervention on flow of smoke air particle concentration from the main room where smoking generates air particle contamination to a room where a child living in the home sleeps. The paper also explores potential modelling techniques to proactively identify and the impact of the smoke air particles with the intent to discourage adults from smoking in the home and thus potentially minimizing the impact to children’s health. The data was analyzed using hierarchical linear models to quantify the impact of intervention. The analysis finds that smoke air particles attenuated, on average, by 31.6% from the main room to the child’s room. Using hierarchical linear models, I also quantified the effect of intervention where the relationship between the main room and child’s room concentrations decreased once the intervention became active (-0.146 to -0.034 based on random slope versus random intercept). I also developed an LSTM model that can proactively identify whether a smoking event would be an impact children’s health. The results of the model are very encouraging, with an accuracy of approximately 80% when using less than 4 minutes of main room data. The two key outcomes from this study are 1) I can quantify the impact of intervention on the flow of air particle concentration between the main room and child’s room and 2) I am able to develop a modelling approach that can proactively identify the potential impact of SHS to health of the child. The study open doors for several possibilities including use of the findings by practitioners in counselling sessions to provide metrics to smoking adults and advice on the potential impact of smoking to the health of the child. The modelling approach also lays a foundation for future research to implement proactive, real time monitoring and notification in smart homes.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_dissertations/29"],"dc:source":["S. Ramchandani, \"Novel techniques for quantifying secondhand smoke diffusion into children's bedroom,\" Ph.D. dissertation, Chapman University, Orange, CA, 2022. https://doi.org/10.36837/chapman.000411"],"dc:subject":["Computational Engineering"],"dc:title":["Novel Techniques for Quantifying Secondhand Smoke Diffusion into Children's Bedroom"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T01:38:24Z"}