Shambhavi Sharma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shambhavi Sharma has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ahtesham Bakht
Kumoh National Institute of Technology, Gumi 39177, Korea
2 shared publications - Duckshin Park
Transportation Environmental Research Department, Korea Railroad Research Institute (KRRI), Cheoldo Bangmulgwanro, Uiwang-si 16105, Republic of Korea
2 shared publications - Hyunsoo Lee
School of Industrial Engineering, Kumoh National Institute of Technology, Gumi, South Korea
1 shared publication - Hyunsoo Lee
Kumoh National Institute of Technology, Gumi 39177, Korea
1 shared publication - Muhammad Jahanzaib
Transportation Environmental Research Team, Korea Railroad Research Institute (KRRI), Uiwang-si 16105, Korea
1 shared publication
Latest Publications
- Open Access
- 9 November 2022
- Published by MDPI AG in Atmosphere
- Vol. 13 (11) , 1863
Evaluation of the Effectiveness of Common Indoor Plants in Improving the Indoor Air Quality of Studio Apartments
People spend most of their time indoors, and prolonged exposure to pollution can harm their health. The degradation of indoor air quality (IAQ) has raised serious issues. Botanical biofilters are an exciting solution for lowering indoor air pollution. However, plants cultivated inside under low light intensity (10–50 μmole PAR m−2 s−1) generate CO2 in the indoor atmosphere. Combining C3 (Calvin Cycle) and Crassulacean Acid metabolism (CAM) plants may be able to address this problem by lowering CO2 emission levels and enhancing the efficiency of pollution removal by removing the primary indoor air pollutants from actual interior settings, including carbon dioxide (CO2), formaldehyde (HCHO), particulate matter (PM2.5 and PM10), and total volatile organic compounds (TVOCs). As a result, a successful botanical biofilter made of several plants was researched. Indoor plants can phytoremediate a variety of indoor contaminants. However, just a few studies have demonstrated its efficacy in practical contexts. Due to the harsh winter, apartments in South Korea are frequently closed, necessitating the measurement of interior air pollution concentration in real-time. Four apartments (APT I through APT IV) with various ventilation and indoor plant setups were selected for this investigation. Various combinations of indoor environments (ventilation, low light) and a combination of C3 and CAM indoor plants as a botanical biofilter were used to study the sites over two months. Current research indicates that combining a botanical biofilter with ventilation can reduce levels of CO2, TVOCs, HCHO, PM2.5, and PM10 by 76%, 87%, 75%, 52%, and 51%, respectively. The current study concluded that different indoor potted plants provide an effective, affordable, self-regulating, sustainable option for enhancing indoor air quality and, consequently, human well-being and productivity in small, cramped places.
- 9 November 2022
- Published by MDPI AG in Atmosphere
- Vol. 13 (11) , 1863
Open AccessEvaluation of the Effectiveness of Common Indoor Plants in Improving the Indoor Air Quality of Studio Apartments
People spend most of their time indoors, and prolonged exposure to pollution can harm their health. The degradation of indoor air quality (IAQ) has raised serious issues. Botanical biofilters are an exciting solution for lowering indoor air pollution. However, plants cultivated inside under low light intensity (10–50 μmole PAR m−2 s−1) generate CO2 in the indoor atmosphere. Combining C3 (Calvin Cycle) and Crassulacean Acid metabolism (CAM) plants may be able to address this problem by lowering CO2 emission levels and enhancing the efficiency of pollution removal by removing the primary indoor air pollutants from actual interior settings, including carbon dioxide (CO2), formaldehyde (HCHO), particulate matter (PM2.5 and PM10), and total volatile organic compounds (TVOCs). As a result, a successful botanical biofilter made of several plants was researched. Indoor plants can phytoremediate a variety of indoor contaminants. However, just a few studies have demonstrated its efficacy in practical contexts. Due to the harsh winter, apartments in South Korea are frequently closed, necessitating the measurement of interior air pollution concentration in real-time. Four apartments (APT I through APT IV) with various ventilation and indoor plant setups were selected for this investigation. Various combinations of indoor environments (ventilation, low light) and a combination of C3 and CAM indoor plants as a botanical biofilter were used to study the sites over two months. Current research indicates that combining a botanical biofilter with ventilation can reduce levels of CO2, TVOCs, HCHO, PM2.5, and PM10 by 76%, 87%, 75%, 52%, and 51%, respectively. The current study concluded that different indoor potted plants provide an effective, affordable, self-regulating, sustainable option for enhancing indoor air quality and, consequently, human well-being and productivity in small, cramped places.
- Open Access
Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms
Particulate matter (PM) of sizes less than 10 µm () and 2.5 µm () found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future and on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed and forecasting framework is demonstrated using comparisons with the different existing deep learning models.Open AccessDeep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms
Particulate matter (PM) of sizes less than 10 µm () and 2.5 µm () found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future and on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed and forecasting framework is demonstrated using comparisons with the different existing deep learning models.