The diffusive uptake rate is essential for using passive samplers to measure indoor volatile organic compounds(VOCs).The traditional theoretical model of passive samplers requires available regression formulas of upta...The diffusive uptake rate is essential for using passive samplers to measure indoor volatile organic compounds(VOCs).The traditional theoretical model of passive samplers requires available regression formulas of uptake rates and physicochemical properties of adsorbents to predict the uptake rate.However,it is difficult to obtain the uptake rates of different VOCs under different sampling periods,and it is also difficult to obtain the physical parameters of adsorbents accurately and effectively.This study provides a reliable numerical prediction method of diffusive uptake rates of VOCs.The modeling was based on the standard automated thermal desorption(ATD)tubes packed with Tenax TA and the mass transfer process during adsorption.The experimental determinations of toluene uptake rate are carried out to verify the prediction model.Diffusive uptake rates of typical indoor VOCs are obtained from the literature to calibrate the key apparent parameters in the model by statistical regression fitting.The predicted model can provide the VOC diffusive uptake rates under different sampling duration with an average deviation of less than 5%.This study can provide the basis for fast and accurate prediction of diffusive uptake rates for various VOC pollutants in built environments.展开更多
Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usa...Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating,ventilation,and air-conditioning systems.In this study,to represent the dynamics of indoor temperature and air quality,a coupled grey-box model is developed.The model is identified and validated using a data-driven approach and real-time measured data of a campus office.To manage building energy usage and indoor air quality,a model predictive control strategy is proposed and developed.The simulation study demonstrated 18.92%energy saving while maintaining good indoor air quality at the testing site.Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones.The results showed 20%–40%energy saving in general while maintaining a predetermined indoor air quality setpoint.Although the infection risk is increased due to the reduced ventilation rate,it is still less than the suggested threshold(2%)in general.展开更多
Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby high...Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.展开更多
基金financially supported by the National Natural Sci-ence Foundation of China(No.52078269)the special funding from Wuhan Second Ship Design and Research Institute.
文摘The diffusive uptake rate is essential for using passive samplers to measure indoor volatile organic compounds(VOCs).The traditional theoretical model of passive samplers requires available regression formulas of uptake rates and physicochemical properties of adsorbents to predict the uptake rate.However,it is difficult to obtain the uptake rates of different VOCs under different sampling periods,and it is also difficult to obtain the physical parameters of adsorbents accurately and effectively.This study provides a reliable numerical prediction method of diffusive uptake rates of VOCs.The modeling was based on the standard automated thermal desorption(ATD)tubes packed with Tenax TA and the mass transfer process during adsorption.The experimental determinations of toluene uptake rate are carried out to verify the prediction model.Diffusive uptake rates of typical indoor VOCs are obtained from the literature to calibrate the key apparent parameters in the model by statistical regression fitting.The predicted model can provide the VOC diffusive uptake rates under different sampling duration with an average deviation of less than 5%.This study can provide the basis for fast and accurate prediction of diffusive uptake rates for various VOC pollutants in built environments.
基金This research was jointly sponsored by Honeywell International Inc.and Syracuse University.
文摘Since the coronavirus disease 2019,the extended time indoors makes people more concerned about indoor air quality,while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating,ventilation,and air-conditioning systems.In this study,to represent the dynamics of indoor temperature and air quality,a coupled grey-box model is developed.The model is identified and validated using a data-driven approach and real-time measured data of a campus office.To manage building energy usage and indoor air quality,a model predictive control strategy is proposed and developed.The simulation study demonstrated 18.92%energy saving while maintaining good indoor air quality at the testing site.Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones.The results showed 20%–40%energy saving in general while maintaining a predetermined indoor air quality setpoint.Although the infection risk is increased due to the reduced ventilation rate,it is still less than the suggested threshold(2%)in general.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP2/45/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R135)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4270206DSR02).
文摘Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.
