Predicting indoor particle dispersion under dynamic ventilation modes with high-order Markov chain model

Stochastic modeling of obesity status in United States adults using Markov Chains: A nationally representative analysis of population health data from 2017-2020

Importance

The prevalence of obesity among United States adults has increased from 34.9% in 2013-2014 to 42.8% in 2017-2018. Developing methods to model the increase of obesity over-time is a necessity to know how to accurately quantify its cost and to develop solutions to combat this national public health emergency.

Methods

A cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES 2017-2020) was conducted in individuals who completed the weight questionnaire and had accurate data for both weight at the time of survey and weight 10 years ago. To model the dynamics of obesity, a Markov transition state matrix was created, which allowed for the analysis of weight transitions over time. Bootstrap simulation was incorporated to account for uncertainty and generate multiple simulated datasets, providing a more robust estimation of the prevalence and trends in obesity within the cohort.

Results

Of the 6146 individuals who met the inclusion criteria, 3024 (49%) individuals were male and 3122 (51%) were female. There were 2252 (37%) White individuals, 1257 (20%) Hispanic individuals, 1636 (37%) Black individuals, and 739 (12%) Asian individuals. The average BMI was 30.16 (SD = 7.15), the average weight was 83.67 kilos (SD = 22.04), and the average weight change was a 3.27 kg (SD = 14.97) increase in body weight. A total of 2411 (39%) individuals lost weight, and 3735 (61%) individuals gained weight. 87 (1%) individuals were underweight (BMI <18.5), 2058 (33%) were normal weight (18.5 ≤ BMI <25), 1376 (22%) were overweight (25 ≤ BMI <30) and 2625 (43%) were in the obese category (BMI >30).

Conclusion

United States adults are at risk of transitioning from normal weight to the overweight or obese category. Markov modeling combined with bootstrap simulations can accurately model long-term weight status.

Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.

Mine ventilation system reliability evaluation based on a Markov chain

Mine ventilation systems play a key role in creating and sustaining a healthy and safe working environment within the mine, and as such, should always be maintained at optimal performance levels. This paper establishes a model based on Markov chain that can quickly evaluate the reliability of the ventilation system. Firstly, the operation status of the ventilation system is divided into normal, risk and failure. Then, according to the failure rate and repair rate of the system, the operation state of the system under the specified total operation time T and time interval Δt is simulated based on Monte Carlo method, the Markov chain state transfer probability matrix of the system can be obtained. Combined with the current operation state of the system, the reliability indexes such as the system operation state transfer probability and the steady state probability in the future can be quickly analyzed to realize the rapid evaluation of the operation reliability of the ventilation system. Finally, the model is used to evaluate the reliability of XQ mine ventilation system, which shows the effectiveness of the model. This research provides theoretical reference and technical support for mine safety production.

Nationwide evaluation of energy and indoor air quality predictive control and impact on infection risk for cooling season

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.

ELECTRONIC SUPPLEMENTARY MATERIAL ESM

The Appendix is available in the online version of this article at 10.1007/s12273-022-0936-6.

Robustness of ventilation systems in the control of walking-induced indoor fluctuations: Method development and case study

Walking-induced fluctuations have a significant influence on indoor airflow and pollutant dispersion. This study developed a method to quantify the robustness of ventilation systems in the control of walking-induced fluctuation control. Experiments were conducted in a full-scale chamber with four different kinds of ventilation systems: ceiling supply and side return (CS), ceiling supply and ceiling return (CC), side supply and ceiling return (SC), and side supply and side return (SS). The measured temperature, flow and pollutant field data was (1) denoised by FFT filtering or wavelet transform; (2) fitted by a Gaussian function; (3) feature-extracted for the range and time scale disturbance; and then (4) used to calculate the range scale and time scale robustness for different ventilation systems with dimensionless equations developed in this study. The selection processes for FFT filtering and wavelet transform, FFT filter cut-off frequency, wavelet function, and decomposition layers are also discussed, as well as the threshold for wavelet denoising, which can be adjusted accordingly if the walking frequency or sampling frequency differs from that in other studies. The results show that for the flow and pollutant fields, the use of a ventilation system can increase the range scale robustness by 19.7%-39.4% and 10.0%-38.8%, respectively; and the SS system was 7.0%-25.7% more robust than the other three ventilation systems. However, all four kinds of ventilation systems had a very limited effect in controlling the time scale disturbance.