Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.Peer reviewe

Mutual Information Input Selector and Probabilistic Machine Learning Utilisation for Air Pollution Proxies

An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.An air pollutant proxy is a mathematical model that estimates an unobserved air pollutant using other measured variables. The proxy is advantageous to fill missing data in a research campaign or to substitute a real measurement for minimising the cost as well as the operators involved (i.e., virtual sensor). In this paper, we present a generic concept of pollutant proxy development based on an optimised data-driven approach. We propose a mutual information concept to determine the interdependence of different variables and thus select the most correlated inputs. The most relevant variables are selected to be the best proxy inputs, where several metrics and data loss are also involved for guidance. The input selection method determines the used data for training pollutant proxies based on a probabilistic machine learning method. In particular, we use a Bayesian neural network that naturally prevents overfitting and provides confidence intervals around its output prediction. In this way, the prediction uncertainty could be assessed and evaluated. In order to demonstrate the effectiveness of our approach, we test it on an extensive air pollution database to estimate ozone concentration.Peer reviewe

The Role of Computed Tomography in Laryngotracheal Trauma: A Case Series

Laryngotracheal injuries are uncommon but often life threatening. Forensically, the assessment of survived cases is usually based on the external findings and several subjective elements such as reports from the involved persons and witnesses. Therefore, the need for more objective methods is crucial for forensic experts. Clinical computed tomography (CT) is sufficiently advanced to provide detailed descriptions of the internal structures. This study aims to evaluate the use of CT in survived and non-survived cases of laryngotracheal trauma. A total of five patients were included in this study (4 survived cases and one deceased). Information and data were collected retrospectively from medical records; radiological images were analyzed. The study involved three cases with injuries which resulted from blunt trauma caused by an alleged boating accident and sporting accidents, as well as two cases with injuries as a result of medical malpractice. During history taking, type of injury may help in early diagnosis and fast provision of treatment to patients. Blunt types of injury may require the help of CT more than acute injuries for early diagnosis and treatment

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O3) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00–17:00) and nighttime (01:00–05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O3 and NOx and was evaluated against a one-year data-base of O3 and nitrogen oxides (NO and NO2) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO2 to NO whereas that for the nighttime period was suggested to be inversely proportional to NO2 concentrations. Knowing that reactions involved in tropospheric O3 formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O3 and NOx, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O3 concentrations, as well as its precursors’ emissions

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O3) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00–17:00) and nighttime (01:00–05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O3 and NOx and was evaluated against a one-year data-base of O3 and nitrogen oxides (NO and NO2) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO2 to NO whereas that for the nighttime period was suggested to be inversely proportional to NO2 concentrations. Knowing that reactions involved in tropospheric O3 formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O3 and NOx, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O3 concentrations, as well as its precursors’ emissions

A Predictive Model for Steady State Ozone Concentration at an Urban-Coastal Site

Ground level ozone (O-3) plays an important role in controlling the oxidation budget in the boundary layer and thus affects the environment and causes severe health disorders. Ozone gas, being one of the well-known greenhouse gases, although present in small quantities, contributes to global warming. In this study, we present a predictive model for the steady-state ozone concentrations during daytime (13:00-17:00) and nighttime (01:00-05:00) at an urban coastal site. The model is based on a modified approach of the null cycle of O-3 and NOx and was evaluated against a one-year data-base of O-3 and nitrogen oxides (NO and NO2) measured at an urban coastal site in Jeddah, on the west coast of Saudi Arabia. The model for daytime concentrations was found to be linearly dependent on the concentration ratio of NO2 to NO whereas that for the nighttime period was suggested to be inversely proportional to NO2 concentrations. Knowing that reactions involved in tropospheric O-3 formation are very complex, this proposed model provides reasonable predictions for the daytime and nighttime concentrations. Since the current description of the model is solely based on the null cycle of O-3 and NOx, other precursors could be considered in future development of this model. This study will serve as basis for future studies that might introduce informing strategies to control ground level O-3 concentrations, as well as its precursors' emissions.Peer reviewe