The latest forecasts indicate wildfire activity in many parts of the world.Wildfire smoke contains hazardous air pollutants such as carbon monoxide,nitrogen dioxide,ozone,particulate matter et cetera.However,predictio...The latest forecasts indicate wildfire activity in many parts of the world.Wildfire smoke contains hazardous air pollutants such as carbon monoxide,nitrogen dioxide,ozone,particulate matter et cetera.However,prediction of this impact and on time medical care are difficult due to the lack of digital decision-making systems.The aim of this study is to assess population health risks associated with the sub-daily exposure to wildfire smoke produced by massive foci of combustion near the populated areas and at a significant distance from them.We consider reflex reactions as a response to a short-term exposure.The maximum value of the 95th percentile from the series of observations at the monitoring point was used to assess the hazard.For the mathematical description of the“concentration-effect”relationship,the model of individual thresholds is applicable.This model describes a dependence as a straight line under the condition that the concentration is expressed in the form of a normalprobabilistic scale.The frequency of additional cases is determined by studying the number of requests for medical assistance(including calls for ambulance)with complaints of respiratory disorders,lacrimation,etc.on the territories affected by wildfires smokes.The indicator is calculated per 1000 population.The probability of negative biological effects in response to the impact of wildfire smoke is associated mainly with the content of CO and TPM in the conditions of the Baikal region.The frequency of additional requests for medical care ranged from 0.137 to 0.933 per 1000 exposed population during the fire period in settlements where risk levels are>0.01.We developed a digital environment that allows us to get information about harmful substances in the outdoor air from different sources and in different formats and data schemes.The digital environment supports implementation of models for assessing hazards to human body organs.展开更多
基金supported by the Ministry of Science and Higher Education of the Russian Federation,Grant No.075-15-2020-787 for implementation of major scientific projects in priority areas of scientific and technological development (Project“Fundamentals,methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory”).
文摘The latest forecasts indicate wildfire activity in many parts of the world.Wildfire smoke contains hazardous air pollutants such as carbon monoxide,nitrogen dioxide,ozone,particulate matter et cetera.However,prediction of this impact and on time medical care are difficult due to the lack of digital decision-making systems.The aim of this study is to assess population health risks associated with the sub-daily exposure to wildfire smoke produced by massive foci of combustion near the populated areas and at a significant distance from them.We consider reflex reactions as a response to a short-term exposure.The maximum value of the 95th percentile from the series of observations at the monitoring point was used to assess the hazard.For the mathematical description of the“concentration-effect”relationship,the model of individual thresholds is applicable.This model describes a dependence as a straight line under the condition that the concentration is expressed in the form of a normalprobabilistic scale.The frequency of additional cases is determined by studying the number of requests for medical assistance(including calls for ambulance)with complaints of respiratory disorders,lacrimation,etc.on the territories affected by wildfires smokes.The indicator is calculated per 1000 population.The probability of negative biological effects in response to the impact of wildfire smoke is associated mainly with the content of CO and TPM in the conditions of the Baikal region.The frequency of additional requests for medical care ranged from 0.137 to 0.933 per 1000 exposed population during the fire period in settlements where risk levels are>0.01.We developed a digital environment that allows us to get information about harmful substances in the outdoor air from different sources and in different formats and data schemes.The digital environment supports implementation of models for assessing hazards to human body organs.
