The article addresses development of methodical approaches to calculating levels of health disorders caused by short-term exposure to ambient air pollution. We have established and parameterized relationships relevant for quantification of probable health outcomes as responses to elevated levels of chemicals in ambient air higher than their reference ones. These relationships were modeled using system analysis techniques and were based on dynamic data series on ambient air quality at the control points and the number of applications for medical aid in settlements with their overall population being more than 5 million people. We have formalized relationships that describe how intensively acute health disorders develop under short-term exposure to chemical levels in ambient air being higher than the reference ones that are identified at the control points. The resulting models rely on official data and can be used to predict and assess public health risks in any area where ambient air quality is monitored. The formalized relationships were tested within identifying levels of incidence associated with acute short-term exposure to ambient air pollution in a large industrial center. It was established that, according to data collected in 2020, the highest associated incidence was caused by exposure to benzene (on average 0.364 mg/m3 higher than the reference level) in ambient air and was detected as per such nosologies as ‘Allergic rhinitis unspecified’ and ‘Predominantly allergic asthma’. We are planning to use the results obtained at this stage in the research in further development of methodical approaches to assessing and predicting chemical health risks in areas influenced by hazardous chemical objects under short-term exposure to high levels of pollutants.

The relevance of the present study follows from the necessity to establish parameterized cause-effect relationships that describe additional disease cases among population caused by chronic exposure to chemical factors. In this study, our aim was to explore relationships within the ‘environment – public health’ system to quantify and predict chronic risks under exposure to chemicals in ambient air. To achieve this, we collected statistical data on some municipalities located in the Russian Federation with different structures and levels of chemical pollution in ambient air. Data on population incidence and ambient air quality were coordinated at places where calculation points were located; these points were centers of residential buildings and their coordinates were applied in the study. Mathematical modeling of the relationships was conducted by using multiple linear regressions. Pollution indicators (chemical concentrations in ambient air) that met the requirements of biological plausibility and statistical significance of pair correlations were selected as independent variables. The obtained regression models contain 190 factors for 36 chemicals occurring in emission into ambient air from stationary and mobile sources, which allow calculating the frequency of additional disease cases for 29 diseases. The established factors make it possible to perform operative estimations of a number of diseases associated with ambient air quality at a place of residence relying on medical aid applications. The resulting relationships can be used to predict chronic health risks. Establishing criteria for ranking chemical health risks in zones influenced by hazardous chemical objects can become a next step in development of the suggested approaches.

The relevance of the present study follows from the necessity to establish parameterized cause-effect relationships that describe additional disease cases among population caused by chronic exposure to chemical factors. In this study, our aim was to explore relationships within the ‘environment – public health’ system to quantify and predict chronic risks under exposure to chemicals in ambient air. To achieve this, we collected statistical data on some municipalities located in the Russian Federation with different structures and levels of chemical pollution in ambient air. Data on population incidence and ambient air quality were coordinated at places where calculation points were located; these points were centers of residential buildings and their coordinates were applied in the study. Mathematical modeling of the relationships was conducted by using multiple linear regressions. Pollution indicators (chemical concentrations in ambient air) that met the requirements of biological plausibility and statistical significance of pair correlations were selected as independent variables. The obtained regression models contain 190 factors for 36 chemicals occurring in emission into ambient air from stationary and mobile sources, which allow calculating the frequency of additional disease cases for 29 diseases. The established factors make it possible to perform operative estimations of a number of diseases associated with ambient air quality at a place of residence relying on medical aid applications. The resulting relationships can be used to predict chronic health risks. Establishing criteria for ranking chemical health risks in zones influenced by hazardous chemical objects can become a next step in development of the suggested approaches.