The present article aims to describe the status quo of the atmospheric air quality in Bishkek and the state measures taken to improve it and to give the perspective of research and policy development. Air pollution is one of the major environmental risks for premature death from respiratory diseases, cancer, strokes, heart attacks, diabetes, and other diseases. It exerts a negative effect on worker productivity and mental health. In the last 30 years, Bishkek, the capital of the Kyrgyz Republic, has turned from one of the cleanest and greenest cities in the former Soviet Union to one of the most polluted cities in the world. The roots of that transformation lie in the negative socio-economic changes taking place in the country, including the population doubling of Bishkek mainly due to internal migration, uncontrolled construction of houses without relevant infrastructure, worsening socio-economic conditions, increased number of used vehicles, and low quality of gasoline. The main sources of air pollution in Bishkek are domestic heating and vehicle exhaust fumes. During the winter, air pollution is aggravated by frequent temperature inversion and air stagnation due to air trapping by high-rise buildings. The state's approaches and measures to address this issue are reflected in its laws and policies. The city and national government have taken a range of strategic measures to transform Bishkek into a green city with a favourable environment. Recommendations on research and policy development are provided in this perspective.

Background. The prognosis of the difficult tracheal intubation remains an essential problem. The effectiveness of using predictors does not allow to foreseen such situation accurately. The purpose of the study was to develop a predictive system and evaluate its effectiveness in difficult tracheal intubation based on facial image analysis combined with the most significant predictors of difficult intubation. Materials and methods. A database based on the registration of difficult intubation predictors was developed. It was based on the patients face images with marked reference points. It allowed to estimate the information signs associated with the difficult tracheal intubation. The degree of intubation severity was determined directly during the intubation process according to the proposed original scale of severity. Results. The classifier was synthesized by using the self-organization neural network method. The trained neural network was the basis of the classifier model implemented as a computer application. The sensitivity of the difficult tracheal intubation prognosis was 90.90%, specificity was 97.02%, the prognostic value of the positive result was 58.82%, the negative one was 99.56%. Conclusions. The proposed decision support system allows patients to be stratified into groups according to the degree of difficult tracheal intubation risk. In addition, the self-learning process of the system continues as the new data become available. This allows to improve its efficiency continuously.