Page 28 - Çevre Şehir ve İklim Dergisi İngilizce - Özel Sayı
P. 28

Breath: the Source of Life
                                 “Air Quality Modelling Applications”

               4 . Conclusions and Recommendations

               Digital applications in effective air quality management launched by the
            Ministry  of  Environment,  Urbanisation  and  Climate  Change  feature  as  the
            leading example in this field. All sources of air pollution have been considered
            in the software that aims to obtain maximum quality data by minimising the
            uncertainty in the calculations.
               Applications developed simultaneously with the digital transformation
            era have enhanced the use of decision support system in the air quality
            management at maximum.
               It is obvious that this managerial approach, which aims at sustainable and
            continuous development with public resources and infrastructure, will be
            useful in improving the air quality of our cities.
               Through implementing the mitigation measures developed for the identified
            hotspots, emission reduction will be achieved, thus the air quality will improve
            and contribution will be made to the net zero target of our country.
               Sharing the digital transformation outputs in air quality management with
            the relevant institutions in line with the green transformation goal is important
            for the effective and efficient use of resources.
               Setting out with the principle of creating a common and transparent
            database, the Ministry of Environment, Urbanisation and Climate Change has
            managed to provide all citizens with data on the air quality of their location.
               It  is  recommended  to  continue  to  benefit  from  advancing  information
            technologies, to use satellite data in studies in particular, and to include
            techniques such as machine learning, artificial neural networks in the studies.
               I would like to extend my thanks to the project team of Istanbul Technical
            University  and  TURKSAT,  my  managers  and  team  members  in  the  Ministry
            of Environment, Urbanisation and Climate Change, who contributed to the
            development of the models in the scope of the study.

               References


               WHO, 2018. Ambient air pollution: health impacts. Retrieved May 10, (2020), from.
            htt ps://www.who.int/airpollution/ambient/health-impacts/en/.
               Cohen,  A.J.,  Brauer,  M.,  Burnett,  R.,  Anderson,  H.R.,  Frostad,  J.,  Estep,  K.,
            Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G.,
            Hubbell, B., Jobling, A., Kan, H., et al., (2017). Estimates and 25-year trends of the
            global  burden  of  disease  attributable  to  ambient  air  pollution:  an  analysis  of  data
            from the Global Burden of Diseases Study 2015. Lancet. 389, 1907–1918. https:// doi.
            org/10.1016/S0140-6736(17)30505-6.



                                                                              15
                                                                    Special Issue / 2024
   23   24   25   26   27   28   29   30   31   32   33