Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks
Само за регистроване кориснике
2019
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors' emissions, population and climate data for the period 2003-2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values. The created models have made majority of predictions ( approximate to 60%) with satisfactory accuracy (relative error lt 20%) on testing, while the best performing model had R-2 = 0.87 and overall relative error of 33.6%. The... domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to lt 14%, after the pool of countries was reduced based on the abovementioned criterion.
Кључне речи:
SOMO35 / ANN / Forecasting / Europe / GRNNИзвор:
Environmental Pollution, 2019, 244, 288-294Издавач:
- Elsevier Sci Ltd, Oxford
Финансирање / пројекти:
- Развој и примена метода и материјала за мониторинг нових загађујућих и токсичних органских материја и тешких метала (RS-MESTD-Basic Research (BR or ON)-172007)
DOI: 10.1016/j.envpol.2018.10.051
ISSN: 0269-7491
PubMed: 30342369
WoS: 000452940700032
Scopus: 2-s2.0-85055965377
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Antanasijević, Davor AU - Pocajt, Viktor AU - Perić-Grujić, Aleksandra AU - Ristić, Mirjana PY - 2019 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/4331 AB - Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors' emissions, population and climate data for the period 2003-2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values. The created models have made majority of predictions ( approximate to 60%) with satisfactory accuracy (relative error lt 20%) on testing, while the best performing model had R-2 = 0.87 and overall relative error of 33.6%. The domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to lt 14%, after the pool of countries was reduced based on the abovementioned criterion. PB - Elsevier Sci Ltd, Oxford T2 - Environmental Pollution T1 - Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks EP - 294 SP - 288 VL - 244 DO - 10.1016/j.envpol.2018.10.051 ER -
@article{ author = "Antanasijević, Davor and Pocajt, Viktor and Perić-Grujić, Aleksandra and Ristić, Mirjana", year = "2019", abstract = "Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum of means over 35 ppb) in their health impact assessments. Is this paper, the artificial neural network (ANN) approach was used to forecast SOMO35 at the national level for a set of 24 European countries, mostly EU members. Available ozone precursors' emissions, population and climate data for the period 2003-2013 were used as inputs. Trend analysis had been performed using the linear regression of SOMO35 over time, and it has demonstrated that majority of the studied countries have a decreasing trend of SOMO35 values. The created models have made majority of predictions ( approximate to 60%) with satisfactory accuracy (relative error lt 20%) on testing, while the best performing model had R-2 = 0.87 and overall relative error of 33.6%. The domain of applicability of the created models was analyzed using slope/mean ratio derivate from the trend analysis, which was successful in distinguishing countries with high from countries with low prediction errors. The overall relative error was reduced to lt 14%, after the pool of countries was reduced based on the abovementioned criterion.", publisher = "Elsevier Sci Ltd, Oxford", journal = "Environmental Pollution", title = "Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks", pages = "294-288", volume = "244", doi = "10.1016/j.envpol.2018.10.051" }
Antanasijević, D., Pocajt, V., Perić-Grujić, A.,& Ristić, M.. (2019). Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks. in Environmental Pollution Elsevier Sci Ltd, Oxford., 244, 288-294. https://doi.org/10.1016/j.envpol.2018.10.051
Antanasijević D, Pocajt V, Perić-Grujić A, Ristić M. Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks. in Environmental Pollution. 2019;244:288-294. doi:10.1016/j.envpol.2018.10.051 .
Antanasijević, Davor, Pocajt, Viktor, Perić-Grujić, Aleksandra, Ristić, Mirjana, "Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks" in Environmental Pollution, 244 (2019):288-294, https://doi.org/10.1016/j.envpol.2018.10.051 . .