Dataset Information

Developing a model to predict air pollution (case study: Tehran City).

ABSTRACT: The technology development, population growth, development of metropolises and subsequent pollution are serious threats to the environment and public health. Therefore, monitoring and evaluation of various emissions and their sources, and also providing practical strategies of pollution reduction, are necessary to solve these problems. In this regard, the use of modern methods to predict the concentration of pollutants can improve decision-making and provide appropriate solutions. Tehran has been ranked as one of the most polluted cities in Iran. In this study, the meteorological monthly data were employed to achieve potent models based on a Box-Jenkins method for the modelling of concentration level of five major air pollutants in Tehran such as NO₂, PM₁₀, O₃, SO₂, CO, and Pollutant Standard Index. The best models were selected using goodness of fit criteria such as Akaike Information Criterion (AIC) and Schwartz Bayesian Criterion (SBC) and least prediction error. Prediction of concentrations of those pollutants can be a powerful tool in order to take preventive measures, such as the reduction of emissions and alerting the affected population. The results indicated that the concentration of pollutants in each period was influenced by their level and shocks they received during previous periods, which is mainly explained by special climatic and geographic conditions of Tehran that accumulates the pollution over time.

Supplementary information

The online version contains supplementary material available at 10.1007/s40201-020-00582-w.

SUBMITTER: Saleh I

PROVIDER: S-EPMC8172820 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Developing a model to predict air pollution (case study: Tehran City).

Saleh Iraj I Abedi Samaneh S Abedi Sara S Bastani Mahdi M Beman Elizabeth E

Journal of environmental health science & engineering 20210107 1

The technology development, population growth, development of metropolises and subsequent pollution are serious threats to the environment and public health. Therefore, monitoring and evaluation of various emissions and their sources, and also providing practical strategies of pollution reduction, are necessary to solve these problems. In this regard, the use of modern methods to predict the concentration of pollutants can improve decision-making and provide appropriate solutions. Tehran has bee ...[more]

PMID: 34150219

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Project description:BackgroundMost existing studies have only investigated the direct effects of the built environment on respiratory diseases. However, there is mounting evidence that the built environment of cities has an indirect influence on public health via influencing air pollution. Exploring the "urban built environment-air pollution-respiratory diseases" cascade mechanism is important for creating a healthy respiratory environment, which is the aim of this study.MethodsThe study gathered clinical data from 2015 to 2017 on patients with respiratory diseases from Tongji Hospital in Wuhan. Additionally, daily air pollution levels (sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter (PM2.5, PM10), and ozone (O3)), meteorological data (average temperature and relative humidity), and data on urban built environment were gathered. We used Spearman correlation to investigate the connection between air pollution and meteorological variables; distributed lag non-linear model (DLNM) was used to investigate the short-term relationships between respiratory diseases, air pollutants, and meteorological factors; the impacts of spatial heterogeneity in the built environment on air pollution were examined using the multiscale geographically weighted regression model (MGWR).ResultsDuring the study period, the mean level of respiratory diseases (average age 54) was 15.97 persons per day, of which 9.519 for males (average age 57) and 6.451 for females (average age 48); the 24 h mean levels of PM10, PM2.5, NO2, SO2 and O3 were 78.056 μg/m3, 71.962 μg/m3, 54.468 μg/m3, 12.898 μg/m3, and 46.904 μg/m3, respectively; highest association was investigated between PM10 and SO2 (r = 0.762, p < 0.01), followed by NO2 and PM2.5 (r = 0.73, p < 0.01), and PM10 and PM2.5 (r = 0.704, p < 0.01). We observed a significant lag effect of NO2 on respiratory diseases, for lag 0 day and lag 1 day, a 10 μg/m3 increase in NO2 concentration corresponded to 1.009% (95% CI: 1.001, 1.017%) and 1.005% (95% CI: 1.001, 1.011%) increase of respiratory diseases. The spatial distribution of NO2 was significantly influenced by high-density urban development (population density, building density, number of shopping service facilities, and construction land, the bandwidth of these four factors are 43), while green space and parks can effectively reduce air pollution (R2 = 0.649).ConclusionPrevious studies have focused on the effects of air pollution on respiratory diseases and the effects of built environment on air pollution, while this study combines these three aspects and explores the relationship between them. Furthermore, the theory of the "built environment-air pollution-respiratory diseases" cascading mechanism is practically investigated and broken down into specific experimental steps, which has not been found in previous studies. Additionally, we observed a lag effect of NO2 on respiratory diseases and spatial heterogeneity of built environment in the distribution of NO2.

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Dataset Information

Developing a model to predict air pollution (case study: Tehran City).

Supplementary information

Publications

Developing a model to predict air pollution (case study: Tehran City).

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