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 |

REPOSITORIES: biostudies-literature

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Project description:Multiple land use regression models (LUR) were developed for different air pollutants to characterize exposure, in the Durban metropolitan area, South Africa. Based on the European Study of Cohorts for Air Pollution Effects (ESCAPE) methodology, concentrations of particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), and nitrogen dioxide (NO2) were measured over a 1-year period, at 41 sites, with Ogawa Badges and 21 sites with PM Monitors. Sampling was undertaken in two regions of the city of Durban, South Africa, one with high levels of heavy industry as well as a harbor, and the other small-scale business activity. Air pollution concentrations showed a clear seasonal trend with higher concentrations being measured during winter (25.8, 4.2, 50.4, and 20.9 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively) as compared to summer (10.5, 2.8, 20.5, and 8.5 µg/m3 for NO2, SO2, PM10, and PM2.5, respectively). Furthermore, higher levels of NO2 and SO2 were measured in south Durban as compared to north Durban as these are industrial related pollutants, while higher levels of PM were measured in north Durban as compared to south Durban and can be attributed to either traffic or domestic fuel burning. The LUR NO2 models for annual, summer, and winter explained 56%, 41%, and 63% of the variance with elevation, traffic, population, and Harbor being identified as important predictors. The SO2 models were less robust with lower R2 annual (37%), summer (46%), and winter (46%) with industrial and traffic variables being important predictors. The R2 for PM10 models ranged from 52% to 80% while for PM2.5 models this range was 61-76% with traffic, elevation, population, and urban land use type emerging as predictor variables. While these results demonstrate the influence of industrial and traffic emissions on air pollution concentrations, our study highlighted the importance of a Harbor variable, which may serve as a proxy for NO2 concentrations suggesting the presence of not only ship emissions, but also other sources such as heavy duty motor vehicles associated with the port activities.

Project description:Introduction: Many studies have reported the association between air pollution and human health based on regulatory air pollution monitoring data. However, because regulatory monitoring networks were not designed for epidemiological studies, the collected data may not provide sufficient spatial contrasts for assessing such associations. Our goal was to develop a monitoring design supplementary to the regulatory monitoring network in Seoul, Korea. This design focused on the selection of 20 new monitoring sites to represent the variability in PM2.5 across people's residences for cohort studies. Methods: We obtained hourly measurements of PM2.5 at 37 regulatory monitoring sites in 2010 in Seoul, and computed the annual average at each site. We also computed 313 geographic variables representing various pollution sources at the regulatory monitoring sites, 31,097 children's homes from the Atopy Free School survey, and 412 community service centers in Seoul. These three types of locations represented current, subject, and candidate locations. Using the regulatory monitoring data, we performed forward variable selection and chose five variables most related to PM2.5. Then, k-means clustering was applied to categorize all locations into several groups representing a diversity in the spatial variability of the five selected variables. Finally, we computed the proportion of current to subject location in each cluster, and randomly selected new monitoring sites from candidate sites in the cluster with the minimum proportion until 20 sites were selected. Results: The five selected geographic variables were related to traffic or urbanicity with a cross-validated R² value of 0.69. Clustering analysis categorized all locations into nine clusters. Finally, one to eight new monitoring sites were selected from five clusters. Discussion: The proposed monitoring design will help future studies determine the locations of new monitoring sites representing spatial variability across residences for epidemiological analyses.

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

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