Risk assessment of extreme air pollution based on partial duration series: IDF approach

Masseran, Nurulkamal; Safari, Muhammad Aslam Mohd

doi:10.1007/s00477-020-01784-2

Cited by 23 publications

(9 citation statements)

References 41 publications

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“…Based on the observed API data, the proportion of unhealthy air pollution events is found to be 2.44%. As described by Masseran and Safari [48], although this proportion is quite small, however, these events provide the most valuable information, particularly on pollution risk management and mitigation. Table 1 show the descriptive statistics for the severity and duration data.…”

Section: Resultsmentioning

confidence: 98%

“…Figure 2 illustrate the schematic process for determining the API value based on five pollutant variables [47]. Based on the API index values, the DOE classifies API values higher than 100 as unhealthy air pollution events [48], and consecutive API values greater than 100 in a given period indicate the duration of an air pollution event. Assuming that the random variable D i represents the duration of any pollution event, then i = 1, 2, 3, .…”

Section: Study Area and Datamentioning

confidence: 99%

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Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Masseran

2021

IJERPH

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This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration and severity characteristics. The bivariate criteria represented by duration and severity indicate their structural dependency, long-tail, and non-identically marginal distributions. A copula approach can provide a good statistical tool to deal with these issues and enable the extraction of valuable information from air pollution data. Based on the copula model, several statistical measurements are proposed for describing the characteristics of unhealthy air pollution events, including the Kendall’s τ correlation of the copula, the conditional probability of air pollution severity based on a given duration, the joint OR/AND return period, and the conditional D|S and conditional S|D return periods. A case study based on air pollution data indices was conducted in Klang, Malaysia. The results indicate that a copula approach is beneficial for deriving valuable information for planning and mitigating the risks of unhealthy air pollution events.

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Section: Resultsmentioning

confidence: 98%

Section: Study Area and Datamentioning

confidence: 99%

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Masseran

2021

IJERPH

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“…Several pollutions data were data namely Nitrogen Oxide (NOx), PM2.5, atmospheric 10-µm particulate matter (PM10), and sulfur dioxide (SO2). PM2.5 (Feng et al, 2016), PM10 (Masseran & Safari, 2020), SO2 (Caraka et al, 2020), NOx (as nitrogen dioxide (NO2) and nitrogen trioxide (NO3)) are the major sources of pollution in urban regions due to high traffic and the massive industrial developments (De Vito et al, 2009).…”

Section: Previous Studiesmentioning

confidence: 99%

Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data

Caraka

Chen

Yasin

et al. 2021

Indonesian J. Sci. Technol

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The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task. In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.

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“…Air pollution control has actually become a program of the Indonesian government through pollution prevention and control also restoration of air quality. However, these activities must begin with continuous monitoring and research to determine developments in the ambient air condition (Masseran and Safari 2020 ). The development of the air condition can be seen by observing data on air pollutants, one of which is ozone, from time to time.…”

Section: Introductionmentioning

confidence: 99%

Using Harris hawk optimization towards support vector regression to ozone prediction

Kurniawan¹,

Setiawan

Caraka

et al. 2022

Stoch Environ Res Risk Assess

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As an area experiencing air pollution, especially ozone concentrations that often exceed the threshold or are unhealthy, JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) seeks to prevent and control pollution as well as restore air quality. Therefore, this study aims to build a predictive model of ozone concentration using Harris hawks optimizationsupport vector regression (HHO-SVR) in 14 sub-districts in JABODETABEK. This goal is achieved by collecting data on ozone concentration as a response variable and meteorological factors as predictor variables from the website that provides the data. Other predictor variables such as time and significant lag detected with partial autocorrelation function of ozone concentration were also used. Then the variables will be selected using the recursive feature elimination-support vector regression (RFE-SVR) to obtain a significant predictor variable that affects the ozone concentration. After that, the prediction model will be built using the HHO-SVR method, support vector regression (SVR) whose parameter values are optimized with the Harris hawks optimization (HHO) algorithm. When the model has been formed, several evaluation metrics used to determine the best model include mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Coefficient of Determination (R 2 ), Variance Ratio (VR), and Diebold-Mariano test. The results of this study indicate that lag 1, lag 2, air temperature, humidity, and UV index are significant predictor variables of the RFE-SVR results for most sub-districts. In general, the HHO process takes longer than other metaheuristic algorithms. On average, 7 of the 14 sub-districts using the HHO-SVR model yielded the best predictions with MAE below 10, RMSE and MAPE below 20, R 2 around 0.97, and VR around 0.98. Then, the results of the Diebold-Mariano test also show that the accuracy of the prediction results and the stability of the performance of the HHO-SVR model is better, especially for the Ciputat and South Bekasi sub-districts. This shows that the two sub-districts are very suitable to use HHO-SVR in predicting ozone concentrations.

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Risk assessment of extreme air pollution based on partial duration series: IDF approach

Cited by 23 publications

References 41 publications

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data

Using Harris hawk optimization towards support vector regression to ozone prediction

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