Research articleIntensity–duration–frequency approach for risk assessment of air pollution events
Introduction
Air pollution is an important issue that needs to be addressed and controlled, particularly in a developing country such as Malaysia. In urban areas, the problem of air pollution worsens every year and becomes alarming as it can affect human health or environmental ecosystems. Thus, rapid development should be carefully planned and monitored so that a sustainable ecosystem and healthy environment can be a priority. For planning and monitoring purposes, an air pollutant index (API) is recorded and supervised by the Department of Environment (DOE) of Malaysia. The API is derived from five major pollutants: ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter less than 10 μm (PM10). The highest value of these five pollutants at a particular time is determined as an API index, as shown in Fig. 1 (AL-Dhurafi et al., 2018).
As shown in Table 1, the DOE evaluates the API values corresponding to breakpoints of 50, 100, 200, and 300 which provide information on the status of air quality and its associated health effects (Department of Environment, 1997, 2000; Alyousifi et al., 2018). High values of API imply intense air pollution at a particular time. A high API value can also be considered as an extreme pollution event. In parallel with the status of high API index, extreme pollution events can have negative effects on human health. These events can also disrupt economic activities and the ecosystem of a country. For example, in 2005 and 2013, the government of Malaysia declared a state of emergency in several areas due to the recorded extreme API value, which referred to the occurrence of haze events (Sahani et al., 2014). Thus, risk assessment on the behavior of extreme pollution events should be effectively designed to reflect the likelihood or probability of these events. To achieve this goal, we can use the method known as intensity–duration–frequency (IDF) curve in describing the relationship of pollution intensity (i) with duration (d) and return period (T) (Mohymont et al., 2004; Van de Vyver, 2015; Willems, 2000). Thus, based on the IDF curve, the information about the probability of a given pollution intensity and duration expected to occur at a particular location can be determined.
The IDF method is a popular approach for studies on extreme rainfall, flood risk management, drought events, and engineering applications. For example, Dupont and Allen (2000) used IDF curves to predict runoff amount of watershed. Tfwala et al. (2017) used the IDF technique to estimate precipitation intensities and their uncertainties in various duration hours. Zhai et al. (2017) applied the IDF technique to analyze the trend of dryness as part of drought risk assessment in China. Sarhadi and Soulis (2017) used the IDF technique to estimate the maximum extent of the probability of exceedance, which is related to extreme storms that change over time. Micovic et al. (2016) used the same technique as a tool for analyzing flood hazards on dams for hydroelectric systems. Based on the information provided by the IDF curves, this technique can be a basis for municipal storm-water management and the design of engineering infrastructures that can withstand floods and extreme precipitation events (Elshorbagy et al., 2018; Cheng and AghaKouchak, 2014; Tung and Wong, 2014). Thus, by adopting the IDF technique in the analysis of extreme pollution data, we can obtain valuable information that can serve as a reference for risk assessment of air pollution events.
Section snippets
Study area and data
Klang, one of the largest cities in Malaysia, is located at latitude 101° 26′ 44.023 E and longitude 3° 2’ 41.701 N. Fig. 2 shows the map of Klang. The densely populated city occupies approximately 573 km2. In addition, Klang is in an active industrialized region where the most important economic activities are import and export operations. In fact, Klang Port has been recognized as the 13th busiest trans-shipment port and 16th busiest container port in the world (Gin, 2009; Masseran et al.,
Statistical model of extreme pollution data
As mentioned previously, extreme pollution events can harm human health and disrupt economic activities and the ecosystem. Thus, investigating the behaviors of extreme pollution events necessitates information on the peak concentrations of air pollutants in predicting the extreme concentrations and assessing their behaviors (Kutchenhoff and Thamerus, 1996). To perform this task, a statistical model known as generalized extreme value (GEV) distribution plays an important role. For example,
Block maxima size based on monsoon seasons
As reported by Azmi et al. (2010), the pollution concentration in Klang is significantly and positively correlated with the ambient temperature. The high temperature increases the quantity of biomass burning and suspended particles such as soil dust from the earth's surface. Azmi et al. (2010) also reported that the pollution concentration in Klang is negatively correlated with a humidity factor. The reason is that high humidity is related to the number of rain occasions, which reduce the
Adopting IDF relationship to extreme pollution events
The IDF relationship is represented by a curve with the duration plotted as abscissa, and the intensity as ordinate with a series of curves, one for each return period. The IDF curve provides information on the expected pollution intensity of a given duration of pollution event, thereby corresponding to the desired frequency of occurrence. In terms of mathematical functions, the IDF relationship between the intensity of extreme event , which is related to the duration, , and the return
Results and discussion
Before a detailed analysis is conducted, descriptive statistics of the data have to be evaluated so that preliminary information can be obtained. Table 2 shows the descriptive statistics of maximum pollution intensity based on monsoon season blocks. The mean of maximum intensity per duration hour is in the range of 129 to 112. In general, the mean decreases as the duration hour increases. The means of all duration hours are above the level of unhealthy API, as described in Table 1. The standard
Conclusion
This study proposes the IDF approach for analyzing the behavior of extreme pollution intensities and uncertainties corresponding to duration hours and return periods. The data of Klang city in Malaysia have been used as a case study. This study concludes that the extreme pollution intensity in this city exhibited a consistent trend during various return periods and duration hours. For any duration hour, the magnitudes of pollution intensity tend to increase in parallel with increasing return
Credit author statement
Nurulkamal Masseran conceived of the presented idea, developed the theory and performed the computations. Muhammad Aslam Mohd Safari have verified the analytical methods. Both authors discussed the results and contributed to the final manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
The author is indebted Malaysian Department of Environment for providing air pollution data. This research would not be possible without the sponsorship from the Universiti Kebangsaan Malaysia (grant number DIP-2018-038).
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