Intensity–duration–frequency approach for risk assessment of air pollution events

Masseran, Nurulkamal; Safari, Muhammad Aslam Mohd

doi:10.1016/j.jenvman.2020.110429

Cited by 23 publications

(14 citation statements)

References 27 publications

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“…First of all, a year needs to pass in order to obtain the levels of these indicators, so the evaluation is always retrospective. Second, the use of mean values is useful to evaluate chronic exposure, but it does not take into account the short-term effects of air pollution during peak events exposure [1,2]. Third, the results of this study and former ones have shown that the chemical PM composition is highly variable in space and time.…”

Section: Health Impact Of Air Pollution In Cataloniamentioning

confidence: 80%

“…Air pollution is a global threat to ecosystems and affects human health, even at shortterm exposure [1,2]. Therefore, there is a growing demand to improve the air quality [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…These directives provide the current framework for the control of ambient air pollution in the EU. Effective action to reduce air pollution and its impacts requires a good understanding of sources, atmospheric transport, and transformation mechanisms, and the effects of these atmospheric pollutants on humans and ecosystems [2]. Pending issues are the contributions of biomass emissions to PM 10 and benzo [a]pyrene in the atmosphere, as well as the formation of secondary aerosols and their influence on PM and their toxicity [3].…”

Section: Introductionmentioning

confidence: 99%

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Source Apportionment and Toxicity of PM in Urban, Sub-Urban, and Rural Air Quality Network Stations in Catalonia

et al. 2021

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Air quality indicators, i.e., PM10, NO2, O3, benzo[a]pyrene, and several organic tracer compounds were evaluated in an urban traffic station, a sub-urban background station, and a rural background station of the air quality network in Catalonia (Spain) from summer to winter 2019. The main sources that contribute to the organic aerosol and PM toxicity were determined. Traffic-related air pollution dominated the air quality in the urban traffic station, while biomass burning in winter and secondary organic aerosol (SOA) in summer impact the air quality in the sub-urban and rural background stations. Health risk assessment for chronic exposure over the past decade, using WHO air quality standards, showed that NO2, PM10 and benzo[a]pyrene from traffic emissions pose an unacceptable risk to the human population in the urban traffic station. PM10 and benzo[a]pyrene from biomass burning were unacceptably high in the sub-urban and rural background stations. Toxicity tests of the PM extracts with epithelial lung cells showed higher toxicity in wintertime samples in the sub-urban and rural stations, compared to the urban traffic station. These results require different mitigation strategies for urban and rural sites in order to improve the air quality. In urban areas, traffic emissions are still dominating the air quality, despite improvements in the last years, and may directly be responsible for part of the SOA and O3 levels in sub-urban and rural areas. In these later areas, air pollution from local biomass burning emissions are dominating the air quality, essentially in the colder period of the year.

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Section: Health Impact Of Air Pollution In Cataloniamentioning

confidence: 80%

“…Air pollution is a global threat to ecosystems and affects human health, even at shortterm exposure [1,2]. Therefore, there is a growing demand to improve the air quality [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Source Apportionment and Toxicity of PM in Urban, Sub-Urban, and Rural Air Quality Network Stations in Catalonia

et al. 2021

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“…Given the available properties on each copula model, the Gaussian copula is useful in assessing the overall dependence structure of pollutant variables, as described in Equation (10). For the SJC copula model, it is useful in exploring the dependence behaviour of the lower and upper tails, as described in Equations (14) and (15). Table 4 shows the results of the parameter estimates for the Gaussian and SJC copulas.…”

Section: Resultsmentioning

confidence: 99%

Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables

Masseran

Hussain

2020

Mathematics

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A correlation analysis of pollutant variables provides comprehensive information on dependency behaviour and is thus useful in relating the risk and consequences of pollution events. However, common correlation measurements fail to capture the various properties of air pollution data, such as their non-normal distribution, heavy tails, and dynamic changes over time. Hence, they cannot generate highly accurate information. To overcome this issue, this study proposes a combination of the Generalized Autoregressive Conditional Heteroskedasticity model, Generalized Pareto distribution, and stochastic copulas as a tool to investigate the dependence structure between the PM10 variable and other pollutant variables, including CO, NO2, O3, and SO2. Results indicate that the dynamic dependence structure between PM10 and other pollutant variables can be described with a ranking of PM10–CO > PM10–SO2 > PM10–NO2 > PM10–O3 for the overall time paths (δ) and the upper tail (τU) or lower tail (τL) dependency measures. This study reveals an evident correlation among pollutant variables that changes over time; such correlation reflects dynamic dependency.

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“…However, small discrepancies in the F determination will lead to substantial discrepancies in F n . In parallel, subsequent analyses using the F n density function will contribute to large errors, consequently leading to incorrect results [52]. To overcome this problem, the density function F can be assumed to be unknown, whereas the determination of the F n density function can be approximated to a particular limiting distribution form as n → ∞ .…”

Section: Bm Approachmentioning

confidence: 99%

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

Masseran

Safari

2021

IJERPH

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This article proposes a novel data selection technique called the mixed peak-over-threshold–block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.

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Intensity–duration–frequency approach for risk assessment of air pollution events

Cited by 23 publications

References 27 publications

Source Apportionment and Toxicity of PM in Urban, Sub-Urban, and Rural Air Quality Network Stations in Catalonia

Source Apportionment and Toxicity of PM in Urban, Sub-Urban, and Rural Air Quality Network Stations in Catalonia

Copula Modelling on the Dynamic Dependence Structure of Multiple Air Pollutant Variables

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

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