Efficiency in reducing air pollutants and healthcare expenditure in the Seoul Metropolitan City of South Korea

Kumbhakar, Subal C.; An, Jiyeon; Rashidghalam, Masoomeh; Heshmati, Almas

doi:10.1007/s11356-020-12122-y

Cited by 5 publications

(4 citation statements)

References 45 publications

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“…The urban AQMSs are evenly distributed across the city as they are located in each of Seoul's 25 administrative districts. Due to insufficient availability of appropriate air pollution data in the period 2018 to 2021, the urban AQMS in the Gangnam-gu district in the south-southeast area of Seoul [45,46] could not be considered in this study. Similarly to the background AQMSs, the urban AQMSs are installed away from major roads on the roofs of public buildings and measure air pollutants at different heights above ground The AQMSs were previously grouped into roadside (R: 13 AQMSs, Appendix Table A1), urban (U: 24 AQMSs, Table A2), and background (B: 5 AQMSs, Table A3) types [44].…”

Section: Methodsmentioning

confidence: 99%

“…The different sources of O 3 , NO 2 , PM 10 , and PM 2.5 , associated chemical precursors, chemical reactions, and meteorological influences on their concentrations in the ambient air have already been studied in Seoul in many ways [6,14,22,23,25,26,[28][29][30][31][32][36][37][38][39][40]42,[45][46][47]55,[57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74]. Therefore, they can be assumed to be known when discussing their annual cycles.…”

Section: Annual Cycles Of Air Pollutants At Aqmssmentioning

confidence: 99%

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Statistical Characteristics of Air Quality Index DAQx*-Specific Air Pollutants Differentiated by Types of Air Quality Monitoring Stations: A Case Study of Seoul, Republic of Korea

Lee¹,

Park

Mayer

2023

Sustainability

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Seoul has a high density of air quality monitoring stations (AQMSs) grouped into roadside, urban, and background types. Using the extensive data from 42 AQMSs in the period 2018 to 2021, the statistical characteristics of air pollutants required to calculate the daily air quality index DAQx* (daily maximum 1 h O3 and NO2 means and daily 24 h PM10 and PM2.5 means) are determined, depending on station types and three temporal periods (individual years, winters, and summers). The results for (i) annual cycles, which include peak concentrations of PM10 (up to 517 µg/m3 in May 2021) and PM2.5 (up to 153 µg/m3 in March 2019) owing to transboundary transport, (ii) annual medians, (iii) annual scattering ranges, (iv) partitioning of frequencies into DAQx*-related concentration ranges, and (v) maximum daily variations within individual station types indicate clear statistical air pollutant characteristics depending on the station types. They were primarily caused by different emission and atmospheric exchange conditions in a circular buffer around each AQMS, which are often approximated by urban form variables. The maximum daily variations were highest in the middle NO2 concentration range of the “satisfying” class for the roadside type (between 53% in summer 2019 and 90% in winter 2020).

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Section: Methodsmentioning

confidence: 99%

Section: Annual Cycles Of Air Pollutants At Aqmssmentioning

confidence: 99%

Statistical Characteristics of Air Quality Index DAQx*-Specific Air Pollutants Differentiated by Types of Air Quality Monitoring Stations: A Case Study of Seoul, Republic of Korea

Lee¹,

Park

Mayer

2023

Sustainability

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“…where v it + ϵ t comes from (17) and K C (c) is the second-order Gaussian kernel function. Therefore, we can obtain the ρ(•) function local to c it = c using the moment conditions.…”

Section: Robustness Checkmentioning

confidence: 99%

COVID-19 Under-reporting: Spillovers and Stringent Containment Strategies of Global Cases*

Wang,

Kumbhakar

2023

Preprint

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Owing to the rapid spread of COVID-19 pandemic, accurately determining the true global infection count has become an exceedingly challenging endeavor. In this context, our study delves into the spatial spillover analysis of COVID-19 cases and assesses the impact of containment policy stringency on these spillovers. Furthermore, we explore the extent of under-reporting of COVID-19 cases at a country-specific level.To account for the diverse spatial dependencies, we employ a semiparametric spatial autoregressive model in which the coefficients are smooth unknown functions of countries' stringency indices. Country-specific under-reporting, represented as a one-sided deterministic function of exogenous variables, is estimated using the sieves method.Our analysis relies on COVID-19 infection data spanning from 2020 to 2021 for 57 countries. We have discovered that spillovers exhibit significant variations at different levels of containment stringency. Moreover, the true number of infections is estimated to be 1.38 to 17.12 times higher than the reported cases. These results align with prior research and bear significant policy implications for improving the precision of COVID-19 reporting and managing spillover effects effectively. JEL Classification No.: C23, C14, I18.

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“…The Seoul of South Korea, is a metropolitan city with a population of 10 million and serious air pollution worldwide (Kumbhakar et al, 2021). Air pollution in Seoul is known to have a serious proportion and risk of PM 10 and PM 2.5 among various air pollutants (Choe et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Correlation Between COVID-19 Infection Rate and Atmospheric Environmental Factors in Seoul Metropolitan City, South Korea

Choi

Kim

2022

Preprint

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The purpose of this study is to statistically analyze whether the change in the COVID-19 virus infection rate is affected by atmospheric environmental factors in Seoul metropolitan city of South Korea. The research period of this study is from January 1, 2020 to March 31, 2021. The correlation relationship between the COVID-19 virus infection rate and atmospheric environmental factors such as PM10, PM2.5, temperature, and humidity was analyzed by multiple regression analysis. In the correlation between PM10 and PM2.5 and the COVID-19 virus infection rate, correlation coefficient between PM10 and infection rate was r=0.114(p=0.019), showing a positive correlation. The correlation coefficient between PM2.5 and infection rate was r=0.147(p=0.002), which indicates a higher positive correlation than PM10. In the correlation between thermal factors and infection rate, the correlation coefficients between temperature and infection rate and between relative humidity and infection rate were r= -0.527(p=0.000) and r= -0.91(p=0.060), respectively. Unlike PM10 and PM2.5, temperature and relative humidity showed a negative correlation with the infection rate. Except for relative humidity, PM10, PM2.5 and temperature had statistically significant correlations with the infection rate (p<0.05). Based on the results obtained from this study, it is recommended to avoid outdoor activities as much as possible in weather with high PM10 and PM2.5 and low temperature and humidity.

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Efficiency in reducing air pollutants and healthcare expenditure in the Seoul Metropolitan City of South Korea

Cited by 5 publications

References 45 publications

Statistical Characteristics of Air Quality Index DAQx*-Specific Air Pollutants Differentiated by Types of Air Quality Monitoring Stations: A Case Study of Seoul, Republic of Korea

Statistical Characteristics of Air Quality Index DAQx*-Specific Air Pollutants Differentiated by Types of Air Quality Monitoring Stations: A Case Study of Seoul, Republic of Korea

COVID-19 Under-reporting: Spillovers and Stringent Containment Strategies of Global Cases*

Correlation Between COVID-19 Infection Rate and Atmospheric Environmental Factors in Seoul Metropolitan City, South Korea

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