Comprehensive Air Quality Index Research Articles

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea

BackgroundCurrently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk. MethodsUsing a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM10, PM2.5, O3, CO, SO2, NO2) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally. ResultsThere were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0–30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0–120 days) showed higher risk of depressive episode in CO, SO2, PM2.5, and PM10. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11–2.54) increase in the risk of depressive episode. ConclusionsShort- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

계절관리제 시행에 따른 통합 공기질 지수 변화 분석 및 미세먼지의 건강 영향 평가

계절관리제 시행에 따른 통합 공기질 지수 변화 분석 및 미세먼지의 건강 영향 평가

The cleaner the environment, the happier the people: Integrating a nationwide public survey and environmental quality monitoring data in South Korea

Environmental studies are eager to prove the contribution of environmental quality (EQ) to happiness, given the recent interest of society in life quality and human well-being. Despite advances in empirical studies testing the relationship between EQ and life satisfaction (LS) at a global, national, and local scale, there is no comprehensive analysis of EQ, satisfaction with the environment (SE), and LS using various EQ parameters. This study tests this relationship by implementing a 5-year public survey on awareness and attitudes towards the evnironment with nationwide air and water quality data. Ordered probit models proved that NO2, PM10, and the comprehensive air-quality index concentrations are related to satisfaction with air quality, combined with the impacts of respondents’ pro-environmental attitude, property, and size of the residential area, whereas SO2, O3, and CO concentrations were ineffective. Total phosphorous was the only water quality parameter significant to satisfaction with water. Structural equation modelling proved that LS was affected by objective EQ through both direct and indirect paths via SE. We estimated the marginal effects of EQ improvement on SE. Understanding the relationship between EQ, SE, and LS can support decision-making in environmental management and public health programmes at the individual to policy levels. This study provides an improved approach and observations on this topic.

Air pollution and daily public transportation ridership: The case of Seoul city

This study examines the relationship between air pollution and daily mobility, focusing on daily bus and subway usage in Seoul using panel data with the fixed effects model. This study empirically examines the effect of air pollutant exposure on the daily moving population volume, including daily bus and subway usage in Seoul over 1271 days. The results show that people's sensitivity to particulate matter is complex. The most remarkable aspect of the results is that people are not sensitive to continuous pollutant levels, but to the comprehensive air-quality index (CAI). In addition, people's sensitivity is different depending on the moving distance and the means of transportation. Considering that CAI is defined by adopting the first air quality guidelines proposed by the World Health Organization (WHO) in 2005, these results explain how sensitive people are to the human health risks of particulate matter exposure recommended by the WHO. Our findings can be used as evidence to establish the new particulate matter standards and policies that can be strengthened by reflecting the recently updated WHO's air quality guidelines in 2021.

Air Quality Index through Inverse Evaluation of Hazard Quotient for Public Indoor Facilities - schools, child daycare centers and elderly nursing homes

Indoor air quality indices (IAQI-S, IAQI-C and IAQI-E) were developed to manage air quality in public facilities for vulnerable groups such as schools, child daycare centers, and elderly nursing homes, respectively. In this study, hazard quotient (HQ) was first designated, then the concentrations of each pollutant were calculated by adding the exposure factor of residents. The presented index was more stringent than comprehensive air quality index (CAI) for outdoor atmosphere. Also, composite indices that integrate individual indices for each pollutant were developed for quick and convenient recognition of the current air quality by administration officers or teachers and to take remedial actions. Among all data collected in field measurements, 75.4%, 71.2% and 35.6% were classified as ‘Good’ or ‘Moderate’ in school classrooms, child daycare centers and elderly nursing homes, respectively

Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction

Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.

Impact Assessment of Energy Transition Policy on Air Quality over a Typical District of the Pearl River Delta Region, China

Energy transition policies have been proposed for the two imperative tasks of carbon dioxide (CO2) emissions peaking and air pollution control in the Pearl River Delta (PRD) region in China. This study assesses the impact of the policies on CO2 emissions mitigation and air quality improvements and provides recommendations for policy implementation. Using Shunde District as a case study, we developed the emission inventories of CO2 and air pollutants, projected the trend of CO2 emissions, and estimated the air quality under three energy transition scenarios using the Long-range Energy Alternatives Planning (LEAP) model and the Weather Research and Forecasting-Community Multiscale Air Quality Modeling (WRF-CMAQ) system. The emission inventory revealed that the power, transportation and industry sources were three key sectors of CO2 and energy-related air pollutant emissions, with a combined contribution of more than 90%. The simulation results of energy transition policy demonstrated that CO2 emissions in Shunde would be unable to peak under the current “business as usual” (BAU) policy, while it could peak at 21.58 million tons (Mt) and 21.18 Mt under the energy transition (ET) and the enhanced energy transition (EET) policies, respectively. The concentrations of all index pollutants could meet the Grade II national standards for air quality in 2025, and the Comprehensive Air Quality Index (CAQI) in 2030 could also significantly decrease by 27.0% relative to the 2019 base year under the most stringent energy transition policies. Our study suggests that the local government should consider taking the power, transportation and industry sources as the priority sectors and implementing a stricter energy transition policy as soon as possible in Shunde District of the PRD region in China.

A data-driven air quality assessment method based on unsupervised machine learning and median statistical analysis: The case of China

Because the equation for calculating the air quality index (AQI) only considers the most serious pollutants, it remains controversial, and a comprehensive AQI has been a focus of subsequent studies. The present research transformed 22,504,440 pollution characteristics per year into datasets of a Louvain community detection clustering analysis. Unsupervised machine learning was applied to classify 367 cities across China into seven categories. The seven representative cities were selected from seven categories, and the augmented Dickey-Fuller test was used to detect their stationarity. A situation-based composite AQI calculation method was proposed. To verify the method, we clarified the difference between it and the AQI. We also compared its relative error with the total AQI. The relative error values in the seven representative cities were 8.9%, 12.16%, 12.91%, 8.75%, 13.42%, 11.41%, and 11.27%. These values were smaller than the total AQI. The median values of IAQICO,IAQINO2,IAQISO2,IAQIPM2.5,andIAQIPM10 remained relatively stable. Only the changes in IAQIO3 were dramatic. High Thour values are generally encountered in more polluted cities. When there is at most one chief pollutant (CP), then Thour=1. When there are several CPs, Thour>1. The findings of this research provide the public with an intuitive understanding of air pollution and guidance on effective assessments of outdoor air quality.

Contaminated Heart: Does Air Pollution Harm Business Ethics? Evidence from Earnings Manipulation

We investigate whether air pollution harms business ethics from the perspective of earnings manipulation, which exerts a real effect on the economy and social welfare. Using a large sample and a comprehensive air quality index in China, we find that firms located in cities with more severe air pollution exhibit higher levels of discretionary accruals and are more likely to restate their financial statements, consistent with exposure to air pollution leading to more earnings manipulation. We further provide causal evidence using propensity score matching and a discontinuity regression design (RDD) exploiting the Qinling Mountain–Huai River Heating Policy Line, which exogenously leads to more air pollution to cities located immediately north of the Line but not those in the south. Our findings are robust to controlling for weather conditions and alternative samples and measures of air pollution and earnings manipulation. Overall, this study unveils how the ecological environment shapes business ethics.

ASSESSMENT OF AIR POLLUTION IN THE CITIES OF PERM REGION

The chemicals emitted from stationary and mobile sources into the air in cities have a harmful effect on the population, contributing to the growth of morbidity and mortality and disturbing the natural environment. In Perm Krai, Russia, industrial facilities discharge about 360 chemicals into the air. The airborne pollution mostly comes from chemical industry, oil and minerals extraction, metallurgical industry, power generation, and transport. This study focused on the assessment of airborne pollution in the cities of Perm Krai. The study used data of the Perm Center for Hydrometeorology and Environmental Monitoring, published in annual environmental reports. It was found that the main pollutant emissions from stationary sources are liquid and gaseous substances. The most common emission components are hydrocarbons coming from pipeline transportation facilities. The second most common substance is carbon monoxide emitted by combustion of various fuels. Gross pollutant emissions tended to increase from 2014 to 2018, with a steady growth of emissions from mobile sources. Airborne pollution was assessed by the comprehensive air quality index, taking into account several substances that make the largest contribution to airborne pollution (this year). The monitoring results from Berezniki, Gubakha, Krasnokamsk, Lysva, Perm, Solikamsk and Tchaikovsky were analyzed. The paper presents the dynamics of air quality index in these cities from 2008 to 2018. The highest index value (‘very high’) was observed in Solikamsk in 2012; the lowest index value, in Tchaikovsky. The airborne pollution in these cities showed a tendency to decrease over the past six years, possibly, due to the use of more advanced emissions cleaning technologies and the decommissioning of some industrial facilities. The cities that have large industrial companies, such as Perm, Berezniki, Lysva and Solikamsk, experience the greatest environmental burden from stationary sources. Emissions of volatile organic compounds and sulfur dioxide from stationary sources increased from 2016 to 2018, although neither ‘high’, nor ‘very high’ pollution was observed in the cities of Perm Krai from 2013 to 2018.

새로운 통합대기환경지수 개발과 호흡기 및 심혈관계 질환 사망에 미치는 영향

대기오염과 기상인자가 호흡기 및 심혈관계 질환에 영향을 미치는 것으로 알려져 있다. 그러나 기존 우리나라에서 수행된 미세먼지와 사망과의 연관성에 관한 연구는 대도시를 중심으로 하였거나, 대기오염물질 각각의 위해성을 분석한 연구에 국한되었다. 이에 본 연구는 대기오염으로 인하여 심혈관 및 호흡기계 질환 사망에 미치는 영향을 종합적으로 고려하고자 통합대기환경기준을 새롭게 제시하였고, 분석대상을 전국으로 확대하였으며, 기상인자를 고려하여 일반화 부가모형(Generalized addictive model)으로 분석하였다. 데이터는 환경부 대기환경정보(Airkorea)에서 2015년부터 2018년까지 각 오염물질의 농도자료를 이용하여 통합대기환경지수를 계산하였고 통계청의 사망자료와 기상청의 기상자료를 이용하였다.<BR>첫 번째로 기준값, 구분값, 가산점 적용을 고려하여 적합성이 높은 모델을 선정하였다. 분석 결과 가중치를 제거한 새로운 통합대기환경지수(NCAI) 모델의 적합성이 높은 것으로 나타났다. 두 번째로, 사망자의 연령과 질환에 따라 어떻게 영향을 미치는지 알아보기 위해서 구분하여 통합대기환경지수(CAI)와 새로운 통합대기환경지수(NCAI)를 비교한 결과, ｢65세 이상 호흡기계 질환사망자｣를 종속변수로 ｢새로운 통합대기환경지수(NCAI)｣를 독립변수로 사용한 모델의 AIC가 6.28로 가장 낮았고, 평균제곱근오차(RMSE) 값도 7.33으로 가장 작게 나타나 적합한 모델로 나타났다. 세 번째로 대기오염이 사망에 영향을 미치는지 알아보기 위해 지연효과(lag effect)를 고려하여 6일전까지 상대위험비(Relative Risk)를 분석하였다. 분석결과 상대위험비는 통합대기환경지수(CAI)와 새로운 통합대기환경지수(NCAI) 모두 1일전(lag)이 가장 높게 나타났으며 65세 이상 호흡기계 사망에 대하여 통합대기환경지수(CAI) 28.584 일 때 1.0577 [1.0111,1.1043]로 초과 사망 위험이 5.7% 증가하며, 새로운 통합대기환경지수(NCAI) 52.89일 때 1.0784[1.0421,1.1146]로 7.84%로 초과사망 위험이 증가하는 것으로 나타났다. 본 연구결과를 통하여 새로운 통합대기환경지수의 상대위험비가 높게 나타나며, 65세 이상 호흡기 관련 사망은 하루 전 대기오염 농도와 높은 영향이 있음을 증명하였다.

Air pollution characteristics, health risks, and source analysis in Shanxi Province, China.

China is confronting an unprecedented air pollution problem. This study discussed the characteristics of air pollution and its risks on human health and conducted source analysis combined with local development in Shanxi Province in 2016 and 2017. Results demonstrated that the air pollution situation in Shanxi was deteriorating, with Taiyuan, Yangquan, Changzhi, Jincheng, Jinzhong, and Linfen being heavily polluted districts. Particulate matter (PM) was considered the major pollutant, but nitrogen dioxide and ozone showed a dominant trend recently. Furthermore, the health risks evaluated on the basis of a comprehensive air quality index (AAQI) and an aggregated risk index revealed a relatively high-risk level in Shanxi. Among the pollutants, the largest contributor was PM, followed by sulfur dioxide and ozone. Southern Shanxi had the largest pollution level and health risks, whereas Datong was the least polluted region. Source analysis suggested that the main driving forces of air pollution, besides natural factors, were urbanization, population size, civil vehicles, coal-based heavy industries, and high-energy consumption. Therefore, strengthening urban greening, vigorously adjusting and optimizing the industrial structure, and formulating a multi-domain cooperative control regime on air pollution, especially PM and ozone, should be promoted.

Forecasting air quality considering the socio-economic development in Xingtai

Air quality has deteriorated in many areas of China. To discuss the impact of the socio-economic factors on air quality and improve air quality in a small city, this article firstly uses the grey correlation analysis method to rank the impact of social-economic factors on Xingtai's air quality. Then, considering the different growth rates of the socio-economic factors, the grey multivariate convolution model is developed to predict the comprehensive air quality index in Xingtai. The results show that the industry has less adverse impact on air quality than the construction industry, and the tertiary industry has positive impact on air quality. The vehicle population and resident population all have adverse impact on air quality. According to the results, it is suggested to change the development direction from construction industry to the industry.

Analyzing the air quality of Beijing, Tianjin, and Shijiazhuang using grey Verhulst model

To predict the comprehensive air quality index, the data of comprehensive air quality index and environmental protection investment are analyzed in Beijing, Tianjin, and Shijiazhuang, respectively. Considering the different growth rates in environmental protection investment, comprehensive air quality index is predicted by grey Verhulst model. The results show that the government’s investment can promote the improvement of air quality to a certain extent. According to local conditions, the improvement of air quality not only relies on government investment, but also needs other ways to reduce the comprehensive air quality index.

Ranking effect in air pollution governance: Evidence from Chinese cities

Information disclosure in environmental governance, or informational governance conceptualized by Arthur Mol, has been increasingly utilized as a policy instrument to incentivize environmental policy enforcement in not only democratic societies but also authoritarian regimes like China. This study uses an explicit regulation, the air pollution ranking in Chinese cities as an example to illustrate how such informational governance policy instrument has an impact on local air pollution governance. Empirical evidence is based on monthly comprehensive air quality index ranking data of 74 key monitoring cities in China from 2013 to 2018. And a regression discontinuity design is applied to explore the relationship between air pollution ranking and air quality improvement in localities. The results show that (1) air quality of the bottom ten cities in the ranking will improve in the coming month compared with their counterparts; (2) such improvement happens upon the releasing of the ranking, but would not last for more than a month; (3) while top ten cities in the ranking do not experience such change in air quality. Therefore, we argue that the air pollution ranking system in China can be a useful informational governance instrument in providing negative incentives for environmental administrations in bottom cities to strengthen air pollution control. However, as a top-down environmental information disclosure program, such ranking institution could only mobilize local air quality governance temporarily. And new institutions ought to be established to further internalized local governments’ environmental externalities.

Spatiotemporal patterns and spatial clustering characteristics of air quality in China: A city level analysis

This study utilized air quality data on six pollutants (PM2.5, PM10, O3, CO, NO2, and SO2) obtained by monitoring for one year (from Jan 1 to Dec 31, 2016) in 338 Chinese cities at or above the prefectural level, and introduced the comprehensive air quality index (CAQI). Specifically, the CAQI was used to measure the comprehensive status of ambient air quality, after which the spatiotemporal distribution of the CAQI and its spatial correlation and clustering were investigated at the month and year levels, respectively. The CAQI values were generally high nationwide, with remarkable spatiotemporal variations. Additionally, cities with higher (or lower) CAQI values were concentrated in north (or south) China, while those with higher CAQI values were also observed in west and east China. The CAQI values exhibited a U-shaped trend from January to December, with the highest values being observed in winter and spring and the lowest during summer. Moreover, particulate matter (PM2.5 and PM10) is the major pollutant during most of the year, with PM2.5 being prevalent in east and central-south China and PM10 in northwest and north China; CAQI values are highly dependent on particulate concentrations. During summer, O3 becomes a major pollutant and contributes greatly to CAQI, with the highest O3 being observed in the Bohai Rim, the middle and lower reaches of the Yellow River, and the eastern coast of China. Finally, there was significant spatial autocorrelation and clustering of the CAQI, with spatial hot spots of CAQI being observed in southwest Xinjiang province, where air pollution issues have not received a great deal of attention, as well in Beijing-Tianjin-Hebei (BTH) and its surrounding areas, while cold spots for CAQI are mainly in south and northeast China.

Factors affecting ambient endotoxin and particulate matter concentrations around air vents of subway stations in South Korea

Levels of airborne endotoxins and particulate matter less than 10 μm and 2.5 μm in diameter (PM) were measured in the air vents of subway stations in Seoul, South Korea, and factors affecting both pollutants were analyzed. The measurements were completed from March 2016 to February 2017 for eight air vents situated at the ground level around the subway stations. A total of 166 air samples were collected and analyzed using the kinetic limulus amebocyte lysate assay. Endotoxin levels ranged from not detected to 1.986 EU m−3, with a mean of 0.227 EU m−3. The results showed significantly different PM levels from the measurements reported by AIRKOREA as part of the comprehensive air quality index. This can be attributed to different sampling sites in the same area. Endotoxin levels tended to be higher in fall compared to summer. Airborne bacteria levels showed a pattern similar to the endotoxin levels, but no significant association was reported between them. The levels of endotoxins around air vents with a glass cover and streets that allowed smoking were significantly higher than those not containing a walled barrier and streets in which smoking was prohibited. Multivariate regression analysis showed that the factors affecting endotoxin levels comprised air vents with a glass cover (coefficient = 0.106, p = 0.014) and season (coefficient = 0.062, p < 0.0001). Therefore, installing barriers on the air vents and prohibiting smoking in streets to which the vents open may be effective ways to lessen exposure to airborne endotoxin levels around air vents.

Harmful impact of air pollution on severe acute exacerbation of chronic obstructive pulmonary disease: particulate matter is hazardous.

IntroductionParticulate matter and air pollution in Korea are becoming worse. There is a lack of research regarding the impact of particulate matter on patients with COPD. Therefore, the purpose of this study was to investigate the effects of various air pollution factors, including particulate matter, on the incidence rate of severe acute exacerbations of COPD (AECOPD) events.MethodsWe analyzed the relationship between air pollutants and AECOPD events that required hospitalization at Guro Hospital in Korea from January 1, 2015 to May 31, 2017. We used general linear models with Poisson distribution and log-transformation to obtain adjusted relative risk (RR). We conducted further analysis through the Comprehensive Air-quality Index (CAI) that is used in Korea.ResultsAmong various other air pollutants, particulate matter was identified as a major source of air pollution in Korea. When the CAI score was over 50, the incidence rate of severe AECOPD events was statistically significantly higher [RR 1.612, 95% CI, 1.065–2.440, P=0.024]. Additionally, the particulate matter levels 3 days before hospitalization were statistically significant [RR 1.003, 95% CI, 1.001–1.005, P=0.006].ConclusionParticulate matter and air pollution increase the incidence rate of severe AECOPD events. COPD patients should be cautioned against outdoor activities when particulate matter levels are high.

A Comprehensive Real-Time Indoor Air-Quality Level Indicator

The growing concern about Indoor Air-Quality has accelerated the development of small, low-cost air-quality monitoring systems. These systems are capable of monitoring various indoor air pollutants in real time, notifying users about the current air-quality status and gathering the information to the central server. However, most Internet of Things (IoT)-based air-quality monitoring systems numerically present the sensed value per pollutant, making it difficult for general users to identify how polluted the air is. Therefore, in this paper, we first introduce a tiny air-quality monitoring system that we developed and, based on the system, we also test the applicability of the comprehensive Air-Quality Index (AQI), which is widely used all over the world, in terms of its capacity for a comprehensive indoor air-quality indication. We also develop design considerations for an IoT-based air-quality monitoring system and propose a real-time comprehensive indoor air-quality level indication method, which effectively copes with dynamic changes and is efficient in terms of processing and memory overhead.

토지이용-교통 통합 모형을 활용한 도시 환경 영향 예측 방법론 개발

이 논문은 도시의 장래 탄소 배출량과 환경적 영향을 예측하는 방법론에 대하여 설명하고 있다. 다양한 저탄소 도시/교통정책의 시행으로 인한 장래의 토지이용과 교통의 변화를 예측하기 위하여 토지이용 통합 모형인 DELTA, OmniTRANS 조합을 적용하였다. 또한, 이러한 모형과 통합하기에 적합한 배출량 산정 모형과 확산모형을 선정하여 사용하였다. 개발 방법론의 결과 값인 토지이용과 통행 활동으로 인한 이산화탄소 및 대기오염 물질의 배출량, 오염 물질의 농도, 통합 대기질 지수 등이 GIS 기반의 지도에 표출되도록 하였다. 수원시를 대상으로 프로토타입을 개발하고 결과 값의 표출 사례를 논문에 예시하였다. 개발 방법론은 환경 친화적인 도시 정책의 효과를 알고자 하는 정책 결정자들에게 유용하게 사용될 수 있을 것으로 기대된다. This paper describes a method that can be used for estimating future carbon emissions and environmental effects. To forecast future land use and transportation changes under various low carbon policies, a DELTA and OmniTRANS combination (a land use-transport integrated model) was applied. Appropriate emission estimation methods and dispersion models were selected and applied in the method. It was designed that the estimated emissions from land use and transportation activity as well as the estimated concentrations of air pollutants and comprehensive air quality index (CAI) are presented on a GIS-based map. The prototype was developed for the city of Suwon and the outcome examples were presented in this paper; it demonstrates what kinds of analysis results are presented in this method. It is expected that the developed method will be very useful for decision makers who want to know the effect of environmental policies in cities.