National Institute Of Environmental Research Research Articles

Associations of Night Shift Status During Pregnancy With Small for Gestational Age and Preterm Births.

Shift work, including night shift work, during pregnancy has been associated with adverse birth outcomes such as small for gestational age (SGA) infants and preterm births. This study, conducted in South Korea using the Korean CHildren's ENvironmental health Study (Ko-CHENS) cohort, aimed to investigate the association between shift work and night shift status during pregnancy and adverse birth outcomes. The Korean Ko-CHENS is a nationwide prospective birth cohort study of children's environmental diseases, conducted by the Ministry of Environment and the National Institute of Environmental Research. This study included pregnant women recruited from 2015 to 2020 for Ko-CHENS Core Cohorts, and 4,944 out of a total of 5,213 pregnant women were selected as final subjects. A logistic regression model was used to identify the risk factors affecting SGA births, preterm births, and low-birth-weight infants, and the odds ratio (OR) was adjusted. This was confirmed by calculating ORs. Maternal age, infant sex, maternal educational status, body mass index, smoking status, alcohol consumption status, parity, gestational diabetes mellitus, preeclampsia, and abortion history were used as adjusted variables. No statistically significant differences were observed in the birth outcomes or maternal working patterns. There were no significant differences in the adjusted odds ratios (aORs) of SGA and preterm births between the non-worker, day worker, and shift worker. However, there was a significant difference in the aORs of SGA between non-workers and night shift workers. (aORs [95% confidence interval], 2.643 [1.193-5.859]). Working during pregnancy did not increase the risk of SGA or preterm birth, and night shift work did not increase the risk of preterm birth. However, night-shift work increases the risk of SGA.

Development of a computational fluid dynamics model adopting a nested grid system: Flow simulations for ideal and real urban settings

We incorporate a nested grid system into a computational fluid dynamics (CFD) model based on the Reynolds-averaged Navier-Stokes equations. First, we evaluate the CFD model by comparing the results simulated in an array of aligned buildings using one- and two-way nested and non-nested coarse (same grid size as the parent domain) and fine grid (same grid size as the child domain) systems. The two-way nested grid simulates double-eddy circulation, wind speeds, and turbulence kinetic energy (TKE) in a street canyon better than the one-way nested grid. The nested simulations produce similar results to the fine grid system but with a significantly reduced CPU time. We also apply the nested grid system to airflow simulations in a building-congested district and validate the simulated results against the wind speed and direction measured by an automated weather system. The nested simulation improves wind direction compared to the WRF model operated by the National Institute of Environmental Research of South Korea. The two-way nested grid successfully reproduces the measured wind speed. The two-way nested grid simulation well represents the continuous flow structure (a smooth flow transition) between the parent and child domains by considering the two-way flow interaction between the two domains.

Evaluation of algal species distributions and prediction of cyanophyte cell counts using statistical techniques.

Safe drinking water sources are crucial for human health. Consequently, water quality management, including continuous monitoring of water quality and algae at sources, is critical to ensure the availability of safe water for local residents. This study aimed to construct statistical prediction models considering probability distributions relevant to cyanophyte cell counts and compare their prediction performance. In this study, water quality parameters at Juam Lake and Tamjin Lake, representative water sources in the Yeongsan and Seomjin rivers, South Korea, were investigated. We used a water quality monitoring network, algae alert system, and hydraulic and hydrological data measured every 7 days from January 2017 to December 2022 from the Water Environment Information System of the National Institute of Environmental Research. Using data for 2017-2021 as a training set and data for 2022 as a test set, the performances of seven models were compared for predicting cyanophyte cell counts. Environmental factors associated with algae in water sources were observed based on the monitoring data, and a prediction model appropriate for the cyanophyte distribution was generated, which also included the risk of toxicity. The extreme gradient boosting with the random forest model had the best predictive performance for cyanophyte cell counts. The study results are expected to facilitate water quality management in various water systems, including water sources.

Evaluation of the emission inventory for large point emission sources in South Korea by applying measured data from the NASA/NIER KORUS-AQ aircraft field campaign

One of the major issues in determining a region’s air quality is the uncertainty of large point sources (LPSs) emissions, which significantly affect the local-regional air quality. In this study, the SO2 and NOx emissions of 5 major LPSs in South Korea were evaluated by comparing the emissions-based concentrations employing a Gaussian dispersion model with aircraft-based measurements from DC-8 “around-the-stack” flights through the National Aeronautics and Space Administration (NASA)/National Institute of Environmental Research (NIER) KORea-U.S. Cooperative Domestic Air Quality (KORUS-AQ) aircraft field campaign. The ratio between modeled and measured concentrations for all 5 LPSs ranged between 0.42 and 1.30 and 0.39 and 1.01 for NOx and SO2, respectively. The results for the Boryeong, Dangjin, and Seocheon power plants (PPs), where the locations and sizes of stacks are easier to specify than industrial complexes (Hyundai Steel and Hankook Glass), yielded better performance, which ranged between 0.82 and 1.30 and 0.79 and 1.01 for NOx and SO2. This level of agreement was very encouraging, considering that the modeled concentrations were based on 30-min averaged emissions compared to less-than-a-minute DC-8 around-the-stack measurements. Based on our analysis, the uncertainty of LPS emissions, at least for NOx and SO2, appears to be small, which implies that the point sources inventory emissions are reasonably accurate. The Dangjin PP’s analysis reveals that the actual measured emissions should be considered in addition to “the official” inventory amounts to reduce emission uncertainty. This detailed comparative analysis verified the method used for this study. The findings of this study are expected to enhance the performance of future LPS emission inventory assessments. In terms of recommendations, the data from the raw emission inventory should include more clear information about the locations of measured stacks to obtain more accurate emission estimates. In addition, the flight measurement duration should be long enough to fly around several times to reduce uncertainties, and the flight positions and altitudes should be varied. By improving LPS inventories through accurate evaluations, more accurate air quality forecasts and better policies could be made. As a result, it is expected that public health can be improved by reducing the time people are exposed to high concentrations of air pollutants.

The Integrated Dataset for Occurrence of Odonata Species in South Korea

This study presents an integrated dataset comprising occurrence records for 102 Odonata species in South Korea. The occurrence information of the Odonata species was collected from 10 independent field survey-based datasets, spanning the period from 2014 to 2023 and provided by three agencies: National Institute of Ecology, National Institute of Environmental Research, and Global Biodiversity Information Facility (GBIF). In addition to occurrence data, the dataset includes information on the survey year, IUCN (International Union for Conservation of Nature) Red List classification, satellite-derived spectral indices and bioclimatic variables to provide valuable insights into the survey sites and habitats of Odonata species. By integrating diverse dataset, this comprehensive Odonata dataset can contribute to the validation of habitat-related traits, enhances the national checklist information, and facilitates the identification of rarity and potential habitats for Odonata species in South Korea. The integrated Odonata dataset serves as a valuable resource for ongoing Odonata research and conservation efforts in the study area.

Dataset on the Distribution of Ecosystem-Disturbing Plants in the Republic of Korea

In this study, we presented distribution data for 16 plant species designated as ecosystem-disturbing species by the Ministry of Environment of the Republic of Korea. These data include location information for the ecosystem-disturbing plants from four survey projects (Monitoring of invasive alien species designated by the wildlife protection act, Nationwide survey of non-native species in Korea, The 3rd and 4th national ecosystem survey) conducted by two agencies (National Institute of Ecology and National Institute of Environmental Research) between 2014 and 2021. Additionally, the data includes habitat environmental characteristics and administrative district information on the survey sites of the ecosystem-disturbing plants. These data have a high potential for utilization as basic information for natural environmental policies and related research by identifying the habitat characteristics of invasive alien species.

Health risks of pest control and disinfection workers after the COVID-19 outbreak in South Korea

The exposure patterns of pest control and disinfection workers have changed after the coronavirus disease 2019 (COVID-19) outbreak, but the health risks of chemical exposure have not been assessed. We identified these workers' chemical exposure patterns and risks before and after the COVID-19 outbreak. We used data conducted between 2018 (pre-pandemic) and 2021 (post-pandemic) from three-year cross-sectional surveys on pest control and disinfection workers. Inhalation and dermal exposure concentrations were estimated using equations based on a biocidal product risk assessment model from the Korean National Institute of Environmental Research. The non-carcinogenic and carcinogenic risks of chemicals were calculated using the United States Environmental Protection Agency risk assessment model. We found that the annual work frequency (50th percentile) of foggers using disinfectants increased the most among all the work types, from 140 uses/year to 176 uses/year after the COVID-19 outbreak. Moreover, all chemicals' non-carcinogenic and carcinogenic risks increased regardless of exposure routes. In the worst scenario (95th percentile), the margin of exposure for citric acid, benzethonium chloride, benzyl-C12-16-alkyldimethyl chlorides, and sodium chlorite of inhalation exposure, and isopropyl alcohol and benzyl-C12-16-alkyldimethyl chlorides of dermal exposure were acceptable (>100) before the COVID-19 outbreak but became unacceptable (<100) after the COVID-19 outbreak. Carcinogenic risks of dichlorvos from inhalation and dermal exposure were above acceptable levels (>10−6) before and after the COVID-19 outbreak but comparatively high after the COVID-19 outbreak. Additionally, significantly more workers experienced health symptoms after the COVID-19 outbreak (p<0.05), with the most common being muscle lethargy (31%), skin/face stinging (28.7%), and breath shortness/neck pain (24.1%).

Evaluating Statistical Machine Learning Algorithms for Classifying Dominant Algae in Juam Lake and Tamjin Lake, Republic of Korea

South Korea’s National Institute of Environmental Research (NIER) operates an algae alert system to monitor water quality at public water supply source sites. Accurate prediction of dominant harmful cyanobacterial genera, such as Aphanizomenon, Anabaena, Oscillatoria, and Microcystis, is crucial for managing water source contamination risks. This study utilized data collected between January 2017 and December 2022 from Juam Lake and Tamjin Lake, which are representative water supply source sites at the Yeongsan River and Seomjin River basins. We performed an exploratory data analysis on the monitored water quality parameters to understand overall fluctuations. Using data from 2017 to 2021 as training data and 2022 data as test data, we compared the dominant algal classification accuracy of 11 statistical machine learning algorithms. The results indicated that the optimal algorithm varied depending on the survey site and evaluation criteria, highlighting the unique environmental characteristics of each site. By predicting dominant algae in advance, stakeholders can better prepare for water source contamination accidents. Our findings demonstrate the applicability of machine learning algorithms as efficient tools for managing water quality in water supply source systems using monitoring data.

Highly resolved mapping of NO2 vertical column densities from GeoTASO measurements over a megacity and industrial area during the KORUS-AQ campaign

Abstract. The Korea–United States Air Quality (KORUS-AQ) campaign is a joint study between the United States National Aeronautics and Space Administration (NASA) and the South Korea National Institute of Environmental Research (NIER) to monitor megacity and transboundary air pollution around the Korean Peninsula using airborne and ground-based measurements. Here, tropospheric nitrogen dioxide (NO2) slant column density (SCD) measurements were retrieved from Geostationary Trace and Aerosol Sensor Optimization (GeoTASO) L1B data during the KORUS-AQ campaign (2 May to 10 June 2016). The retrieved SCDs were converted to tropospheric vertical column densities using the air mass factor (AMF) obtained from a radiative transfer calculation with trace gas profiles and aerosol property inputs simulated with the Community Multiscale Air Quality (CMAQ) model and surface reflectance data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). For the first time, we examine highly resolved (250 m × 250 m resolution) tropospheric NO2 over the Seoul and Busan metropolitan regions and the industrial region of Anmyeon. We reveal that the maximum NO2 vertical column densities (VCDs) were 4.94×1016 and 1.46×1017 molec. cm−2 at 09:00 and 15:00 LT over Seoul, respectively, 6.86×1016 and 4.89×1016 molec. cm−2 in the morning and afternoon over Busan, respectively, and 1.64×1016 molec. cm−2 over Anmyeon. The VCDs retrieved from the GeoTASO airborne instrument were correlated with those obtained from the Ozone Monitoring Instrument (OMI) (r=0.48), NASA's Pandora Spectrometer System (r=0.91), and NO2 mixing ratios obtained from in situ measurements (r=0.07 in the morning, r=0.26 in the afternoon over the Seoul, and r&gt;0.56 over Busan). Based on our results, GeoTASO is useful for identifying NO2 hotspots and their spatial distribution in highly populated cities and industrial areas.

팔당호의 호소 부영양화 관리를 위한 수질인자 분석

It is necessary to comprehensively understand about lake water quality, environmental factors to successfully manage environmental problems such as eutrophication and mass propagation of phytoplankton in lakes. When it comes to a method of eutrophication evaluation, Carlson’s trophic state index (TSI) has been used so far but was not suitable for artificial lakes in Korea. Accordingly, a new Korean trophic state index (TSIKO), was developed and utilized by National Institute of Environmental Research. The purpose of this research is to identify current situation of water quality in the six spots of artificial lakes, to measure eutrophication through Korean TSIKO, to finally provide information on lake eutrophication management. As a result, Chl-a (Chlorophyll a) of KAC9 showed higher than others. According to Korean TSIKO, nutritional status by lake In 2016 showed that Paldang Lake was mesotrophic·eutrophic In the long-term from 2008 to 2016, Korean TSIKO of Paldang Lake ranged from 44 to 50 and it was assessed mesotrophic·eutrophic as well. To conclude, in the Han River That is why going downstream and increasing inflow link to increasing retention time in the lake as well as an increase of algae, nutrients and so on, it could be reasons of eutrophication and needs management continuously with nonpoint pollution. This paper could be applied a basic resource for comprehensive management of lake eutrophication by types of lake.

PM2.5 Forecast in Korea using the Long Short-Term Memory (LSTM) Model

The National Institute of Environmental Research, under the Ministry of Environment of Korea, provides two-day forecasts, through AirKorea, of the concentration of particulate matter with diameters of ≤ 2.5 μm (PM2.5) in terms of four grades (low, moderate, high, and very high) over 19 districts nationwide. Particulate grades are subjectively designated by human forecasters based on forecast results from the Community Multiscale Air Quality (CMAQ) and artificial intelligence (AI) models in conjunction with weather patterns. This study evaluates forecasts from the long short-term memory (LSTM) algorithm relative to those from CMAQ-solely and AirKorea using observations from 2019. The skills of the one-day PM2.5 forecasts over the 19 districts were 39–70% for CMAQ, 72–79% for LSTM, and 73–80% for AirKorea; the AI forecasts showed comparable skills to the human forecasters at AirKorea. The one-day forecast skill levels of high and very high PM2.5 pollution grades are 31–98%, 31–74%, and 39–81% for the CMAQ-solely, the LSTM, and the AirKorea forecasts, respectively. Despite good skills for forecasting the high and very high events, CMAQ-solely forecasts also generate substantially higher false alarm rates (up to 86%) than the LSTM and AirKorea forecasts (up to 58%). Hence, applying only the LSTM model to the CMAQ forecasts can yield reasonable forecast skill levels comparable to the operational AirKorea forecasts that elaborately combine the CMAQ model, AI models, and human forecasters. The present results suggest that applications of appropriate AI models can greatly enhance PM2.5 forecast skills for Korea in a more objective way.

Association between short-term exposure to fine particulate matter components and cause-specific mortality in Korea: a multi-city time-series study

Background/Aim: Many studies have reported fine particulate matter (PM2.5) has various health effects. The effects of PM2.5 have been shown to vary in space and time. One of the underlying explanations was the variability in the distribution of chemical components in PM2.5. Therefore, identifying whether the health effects of each component of PM2.5 differ can provide evidence for the heterogeneous mortality risk of PM2.5. Our study aims to estimate the associations between short-term exposure to components and cause-specific mortality across six major cities in Korea. Methods: Daily mass concentration of PM2.5 and 25 components of PM2.5 were obtained from the National Institute of Environmental Research for 2012-2019. We used six intensive air monitoring stations in each of the six major cities: Seoul, Incheon, Daejeon, Gwangju, Ulsan, and Jeju. We employed a two-stage analysis. In the first stage, we conducted generalized additive model with quasi-Poisson regression to estimate city-specific associations of mortality with PM2.5 and its components on the same day. We excluded Asian dust days and adjusted the model for temperature, relative humidity, and time trend. In the second stage, we used fixed-effects meta-analysis to pool the city-specific estimates. Results: We could not find statistically significant association of PM2.5 and all components with all-cause mortality. Potassium was associated with 2.2% increment (95% CI:0.4%, 4.0%) in cardiovascular mortality per IQR increase (0.24&amp;#x3bc;g/m3). Magnesium ion was associated with 0.7% increment (95% CI: 0.1%, 1.4%) in respiratory mortality per IQR increase (0.01&amp;#x3bc;g/m3). Conclusion: This study suggests potential adverse health effects of particles produced by dust, salt, or traffic. In contrast to previous studies, our results showed no association of elemental carbon or nitrate with any mortality. Further study would be needed to account for the joint and lagged effect of components and meteorological variables. Keyword: fine particulate matter, components, multi-city analysis, epidemiology, health

Physicochemical Characteristics of PM2.5 Based on Long-term Hourly Data at National Intensive Monitoring Sites in Korea

The objective of this study was to analyze the temporal and spatial characteristics of fine particulate matters by using huge hourly datasets of PM2.5, including chemical information monitored at the 6 national intensive monitoring sites (NIMSs) from 2013 to 2018 in Korea. Hourly PM2.5 raw datasets were obtained from the National Institute of Environmental Research (NIER) in Korea. Monitoring sites included urban, rural/agricultural, industrial, and marine environments. Since the PM2.5 concentration steadily decreased nationwide, each species concentration also decreased in general. One of key reasons for decreasing PM2.5 might be explained by the implementation of domestic fine dust reduction policies and external influences such as PM2.5 concentration reduction in China. It was observed that 45.0% of all datasets for 6 years were classified as good condition. The average sum of 14 elements over all sites in 2018 was calculated to be 501.5 ng/m3, and its mass ratio for PM2.5 (21.9 μg/m3) was 2.30%. The inorganic elements were generally higher in industrial/urban areas than in agricultural areas. In addition, the average TC (total carbon) over all 6 sites was 28.3% of PM2.5 with the range of 23.6% to 31.4%. The TC in small urban areas was much higher than that in marine areas or even that in large, populated urban area/industrial areas. It seemed that the latter areas were better controlled than the former area in terms of combustion activities of fossil fuels. It is suggested that these results could be play an important role as important basic data to manage ambient air quality and establish effective emission reduction strategies in each region.

Validating Aerosol Optical Depth Estimation Methods Using the National Institute of Environmental Research Operational Numerical Forecast Model

Currently, significant efforts are being made to enhance the performance of the National Institute of Environmental Research (NIER) operational model. However, the model performance concerning Aerosol Optical Depth (AOD) estimation remains uninvestigated. In this study, three different estimation methods for AOD were implemented using the NIER operational model and validated with satellite and ground observations. In the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) method, AOD exponentially increases with relative humidity owing to a hygroscopic growth factor. However, alternative methods show better performance, since AOD estimation considers the size dependency of aerosol particles and is not sensitive to high relative humidity, which reduces the high AOD in areas with large cloud fractions. Although some R values are significantly low, especially for a single observational comparison and small numerical domain analysis, one of the alternative estimation methods achieves the best performance for diagnosing AOD in the East Asia region.

Predicting Regional Outbreaks of Hepatitis A Using 3D LSTM and Open Data in Korea

In 2020 and 2021, humanity lived in fear due to the COVID-19 pandemic. However, with the development of artificial intelligence technology, mankind is attempting to tackle many challenges from currently unpredictable epidemics. Korean society has been exposed to various infectious diseases since the Korean War in 1950, and to overcome them, the six most serious cases in National Notifiable Infectious Diseases (NNIDs) category I were defined. Although most infectious diseases have been overcome, viral hepatitis A has been on the rise in Korean society since 2010. Therefore, in this paper, the prediction of viral hepatitis A, which is rapidly spreading in Korean society, was predicted by region using the deep learning technique and a publicly available dataset. For this study, we gathered information from five organizations based on the open data policy: Korea Centers for Disease Control and Prevention (KCDC), National Institute of Environmental Research (NIER), Korea Meteorological Agency (KMA), Public Open Data Portal, and Korea Environment Corporation (KECO). Patient information, water environment information, weather information, population information, and air pollution information were acquired and correlations were identified. Next, an epidemic outbreak prediction was performed using data preprocessing and 3D LSTM. The experimental results were compared with various machine learning methods through RMSE. In this paper, we attempted to predict regional epidemic outbreaks of hepatitis A by linking the open data environment with deep learning. It is expected that the experimental process and results will be used to present the importance and usefulness of establishing an open data environment.

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM2.5 concentrations. The purpose is to accurately predict high-peak PM2.5 concentration data that cannot be trained in general deep learning models. For the training of the proposed model, we used all available weather and air quality data for three years from 2015 to 2017 from 25 stations of the National Institute of Environmental Research (NIER) and the Korea Meteorological Administration (KMA) observatory in Seoul, South Korea. Our model trained three years of data and predicted high-peak PM2.5 concentrations for the year 2018. In addition, we propose a Gaussian filter algorithm as a preprocessing method for capturing high concentrations of PM2.5 in the Seoul area and predicting them more accurately. This model overcomes the limitations of conventional deep learning approaches that are unable to predict high peak PM2.5 concentrations. Comparing model measurements at each of the 25 monitoring sites in 2018, we found that the deep learning model with a Gaussian filter achieved an index of agreement of 0.73–0.89 and a proportion of correctness of 0.89–0.96, and compared to the conventional deep learning method (average POC = 0.85), the Gaussian filter algorithm (average POC = 0.94) improved the accuracy of high-concentration PM2.5 prediction by an average of about 9%. Applying this algorithm in the preprocessing stage could be updated to predict the risk of high PM2.5 concentrations in real time.

An Investigation into the Effect of Emissions from Industrial Complexes on Air Quality in the Ulsan Metropolitan City Utilizing Trace Components in PM2.5

This study investigates the impact of industrial complexes on the air quality in the Ulsan Metropolitan City, Korea, by analyzing the concentration of trace substances. Importantly, this study performs segmentation and analysis of the components of particulate matter for tracking emission sources. Concentrations of particulate matter with aerodynamic diameters ≤10 and ≤2.5 µm (PM10 and PM2.5, respectively) and 19 substances comprising PM2.5 (such as ions, carbon, and nine elements) were measured hourly during the year 2017 in the southeastern intensive air quality monitoring station of the National Institute of Environmental Research, Korea. This study identified and investigated the time periods during which the vanadium content in PM2.5 was higher than the annual mean (1.026 ng/µg) through selection cases (SCs). The annual mean concentrations of PM2.5 and PM10 were 18.50 and 32.35 µg/m3, respectively, and were higher (i.e., 26.54 and 45.84 µg/m3, respectively) in SCs. Notably, the concentrations were high even when the main wind direction of SCs was southeasterly, which was mainly the case in summer. Furthermore, the emission sources contributing to PM2.5 were estimated using the correlations of organic carbon, elemental carbon, zinc, iron, manganese, and titanium concentrations in the SCs. This study demonstrated that a detailed tracking of the emission sources at a local scale is possible by analyzing the composition of the components of PM2.5.

Particular matter influences the incidence of acute otitis media in children

Particulate matter (PM) is the main component of air pollution. Children are vulnerable to PM and acute otitis media (AOM), which is one of the most common diseases in children. However, studies on the relationship between AOM in children and PM are rare and their results are inconsistent. The aim of this study is to investigate the effect of PM on AOM in children on the basis of the Korea National Health Insurance service (NHIS) claims data. NHIS claim data from 2008 to 2015 was used to identify outpatient visits, antibiotic use to treat AOM, and demographic data. This data was combined with the data on PM2.5 (≤ 2.5 μm) and PM10 (≤ 10 μm according to its aerodynamic diameter) level extracted from air pollution data from Korean National Institute of Environmental Research for 16 administrative regions. The children with AOM were divided into three age groups (< 2, 2–4, 5–10 years). Generalized linear Poisson regression model was used to estimate the association between AOM and PM using daily counts of AOM and daily mean PM concentrations. It was adjusted to temperature, wind, humidity, season, year, age, and region. With an increase in PM2.5 of 10 μg/m3, the relative risk of OM increased by 4.5% in children under 2 years of age. The effect of PM2.5 was strongest influence on the day of exposure. The exposure to PM10 was related to the incidence of AOM on the day of exposure and the following seven days in all three age groups. The PM concentrations did not strongly affect either AOM duration or the use of antibiotics to cure AOM. The RR in the each lag day after exposure to PM10 was diverse according to the age groups. Regardless of PM size and children’s age, the PM levels are positively related to the incidence of AOM. Both PM2.5 and PM10 have the most adverse effects on children under 2 years of age and on the day of exposure.

Associations of PM2.5 components with urban characteristics and mortality in South Korea

BACKGROUND AND AIM: Fine particulate matter (PM2.5) is a mixture of many chemical materials including carbon, ions, and trace elements. Although Information on which components of PM2.5 is most harmful and associated with certain disease has gained increasing attentions, few studies have investigated the association of the components with health in South Korea because of the limited access to components data. Therefore, we aim to investigate the associations of PM2.5 components with district level urban characteristics and non-external, respiratory, and cardiovascular mortality rate. METHODS: Air pollution data from the National Institute of Environmental Research for the year 2019 was consisted of annual mean concentration of PM2.5, cation, anion, organic carbon (OC), elemental carbon (EC), crust metal, and trace metal and measured at 38 monitor stations nationwide. The study area was selected in accordance with the regions where monitor stations are located. We used multivariable linear regression to evaluate association of the components ratio with district level variables including population density, park/city area ratio, basic livelihood security recipient ratio, smoking, and walking. Further, the associations of the components concentration with age-standardized non-external, respiratory, and cardiovascular mortality were analyzed using overdispersed-poisson regression analysis. Adjustments were made for sex and urban characteristics. RESULTS:The 1 person/km2 increase in population density was associated with 0.2% increase in anion ratio (p-value = 0.02). Basic livelihood security recipient had positive but statistically non-significant association with increase in OC ratio. Park/city area ratio had adverse association with increase in cation and anion ratio but it was also statistically non-significant. In addition, increased OC concentration and cation had positive association with respiratory mortality. We did not observe significant associations with non-external and cardiovascular mortality. CONCLUSIONS:To our knowledge, this is first study using nationwide components data of PM2.5. Further studies with larger data are required to provide robust evidence. KEYWORDS: Particle components, Mixtures, respiratory outcomes, Environmental epidemiology.

Clinical outcomes associated with long-term exposure to airborne particulate pollution in kidney transplant recipients

BackgroundResearchers have yet to investigate the specific association between 10-μm particulate matter (PM10) levels and the risk of graft failure, kidney disease, or the functional decline of transplanted kidneys, in kidney transplant recipients (KTRs). Furthermore, we know very little about the association between PM10 levels and the development of allograft rejection in transplanted kidneys. Identification of air pollution as a potential contributor to kidney disease could help reduce future disease burden, stimulate policy discussions on the importance of reducing air pollution with respect to health and disease, and increase public awareness of the hazards of air pollution. We aimed to evaluate the relationship of PM10 with the risk of graft failure, mortality, and decline of graft function in KTRs.MethodsAir pollutant data were obtained from the Korean National Institute of Environmental Research. We then investigated potential associations between these data and the clinical outcomes of 1532 KTRs who underwent kidney transplantation in a tertiary hospital between 2001 and 2015. Survival models were used to evaluate the association between PM10 concentrations and the risk of death-censored graft failure (DCGF), all-cause mortality, and biopsy-proven rejection (BPR), over a median follow-up period of 6.31 years.ResultsThe annual mean PM10 exposure after kidney transplantation was 27.1 ± 8.0 μg/m3. Based on 1-year baseline exposure, 1 μg/m3 increase in PM10 concentration was associated with an increased risk of DCGF (hazard ratio (HR): 1.049; 95% confidence interval (CI): 1.014–1.084) and BPR (HR: 1.053; 95% CI: 1.042–1.063). Fully adjusted models showed that all-cause mortality was significantly associated with 1-year average PM10 concentrations (HR, 1.09; 95% CI, 1.043 to 1.140).ConclusionsLong-term PM10 exposure is significantly associated with BPR, DCGF, and all-cause mortality in KTRs.