Air Quality Monitoring Sites Research Articles

Impact of Remote and Local Sources on Particulate Matter in Urban Environment

In this paper, the impact of natural and anthropogenic sources on particulate matter with diameter less than 10 μm (PM10) was investigated in Jeddah urban area, Saudi Arabia to identify and quantify the major particle pollution source classes. Hourly data of PM10 and other gaseous pollutants (NOx, CO, SO2 and O3) and meteorological factors (temperature, relative humidity, atmospheric pressure, wind speed and wind direction) were collected at two air quality monitoring sites for the period of March 2008 to February 2009. The air mass origin was determined using 5-day backward trajectories arriving to Jeddah by using HYSPLIT model. Results show that the PM10 Daily Limit Value was exceeded. The most frequent air masses entering Jeddah and thereby influencing PM10 concentrations come from the East, specifically the SE and NE directions, 84% of the trajectories were originated over the Arabian Peninsula including Saudi Arabia mainland and the Arabian Gulf. The Mediterranean Sea and southern Europe were the origin source of 8% of the air mass trajectories, another 8% were originated from North Africa and Sahara Desert. The majority of PM10 episodes were attributed to the intrusion of dust to Jeddah urban air.

A Simple Tool to Identify Representative Wind Sites for Air Pollution Modelling Applications

This paper investigates the use of the Site-Optimized Semiempirical (SOSE) air pollution model to identify the surface wind measurement site characteristics that yield the best air pollution predictions for urban locations. It compares the modelling results from twelve meteorological sites with varying anemometer heights, located at different distances from the air pollution measurements and exhibiting different land use characteristics. The results show that the index of agreement (IA) between observed and predicted concentrations can be improved from 0.4 to 0.8 by using the most compared to the least representative wind data as input to the air pollution model. Although improvements can be achieved using wind data from a site closer to the air quality monitoring site, choosing the closest wind site does not necessarily yield the best results, especially if the meteorological station is located in a region of complex land use. In addition, both the height of the anemometer and the openness of the terrain surrounding the anemometer were found to be equally important in obtaining good model predictions. The simple SOSE model can therefore be used to complement regulatory meteorological guidelines by providing a quantitative assessment of wind site representativeness for air quality applications in complex urban environments.

Association of carbon monoxide levels with allergic diseases in children.

Air pollution is regarded as a potential reason for the development or aggravation of allergic diseases. To investigate the relationship between exposure to outdoor air pollution during the first year of life and the development of allergic diseases, and to determine the association between outdoor air pollution near the residence and recent symptoms of allergic diseases in children. A nationwide cross-sectional survey was conducted in 6- to 7-year-old children. Among them, subjects who had been born (n = 1828) or lived (n = 1894) within 2 km of the nearest air quality monitoring sites were selected. The prevalence of allergic diseases was assessed by using questionnaires. Each subject was examined by pediatricians to determine the presence of eczema. Publicly available data were used regarding the daily ambient concentrations of sulfur dioxide, nitrogen dioxide, ozone, carbon monoxide (CO), and particulate matter. For a 100 ppb increase in the mean daily CO level during the first year of life, the adjusted odds ratio for lifetime allergic rhinitis was 1.10 (95% confidence interval, 1.03-1.19). For a 1 ppm increase in the annual average CO level during the past 12 months, the adjusted odds ratio for the presence of atopic dermatitis on the day of the survey was 8.11 (95% confidence interval, 1.06-62.12). However, the other air pollutants showed no statistical significance. Higher exposure to CO during infancy increased the risk of development of allergic rhinitis. The risk of current atopic dermatitis symptoms increased in children who were exposed to higher CO levels during the past 12 months.

Correction Factors for Outdoor Concentrations of PM2.5Measured with Portable Real-time Monitors Compared with Gravimetric Methods: Results from South Korea

This study investigated the association between <TEX>$PM_{2.5}$</TEX> concentrations obtained with portable real-time monitors and those obtained with gravimetric methods in national urban air-quality monitoring sites in Seoul, South Korea. We used the SidePak AM510 Personal Aerosol Monitor (TSI Inc., 500 Cardigan Road Shoreview, MN) and DustTrak DRX 8533 (TSI Inc., 500 Cardigan Road Shoreview, MN) as portable real-time monitors for measuring <TEX>$PM_{2.5}$</TEX> concentrations and compared these values with those measured with the PMS-103 or SEQ 47/50 models operated by Federal Reference Method (FRM) or the European Committee for Standardization(ECS), respectively, in national urban air-quality monitoring sites in Seoul. Measurements were conducted every other day in the winter and spring seasons of 2014. The estimated daily mean concentrations of <TEX>$PM_{2.5}$</TEX> ranged between 13.4 and <TEX>$161.9{\mu}g/m^3$</TEX> using AM 510 and between 22.0 and <TEX>$156.0{\mu}g/m^3$</TEX> using DustTrak. The Spearman correlation coefficient for <TEX>$PM_{2.5}$</TEX> concentrations between AM 510 and gravimetric results was 0.99, and the correlation between DustTrak and gravimetric results was 0.87. The correction factor suggested was 0.42 and 0.29 for AM 510 and DustTrak, respectively. We found that <TEX>$PM_{2.5}$</TEX> concentrations measured with real-time monitors could overestimate true <TEX>$PM_{2.5}$</TEX> concentrations and therefore the application of a correction factor (0.43) is strongly suggested for quantification when Real-time monitors were operated of <TEX>$PM_{2.5}$</TEX> levels at urban atmospheric environment of South Korea.

Spatiotemporal Characterization of Ambient PM2.5 Concentrations in Shandong Province (China).

China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space-time variability of PM2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space-time PM2.5 distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM2.5 exceeded 75, 115, 150, and 250 μg m(-3) varied: 43-253, 13-128, 4-66, and 0-15 days, respectively. The PM2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM2.5 exceeded 150 μg m(-3) during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM2.5 pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold.

Wavelets-based clustering of air quality monitoring sites.

This paper aims at providing a variance/covariance profile of a set of 36 monitoring stations measuring ozone (O3) and nitrogen dioxide (NO2) hourly concentrations, collected over the period 2005-2013, in Portugal mainland. The resulting individual profiles are embedded in a wavelet decomposition-based clustering algorithm in order to identify groups of stations exhibiting similar profiles. The results of the cluster analysis identify three groups of stations, namely urban, suburban/urban/rural, and a third group containing all but one rural stations. The results clearly indicate a geographical pattern among urban stations, distinguishing those located in Lisbon area from those located in Oporto/North. Furthermore, for urban stations, intra-diurnal and daily time scales exhibit the highest variance. This is due to the more relevant chemical activity occurring in high NO2 emissions areas which are responsible for high variability on daily profiles. These chemical processes also explain the reason for NO2 and O3 being highly negatively cross-correlated in suburban and urban sites as compared with rural stations. Finally, the clustering analysis also identifies sites which need revision concerning classification according to environment/influence type.

Assessment of PM10 pollution level and required source emission reduction in Belgrade area

The aim of this study was to assess PM10 pollution level and estimate required source emission reduction in Belgrade area, the second largest urban center in the Balkans. Daily mass concentrations and trace metal content (As, Cd, Cr, Mn, Ni, Pb) of PM10 were evaluated for three air quality monitoring sites of different types: urban-traffic (Slavija), suburban (Lazarevac) and rural (Grabovac) under the industrial influence, during the period of 2012–13. Noncompliance with current Air Quality Standards (AQS) was noticeable: annual means were higher than AQS at Slavija and Lazarevac, and daily frequency threshold was exceeded at all three locations. Annual means of As at Lazarevac were about four times higher than the target concentration, which could be attributed to the proximity of coal-fired power plants, and dust resuspension from coal basin and nearby ash landfills. Additionally, levels of Ni and Cr were significantly higher than in other European cities. Carcinogenic health risk of inhabitants' exposure to trace metals was assessed as well. Cumulative cancer risk exceeded the upper limit of acceptable US EPA range at two sites, with Cr and As as the major contributors. To estimate source emission reduction, required to meet AQS, lognormal, Weibull and Pearson 5 probability distribution, functions (PDF) were used to fit daily PM10 concentrations. Based on the rollback equation and best fitting PDF, estimated reduction was within the range of 28–98%. Finally, the required reduction obtained using two-parameter exponential distribution suggested that risks associated to accidental releases of pollutants should be of greater concern.

Incorporating an advanced aerosol activation parameterization into WRF‐CAM5: Model evaluation and parameterization intercomparison

AbstractAerosol activation into cloud droplets is an important process that governs aerosol indirect effects. The advanced treatment of aerosol activation by Fountoukis and Nenes (2005) and its recent updates, collectively called the FN series, have been incorporated into a newly developed regional coupled climate‐air quality model based on the Weather Research and Forecasting model with the physics package of the Community Atmosphere Model version 5 (WRF‐CAM5) to simulate aerosol‐cloud interactions in both resolved and convective clouds. The model is applied to East Asia for two full years of 2005 and 2010. A comprehensive model evaluation is performed for model predictions of meteorological, radiative, and cloud variables, chemical concentrations, and column mass abundances against satellite data and surface observations from air quality monitoring sites across East Asia. The model performs overall well for major meteorological variables including near‐surface temperature, specific humidity, wind speed, precipitation, cloud fraction, precipitable water, downward shortwave and longwave radiation, and column mass abundances of CO, SO2, NO2, HCHO, and O3 in terms of both magnitudes and spatial distributions. Larger biases exist in the predictions of surface concentrations of CO and NOx at all sites and SO2, O3, PM2.5, and PM10 concentrations at some sites, aerosol optical depth, cloud condensation nuclei over ocean, cloud droplet number concentration (CDNC), cloud liquid and ice water path, and cloud optical thickness. Compared with the default Abdul‐Razzack Ghan (2000) parameterization, simulations with the FN series produce ~107–113% higher CDNC, with half of the difference attributable to the higher aerosol activation fraction by the FN series and the remaining half due to feedbacks in subsequent cloud microphysical processes. With the higher CDNC, the FN series are more skillful in simulating cloud water path, cloud optical thickness, downward shortwave radiation, shortwave cloud forcing, and precipitation. The model evaluation identifies several areas of improvements including emissions and their vertical allocation as well as model formulations such as aerosol formation, cloud droplet nucleation, and ice nucleation.

Effects of Independence Day fireworks on atmospheric concentrations of fine particulate matter in the United States

Previous case studies have documented increases in air pollutants, including particulate matter (PM), during and following fireworks displays associated with various holidays and celebrations around the world. But no study to date has explored fireworks effects on air quality over large regions using systematic observations over multiple years to estimate typical regional PM increases. This study uses observations of fine PM (with particle diameters < 2.5 μm, PM2.5) from 315 air quality monitoring sites across the United States to estimate the effects of Independence Day fireworks on hourly and 24-hr average concentrations. Hourly PM2.5 concentrations during the evening of July 4 and morning of July 5 are higher than on the two preceding and following days in July, considered as control days. On national average, the increases are largest (21 μg/m3) at 9–10 pm on July 4 and drop to zero by noon on July 5. Average concentrations for the 24-hr period beginning 8 pm on July 4 are 5 μg/m3 (42%) greater than on control days, on national average. The magnitude and timing of the Independence Day increases vary from site to site and from year to year, as would be expected given variations in factors such as PM2.5 emissions from fireworks, local meteorological conditions, and distances between fireworks displays and monitoring sites. At one site adjacent to fireworks, hourly PM2.5 levels climb to ∼500 μg/m3, and 24-hr average concentrations increase by 48 μg/m3 (370%). These results have implications for potential improvements in air quality models and their predictions, which currently do not account for this emissions source.

Back-extrapolating a land use regression model for estimating past exposures to traffic-related air pollution.

Land use regression (LUR) models rely on air pollutant measurements for their development, and are therefore limited to recent periods where such measurements are available. Here we propose an approach to overcome this gap and calculate LUR models several decades before measurements were available. We first developed a LUR model for NOx using annual averages of NOx at all available air quality monitoring sites in Israel between 1991 and 2011 with time as one of the independent variables. We then reconstructed historical spatial data (e.g., road network) from historical topographic maps to apply the model's prediction to each year from 1961 to 2011. The model's predictions were then validated against independent estimates about the national annual NOx emissions from on-road vehicles in a top-down approach. The model's cross validated R2 was 0.74, and the correlation between the model's annual averages and the national annual NOx emissions between 1965 and 2011 was 0.75. Information about the road network and population are persistent predictors in many LUR models. The use of available historical data about these predictors to resolve the spatial variability of air pollutants together with complementary national estimates on the change in pollution levels over time enable historical reconstruction of exposures.

Epiphytic macrolichen indication of air quality and climate in interior forested mountains of the Pacific Northwest, USA

Biomonitoring can provide cost-effective and practical information about the distribution of nitrogen (N) deposition, particularly in regions with complex topography and sparse instrumented monitoring sites. Because of their unique biology, lichens are very sensitive bioindicators of air quality. Lichens lack a cuticle to control absorption or leaching of nutrients and they dynamically concentrate nutrients roughly in proportion to the abundance in the atmosphere. As N deposition increases, nitrogen-loving eutrophic lichens become dominant over oligotrophic lichens that thrive in nutrient-poor habitats. We capitalize on these characteristics to develop two lichen-based indicators of air-borne and depositional N for interior forested mountain ecosystems of the Pacific Northwest and calibrate them with N concentration measured in PM2.5 at 12 IMPROVE air quality monitoring sites in the study area. The two lichen indices and peak frequencies of individual species exhibited continuous relationships with inorganic N pollution throughout the range of N in ambient PM2.5, suggesting that the designation of a critical level or critical load is somewhat arbitrary because at any level above background, some species are likely to experience adverse impacts. The concentration of N in PM2.5 near the city of Spokane, Washington was the lowest measured at an instrumented monitoring site near known N pollution sources. This level, 0.37μg/m3/year, served as a critical level, corresponding to a concentration of 1.02% N in the lichen Letharia vulpina, which is similar to the upper end of background lichen N concentrations measured elsewhere in the western United States. Based on this level, we estimate critical loads to be 1.54 and 2.51kg/ha/year of through-fall dissolved inorganic N deposition for lichen communities and lichen N concentration, respectively. We map estimated fine-particulate (PM2.5) N in ambient air based on lichen community and lichen N concentration indices to identify hotspots in the region. We also develop and map an independent lichen community-based bioclimatic index, which is strongly related to gradients in moisture availability and temperature variability. Lichen communities in the driest climates were more eutrophic than those in wetter climates at the same levels of N air pollution.

Extensive spatiotemporal analyses of surface ozone and related meteorological variables in South Korea for the period 1999–2010

Abstract. Spatiotemporal characteristics of surface ozone (O3) variations over South Korea are investigated with consideration of meteorological factors and timescales based on the Kolmogorov–Zurbenko filter (KZ filter), using measurement data at 124 air quality monitoring sites and 72 weather stations for the 12 yr period of 1999–2010. In general, O3 levels at coastal cities are high due to dynamic effects of the sea breeze while those at the inland and Seoul Metropolitan Area (SMA) cities are low due to the NOx titration by local precursor emissions. We examine the meteorological influences on O3 using a combined analysis of the KZ filter and linear regressions between O3 and meteorological variables. We decomposed O3 time series at each site into short-term, seasonal, and long-term components by the KZ filter and regressed on meteorological variables. Impact of temperature on the O3 levels is significantly high in the highly populated SMA and inland region, but low in the coastal region. In particular, the probability of high O3 occurrence doubles with 4 °C of temperature increase in the SMA during high O3 months (May–October). This implies that those regions will experience frequent high O3 events in a future warming climate. In terms of short-term variation, the distribution of high O3 probability classified by wind direction shows the effect of both local precursor emissions and long-range transport from China. In terms of long-term variation, the O3 concentrations have increased by +0.26 ppbv yr−1 (parts per billion by volume) on nationwide average, but their trends show large spatial variability. Singular value decomposition analyses further reveal that the long-term temporal evolution of O3 is similar to that of nitrogen dioxide, although the spatial distribution of their trends is different. This study will be helpful as a reference for diagnostics and evaluation of regional- and local-scale O3 and climate simulations, and as a guide to appropriate O3 control policy in South Korea.

Finite mixture models to characterize and refine air quality monitoring networks

BackgroundExisting air quality monitoring programs are, on occasion, not updated according to local, varying conditions and as such the monitoring programs become non-informative over time, under-detecting new sources of pollutants or duplicating information. Furthermore, inadequate maintenance may cause the monitoring equipment to be utterly deficient in providing information. To deal with these issues, a combination of formal statistical methods is used to optimize resources for monitoring and to characterize the monitoring networks, introducing new criteria for their refinement. MethodsMonitoring data were obtained on key pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter (PM10) and sulfur dioxide (SO2) from 12 air quality monitoring sites in Seville (Spain) during 2012. A total of 49 data sets were fit to mixture models of Gaussian distribution using the expectation–maximization (EM) algorithm. To summarize these 49 models, the mean and coefficient of variation were calculated for each mixture and carried out a hierarchical clustering analysis (HCA) to study the grouping of the sites according to these statistics. To handle the lack of observational data from the sites with unmonitored pollutants, the missing statistical values were imputed by applying the random forests technique and then later, a principal component analysis (PCA) was carried out to better understand the relationship between the level of pollution and the classification of monitoring sites. All of the techniques were applied using free, open-source, statistical software. Results and conclusionOne example of source attribution and contribution is analyzed using mixture models and the potential for mixture models is posed in characterizing pollution trends. The mixture statistics have proven to be a fingerprint for every model and this work presents a novel use of them and represents a promising approach to characterizing mixture models in the air quality management discipline. The imputation technique used is allowed for estimating the missing information from key unmonitored pollutants to gather information about unknown pollution levels and to suggest new possible monitoring configurations for this network. Posterior PCA confirmed the misclassification of one site detected with HCA. The authors consider the stepwise approach used in this work to be promising and able to be applied to other air monitoring network studies.

Characterising the temporal variations of ground-level ozone and its relationship with traffic-related air pollutants in the united kingdom: a quantile regression approach

Ground-level ozone is a secondary air pollutant and is photochemically produced by solar radiation from the reaction of volatile organic compounds (VOCs) and nitrogen oxides (NOx). Ground-level ozone is considered a harmful pollutant due to its adverse impact on human health, agricultural crops and materials. The concerning factor is that in spite of decreasing trends in some other air pollutants (e.g. NOx), ozone concentrations are still increasing. This paper describes the temporal variations of ozone at four air quality monitoring sites (Harwell, Leeds, Marylebone and Strath Vaich) in the United Kingdom for the year of 2008. The association of ozone with some traffi c-related air pollutants has been explored applying a quantile regression model (QRM). The traffi c-related air pollutants considered as predictors for this study are hydrocarbons (HC), nitric oxides (NO), nitrogen dioxides (NO 2 ), carbon monoxides (CO) and particulate matter (PM 2.5 ). QRM can handle the non-linearities in the relationship of ozone and its predictors and is applicable to non-normal air quality data distribution. The behaviour and interaction of ozone with its predictors vary at different regimes of ozone distributions, which remains hidden when applying an ordinary least square regression model. QRM explains signifi cantly more variations in ozone concentrations (global goodness of fi 1 = 0.88) as compared to ordinary least square regression (coeffi cient of determination R 2 = 0.32) and is therefore better suited for ozone data analysis and prediction.

황사 발생 기간 동안 WRF-Chem 모델을 이용한 미세먼지 예측과 관련 기상장에 대한 민감도 분석

이 연구의 목적은 2008년 5월 29일 우리나라에 영향을 미치는 황사를 예측하기 위해 WRF-Chem 모델 내 에어로졸 스킴과 광물성 먼지 옵션에 따른 미세먼지 농도 변화와 그에 따른 기상장의 민감도를 분석하는 것이다. 미세먼지의 인위적 배출량에 대해서는 <TEX>$0.5^{\circ}{\pm}0.5^{\circ}$</TEX> RETRO 전구 배출량을, 광해리의 경우 Fast-J 광해리 스킴을, 그리고 황사 발생량을 추정하기 위해 RADM2 화학메커니즘 및 MADE/SORGAM 에어로졸 시나리오, MOSAIC 8 섹션 에어로졸 시나리오, 그리고 GOCART 먼지 침식 시나리오를 각각 적용하였다. 그 결과 RADM2 화학메커니즘 및 MADE/SORGAM 에어로졸 시나리오가 다른 시나리오들보다 우리나라 황사 먼지 농도와 배경 PM 농도를 더 높게 모사하였다. 그리고 이 시나리오와 서울의 각 대기질 측정망의 평균 PM10 농도와의 비교 결과, 상관계수는 0.67, 평균제곱근오차는 <TEX>$44{\mu}gm^{-3}$</TEX>으로 나타났다. 또한 WRF-Chem 모델에서 상기 3가지 시나리오와 이들 시나리오가 없는 순수 기상에서의 온도, 풍속, 경계층 높이, 장파복사의 기상 민감도를 분석한 결과, 1,800-3,000 m 경계층 높이와 <TEX>$2-16ms^{-1}$</TEX> 풍속 U 성분의 공간적 분포가 황사 먼지 발생의 공간적 분포와 유사하게 나타났다. 그리고 GOCART 먼지 침식 시나리오와 RADM2 화학메커니즘 및 MADE/SORGAM 에어로졸 시나리오는 황사 먼지 또는 에어로졸과 기상이 온라인으로 상호작용함으로써 지구장파복사가 더 낮게 모사되었다. The purpose of this study was to analyze the sensitivity of meteorological fields and the variation of concentration of particulate matters (PMs) due to aerosol schemes and dust options within the WRF-Chem model to estimate Asian dusts affected on 29 May 2008 in the Korean peninsula. The anthropogenic emissions within the model were adopted by the <TEX>$0.5^{\circ}{\pm}0.5^{\circ}$</TEX> RETRO of the global emissions, and the photolysis option was by Fast-J photolysis. Also, three scenarios such as the RADM2 chemical mechanism and MADE/SORGAM aerosol, the MOSAIC 8 section aerosol, and the GOCART dust erosion were simulated for calculating Asian dust emissions. As a result, the scenario of the RADM2 chemical mechanism & MADE/SORGAM aerosol depicted higher concentration than the others' in both Asian dusts and the background concentration of PMs. By comparing of the daily mean of PM10 measured at each air quality monitoring site in Seoul with the scenario results, the correlation coefficient was 0.67, and the root mean square error was <TEX>$44{\mu}gm^{-3}$</TEX>. In addition, the air temperature, the wind speed, the planetary boundary layer height, and the outgoing long-wave radiation were simulated under conditions of no chemical option with these three scenarios within the WRF or WRF-Chem model. Both the spatial distributions of the PBL height and the wind speed of u component among the meteorological factors were similar to those of the Asia dusts in range of 1,800-3,000 m and <TEX>$2-16ms^{-1}$</TEX>, respectively. And, it was shown that both scenarios of the RADM2 chemical mechanism and MADE/SORGAM aerosol and the GOCART dust erosion were interacted on-line between meteorological factors and Asian dusts or aerosols within the model because the outgoing long-wave radiation was changed to lower than the others.

An In Vitro alveolar macrophage assay for the assessment of inflammatory cytokine expression induced by atmospheric particulate matter

Exposures to air pollution in the form of particulate matter (PM) can result in excess production of reactive oxygen species (ROS) in the respiratory system, potentially causing both localized cellular injury and triggering a systemic inflammatory response. PM-induced inflammation in the lung is modulated in large part by alveolar macrophages and their biochemical signaling, including production of inflammatory cytokines, the primary mechanism via which inflammation is initiated and sustained. We developed a robust, relevant, and flexible method employing a rat alveolar macrophage cell line (NR8383) which can be applied to routine samples of PM from air quality monitoring sites to gain insight into the drivers of PM toxicity that lead to oxidative stress and inflammation. Method performance was characterized using extracts of ambient and vehicular engine exhaust PM samples. Our results indicate that the reproducibility and the sensitivity of the method are satisfactory and comparisons between PM samples can be made with good precision. The average relative percent difference for all genes detected during 10 different exposures was 17.1%. Our analysis demonstrated that 71% of genes had an average signal to noise ratio (SNR) ≥ 3. Our time course study suggests that 4 h may be an optimal in vitro exposure time for observing short-term effects of PM and capturing the initial steps of inflammatory signaling. The 4 h exposure resulted in the detection of 57 genes (out of 84 total), of which 86% had altered expression. Similarities and conserved gene signaling regulation among the PM samples were demonstrated through hierarchical clustering and other analyses. Overlying the core congruent patterns were differentially regulated genes that resulted in distinct sample-specific gene expression "fingerprints." Consistent upregulation of Il1f5 and downregulation of Ccr7 was observed across all samples, while TNFα was upregulated in half of the samples and downregulated in the other half. Overall, this PM-induced cytokine expression assay could be effectively integrated into health studies and air quality monitoring programs to better understand relationships between specific PM components, oxidative stress activity and inflammatory signaling potential.

대구지역 대기 중 PM-10과 PM-2.5의 농도분포 특성

The three air quality monitoring sites, analysed simultaneously PM-10 and PM-2.5, ie. Ihyeondong in industrial area, Manchondong in residential area, Pyeongnidong in streetside, among 13 air quality monitoring sites in Daegu area, were investigated the concentration distribution characteristics of PM-2.5 and PM-10 in the last 2 years (2011~2012). PM-10 concentrations exceeded annual average reference value () in Ihyeondong () and Pyeongnidong () but satisfied in Manchondong (). All PM-2.5 concentrations exceeded EPA annual standard value of the United States () in three points, but also exceeded new control annual standard value () coming into effect in 2015. Seasonal concentration of PM-10 appeared the order of spring > winter > fall > summer, and in the case of PM-2.5, the order was winter > spring > fall > summer. Monthly concentrations of PM-10 and PM-2.5 were highest in February and lowest in September. Diurnal concentrations of PM-10 and PM-2.5 increased from 7:00 AM, and recorded the highest concentration between 10:00 AM and 11:00 AM. And after 6:00 PM it lowered continuously and tended to show fixed concentrations from evening until early morning. In addition, the concentration of fine particles during the week was higher than the weekend. The fluctuation in industrial area was larger than the residential area. At the PM-2.5/PM-10 ratio, summer was generally high, spring was the lowest. And, when yellow sand occurred, it was 0.32 to 0.42. It was very low compared to 0.54 to 0.64 during non-yellow sand times. This paper for the state and the characteristics of Daegu` fine particles (PM-10, PM-2.5) will be valuable to future researches of fine particles and air pollution management.

이동 측정방법을 사용한 부산지역 주요 도로의 대기오염도 조사

Mobile sources produce a significant fraction of total anthropogenic emissions in Korea and have harmful effects on air quality. Mobile emissions are intrinsically difficult to estimate due to complicated road networks and variations of traffic volume with location and time. To measure traffic pollutants with high temporal and spatial resolution under real conditions a mobile laboratory was designed. The mobile laboratory provide concentrations of , CO, NO, and location coordinate value. This approach allowed for pollutant level measurements on many roads within short periods of time. In this study, on-road concentrations of , CO, NO and were measured using mobile platform measurement along the 25 main roads in Busan to estimate the average air pollution level in short time difference. The measurements were conducted on favorable meteorological days from 2010 to 2012 and the overall concentrations of , CO, NO and were 0.006, 0.8, 0.182 and 0.055 ppm respectively. The result showed that the concentration of CO, NO and on road were twice, 18 times and 2.5 times higher than regional air quality monitoring sites mean in same period.

The association between air pollution and lung function in Children

Background: Various studies have found respiratory impacts on adults from exposures to air pollutants in earlier stages of life. The relationships between respiratory illnesses and air pollution were examined largely with single air pollutant, but the combined effects from multiple air pollutants remained unclear. Aims: This study evaluated the association between children's lung function and various air pollutants. Methods: Forced vital capacity (FVC) and forced expiratory flow in one second (FEV1) were measured from 2,919 students aged 12-16 in a mass screening program for asthma in Taiwan. Daily concentration records for PM10, SO2, CO, NO2 and O3 were collected from five ambient air quality monitoring sites in Taipei City. Principal component analysis was performed to extract components to express air pollution level because of high correlation between CO, SO2, NO2 and PM10 (Pearson’s correlation r&gt;0.5). Two components, the “original air pollutions” and “secondary air pollutions” were constructed. The combination of “original air pollutions” was 0.88SO2+0.93CO+0.93NO2-0.32O3+0.78PM10; the combination of “secondary air pollutions” was 0.16SO2-0.10CO-0.18NO2+0.92O3+0.51PM10. The association of FVC and FEV1 with the two extracted components were determined and adjusted for age, gender, height, weight, students’ living district, temperature, and rainfall. Results: The mean of FVC and FEV1of all students was 3.26L and 2.98L, respectively.Ranges of Measurements of SO2, CO, O3, PM10 and NO2, respectively, were 1.8-10.0 ppb, 0.60–2.10 ppm, 10.7–30.5 ppb, 35.3–98.3 ug/m3 and 16.2–49.3 ppb. Negative association was found between “secondary air pollution” and FVC and FEV1: a unit increase of “secondary air pollution” was associated with reduction of 40.26 mL (95% CI: -69.64, -10.88 mL) in FVC and 29.84 mL (95% CI: -58.00, -1.68 mL) in FEV1. Conclusions: Exposure to secondary air pollution was negative associated with FVC and FEV1.

Air pollution indicators predict outbreaks of asthma exacerbations among elementary school children: integration of daily environmental and school health surveillance systems in Pennsylvania

Objectives of this study are to determine if a relationship exists between asthma exacerbations among elementary school children in industrialized countries (with climatic seasons) and exposure to daily air pollution with particulate matter, sulfur dioxide, nitrogen dioxide, nitrogen oxides, carbon monoxide, and ozone, when controlled for potential confounders; and, if so, to derive a statistical model that predicts variation of asthma exacerbations among elementary school children. Using an ecological study design, health records of 168,25 students from elementary schools in 49 Pennsylvania counties employing "Health eTools for Schools" were analyzed. Asthma exacerbations were recorded by nurses as treatment given during clinic visits each day. Daily air pollution measurements were obtained from the EPA's air quality monitoring sites. The distribution of asthmatic grouping for pollen and calendar seasons was developed. A Poisson regression model was used to predict the number of asthma exacerbations. The greatest occurrence of asthma exacerbations was in autumn, followed by summer, spring and winter. If the number of asthma exacerbations on a day is N and the daily mean of asthma exacerbations for the three-year period is 48, the probabilities of N > 48 in tree pollen and grass pollen seasons were 56.5% and 40.8%, respectively (p < 0.001). According to the Poisson regression, the week number and prior day CO, SO₂, NO₂, NOx, PM₂.₅, and O₃ had significant effects on asthma exacerbations among students. Monitoring of air pollutants over time could be a reliable new means for predicting asthma exacerbations among elementary school children. Such predictions could help parents and school nurses implement effective precautionary measures.