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Characteristics of PM_(10) Levels Monitored for More than a Decade in Subway Stations in South Korea

This study aimed to evaluate the variation in PM_(10) concentration and identify the factors influencing it in Korean subways during the past decade. The PM_(10) measured internally by subway companies according to legal requirements was categorized by the subway's characteristics, which were statistically examined using a mixed effects model to identify the relevant parameters. The average levels monitored near or on the platforms and in the waiting rooms ranged from 53.9 to 92.4 μg m^(-3), remaining below the Indoor Air Quality Control Act regulatory standard of 150 μg m^(-3). However, the levels monitored on the platforms far exceeded the average yearly atmospheric environmental standard (50 μg m^(-3)). Based on both univariate and multiple analyses, several subway characteristics, including the presence of a platform screen door (PSD), were found to significantly correlate with the concentration, although slight differences in the significant factors were detected between the cities. Particularly, the absence of transfer lines and the presence of a PSD reduced the platform concentration, except at Busan and during specific years.

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Evolution and Assessment of the Atmospheric Composition in Hangzhou and its Surrounding Areas during the G20 Summit

To evaluate the effectiveness of control strategies during the G20 Summit (August 24-September 6, 2016), five sites were selected for investigating the variation in air pollution in the city of Hangzhou and its surrounding areas. The results showed that the air quality in Hangzhou greatly improved after the implementation of strict emission controls. Compared to the same dates from the five preceding years, the G20 period exhibited low relative humidity during the early stage (August 27-31) and no significant differences in other meteorological conditions. The SO_2, NO_2, PM_(10), and PM_(2.5) concentrations averaged across the five sites in Hangzhou decreased by 42.6%, 57.1%, 36%, and 38.5%, respectively, although the average O_3 concentration increased by 19%. These changes indicate that the emission reduction measures decreased the concentrations of several pollutants. Compared to the same dates from the previous year (August 24-September 6, 2015), the G20 period exhibited significantly decreased concentrations for most of the chemical components of PM_(2.5), particularly SO_4^(2-), NO_3^-, and NH_4^+. Additionally, the aerosol optical depth (AOD) and the NO_2 column densities were found to exhibit similar trends with that of the pollutants in this city.

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Air Pollution Characteristics and Meteorological Correlates in Lin'an, Hangzhou, China

The concentration and distribution of atmospheric particulate matter depend primarily on the meteorological conditions associated with a fixed pollution source. The effects of meteorological factors on particulate matter have been analyzed on the meteorological seasonal scale, but few researchers have considered the climatic season, which is divided based on the distribution feature of climatic factors. In addition, the hysteresis effect of meteorological factors is easily neglected. Here, we reviewed the characteristics and influential factors of particle pollution based on particle concentration and meteorological data from January 2013 through December 2013. Results from nonparametric tests and Spearman's nonparametric correlation coefficient showed that particle pollution exhibited a statistically significant seasonal trend. The pollution on workdays was slightly less than that on holidays, but no significant difference was found. The air pressure 1-2 days earlier showed a higher positive correlation with the current particle concentrations (except in winter), and the temperature 2-3 days earlier in summer and fall showed a stronger negative correlation with the particle concentration. Lower moisture and frequent precipitation would significantly reduce the pollution on the current day and the next day (except in summer). The variation of particulate matter concentration in summer exhibited a high-low-high variation, caused mainly by temperature and precipitation; the air quality during the plum rain period was significantly better than that in the period before the plum rain. The fine particle pollution level during the high-temperature and heat wave days was the lowest, after which the concentration increased.

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Effects of Blending Ethanol with Gasoline on the Performance of Motorcycle Catalysts and Airborne Pollutant Emissions

This study investigated the effects of blending ethanol with gasoline on the exhaust emissions of fuel-injected motorcycles. Regulated gasoline (RF), and 15 (E15) and 30 (E30) vol% ethanol fuel were used as test fuels. Measurements of several air pollutants (CO, HC, and NO_x) and organic air pollutant groups were conducted for two new fuel-injected four-stroke motorcycles. In addition, various catalysts were inserted into the motorcycles' tailpipes to determine the characteristics and performance of the catalysts in treating the exhaust. Compared to using RF, we found that using blended fuel potentially reduced the CO and HC emissions by 30-37% and 19-28%, respectively. New catalytic systems, in conjunction with using different fuels, reduced CO, HC, and NO_x emissions in the tailpipe exhaust by 12-61%, 32-39%, and 81-85%, respectively. The CO and HC emissions were directly proportional in quantity to the running mileage of the catalyst, but the NO_x emissions were unaffected by this mileage, although they increased as the catalyst aged. We also discovered that at identical running mileages for a catalyst, the fuel consumption increased by -1.7-6.5% and 4.1-15% when using E15 and E30 fuel instead of RF. Furthermore, the specific surface area and pore volume of the catalyst decreased with the aged catalyst the phosphorus and sulfur content in the catalyst increased with the catalyst's running mileage; adding ethanol to the fuel decreased emissions of paraffins, olefins, and aromatics but increased those of carbonyls; and the ozone formation potential of volatile organic compounds (VOCs) in the tailpipe exhaust was 16.7-17.2% for paraffins, 22-33% for olefins, 26-45% for aromatics, and 4.9-25% for carbonyls.

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Infants' Neurodevelopmental Effects of PM_(2.5) and Persistent Organohalogen Pollutants Exposure in Southern Taiwan

Several studies have stated the harmful effects of PM_(2.5) to population health, including disruption of neurological development. However, the mechanism behind the neurodevelopmental effects of ambient PM_(2.5) and postnatal PBDEs and OCPs exposure is still unknown. Our goal was to determine influence of breastmilk residues, polybrominated diphenyl ethers (PBDEs) and organochlorine pesticides (OCPs), to the infants' neurodevelopment with respect to high and low PM_(2.5) exposure areas. The participants were recruited from high PM_(2.5) exposure areas (n = 32) and low PM_(2.5) exposure areas (n = 23) of southern Taiwan. The extracted 14 PBDEs and 20 OCPs compounds were analyzed using gas chromatography coupled with mass spectrometer. The infants, aging from 8-12 months, were examined by Bayley Scales of Infants and Toddlers Development, Third Edition (Bayley-III) for neurodevelopment. Results showed that high PM_(2.5) exposure caused reduced head circumference and had significant effects on the motor skill and social emotional development. For breastmilk PBDEs, a positive correlation between BDE-196 and social emotion, after multivariate analysis with adjustment of confounders, was observed while BDE-99, 196, 197, and 207 showed higher magnitudes in low PM_(2.5) areas than in high PM_(2.5) areas. For OCPs, only γ- hexachlorcyclohexanes (γ-HCH) presented the significant difference between high and low PM_(2.5) exposure areas. Most breastmilk OCPs residues, including 4,4'-dichlorodiphenyltrichloroethane (4,4'-DDT), γ-HCH, endrin, and heptachlor epoxide showed negative impact on the Bayley-III scores after multivariate analysis. In conclusion, infants' neurodevelopment was significantly correlated with the location of PM_(2.5) exposure and breastmilk intake of certain PBDEs and OCPs. Breastmilk OCPs might obviously affect infants' neurodevelopment more compared to breastmilk PBDEs based on our finding. Moreover, this study further employs awareness about viable effects of PM_(2.5) in infants' neurodevelopment.

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Respirable Crystalline Silica Exposure among Concrete Finishing Workers at Apartment Complex Construction Sites

The objective of this study was to evaluate the level of exposure to respirable crystalline silica (RCS) among concrete finishers and to identify the size distribution of particles from concrete finishing work at apartment complex construction sites. Active personal air sampling (n = 129) was conducted at eight sites using filters with aluminum cyclones, and local air sampling for the size distribution of the particles (n = 6) was conducted using a Mylar substrate with cascade impactors. Crystalline silica was analyzed by Fourier-transform infrared spectroscopy (FT-IR). The collected personal samples were sorted by the type of task (concrete chipping, grinding, or plastering) for four kinds of working places (exterior walls, interior walls of apartment units, staircases, and underground parking lots). The geometric mean (GM) of the RCS concentration was highest, although varying according to the type of structure, for concrete grinding (2.06 mg m^(-3)), followed by chipping (0.12 mg m^(-3)) and plastering (0.003 mg m^(-3)). The maximum RCS concentration was measured in the staircases (4.18 mg m^(-3)), followed by the interior walls of apartment units (2.76 mg m^(-3)), underground parking lots (1.30 mg m^(-3)), and exterior walls (0.89 mg m^(-3)). The mass fraction of inhalable, thoracic, and respirable crystalline silica was respectively 73.9%, 40.2%, and 17.9% in chipping and 76.0%, 46.3%, and 19.7% in grinding. Concrete finishing workers at apartment complex construction sites are exposed to unacceptably high concentrations of RCS. To protect workers' health, dust-minimizing construction methods as well as high-efficiency respirators paired with local exhaust ventilation systems or wet methods must be employed. Further efforts to reduce the total working time and to use full-faced air-purifying respirators are required.

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Forecasting Surface O_3 in Texas Urban Areas Using Random Forest and Generalized Additive Models

We developed and evaluated three types of statistical forecasting models (quantitative, probabilistic, and classification) for predicting the maximum daily 8-hour average concentration of ozone based on meteorological and ozone monitoring data for six Texas urban areas from 2009 to 2015. The quantitative and probabilistic forecasting models were generalized additive models (GAMs), whereas the classification forecast used the random forest machine learning method. We found that for the quantitative forecasting models, five of the eight predictors (the day of week, day of the year, water vapor density, wind speed, and previous day's ozone measurement) were significant at the α = 0.001 level for all urban areas, whereas the other three varied in significance according to the location. The quantitative forecasting for the 2016 ozone season agreed well with the associated measurements (R^2 of ~0.70), but it tended to under-predict the ozone level for the days with the highest concentrations. By contrast, the probabilistic forecasting models showed little accuracy in determining the probability of concentrations exceeding policy-relevant thresholds during this season. The success rate for the random forest classification models typically exceeded 75% and would likely increase if the training data sets contained more extreme events.

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Source Identification of Episodic Rain Pollutants by a New Approach: Combining Satellite Observations and Backward Air Mass Trajectories

In this study, various source apportionment methods, viz., enrichment factor, relative source contribution, and factor analysis in addition to cluster analysis coupled with satellite observations, were used to investigate the origin of rainwater pollutants in an arid urban area of Saudi Arabia. The rainwater samples were collected by an automatic wet-only sequential rain sampler and analyzed for their pH, electrical conductivity (EC), and major ions (Cl^-, NO_3^-, SO_4^(2-), HCO_3^-, Na^+, K^+,NH_4^+, Ca^(2+), and Mg^(2+)). The results revealed that the chemical composition was dominated by Ca^(2+), SO_4^(2-), Cl^-, HCO_3^-, and Na^+, which represented 80% of the total ionic equivalent concentration. Ca^(2+), the most abundant ion, exhibited a mean concentration of 480 μeq L^(-1) and accounted for 30% of the total ionic equivalent concentration. The study area received a total ionic wet deposition flux (F_(WD)) of 4.07 tons km^(-2) y^(-1), and SO_4^(2-) and NO_3^- contributed 76% and 24%, respectively, of the rainwater acidity. Ca^(2+) was responsible for 80% of the rainwater's neutralization. The source apportionment indicated that intense local human activity and medium- to long-range transport from Kuwait, Iraq, Qatar, Riyadh, and the Western Province produced the majority of the anthropogenic components (SO_4^(2-), NO_3^-, and NH_4^+), which represented 47% of the total ionic FWD. However, moderate to high particulate matter loads over the southern part of Saudi Arabia and long-range transport from Iraq and Kuwait contributed the crustal components (Ca^(2+), HCO_3^-, K^+, and Mg^(2+)), which accounted for 36% of the total ionic FWD, whereas the Arabian Gulf was the primary source of the marine components (Na^+ and Cl^-). We found combining satellite observations and cluster analysis of backward air mass trajectories to be an effective new approach for assessing the source-receptor relationships for atmospheric air pollutants.

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Identifying Leading Nodes of PM_(2.5) Monitoring Network in Taiwan with Big Data-oriented Social Network Analysis

TEPA (Taiwan Environmental Protection Administration) currently has regulated six types of air pollutants based on the AQI. Among these, the three items most prone to exceeding the standard are PM_(2.5), PM_(10), and O_3, in that order. PM_(2.5) pollution episodes in Taiwan mainly occur in winter and spring when the northeast monsoon prevails. In addition to local pollution sources, transboundary air pollution affects Taiwan. Obviously, the existing AQ monitoring data analyzed by the BD-oriented perspective not only simplifies the simulation calculation and verification resources of the AQ model but also assists in real-time insight into the causal relationships between the AQ and important parameters of meteorology, pollution sources, and regions. This study integrates the BD-oriented Social Network Analysis (SNA) approach and data visualization tools to analyze the event co-occurrence and spatial correlation characteristics of two pollution scenarios for AQ monitoring stations based on two severe PM_(2.5) pollution conditions: (1) the Z-value of PM_(2.5) daily average concentration is higher than 1.65 (Scenario I), and (2) the daily average concentration of PM_(2.5) exceeds TEPA's regulation on the AQ standard (Scenario II), to identify the regional leading nodes suitable for different pollution scenarios. Furthermore, Principal Component Analysis (PCA) and time series data are employed to verify the spatial-temporal representation of these leading nodes, which can be regarded as means to the real-time AQ management decision-making as well as instant transboundary pollution precaution in the future. This study contributes to the application of the discrete data-driven approach (SNA) and the continuous data-driven approach (PCA) in an ambient AQ monitoring network, which can clearly explain and analyze the regional high pollution characteristics of PM_(2.5) in Scenarios I and II. The results of this study are consistent with previously relevant findings in Taiwan.

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High Photocatalyst Module on Degradation of Extracted Gas from Soil under Visible Light

Photocatalytic oxidation (PCO) is a promising technology for air purification due to low operating cost, potentially long service life, and low maintenance. In order to establish a high efficiency for removal of solid vapor extraction (SVE), in this study activated carbon was used for adsorption of SVE and lanthanum-doped titanium dioxide as a photocatalyst. The photocatalyst was coated on the glass fiber cloth and applied to the photocatalytic module and then performed for degradation of 15 ppm toluene. The adsorption module was used to eliminate the residual contaminants. The X-ray diffraction (XRD), Scanning electron microscopy (SEM) and the diffused reflectance spectroscopy (DRS), showed the synthesis of the catalyst were successfully prepared and the photocatalytic commercial module was designed by four photocatalytic glass fiber cloths under visible light. The adsorption module was designed by five layers of activated carbon each layer contained 30 g of activated carbon sets in the photocatalytic module. When the ambient humidity is less than 4 RH%, the removal efficiency was overtaken more than 95% within 1 hour. The experimental results depict that the photocatalytic commercial module is assembled with flow rate less than 3 liters per minute and the removal efficiency can be stable for more than 72 hours.