Characterizing emission factors and oxidative potential of motorcycle emissions in a real-world tunnel environment.

Transportation emissions significantly affect human health, air quality, and climate in urban areas. This study conducted experiments in an urban tunnel in Taipei, Taiwan, to characterize vehicle emissions under real driving conditions, providing emission factors of PM2.5, eBC, CO, and CO2. By applying multiple linear regression, it derives individual emission factors for heavy-duty vehicles (HDVs), light-duty vehicles (LDVs), and motorcycles (MCs). Additionally, the oxidative potential using dithiothreitol assay (OPDTT) was established to understand PM2.5 toxicity. Results showed HDVs dominated PM2.5 and eBC concentrations, while LDVs and MCs influenced CO and CO2 levels. The CO emission factor for transportation inside the tunnel was found to be higher than those in previous studies, likely owing to the increased fraction of MCs, which generally emit higher CO levels. Among the three vehicle types, HDVs exhibited the highest PM2.5 and eBC emission factors, while CO and CO2 levels were relatively higher for LDVs and MCs. The OPDTTm demonstrated that fresh traffic emissions were less toxic than aged aerosols, but higher OPDTTv indicated the impact on human health cannot be ignored. This study updates emission factors for various vehicle types, aiding in accurate assessment of transportation emissions' effects on air quality and human health, and providing a guideline for formulating mitigation strategies.

White matter pathology in alzheimer's transgenic mice with chronic exposure to low-level ambient fine particulate matter.

BACKGROUND: Air pollution, especially fine particulate matter (PM), can cause brain damage, cognitive decline, and an increased risk of neurodegenerative disease, especially alzheimer's disease (AD). Typical pathological findings of amyloid and tau protein accumulation have been detected in the brain after exposure in animal studies. However, these observations were based on high levels of PM exposure, which were far from the WHO guidelines and those present in our environment. In addition, white matter involvement by air pollution has been less reported. Thus, this experiment was designed to simulate the true human world and to discuss the possible white matter pathology caused by air pollution. RESULTS: 6 month-old female 3xTg-AD mice were divided into exposure and control groups and housed in the Taipei Air Pollutant Exposure System (TAPES) for 5 months. The mice were subjected to the Morris water maze test after exposure and were then sacrificed with brain dissection for further analyses. The mean mass concentration of PM2.5 during the exposure period was 13.85 µg/m3. After exposure, there was no difference in spatial learning function between the two groups, but there was significant decay of memory in the exposure group. Significantly decreased total brain volume and more neuronal death in the cerebral and entorhinal cortex and demyelination of the corpus callosum were noted by histopathological staining after exposure. However, there was no difference in the accumulation of amyloid or tau on immunohistochemistry staining. For the protein analysis, amyloid was detected at significantly higher levels in the cerebral cortex, with lower expression of myelin basic protein in the white matter. A diffuse tensor image study also revealed insults in multiple white matter tracts, including the optic tract. CONCLUSIONS: In conclusion, this pilot study showed that even chronic exposure to low PM2.5 concentrations still caused brain damage, such as gross brain atrophy, cortical neuron damage, and multiple white matter tract damage. Typical amyloid cascade pathology did not appear prominently in the vulnerable brain region after exposure. These findings imply that multiple pathogenic pathways induce brain injury by air pollution, and the optic nerve may be another direct invasion route in addition to olfactory nerve.

A numerical study of reducing the concentration of O3 and PM2.5 simultaneously in Taiwan.

Since the 24-hr PM2.5 (particle aerodynamic diameter less than 2.5 µm) concentration standard was regulated in Taiwan in 2012, the PM2.5 concentration has been decreasing year by year, but the ozone (O3) concentration remains almost the same. In particular, the daily maximum 8-hr average O3 (MDA8 O3) concentration frequently exceeds the standard. The goal of this study is to find a solution for reducing PM2.5 and O3 simultaneously by numerical modeling. After the Volatile Organic Compounds (VOCS)-limited and nitrogen oxides (NOX)-limited areas were defined in Taiwan, then, in total, 50 scenarios are simulated in this study. In terms of the average in Taiwan, the effect of VOCS emission reduction is better than that of NOX on the decrease in PM2.5 concentration, when the same reduction proportion (20%, 40%) is implemented. While the effect of further NOX emission reduction (60%) will exceed that of VOCS. The decrease in PM2.5 is proportional to the reduction in precursor emissions such as NOX, VOCS, sulfur dioxides (SO2), and ammonia (NH3). The lower reduction of NOX emission for whole Taiwan caused O3 increases on average but higher reduction can ease the increase, which suggests the implement of NOX emission reductions must be cautious. When comparing administrative jurisdictions in terms of grids, districts/towns, and cities/counties, it was found that controlling NOX and VOCS at a finer spatial resolution of control units did not benefit the decrease in PM2.5 but did benefit the decrease in O3. The enhanced O3 control strategies obviously cause a higher decrease of O3 throughout Taiwan due to NOX and VOCS emission changes when they are implemented in the right places. Finally, three sets of short-term and long-term goals of controlling PM2.5 and O3 simultaneously are drawn from the comprehensive rankings for all simulated scenarios, depending on whether PM2.5 or O3 control is more urgent. In principle, the short-term scenarios could be ordinary or enhanced version of O3 decrease with lower NOX/VOCS emissions, while the long-term scenario is enhanced version of O3 decrease plus high emission reductions for all precursors.

Investigation of East Asian Emissions of CFC-11 Using Atmospheric Observations in Taiwan.

Recent findings of an unexpected slowdown in the decline of CFC-11 mixing ratios in the atmosphere have led to the conclusion that global CFC-11 emissions have increased over the past decade and have been attributed in part to eastern China. This study independently assesses these findings by evaluating enhancements of CFC-11 mixing ratios in air samples collected in Taiwan between 2014 and 2018. Using the NAME (Numerical Atmospheric Modeling Environment) particle dispersion model, we find the likely source of the enhanced CFC-11 observed in Taiwan to be East China. Other halogenated trace gases were also measured, and there were positive interspecies correlations between CFC-11 and CHCl3, CCl4, HCFC-141b, HCFC-142b, CH2Cl2, and HCFC-22, indicating co-location of the emissions of these compounds. These correlations in combination with published emission estimates of CH2Cl2 and HCFC-22 from China, and of CHCl3 and CCl4 from eastern China, are used to estimate CFC-11 emissions. Within the uncertainties, these estimates do not differ for eastern China and the whole of China, so we combine them to derive a mean estimate that we term as being from "(eastern) China". For 2014-2018, we estimate an emission of 19 ± 5 Gg year-1 (gigagrams per year) of CFC-11 from (eastern) China, approximately one-quarter of global emissions. Comparing this to previously reported CFC-11 emissions estimated for earlier years, we estimate CFC-11 emissions from (eastern) China to have increased by 7 ± 5 Gg year-1 from the 2008-2011 average to the 2014-2018 average, which is 50 ± 40% of the estimated increase in global CFC-11 emissions and is consistent with the emission increases attributed to this region in an earlier study.

A comprehensive examination of temporal-seasonal variations of PM1.0 and PM2.5 in taiwan before and during the COVID-19 lockdown.

COVID-19 has been a significant global concern due to its contagious nature. In May 2021, Taiwan experienced a severe outbreak, leading the government to enforce strict Pandemic Alert Level 3 restrictions in order to curtail its spread. Although previous studies in Taiwan have examined the effects of these measures on air quality, further research is required to compare different time periods and assess the health implications of reducing particulate matter during the Level 3 lockdown. Herein, we analyzed the mass concentrations, chemical compositions, seasonal variations, sources, and potential health risks of PM1.0 and PM2.5 in Central Taiwan before and during the Level 3 lockdown. As a result, coal-fired boilers (47%) and traffic emissions (53%) were identified as the predominant sources of polycyclic aromatic hydrocarbons (PAHs) in PM1.0, while in PM2.5, the dominant sources of PAHs were coal-fired boilers (28%), traffic emissions (50%), and iron and steel sinter plants (22.1%). Before the pandemic, a greater value of 20.9 ± 6.92 µg/m3 was observed for PM2.5, which decreased to 15.3 ± 2.51 µg/m3 during the pandemic due to a reduction in industrial and anthropogenic emissions. Additionally, prior to the pandemic, PM1.0 had a contribution rate of 79% to PM2.5, which changed to 89% during the pandemic. Similarly, BaPeq values in PM2.5 exhibited a comparable trend, with PM1.0 contributing 86% and 65% respectively. In both periods, the OC/EC ratios for PM1.0 and PM2.5 were above 2, due to secondary organic compounds. The incremental lifetime cancer risk (ILCR) of PAHs in PM2.5 decreased by 4.03 × 10-5 during the pandemic, with PM1.0 contributing 73% due to reduced anthropogenic activities.

A traffic-induced shift of ultrafine particle sources under COVID-19 soft lockdown in a subtropical urban area.

During the unprecedented COVID-19 city lockdown, a unique opportunity arose to dissect the intricate dynamics of urban air quality, focusing on ultrafine particles (UFPs) and volatile organic compounds (VOCs). This study delves into the nuanced interplay between traffic patterns and UFP emissions in a subtropical urban setting during the spring-summer transition of 2021. Leveraging meticulous roadside measurements near a traffic nexus, our investigation unravels the intricate relationship between particle number size distribution (PNSD), VOCs mixing ratios, and detailed vehicle activity metrics. The soft lockdown era, marked by a 20-27% dip in overall traffic yet a surprising surge in early morning motorcycle activity, presented a natural experiment. We observed a consequential shift in the urban aerosol regime: the decrease in primary emissions from traffic substantially amplified the role of aged particles and secondary aerosols. This shift was particularly pronounced under stagnant atmospheric conditions, where reduced dilution exacerbated the influence of alternative emission sources, notably solvent evaporation, and was further accentuated with the resumption of normal traffic flows. A distinct seasonal trend emerged as warmer months approached, with aromatic VOCs such as toluene, ethylbenzene, and xylene not only increasing but also significantly contributing to more frequent particle growth events. These findings spotlight the criticality of targeted strategies at traffic hotspots, especially during periods susceptible to weak atmospheric dilution, to curb UFP and precursor emissions effectively. As we stand at the cusp of widespread vehicle electrification, this study underscores the imperative of a holistic approach to urban air quality management, embracing the complexities of primary emission reductions and the resultant shifts in atmospheric chemistry.

Spatiotemporal characterization of PM2.5, O3, and trace gases associated with East Asian continental outflows via drone sounding.

East Asian continental outflows with PM2.5, O3, and other species may determine the baseline conditions and affect the air quality in downwind areas via long-range transport (LRT). To gain insight into the impact and spatiotemporal characteristics of airborne pollutants in East Asian continental outflows, a versatile multicopter drone sounding platform was used to simultaneously observe PM2.5, O3, CO2, and meteorological variables (temperature, specific humidity, pressure, and wind vector) above the northern tip of Taiwan, Cape Fuiguei, which often encounters continental outflows during winter monsoon periods. By coordinating hourly high-spatial-resolution profiles provided by drone soundings, WRF/CMAQ model air quality predictions, HYSPLIT-simulated backward trajectories, and MERRA-2 reanalysis data, we analyzed two prominent phenomena of airborne pollutants in continental outflows to better understand their physical/chemical characteristics. First, we found that pollutants were well mixed within a sounding height of 500 m when continental outflows passed through and completely enveloped Cape Fuiguei. Eddies induced by significant fluctuations in wind speeds coupled with minimal temperature inversion and LRT facilitated vertical mixing, possibly resulting in high homogeneity of pollutants within the outflow layer. Second, the drone soundings indicated exceptionally high O3 concentrations (70-100 ppbv) but relatively low concentrations of PM2.5 (10-20 µg/m3), CO2 (420-425 ppmv), and VOCs in some air masses. The low levels of PM2.5, CO2, and VOCs ruled out photochemistry as the cause of the formation of high-level O3. Further coordination of spatiotemporal data with air mass trajectories and O3 cross sections provided by MERRA-2 suggested that the high O3 concentrations could be attributed to stratospheric intrusion and advection via continental outflows. High-level O3 concentrations persisted in the lower troposphere, even reaching the surface, suggesting that stratospheric intrusion O3 may be involved in the rising trend in O3 concentrations in parts of East Asia in recent years in addition to surface photochemical factors.

Analysis of the major factors affecting the visibility degradation in two stations.

UNLABELLED: There are four types of PM10 (particulate matter with an aerodynamic diameter <10 microm) episodes that occur frequently in central Taiwan: long-range transport with dust storms (DS), long-range transport with frontal pollution (FP), river dust (RD), and stagnant weather (SW). During the periods of the four episodes, poor visibility usually results. Multiple linear regression was applied to visibility using eight potential influential variables (temperature, relative humidity, wind speed, PM2.5, PM2.5-10, SO2, NO2, and NO) as independent variables. Of the eight variables, PM2.5 showed the greatest explainable percentage of about 48.6% and 58.1% for Taichung and Wuchi stations, respectively. This suggested that PM2.5 was the most important contributor to reduced visibility. Compared with other type of episodes, the aerosols tended to be offine size during the SWepisodes. This is the main reason that the poorest visibility occurred during the SWepisodes. Good correlation between visibility and secondary inorganic salts (NH4+, NO3, and SO4(2-)) were found at Taichung station (r = 0.71) and Wuchi station (r = 0.81), suggesting that secondary inorganic salts did contribute significantly to the degradation ofvisibility. The visibility degradation due to the effects ofNO3- was much higher than that due to SO4(2-) and NH4+ in the urban area, whereas the visibility degradation due to the effects of NO3 , SO42-, and NH4+ did not show significant diference in the rural area. IMPLICATIONS: Of the eight potential influential variables, PM2.5 showed the greatest effects on reduced visibility. Compared with other type of episodes, the aerosols tend to be fine size during the episodes of stagnant weather. This is the main reason why the poorest visibility occurred during the SW episodes. Good correlations between visibility and secondary inorganic salts (NH4+, NO3-, and SO4(2-)) suggested that secondary inorganic salts did contribute significantly to the degradation of visibility. Among the three inorganic salts, nitrates played a leading role for visibility degradation in urban areas in central Taiwan.

Spatial and seasonal variations in the carbon and lead isotopes of PM2.5 in air of residential buildings and their applications for source identification.

To understand isotope distributions of PM2.5 in residential buildings and apply them for source identification, carbon (δ13C) and lead (Pb) isotope ratios in indoor and outdoor air of residential buildings were analyzed. Moreover, factor analysis (FA) was employed to investigate sources, which were compared through isotopic analyses. The average δ13C values of indoor air are -26.94 ± 1.22‰ and -27.04 ± 0.44‰ in warm (August to October) and cold (February to March) seasons, respectively, and the corresponding values for outdoor air are -26.77 ± 0.54‰ and -26.57 ± 0.39‰. The average 206Pb/207Pb (208Pb/207Pb) ratios of indoor air are 1.1584 ± 0.0091 (2.4309 ± 0.0125) and 1.1529 ± 0.0032 (2.4227 ± 0.0081) in warm and cold seasons, respectively, and the corresponding values for outdoor air are 1.1594 ± 0.0069 (2.4374 ± 0.0103) and 1.1538 ± 0.0077 (2.4222 ± 0.0085). Seasonal variation in δ13C values or Pb isotope ratios of indoor air was not significant, and similar results were obtained for outdoor air. Significant differences were not observed between δ13C values or Pb isotope ratios of indoor and outdoor air. Traffic emission is the major contributor to indoor and outdoor PM2.5 based on isotopic analyses; this result was consistent with the results of FA. The δ13C values of indoor air in buildings with poor ventilation conditions were significantly lighter than those of outdoor air. In summary, the spatial and seasonal variations of isotopes were similar in residential buildings, which can be used to identify sources of indoor PM2.5, and ventilation condition is an influencing factor.

Effects of boundary layer variations on physicochemical characteristics of aerosols in mid-low-altitude regions.

Variations in the height of the boundary layer have a critical impact on the vertical transport of near-surface aerosols. Variations can affect the interactions between aerosols and clouds/fog by altering the scattering and absorption of solar radiation, significantly changing radiative forcing, convective precipitation, and regional climate. In this study, we simultaneously monitored air pollution and meteorological factors in a flat urban area (YunTech site, 50 m asl) and its peripheral mountainous region (MeiShan site, 980 m asl), analyzed the characteristics of pollutants under different atmospheric conditions, and explored the differences in the chemical reaction mechanisms of aerosols at various altitudes, aiming to clarify the evolution of the boundary layer in urban and suburban areas and its impact on the transport of pollutants. The results show that even without anthropogenic emissions, urban ground-level pollutants could be transported to peripheral mountainous areas through boundary layer height variations and local circulations, such as mountain-valley breezes. The PM2.5 concentration was higher at the urban site (average 31.14 ± 14.82µgm-3) and could be transported aloft by valley winds, leading to the gradual accumulation of daytime PM2.5 with an afternoon peak at the mountain site. Moreover, the nitrogen oxidation rate (NOR = [NO3-]/[NO3-] + [NO2]) exhibited clear site variations, the mountain site (average 0.41 ± 0.20) was higher than the urban site (average 0.19 ± 0.07), likely due to the atmospheric environment with thick clouds/fog and strong oxidation capacity in the mountain area. Our study has verified that aerosol characteristics, origins, formation pathways and transport mechanisms at the two measurement sites are significantly different under different conditions, which provides a theoretical basis for future air pollution prevention and regional climate research.

A study to characterize the lead isotopic fingerprint in PM2.5 emitted from incense stick and cigarette burning.

The incense sticks and cigarettes burning are key sources of particulate matter with a diameter of ≤ 2.5 µm (PM2.5) in indoor and outdoor air. While lead (Pb) isotope ratios provide valuable insights into the origin of particle pollution, their applicability for investigating these source remains unclear. The Pb isotope ratios in the PM2.5 emitted from these two sources were analyzed, and effects of brands or nicotine contents on the ratios were assessed. In addition, As, Cr, and Pb were analyzed to investigate whether Pb isotope ratios can serve as an indicator for the source investigation of these metals. We found that average ratios of 206Pb/204Pb, 206Pb/207Pb, and 208Pb/207Pb in cigarettes were heavier than those in incense sticks. Scatter plots of Pb isotope ratios indicated an overlap of values for incense sticks or cigarettes linked to different brands, in that ratios for cigarettes with high nicotine content were heavier than for those with low nicotine content. Scatter plots of As, Cr, or Pb concentration against Pb isotope ratios clearly distinguished the effects of cigarette burning versus incense sticks with respect to PM2.5 of these metals. Results indicate that brand differences did not affect the determination of PM2.5 in these two sources. We suggest that Pb isotope ratios can be a useful tool in investigating the influence of incense sticks and of cigarettes (with high or low nicotine content) burning to PM2.5 and associated metals.

Embedded information of aerosol type, hygroscopicity and scattering enhancement factor revealed by the relationship between PM2.5 and aerosol optical depth.

Satellite aerosol optical depth (AOD) provides an alternative way to depict the spatial distribution of near-surface PM2.5. In this study, a mathematical formulation of how PM2.5 is related to AOD is presented. When simplified to a linear equation, a functional dependence of the slope on the aerosol type, scattering enhancement factor f(RH), and boundary layer height is revealed, while the influence of the vertical aerosol profile is embedded in the intercept. Specifically, we focus on the effects of aerosol properties and employ a new aerosol index (Normalized Gradient Aerosol Index, NGAI) for classifying aerosol subtypes. The combination of AOD difference at shorter wavelengths over longer-wavelength AOD from AERONET data could distinguish and subclassify aerosol types previously indistinguishable by AE (i.e., urban-industrial pollution, U/I, and biomass burning, BB). AOD-PM2.5 regressions are performed on these aerosol subtypes at various relative humidity (RH) levels. The results suggest that BB aerosols are nearly hydrophobic until the RH exceeds 80 %, while the AOD-PM2.5 regressions for U/I depend on RH levels. Moreover, the scattering enhancement factor f(RH) can be calculated by taking the ratio of intercepts between dry and humidity conditions, which is proposed and tested for the first time in this study. Our results show an f(RH ≥ 80 %) of ∼2.6 for U/I-dominated aerosols, whereas the value is not over 1.5 for BB aerosols. The f(RH) can be further used to derive the optical hygroscopicity parameter (κsca), demonstrating that the NGAI can be used to exploit differences in aerosol hygroscopicity and improve the AOD-PM2.5 relationship.

Using drone soundings to study the impacts and compositions of plumes from a gigantic coal-fired power plant.

The immense impacts of coal-fired power plant plumes on the atmospheric environment have caused great concern linked to climate and health issues. However, studies on the field observations of aerial plumes are relatively limited, mainly due to the lack of suitable plume observation tools and techniques. In this study, we use a multicopter unmanned aerial vehicle (UAV) sounding technique to study the influences of the aerial plumes of the world's fourth-largest coal-fired power plant on the atmospheric physical/chemical conditions and air quality. A set of species, including 106 volatile organic compounds (VOCs), CO, CO2, CH4, PM2.5, and O3, and meteorological variables of temperature (T), specific humidity (SH), and wind data, are collected by the UAV sounding technique. The results reveal that the large-scale plumes of the coal-fired power plant cause local temperature inversion and humidity changes, and even affect the dispersion of pollutants below. The chemical compositions of coal-fired power plant plumes are significantly different from those of another ubiquitous vehicular source. High fractions of ethane, ethene, and benzene and low fractions of n-butane and isopentane found in plumes could serve as the key features to help distinguish the influences of coal-fired power plant plumes from other pollution sources in a particular area. By taking the ratios of pollutants (e.g., PM2.5, CO, CH4, and VOCs) to CO2 in plumes and the CO2 emission amounts of the power plant into calculation, we enable the easy quantification of the specific pollutant emissions released from power plant plumes to the atmosphere. In summary, observation by using drone soundings dissecting the aerial plumes provides a new methodology that allows aerial plumes to be readily detected and characterized. Furthermore, the influences of the plumes on the atmospheric physical/chemical conditions and air quality can be assessed rather straightforwardly, which was not easily achievable in the past.

Integrated ground and vertical measurement techniques to characterize overhead atmosphere: Case studies of local versus transboundary pollution.

Much attention has been found to the long-range transport (LRT) of air pollutants and their adverse effects on downwind air qualities resulting from the Chinese haze, which frequently occurs in association with winter monsoon. This study integrates ground-based measurements, unmanned aerial vehicles (UAVs), and model simulations to characterize the meteorological, chemical, and particulate matter (PM) properties comprehensively for the events that were LRT or local pollution (LP) dominated in northern Taiwan during the wintertime of 2017. During the two types of episodes, various approaches were made to investigate the vertical mixing conditions and PM properties with UAV flights. A confined and PM accumulated feature near ground level with a temperature inversion was found during the LP event. In contrast, a vertically homogeneous atmospheric structure with strong winds was suggested during the LRT event. Independent measurements of criteria air pollutants, meteorological variables, volatile organic compounds (VOCs), and micropulse lidar (MPL) made at the ground level were closely supported by the vertical measurements. When synchronizing all these observational and numerical tools in a three-dimensional manner, the characterization of air masses and possible origins of pollution, such as LP vs. LRT, has now become more versatile and capable of gaining a complete picture of atmospheric conditions that define air quality.

Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data.

Reliable long-horizon PM2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM2.5 forecasts at long horizons very challenging. This study proposed a novel machine learning-based model (MCNN-BP) that fused multiple convolutional neural networks (MCNN) with a back-propagation neural network (BPNN) for making spatiotemporal PM2.5 forecasts for the next 72 h at 74 stations covering the whole Taiwan simultaneously. Model configuration involved an ensemble of massive hourly air quality and meteorological monitoring datasets and the existing publicly-available PM2.5 simulated (forecasted) datasets from an atmospheric chemical transport (ACT) model. The proposed methodology collaboratively constructed two CNNs to mine the observed data (the past) and the forecasted data from ACT (the future) separately. The results showed that the MCNN-BP model could significantly improve the accuracy of spatiotemporal PM2.5 forecasts and substantially reduce the forecast biases of the ACT model. We demonstrated that the proposed MCNN-BP model with effective feature extraction and good denoising ability could overcome the curse of dimensionality and offer satisfactory regional long-horizon PM2.5 forecasts. Moreover, the MCNN-BP model has considerably shorter computational time (5 min) and lower computational load than the compute-intensive ACT model. The proposed approach hits a milestone in multi-site and multi-horizon forecasting, which significantly contributes to early warning against regional air pollution.

Distinct brain lipid signatures in response to low-level PM2.5 exposure in a 3xTg-Alzheimer's disease mouse inhalation model.

Fine particulate matter (PM2.5) poses a significant risk to human health. The molecular mechanisms underlying low-level PM2.5-induced neurotoxicity in the central nervous system remain unclear. In addition, changes in lipids in response to PM2.5 exposure have not yet been fully elucidated. In this study, 3xTg-Alzheimer's disease (AD) mice experienced continuous whole-body exposure to non-concentrated PM2.5 for three consecutive months, while control mice inhaled particulate matter-filtered air over the same time span. A liquid chromatography-mass spectrometry-based lipidomic platform was used to determine the distinct lipid profiles of various brain regions. The average PM2.5 concentration during the exposure was 11.38 µg/m3, which was close to the regulation limits of USA and Taiwan. The partial least squares discriminant analysis model showed distinct lipid profiles in the cortex, hippocampus, and olfactory bulb, but not the cerebellum, of mice in the exposure group. Increased levels of fatty acyls, glycerolipids, and sterol lipids, as well as the decreased levels of glycerophospholipids and sphingolipids in PM2.5-exposed mouse brains may be responsible for the increased energy demand, membrane conformation, neuronal loss, antioxidation, myelin function, and cellular signaling pathways associated with AD development. Our research suggests that subchronic exposure to low levels of PM2.5 may cause neurotoxicity by changing the lipid profiles in a susceptible model. Lipidomics is a powerful tool to study the early effects of PM2.5-induced AD toxicity.

Isotopic signatures and source apportionment of Pb in ambient PM2.5.

Particulate lead (Pb) is a primary air pollutant that affects society because of its health impacts. This study investigates the source sectors of Pb associated with ambient fine particulate matter (PM2.5) over central-western Taiwan (CWT) with new constraints on the Pb-isotopic composition. We demonstrate that the contribution of coal-fired facilities is overwhelming, which is estimated to reach 35 ± 16% in the summertime and is enhanced to 57 ± 24% during the winter monsoon seasons. Moreover, fossil-fuel vehicles remain a major source of atmospheric Pb, which accounts for 12 ± 5%, despite the current absence of a leaded gasoline supply. Significant seasonal and geographical variations in the Pb-isotopic composition are revealed, which suggest that the impact of East Asian (EA) pollution outflows is important in north CWT and drastically declines toward the south. We estimate the average contribution of EA outflows as accounting for 35 ± 15% (3.6 ± 1.5 ng/m3) of the atmospheric Pb loading in CWT during the winter monsoon seasons.

Characteristics of major secondary ions in typical polluted atmospheric aerosols during autumn in central Taiwan.

In autumn of 2008, the chemical characteristics of major secondary ionic aerosols at a suburban site in central Taiwan were measured during an annually occurring season of high pollution. The semicontinuous measurement system measured major soluble inorganic species, including NH(4)(+), NO(3)(-), and SO(4)(2-), in PM(10) with a 15 min resolution time. The atmospheric conditions, except for the influences of typhoons, were dominated by the local sea-land breeze with clear diurnal variations of meteorological parameters and air pollutant concentrations. To evaluate secondary aerosol formation at different ozone levels, daily ozone maximum concentration (O(3,daily max)) was used as an index of photochemical activity for dividing between the heavily polluted period (O(3,daily max) ≧80 ppb) and the lightly polluted period (O(3,daily max)<80 ppb). The concentrations of PM(10), NO(3)(-), SO(4)(2-), NH(4)(+) and total major ions during the heavily polluted period were 1.6, 1.9, 2.4, 2.7 and 2.3 times the concentrations during the lightly polluted period, respectively. Results showed that the daily maximum concentrations of PM(10) occurred around midnight and the daily maximum ozone concentration occurred during daytime. The average concentration of SO(2) was higher during daytime, which could be explained by the transportation of coastal industry emissions to the sampling site. In contrast, the high concentration of NO(2) at night was due to the land breeze flow that transport inland urban air masses toward this site. The simulations of breeze circulations and transitions were reflected in transports and distributions of these pollutants. During heavily polluted periods, NO(3)(-) and NH(4)(+) showed a clear diurnal variations with lower concentrations after midday, possibly due to the thermal volatilization of NH(4)NO(3) during daytime and transport of inland urban plume at night. The diurnal variation of PM(10) showed the similar pattern to that of NO(3)(-) and NH(4)(+) aerosols. This indicated that the formatted secondary aerosols in the inland urban area could be transported to the coastal area by the weak land breeze and deteriorated the air quality in the coastal area at night.

Characteristics of PCDD/Fs in PM2.5 from emission stacks and the nearby ambient air in Taiwan.

This study aimed to find the characteristics of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in fine particulate matter from different stationary emission sources (coal-fired boiler, CFB; municipal waste incinerator, MWI; electric arc furnace, EAF) in Taiwan and the relationship between PM2.5 and PM2.5-bound PCDD/Fs with Taiwanese mortality risk. PM2.5 was quantified using gravimetry and corresponding chemical analyses were done for PM2.5-bound chemicals. Mortality risks of PM2.5 exposure and PCDD/Fs exposure were calculated using Poisson regression. The highest concentration of PM2.5 (0.53 ± 0.39 mg/Nm3) and PCDD/Fs (0.206 ± 0.107 ng I-TEQ/Nm3) was found in CFB and EAF, respectively. Higher proportions of PCDDs over PCDFs were observed in the flue gases of CFB and MWI whereas it was reversed in EAF. For ambient air, PCDD/F congeners around the stationary sources were dominated by PCDFs in vapor phase. Positive matrix factorization (PMF) analysis found that the sources of atmosphere PCDD/Fs were 14.6% from EAF (r = 0.81), 52.6% from CFB (r = 0.74), 18.0% from traffic (r = 0.85) and 14.8% from MWI (r = 0.76). For the dioxin congener distribution, PCDDs were dominant in flue gases of CFB and MWI, PCDFs were dominant in EAF. It may be attributed to the different formation mechanisms among wastes incineration, steel-making, and coal-burning processes.

Lipid changes in extrapulmonary organs and serum of rats after chronic exposure to ambient fine particulate matter.

Fine particulate matter (PM2.5) is able to pass through the respiratory barrier to enter the circulatory system and can consequently spread to the whole body to cause toxicity. Although our previous studies have revealed significantly altered levels of phosphorylcholine-containing lipids in the lungs of rats after chronic inhalation exposure to PM2.5, the effects of PM2.5 on phosphorylcholine-containing lipids in the extrapulmonary organs have not yet been elucidated. In this study, we examined the lipid effects of chronic PM2.5 exposure on various organs and serum by using a rat inhalation model followed by a mass spectrometry-based lipidomic approach. Male Sprague-Dawley rats were continuously exposed at the whole body level to nonfiltered and nonconcentrated ambient air from the outside environment of Taipei city for 8 months, while the control rats inhaled filtered air simultaneously. After exposure, serum samples and various organs, including the testis, pancreas, heart, liver, kidney, spleen, and epididymis, were collected for lipid extraction and analysis to examine the changes in phosphorylcholine-containing lipids after exposure. The results from the partial least squares discriminant analysis models demonstrated that the lipid profiles in the PM2.5 exposure group were different from those in the control group in the rat testis, pancreas, heart, liver, kidney and serum. The greatest PM2.5-induced lipid effects were observed in the testes. Decreased lyso-phosphatidylcholines (PCs) as well as increased unsaturated diacyl-PCs and sphingomyelins in the testes may be related to maintaining the membrane integrity of spermatozoa, antioxidation, and cell signaling. Additionally, our results showed that decreased PC(16:0/18:1) was observed in both the serum and testes. In conclusion, exposure to chronic environmental concentrations of PM2.5 caused lipid perturbation, especially in the testes of rats. This study highlighted the susceptibility of the testes and suggested possible molecular events for future study.