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.

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.

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.

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.

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.

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, PM_2.5, 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., PM_2.5, 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.

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 (PM_2.5) 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 PM_2.5 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 PM_2.5 of these metals. Results indicate that brand differences did not affect the determination of PM_2.5 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 PM_2.5 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 PM_2.5. In this study, a mathematical formulation of how PM_2.5 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-PM_2.5 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-PM_2.5 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 (κ_sca), demonstrating that the NGAI can be used to exploit differences in aerosol hygroscopicity and improve the AOD-PM_2.5 relationship.

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 PM_2.5 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 PM_2.5 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 PM_2.5, and ventilation condition is an influencing factor.

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

Since the 24-hr PM_2.5 (particle aerodynamic diameter less than 2.5 μm) concentration standard was regulated in Taiwan in 2012, the PM_2.5 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 PM_2.5 and O₃ simultaneously by numerical modeling. After the Volatile Organic Compounds (VOC_S)-limited and nitrogen oxides (NO_X)-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 VOC_S emission reduction is better than that of NO_X on the decrease in PM_2.5 concentration, when the same reduction proportion (20%, 40%) is implemented. While the effect of further NO_X emission reduction (60%) will exceed that of VOC_S. The decrease in PM_2.5 is proportional to the reduction in precursor emissions such as NO_X, VOC_S, sulfur dioxides (SO₂), and ammonia (NH₃). The lower reduction of NO_X emission for whole Taiwan caused O₃ increases on average but higher reduction can ease the increase, which suggests the implement of NO_X emission reductions must be cautious. When comparing administrative jurisdictions in terms of grids, districts/towns, and cities/counties, it was found that controlling NO_X and VOC_S at a finer spatial resolution of control units did not benefit the decrease in PM_2.5 but did benefit the decrease in O₃. The enhanced O₃ control strategies obviously cause a higher decrease of O₃ throughout Taiwan due to NO_X and VOC_S emission changes when they are implemented in the right places. Finally, three sets of short-term and long-term goals of controlling PM_2.5 and O₃ simultaneously are drawn from the comprehensive rankings for all simulated scenarios, depending on whether PM_2.5 or O₃ control is more urgent. In principle, the short-term scenarios could be ordinary or enhanced version of O₃ decrease with lower NO_X/VOC_S emissions, while the long-term scenario is enhanced version of O₃ decrease plus high emission reductions for all precursors.

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

Fine particulate matter (PM_2.5) poses a significant risk to human health. The molecular mechanisms underlying low-level PM_2.5-induced neurotoxicity in the central nervous system remain unclear. In addition, changes in lipids in response to PM_2.5 exposure have not yet been fully elucidated. In this study, 3xTg-Alzheimer's disease (AD) mice experienced continuous whole-body exposure to non-concentrated PM_2.5 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 PM_2.5 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 PM_2.5-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 PM_2.5 may cause neurotoxicity by changing the lipid profiles in a susceptible model. Lipidomics is a powerful tool to study the early effects of PM_2.5-induced AD toxicity.

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

Reliable long-horizon PM_2.5 forecasts are crucial and beneficial for health protection through early warning against air pollution. However, the dynamic nature of air quality makes PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

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 PM_2.5 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 PM_2.5 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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-022-00485-8.

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

Fine particulate matter (PM_2.5) 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 PM_2.5, the effects of PM_2.5 on phosphorylcholine-containing lipids in the extrapulmonary organs have not yet been elucidated. In this study, we examined the lipid effects of chronic PM_2.5 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 PM_2.5 exposure group were different from those in the control group in the rat testis, pancreas, heart, liver, kidney and serum. The greatest PM_2.5-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 PM_2.5 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.

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 PM_2.5 and PM_2.5-bound PCDD/Fs with Taiwanese mortality risk. PM_2.5 was quantified using gravimetry and corresponding chemical analyses were done for PM_2.5-bound chemicals. Mortality risks of PM_2.5 exposure and PCDD/Fs exposure were calculated using Poisson regression. The highest concentration of PM_2.5 (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.

Long-term (2003-2018) trends in aerosol chemical components at a high-altitude background station in the western North Pacific: Impact of long-range transport from continental Asia

This study examined the long-term trends in chemical components in PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) samples collected at Lulin Atmospheric Background Station (LABS) located on the summit of Mt. Lulin (2862 m above mean sea level) in Taiwan in the western North Pacific during 2003-2018. High ambient concentrations of PM_2.5 and its chemical components were observed during March and April every year. This enhancement was primarily associated with the long-range transport of biomass burning (BB) smoke emissions from Indochina, as revealed from cluster analysis of backward air mass trajectories. The decreasing trends in ambient concentrations of organic carbon (-0.67% yr^-1; p = 0.01), elemental carbon (-0.48% yr^-1; p = 0.18), and non-sea-salt (nss) K⁺ (-0.71% yr^-1; p = 0.04) during 2003-2018 indicated a declining effect of transported BB aerosol over the western North Pacific. These findings were supported by the decreasing trend in levoglucosan (-0.26% yr^-1; p = 0.20) during the period affected by the long-range transport of BB aerosol. However, NO₃^- displayed an increasing trend (0.71% yr^-1; p = 0.003) with considerable enhancement resulting from the air masses transported from the Asian continent. Given that the decreasing trends were for the majority of the chemical components, the columnar aerosol optical depth (AOD) also demonstrated a decreasing trend (-1.04% yr^-1; p = 0.0001) during 2006-2018. Overall decreasing trends in ambient (carbonaceous aerosol and nss-K⁺) as well as columnar (e.g., AOD) aerosol loadings at the LABS may influence the regional climate, which warrants further investigations. This study provides an improved understanding of the long-term trends in PM_2.5 chemical components over the western North Pacific, and the results would be highly useful in model simulations for evaluating the effects of BB transport on an area.

Vertical distribution of source apportioned PM2.5 using particulate-bound elements and polycyclic aromatic hydrocarbons in an urban area

Receptor models have been widely used for identifying and quantifying source-specific contributions from mixtures of air pollutants. Nonetheless, our knowledge is still limited on how various components of air pollution are vertically distributed and the sources of such pollutants. In this study, we collected 135 samples of PM_2.5 (particles with aerodynamic diameter less than or equal to 2.5 µm) from building balconies at three altitudes in the metropolis of Taipei (Taiwan) and analyzed the samples for elements and polycyclic aromatic hydrocarbons that could be used to identify the sources of those pollutants. We used positive matrix factorization (PMF) to identify seven likely sources, including combustion, sulfur-rich aerosol, fresh traffic, industry/Cr-rich, oil combustion/vehicle, dust, and traffic. Although PM_2.5 mass differed significantly between low-level and mid-level sites, the largest contributor to PM_2.5 mass (sulfur-rich aerosol, 35.2%) showed nonsignificant variation in the vertical distribution. In contrast, oil combustion/vehicle, which exhibited significant difference between mid-level sites and the other two altitudes, might be a determinant in the vertical variation of PM_2.5. We also observed negative trends with sampling height for combustion and traffic emissions.

Source apportionment of urban air pollutants using constrained receptor models with a priori profile information

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM_2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM_2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM_2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM_2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%).

Wintertime haze deterioration in Beijing by industrial pollution deduced from trace metal fingerprints and enhanced health risk by heavy metals

Airborne particulate matter (PM) was collected in Beijing between 24 February and 12 March 2014 to investigate chemical characteristics and potential industrial sources of aerosols along with health risk of haze events. Results showed secondary inorganic aerosol was the major contributor to PM2.5 during haze days. Utilizing specific elements, including Fe, La, Tl and As, as fingerprinting tracers, four emission sources, namely iron and steel manufacturing, petroleum refining, cement plant, and coal combustion were explicitly identified; their elevated contributions to PM during haze days were also estimated. The average cancer risk from exposure to inhalable PM toxic metals was 1.53 × 10(-4) on haze days, which is one order of magnitude higher than in other developed cities. These findings suggested heavy industries emit large amounts of not only primary PM but also precursor gas pollutants, leading to secondary aerosol formation and harm to human health during haze days.

Source and risk apportionment of selected VOCs and PM₂.₅ species using partially constrained receptor models with multiple time resolution data

This study was conducted to identify and quantify the sources of selected volatile organic compounds (VOCs) and fine particulate matter (PM2.5) by using a partially constrained source apportionment model suitable for multiple time resolution data. Hourly VOC, 12-h and 24-h PM2.5 speciation data were collected during three seasons in 2013. Eight factors were retrieved from the Positive Matrix Factorization solutions and adding source profile constraints enhanced the interpretability of source profiles. Results showed that the evaporative emission factor was the largest contributor (25%) to VOC mass concentration, while the largest contributor to PM2.5 mass concentration was soil dust/regional transport related factor (26%). In terms of risk prioritization, traffic/industry related factor was the major cause for benzene, ethylbenzene, Cr, and polycyclic aromatic hydrocarbons (29-69%) while petrochemical related factor contributed most to the Ni risk (36%). This indicated that a larger contributor to mass concentration may not correspond to a higher risk.

Subchronic effects of inhaled ambient particulate matter on glucose homeostasis and target organ damage in a type 1 diabetic rat model

Epidemiological studies have reported associations between particulate matter (PM) and cardiovascular effects, and diabetes mellitus (DM) patients might be susceptible to these effects. The chief chronic injuries resulting from DM are small vascular injuries (micro-vascular complications) or large blood vessel injuries (macro-vascular complications). However, toxicological data regarding the effects of PM on DM-related cardiovascular complications is limited. Our objective was to investigate whether subchronic PM exposure alters glucose homeostasis and causes cardiovascular complications in a type 1 DM rat model. We constructed a real world PM2.5 exposure system, the Taipei Air Pollution Exposure System for Health Effects (TAPES), to continuously deliver non-concentrated PM for subchronic exposure. A type 1 DM rat model was induced using streptozotocin. Between December 22, 2009 and April 9, 2010, DM rats were exposed to PM or to filtered air (FA) using TAPES in Taipei, Taiwan, 24h/day, 7days/week, for a total of 16weeks. The average concentrations (mean [SD]) of PM2.5 in the exposure and control chambers of the TAPES were 13.30 [8.65] and 0.13 [0.05]μg/m(3), respectively. Glycated hemoglobin A1c (HbA1c) was significantly elevated after exposure to PM compared with exposure to FA (mean [SD], 7.7% [3.1%] vs. 4.7% [1.0%], P<0.05). Interleukin 6 and fibrinogen levels were significantly increased after PM exposure. PM caused focal myocarditis, aortic medial thickness, advanced glomerulosclerosis, and accentuation of tubular damage of the kidney (tubular damage index: 1.76 [0.77] vs. 1.15 [0.36], P<0.001). PM exposure might induce the macro- and micro-vascular complications in DM through chronic hyperglycemia and systemic inflammation.

Source apportionment of particulate matter and selected volatile organic compounds with multiple time resolution data

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) co-exist in ambient air and contribute to adverse health effects in human populations. This study was conducted to demonstrate the feasibility of utilizing a composite data set which included PM2.5 and VOC data with multiple time resolutions for source apportionment. Hourly VOC and 12-h PM2.5 speciation data were combined into an improved source apportionment model to quantify different pollutant source contributions to PM2.5 and VOC mixtures. Five factors were retrieved, including vehicle 1, vehicle 2, industrial processing, transported regional, and secondary pollution sources. The largest contributors were vehicular emissions for VOCs (62%) and PM2.5 (35%). Nonetheless, transported regional and secondary pollution sources accounted for a noteworthy portion of PM2.5 (27% and 25%, respectively) relative to VOCs (8% and 5%, respectively). Additional sensitivity analyses showed that excluding the PM2.5 data or reducing the associated temporal resolution (12-h to 24-h) retrieved fewer source factors and increased the errors of source contribution estimates.

Particulate matter characteristics during agricultural waste burning in Taichung City, Taiwan

Agricultural waste burning is performed after harvest periods in June and November in Taiwan. Typically, farmers use open burning to dispose of excess rice straw. PM(2.5) and PM(2.5-10) measurements were conducted at National Chung Hsing University in Taichung City using a dichotomous sampler. The sampling times were during straw burning periods after rice harvest during 2002-2005. Ionic species including SO(4)(2-), NO(3)(-), NH(4)(+), K(+), Ca(2+), Cl(-) and Na(+) and carbonaceous species (EC and OC) in PM(2.5) and PM(2.5-10) were analyzed. The results showed that the average PM(2.5) and PM(2.5-10) concentrations were 123.6 and 31.5 microg m(-3) during agricultural waste burning periods and 32.6 and 21.4 microg m(-3) during non-waste burning periods, respectively. The fine aerosol ionic species including Cl(-), K(+) and NO(3)(-) increased 11.0, 6.7 and 5.5 times during agricultural burning periods compared with periods when agricultural waste burning is not performed. K(+) was found mainly in the fine mode during agricultural burning. High nitrogen oxidation ratio was found during agricultural waste burning periods which might be caused by the conversion of Nitrogen dioxide (NO(2)) to NO(3)(-). It is concluded that agricultural waste burning with low dispersion often causes high PM(2.5) and gases pollutant events.

Lidar observations of the diurnal variations in the depth of urban mixing layer: a case study on the air quality deterioration in Taipei, Taiwan

An aerosol light detection and ranging (LIDAR) system was used to measure the depth of the atmospheric mixing layer over Taipei, Taiwan in the spring of 2005. This paper presents the variations of the mixing height and the mixing ratios of air pollutants during an episode of air quality deterioration (March 7-10, 2005), when Taipei was under an anti-cyclonic outflow of a traveling high-pressure system. It was found that, during those days, the urban mixing height reached its daily maximum of 1.0-1.5 km around noon and declined to 0.3-0.5 km around 18:00 (LST). In terms of hourly averages, the mixing height increased with the ambient temperature linearly by a slope of 166 m/degrees C in daytime. The consistency between the changes in the mixing height and in the ambient temperature implied that the mixing layer dynamics were dominated by solar thermal forcing. As the cap of the mixing layer descended substantially in the afternoon, reduced dispersion in the shallow mixing layer caused the concentrations of primary air pollutants to increase sharply. Consequently, the pollutant concentration exhibited an anti-correlation with the mixing height. While attentions are usually focused on the pollution problems occurring in a morning inversion layer, the results of this study indicate that the air pollution and its health impacts could be even more severe as the mixing layer is getting shallow in the afternoon.

Source identifications of PM10 aerosols depending on hourly measurements of soluble components characterization among different events in Taipei Basin during spring season of 2004

Continuous measurements of hourly PM10 soluble ions were performed by the in situ IC technology in order to assess the impact of Asian outflows on local air quality. The intensive aerosol observation was carried in Taipei from 11 February 22:00 to 7 April 19:00, 2004. Concentrations of the water-soluble ions (Cl(-), NO(-)(2), NO(-)(3), SO(2-)(4), Na(+), NH(+)(4), K(+), and Ca(2+)) were measured in a total of 3,300 samples. The characteristics of air pollutant events in Taipei Basin were classified as frontal dust, dust, northeast monsoon, south wind and sea/land breeze according to the hourly meteorology and air pollutant concentrations. Factor analysis was conducted based on hourly data for 13 variables to find the group of variables with similar behavior. According to the source characteristics of high loading species, the possible sources of PM10 aerosols in each group were identified. Three to four factors were identified for each event. The total variances of frontal dust, dust, northeast trade, south wind, and sea/land breeze events were explained about 85%, 86%, 76%, 77%, and 80%, respectively, indicating that the identified factors were satisfactory.