Neuropathology changed by 3- and 6-months low-level PM2.5 inhalation exposure in spontaneously hypertensive rats.

BACKGROUND: Epidemiological evidence has linked fine particulate matter (PM2.5) to neurodegenerative diseases; however, the toxicological evidence remains unclear. The objective of this study was to investigate the effects of PM2.5 on neuropathophysiology in a hypertensive animal model. We examined behavioral alterations (Morris water maze), lipid peroxidation (malondialdehyde (MDA)), tau and autophagy expressions, neuron death, and caspase-3 levels after 3 and 6 months of whole-body exposure to urban PM2.5 in spontaneously hypertensive (SH) rats. RESULTS: SH rats were exposed to S-, K-, Si-, and Fe-dominated PM2.5 at 8.6 ± 2.5 and 10.8 ± 3.8 µg/m3 for 3 and 6 months, respectively. We observed no significant alterations in the escape latency, distance moved, mean area crossing, mean time spent, or mean swimming velocity after PM2.5 exposure. Notably, levels of MDA had significantly increased in the olfactory bulb, hippocampus, and cortex after 6 months of PM2.5 exposure (p < 0.05). We observed that 3 months of exposure to PM2.5 caused significantly higher expressions of t-tau and p-tau in the olfactory bulb (p < 0.05) but not in other brain regions. Beclin 1 was overexpressed in the hippocampus with 3 months of PM2.5 exposure, but significantly decreased in the cortex with 6 months exposure to PM2.5. Neuron numbers had decreased with caspase-3 activation in the cerebellum, hippocampus, and cortex after 6 months of PM2.5 exposure. CONCLUSIONS: Chronic exposure to low-level PM2.5 could accelerate the development of neurodegenerative pathologies in subjects with hypertension.

Trajectory-Assisted Source Apportionment of Winter-Time Aerosol Using Semi-continuous Measurements.

Receptor models, e.g., positive matrix factorization (PMF), are beneficial in designing effective control strategies to improve air quality. Additionally, integrating the trajectory analysis data into receptor modeling facilitates identifying the contributions from long-range transported aerosols. This study was conducted in Taipei City of Taiwan, a representative urban area with high population density, heavy traffic, and residential-commercial complexes. Hourly measurements were applied into an integrated trajectory-source apportionment approach. PMF was used to identify seven potential sources, including ammonium sulfate related, oil combustion, firework/firecracker, dust, vehicle, coal/marine, and industry/vehicle. Ammonium sulfate related source (33%) was characterized as the largest contributor, followed by coal/marine (18%) and industry/vehicle (16%). Through this integrated method, contribution estimates of the ammonium sulfate related factor from distant potential source regions were differentiated. Additionally, detailed distributions of source contributions to PM2.5 event periods were revealed by applying these highly time-resolved measurements.

Vertical variation of source-apportioned PM2.5 and selected volatile organic compounds near an elevated expressway in an urban area.

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) are associated with adverse health effects and show spatial variation in three dimensions. The present study attempted to evaluate source contributions of PM2.5 and toxic VOCs in a metropolitan area focusing on the associated vertical variations. A special emphasis is put on the effects of the elevated expressway on the vertical variability of contribution estimates of the identified sources. Nine source factors, i.e., soil dust, sea salt/oil combustion, secondary nitrate, industrial emission, aged VOCs/secondary aerosol, traffic-related I, solvent use/industrial process, secondary sulfate, and traffic-related II, were identified using positive matrix factorization (PMF). The main contributors to PM2.5 were secondary sulfate (19.1%) and traffic-related emissions (traffic-related I and II, 16.1%), whereas the largest contributors to VOCs were traffic-related emissions (37.6%). The influence of the elevated expressway is suggested to be particularly critical on vertical variations of traffic-related emissions, including aging and secondary formation of locally accumulated air pollutants near roads. Increasing the building porosity under the viaduct could reduce the accumulation of air pollutants caused by the shelter effect. Additionally, in-street barriers would be beneficial in reducing population exposure to traffic-related emissions by altering the airflows near roads.

Identifying and quantifying PM2.5 pollution episodes with a fusion method of moving window technique and constrained Positive Matrix Factorization.

PM2.5 pollution episodes rapidly and significantly deteriorate the air quality and are a critical concern worldwide. This study developed a fusion method based on the moving window dataset technique and constrained Positive Matrix Factorization (PMF) to differentiate and characterize potential factors in a PM2.5 episode case assuming having one new contributor. The hourly PM2.5 compositions of elements, ions and carbonaceous components, were collected from September to December 2020 in Taipei, Taiwan. Constraint targets based on the bootstrap analysis result of a PMF model using a long-term input dataset were imposed on the modeling of each moving window to ensure similar features of the retrieved factors. The constituents of an additionally differentiated factor to the episode, which was identified as regional transport, were stable among each moving window that covered the occurrence of the episode as revealed by the profile matching index. The results showed that the largest contributor to the PM2.5 mass during the episode period of 12/12/2020 was regional transport (61%), whereas that of 12/13 was the regular pollution of industry/ammonium sulfate related (43%). According to our review of the literature, this study is the first to apply both the moving window technique and constrained PMF to characterize the episode. The findings provide valuable information that can be used to explore the causes of PM2.5 episodes and implement air pollution control strategies.

Targeted profiling of chlorinated transformation products and the parent micropollutants in the aquatic environment: A comparison between two coastal cities.

This study investigated chlorinated transformation products (TPs) and their parent micropollutants, aromatic pharmaceuticals and personal care products (PPCPs) in the urban water bodies of two metropolitan cities. Nine PPCPs and 16 TPs were quantitatively or semi-quantitatively determined using isotope dilution techniques and liquid chromatography-tandem mass spectrometry. TPs and most PPCPs were effectively removed by conventional wastewater treatments in a wastewater treatment plant (WWTP). Chlorinated parabens and all PPCPs (at concentrations below 1000 ng/L) were present in the waters receiving treated wastewater. By contrast, the waters receiving untreated wastewater contained higher levels of PPCPs (up to 9400 ng/L) and more species of chlorinated TPs including chlorinated parabens, triclosan, diclofenac, and bisphenol A. The very different chemical profiles between the water bodies of the two cities of similar geographical and climatic properties may be attributed to their respective uses of chemicals and policies of wastewater management. No apparent increase in the number of species or abundances of TPs was observed in either the chlorinated wastewater or the seawater rich in halogens. This is the first study to elucidate and compare the profiles of multiple TPs and their parent PPCPs in the water bodies of coastal cities from tropical islands. Our findings suggest that chlorinated derivatives of bisphenol A, diclofenac, triclosan, and parabens in the surface water originate from sources other than wastewater disinfection or marine chlorination. Although further studies are needed to identify the origins, conventional wastewater treatments may protect natural water bodies against contamination by those chlorinated substances.

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 PM2.5 (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 PM2.5 mass differed significantly between low-level and mid-level sites, the largest contributor to PM2.5 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 PM2.5. 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 (PM2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM2.5 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 PM2.5 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%).

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.

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.

Simultaneous analysis of chlorpyrifos and cypermethrin in cord blood plasma by online solid-phase extraction coupled with liquid chromatography-heated electrospray ionization tandem mass spectrometry.

Chlorpyrifos and cypermethrin are the most used insecticides in Taiwan. Exposure to both pesticides has been associated with reproductive and developmental health effects in humans and animals. This study describes an online solid-phase extraction coupled with liquid chromatography-heated electrospray ionization tandem mass spectrometry (online SPE-LC/HESI/MS/MS) method to analyze chlorpyrifos and cypermethrin in cord blood of pregnant women. Calibration curves showed good linearity (r² > 0.998) for both pesticides within the range of 0.1-100 ppb. Limits of detection (LODs) were 0.01 and 0.05 ppb and recoveries in cord blood were 97.2 ± 4.8% and 93.5 ± 9.5% for chlorpyrifos and cypermethrin respectively. After analysis of 396 samples, the mean concentrations of chlorpyrifos and cypermethrin were 0.38 and 1.08 ppb respectively. These results demonstrate that LC/HESI/MS/MS is effective for the simultaneous analysis of chlorpyrifos and cypermethrin in cord blood with excellent sensitivity and specificity and may also be effective for high throughput assay in future epidemiology studies.

Analysis of urinary aristolactams by on-line solid-phase extraction coupled with liquid chromatography-tandem mass spectrometry.

Aristolochic acids (AAs), nephrotoxicants and known human carcinogens, are a mixture of structurally related derivatives of nitrophenanthrene carboxylic acids with the major components being aristolochic acid I and aristolochic acid II. People may ingest small amounts of AAs from its natural presence in medicinal plants and herbs of the family Aristolochiaceae, including the genera Aristolochia and Asarum, which have been used worldwide in folk medicine for centuries. In order to assess AA intake, an on-line solid-phase extraction coupled with liquid chromatography-tandem mass spectrometry (on-line SPE-LC/MS/MS) method was developed to analyze their most abundant corresponding metabolites, aristolactams (ALs), in urine to serve as biomarkers. The limits of quantitation were 0.006 ng for aristolactam I (AL-I), and 0.024 ng for aristolactam II (AL-II) on column. Recovery varied from 98.0% to 99.5%, and matrix effects were within 75.3-75.4%. This method was applied to analyze ALs in the urine samples collected on days 1, 2, 4, and 7 from mice treated with 30 mg/kg or 50mg/kg AAs. Their half lives were estimated to be 3.55 h and 4.00 for AL-I, and 4.04 and 4.83 h for AL-II, depending on AAs doses. These results demonstrated that the first simple on-line SPE-LC/MS/MS method was successfully developed to analyze urinary ALs with excellent sensitivity and specificity to serve as biomarkers to assess current AA intake from AAs-containing Chinese herbs.