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.

Characterization of Fe-Containing and Pb-Containing Nanoparticles Resulting from Corrosion of Plumbing Materials in Tap Water Using a Hyphenated ATM-DMA-spICP-MS System.

Single-particle inductively coupled plasma mass spectrometry (spICP-MS) has been used to characterize metallic nanoparticles (NPs) assuming that all NPs are spherical and composed of pure element. However, environmental NPs generally do not meet these criteria, suggesting that spICP-MS may underestimate their true sizes. This study employed a system hyphenating the atomizer (ATM), differential mobility analyzer (DMA), and spICP-MS to characterize metallic NPs in tap water. Its performance was validated by using reference Au nanoparticles (AuNPs) and Ag-shelled AuNPs. The hyphenated system can determine the actual size and metal composition of both NPs with additional heating after ATM, while stand-alone spICP-MS misidentified the Ag-shelled AuNPs as smaller individual AgNPs and AuNPs. Dissolved metal ions could introduce artifact NPs after heating but could be eliminated by centrifugation. The hyphenated system was applied to characterize Fe-containing and Pb-containing NPs resulting from the corrosion of plumbing materials in tap water. The mode sizes of Fe-containing and Pb-containing NPs were determined to be 110 and 100 nm and the particle number concentrations were determined to be 4.99 × 107 and 1.40 × 106 #/mL, respectively. Cautions should be paid to potential changes in particle size induced by heating for metallic NPs with a low melting point or a high organic content.

Unmasking air quality: A novel image-based approach to align public perception with pollution levels.

In the quest to reconcile public perception of air pollution with scientific measurements, our study introduced a pioneering method involving a gradient boost-regression tree model integrating PM2.5 concentration, visibility, and image-based data. Traditional stationary monitoring often falls short of accurately capturing public air quality perceptions, prompting the need for alternative strategies. Leveraging an extensive dataset of over 20,000 public visibility perception evaluations and over 8,000 stationary images, our models effectively quantify diverse air quality perceptions. The predictive prowess of our models was validated by strong performance metrics for perceived visibility (R = 0.98, RMSE = 0.19), all-day PM2.5 concentrations (R: 0.77-0.78, RMSE: 8.31-9.40), and Central Weather Bureau visibility records (R = 0.82, RMSE = 9.00). Interestingly, image contrast and light intensity hold greater importance than scenery clarity in the visibility perception model. However, clarity is prioritized in PM2.5 and Central Weather Bureau models. Our research also unveiled spatial limitations in stationary monitoring and outlined the variations in predictive image features between near and far stations. Crucially, all models benefit from the characterization of atmospheric light sources through defogging techniques. The image-based insights highlight the disparity between public perception of air pollution and current policy implementation. In other words, policymakers should shift from solely emphasizing the reduction of PM2.5 levels to also incorporating the public's perception of visibility into their strategies. Our findings have broad implications for air quality evaluation, image mining in specific areas, and formulating air quality management strategies that account for public perception.

Climate change, air quality, and respiratory health: a focus on particle deposition in the lungs.

This review article delves into the multifaceted relationship between climate change, air quality, and respiratory health, placing a special focus on the process of particle deposition in the lungs. We discuss the capability of climate change to intensify air pollution and alter particulate matter physicochemical properties such as size, dispersion, and chemical composition. These alterations play a significant role in influencing the deposition of particles in the lungs, leading to consequential respiratory health effects. The review paper provides a broad exploration of climate change's direct and indirect role in modifying particulate air pollution features and its interaction with other air pollutants, which may change the ability of particle deposition in the lungs. In conclusion, climate change may play an important role in regulating particle deposition in the lungs by changing physicochemistry of particulate air pollution, therefore, increasing the risk of respiratory disease development.

Climate change influences particle deposition in the lungs by modifying the physicochemical properties of particulate air pollution, thereby escalating the risk of respiratory disease development.It is crucial for healthcare providers to educate patients about the relationship between climate change and respiratory health.People with conditions such as asthma, COPD, and allergies must understand how changes in weather, air pollution, and allergens can exacerbate their symptoms.Instruction on understanding air quality indices and pollen predictions, along with recommendations on adapting everyday activities and medication regimens in response, is essential.

Assessing traffic-related air pollution-induced fiber-specific white matter degradation associated with motor performance declines in aged rats.

Fine particulate matter (PM2.5) is thought to exacerbate Parkinson's disease (PD) in the elderly, and early detection of PD progression may prevent further irreversible damage. Therefore, we used diffusion tensor imaging (DTI) for probing microstructural changes after late-life chronic traffic-related PM2.5 exposure. Herein, 1.5-year-old Fischer 344 rats were exposed to clean air (control), high-efficiency particulate air (HEPA)-filtered ambient air (HEPA group), and ambient traffic-related PM2.5 (PM2.5 group, 9.933 ± 1.021 µg/m3) for 3 months. Rotarod test, DTI tractographic analysis, and immunohistochemistry were performed in the end of study period. Aged rats exposed to PM2.5 exhibited motor impairment with decreased fractional anisotropy and tyrosine hydroxylase expression in olfactory and nigrostriatal circuits, indicating disrupted white matter integrity and dopaminergic (DA) neuronal loss. Additionally, increased radial diffusivity and lower expression of myelin basic protein in PM2.5 group suggested ageing progression of demyelination exacerbated by PM2.5 exposure. Significant production of tumor necrosis factor-α was also observed after PM2.5 exposure, revealing potential inflammation of injury to multiple fiber tracts of DA pathways. Microstructural changes demonstrated potential links between PM2.5-induced inflammatory white matter demyelination and behavioral performance, with indication of pre-manifestation of DTI-based biomarkers for early detection of PD progression in the elderly.

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.

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.

Impacts of COVID-19's restriction measures on personal exposure to VOCs and aldehydes in Taipei City.

The Coronavirus Disease 2019 (COVID-19) pandemic provided an unprecedented natural experiment, that allowed us to investigate the impacts of different restrictive measures on personal exposure to specific volatile organic compounds (VOCs) and aldehydes and resulting health risks in the city. Ambient concentrations of the criteria air pollutants were also evaluated. Passive sampling for VOCs and aldehydes was conducted for graduate students and ambient air in Taipei, Taiwan, during the Level 3 warning (strict control measures) and Level 2 alert (loosened control measures) of the COVID-19 pandemic in 2021-2022. Information on the daily activities of participants and on-road vehicle counts nearby the stationary sampling site during the sampling campaigns were recorded. Generalized estimating equations (GEE) with adjusted meteorological and seasonal variables were used to estimate the effects of control measures on average personal exposures to the selected air pollutants. Our results showed that ambient CO and NO2 concentrations in relation to on-road transportation emissions were significantly reduced, which led to an increase in ambient O3 concentrations. Exposure to specific VOCs (benzene, methyl tert-butyl ether (MTBE), xylene, ethylbenzene, and 1,3-butadiene) associated with automobile emissions were remarkably decreased by ~40-80 % during the Level 3 warning, resulting in 42 % and 50 % reductions of total incremental lifetime cancer risk (ILCR) and hazard index (HI), respectively, compared with the Level 2 alert. In contrast, the exposure concentration and calculated health risks in the selected population for formaldehyde increased by ~25 % on average during the Level 3 warning. Our study improves knowledge of the influence of a series of anti-COVID-19 measures on personal exposure to specific VOCs and aldehydes and its mitigations.

A hybrid model for estimating the number concentration of ultrafine particles based on machine learning algorithms in central Taiwan.

Modeling is a cost-effective measure to estimate ultrafine particle (UFP) levels. Previous UFP estimates generally relied on land-use regression with insufficient temporal resolution. We carried out in-situ measurements for UFP in central Taiwan and developed a model incorporating satellite-based measurements, meteorological variables, and land-use data to estimate daily UFP levels at a 1-km resolution. Two sampling campaigns were conducted for measuring hourly UFP concentrations at six sites between 2008-2010 and 2017-2021, respectively, using scanning mobility particle sizers. Three machine learning algorithms, namely random forest, eXtreme gradient boosting (XGBoost), and deep neural network, were used to develop UFP estimation models. The performances were evaluated with a 10-fold cross-validation, temporal, and spatial validation. A total of 1,022 effective sampling days were conducted. The XGBoost model had the best performance with a training coefficient of determination (R2) of 0.99 [normalized root mean square error (nRMSE): 6.52%] and a cross-validation R2 of 0.78 (nRMSE: 31.0%). The ten most important variables were surface pressure, distance to the nearest road, temperature, calendar year, day of the year, NO2, meridional wind, the total length of roads, PM2.5, and zonal wind. The UFP levels were elevated along the main roads across different seasons, suggesting that traffic emission is an important contributor to UFP. This hybrid model outperformed prior land use regression models and thus can provide more accurate estimates of UFP for epidemiological studies.

Data on lung and intestinal microbiome after air pollution exposure in ageing rats.

Air pollution has been linked to respiratory diseases, and urban air pollution can be attributed to a number of emission sources. The emitted particles and gases are the primary components of air pollution that enter the lungs during respiration. Particulate matter with an aerodynamic diameter of ≤ 2.5 µm (PM2.5) can deposit deep into the respiratory tract via inhalation and has been proposed as a causative agent for adverse respiratory health. In addition, the lung contains a diverse microbial community (microbiome) that maintains normal homeostasis and is significantly altered in a variety of pulmonary disorders. Air pollution, specifically PM2.5, has previously been shown to significantly alter the composition of the lower airway microbiome, which has been linked to decreased lung function in chronic obstructive pulmonary disease (COPD) patients. Surprisingly, the intestinal microbiome has also been implicated in the modulation of pulmonary inflammatory diseases. Therefore, dysbiosis of the lung and intestinal microbiomes pose significant negative effects on human health. This dataset describes the microbial community profiles of the lungs and intestines of ageing rats exposed to ambient unconcentrated traffic-related air pollution for three months. The whole-body exposure system was equipped with and without high efficiency particulate air (HEPA) filtration (gaseous vs. PM2.5 pollution). The data can provide valuable information on lung and intestinal microbiome changes, including that which was only found after traffic-related air pollution exposure.

Source-oriented risk and lung-deposited surface area (LDSA) of ultrafine particles in a Southeast Asia urban area.

Submicron and ultrafine particle (UFP) exposure may be epidemiologically and toxicologically linked to pulmonary, neurodegenerative, and cardiovascular diseases. This study explores UFP and fine particle sources using a positive matrix factorization (PMF) model based on PM2.5 chemical compositions and particle number size distributions (PNSDs). The particle chemical composition and size distribution contributions are simultaneously identified to evaluate lung deposition and excess cancer risks. High correlations between the PNSD and chemical composition apportionment results were observed. Fresh and aged traffic particles dominated the number concentrations, while heterogeneous, photochemical reactions and/or regional transport may have resulted in secondary aerosol formation. Fresh and aged road traffic particle sources mostly contributed to the lung deposition dosage in the pulmonary region (~53 %), followed by the tracheobronchial (~30.4 %) and head regions (~16.6 %). However, lung-deposited surface area (LDSA) concentrations were dominated by aged road traffic (~39.2 %) and secondary aerosol (~33.2 %) sources. The excess cancer risks caused by Cr6+, Ni, and As were also mainly contributed to by aged road traffic (~31.7 %) and secondary aerosols (~67 %). The source apportionments based on the physical and chemical properties of aerosol particles are complementary, offering a health impact benchmark of UFPs in a Southeast Asia urban city.

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.

A mobile platform for characterizing on-road tailpipe emissions and toxicity of ultrafine particles under real driving Conditions.

Acute exposure to fresh traffic-related air pollutants (TRAPs) can be high for road users, including motorbike drivers, cyclists, and pedestrians. However, evaluating the toxicity of fresh traffic emissions from on-road vehicles is challenging since pollution properties can change dynamically within a short distance and time. This study demonstrated a mobile platform equipped with an On-Board Diagnostic II (OBDII) system, a tailor-made portable emission measurement system, and an electrostatic air-liquid interface exposure system with human monocytic THP-1 cells to characterize on-road tailpipe emissions under real driving conditions. High number concentrations up to 106-107 # cm-3 of ultrafine particles (UFPs) were observed for a gasoline engine at the cold-start stage and a diesel engine during particulate filter regeneration. In particular, a substantial fraction of freshly emitted UFPs within the size less than 23 nm were observed and should be cautioned. The potential toxicity of fresh TRAPs was quantified by cell viability, cytotoxicity, oxidative stress, and inflammatory biomarkers. Results show that the decreased cell viability, increased lactate dehydrogenase (LDH) activity, and high oxidative stress induced by the fresh TRAPs were potentially contributed by gaseous pollutants as well as particles, especially driving with the high idling frequency. Moreover, the dominant contributor to the toxicity is different for gasoline's and diesel's TRAPs. Characterizing on-road air pollutant toxicity as well as physicochemical properties using an innovative mobile platform can fill this knowledge gap.

Sources, transport, and visibility impact of ambient submicrometer particle number size distributions in an urban area of central Taiwan.

This study applied positive matrix factorization (PMF) to identify the sources of size-resolved submicrometer (10-1000 nm) particles and quantify their contributions to impaired visibility based on the particle number size distributions (PNSDs), aerosol light extinction (bp), air pollutants (PM10, PM2.5, SO2, O3, and NO), and meteorological parameters (temperature, relative humidity, wind speed, wind direction, and ultraviolet index) measured hourly over an urban basin in central Taiwan between 2017 and 2021. The transport of source-specific PNSDs was evaluated with wind and back trajectory analyses. The PMF revealed six sources to the total particle number (TPN), surface (TPS), volume (TPV), and bp. Factor 1 (F1), the key contributor to TPN (35.0 %), represented nucleation (<25 nm) particles associated with fresh traffic emission and secondary new particle formation, which were transported from the west-southwest by stronger winds (>2.2 m s-1). F2 represented the large Aitken (50-100 nm) particles transported regionally via northerly winds, whereas F3 represented large accumulation (300-1000 nm) particles, which showed elevated concentrations under stagnant conditions (<1.1 m s-1). F4 represented small Aitken (25-50 nm) particles arising from the growth and transport of the nucleation particles (F1) via west-southwesterly winds. F5 represented large Aitken particles originating from combustion-related SO2 sources and carried by west-northwesterly winds. F6 represented small accumulation (100-300 nm) particles emitted both by local sources and by the remote SO2 sources found for F5. Overall, large accumulation particles (F3) played the greatest role in determining the TPV (66.4 %) and TPS (34.8 %), and their contribution to bp increased markedly from 17.3 % to 40.7 % as visibility decreased, indicating that TPV and TPS are better metrics than TPN for estimating bp. Furthermore, slow-moving air masses-and therefore stagnant conditions-facilitate the build-up of accumulation mode particles (F3 + F6), resulting in the poorest visibility.

Importance of surface charge of soot nanoparticles in determining inhalation toxicity in mice.

Physicochemical properties of nanoparticles are important in regulating nanoparticle toxicity; however, the contribution of nanoparticle charge remains unclear. The objective of this study was to investigate the pulmonary effects of inhalation of charged soot nanoparticles. We established a stably charged nanoparticle generation system for whole-body exposure in BALB/c mice, which produced positively charged, negatively charged, and neutral soot nanoparticles in a wide range of concentrations. After a 7-day exposure, pulmonary toxicity was assessed, together with proteomics analysis. The charged soot nanoparticles on average carried 1.17-1.35 electric charges, and the sizes for nanoparticles under different charging conditions were all fixed at 69 ~ 72 nm. We observed that charged soot nanoparticles induced cytotoxic LDH and increased lung permeability, with the release of 8-isoprostane and caspase-3 and systemic IL-6 in mice, especially for positively charged soot nanoparticles. Next, we observed that positive-charged soot nanoparticles upregulated Eif2, Eif4, sirtuin, mammalian target of rapamycin (mTOR), peroxisome proliferator-activated receptors (PPAR), and HIPPO-related signaling pathways in the lungs compared with negatively charged soot nanoparticles. HIF1α, sirt1, E-cadherin, and Yap were increased in mice's lungs by positively charged soot nanoparticle exposure. In conclusion, carbonaceous nanoparticles carrying electric ions, especially positive-charged, are particularly toxic when inhaled and should be of concern in terms of pulmonary health protection.

Effects of antibiotics and metals on lung and intestinal microbiome dysbiosis after sub-chronic lower-level exposure of air pollution in ageing rats.

We investigated the effects of antibiotics, drugs, and metals on lung and intestinal microbiomes after sub-chronic exposure of low-level air pollution in ageing rats. Male 1.5-year-old Fischer 344 ageing rats were exposed to low-level traffic-related air pollution via whole-body exposure system for 3 months with/without high-efficiency particulate air (HEPA) filtration (gaseous vs. particulate matter with aerodynamic diameter of ≤2.5 µm (PM2.5) pollution). Lung functions, antibiotics, drugs, and metals in lungs were examined and linked to lung and fecal microbiome analyses by high-throughput sequencing analysis of 16 s ribosomal (r)DNA. Rats were exposed to 8.7 µg/m3 PM2.5, 10.1 ppb NO2, 1.6 ppb SO2, and 23.9 ppb O3 in average during the study period. Air pollution exposure decreased forced vital capacity (FVC), peak expiratory flow (PEF), forced expiratory volume in 20 ms (FEV20), and FEF at 25∼75% of FVC (FEF25-75). Air pollution exposure increased antibiotics and drugs (benzotriazole, methamphetamine, methyl-1 H-benzotriazole, ketamine, ampicillin, ciprofloxacin, pentoxifylline, erythromycin, clarithromycin, ceftriaxone, penicillin G, and penicillin V) and altered metals (V, Cr, Cu, Zn, and Ba) levels in lungs. Fusobacteria and Verrucomicrobia at phylum level were increased in lung microbiome by air pollution, whereas increased alpha diversity, Bacteroidetes and Proteobacteria and decreased Firmicutes at phylum level were occurred in intestinal microbiome. Lung function decline was correlated with increasing antibiotics, drugs, and metals in lungs as well as lung and intestinal microbiome dysbiosis. The antibiotics, drugs, and Cr, Co, Ca, and Cu levels in lung were correlated with lung and intestinal microbiome dysbiosis. The lung microbiome was correlated with intestinal microbiome at several phylum and family levels after air pollution exposure. Our results revealed that antibiotics, drugs, and metals in the lung caused lung and intestinal microbiome dysbiosis in ageing rats exposed to air pollution, which may lead to lung function decline.

Secondary inorganic aerosol chemistry and its impact on atmospheric visibility over an ammonia-rich urban area in Central Taiwan.

This study investigated the hourly inorganic aerosol chemistry and its impact on atmospheric visibility over an urban area in Central Taiwan, by relying on measurements of aerosol light extinction, inorganic gases, and PM2.5 water-soluble ions (WSIs), and simulations from a thermodynamic equilibrium model. On average, the sulfate (SO42-), nitrate (NO3-), and ammonium (NH4+) components (SNA) contributed ∼90% of WSI concentrations, which in turn made up about 50% of the PM2.5 mass. During the entire observation period, PM2.5 and SNA concentrations, aerosol pH, aerosol liquid water content (ALWC), and sulfur and nitrogen conversion ratios all increased with decreasing visibility. In particular, the NO3- contribution to PM2.5 increased, whereas the SO42- contribution decreased, with decreasing visibility. The diurnal variations of the above parameters indicate that the interaction and likely mutual promotion between NO3- and ALWC enhanced the hygroscopicity and aqueous-phase reactions conducive for NO3- formation, thus led to severely impaired visibility. The high relative humidity (RH) at the study area (average 70.7%) was a necessary but not sole factor leading to enhanced NO3- formation, which was more directly associated with elevated ALWC and aerosol pH. Simulations from the thermodynamic model depict that the inorganic aerosol system in the study area was characterized by fully neutralized SO42- (i.e. a saturated factor in visibility reduction) and excess NH4+ amidst a NH3-rich environment. As a result, PM2.5 composition was most sensitive to gas-phase HNO3, and hence NOx, and relatively insensitive to NH3. Consequently, a reduction of NOx would result in instantaneous cuts of NO3-, PM2.5, and ALWC, and hence improved visibility. On the other hand, a substantial amount of NH3 reduction (>70%) would be required to lower the aerosol pH, driving more than 50% of the particulate phase NO3- to the gas phase, thereby making NH3 a limiting factor in shifting PM2.5 composition.

A two-dimensional nanoparticle characterization method combining differential mobility analyzer and single-particle inductively coupled plasma-mass spectrometry with an atomizer-enabled sample introduction (ATM-DMA-spICP-MS): Toward the analysis of heteroaggregated nanoparticles in wastewater.

Characterizing engineered nanoparticles (ENPs) in complex environmental matrices remains a challenging task. This work presents a two-dimensional size analysis method by combining differential mobility analyzer (DMA) and single-particle inductively coupled plasma-mass spectrometry (spICP-MS) with a new atomizer (ATM)-enabled sample introduction that is relatively easy to operate. The tailing of electrical mobility size distributions was solved by heating the aerosol flow, where water-shelled gold nanoparticles (AuNPs) were dehydrated, effectively eliminating the tailing. The improved method has a good sizing performance and can resolve the size fractions of mixed 30 nm and 50 nm AuNPs. It can reliably analyze 7.8 × 105 to 1.9 × 107 # of 50 nm AuNPs (or 4.1 × 105 to 107 # NPs/mL, equivalent to 0.6 to 14.3 µg Au/L) with a linear response and a limit of detection of 7.8 × 105 # AuNPs (equivalent to 4.1 × 105 # AuNPs/mL) that is relevant to NP concentrations in surface water and wastewater samples. The potential of this method to analyze environmental samples was demonstrated by characterizing AuNPs and silver nanoparticles (AgNPs) spiked in wastewater, where both NPs were revealed to form heteroaggregates with colloids existing in wastewater. The method can even directly analyze nanosized Ag particles inherent in the wastewater before adding external AgNPs. The result indicates that ATM-DMA-spICP-MS is a relatively simple two-dimensional size analysis method that has a great potential to characterize heteroaggregated NPs in aqueous environmental samples.