Chemical characterization of PM2.5 emissions and atmospheric metallic element concentrations in PM2.5 emitted from mobile source gasoline-fueled vehicles

Enhanced Intrusion of Exogenous Airborne Fine Particles toward Eyes in Humans and Animals: Where Damaged Blood-Ocular Barrier Plays a Crucial Role

Emerging data suggest a close correlation between ambient fine particle (AFP) exposure and eye disorders and pinpoint potential threats of AFPs to eye health in humans. However, the possible passage (including direct intrusion) and the interactions of AFPs with the eye microenvironment in addition to morphological and physiological injuries remain elusive. To this end, the likely transport of AFPs into the eyes via blood-ocular barrier (BOB) in humans and animals was investigated herein. Exogenous particles were recognized inside human eyes with detailed structural and chemical fingerprints. Importantly, comparable AFPs were found in sera with constant structural and chemical fingerprints, hinting at the translocation pathway from blood circulation into the eye. Furthermore, we found that the particle concentrations in human eyes from patients with diabetic retinopathy were much higher than those from patients with no fundus pathological changes (i.e., myopia), indicating that the damaged BOB increased the possibility of particle entrance. Our diseased animal model further corroborated these findings. Collectively, our results offer a new piece of evidence on the intrusion of exogenous particles into human eyes and provide an explanation for AFP-induced eye disorders, with substantially increased risk in susceptible individuals with BOB injuries.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Characteristics of number concentration, size distribution and components of particulate matter emitted from a typical large civil airport

Civil airports are recognized as significant contributors to fine particulate matter, especially ultra-fine particulate matter (UFP). The pollutants from airport activities have a notable adverse impact on global climate, urban air quality, and public health. However, there is a lack of practical observational studies on the characterization of integrated pollutant emissions from large civil airports. This study aims to focus on the combined emission characteristics of particulate number concentration (PNC), size distribution, and components at a large civil airport, especially UFP. The findings reveal that airport activities significantly contribute to elevated PNC levels during aircraft activity in downwind conditions (four times higher than background levels) and upwind conditions (7.5 times higher). UFP dominates the PNC around the airport. The particle size distribution shows two peaks occurring around 10-30 nm and 60-80 nm. Notably, particles within the ranges of 17-29 nm and 57-101 nm account for 65.9 % and 12.0 % of the total PNC respectively. Aircraft landing has the greatest impact on particles sized between 6 and 17 nm while takeoff affects particles sized between 29 and 57 nm resulting in a respective increase in PNC by factors of approximately 3.27 and 35.4-fold increase compared to background levels. Different aircraft types exhibit varying effects on PNC with A320 and A321 showing more pronounced effects during takeoff and landing.The presence of airports leads to roughly five-fold rise in elemental component concentrations with Si being highest followed by OC, Ca, Al, Fe, Ca2+, EC, and Mg2+. The OC/EC ratio under high aircraft activity in downwind conditions falls within range of approximately 2.5-3.5. These characteristic components and ratio can be considered as identifying species for civil airports. PMF model show about 75 % of the particulate emissions at the airport boundary were related to airport activities.

Impacts of ship emissions and sea-land breeze on urban air quality using chemical characterization, source contribution and dispersion model simulation of PM2.5 at Asian seaport

Fine particulate matter (PM2.5) emitted from marine transportation, bulk materials handling at the docks, and dust dispersion has garnered increased attention, particularly in the interface between port and urban areas. This study explored the inter-transport of PM2.5 between Kaohsiung Harbor and neighboring Metro Kaohsiung. Chemical analyses of PM2.5 samples from four sites include water-soluble ions, metallic elements, carbons, anhydrosugars, and organic acids to establish PM2.5's chemical fingerprints. The CALPUFF air dispersion model is employed to simulate the spatiotemporal distribution of PM2.5 in Kaohsiung Harbor and adjacent urban areas. A clear seasonal and diurnal variation of PM2.5 concentrations and chemical composition was observed in both harbor and urban areas. The high correlation of nighttime PM2.5 levels between the port and urban areas suggests inter-transport phenomena. Sea salt spray, ship emissions, secondary aerosols, and heavy fuel-oil boilers exhibit higher levels in the port area than in the urban area. In Metro Kaohsiung, mobile sources, fugitive dust, and waste incinerators emerge as major PM2.5 contributors. Furthermore, sea breeze significantly influences PM2.5 dispersion from Kaohsiung Harbor to Metro Kaohsiung, particularly in the afternoon. The average contribution of PM2.5 from ships' main engines in Kaohsiung Harbor ranges from 2.9% to 5.3%, while auxiliary engines contribute 3.8%-8.3% of PM2.5 in Metro Kaohsiung.

The impact of dynamic traffic and wind conditions on green infrastructure performance to improve local air quality

Road traffic represents the dominant source of air pollution in urban street canyons. Local wind conditions greatly impacts the dispersion of these pollutants, yet street trees complicate ventilation in such settings. This case study adopts a novel modelling framework to account for dynamic traffic and wind conditions to identify the optimal street tree configuration that prevents a deterioration in air quality. Measurement data from a shallow to moderately deep street canyon (average 0.5 H/W aspect ratio and four lanes of 1-way traffic) in Dublin, Ireland was used for model calibration. The computational fluid dynamics (CFD) models were used to examine scenarios of dynamic traffic flows within each traffic lane with respect to its impact on local PM2.5 concentrations on adjacent footpaths, segmenting air quality monitoring results based on different wind conditions for model calibration. The monitoring campaign identified higher PM2.5 concentrations on the leeward (north) footpath, with average differences of 14.1 % (2.15 μg/m3) for early evening peaks. The modelling results demonstrated how street trees negatively impacted air quality on the windward footpath in parallel wind conditions regardless of leaf area density (LAD) or tree spacing, with mixed results observed on the leeward footpath in varying traffic flows and wind speeds. Perpendicular wind direction models and high wind speed exacerbated poor air quality on the windward footpath for all tree spacing models, while improving the air quality on the leeward footpath. The findings advise against planting high-LAD trees in this type of street, with a minimum of 20 m spacing for low-LAD trees to balance reducing local air pollution and ventilation capacity in the street. This study highlights the complexities of those in key decision-marking roles and demonstrates the need to adopt a transparent framework to ensure adequate modelling evidence can inform tree planting in city streets.

Estimating the geographical patterns and health risks associated with PM2.5-bound heavy metals to guide PM2.5 control targets in China based on machine-learning algorithms

PM2.5 is the main component of haze, and PM2.5-bound heavy metals (PBHMs) can induce various toxic effects via inhalation. However, comprehensive macroanalyses on large scales are still lacking. In this study, we compiled a substantial dataset consisting of the concentrations of eight PBHMs, including As, Cd, Cr, Cu, Mn, Ni, Pb and Zn, across different cities in China. To improve prediction accuracy, we enhanced the traditional land-use regression (LUR) model by incorporating emission source-related variables and employing the best-fitted machine-learning algorithm, which was applied to predict PBHM concentrations, analyze geographical patterns and assess the health risks associated with metals under different PM2.5 control targets. Our model exhibited excellent performance in predicting the concentrations of PBHMs, with predicted values closely matching measured values. Noncarcinogenic risks exist in 99.4% of the estimated regions, and the carcinogenic risks in all studied regions of the country are within an acceptable range (1 × 10-5-1 × 10-6). In densely populated areas such as Henan, Shandong, and Sichuan, it is imperative to control the concentration of PBHMs to reduce the number of patients with cancer. Controlling PM2.5 effectively decreases both carcinogenic and noncarcinogenic health risks associated with PBHMs, but still exceed acceptable risk level, suggesting that other important emission sources should be given attention.

Transcriptomics profile of human bronchial epithelial cells exposed to ambient fine particles and influenza virus (H3N2)

Fine particulate matter (PM2.5) pollution remains a major threat to public health. As the physical barrier against inhaled air pollutants, airway epithelium is a primary target for PM2.5 and influenza viruses, two major environmental insults. Recent studies have shown that PM2.5 and influenza viruses may interact to aggravate airway inflammation, an essential event in the pathogenesis of diverse pulmonary diseases. Airway epithelium plays a critical role in lung health and disorders. Thus far, the mechanisms for the interactive effect of PM2.5 and the influenza virus on gene transcription of airway epithelial cells have not been fully uncovered. In this present pilot study, the transcriptome sequencing approach was introduced to identify responsive genes following individual and co-exposure to PM2.5 and influenza A (H3N2) viruses in a human bronchial epithelial cell line (BEAS-2B). Enrichment analysis revealed the function of differentially expressed genes (DEGs). Specifically, the DEGs enriched in the xenobiotic metabolism by the cytochrome P450 pathway were linked to PM2.5 exposure. In contrast, the DEGs enriched in environmental information processing and human diseases, such as viral protein interaction with cytokines and cytokine receptors and epithelial cell signaling in bacterial infection, were significantly related to H3N2 exposure. Meanwhile, co-exposure to PM2.5 and H3N2 affected G protein-coupled receptors on the cell surface. Thus, the results from this study provides insights into PM2.5- and influenza virus-induced airway inflammation and potential mechanisms.

Neurotoxicity of the air-borne particles: From molecular events to human diseases

Exposure to PM_2.5 is associated with an increased incidence of CNS diseases in humans, as confirmed by numerous epidemiological studies. Animal models have demonstrated that PM_2.5 exposure can damage brain tissue, neurodevelopmental issues and neurodegenerative diseases. Both animal and human cell models have identified oxidative stress and inflammation as the primary toxic effects of PM_2.5 exposure. However, understanding how PM_2.5 modulates neurotoxicity has proven challenging due to its complex and variable composition. This review aims to summarize the detrimental effects of inhaled PM_2.5 on the CNS and the limited understanding of its underlying mechanism. It also highlights new frontiers in addressing these issues, such as modern laboratory and computational techniques and chemical reductionism tactics. By utilizing these approaches, we aim to fully elucidate the mechanism of PM_2.5-induced neurotoxicity, treat associated diseases, and ultimately eliminate pollution.

Chemical characteristics and spatiotemporal variation of marine fine particles for clustered channels of air masses transporting toward remote background sites in East Asia

This study investigated the chemical characteristics, spatiotemporal distribution, and source apportionment of marine fine particles (PM_2.5) for clustered transport channels/routes of air masses moving toward three remote sites in East Asia. Six transport routes in three channels were clustered based on backward trajectory simulation (BTS) in the order of: West Channel > East Channel > South Channel. Air masses transported toward Dongsha Island (DS) came mainly from the West Channel, while those transported toward Green Island (GR) and Kenting Peninsula (KT) came mostly from the East Channel. High PM_2.5 commonly occurred from late fall to early spring during the periods of Asian Northeastern Monsoons (ANMs). Marine PM_2.5 was dominated by water-soluble ions (WSIs) which were predominated by secondary inorganic aerosols (SIAs). Although the metallic content of PM_2.5 was predominated by crustal elements (Ca, K, Mg, Fe, and Al), enrichment factor clearly showed that trace metals (Ti, Cr, Mn, Ni, Cu, and Zn) came mainly from anthropogenic sources. Organic carbon (OC) was superior to elemental carbon (EC), while OC/EC and SOC/OC ratios in winter and spring were higher than those in other two seasons. Similar trends were observed for levoglucosan and organic acids. The mass ratio of malonic acid and succinic acid (M/S) was commonly higher than unity, showing the influences of biomass burning (BB) and secondary organic aerosols (SOAs) on marine PM_2.5. We resolved that the main sources of PM_2.5 were sea salts, fugitive dust, boiler combustion, and SIAs. Boiler combustion and fishing boat emissions at DS had higher contribution than those at GR and KT. The highest/lowest contribution ratios of cross-boundary transport (CBT) were 84.9/29.6% in winter and summer, respectively.

Metal contents in house geckos (Squamata: Gekkonidae) from industrial and urban areas of southern Tamaulipas, Mexico and western Andalucía, Spain, may reflect airborne metal pollution

House geckos share living quarters with humans in the tropical and subtropical regions inhabited by these reptiles. Gecko behavior, biological traits, continuous exposure to suspended particulate matter 0 µm in diameter (PM₁₀) and dust, as well as status as exotic species, motivated the choice of these species to examine environmental exposure to ambient air pollutants, in particular metals, and subsequent accumulation in these organisms. One part of the study was conducted in Tamaulipas (Mexico) where Hemydactylus frenatus is abundant in urban and industrial environments, the other part was conducted in Andalucia (Spain) where Tarentola mauritanica is found in similar environments. Adult geckos were collected on buildings in locations affected by various air pollution sources. For both species, higher metal contents were observed in whole-body (including digestive tracts) analysis and were markedly different between collection sites. Contents in tails, digestive tracts, and carcasses without digestive tracts were not correlated. Based on contamination factor values, bioaccumulation in H. frenatus tissues occurred for 12 of the 15 metals analyzed. Data suggest that H. frenatus might serve as a biomonitor for Cu, Ni, Pb, Cr, Li, and V, whereas T. mauritanica might be a biomonitor for Cu, Ni, Pb, and Cr. To our knowledge, metal contents for H. frenatus are reported here for the first time. House gecko data could be integrated into a highly representative monitoring system and health risk assessments related to air quality in residential areas.

Extra-Pulmonary Translocation of Exogenous Ambient Nanoparticles in the Human Body

As one of the major pollutants in the air, ambient fine particles are gaining considerable attention in terms of public health concerns. Significant progress has been achieved in recent years in understanding the biological effects and mechanisms of ambient fine particles. The airborne particles can enter the human body through various pathways and translocate to a range of different organs and further stay in these organs for extended periods. Current studies are making substantial achievements, while many challenges remain. On one hand, the whole picture of the concurrent exposure pathways and the translocation of particles in the human body should be explored, requiring technological advances and systematic biobanking of human samples for analysis. On the other hand, the correlation between the environmental exposure concentration of ambient particles and internal fate (i.e., dose, distribution patterns, and kinetics) of invasive particles needs to be investigated. Moreover, the biotransformation of particles in vivo should be considered, and more information is needed to differentiate exogenous particles from biological macromolecules and biogenically formed particles. We recapitulate the current knowledge gaps in understanding the fate of exogenous ambient fine particles in extra-pulmonary organs of the human body and the related biological effects and also propose future research directions to support both fundamental studies and policy making.

Assessment of indoor air quality and their inter-association in hospitals of northern India-a cross-sectional study

This study was commenced to evaluate the indoor and outdoor air quality concentrations of PM_2.5, sub-micron particles (PM_>2.5, PM_1.0-2.5, PM_{0.50 -1.0}, PM_0.25-0.50, and PM_<0.25), heavy metals, and microbial contaminants along with their identification in three different hospitals of Lucknow City. The study was conducted from February 2022 to April 2022 in hospitals situated in the commercial, residential, and industrial belts of the city. The indoor concentration trend of particulate matter as observed during the study suggested that most of the highest concentrations belonged to the hospital situated in an industrial area. The highest obtained indoor and outdoor concentrations for PM_1.0-2.5, PM_0.50-1.0, PM_0.25-0.50, and PM_<0.25 are 40.44 µg/m³, 56.08 µg/m³, 67.20 µg/m³, 74.50 µg/m³, 61.9 µg/m³, 79.3 µg/m³, 82.0 µg/m³, and 93.9 µg/m³, respectively, which belonged to hospital C situated in the industrial belt. However, for PM_>2.5, the highest indoor concentration obtained belonged to hospital B, i.e., 30.7 µg/m³, which is situated in the residential belt of the city. Regarding PM_2.5, the highest indoor and outdoor concentrations obtained are 149.41 µg/m³ and 227.45 µg/m³, which were recorded at hospital A and hospital C, respectively. The present study also observed that a high bacterial load of 1389.21 CFU/m³ is recorded in hospital B, and the fungi load was highest in hospital C with 786.34 CFU/m³. Henceforth, the present study offers thorough information on the various air pollutants in a crucial indoor setting, which will further aid the researchers in the field to identify and mitigate the same more precisely.

Specific size distributions of elemental and organic carbon, and polycyclic aromatic hydrocarbons emitted from petrol engine using two fuel grades

The most common commercial oils in the Chinese market are two petrol types with octane levels of 93 and 97. To determine the source spectrum of air pollutant emissions, we herein investigated the specific emission sizes of the total suspended particles (TSP), total carbon (TC), elemental carbon (EC), organic carbon (OC), and polycyclic aromatic hydrocarbons (PAHs) from a petrol engine fueled with 93# and 97# petrol in 2016 (based on Chinese national IV gasoline standard). We found that while 93# emitted a higher TSP content, 97# emitted greater TC, EC, OC, and PAHs. The highest carbon contents were found in the < 0.25 µm and 0.44-1.0 µm size fractions for the 93# and 97# petrol, respectively. OC content showed a significant positive correlation with EC, and EC2 (at 740 ℃) was the main carbon fragment in both petrol exhausts. The highest PAH content occurred in the 0.25-0.44 µm size-bin, differing from the results for TC, EC, and OC, and medium molecular weight (4 rings) PAHs were the primary component in the emissions. These results indicate that fuel composition and octane sensitivity have a prominent effect on the size distributions of TC (including EC, OC, and PAHs). Thus, more studies on the carbon content at specific emission sizes in petrol exhaust should be conducted to clarify the main factors impacting these variations.

Mass concentrations and elemental analysis of PM2.5 and PM10 in a coastal Mediterranean site: A holistic approach to identify contributing sources and varying factors

PM₁₀ and PM_2.5 were measured at a suburban coastal Mediterranean site. Daily mass concentrations were generally lower than the WHO air quality limits but showed significant diurnal and seasonal variations, with intensive African dust events and residential heating emissions during wintertime causing the higher % increase of ambient concentrations (32% and 59% respectively). However, analysis of the elemental content revealed intrinsic characteristics. Statistically significant seasonal variability was found for S with higher concentrations during warm seasons directly associated with the formation of secondary sulfates. Increased S was the driving reason for the significant depletion of both Cl and Br. On the other hand, elevated K during wintertime was attributed to biomass burning. Analysis of the mineral content of airborne particles showed that typical soil-derived particles were significantly increased during dust events (Mg, Al, Si, K, Ca, Ti, Mn, Fe) although the corresponding mass concentrations preserved relative low levels (<30 μg m^-3). It was also found that dust events contributed to P, S, V, Zn, Sr and Pb with elemental ratios including Si (Si/Al, Si/Fe, Si/Ca) and Ca/Al serving as good markers for African dust. Heavy metals were found to have both direct and indirect influence on PM_2.5 and PM₁₀. PM_2.5 were primarily enriched with metals produced from motor vehicles (Cu, Zn, Pb) and ships (V, Ni), whilst, PM₁₀ enriched indirectly with Cr, Zn, Br and Pb due to road dust. At the end, source appointment has shown that the dominant contributing factor was mineral dust for both metrics (>30%) but significant contributions arose from secondary sulfates and traffic, (aged) sea salt, biomass burning (only for PM_2.5) and road dust (only for PM₁₀). Overall, this work provides a novel insight on the characteristics and chemical profile of atmospheric particles in a suburban environment largely affected by local, regional and long-range sources.

Passage of exogeneous fine particles from the lung into the brain in humans and animals

Addressing the knowledge gaps regarding the access to and harmful effects of airborne fine particles on the central nervous system is critical. Various exogenous fine particles were found in human cerebrospinal fluids, including commonly found particles and others that have not been reported previously. Animal experiments provided mechanistic explanations and verifications of the translocation of inhaled particles into the brain. Moreover, retention of particles in the brain for longer durations than in other organs was demonstrated through isotope labeling–based biodistribution studies in mice, suggesting possible long-term effects on the brain. This work unravels indications and associations between inhalation and particle transport and adds evidence on the relationship between air pollution and detrimental effects of exogenous particles on the brain.

There are still significant knowledge gaps in understanding the intrusion and retention of exogeneous particles into the central nervous system (CNS). Here, we uncovered various exogeneous fine particles in human cerebrospinal fluids (CSFs) and identified the ambient environmental or occupational exposure sources of these particles, including commonly found particles (e.g., Fe- and Ca-containing ones) and other compositions that have not been reported previously (such as malayaite and anatase TiO₂), by mapping their chemical and structural fingerprints. Furthermore, using mouse and in vitro models, we unveiled a possible translocation pathway of various inhaled fine particles from the lung to the brain through blood circulation (via dedicated biodistribution and mechanistic studies). Importantly, with the aid of isotope labeling, we obtained the retention kinetics of inhaled fine particles in mice, indicating a much slower clearance rate of localized exogenous particles from the brain than from other main metabolic organs. Collectively, our results provide a piece of evidence on the intrusion of exogeneous particles into the CNS and support the association between the inhalation of exogenous particles and their transport into the brain tissues. This work thus provides additional insights for the continued investigation of the adverse effects of air pollution on the brain.

The Contents of Potentially Toxic Elements and Emission Characteristics of PM2.5 in Soil Fugitive Dust around Six Cities of the Yunnan-Guizhou Plateau in China

The contents of potentially toxic elements (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) and emission characteristics of PM2.5 in soil fugitive dust (SFD) in six Yunnan cities (Baoshan, Kunming, Wenshan, Honghe, Yuxi, and Zhaotong) were investigated in this research. The results showed that the contents of Zn and Pb in PM2.5 of SFD were the highest around Honghe and Yuxi, respectively, while the contents of Mn were the highest in PM2.5 of SFD around the other four cities. The enrichment factor and correlation indicated that the potentially toxic elements’ pollution degrees of PM2.5 of SFD around Kunming, Yuxi, and Honghe were higher than those around the other three cities and that potentially toxic elements were generally affected by metal smelting activities, and in Zhaotong, were affected by coal burning activities, while in Wenshan and Baoshan were less affected by human activities. The total emission of PM2.5 of SFD in the six cities was 7705.49 t in 2018. The total emission factor of PM2.5 of SFD reached the highest level from January to May and the lowest level from July to October. The health risk assessment showed that the potentially toxic elements in PM2.5 of SFD for children in the six cities and for adults in Baoshan, Kunming, Honghe, and Zhaotong had non-carcinogenic risk (non-carcinogenic risk thresholds were greater than 1), and As contributes most to non-carcinogenic risk. The carcinogenic risk value of Cr in PM2.5 of SFD in Kunming and Zhaotong was between 1 × 10−6 and 1 × 10−4, which had a certain carcinogenic risk. More attention should be paid to alleviate health risks posed by particle-bound potentially toxic elements through SFD.

Source identification of exhaust and non-exhaust traffic emissions through the elemental carbon fractions and Positive Matrix Factorization method

Our study implies the importance of exhaust and non-exhaust emissions in a highway road tunnel, which is crucial to assess their impacts on air quality, human health, climate and developing functional methods for controlling. The total suspended particulates (TSP) and road dust (RD) samples were collected by PUF samplers and manually sweeping, respectively. Campaigns were performed in the summer and winter of 2014 in a highway road tunnel in Bolu, Turkey. Chemical analyses were presented to characterize the contents of organic carbon (OC) fractions (OC1,2,3,4), elemental carbon (EC) fractions (EC1,2,3,4,5,6), polycyclic aromatic hydrocarbons (PAHs) and metals (Al, Ba, Ca, Cu, Mg, Mn, Pb, Sr, Cr and Fe) in the collected TSP and RD samples. Positive matrix factorization (PMF) and orthogonal (Deming) regression analysis were applied to find out the exhaust and non-exhaust vehicle emissions of metal and carbonaceous species in the tunnel. The results showed that the identified source profiles included resuspended road dust (43%), non-exhaust emissions (37%), diesel exhaust emissions (13%), and gasoline exhaust emissions (7%). The relationship between emission markers of metal species and EC carbon fractions was supported by correlation studies. Among these EC fractions, EC4 and EC2 were the most abundant fractions in aerosol and RD samples, respectively and so they highly represented the diesel and non-exhaust emissions. Besides, the EC1 fraction was the indicator of gasoline-fueled emissions. Lower EC1 and higher soot-EC contribution obtained in tunnel aerosol (AS) samples showed the dominance of diesel-fueled vehicles in the tunnel. The data represented herein would help to identify the characteristic of vehicle emissions.

Exposure to PM2.5 induces neurotoxicity, mitochondrial dysfunction, oxidative stress and inflammation in human SH-SY5Y neuronal cells

Ambient air pollution is a global public health issue. Recent evidence suggests that exposure to fine aerosolized particulate matter (PM) as small as ≤2.5 microns (PM_2.5) is neurotoxic to brain structures. Many studies also suggest exposure to PM_2.5 may cause neurotoxicity and affect brain function. However, the molecular mechanisms by which PM_2.5 exerts these effects are not fully understood. Thus, we evaluated the hypothesis that PM_2.5 exposure exerts its neurotoxic effects via increased oxidative and inflammatory cellular damage and mitochondrial dysfunction using human SH-SY5Y neuronal cells. Here, we show PM_2.5 exposure significantly decreases viability, and increases caspase 3 and 9 protein expression and activity in SH-SY5Y cells. In addition, PM_2.5 exposure decreases SH-SY5Y survival, disrupts cell and mitochondrial morphology, and significantly decreases ATP levels, D-loop levels, and mitochondrial mass and function (maximal respiratory function, COX activity, and mitochondrial membrane potential) in SH-SY5Y cells. Moreover, SH-SY5Y cells exposed to PM_2.5 have significantly decreased mRNA and protein expression levels of survival genes (CREB and Bcl-2) and neuroprotective genes (PPARγ and AMPK). We further show SH-SY5Y cells exposure to PM_2.5 induces significant increases in the levels of oxidative stress, and expression levels of the inflammatory mediator's TNF-α, IL-1β, and NF-κB. Taken together, these results provide the first evidence of the biochemical, molecular and morphological effects of PM_2.5 on human neuronal SH-SY5Y cells, and support our hypothesis that increased mitochondrial disruption, oxidative stress and inflammation are critical mediators of its neurotoxic effects. These findings further improve our understanding of the neuronal cell impact of PM_2.5 exposure, and may be useful in the design of strategies for the treatment and prevention of human neurodegenerative disorders.

The impacts of road traffic on urban air quality in Jinan based GWR and remote sensing

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R² values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R² based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice.

A high-resolution index suitable for multi-pollutant monitoring in urban areas

In view of the problems involved in remote sensing monitoring of urban air quality, including low spatial resolution, only for a single pollutant, complex inversion algorithms, and difficultly obtaining parameter values, in this study, a new difference smog index (DSI) was developed, and then a comparison with the normal difference haze index, the difference index, and the MODIS aerosol optical depth products. The results show that the DSI model developed in this study has a higher accuracy and a better monitoring effect in urban areas, and it has a higher resolution (30 m), which greatly improves the degree of refinement of the remote sensing monitoring. The DSI model has a higher extensibility, and it is suitable for monitoring the AQI, PM_2.5, NO₂. The DSI model proposed in this paper is simple and easy to use, and thus, it has a high potential for application and deserves promotion in urban air quality monitoring.

Characterization, possible sources and health risk assessment of PM2.5-bound Heavy Metals in the most industrial city of Iran

Air pollution associated with particulate matters results in different types of disease including allergy, lung destruction, heart failure, and related problems. This study has been designed and performed to examine the concentration of PM_2.5-bound heavy metals, risk assessment, possible sources and effect of meteorological parameters on 17 sites of the air of the most industrial city of Iran (Karaj) in 2018-19. For this purpose, four samples were taken from every point of Karaj air over one year using a pump (Leland Legacy (SKC)) with flow rate of 3 L/min on PTFE filter for 24 h. Overall, 68 samples of PM_2.5-bound heavy metals were collected. Note that during the sampling, atmospheric parameters including temperature, pressure, humidity, and wind speed were regularly recorded using PHB318 portable device. In examining the chemical composition of these particles, the concentration of metals (Al-Zn- Ar-Cd-Cr-Cu-Fe-Hg-Mn-Ni-Pb) was determined after digestion of the collected samples and through injection into ICP-OEC device. The results indicated that the mean annual concentration of PM_2.5 particles range from 21.84 to 72.75 µg/m³. The mean concentration of heavy metals lied within the range of 25.63 to 336.27 ng/m³. Among heavy metals, the maximum concentration belonged to aluminum (277.95 ng/m³) and iron (336.27 ng/m³), which are known as elements with a ground source (sources such as car fuels, exhaust gases, decorative materials, batteries, indoor smoking, the paint used for painting walls, erosion and corrosion of rubber of cars). Meanwhile, there was a positive relationship between heavy metals and temperature(r: 0.418, p < 0.019), pressure (r: 0.184, p < 0.0.402), as well as wind speed (r: 0.38, p < 0.017), while an inverse relationship was observed with relative humidity (r: -0.219, p < 0.018). The ecological risk of the metals calculated was very notable, with the maximum environmental risk being related to cadmium in children (6.61) and manganese in adults (0.82). The largest HQ in children and adults was associated with Cr. Finally, ILCR values for cadmium in both children (1.19 E-04) and adult (4.81 E-04) groups indicated high risk of developing cancer in humans.

Preparation of ashless cellulose paper standards for rapid determination of multi-element concentrations in airborne fine particulate matter using laser ablation inductively coupled plasma mass spectrometry

In this study, we developed ashless cellulose filter papers as calibration standards in laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to rapidly determine multi-element concentrations in airborne fine particulate matter (PM2.5). To achieve this, the papers were treated by immersion in standard solutions, followed by evaporation of the solutions. The homogeneity of the paper standards was studied, and the results demonstrated that the elements were homogeneously distributed at the paper centers with slight fluctuations (i.e., relative standard derivation ≦ 8%). The instrument signal drift and instability were compensated using a pseudo internal standard (197Au). The limits of detection established for LA-ICP-MS were obtained by the ablation of 11 lines on the procedural blank filter paper containing 0.5% HNO3, with values ranging from 0.01 (Sr) to 0.49 μg g-1 (Fe). The accuracy of the LA-ICP-MS determinations was validated using certified reference materials (CRMs) and analyzed using six line scans. The results showed acceptable analytical errors (<13%). Thus, our method was applied to analyze actual PM2.5 samples. Moreover, the sources of PM2.5 in Hangzhou were also investigated. Additionally, this method has considerable potential for multi-element analysis in other airborne dusts.