PM Size Fractions Research Articles

Characterisation of urban aerosol size distribution by radiocarbon and PIXE analyses in a middle-European urban environment for source identification: a pilot study

This study, conducted in Debrecen, Hungary, aimed to analyse atmospheric particulate matter (APM or PM) through radiocarbon and PIXE analyses during the winter smog (23–25 January) and spring (15–18 May) seasons. The information presented in this pilot study aims to provide insight into the importance of utilising detailed characteristics of the mass size distributions of fossil carbon (ff) and contemporary carbon (fC) content. Additionally, it seeks to compare these characteristics with the size distributions of various elements to enable even more accurate PM source identification. In winter, APM concentrations were 86.27 μg/m3 (total), 17.07 μg/m3 (fC) and 10.4 μg/m3 (ff). In spring, these values changed to 29.5 μg/m3, 2.64 μg/m3 and 7.01 μg/m3, respectively. Notably, differences in mass size distribution patterns were observed between the two seasons, suggesting varied sources for contemporary carbon. Biomass burning emerged as a crucial source during the smog period, supported by similar MMAD (Mass Median Aerodynamic Diameter) values and a strong correlation (r = 0.95, p < 0.01) between potassium and fC. In spring, a significant change in the concentration and distribution of fC occurred, with a broad, coarse mode and a less prominent accumulation mode. Ff was found to have similar distributions as PM, with nearly the same MMADs, during both periods. Finally, a comprehensive comparison of modal characteristics identified specific sources for the various components, including biomass burning, vehicle exhaust, coal and oil combustion, vehicle non-exhaust, road dust, tyre abrasion, mineral dust and biogenic emission. This study showcases how using radiocarbon and PIXE analysis in size distribution data can enhance our understanding of the sources of PM and their effects on different size fractions of PM.

Pre-pregnancy ozone and ultrafine particulate matter exposure during second year of life associated with decreased cognitive and adaptive functioning at aged 2–5 years

BackgroundThis study sought to investigate the association of prenatal and early life exposure to a mixture of air pollutants on cognitive and adaptive outcomes separately in children with or without autism spectrum disorder (ASD). MethodsUtilizing data from the CHARGE case-control study (birth years: 2000–2016), we predicted daily air concentrations of NO2, O3, and particulate matter <0.1 μm (PM0.1), between 0.1 and 2.5 μm (PM0.1-2.5), and between 2.5 and 10 μm (PM2.5-10) using chemical transport models with ground-based monitor adjustments. Exposures were evaluated for pre-pregnancy, each trimester, and the first two years of life. Individual and combined effects of pollutants were assessed with Vineland Adaptive Behavior Scales (VABS) and Mullen Scales of Early Learning (MSEL), separately for children with ASD (n = 660) and children without ASD (typically developing (TD) and developmentally delayed (DD) combined; n = 753) using hierarchical Bayesian Kernel Machine Regression (BKMR) models with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. ResultsPre-pregnancy Ozone was strongly negatively associated with all scores in the non-ASD group (group posterior inclusion probability (gPIP) = 0.83–1.00). The PM group during year 2 was also strongly negatively associated with all scores in the non-ASD group (gPIP = 0.59–0.93), with PM0.1 driving the group association (conditional PIP (cPIP) = 0.73–0.96). Weaker and less consistent associations were observed between PM0.1-2.5 during pre-pregnancy and ozone during year 1 and VABS scores in the ASD group. ConclusionsThese findings prompt further investigation into ozone and ultrafine PM as potential environmental risk factors for neurodevelopment.

Estimation of pan-European, daily total, fine-mode and coarse-mode Aerosol Optical Depth at 0.1° resolution to facilitate air quality assessments

Aerosol Optical Depth (AOD) data derived from satellites is crucial for estimating spatially-resolved PM concentrations, but existing AOD data over land remain affected by several limitations (e.g., data gaps, coarser resolution, higher uncertainty or lack of size fraction data), which weakens the AOD-PM relationship. We developed a 0.1° resolution daily AOD data set over Europe over the period 2003–2020, based on two-stage Quantile Machine Learning (QML) frameworks. Our approach first fills gaps in satellite AOD data and then constructs three components' models to obtain reliable full-coverage AOD along with Fine-mode AOD (fAOD) and Coarse-mode AOD (cAOD). These models are based on AERONET (AErosol RObotic NETwork) observations, Gap-filled satellite AOD, climate and atmospheric composition reanalyses. Our QML AOD products exhibit better quality with an out-of-sample R2 equal to 0.68 for AOD, 0.66 for fAOD and 0.65 for cAOD, which is 23–92 %, 11–13 % and 115–132 % higher than the corresponding satellite or reanalysis products, respectively. Over 91.6 %, 81.6 %, and 88.9 % of QML AOD, fAOD and cAOD predictions fall within ±20 % Expected Error (EE) envelopes, respectively. Previous studies reported that a weak satellite AOD-PM correlation across Europe (Pearson correlation coefficient (PCC) around 0.1). Our QML products exhibit higher correlations with ground-level PMs, particularly when broadly matched by size: AOD with PM10, fAOD with PM2.5, cAOD with PM coarse (R = 0.41, 0.45 and 0.26, respectively). Different AOD fractions more effectively distinct PM size fractions, than total AOD. Our QML aerosol dataset and models pioneer full-coverage, daily high-resolution monitoring of fine-mode and coarse-mode aerosols, effectively addressing existing AOD challenges for further PMs exposures' estimations. This dataset opens avenues for more in-depth exploration of the impacts of aerosols on human health, climate, visibility, and biogeochemical processes, offering valuable insights for air quality management and environmental health risk assessment.

PM1, PM2.5 and PM10 size fraction distribution under steady-state conditions in a walk-in type 222Rn calibration chamber facility.

Attachment of 222Rn progenies, upon their formation, to the atmospheric aerosols and inhalation of these radioactive aerosols causes inhalation dose to the human being. Aerosols have the characteristics of small particle size, long-time suspension and long-distance transmission and easy access to the deep respiratory tract. Aerosols are responsible for viral infection risk such as the recent worldwide pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2, or COVID-19). Understanding the formation and behaviour of aerosols in a confined environment in various human habitations is essential to combat such detrimental exposures. Experiments have been performed to study the distribution of aerosol size fractions in the walk-in type 222Rn calibration chamber. The real-time applied particle technology monitors (APT-Maxima stationary monitors) were used for the simultaneous measurements of PM1, PM2.5, and PM10 size fractions. The variation of the mass densities (μgm-3) of different size fractions at different positions inside the chamber was monitored by placing APTs. The PM1, PM2.5, and PM10 sizes fractions were distributed homogeneously within the chamber volume and the concentration ratios of these fractions were 1:1.5:1.6 for concentration values of < 1500μgm-3, and 1:7:9 for the concentration values of > 1500μgm-3.

Aerosolized Cyanobacterial Harmful Algal Bloom Toxins: Microcystin Congeners Quantified in the Atmosphere.

Cyanobacterial harmful algal blooms (cHABs) have the potential to adversely affect public health through the production of toxins such as microcystins, which consist of numerous molecularly distinct congeners. Microcystins have been observed in the atmosphere after emission from freshwater lakes, but little is known about the health effects of inhaling microcystins and the factors contributing to microcystin aerosolization. This study quantified total microcystin concentrations in water and aerosol samples collected around Grand Lake St. Marys (GLSM), Ohio. Microcystin concentrations in water samples collected on the same day ranged from 13 to 23 μg/L, dominated by the d-Asp3-MC-RR congener. In particulate matter <2.5 μm (PM2.5), microcystin concentrations up to 156 pg/m3 were detected; the microcystins were composed primarily of d-Asp3-MC-RR, with additional congeners (d-Asp3-MC-HtyR and d-Asp3-MC-LR) observed in a sample collected prior to a storm event. The PM size fraction containing the highest aerosolized MC concentration ranged from 0.44 to 2.5 μm. Analysis of total bacteria by qPCR targeting 16S rDNA revealed concentrations up to 9.4 × 104 gc/m3 in aerosol samples (≤3 μm), while a marker specific to cyanobacteria was not detected in any aerosol samples. Concentrations of aerosolized microcystins varied even when concentrations in water were relatively constant, demonstrating the importance of meteorological conditions (wind speed and direction) and aerosol generation mechanism(s) (wave breaking, spillway, and aeration systems) when evaluating inhalation exposure to microcystins and subsequent impacts on human health.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

Meteorological variables and PM10 exceedances effect on aerosol particles in a low emission zone using compositional data analysis

Particulate air pollution is a significant environmental concern due to its potential effects on the environment and human health. Meteorological variables can affect greatly on PM accumulation, also establishing a possible obstacle to the achievement of air quality standards. The objective of the study is to evaluate the effect of meteorological variables such as temperature, relative humidity and daily precipitation on coarse and fine size fraction of PM with and without considering PM10 exceedances conditions using a multiple compositional linear model. The study was conducted in an environmental traffic site placed in a low emission zone (LEZ) of Milan, which is located in the Po Valley (Northern Italy), during the cold season. The results indicated that an increasing in temperature of about 4.7 °C determined a decrease in fine size fraction of PM of about 6.2 % and 24.1 % with and without exceedances, respectively. Whereas an increase in relative humidity of about 19 % determined an increase in fine size fraction of PM of about 10.5 %. In addition, the analysis showed that the PM10 measured when there were PM10 exceedances was compositionally different from that measured when there were not PM10 exceedances as it was richer in fine particles of about 17.2 %. The study showed that PM10 exceedances were mainly caused by an increase in the fine size fraction of PM rather than the coarse one. Furthermore, research results indicated that changes in temperature and relative humidity could affect the particle size fractions of PM. A decrease in temperature and an increase in relative humidity led to an increase in the PM fine size fraction. The study provides insights into the underlying mechanisms affecting both fine and coarse size PM fractions.

Trait-mediated leaf retention of atmospheric particulate matter in fourteen tree species in southern China.

Particulate air pollution is a serious threat to human health, especially in urban areas, and trees can act as biological filters and improve air quality. However, studies on greening tree species selection are rare. We measured three particular matter adsorption metrics (PM2.5, PM2.5-10, and PM>10 captured per leaf area) and six functional traits for each of fourteen species and estimated their minimum light requirements based on field surveys. We found that shade-tolerant species captured more coarse particles (PM2.5-10) than light-demanding species. For traits, a strong negative correlation was found between photosynthetic capacity and adsorption capacity for all three PM size fractions, indicating that in comparison to acquisitive species, conservative species captured larger amounts of particles. Moreover, denser wood species and smaller leaves were more efficient in capturing large particles (PM>10), while species with "expensive" leaves (high leaf N or P) were more efficient in capturing fine particles (PM2.5), indicating that capturing large and fine particles was related to mechanical stability traits and leaf surface traits, respectively. Our results demonstrated that the metabolism (e.g., photosynthetic capacity) and chemistry (e.g., leaf N and leaf P) of leaves help explain species capacity to capture PM. We encourage future studies to investigate the ecosystem functions and stress tolerance of tree species with the same framework and trait-based methods.

Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain

Ambient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of PM10, PM2.5, and PM1 were collected roughly every four days from January 2018 until March 2019 at a Barcelona urban background site and Montseny rural background site in northeastern Spain. We determined the chemical composition of samples, allowing us to perform source apportionment using positive matrix factorization. The OP of PM was determined by measuring reactive oxygen species using dithiothreitol and ascorbic acid assays. Finally, to link the sources with the measured OP, both a Pearson's correlation and a multiple linear regression model were applied to the dataset. The results showed that in Barcelona, the OP of PM10 was much higher than those of PM2.5 and PM1, whereas in Montseny results for all PM sizes were in the same range, but significantly lower than in Barcelona. In Barcelona, several anthropogenic sources were the main drivers of OP in PM10 (Combustion + Road Dust + Heavy Oil + OC-rich) and PM2.5 (Road Dust + Combustion). In contrast, PM1 -associated OP was driven by Industry, with a much lower contribution to PM10 and PM2.5 mass. Meanwhile, Montseny exhibited no clear drivers for OP evolution, likely explaining the lack of a significant difference in OP between PM10, PM2.5, and PM1. Overall, this study indicates that size fraction matters for OP, as a function of the environment typology. In an urban context, OP is driven by the PM10 and PM1 size fractions, whereas only the PM1 fraction is involved in rural environments.

Factors affecting particle number size distributions at a suburban and a rural site at the Anatolian Plateau

In this work, number size distributions of particles between 0.2 and 34 μm were measured with a laser spectrometer. Temporal and spatial variability in size distributions were investigated. Measurements were carried out at two stations; the Mediterranean coast of Turkey (Marmaris) and central Anatolia (Ankara). PM1, PM2.5, and PM10 mass concentrations were measured. Flow climatology indicated that air masses spent more time in the south of Mediterranean basin in winter and in the North of the basin during summer months. Median PM10 concentrations measured in Marmaris and Ankara stations were 17 and 38 μg m−3, respectively. North African dust transport was not the main PM10 source in the suburban station, but it was significant in the rural station. The median PM2.5 concentration was higher at Ankara due to anthropogenic emissions which was not observed in Marmaris. Median PM1 concentrations were comparable in both stations. 90–95% of the particles were between 0.26 and 0.42 μm size ranges. Coarse particle concentrations were higher at the suburban station, whereas fine particles were higher at the rural station. Concentrations depicted a well-defined seasonal pattern in the rural station, with higher concentrations in summer season. Although concentrations of PM size fractions were also higher in the summer season in Ankara station, seasonal differences were not as well-defined as in the rural station. PM1 and PM2.5 concentrations did not show any difference between weekends and weekdays, but PM10 concentrations, particularly the 2.5 μm < D < 10 μm, were higher on weekdays due to contribution of resuspended road dust.

Airborne microbial community structure and potential pathogen identification across the PM size fractions and seasons in the urban atmosphere

As a vital component of airborne bioaerosols, bacteria and fungi seriously endanger human health as pathogens and allergens. However, comprehensive effects of environmental variables on airborne microbial community structures remain poorly understood across the PM sizes and seasons. We collected atmospheric PM1.0, PM2.5, and PM10 samples in Hefei, a typical rapidly-developing city in East China, across three seasons, and performed a comprehensive analysis of airborne microbial community structures using qPCR and high-throughput sequencing. Overall the bacterial and fungal abundances in PM1.0 were one to two orders of magnitude higher than those in PM2.5 and PM10 across seasons, but their α-diversity tended to increase from PM1.0 to PM10. The bacterial gene abundances showed a strong positive correlation (P < 0.05) with atmospheric SO2 and NO2 concentrations and air quality index. The bacterial gene abundances were significantly higher (P = 0.001) than fungi, and the bacterial diversity showed stronger seasonality. The PM sizes influenced distribution patterns for airborne microbial communities within the same season. Source-tracking analysis indicated that soils, plants, human and animal feces represented important sources of airborne bacteria with a total relative abundance of more than 60% in summer, but total abundance from the unidentified sources surpassed in fall and winter. Total 10 potential bacterial and 12 potential fungal pathogens were identified at the species level with the highest relative abundances in summer, and their abundances increased with the PM sizes. Together, our results indicated that a complex set of environmental factors, including water-soluble ions in PM, changes in air pollutant levels and meteorological conditions, and shifts in the relative importance of available microbial sources, acted to control the seasonal compositions of microbial communities in the urban atmosphere.

Air Quality Sensors Systems as Tools to Support Guidance in Athletics Stadia for Elite and Recreational Athletes.

While athletes have high exposures to air pollutants due to their increased breathing rates, sport governing bodies have little guidance to support events scheduling or protect stadium users. A key limitation for this is the lack of hyper-local, high time-resolved air quality data representative of exposures in stadia. This work aimed to evaluate whether air quality sensors can describe ambient air quality in Athletics stadia. Sensing nodes were deployed in 6 stadia in major cities around the globe, monitoring NO2, O3, NO, PM10, PM2.5, PM1, CO, ambient temperature, and relative humidity. Results demonstrated that the interpretation of hourly pollutant patterns, in combination with self-organising maps (SOMs), enabled the interpretation of probable emission sources (e.g., vehicular traffic) and of atmospheric processes (e.g., local vs. regional O formation). The ratios between PM size fractions provided insights into potential emission sources (e.g., local dust re-suspension) which may help design mitigation strategies. The high resolution of the data facilitated identifying optimal periods of the day and year for scheduling athletic trainings and/or competitions. Provided that the necessary data quality checks are applied, sensors can support stadium operators in providing athlete communities with recommendations to minimise exposure and provide guidance for event scheduling.

Integrated Assessment of Affinity to Chemical Fractions and Environmental Pollution with Heavy Metals: A New Approach Based on Sequential Extraction Results.

To assess the affinity degree of heavy metals (HMs) to geochemical phases, many indices with several limitations are used. Thus, this study aims to develop a new complex index for assessing contamination level and affinity to chemical fractions in various solid environmental media. For this, a new integrated approach using the chemical affinity index (CAF) is proposed. Comparison of CAF with %F on the literature examples on fractionation of HMs from soils, bottom sediments, atmospheric PM10, and various particle size fractions of road dust proved a less significant role of the residual HMs fraction and a greater contribution of the rest of the chemical fractions in the pollution of all studied environments. This fact is due to the normalization relative to the global geochemical reference standard, calculations of contribution of an individual element to the total pollution by all studied HMs, and contribution of the particular chemical fraction to the total HMs content taken into account in CAF. The CAF index also shows a more significant role in pollution and chemical affinity of mobile and potentially mobile forms of HMs. The strong point of CAF is the stability of the obtained HM series according to the degree of chemical affinity and contamination. Future empirical studies are necessary for the more precise assessment of CAF taking into account the spatial distribution of HMs content, geographic conditions, geochemical factors, the intensity of anthropogenic impact, environmental parameters (temperature, humidity, precipitation, pH value, the content of organic matter, electrical conductivity, particle size distribution, etc.). The combined use of CAF along with other indices allows a more detailed assessment of the strength of HMs binding to chemical phases, which is crucial for understanding the HMs’ fate in the environment.

Associations of Particulate Matter Sizes and Chemical Constituents with Blood Lipids: A Panel Study in Guangzhou, China.

Existing evidence is scarce concerning the various effects of different PM sizes and chemical constituents on blood lipids. A panel study that involved 88 healthy college students with five repeated measurements (440 blood samples in total) was performed. We measured mass concentrations of particulate matter with diameters ≤ 2.5 μm (PM2.5), ≤1.0 μm (PM1.0), and ≤0.5 μm (PM0.5) as well as number concentrations of particulate matter with diameters ≤ 0.2 μm (PN0.2) and ≤0.1 μm (PN0.1). We applied linear mixed-effect models to assess the associations between short-term exposure to different PM size fractions and PM2.5 constituents and seven lipid metrics. We found significant associations of greater concentrations of PM in different size fractions within 5 days before blood collection with lower high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A (ApoA1) levels, higher apolipoprotein B (ApoB) levels, and lower ApoA1/ApoB ratios. Among the PM2.5 constituents, we observed that higher concentrations of tin and lead were significantly associated with decreased HDL-C levels, and higher concentrations of nickel were associated with higher HDL-C levels. Our results suggest that short-term exposure to PM in different sizes was deleteriously associated with blood lipids. Some constituents, especially metals, might be the major contributors to the detrimental effects.

Assessment of pedestrian exposure and deposition of PM10, PM2.5 and ultrafine particles at an urban roadside: A case study of Xi'an, China

Assessment of pedestrian exposure and lung deposition is critical to better understanding the health risks resulting from exposure to ambient particles, and to develop site-specific risk control countermeasures. In this study, real-time mobile-monitoring campaigns of three different PM size fractions (PM10, PM2.5 and UFP) were performed during morning and evening peak hours in two seasons (summer and winter), along road sections with differing characteristics (commercial and residential) in Xi'an, China. The Multiple-Path Particle Dosimetry model (MPPD) was utilized to quantify the total and regional depositions for both adults and teens of different genders. Observed PM10, PM2.5 and UFP concentrations showed pronounced regional, seasonal, and diurnal variabilities. The total deposition dose was higher in males than in females for all particle-size fractions in both age groups, except for UFPs in the teen group, the total deposition dose was higher in females. As for regional depositions, these were observed to be highest in the head region, compared with the tracheobronchial and pulmonary regions, regardless of a participant's age or gender. PM10 and PM2.5 showed the highest depositions in the head region for all age groups (PM10: 90%; PM2.5: 67%), while UFP was highly deposited in the pulmonary region (66%). The dominant percentage of UFP deposition in the pulmonary regions strengthens the need to incorporate UFP into the current air-quality index.

Monitoring uranium mine pollution on Native American lands: Insights from tree bark particulate matter on the Spokane Reservation, Washington, USA

The uranium boom in the United States from the 1940's to the 1980's was a period of extensive uranium mining on Native American lands. However, detailed environmental investigations of the resulting uranium pollution are sparse and typically ignore contributions from airborne particulate matter. The Midnite Mine is a 350-acre inactive open pit uranium mine located on the Spokane Indian Reservation in eastern Washington that operated from 1954 to 1981. Approximately 2.4 million tons of ore and 33 million tons of waste rock were left behind in stockpiles and have also been utilized as gravel on access and haul roads. Although the Midnite Mine is now a Superfund Site, and governmental investigations of water and soil contamination have been done, no investigations of airborne particulate matter pollution have been conducted. This study applies tree bark from 31 Pinus ponderosa trees as a biomonitor of this airborne particulate matter. Bulk trace elemental analyses via inductively coupled plasma – mass spectrometry (ICP-MS) of tree bark show that U is the most abundant trace element of interest present up to 232 ppb. Other metals that are of potential human health concern include Th, Pb, and As which are present at 20 ppb, 104 ppb, and 20 ppb respectively. Calculated geoaccumulation indices determine these metals to be at high (U), moderate (Th), and low (Pb and As) levels of contamination. Detailed scanning electron microscopy (SEM) investigations of particulate matter from the surface of tree bark confirm that U and Th-bearing particulate matter exist in the <PM10 size fraction while geospatial analyses indicate that uranium, thorium, and arsenic contamination are centralized along the Midnite Mine access road and at the nearby Dawn Mill where ore was further processed. Combined, these findings indicate that the nature and distribution of historic airborne particulate matter from the Midnite Mine and Dawn Mill provide context for potentially understanding past and current illness on the reservation. In addition, much needed context for future health and environmental studies for both local and national Native American populations is provided.

Carbonaceous Aerosols in PM1, PM2.5, and PM10 Size Fractions over the Lanzhou City, Northwest China

Carbonaceous particles have been confirmed as major components of ambient aerosols in urban environments and are related to climate impacts and environmental and health effects. In this study, we collected different-size particulate matter (PM) samples (PM1, PM2.5, and PM10) at an urban site in Lanzhou, northwest China, during three discontinuous one-month periods (January, April, and July) of 2019. We measured the concentrations and potential transport pathways of carbonaceous aerosols in PM1, PM2.5, and PM10 size fractions. The average concentrations of OC (organic carbon) and EC (elemental carbon) in PM1, PM2.5, and PM10 were 6.98 ± 3.71 and 2.11 ± 1.34 μg/m3, 8.6 ± 5.09 and 2.55 ± 1.44 μg/m3, and 11.6 ± 5.72 and 4.01 ± 1.72 μg/m3. The OC and EC concentrations in PM1, PM2.5, and PM10 had similar seasonal trends, with higher values in winter due to the favorable meteorology for accumulating pollutants and urban-increased emissions from heating. Precipitation played a key role in scavenge pollutants, resulting in lower OC and EC concentrations in summer. The OC/EC ratios and principal component analysis (PCA) showed that the dominant pollution sources of carbon components in the PMs in Lanzhou were biomass burning, coal combustion, and diesel and gasoline vehicle emissions; and the backward trajectory and concentration weight trajectory (CWT) analysis further suggested that the primary pollution source of EC in Lanzhou was local fossil fuel combustion.

Chemical characterization and source apportionment of fine and coarse atmospheric particulate matter in Doha, Qatar

Atmospheric particulate matter of two size fractions (i.e., PM2.5 and PM10) were sampled simultaneously at an urban site in Doha, Qatar, and characterized to measure chemical constituents (including trace elements, organic and elemental carbon (OC/EC), and water-soluble ions). The composition data were applied to mass closure analysis and Positive Matrix Factorization (PMF) model along with detailed wind direction analysis for quantifying and identifying the probable sources of both PM size fractions. The average concentrations of PM2.5 (40 μg m−3) and PM10 (146 μg m−3) were greatly exceeded the WHO air quality thresholds for 78–92% of the sampling days. Mass closure indicated the predominance of secondary inorganic aerosol (39%) and EC (7%) in fine PM, while mineral dust contributes 50% to the total mass of coarse particles. PMF identified six major sources of fine and coarse PM, including secondary inorganic aerosols, vehicular emissions, heavy oil combustion, marine aerosols, mineral and fugitive dust. The wind direction analysis indicated that both local and regional sources impact Qatar's air quality attributed to specific wind conditions. Two main sources of heavy oil combustion and secondary aerosols are closely related to regional emissions from shipping vessels and the petrochemical industry in the Gulf region. The desert dust transport mixed with local re-suspended dust and sea-salt particles also contributes considerably to the local PM concentrations. The significant contributions of regional anthropogenic and natural emissions pose a significant challenge in meeting local PM standards, signifying the importance of regional collaboration for better control policies on reducing transboundary air pollution.

Biomass-Derived Provenance Dominates Glacial Surface Organic Carbon in the Western Himalaya.

The origin, transport pathway, and spatial variability of total organic carbon (OC) in the western Himalayan glaciers are poorly understood compared to those of black carbon (BC) and dust, but it is critically important to evaluate the climatic role of OC in the region. By applying the distribution of OC activation energy; 14C activity; and radiogenic isotopes of 208Pb/204Pb, 207Pb/204Pb, and 206Pb/204Pb in glacial debris and atmospheric particulate matter (PM10 size fraction), we demonstrate that 98.3 ± 1.6 and 1.7 ± 1.6% of OC in western Himalayan glaciers are derived from biomass and petrogenic sources, respectively. The δ13C and N/C composition indicates that the biomass is a complex mixture of C3 vegetation and autochthonous photoautotrophic input modified by heterotrophic microbial activity. The data set reveals that the studied western Himalayan glacier has negligible contributions from fossil-fuel-derived particles, which contrasts to the central and eastern Himalayan glaciers that have significant contributions from fossil fuel sources. We show that this spatial variability of OC sources relates to regional differences in air mass transport pathways and precipitation regimes over the Himalaya. Moreover, our observation suggests that biomass-derived carbon could be the only primary driver of carbon-induced glacier melting in the western Himalaya.

Characterization, pro-inflammatory response and cytotoxic profile of bioaerosols from urban and rural residential settings in Pune, India

Microbiota associated with airborne particulate matter (PM) is an important indicator of indoor pollution as they can be pathogenic and cause serious health threats to the exposed occupants. Present study aimed to investigate the level of culturable microbes associated with PM and their toxicological characterization in urban and rural houses of Pune city. Highest concentration of bacterial aerosols observed to be associated with PM10 size fraction in urban site (2136 ± 285 CFU/m3) whereas maximum fungal concentration has been measured in rural houses (1521 ± 302 CFU/m3). Predominantly found bacterial species were Bacillus sp., S. aureus, and Pseudomonas aeruginosa and fungal species were Aspergillus sp., Cladosporium sp., and Penicillium sp. in both urban and rural residential premises. Concentration of endotoxin measured using the kinetic Limulus Amebocyte Lysate assay exhibited that the level of endotoxin in both urban and rural sites are associated with household characteristics and the activities performed in indoor as well as outdoor. Cell free DTT assay confirmed the ability of these airborne microbes to induce the production of reactive oxygen species (ROS) varying along with the types of microorganisms. On exposure of A549 cells to airborne microbes, a significant decrease in cell viability was observed in terms of both necrosis and apoptosis pathway. Elevated production of nitric oxide (NO) and proinflammatory cytokines in epithelial cells and macrophages clearly suggest the inflammatory nature of these airborne microbes. Results derived from the present study demonstrated that the indoor air of urban and rural houses of Pune is contaminated in terms of microbial load. Therefore, attention should be paid to control the factors favoring the microbial growth in order to safeguard the health of exposed inhabitants.