Mass Concentrations Of PM2 Research Articles

The significant contribution of nitrate to a severe haze event in the winter of Guangzhou, China

A severe haze pollution occurred in Guangzhou from January 14 to 16, 2021, during which the mass concentration of PM2.5 ranged from 76 to 243 μg m−3. This level of pollution was rarely observed in recent years considering the improved air quality in Guangzhou. Therefore, it is crucial to comprehensively study the formation mechanisms of this severe haze pollution to prevent its reoccurrence. During the haze period, the concentrations of NO and NO2 sharply increased by 7.4 and 3.8 times, respectively, and total volatile organic compounds (TVOCs) increased 7 times, suggesting enhanced primary emissions from vehicles due to stagnant meteorological conditions. Nitrate concentration (43 ± 20 μg m−3) increased 6.7 times and became the dominant species in PM2.5 during the haze period. Notably, gaseous NH3, HONO and HNO3 also exhibited a sharp increase, suggesting the important role of nitrate chemistry in the evolution of haze pollution. The simulation results from chemical box model revealed that the OH + NO2 reaction was the dominant formation pathway for nitrate production (82 %) during the haze period. The net production rate of ROx radicals (including OH, HO2 and RO2) was 4.4 times higher during the haze period (5.8 ppb h−1) compared to the pre-haze period (1.3 ppb h−1). This was mainly attributed to the enhanced HONO and OVOCs photolysis, which increased from 0.6 ppb h−1 to 3.1 ppb h−1 and 0.4 ppb h−1 to 2.1 ppb h−1, respectively. Furthermore, the sensitivity tests demonstrated the reductions in VOCs and NOx were both beneficial for controlling nitrate production by influencing OH production and N2O5 uptake rate. These findings provide insights into the formation mechanisms of nitrate production during severe haze pollution and suggest that joint mitigation of PM2.5 and O3 can be achieved through the control of VOCs emission.

Evaluation of indoor particulate matter control based on health risks and oxidative potential in a metro station

Particulate matter (PM) from different sources exhibits diverse chemical and physical characteristics, and it is an essential carrier of toxic chemicals. Ignoring PM properties is inappropriate for indoor air quality (IAQ) control in places with internal sources, such as metro stations. PM control measures aimed at health protection should be established based on PM health effects. The current PM control limit for metro stations may be insufficient for health protection. It is important to find a method that comprehensively evaluates PM features to set PM exposure limits. This study evaluated the health risks induced by chemical components and the oxidative potential of PM in the Tianjinzhan metro station. A PM control strategy was suggested based on health effect analysis. PM on the platform had notable underlying adverse health effects. An in-depth toxicology analysis was suggested to assess the high Ba and Mn concentrations in PM. The risk from Cr raised concern regarding the potential for carcinogenicity. As a potential composite index, the dithiothreitol (DTT) activity may be appropriate to evaluate the general adverse health effects caused by PM. The DTT activity of the platform was 3.3–5.4 times that of the ventilation shaft. Removing a unit mass of PM1 in the station hall would produce more health benefits than the removal of a unit mass of PM2.5. However, the DTT results indicated that control strategies on the platform should first focus on PM2.5, which would achieve the same protection effect as that when the PM1 mass concentration is decreased by the same level. A revision of the PM2.5 control limit for China metro stations was recommended to control oxidative potential on the platform.

Effects of personal exposure to the oxidative potential of PM2.5 on oxidative stress biomarkers in pregnant women

Oxidative stress is a prominent pathway for the health effects associated with fine particulate matter (PM2.5) exposure. Oxidative potential (OP) of PM has been associated to several health endpoints, but studies on its impact on biomarkers of oxidative stress remains insufficient. 300 pregnant women from the SEPAGES cohort (France) carried personal PM2.5 samplers for a week and OP was measured using ascorbic acid (AA) and dithiothreitol (DTT) assays, and normalized by 1) PM2.5 mass (OPm) and 2) sampled air volume (OPv). A pool of three urine spots collected on the 7th day of PM sampling was analyzed for biomarkers, namely 8-hydroxy-2-deoxyguanosine (8-OHdG), malondialdehyde (MDA) and 8-isoprostaglandin-F2α (8-isoPGF2α). Associations were investigated using adjusted multiple linear regressions. OP effects were additionally investigated by stratifying by median PM2.5 concentration (14 μg m−3). In the main models, no association was observed with 8-isoPGF2α, nor MDA. An interquartile range (IQR) increase in OPmAA exposure was associated with increased 8-OHdG (percent change: 6.2 %; 95 % CI: 0.2 % to 12.6 %). In the stratified analysis, exposure to OPmAA was associated with 8-OHdG for participants exposed to low levels of PM2.5 (percent change: 11.4 %; 95 % CI: 3.3 % to 20.1 %), but not for those exposed to high levels (percent change: −1.0 %; 95 % CI: −10.6 % to 9.6 %). Associations for OPmDTT also followed a similar pattern (p-values for OPmAA-PM and OPmDTT-PM interaction terms were 0.12 and 0.11, respectively). Overall, our findings suggest that OPmAA may be associated with increased DNA oxidative damage. This association was not observed with PM2.5 mass concentration exposure. The effects of OPmAA in 8-OHdG tended to be stronger at lower (below median) vs. higher concentrations of PM2.5. Further epidemiological, toxicological and aerosol research are needed to further investigate the OPmAA effects on 8-OHdG and the potential modifying effect of PM mass concentration on this association.

Spatial and Temporal Variations in PM1 Concentration and its Correlation with AOD in China from 2014 to 2017

Based on the PM1 mass concentration data from all the air quality monitoring stations in China from 2014 to 2017, the temporal and spatial distribution characteristics of PM1 concentration were studied using the time series statistical and spatial hierarchical clustering methods, and the PM1 spatiotemporal evolution characteristics were revealed. Combined with AOD data of the MODIS remote-sensing satellite, the temporal and spatial variation in PM1-AOD correlation was analyzed on a fine scale. The results showed that, from 2014 to 2017, the annual average PM1 concentration in China decreased yearly, the seasonal PM1 concentration showed the characteristics of "high in winter and low in summer," and the monthly average PM1 concentration showed a "U"-shaped variation. An "M"-shaped PM1 variation pattern was presented before and after the holidays. Weekly variation showed that high PM1 values occurred on Mondays and Fridays, and low ones occurred on Sundays. Based on the spatial clustering method, the national average annual PM1 concentration in China was divided into seven categories, and the overall spatial distribution pattern was "high in the east and low in the west and high in the north and low in the south." The highest and the lowest values of average PM1 concentration occurred in central China(54.59 μg·m-3) and in Xinjiang-Qinghai-Xizang(11.37 μg·m-3), respectively. The PM1-AOD relationship was positively correlated as a whole, the highest correlation coefficient was 0.55 in central China, and the lowest value was 0.36 in central and southern China.

Justification of an Energy-Efficient Air Purification System in Subways Based on Air Dust Content Studies

It is not uncommon that subways count as densely populated areas, so air quality standards, including fine dust concentration, have been established for them. As passengers and subway staff are exposed to potentially harmful airborne particles, addressing this issue is vital to ensuring a safe and healthy environment on the subway. To reduce the dust concentration in subway systems, the authors propose installing filters to capture dust in ventilation failures between subway tunnels near metro stations. A novel aspect of the proposed method is the fact that airflow will be moved through filters by using the piston action of trains passing through the tunnels. The result of this research provides empirical evidence regarding dust content and mass concentrations of PM2.5 and PM10 in subway environments. While some existing literature discusses air quality in subways, the inclusion of specific measurements and data from the experiment strengthens the understanding of the severity of dust-related air quality issues in such environments. The data for this study were collected in the Almaty subway (Republic of Kazakhstan) at four stations: Raiymbek Batyr, Almaty, Baikonur and Alatau. Measuring points were located on passenger platforms, in the halls and at the entrances to the station. The lab scale tests determined the percentage of particles by their diameters relative to the total volume of dust, the percentage of dust particles smaller than a certain diameter, the percentage of various metal oxides and the average dust density. A preliminary energy assessment has been done on the proposed method of air purification from dust. With a frequency of 24 pairs of trains per hour, the energy savings per ventilation failure will be 240.170 kWh.

Conditionally Background Level of Aerosol Pollution of Near-Surface Air in Moscow and One of Its Suburbs: Seasonal Variations

The data of continuous observations of aerosol composition in the near-surface atmosphere in Moscow (in the city center) and in Moscow region (near Zvenigorod, Moscow region) for three years, from autumn 2019 to the end of 2022, are analyzed. The obtained data were compared with the results of observations on the Moscow network stations “Mosecomonitoring”. The concept of conditionally background aerosol pollution of the atmosphere in Moscow is introduced for those days when the average daily concentration of PM10 is less than the MPC value (60 µg/m3). Previously, the authors found that all episodes of increased aerosol pollution in Moscow with daily average PM10 concentration higher than the MPC value, are associated either with the presence of a close local source in the city itself, or with the long-range transport of fire aerosols and/or dust from other territories to the Moscow region. The average daily PM2.5 concentration in the city and the suburb is lower than the MPC (35 µg/m3) all year round. The days corresponding to the introduced conditional background make up more than 91% over three years in the center of Moscow. Such a conditional background is formed by both natural and anthropogenic sources of aerosols, and not only of local, but also of remote origin. It implicitly takes into account the influence of meteorological conditions on sources and sinks of aerosols, as well as advective air mass transport of aerosol to and from the city. Seasonal variations in mass concentration of PM10, PM2.5 particles and individual chemical elements, as well as in the distribution of chemical elements by the size of aerosol particles in near-surface atmosphere under conditionally background pollution are analyzed. The emphasis is placed on the similarity and difference in the conditionally background near-surface aerosol for the city and the suburb in different seasons.

Insights the dominant contribution of biomass burning to methanol-soluble PM2.5 bounded oxidation potential based on multilayer perceptron neural network analysis in Xi'an, China

Atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality due to its ability to generate reactive oxygen species (ROS). Ambient PM2.5 samples were collected during heating and nonheating seasons in Xi'an, China, and the ROS-generation potential of PM2.5 was quantified using the dithiothreitol (DTT) assay. Additionally, positive matrix factorization combined with multilayer perceptron was employed to apportion sources contributing to the oxidation potential of PM2.5. Both the mass concentration of PM2.5 and the volume-based DTT activity (DTTv) were higher during the heating season than during the nonheating season. The primary contributors to DTTv were combustion (biomass and coal) sources during the heating season (>52 %), whereas secondary formation dominated DTT activity during the nonheating season (35.7 %). In addition, the secondary reaction process promoted the generation of intrinsic oxidation potential (OP) of sources. Among all the sources investigated (traffic source, industrial emission, mineral dust, biomass burning, secondary formation and coal combustion), the inherent oxidation potential of biomass burning was the highest, whereas that of mineral dust was the lowest. Our study indicates that anthropogenic sources, especially biomass burning, should be prioritized in PM2.5 toxicity control strategies.

Development of a physics-based method for calibration of low-cost particulate matter sensors and comparison with machine learning models

Low cost particulate matter sensors are receiving significant attention as they can be used in large number for spatial and temporal measurement of PM mass and number concentration. However, the data reliability is questionable as these sensors are affected by numerous parameters such as temperature, relative humidity, and hygroscopicity of particles. To ensure accurate and reliable measurement of particulate matter concentrations, performance evaluation and calibration of LCS co-located with reference instrument at site is essential. In this study, the performance of the low-cost sensor (APT Maxima) was evaluated with the reference instrument SASS (Speciation Air Sampling System) sampler for developing suitable calibration factor that include impact of the meteorological parameter such as relative humidity and temperature, and hygroscopicity. In this study, we have demonstrated a systematic physics-based method for calibration of LCS based on κ-Köhler theory and Mie theory. For comparison with statistical models, linear regression and machine learning algorithm were also applied. In physics-based model, calculated total light scattered intensity shows good linearity with reference PM2.5 measurements. Physics based model performed better for both the sites as compared to MLR, kNN, RF, and GB ML algorithms with R2, RMSE, and MAE values of 0.72,18.21, and 13.36 for Bhopal site, and 0.91, 7.84, and 5.76 for Kashmir site, respectively. Study indicates that physics-based approach for LCS calibration is suitable and can be transferable to different sites.

A PM10 chemically characterized nation-wide dataset for Italy. Geographical influence on urban air pollution and source apportionment

Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can different topographies generate significant differences on the PM10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM10 mass concentration and chemical composition data was built, covering the decade 2005–2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modeling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modeling performance.Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories.These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks.

Characterization of summertime single aerosol particles in Chengdu (China): Interannual evolution and impact of COVID-19 lockdown

To investigate the interannual evolution of air pollution in summer and the impact of the COVID-19 lockdown on local pollution in Chengdu, China, single aerosol particles were continuously measured in three summer periods: the regular period in 2020 (RP2020); the regular period in 2022 (RP2022); and the lockdown period in 2022 (LP2022). It was found that, from RP2020 to RP2022, the mass concentrations of PM2.5, PM10, SO2 and NO2 decreased by 25.6 %, 24.7 %, 28.8 % and 38.5 %, respectively, while the concentration of O3 increased by 11.0 %. Affected by regional transport, there was no significant decrease in the concentrations of various pollutants during LP2022. All single aerosol particles could be classified into seven categories: vehicle emissions (VE), dust, biomass burning (BB), coal combustion (CC), K mixed with sulfate (KSO4), K mixed with nitrate (KNO3) and K mixed with sulfate and nitrate (KSN) particles. From RP2020 to RP2022, the contributions of BB and CC particles decreased by 12.1 % and 0.9 %, respectively, while VE and dust particles increased by 3.6 % and 2.5 %, respectively; and compared to RP2022, the contributions of VE, dust and CC particles in LP2022 decreased by 22.2 %, 11.0 % and 12.7 %, respectively. The high PM2.5 pollution events in RP2020 and RP2022 were mainly caused by combustion sources (BB and CC, 51.6 %) and VE (38.3 %) particles, respectively, while the pollution event in LP2022 was contributed by BB (27.0 %) and secondary inorganic (KSO4, KNO3 and KSN, 60.2 %) particles. The formation mechanisms of different pollution events were further validated by WRF-Chem results. Although the potential source areas of particles showed a shrinking trend from RP2020 to RP2022, regional transport still caused high PM2.5 pollution events during LP2022. Photochemical processes dominated the formation of KSO4 particles, while the KNO3 and KSN particles were mainly generated by liquid-phase reactions, and this effect increased year by year.

Long-term spatial and temporal evaluation of the PM2.5 and PM10 mass concentrations in Lithuania

Evaluating the spatio-temporal trends of particulate mass concentration is beneficial for appraising the risk to human health and environmental quality. In this study, the long-term observational data of particulate matter mass concentration (PM2.5, PM10), and PM2.5/PM10 ratios' variability of sixteen EPA air pollution monitoring stations in Lithuania over 17 years (2006–2022), were analyzed. A seasonal-trend decomposition procedure using Loess and Mann-Kendall regression with Sen's slope was used to qualify long-term trends and seasonality. The Theil-Sen analysis estimated significant reductions of −2.66, −4.42, and −1.89% per year for PM10, PM2.5, and PM2.5/PM10 ratios after 2013. The possible reasons for these changes are the reduction in liquid fuel consumption, the mitigation of energy sector emissions, as well as Lithuania's positive gross domestic product (GDP) in recent years. The results also revealed a significant increasing trend (+4.48%/year) for PM10 in rural areas, due to increased agricultural emissions. Trend analysis was also applied to different quartiles. The upper and middle values of PM2.5 and the upper values of PM10, responsible for high PM mass concentration, underwent a significant decrease.

Physicochemical characteristics of atmospheric particles in south western (Pune) region of India: Elemental size distribution, source analysis and risk assessment

Chemical characteristics of size-resolved particulate matter (PM) and bound elemental species were investigated under an intensive measurement campaign during winter of 2021 in urban dwelling of south western (Pune) region of India. During the study period, average mass concentrations of PM10, PM4, PM2.5 and PM1 were 182.4 ± 48.7 μgm−3, 76.2 ± 26.2 μgm−3, 63.5 ± 22.4 μgm−3, 17.7 ± 6.9 μgm−3 respectively. Diurnal variability indicated significant high concentration during night and early morning hours attributed to the high volume of heavy-duty vehicles. Bimodal distributions of PM mass concentrations in coarse fraction (Mass Median Aerodynamic Diameter (MMAD) ± Geometric Standard Deviation (GSD) = 4.32 ± 1.27) was noted while accumulation of elements was found higher in fine(0.43–2.1 μm) > coarse(2.1–5.8 μm) > large mode(5.8–10 μm) particles. As per size distribution characteristics, Mn, Pb, Cu, Sn, Zn, Mo were concentrated in coarse mode; As, Cr, Fe, Ni was concentrated in both large and coarse mode while Al, Si depicted multimodal distribution across coarse and fine range. High MMAD (≥1.8) and GSD (>1.2) of all elements pointed towards significance of coarse particles in this study region. Ni and Cr (VI) via inhalation exposure pathway pose highest hazard quotient (HQ) and excess lifetime cancer risk (ELCR) respectively within respirable size range(2.1–3.3 μm) while children were subject to potential health risk than adults mainly through ingestion mode of exposure. Principal Component Analysis (PCA) suggested crustal origin, resuspended road dust along with vehicular traffic and industrial emissions as major sources.

Variability of airborne microbiome at different urban sites across seasons: a case study in Rome

Introduction: Biogenic fraction of airborne PM10 dominated by bacteria and fungi, has been recognized as serious environmental and human health issues in cities.Methods: In the present study, we combined a high-throughput amplicon sequencing of the bacterial 16S rRNA gene and the fungal internal transcribed spacer (ITS) region, with elemental analysis of airborne particulate matter (PM10) to investigate the community compositions and structures of PM10-associated bacteria and fungi across four different seasons in three urban sites of Rome with differential pollution rate.Results: In this study, а clear seasonal shift of bacterial and fungal community structure driven by PM10 mass concentrations and environmental factors, such as temperature and precipitations, has been identified. In addition, the seasonal impact of local sources and long-range transported air masses on the community structures of the microbes has been also postulated. Our data revealed that the lack of precipitation and the subsequent resuspension of dust produced by vehicular traffic might contribute to the maximum abundance of soil-associated microbes in winter and summer. However, the increase of PM10 concentrations favoured also by climatic conditions, domestic heating and dust advection event from African desert further shaped the community structure of winter. Across three seasons, the pollutant removal-hydrogen oxidation bacteria and the opportunist-human pathogenic fungi progressively increased with pollution levels, in the sequence from green to residential and/or polluted area close to the traffic roads, with highest fraction during winter.Discussion: Hence, our results highlight a close interrelationship between pollution, climatic factors and abundance of certain bacterial and fungal predicted functional groups also with potential implications for human health.

Impact of 2020 COVID-19 lockdowns on particulate air pollution across Europe

Abstract. To fight against the first wave of coronavirus disease 2019 (COVID-19) in 2020, lockdown measures were implemented in most European countries. These lockdowns had well-documented effects on human mobility. We assessed the impact of the lockdown implementation and relaxation on air pollution by comparing daily particulate matter (PM), nitrogen dioxide (NO2) and ozone (O3) concentrations, as well as particle number size distributions (PNSDs) and particle light absorption coefficient in situ measurement data, with values that would have been expected if no COVID-19 epidemic had occurred at 28 sites across Europe for the period 17 February–31 May 2020. Expected PM, NO2 and O3 concentrations were calculated from the 2020 Copernicus Atmosphere Monitoring Service (CAMS) ensemble forecasts, combined with 2019 CAMS ensemble forecasts and measurement data. On average, lockdown implementations did not lead to a decrease in PM2.5 mass concentrations at urban sites, while relaxations resulted in a +26 ± 21 % rebound. The impacts of lockdown implementation and relaxation on NO2 concentrations were more consistent (−29 ± 17 and +31 ± 30 %, respectively). The implementation of the lockdown measures also induced statistically significant increases in O3 concentrations at half of all sites (+13 % on average). An enhanced oxidising capacity of the atmosphere could have boosted the production of secondary aerosol at those places. By comparison with 2017–2019 measurement data, a significant change in the relative contributions of wood and fossil fuel burning to the concentration of black carbon during the lockdown was detected at 7 out of 14 sites. The contribution of particles smaller than 70 nm to the total number of particles significantly also changed at most of the urban sites, with a mean decrease of −7 ± 5 % coinciding with the lockdown implementation. Our study shows that the response of PM2.5 and PM10 mass concentrations to lockdown measures was not systematic at various sites across Europe for multiple reasons, the relationship between road traffic intensity and particulate air pollution being more complex than expected.

Effects of stubble burning and firecrackers on the air quality of Delhi.

Every year at the onset of winter season (October-November), crop residue/parali/stubble burning starts in Punjab and Haryana, leading to heavy air pollution in Delhi, and adversely affecting human and environmental health. During this time, the combination of unfavourable meteorological conditions, additional emissions from stubble burning, and firework activities in this area causes the air quality to furtherdeteriorate. In this study, we have attempted to understand the influence of parali and firecracker incidents on air pollutants' variability over Delhi during the last three years (2020 to 2022). For this purpose, daily average particulate matter and gaseous pollutants data were fetched from the Central Pollution Control Board (CPCB), and daily total fire counts and fire radiative power (FRP) data were retrieved from NASA's Fire Information for Resource Management System (FIRMS). A bigger area of severe burning is suggested by higher FRP values and higher fire counts in the middle of November in all the years considered. Three years satellite-based FIRMS data over Punjab and Haryana show the highest number of active fire counts in 2021 (n = 80,505) followed by 2020 (n = 75,428), and 2022 (n = 49,194). More than 90% parali burning incidents were observed in Punjab state only despite the considerable variability in numbers among the years. The significant effect of parali burning was seen on pollutant concentration variability. As the number of fire count increases or decreases in Punjab and Haryana, there is a corresponding increase or decrease in the particulate matter concentration with a time lag of few days (1 to 2days). The trend in backward air mass trajectories suggests that the variable response time of pollutants' concentration is due to local and distant sources with different air mass speeds. Our estimates suggest that stubble burning contributes 50-75% increment in PM2.5 and 40 to 45% increase in PM10 concentration between October and November. A good positive correlation between PM2.5, PM10, NOX, and CO and fire counts (up to 0.8) suggests a strong influence of stubble burning on air quality over Delhi. Furthermore, the firecracker activities significantly increase the concentration of particulate matter with ~100% increment in PM2.5 and ~55% increment in PM10 mass concentrations for a relatively shorter period (1 to 2days).

In-vivo exposure of a plant model organism for the assessment of the ability of PM samples to induce oxidative stress

This study aims to propose an innovative, simple, rapid, and cost-effective method to study oxidative stress induced by PM through in-vivo exposure of the plant model organism Arabidopsis thaliana. A. thaliana seedlings were exposed to urban dust certified for its elemental content and to PM2.5 samples collected in an urban-industrial area of Northern Italy. An innovative technique for the detachment and suspension in water of the whole intact dust from membrane filters was applied to expose the model organism to both the soluble and insoluble fractions of PM2.5, which were analyzed for 34 elements by ICP-MS. Oxidative stress induced by PM on A. thaliana was assessed by light microscopic localization and UV–Vis spectrophotometric determination of superoxide anion (O2−) content on the exposed seedlings by using the nitro blue tetrazole (NBT) assay. The results showed a good efficiency and sensitivity of the method for PM mass concentrations >20 μg m−3 and an increase in O2− content in all exposed seedlings, which mainly depends on the concentration, chemical composition, and sources of the PM administered to the model organism. Particles released by biomass burning appeared to contribute more to the overall toxicity of PM. This method was found to be cost-effective and easy to apply to PM collected on membrane filters in intensive monitoring campaigns in order to obtain valuable information on the ability of PM to generate oxidative stress in living organisms.

Characterization of dust-related new particle formation events based on long-term measurement in the North China Plain

Abstract. Mineral dust is a major natural atmospheric aerosol that impacts the Earth's radiation balance. The significant scavenging process of fine particles by the strong wind during the dust episodes provided a relatively clean environment that was favourable for new particle formation (NPF) occurrence. In this study, the NPF occurred following the dust episodes (dust-related NPF), and other cases under clean and polluted conditions were classified based on the long-term particle number size distribution (PNSD) in urban Beijing in spring from 2017 to 2021. It was found that the observed formation (Jobs) and growth rate (GR) of dust-related NPF events were approximately 50 % and 30 % lower than the values of other NPF days, respectively. A typical severe dust storm that originated from Mongolia and swept over northern China on 15–16 March 2021 was analysed, to illustrate how the dust storm influences the NPF event. The maximum hourly mean PM10 mass concentration reached 8000 µg m−3 during this dust storm. The occurrence of an NPF event after a dust storm was facilitated due to the low condensation sink (∼ 0.005 s−1) caused by the strong dilution process of pre-existing particles. However, a downward trend of particle hygroscopicity was found during dust storm and NPF event as compared with the polluted episode, resulting in an increasing trend of the critical diameter at different supersaturations (ss), where aerosols are activated as cloud condensation nuclei (CCN), although the NPF event occurred when dust faded. The critical diameter was elevated by approximately 6 %–10 % (ss = 0.2 % and 0.7 %) during the dust storm, resulting in a lower CCN activation ratio, especially at low supersaturation. Modifications of the nucleation and growth process, as well as the particle-size distribution and hygroscopicity of the dust particles, provide valuable information that reveals the underlying climate and air quality effects of Asian mineral dust.

Spatial distributions of PM10-bound metal elements in the central part of western Taiwan and their potential emission sources and the carcinogenic health risks.

This study aimed to investigate the spatial distribution of metal elements in PM10 and their potential sources and associated health risks over a period of two years in eight locations in the central part of western Taiwan. The study revealed that the mass concentration of PM10 and the total mass concentration of 20 metal elements in PM10 were 39.0 μg m-3 and 4.74 μg m-3, respectively, with total metal elements accounting for approximately 13.0% of PM10. Of the total metal elements, 95.6% were crustal elements (Al, Ca, Fe, K, Mg, and Na), with trace elements (As, Ba, Cd, Cr, Co, Cu, Ga, Mn, Ni, Pb, Sb, Se, V, and Zn) contributing only 4.4%. Spatially, the inland areas exhibited higher PM10 concentrations due to lee-side topography and low wind speeds. In contrast, the coastal regions exhibited higher total metal concentrations because of the dominance of crustal elements from sea salt and crustal soil. Four primary sources of metal elements in PM10 were identified as sea salt (58%), re-suspended dust (32%), vehicle emissions and waste incineration (8%), and industrial emissions and power plants (2%). The positive matrix factorization (PMF) analysis results indicated that natural sources like sea salt and road dust contributed up to 90% of the total metal elements in PM10, while only 10% was attributed to human activities. The excess cancer risks (ECRs) associated with As, Co, and Cr(VI) were greater than 1 × 10-6, and the total ECR was 6.42 × 10-5. Although only 10% of total metal elements in PM10 came from human activities, they contributed to 82% of the total ECR.

An ephemeral increase in organic carbon, ion ratios, and heavy metal-containing fine particles was screened in a maritime demarcation zone between North and South Korea

Due to its location in a politically and militarily sensitive area as a maritime demarcation between North and South Korea, increasing attention has been given to the chemical and physical changes in atmospheric aerosols on Baengnyeong Island. To observe modifications in the concentrations and components of basic air pollutants, a Korean State Air Quality Monitoring Superstation was established on the island. The monitoring data obtained from the superstation revealed a significant, short-lived increase in mass concentration of SO2, NO2, and nitrate, organic carbon, and heavy metals (especially Pb) in ambient PM2.5, as well as a gradual decrease in mass concentration of PM10, PM2.5, and sulfate in PM2.5 during September 24–27, 2012. Building upon this data, we conducted source appointment of PM2.5 using the positive matrix factorization receptor model (PMF) and identified six major sources: sea-spray aerosols (29.8%), mobile and biogenic aerosols (24.3%), secondary aerosols (20.0%), gunshot residue (11.1%), mineral dust (8.6%), and oil combustion (6.3%). To examine the physicochemical properties of atmospheric particles, 16 samples collected during that period were investigated using a quantitative energy-dispersive electron probe X-ray microanalysis (ED-EPMA). We analyzed the morphology, mixing state, elemental constituents, and relative elemental atomic concentrations of 2545 individual particles from the aforementioned six sources, with a focus on particles containing Ca, organic carbon, and Pb. The complementary use of PMF and ED-EPMA confirmed that an episode of air pollution, likely resulting from a military exercise or activity, occurred from September 25 to 26 on or near Baengnyeong Island, leading to an increase in aerosols from gunshot residue and oil combustion. The significant increase in the relative number abundance of organic carbon and heavy metal-containing particles precisely matched their mass concentrations from the monitoring data. These findings imply that multiple methods for bulk and single-particle analysis are necessary when addressing important issues related to source appointment of atmospheric particulate matter.

Adding the impacts of biological crusts on sand and dust storm emission in Asia

Although biological crusts (biocrusts) grant stability to dust source areas by inhibiting dust emission, only a few models have explicitly considered their inhibitory effect. In this study, we used the China Meteorological Administration Unified Atmospheric Chemistry Environment/Dust model and combined retrieved biocrust information with two biocrust inhibition schemes (roughness length and biocrust strength schemes) to study numerically the inhibitory effect of biocrusts on a strong dust storm that occurred in Central and East Asia on 25−30 Mar. 2018. The inhibitory effect of biocrusts on dust emission increased with biocrust coverage. The total dust emission forecasted by the roughness length and biocrust strength schemes was reduced by approximately 56.7% and 47.9% on average, respectively. In downstream sites in China, the surface mass concentrations of PM10 forecasted by the roughness length and biocrust strength schemes decreased by 36%–57% and 24%–41%, respectively. When biocrusts were considered, the forecast for stations near the dust source area considerably improved and that for stations distant from the source area became more reasonable but with larger bias under the combined effects of emission, transport and deposition.