Sources Of PM2 Research Articles

Association between air pollutants, sources, and components of PM2.5 and pediatric outpatient visits for respiratory diseases in Shanghai, China

BackgroundAlthough exposure to ambient air pollution has been associated with outpatient visits for respiratory diseases, there have been few recent studies investigating the effect of the chemical components of ambient particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) on pediatric outpatient visits for respiratory diseases. In addition, there remains scarce evidence regarding PM2.5 components and sources that are most relevant to pediatric outpatient visits for respiratory diseases. This study aimed to determine the association between four ambient pollutants (SO2, NO2, PM10, and PM2.5), sources and chemical components of PM2.5, and pediatric outpatient visits for respiratory diseases. MethodsThis time-series study was conducted between January 1, 2018 and December 31, 2019 in Shanghai, China. A positive matrix factorization model was used to determine the source apportionment of PM2.5, and a generalized additive model was employed to evaluate the effect of air pollutants and the components and sources of PM2.5 on pediatric outpatient visits for respiratory diseases. In addition, a two-pollutant model was used to investigate the potential confounding effects of the copollutants. ResultsThe study revealed that a 10 μg/m3 increase in SO2, NO2, PM10, and PM2.5 concentrations corresponded to increases of 3.4% (95% CI: 2.3%–4.6%), 1.9% (95% CI: 1.7%–2.2%), 0.5% (95% CI: 0.4%–0.7%), and 0.5% (95% CI: 0.4%–0.7%), respectively, in pediatric outpatient visits for respiratory diseases on the day with the most significant lag effect. Notably, Ni exposure more strongly correlated with increased pediatric outpatient visits for respiratory diseases, with the percent of increase by 10 ng/m3 ranging from 7.6% to 16.5%. Moreover, among the PM2.5 sources researched in this study, oil and coal combustion were the most important for increasing pediatric outpatient visits for respiratory diseases. ConclusionThis study demonstrates that ambient pollutants and the chemical components and sources of PM2.5 were significantly associated with an elevated risk of pediatric outpatient visits for respiratory diseases and provides insights into the differential health risks of PM2.5 components.

Analysing role of airborne particulate matter in abetting SARS-CoV-2 outbreak for scheming regional pandemic regulatory modalities

The mutating SARS-CoV-2 necessitates gauging the role of airborne particulate matter in the COVID-19 outbreak for designing area-specific regulation modalities based on the environmental state-of-affair. To scheme the protocols, the hotspots of air pollutants such as PM2.5, PM10, NH3, NO, NO2, SO2, and and environmental factors including relative humidity (RH), and temperature, along with COVID-19 cases and mortality from January 2020 till December 2020 from 29 different ground monitoring stations spanning Delhi, are mapped. Spearman correlation coefficients show a positive relationship between SARS-COV-2 with particulate matter (PM2.5 with r > 0.36 and PM10 with r > 0.31 and p-value <0·001). Besides, SARS-COV-2 transmission showed a substantial correlation with NH3 (r = 0.41), NO2 (r = 0.36), and NO (r = 0.35) with a p-value <0.001, which is highly indicative of their role in SARS-CoV-2 transmission. These outcomes are associated with the source of PM and its constituent trace elements to understand their overtone with COVID-19. This strongly validates temporal and spatial variation in COVID-19 dependence on air pollutants as well as on environmental factors. Besides, the bottlenecks of missing latent data, monotonous dependence of variables, and the role air pollutants with secondary environmental variables are discussed. The analysis set the foundation for strategizing regional-based modalities considering environmental variables (i.e., pollutant concentration, relative humidity, temperature) as well as urban and transportation planning for efficient control and handling of future public health emergencies.

Relationship between PM1.0 and PM2.5 in urban and background areas of Republic of Korea

Seasonal intensive measurements of atmospheric particulate matter with aerodynamic diameters less than or equal to a nominal 1.0 μm (PM1.0) and 2.5 μm (PM2.5) were conducted at an urban (Seoul, SL) and a background site (Baengnyeong, BN, an island about 200 km from SL) in Korea to fully understand the characteristics of PM1.0 within PM2.5, as well as the differences in PM characteristics between urban and background sites. A single-channel particle sampler with PM1.0 and PM2.5 cyclones was used to collect the samples simultaneously. Eight ions (Cl−, NO3−, SO42−, Na+, NH4+, K+, Ca2+, and Mg2+), carbonaceous components (organic carbon (OC) and elemental carbon (EC)), and ten elements (As, Cd, Cu, Zn, Pb, Cr, Mn, Fe, Ni, and Al) were analyzed. The ratio of PM1.0 to PM2.5 mass concentration was approximately 80%, and the coefficients of determination (r2) between PM1.0 and PM2.5 was higher than 0.95 at both sites. PM1.0 was primarily responsible for the chemical composition of PM2.5. At both sites, OC was the most prevalent component of PM1.0 and PM2.5, followed by NO3−, SO42−, NH4+, and EC. According to source apportionment of the PMF model, PM1.0 and PM2.5 were explained from nine and eight factors at both SL and BN, respectively. At both sites, the significantly larger proportion of the factors of PM1.0 and PM2.5 were secondary inorganic aerosol and the contributions of sources for PM2.5 in SL and BN were comparable to those of PM1.0. This study confirmed that (1) PM1.0 and PM2.5 showed similar characteristics at both SL and BN and (2) PM1.0 contributed significantly to the chemical composition and sources of PM2.5 both in SL and BN, implying that PM1.0-2.5 had a minor impact on PM2.5 in these two sites. Thus, though further studies are needed, it is likely that the current air quality management direction for PM2.5 is also effective in managing PM1.0 in Korea.

Source apportionment of PM2.5 in Thailand's deep south by principal component analysis and impact of transboundary haze.

Atmospheric particulate matter smaller than 2.5 micron (PM2.5) was evaluated at four sites in the lower southern part of Thailand during 2019-2020 to understand the impact of PM2.5 transport from peatland fires in Indonesia on air quality during the southwest monsoon season. Mass concentration and chemical bound-PM, including carbon composition, e.g., organic carbon (OC) and elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and inorganic elements, were analyzed. The PM2.5 emission sources were identified by principal components analysis (PCA). The average mass concentrations of PM2.5 in the normal period, which represents clean background air, from four sites was 3.5-5.1µg/m3, whereas during the haze period, it rose to 5.4-13.5µg/m3. During the haze period, both OC and EC were 3.5 times as high as in the normal period. The average total PAHs and BaP-TEQ of PM2.5 during the haze period were ~ 1.3-1.7 and ~ 1.2-1.9 times higher than those in the normal period. The K concentrations significantly increased during haze periods. SO42- dominated throughout the year. The effects of external sources, especially the transboundary haze from peatland fires, were significantly enhanced, because the background air in the study locations was generally clean. PCA indicated that vehicle emission, local biomass burning, and secondary particles played a key role during normal period, whereas open biomass burning dominated during the haze phenomena. This was consistent with the OC/EC and PAH diagnostic ratios. Backward trajectories confirmed that the sources of PM during the haze period were predominantly peatland fires in Sumatra, Indonesia, due to southwest wind.

In vitro toxicity and lung cancer risk: Atmospheric particulate matter from a city in southeastern Brazil impacted by biomass burning

The effects of PM10 on human health were investigated using samples collected in São Carlos city (São Paulo state), by the determination of the concentrations of PAHs and derivatives, together with evaluations of cytotoxicity and the formation of ROS in in vitro tests. In 2016, the mean concentrations of PM10, ΣPAHs, Σoxy-PAHs, Σnitro-PAHs, Σsaccharides, and Σions were 21.12 ± 9.90 μg m−3, 1.47 ± 1.70 ng m−3, 0.37 ± 0.31 ng m−3, 0.84 ng m−3, 119.91 ± 62.14 ng m−3, and 5.66 ± 4.52 μg m−3, respectively. The PM10 concentrations did not exceed the limit thresholds set by national legislation, however, the annual lung cancer risk calculated was 2.59 ± 1.22 cases per 100,000 people, in the dry season, which accounts for the annual risk (April to September). Moreover, the carcinogenic activities of the PAHs mixture were more than 1000-fold higher in the dry season (dry season: BaPeq = 0.30 ng m−3; wet season BaPeq = 0.02 ng m−3). The concentrations of most analytes were also higher during the dry season, as had already been demonstrated in the same city. This was due to reductions in precipitation, relative humidity and air temperature, and increased biomass burning, which was the main source of PM10 in the city in 2016 (contribution rate of more than 50%). Toxicological results also showed the negative impacts of PM10, exposure to PM10 extracts for 72 h reduced the viability of A549 and MRC5 cells, and the formation of ROS was observed. The cellular responses obtained using combined and individual extracts of PM10 differed and were sometimes associated with specific compounds. These demonstrate the importance of monitoring PM toxicity using different approaches and the main anthropogenic sources’ contribution. Therefore, to improve air quality and human health, existing legislation needs to be modified to incorporate these tests.

Acute health impact of wildfire-related and conventional PM2.5 in the United States: A narrative review

• Economic activities and wildfire events lead U.S. PM 2.5 concentration climbing back • Few studies focused on summarizing health effects from short-term PM 2.5 exposure • Both sources of PM 2.5 have acute impacts on cardiovascular-respiratory disorders • Less is known of PM 2.5 on other health outcomes, especially on work loss days • Information of PM 2.5 on vulnerable populations is needed to inform interventions The impacts of air pollution on public health have become great concerns worldwide. Particulate matter with an aerodynamic diameter small than 2.5μm (PM 2.5 ), either from conventional sources such as traffic emissions or wildfire smoke, is among the most damaging air pollutants and has been documented to cause both acute and chronic diseases and their exacerbations. Owing to increasing economic activities, consumption of natural gasses, and wildfires, the concentration of PM 2.5 in the United States climbed back again. Compared with a large body of research summarizing PM 2.5 chronic effects on health, fewer studies focused on its acute health impacts. We conducted a narrative review of the available epidemiology studies evaluating the associations between short-term PM 2.5 exposure and acute health outcomes including work loss days. Compared to the studies investigating wildfire PM 2.5 effects, we found a larger body of evidence indicating the acute impact of conventional PM 2.5 exposure on various health effects, especially on respiratory and cardiovascular disorders, including related to emergency and hospital visits, as well as disease-specific and all-cause mortalities. More studies are needed to fully understand the extent of the acute health impact of PM 2.5 exposure from both sources, especially from wildfires, and on neuropsychological, and metabolic disorders and birth outcomes; and on outcomes such as work loss days, which are not perceived as severe enough to necessitate medical assistance but affect a large population. More research on vulnerable populations from wildfire PM 2.5 exposure is also needed to inform public health interventions. :

Fine aerosol sources at an urban background site in the Eastern Mediterranean (Nicosia; Cyprus): Insights from offline versus online source apportionment comparison for carbonaceous aerosols

A total of 348 daily PM2.5 samples were collected at an urban background site of Nicosia, capital of Cyprus, for one-year period (October 2018–October 2019) to assess the origin and sources of fine PM at the Eastern Mediterranean, a poorly characterized area of the world.The samples were analysed for water soluble ionic species, elemental and organic carbon, carbohydrates and trace metals, the combination of which were utilized to identify pollution sources by applying Positive Matrix Factorization (PMF). Six PM2.5 sources, namely long-range transport (LRT; 38 %), traffic (20 %), biomass burning (16 %), dust (10 %), sea salt (9 %) and heavy oil combustion (7 %), were identified.Despite sampling in an urban agglomeration, the chemical fingerprint of the aerosol is largely dictated by air mass origin rather than local sources. Springtime is characterized by the most elevated particulate levels due to the southerly air masses carrying particles from the Sahara Desert. Northerlies are observed throughout the year but are predominant during summer allowing the LRT source to peak (54 % during summer). Only during winter, due to extensive use of biomass combustion for domestic heating (36.6 % during winter), local sources dominate.A co-located online PMF source apportionment of submicron carbonaceous aerosols (Organic Aerosols, OA; Black Carbon) was performed by the means of an Aerosol Chemical Speciation Monitor (for OA) and an Aethalometer (for BC) for a four-month period. The comparison between the two methodologies allowed to better assess the robustness and limitations of the two methodologies. More specifically, LRT OA and biomass burning BC apportioned by the offline PMF showed a strong consistency with the online apportioned more oxidized oxygenated OA and BCwb, respectively; cross validating these sources. On the other hand, our traffic factor may contain additional hydrocarbon-like OA and BC from fossil fuel sources other than just vehicular emissions. Finally, the offline biomass burning OA source is likely to contain both primary and secondary OA.

Towards a better understanding of fine PM sources: Online and offline datasets combination in a single PMF

Source apportionment (SA) techniques allocate the measured ambient pollutants with their potential source origin; thus, they are a powerful tool for designing air pollution mitigation strategies. Positive Matrix Factorization (PMF) is one of the most widely used SA approaches, and its multi-time resolution (MTR) methodology, which enables mixing different instrument data in their original time resolution, was the focus of this study. One year of co-located measurements in Barcelona, Spain, of non-refractory submicronic particulate matter (NR-PM1), black carbon (BC) and metals were obtained by a Q-ACSM (Aerodyne Research Inc.), an aethalometer (Aerosol d.o.o.) and fine offline quartz-fibre filters, respectively. These data were combined in a MTR PMF analysis preserving the high time resolution (30 min for the NR-PM1 and BC, and 24 h every 4th day for the offline samples). The MTR-PMF outcomes were assessed varying the time resolution of the high-resolution data subset and exploring the error weightings of both subsets. The time resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental interpretability. The MTR-PMF resolved eight PM1 sources: ammonium sulphate + heavy oil combustion (25%), ammonium nitrate + ammonium chloride (17%), aged secondary organic aerosol (SOA) (16%), traffic (14%), biomass burning (9%), fresh SOA (8%), cooking-like organic aerosol (5%), and industry (4%). The MTR-PMF technique identified two more sources relative to the 24 h base case data subset using the same species and four more with respect to the pseudo-conventional approach mimicking offline PMF, indicating that the combination of both high and low TR data is significantly beneficial for SA. Besides the higher number of sources, the MTR-PMF technique has enabled some sources disentanglement compared to the pseudo-conventional and base case PMF as well as the characterisation of their intra-day patterns.

The influence of COVID-19 pandemic on PM2.5 air quality in Northern Taiwan from Q1 2020 to Q2 2021

The study aimed to investigate the PM2.5 variations in different periods of COVID-19 control measures in Northern Taiwan from Quarter 1 (Q1) 2020 to Quarter 2 (Q2) 2021. PM2.5 sources were classified based on long-range transport (LRT) or local pollution (LP) in three study periods: one China lockdown (P1), and two restrictions in Taiwan (P2 and P3). During P1 the average PM2.5 concentrations from LRT (LRT-PM2.5-P1) were higher at Fuguei background station by 27.9% and in the range of 4.9–24.3% at other inland stations compared to before P1. The PM2.5 from LRT/LP mix or pure LP (Mix/LP-PM2.5-P1) was also higher by 14.2–39.9%. This increase was due to higher secondary particle formation represented by the increase in secondary ions (SI) and organic matter in PM2.5-P1 with the largest proportion of 42.17% in PM2.5 from positive matrix factorization (PMF) analysis. A similar increasing trend of Mix/LP-PM2.5 was found in P2 when China was still locked down and Taiwan was under an early control period but the rapidly increasing infected cases were confirmed. The shift of transportation patterns from public to private to avoid virus infection explicated the high correlation of the increasing infected cases with the increasing PM2.5. In contrast, the decreasing trend of LP-PM2.5-P3 was observed in P3 with the PM2.5 biases of ∼45% at all the stations when China was not locked down but Taiwan implemented a semi-lockdown. The contribution of gasoline vehicle sources in PM2.5 was reduced from 20.3% before P3 to 10% in P3 by chemical signatures and source identification using PMF implying the strong impact of strict control measures on vehicle emissions. In summary, PM2.5 concentrations in Northern Taiwan were either increased (P1 and P2) or decreased (P3) during the COVID-19 pandemic depending on control measures, source patterns and meteorological conditions.

Air Quality Impacts on the Giant Panda Habitat in the Qinling Mountains: Chemical Characteristics and Sources of Elements in PM2.5

The wild giant panda habitat is inaccessible and far away from the main areas of human activity, so environmental pollutants entering the habitat are mainly the result of external migration and spread through the atmospheric advection and diffusion processes and particulate matter deposition. To research the variation, transmission route, chemical characteristics, and source of PM2.5 in the habitat of wild giant pandas, we set up a PM2.5 sampling point near the Shaanxi Foping National Nature Reserve (SFNNR), which is the area with the highest population density of wild giant pandas in the Qinling Mountains. The 12-month average concentration of PM2.5 was 11.3 ± 7.9 μg/m3 from July 2021 to June 2022, and the 12-month average concentration did not exceed the limit value set in the standard. In the results of our analysis of element concentrations, As and Pb were much lower than the limit standard. Si, S, P, and Cl accounted for 99.60% of nonmetallic elements, while the proportion of the six metallic elements, Na, Mg, Al, K, Ca, and Fe, was 96.27%. According to the analysis results of enrichment factor (EF) and pollutant emission sources, there were four sources for the total elements in PM2.5, which were mainly distributed in the areas around the reserve. These included dust, coal combustion, biomass burning, and traffic-related emissions, which contributed 55.10%, 24.78%, 11.91%, and 8.22% of the total element mass in PM2.5, respectively. Additionally, Pb, Cu, Zn, As, Sc, Co, Ga, Mg, and, especially, Se were severely affected by human activities (coal burning, biomass burning, and traffic-related emissions). In the villages and towns around the wild giant panda habitat, the majority of energy for cooking and heating comes from coal and biomass burning, and older vehicles with high emissions are used more frequently. Therefore, to better protect the health of and reduce the impact of environmental pollution on wild giant pandas, we put forward relevant recommendations, including upgrading the energy structure of towns and villages near the habitat to increase the proportion of clean energy, such as photovoltaic power generation, natural gas, etc.; decreasing the combustion of coal and biomass; encouraging the upgrading of agricultural diesel machines and older vehicles used in these areas; and setting limits on vehicle emissions in areas surrounding the habitat.

Chemical Characterization and Source Apportionment of PM10 Using Receptor Models over the Himalayan Region of India

This study presents the source apportionment of coarse-mode particulate matter (PM10) extracted by 3 receptor models (PCA/APCS, UNMIX, and PMF) at semi-urban sites of the Indian Himalayan region (IHR) during August 2018–December 2019. In this study, water-soluble inorganic ionic species (WSIIS), water-soluble organic carbon (WSOC), carbon fractions (organic carbon (OC) and elemental carbon (EC)), and trace elements of PM10 were analyzed over the IHR. Nainital (62 ± 39 µg m−3) had the highest annual average mass concentration of PM10 (average ± standard deviation at 1 σ), followed by Mohal Kullu (58 ± 32 µg m−3) and Darjeeling (54 ± 18 µg m−3). The annual total ∑WSIIS concentration order was as follows: Darjeeling (14.02 ± 10.01 µg m−3) &gt; Mohal-Kullu (13.75 ± 10.21 µg m−3) &gt; Nainital (10.20 ± 6.30 µg m−3), contributing to 15–30% of the PM10 mass. The dominant secondary ions (NH4+, SO42−, and NO3−) suggest that the study sites were strongly influenced by anthropogenic sources from regional and long-range transport. Principal component analysis (PCA) with an absolute principal component score (APCS), UNMIX, and Positive Matrix Factorization (PMF) were used for source identification of PM10 at the study sites of the IHR. All three models showed relatively similar results of source profiles for all study sites except their source number and percentage contribution. Overall, soil dust (SD), secondary aerosols (SAs), combustion (biomass burning (BB) + fossil fuel combustion (FFC): BB+FFC), and vehicular emissions (VEs) are the major sources of PM10 identified by these models at all study sites. Air mass backward trajectories illustrated that PM10, mainly attributed to dust-related aerosols, was transported from the Thar Desert, Indo-Gangetic Plain (IGP), and northwestern region of India (i.e., Punjab and Haryana) and Afghanistan to the IHR. Transported agricultural or residual burning plumes from the IGP and nearby areas significantly contribute to the concentration of carbonaceous aerosols (CAs) at study sites.

Physicochemical Assessment of the Road Vehicle Traffic Pollution Impact on the Urban Environment

Vehicle traffic pollution requires complex physicochemical analysis besides emission level measuring. The current study is focused on two campaigns of emissions measurements held in May and September 2019 in Alba Iulia City, Romania. There was found a significant excess of PM2.5 for all measuring points and PM10 for the most circulated points during May, along with significant VOC and CO2 emissions. September measurements reveal threshold excess for all PM along with increased values for VOC and CO2 emissions. These are the consequences of the complex environmental interaction of the traffic. Street dust and air-suspended particle samples were collected and analyzed to evidence the PM2.5 and PM10 sources. Physicochemical investigation reveals highly mineralized particulate matter: PM2.5 fractions within air-suspended particle samples predominantly contain Muscovite, Kaolinite, and traces of Quartz and Calcite, while PM10 fractions within air-suspended particle samples predominantly contain Quartz and Calcite. These mineral fractions originate in street dust and are suspended in the atmosphere due to the vehicles’ circulation. A significant amount of soot was found as small micro-sized clusters in PM2.5 and fine micro-spots attached over PM10 particles, as observed by Mineralogical Optical Microscopy (MOM) and Fourier Transformed Infrared Spectroscopy (FTIR). GC-MS analysis found over 53 volatile compounds on the investigated floating particles that are related to the combustion gases, such as saturated alkanes, cycloalkanes, esters, and aromatic hydrocarbons. It proves a VOC contamination of the measured particulate matters that make them more hazardous for the health. Viable strategies for vehicle traffic-related pollutants mitigation would be reducing the street dust occurrence and usage of modern catalyst filters of the combustion gas exhausting system.

Risk Assessment and Source Apportionment of Metals on Atmospheric Particulate Matter in a Suburban Background Area of Gran Canaria (Spain).

Concentration levels of 11 heavy metals were analyzed in PM10 and PM2.5 samples from a suburban area frequently affected by Saharan dust in which is located a school. The heavy metals risk assessment was carried out by the 2011 U.S. Environmental Protection Agency method, estimating the chronic and carcinogenic hazard levels both in adults and children. The highest level of chronic hazard was reached for Cr, with values of approximately 8 (PM10, adulthood), 2 (PM10, childhood) and 1.5 (PM2.5, adult age), significantly exceeding the limit value (equal to 1). Regarding the carcinogenic risk level, it was also high for Cr, with values between 10-3 and 10-1 for both study populations and particle size. For the rest of the studied metals, no health risk levels of concern were obtained. The positive matrix factorization method was used for the estimation of heavy metal emission sources apportionment. Non-exhaust vehicle emissions were the main source of Cr emissions under PM2.5, while industrial processes were the main source for PM10. Mineral dust and marine aerosol were common emission sources for both particles sizes-with different contributions. Vehicle emissions, construction and agricultural activities were the main emission sources for PM10, and fossil fuel combustion, road dust re-suspension and ammonium sulfate were for PM2.5. The results obtained in this study support the need to continue applying mitigation measures in suburban areas which are affected by nearby anthropogenic emissions, causing the consequent emission of materials hazardous to human health.

PM10 within Indian standard is achievable by mitigating the sources of PM1: A thirteen years (2009–2021) long study and future prediction (2024) over the eastern Himalayas, India

A thirteen years-long (2009–2021) study was conducted on PM10 pollution over a high-altitude station, Darjeeling (27.1° N and 88.15° E, 2000 m amsl) in the eastern Himalayas in India. To better understand the sources and build a proper mitigation strategy for PM10, we have segregated PM10 into PM1 and PM1-10 using the Anderson cascade impactor. A total of 620 sets of samples were collected during the entire study period. PM10 was found to remain within its Indian standard (60 μg m−3) in every year of the study period (long-term average PM10: 46 ± 8 μg m−3). PM10 pollution during winter (74.0 ± 15.1 μg m−3) and premonsoon (73.1 ± 12.1 μg m−3) was highest compared to other seasons. Bi-modal mass size distribution of composite PM10 was observed for all the seasons with maximum peak intensity at the finer mode (PM1). Most of the anthropogenic water-soluble ionic components as well as carbonaceous aerosols exhibited maximum intensity at PM1 too. PM10 exhibited a steady decrease from 2009 to 2013 but since 2014, a sharp rise was observed. Further, we have observed that premonsoon and winter-time PM1 pollution was the key factor for a such high rise in PM10. PM1 increased sharply at a rate of 4.1 μg m−3 yr−1 in premonsoon and 3.3 μg m−3 yr−1 in winter since 2014. Auto regressive integrated moving average (ARIMA) model for future prediction revealed that PM10 would cross its Indian standard and PM1 alone would cross the PM2.5 standard in 2024 if the current scenario remains the same and the city would be enlisted among the “non-attainment” cities of the country under “National Clean Air Program (NCAP)” of Government of India. The Positive matrix factorization (PMF) model was run to apportion the sources of PM1 and PM10. It was observed that the vehicular emissions in premonsoon (33% in PM1; 28% in PM10; contribution of vehicular emission in PM1 to PM10 > 90%) and biomass burning in winter (27% in PM1; 23% in PM10; contribution of PM1 to PM10 > 80%) are the most influencing sources that need to be curbed to mitigate PM1 and hence PM10 pollution over Darjeeling.

Bangkok port and coastal regions of Thailand under atmospheric PM2.5 pollution: A hypothetical nuclear power plant accident

The atmospheric transport pathways of 1) a hypothetical accident of aerosol at Ninh Thuan Nuclear Power Plant (NPP) in Vietnam to the coastal area of Thailand and 2) the source of PM2.5 near the area of Bangkok port were selected for case studies. The overviews of atmospheric transport patterns were analyzed using the long-term mean wind and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for simulating the transport pathway. The results found that the possible pathway of aerosol from Ninh Thuan NPP to coastal regions of Thailand was present over the entire year, except for the southwest monsoon month. The percentage of aerosol transport reaching the coast of Thailand is highest in the southern coastal part, found in January-April and October-December, with a maximum in January (approximately 84 %). For PM2.5 pollutants on a poor day, strongly positive and negative significant relationships with relative humidity (RH) (r = 0.44, rs = 0.46) and wind speed (r = -0.46, rs = -0.47) were found, respectively. The concentration of PM2.5 is slightly negatively correlated with wind direction. In addition, the upper air transport may bring PM2.5 pollutants from neighboring provinces or Southeast Asia to Bangkok port, Thailand, which appeared in anticyclone form in the upper atmosphere before the day which exceeded the safety level of PM2.5. Highlights The possible pathway of aerosol from Ninh Thuan NPP to coastal regions of Thailand is present over the entire year The percentage of aerosol transport from Ninh Thuan NPP reaching the coast of Thailand is highest in the southern coastal part during the northeast monsoon The exceeding air pollution problem in Bangkok comes from unfavorable meteorological conditions The anticyclone center in the upper atmosphere of Vietnam, Laos, and Cambodia formed before the day, which exceeded the safety level at a poor level

The impact of seasonality and meteorological conditions on PM2.5 carbonaceous fractions coupled with carbon isotope analysis: Advantages, weaknesses and interpretation pitfalls

PM2.5 samples were collected from October 2020 to September 2021 in Legnica in the Lower Silesia Voivodeship (SW Poland), where environmental guidelines are often exceeded. In order to have a better insight into the respective inputs of local and regional sources concentrations of PM2.5, carbonaceous fractions (TC/OC/EC), carbon isotope composition (δ13CTC) and FTIR spectra of PM2.5 were studied in parallel. The objectives of this study were to (i) identify the emission sources controlling the local aerosol budget and to (ii) discuss the potential limits of the approach used. We highlighted a seasonality in the concentrations of PM2.5, TC, OC, and EC, with higher concentrations in winter resulting of an increased activity of local emission sources coupled to the occurrence of specific meteorological conditions. While we identified coal burning as the main source of pollution in winter, a mix of emissions from coal and bio/organic combustion and road traffic explains the pollution levels in summer. Coupling these findings with the study of FTIR spectra, we corroborated that incomplete combustion of fossil fuels is a major source in winter whereas biogenic particles are in summer. Despite the observed seasonal variations in the OC and EC concentrations, the OC/EC ratios did not show significant variations. It suggests that this ratio, if used as a single proxy to infer sources of carbonaceous aerosols, may lead to interpretation pitfalls. Our results strongly suggest that a multi-proxy approach that couples TC/OC/EC and FTIR to carbon isotope geochemistry provides a reliable evaluation tool for air quality and brings strong constraints on the corresponding sources of PM2.5.

A supervised machine learning approach to classify traffic-derived PM sources based on their magnetic properties.

Environmental magnetism techniques are increasingly used to map the deposition of particulate pollutants on any type of accumulative surfaces. The present study is part of a collective effort that begun in recent years to evaluate the efficiency of these techniques involving a large range of measurements to trace the source signals. Here we explore the possibilities provided by the very simple but robust k-near-neighbors algorithm (kNN) for classification in a source-to-sink approach. For this purpose, in a first phase, the magnetic properties of the traffic-related sources of particulate matter (tire, brake pads, exhaust pipes, etc.) are used to parameterize and train the model. Then, the magnetic parameters measured on accumulating surfaces exposed to a polluted air as urban plant leaves and passive filters are confronted to the model. The results are very encouraging. The algorithm predicts the dominant traffic-related sources for different kinds of accumulative surfaces. The model predictions are generally consistent according to the sampling locations. Its resolution seems adequate since different dominant sources could be identified within one street. We demonstrate the possibility to trace traffic-derived pollutants from sources to sinks based only on magnetic properties, and to eventually quantify their contributions in the total magnetic signal measured. Because magnetic mapping has a high-resolution efficiency, these results open the opportunity to complement conventional methods used to measure air quality and to improve the numerical models of pollutant dispersion.

Tracking changes in atmospheric particulate matter at a semi-urban site in Central France over the past decade

Nearly 10-year (2013−2022) data on atmospheric particulate matters (PMs) were collected to investigate the air quality in a suburban site of Orléans city (France). The PM10 concentration decreased slightly between 2013 and 2022. PMs concentrations showed a monthly variation with higher concentration in cold periods. PM10 presented a clear bimodal diurnal variation peaking at morning rush hour and midnight, whereas the fine PMs such as PM2.5 and PM1.0 only had significant peaks during nighttime. Further, PM10 had more pronounced week-end effect than other fine PMs. COVID-19 lockdown impact on PMs levels was further investigated, showing that the lockdown during cold season could result in an increase of PMs concentrations because of the enhanced household heating. We concluded that PM10 could originate from biomass burning and fossil fuel related activities, air parcels from the western Europe through Paris were also important source of PM10 in the investigated area. Fine PMs, such as PM2.5 and PM1.0, originated mainly from biomass burning in addition to secondary formation at the local scale. This study provides a long-term PMs measurement database to explore the sources and characterization of PMs in central France, which could support future regulation and formulation of air quality standards.

Mitigation of airborne contaminants dispersion in an educational building and investigate its impacts on indoor air quality and energy performance

In today's modern world, people spend most of their time inside buildings, highlighting the importance of indoor air quality (IAQ) and providing clean air to the occupants. In this regard, a simple educational building is modeled using CONTAM-EnergyPlus co-simulation to investigate IAQ enhancement strategies and their role in the building's energy performance. Three contaminants, including CO2, PM2.5, and SARS-CoV-2, are considered to be generated from various sources. The occupants generate CO2, a source of PM2.5 is assumed in the lunchroom to represent cooking activities, and a person who sheds SARS-CoV-2 moves around the zones. The main goal of this study is to apply various pollutant mitigation methods to the model, such as increasing ventilation rate and outdoor air (OA) percentage, natural ventilation, installing filters and air cleaners, and UVGI lights. Then, their performance and impact on the defined contaminants are studied individually and in combination.In this regard, a scenario with 80% outdoor air (OA) and 100% ventilation rate has been shown to be effective in reducing all three contaminants' concentrations to acceptable levels in most zones, but this results in 50% higher energy consumption compared to the model with no outdoor air. However, to achieve a safe level of PM2.5 in the lunchroom, a combination of all the strategies presented in the (0.8OA+1Vent+NatVent+all) scenario is required. Furthermore, HEPA air cleaners are more effective in diluting contaminants in all zones than UVGI lights and MERV 13 filters. Additionally, this study has shown that HVAC systems operating with little or no outside air can increase the risk of pollutants being transmitted between adjacent zones through the ducts, making it necessary to install in-duct filters.

Decryption analysis of antimony pollution sources in PM2.5 through a multi-source isotope mixing model based on lead isotopes

Antimony (Sb) in PM2.5 has attracted close attention as a new air pollutant due to its extensive use in daily life. The identification of antimony sources in PM2.5 by scientific methods is important to control its pollution. In this study, the Sb and other elements concentrations and Pb isotopic compositions in PM2.5 and possible pollution sources (soil, road dust, traffic emission, coal-fired fly ash, local factory emission dust and cement dust) were analyzed. The results showed that the Sb in the PM2.5 samples had seasonal change. The enrichment factors of Sb in PM2.5 samples were all above 100 in four seasons, which indicated anthropogenic pollution. The average value of potential ecological risk index was at extremely high-risk level greater than 320. Based on Pearson correlation coefficient and hierarchical cluster analysis results, the pollution sources of antimony and lead in PM2.5 samples were highly consistent which means that Pb isotopes might be a new and feasible tracer for Sb pollution in air. The sources analysis results based on Pb isotopes indicated that the proportion of Pb and Sb from coal-fired fly ash was the highest in winter (47.7%) and inclined to road dust in spring (34.5%), but it was mainly from traffic emissions in summer and autumn (34.2% and 32.8%). This study showed that Pb isotope tracing can be applied to predict the potential pollution sources, and it was also a feasible substitute for tracing Sb pollution in PM2.5.