Sources Of PM2 Research Articles

Improving Air Quality Standards in Europe: Comparative Analysis of Regional Differences, with a Focus on Northern Italy

The study reports a consistent comparison of emission inventories, concentration trends, and PM source apportionment in different European regions and, mostly, a thorough investigation of meteorological parameters influencing atmospheric pollutants’ dispersion. The study focuses on the reasons why Northern Italy still has difficulties complying with EU air quality standards for PM10 and NO2, despite strong emission reductions. The study demonstrates that, in the colder seasons, wind speed, PBL height, and atmospheric pressure in the Po basin are three to five times less efficient at diluting and dispersing pollutants than those occurring in regions north of the Alps. Since air quality standards aim at countering health impacts, it is advisable to consider atmospheric particulate toxicity in addition to PM10/PM2.5 mass concentration as a limit value. A discussion is reported about PM toxicity factors depending on source-specific aerosols and PM composition. We obtained PM toxicity factors that can vary by 10 times (according to carbonaceous content) across Europe, suggesting that, even at the same mass concentration, the effects of PM10/PM2.5 on human health are significantly variable. Modern PM source apportionment and reliable toxicity and epidemiological analyses represent the correct tools to build a new consistent health metric for ambient PM.

Chemical characterizations and sources of PM2.5 over the offshore Eastern China sea: Water soluble ions, stable isotopic compositions, and metal elements

Marine aerosols are mainly sea salt or secondary-formation non-sea salt (nss) inorganic ions and organic compounds released from the ocean, which are modified by substances from terrestrial sources via long-distance transport. Atmospheric particulate matter (PM2.5) was collected during a ship campaign over the offshore eastern China Sea (OECS) from April 20, 2018 to May 18, 2018. Nine water-soluble ions (WSIs), organic carbon (OC), elemental carbon (EC), stable carbon/nitrogen isotopes, and twenty-six metal elements were analyzed. The average concentrations of WSIs, OC, and EC were 13.73 ± 4.89, 2.55 ± 1.95, and 0.72 ± 0.56 μg/m3, respectively. SO42−, NH4+, and NO3− were the predominant species among the WSIs, accounting for 53.2, 16.1, and 11.6% of the total ion mass, respectively. A strong correlation between OC and EC (r2 = 0.79) was found, indicating that they may be derived from common sources. The δ13CTC values ranged from −26.44 to −23.63‰, with an average of −25.82 ± 0.67‰, while δ15NTN values were in the range of −2.19 and 6.97‰, with an average of 5.40 ± 2.05‰. The overlapping isotopic ratios combined with the correlation results suggested that emissions from coal-fired activities and biomass burning were major factors affecting marine aerosols in the OECS. Twenty-six metal elements were classified into natural, anthropogenic, or mixed sources based on their enrichment factors, and more than 60% of the elements were affected by anthropogenic sources. Positive matrix factorization (PMF) analysis identified five potential source factors of PM2.5, including marine sources, vehicular exhaust, crustal sources, secondary formation, and combustion sources, while anthropogenic sources (vehicular exhaust, secondary formation, and combustion sources) accounted for 78.0% of all sources.

Source identification of the elemental fraction of particulate matter using size segregated, highly time-resolved data and an optimized source apportionment approach

Source emissions with high covariance degrade the performance of multivariate models, and often highly-time resolved data is needed to accurately extract the contribution of different emissions. Here, we use highly time-resolved size segregated elemental composition data to apportion the sources of the elemental fraction of PM in Zürich (May 2019–May 2020). For data collection, we have used an ambient metals monitor, Xact 625i, equipped with a sampling inlet alternating between PM 2.5 and PM 10 . By implementing interpolation and a newly proposed uncertainty estimation methodology, it was possible to obtain and use in PMF a combined dataset of PM 2.5 and PM coarse (PM 10-2.5 ) having data from only one instrument. The combination of the inlet switching system, the instrument's high time resolution, and the use of advanced source apportionment approaches yielded improved source apportionment results in terms of the number of identified sources, as the model, additionally to the diurnal and seasonal variation of the dataset, also utilizes the variation from the size segregated data. Thirteen sources of elements were identified, i.e., sea salt (5.4%), biomass burning (7.2%), construction (4.3%), industrial (3.3%), light-duty vehicles (5.4%), Pb (0.7%), Zn (0.7%), dust (22.1%), transported dust (9.5%), sulfates (15.4%), heavy-duty vehicles (17%), railway (6.6%) and fireworks (2.4%). The Covid-19 lockdown effect in PM sources in the area was also quantified. High-intensity events disproportionally affect the PMF solution, and in many cases, they are getting discarded before analysis, removing thus valuable information from the dataset. In this study, a three-step source apportionment approach was used to get a well-resolved unmixed solution when firework data points were included in the analysis. This approach can also be used for other sources and/or events with very high contributions that distort source apportionment analysis. Optimized source apportionment techniques are necessary for effective air pollution monitoring. • Combined PM 2.5 and PM coarse elemental composition data using a single instrument. • Methodology for the estimation of the uncertainty of interpolated PM elemental composition data. • Source apportionment method for the use of data that include high-intensity events. • The effect of Covid-19 lockdown to PM source contributions. • Quantification of heavy duty & low duty vehicular emissions.

Mantle Xenoliths from Huanul Volcano (Central-West Argentina): A Poorly Depleted Mantle Source under Southern Payenia

Huanul is a shield volcano with several lava flows hosting mantle xenoliths erupted during the Pleistocene (0.84 ± 0.05 Ma). It is located in the southern part of the Payenia Volcanic Province, which is among the largest Neogene-Quaternary volcanic provinces of South America. The volcanism here has been ascribed as the northernmost expression of the back-arc volcanism of the Andean Southern Volcanic Zone. We present the first petrographic and mineral chemistry study of mantle xenoliths collected from Huanul lavas with the aim of reconstructing directly the mantle source of the Payenia Volcanic Province. Xenoliths are commonly small (<5 cm in radius) but scarcely crossed by basaltic veins. All xenoliths have a fertile lherzolitic modal composition and are equilibrated in the spinel-facies. Most of them exhibit an almost primitive-mantle geochemical affinity, characterized by slightly depleted clinopyroxene REE patterns reproducible by partial melting degrees between 0 and 4% of a PM source. Geothermobarometric P-T estimates of clinopyroxene-orthopyroxene couples form a linear trend between 10 and 24 kbar with constant increase of T from 814 to 1170 °C along a 50–60 mW/m2 geotherm. Evidences of interaction with the host basalts occur as spongy textures in clinopyroxene and reacted spinel, which tend to became more restitic in composition and show chromatographic or complete overprinting of the trace element compositions. The presence of plagioclase and calculated P-T values constrain this melt/rock reaction process between 6 and 14 kbar, during magma ascent, and fit the mantle adiabat model. Calculated melts in equilibrium with the primary clinopyroxenes do not fit the composition of the host basalt and, together with the geothermobarometric estimations, point to an asthenospheric mantle source for the magmatism in southern Payenia. The PM geochemical affinity of the xenoliths of Huanul is an extremely rare finding in the South America lithospheric mantle, which is commonly extensively refertilized by subduction-derived melts.

Evaluation of modelled LOTOS-EUROS with observational based PM10 source attribution

Due to its serious health impact particulate matter is one of the air pollutants subject to abatement policies. Information on the main sources responsible for high concentrations of pollutants is therefore crucial to enable effective policy measures. In this study we compared two different methods for attribution of particulate matter concentrations to different sources: A tagging approach within the regional chemistry transport model LOTOS-EUROS and an observational method using speciated particulate matter observations and Positive Matrix Factorisation (PMF). The methods have been applied for winter 2016/2017 over Eastern Germany where in wintertime high woodburning emissions, cold temperatures and regular easterly winds can lead to a build-up of pollutant concentrations. The comparison allows the validation of the modelled source attribution for a selection of source categories. The contributions for biomass and total combustion compare well between both methods providing trust in the determined contributions, applied emissions including their timing. The total contribution from combustion is estimated between 3.3-7.7 μg/m3 (PMF) and 3.3-7.2 μg/m3 (LOTOS-EUROS) for the 9 stations incorporated in the study. The temporal Pearson correlation coefficient ranges between 0.3-0.64 for total combustion and 0.34 and 0.7 for biomass combustion. The mean absolute contributions for traffic at background stations also compare well with most values between 1.5-2.0 μg/m3 for PMF and 1–1.6 μg/m3 for LOTOS-EUROS. A lack of correlation for this contribution however suggests that the model has difficulty in representing the source category traffic in a time consistent manner and developments are needed to improve the temporal distribution of the traffic emissions within the model. The modelled particulate matter concentrations displayed a 20–40% underestimation of the observed concentrations with an increasing bias during high pollution events. The underestimation showed a high correlation with the observed contribution from combustion and secondary particulate matter including ammonium sulfate and organic carbon suggesting that at least a part of the missing mass in LOTOS-EUROS is related to transformation of volatile combustion emissions, likely from solid fuels, to secondary particle mass and missing enhanced formation of sulfate. Implementation of these missing processes would help to improve the source attribution of particulate matter with the LOTOS-EUROS model.

Particulate Matter Pollution in Urban Cities of India During Unusually Restricted Anthropogenic Activities

The outbreak of COVID-19 is a global public health challenge and has affected many countries, including India. The nationwide lockdown was imposed in India from March 25 to May 31, 2020 to prevent the transmission of COVID-19. The study intends to assess the impact of the absence of major anthropogenic activities during the various phases of the COVID-19 lockdown (LDN) period on the daily mean concentrations of PM2.5 and PM10 in six populated cities of Jaipur, Jodhpur, Kota, Udaipur, Ajmer, and Alwar in the state of Rajasthan. Investigation has been done for the different periods, including the pre-lockdown—PRELD (January 1–March 4, 2020), partial lockdown—PLDN (March 5–24, 2020), COVID-19 lockdown—LDN (March 25–May 31, 2020), and unlocking—ULC (June 1–August 31, 2020) phases. We have also compared the mean concentrations of PM2.5 and PM10 with the same period of the year 2019. A significant improvement in air quality during the COVID-19 LDN period was noticed in all cities compared to 2019 and for the same period of the year 2020. However, the levels of PM2.5 and PM10 were seen to rise during the second, third, and fourth LDN phases compared to the first LDN, indicating that the subsequent lockdowns started with some relaxations and dusty conditions. On the other hand, wind-blown dust is another vital source of PM10, resulting in high concentrations in the summer months (April–May). Significant reductions in PM2.5 (~25–50%) and PM10 (20–37%) in all six cities during the LDN period compared with PRELD were estimated. However, with significant variations from city to city, the lowest reductions in PM2.5 (~25%) and PM10 (~20%) were measured in Jodhpur and Ajmer, respectively. It was noticed that the episodes of rainfall and transport of oceanic air masses resulted in a reduction of particles during the ULC period compared to the LDN period. The air quality index was, more or less, in the “good to satisfactory” category during the first 3 LDN periods, whereas it was moderate for Jodhpur, Jaipur, and Ajmer during the last LDN period. The study will be helpful to determine mitigation policies to minimize air pollution, especially in developing regions.

Assessment of the link between atmospheric dispersion and chemical composition of PM10 at 2-h time resolution

The concentration of air pollutants is governed by both emission rate and atmospheric dispersion conditions. The role played by the atmospheric mixing height in determining the daily time pattern of PM components at the time resolution of 2 h was studied during 21 days of observation selected from a 2-month field campaign carried out in the urban area of Rome, Italy.Natural radioactivity was used to obtain information about the mixing properties of the lower atmosphere throughout the day and allowed the identification of advection and stability periods. PM10 composition was determined by X-ray fluorescence, ion chromatography, inductively coupled plasma-mass spectrometry and thermo-optical analysis. A satisfactory mass closure was obtained on a 2-h basis, and the time pattern of the PM10 macro-sources (soil, sea, secondary inorganics, organics, traffic exhaust) was acquired at the same time scale.After a complete quality control procedure, 27 main components and source tracers were selected for further elaboration. On this database, we identified some groups of co-varying species related to the main sources of PM. Each group showed a peculiar behaviour in relation to the mixing depth. PM components released by soil, biomass burning and traffic exhaust, and, particularly, ammonium nitrate, showed a clear dependence on the mixing properties of the lower atmosphere. Biomass burning components and organics peaked during the night hours (around midnight), following the atmospheric stabilization and increased emission rate. Traffic exhausts and non-exhausts species also peaked in the evening, but they showed a second, minor increase between 6:00 and 10:00 when the strengthening of the emission rate (morning rush hour) was counterbalanced by the dilution of the atmosphere (increasing mixing depth). In the case of ammonium nitrate, high concentrations were kept during the whole night and morning.

Characterization, source apportionment and associated health risk assessment of respirable air particulates in Metro Manila, Philippines

Coarse (PM10) and fine (PM2.5) aerosols were sampled from June to November 2016 at three different sites in Metro Manila Philippines (Valenzuela, NAMRIA, and MMDA). PM2.5 average levels in all sites exceeded the World Health Organization annual and daily limits, indicating that the ambient air qualities in all sites were unhealthy. Black Carbon (BC), a fingerprint of incomplete combustion, constituted 31–46% of PM2.5. Multi-element analysis of PM indicated that the sulfur, lead, and zinc had substantial contributions to PM2.5, with the highest level found in Valenzuela. Receptor modeling highlighted six important sources: vehicular emissions, biomass burning, industrials, secondary sulfur, sea spray, and fine soil. Among the sources, vehicular emission was the most dominant source, comprising 20–35% of the apportioned sources. An industrial source, predominantly with Zn and Pb, was seen in Valenzuela, contributing about 5.4% of its PM2.5. The health risks of fine aerosol, BC, and major elements were evaluated also in this study. Elements studied showed negligible potential (HQ < 1) to cause non-carcinogenic health effects and very low (<1 × 10−6) carcinogenic health effects to children and adults. The observed trend for carcinogenic risks of PM2.5 in children was Valenzuela > MMDA > NAMRIA while a medium carcinogenic risk was observed for adults at all sites (CR > 1 × 10−5). The trend for PM2.5 average HQ values in both age groups was Valenzuela > MMDA > NAMRIA. Our results showcased varying levels and sources of PM and its components that can impact the health upon exposure and should be addressed in future policies to safeguard public health.

Insights about the Sources of PM2.5 in an Urban Area from Measurements of a Low-Cost Sensor Network

PM2.5 measurements using a network of lost-cost sensors were conducted during 2017–2019 in the greater area of Patras, Greece. The average PM2.5 concentration in all sites during the study period was 9.4 μg m−3, varying from 6.2 μg m−3 in the background areas to 12.8 μg m−3 at the city center. The site with the peak PM2.5 levels was not located in an area with high traffic density but rather in a square with pedestrian-only zones and a high restaurant density. The highest PM2.5 concentrations were observed during the colder period (November–March) due to high emissions from residential wood burning for heating purposes. The measurements of the sensors were used to estimate the importance of regional and local PM2.5 sources. During the warm period, regional transport dominated, contributing approximately 80–85% of the PM2.5 in the city center; however, during the colder period, the local sources were responsible for approximately half the PM2.5. The network operated reliably during this multiyear study. Such measurements provide, at a very low cost, valuable insights not only about the temporal and spatial variability of PM2.5 in a city but also about its sources, including the role of regional transport.

Source Apportionment of Atmospheric PM10 in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model.

In this paper, the emission sources of PM10 are characterised by analysing its trace elements (TE) and ions contents. PM10 samples were collected for a year (2019–2020) at five sites and analysed. PM10 speciated data were analysed using graphical visualization, correlation analysis, generalised additive model (GAM), and positive matrix factorization (PMF). Annual average PM10 concentrations (µg/m3) were 304.68 ± 155.56 at Aziziyah, 219.59 ± 87.29 at Misfalah, 173.90 ± 103.08 at Abdeyah, 168.81 ± 82.50 at Askan, and 157.60 ± 80.10 at Sanaiyah in Makkah, which exceeded WHO (15 µg/m3), USEPA (50 µg/m3), and the Saudi Arabia national (80 µg/m3) annual air quality standards. A GAM model was developed using PM10 as a response and ions and TEs as predictors. Among the predictors Mg, Ca, Cr, Al, and Pb were highly significant (p < 0.01), Se, Cl, and NO2 were significant (p < 0.05), and PO4 and SO4 were significant (p < 0.1). The model showed R-squared (adj) 0.85 and deviance explained 88.1%. PMF identified four main emission sources of PM10 in Makkah: (1) Road traffic emissions (explained 51% variance); (2) Industrial emissions and mineral dust (explained 27.5% variance); (3) Restaurant and dwelling emissions (explained 13.6% variance); and (4) Fossil fuel combustion (explained 7.9% variance).

A comprehensive analysis of the intervention of a fresh air ventilation system on indoor air quality in classrooms

Adequate classroom air quality is vital to student health and learning outcomes. Prolonged periods of air conditioner use in enclosed classrooms may result in poor air quality. However, opening classroom windows may expose students to air pollutants if the outdoor air quality is poor. This study investigated CO2 and PM2.5 concentrations in 86 classrooms installed with air conditioners in elementary and middle schools in southern Taiwan, each additionally installed with a fresh air ventilation system equipped with an E11-class filter. Enclosed air-conditioned classrooms generally have a CO2 concentration greater than 2500 ppm; occasionally, this figure reaches 3500 ppm. With the installed air ventilation system operating, the mean classroom CO2 concentration decreased to 1084 ± 203 ppm, and the median CO2 concentration decreased to 1120 ppm. However, the CO2 concentrations of over half the classrooms remained higher than the concentration advised by the World Health Organization (1000 ppm). This high CO2 concentration was a result of the short distance (1.5–2 m) between the indoor air outlet and the recirculated air inlet resulting in a shortened circuit of air travel. Also, the initial filter installed over the indoor air outlet of the system reduced air penetration into the classroom. When the ventilation system was manually activated without activating the air conditioners, the median PM2.5 concentration measured at the indoor air outlet and the median classroom PM2.5 concentration were 2 μg/m3 and 20 μg/m3, respectively. This implied the presence of other sources of PM2.5 in classrooms, particularly ceiling and wall-mounted fans. The activation of these fans spread dust onto classroom furniture. Overall, fresh air ventilation systems providing filtered air improve classroom ventilation efficiency and indoor air quality by reducing CO2 and PM2.5 concentrations, but the cleanliness of fans and furniture must be maintained.

Application of an improved gas-constrained source apportionment method using data fused fields: A case study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002–2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

PM2.5 exceedances and source appointment as inputs for an early warning system

Between June 2018 and April 2019, a sampling campaign was carried out to collect PM2.5, monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM2.5 exceedances (≥ 12.5 µg m−3). Source appointment was done using elemental composition and morphology of PM by scanning electron microscopy coupled with energy dispersive spectroscopy (SEM–EDS). A proposal of an early warning system (EWS) for PM pollution episodes is presented. The logistic model has a holistic success rate of 94%, with forest fires and motor vehicle flows as significant variables. Source appointment analysis by occurrence of events showed that samples with higher concentrations of PM had carbon-rich particles and traces of K associated with biomass burning, as well as aluminosilicates and metallic elements associated with resuspension of soil dust by motor-vehicles. Quantitative source appointment analysis showed that soil dust, garbage burning/marine aerosols and wildfires are three majority sources of PM. An EWS for PM pollution episodes around the Sartenejas Valley is proposed considering the variables and elements mentioned.

Variation in Coarse Particulate Matter (PM10) and Its Characterization at Multiple Locations in the Semiarid Region

Introduction: The elemental composition and morphological study of particulate matter are very important to understand the nature of particles influencing the environment, climate, soil, and health.Methods: The PM10 samples were collected during the winter season (2018) in Nowshera city, KPK, Pakistan, in three locations, namely, urban, industrial, and suburban. Scanning electron microscopy (SEM) and electron-dispersive X-ray (EDX) spectroscopy were used to examine the PM samples for morphological examination and elemental composition.Results: The average mass concentrations of particulate matter (PM10) at the urban, industrial, and suburban locations were 238.5, 505.1, and 255.0 μg m−3, respectively. The average PM10 mass concentration was higher than the WHO and National Ambient Air Quality Standards (NAAQS). The results of EDX showed that samples contained variable amounts of thirteen elements, such as oxygen, carbon, silicon, magnesium, sodium, calcium, iron, aluminum, potassium, sulfur, titanium, gold, and chlorine. The probable sources of PM were biogenic like plant debris, pollen, and diatoms; geogenic like road dust and resuspended soil dust; and anthropogenic like carbonaceous particles and fly ash, as confirmed by SEM–EDX. The carbonaceous species, that is, OC and EC, had average values of 55.8 ± 13.1 and 4.6 ± 0.6, 5.2 ± 3.2, and 36.4 ± 10.4, 40.0 ± 2.6 and, 6.3 ± 0.2 in industrial, urban, and suburban locations, respectively. Similarly, OC/EC had average values of 12.0 ± 1.2, 8.0 ± 3.0, and 6.3 ± 0.2 in industrial, urban, and suburban locations, respectively. Highly significant correlations among water-soluble ions (K+), OC, and EC were found in each location.Conclusions: The examined PM10 mass concentration in Nowshera city was above the thresholds of National Ambient Air Quality Standards (NAAQS) set by the U.S. Environmental Protection Agency (EPA). In addition, the concentration of pollutants was the highest at the industrial site compared to the other sites. The HYSPLIT model showed that the air mass originated from local sources like cement industries, brick kiln industries, and others.

Numerical Simulation of Topography Impact on Transport and Source Apportionment on PM2.5 in a Polluted City in Fenwei Plain

The unique energy structure, high intensity of coal production, and complex terrain, make Fenwei Plain a highly polluted region in China. In this study, we characterized the transport characteristic and sources of PM2.5 (the fraction of particulate matter ≤ 2.5 μm) in Sanmenxia, a polluted city in canyon terrain. The results showed that special topography in Sanmenxia had an important role in the transport of particulates. Sanmenxia is located between two northeast-southwest facing mountains, showing a special local circulation. The local circulation was dominated by a downslope wind at nighttime, while the cross−mountain airflow and zonal wind were dominant during the daytime in the canyon terrain. PM2.5 accumulated near Sanmenxia with the influence of downslope, zonal wind, and topography. The main regional transport paths could be summarized into an eastern path, a northern path, and a western path during the severe haze episodes. The PM2.5 source apportionment revealed by an on-line tracer-tagged of the Nested Air Quality Prediction Model System (NAQPMS) showed that the main regional sources of Sanmenxia were Yuncheng, Sanmenxia, and Weinan. The contribution to PM2.5 concentration in Sanmenxia was 39%, 25%, and 11%, respectively. The northern path had the most important impact on Sanmenxia. The results can provide scientific basis for the establishment of severe haze control in Sanmenxia and regional joint control.

Characteristics of PM2.5 Pollution in Osorno, Chile: Ion Chromatography and Meteorological Data Analyses

Over the decades, air pollution has become a serious problem in Osorno, Chile. This study aims to clarify the source of PM2.5 by comprehensively analyzing its chemical composition and comparing it with meteorological conditions. The PM2.5 and filter samples were collected during April 2019–August 2019 using a continuous particulate monitor. The analyses were conducted using Image J software, ion chromatography, and backward trajectory. The ion composition and the PM2.5 were compared. The results on the PM2.5 and potassium (K+) concentrations indicated a correlation factor of 0.93, indicating that biomass combustion, such as wood burning, is the dominant source of PM2.5 in Osorno. High PM2.5 concentrations of over 170 to 1124 µg/m3 were observed in low temperature, low precipitation, and low wind speed periods—meteorological conditions contributed to the development of a thermal inversion layer. In addition, correlations of 0.61 to 0.67 were found among the detected ions that are often found in seawater. The backward trajectory analyses showed dominant air mass transport from the South Pacific Ocean, suggesting that part of the detected PM2.5 was derived from the marine environment. Continuous monitoring and mitigation strategies focusing on wood combustion activities are necessary to alleviate the current air pollution problem in Osorno city.

A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach

We investigate the impact of the COVID-19 outbreak on PM2.5 levels in eleven urban environments across the United States: Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, Phoenix, and Seattle. We estimate daily PM2.5 levels over the contiguous U.S. in March–May 2019 and 2020, and leveraging a deep convolutional neural network, we find a correlation coefficient, an index of agreement, a mean absolute bias, and a root mean square error of 0.90 (0.90), 0.95 (0.95), 1.34 (1.24) μg/m3, and 2.04 (1.87) μg/m3, respectively. Results from Google Community Mobility Reports and estimated PM2.5 concentrations show a greater reduction of PM2.5 in regions with larger decreases in human mobility and those in which individuals remain in their residential areas longer. The relationship between vehicular PM2.5 (i.e., the ratio of vehicular PM2.5 to other sources of PM2.5) emissions and PM2.5 reductions (R = 0.77) in various regions indicates that regions with higher emissions of vehicular PM2.5 generally experience greater decreases in PM2.5. While most of the urban environments ⸺ Washington DC, New York, Boston, Chicago, Los Angeles, Houston, Dallas, Philadelphia, Detroit, and Seattle ⸺ show a decrease in PM2.5 levels by 21.1%, 20.7%, 18.5%, 8.05%, 3.29%, 3.63%, 6.71%, 4.82%, 13.5%, and 7.73%, respectively, between March–May of 2020 and 2019, Phoenix shows a 5.5% increase during the same period. Similar to their PM2.5 reductions, Washington DC, New York, and Boston, compared to other cities, exhibit the highest reductions in human mobility and the highest vehicular PM2.5 emissions, highlighting the great impact of human activity on PM2.5 changes in eleven regions. Moreover, compared to changes in meteorological factors, changes in pollutant concentrations, including those of black carbon, organic carbon, SO2, SO4, and especially NO2, appear to have had a significantly greater impact on PM2.5 changes during the study period.

Particulate Matter in an Urban–Industrial Environment: Comparing Data of Dispersion Modeling with Tree Leaves Deposition

Particulate matter represents a serious hazard to human health, and air quality models contribute to the understanding of its dispersion. This study describes particulate matter with a ≤10 μm diameter (PM10) dynamics in an urban–industrial area, through the comparison of three datasets: modeled (TAPM—The Air Pollution Model), measured concentration (environmental control stations—ECS), and leaf deposition values. Results showed a good agreement between ECS and TAPM data. A steel plant area was used as a PM10 emissions reference source, in relation to the four sampling areas, and a distance/wind-based factor was introduced (Steel Factor, SF). Through SF, the three datasets were compared. The SF was able to describe the PM10 dispersion values for ECS and leaf deposition (r2 = 0.61–0.94 for ECS; r2 = 0.45–0.70 for leaf); no relationship was found for TAPM results. Differences between measured and modeled data can be due to discrepancies in one district and explained by a lack of PM10 inventory for the steel plant emissions. The study suggests the use of TAPM as a suitable tool for PM10 modeling at the urban scale. Moreover, tree leaves are a low-cost tool to evaluate the urban environmental quality, by providing information on whether and when data from leaf deposition can be used as a proxy for air pollution concentration. Further studies to include the re-suspension of particles as a PM10 source within emission inventories are suggested.

Fine particulate matter pollution characteristics and source apportionment of Changchun atmosphere.

In order to study the pollution characteristics and main sources of fine particulate matter in the atmosphere of the city of Changchun, PM2.5 samples were collected during the four seasons in 2014, and representative months for each season are January, April, July, and October. Sample collection was carried out on 10 auto-monitoring stations in Changchun, and PM2.5 mass concentration and its chemical components (including inorganic elements, organic carbon, elemental carbon, and water-soluble ions) were measured. The results show that the annual average mass concentration of PM2.5 in Changchun in 2014 was about 66.77μg/m3. Organic matter was the highest component in PM2.5, followed by secondary inorganic ions (SNA), mineral dust (MIN), elemental carbon (EC), and trace elements (TE). Positive matrix factorization (PMF) results gave seven factors, namely, industrial, biomass and coal burning, industrial and soil dust, motor vehicle, soil and secondary ion, light industrial, and hybrid automotive and industrial sources in PM2.5, with contributing values of 18.9%, 24.2%, 5.7%, 23.0%, 11.5%, 13.0%, and 3.6%, respectively.

Atmospheric Emission Sources in the Po-Basin from the LIFE-IP PREPAIR Project

This paper presents the focus on emission estimates in the Italian Regions of the Po-basin obtained by the development of a common air pollutant emission dataset on the Po-basin and Slovenia foreseen in the project LIFE PREPAIR (https://www.lifeprepair.eu/). The objective is to update emission inventories developed by the environmental protection agencies and regions of Lombardy, Emilia-Romagna, Piedmont, Veneto, Friuli Venezia Giulia, Valle d’Aosta, the province of Bolzano (participating as stakeholder) and the province of Trento. A data flux is defined considering the activities on emission estimates by the different administrations according to the current Italian legislation. This activity has allowed the completion of two different datasets on the area for 2013 and 2017. The estimates of primary emissions of the main atmospheric pollutants have a high spatial resolution defined at the municipal level. The non-industrial combustion of biomass in small domestic appliances is the main source of primary PM10 in the Po-basin. NOx primary emissions are determined for quite of a half by road transport. Manure management and fertilization in the agriculture sector are the sources of NH3. The ensemble of the collected data shows a very good comparability even if all local compilers perform independently the estimates, thanks to a good alignment in using reference methodologies and to projects of common methodological development, as reported by the INEMAR project (https://www.inemar.eu/). The estimates of PM10, NOx and NH3 are comparable with data reported by the European Environment Agency EEA for the European Member States EU-28 (until 1 February 2020) and for Italy, reported under the UNECE Convention on Long-range Transboundary Air Pollution and European Union National Emission Ceiling Directive.