Measurements Of PM2 Research Articles

Using in-situ CO2, PM1, PM2.5, and PM10 measurements to assess air change rates and indoor aerosol dynamics

A common technique to assess indoor air quality is to estimate a space's air change rate by quantifying the decay of a tracer species, such as metabolic carbon dioxide (CO2). However, CO2 decay does not fully represent the complexities of aerosol dynamics. In this study, low-cost sensors (QuantAQ/Aerodyne Research ARISense v200 and QuantAQ MODULAIR-PM) were used for continuous measurements of size-resolved particulate matter (PM1, PM2.5, PM10) and CO2 in a university classroom across >5 months in 2021. Occupant-generated emissions from classroom activity and cleaning (fogging aerosols) events were used to determine the decay time constant of each pollutant, which varied substantially (e.g., 25–86 min for CO2, 14–66 min for class PM10 and 24–103 min, 25–82 min, and 18–56 min for fogging PM1, PM2.5, and PM10, respectively across 3 months in the Spring). The range of measured CO2 and PM decay rates was comparable, indicating that quantifying metabolic CO2 decay is a viable method to estimate the timescale of indoor aerosol decay, and that both species provide comparable air change rate estimates. However, the effect of deposition on PM decay was evident and uncertainties in using occupant-generated tracer decay to determine air change rates should be considered. These results provide insights into the practicality and limitations of using in-situ CO2 and PM decay measurements to assess ventilation, and of using CO2 decay to estimate aerosol decay. This work also highlights the importance of performing continuous measurements over extended periods of time to quantify a range of air change rates.

TAPaC—tobacco-associated particulate matter emissions inside a car cabin: establishment of a new measuring platform

BackgroundParticulate matter (PM) emission caused by tobacco combustion leads to severe health burdens worldwide. Second-hand smoke exposure is extraordinarily high in enclosed spaces (e.g., indoor rooms, car cabins) and poses a particular threat to the health of vulnerable individuals (e.g., children, elderly, etc.). This study aimed to establish a new measuring platform and investigate PM emissions under four different ventilation conditions inside a car cabin without exposing any person to harmful tobacco smoke.MethodsPM concentrations were measured during the smoking of 3R4F reference cigarettes in a Mitsubishi Space Runner (interior volume 3.709 m3). The cigarettes were smoked with a machine, eliminating exposure of the researchers. Cigarettes were extinguished 4.5 min after ignition, and PM measurements continued until 10 min after ignition.ResultsHigh mean PM concentrations were measured for cigarettes without ventilation after 4.5 min (PM10: 1150 µg/m3, PM2.5: 1132 µg/m3, PM1: 861.6 µg/m3) and after 10 min (PM10: 1608 µg/m3, PM2.5: 1583 µg/m3, PM1: 1133 µg/m3). 3R4F smoked under conditions with turned on ventilation resulted in reduction of PM compared to those smoked without ventilation after 4.5 min (PM10:-47.5 to -58.4%, PM2.5:-47.2 to -58%, PM1:-39.6 to -50.2%) and after 10 min (PM10:-70.8 to -74.4%, PM2.5:-70.6 to -74.3%, PM1:-64.0 to -68.0%). Cigarettes smoked without ventilation generated high PM peaks at 4.5 min (PM10: 2207 µg/m3, PM2.5: 2166 µg/m3, PM1: 1421 µg/m3) and at 10 min (PM10: 1989 µg/m3, PM2.5: 1959 µg/m3, PM1: 1375 µg/m3). PM peaks of cigarettes smoked under different ventilation modes varied at 4.5 min (PM10: 630-845 µg/m3, PM2.5: 625-836 µg/m3, PM1: 543 - 693 µg/m3) and 10 min (PM10: 124 - 130 µg/m3, PM2.5: 124 - 129 µg/m3, PM1: 118 - 124 µg/m3).ConclusionThe new measuring platform provides a safer way for researchers to investigate PM emissions of cigarettes. These data are comparable to published research and show that smoking in a parked vehicle with the windows closed generates harmful PM emissions even when the vehicle ventilation is in operation. Future studies should be carried out using the new measuring platform investigating PM exposure and PM distribution of in-vehicle smoking under a wide range of conditions.

Comparison of two air quality models in complex terrain near sea shore

Air pollution is the most important environmental problem in Zonguldak, Turkey due to excessive coal combustion and thermal power plant emissions. The city center is located on a complex terrain near the Black Sea shore. There exist some previous studies about PM10 pollution in the study area, but none of them is related to the spatial distribution of the pollutant. This air quality modeling study aims to fill this gap in the literature. Firstly, PM10 emission inventory has been prepared for point, line, and area sources for the year 2011. For that period, bituminous coal was the principal fuel for domestic heating in houses and generating electricity in thermal power plants, therefore particulate matter (PM10) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 tons of PM10 have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling results, the PM10 pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher than that of the AERMOD model. The model predicted values plus background concentration were validated against the PM10 measurements by using fractional bias, index of agreement, geometric mean bias, and geometric mean-variance. According to the model performance analysis, CALPUFF showed slightly better performance as compared to AERMOD.

Evaluation of vertical and horizontal distribution of particulate matter near an urban roadway using an unmanned aerial vehicle.

Measurement of traffic emissions has gained a lot of interest in recent times due to its contribution to urban pollution. This paper reports the outcome from an unmanned aerial vehicle (UAV) based measurement of PM concentration near an urban roadway at Kolkata, India. A total of 54 flights were carried out for simultaneous measurements of PM1, PM2.5 and PM10 mass concentration and meteorological parameters in vertical as well as in horizontal direction. Results for the vertical flight up to 100 m showed that the PM1, PM2.5 and PM10 concentrations at higher altitudes are less (mean; 24.6, 39.9 and 103.8 μg m-3) compared to the respective ground level concentrations (mean; 26.3, 50.4 and 201.9 μg m-3). For all the three particle sizes, the majority of the cases of higher PM concentration at higher altitudes happened during the evening flight. Low mixing height and low wind speed are suggested to be the reasons for the poor dispersion of pollutants in the evening. While there was a 7-10% fall of fine particles (PM1 and PM2.5) mass concentrations up to 90 m away from the road, no trend could be seen for PM10. The random forest model to predict the UAV/Ground concentration ratio showed high accuracy (R2 = 0.82-0.95) for all three particle sizes. This is an important finding from this study, which shows how UAV measurement data can be used to generate models that can predict the higher altitude concentrations from the ground based measurements.

Survey of residential indoor particulate matter measurements 1990-2019.

We surveyed literature on measurements of indoor particulate matter in all size fractions, in residential environments free of solid fuel combustion (other than wood for recreation or space heating). Data from worldwide studies from 1990 to 2019 were assembled into the most comprehensive collection to date. Out of 2752 publications retrieved, 538 articles from 433 research projects met inclusion criteria and reported unique data, from which more than 2000 unique sets of indoor PM measurements were collected. Distributions of mean concentrations were compiled, weighted by study size. Long-term trends, the impact of non-smoking, air cleaners, and the influence of outdoor PM were also evaluated. Similar patterns of indoor PM distributions for North America and Europe could reflect similarities in the indoor environments of these regions. Greater observed variability for all regions of Asia may reflect greater heterogeneity in indoor conditions, but also low numbers of studies for some regions. Indoor PM concentrations of all size fractions were mostly stable over the survey period, with the exception of observed declines in PM2.5 in European and North American studies, and in PM10 in North America. While outdoor concentrations were correlated with indoor concentrations across studies, indoor concentrations had higher variability, illustrating a limitation of using outdoor measurements to approximate indoor PM exposures.

A 3-Dimensional analysis of long-range transported particulate matter to the Eastern Mediterranean: Implication for the chemical components of PM1 and PM10

This study aims to better characterize the location of the strong regional emitters of aerosols affecting the Eastern Mediterranean basin, through the use of a recently developed three-dimensional (3D) version of a Concentration Weighted Trajectory (CWT) model which can identify the prevalent transport pattern of polluted air masses as a function of their altitude (0 m ≤ Layer 1 < 1000 m (near ground layer), 1000 m ≤ Layer 2 < 2000m (intermediate layer), 2000m ≤ Layer 3 (upper layer)). Daily PM10 and PM1 chemical composition measurements from a regional background station on the island of Cyprus were incorporated in the model. Intrusions of PM1 constituents of an anthropogenic origin (Secondary Inorganic Aerosols (SIA) (SO42− and NH4+), K+, EC and OC), were mainly associated with Eastern continental airflows (Middle East regions) in Layers 1 and 2 during the cold periods (16 October to 15 April). During the warm periods (16 April to 15 October) the prevalence of Northern airflows in all layers mainly through Turkey enhanced the levels of SIA and K+ in PM1. Sea salt constituents in PM10 were particularly carried by West – Northwest Mediterranean airflows in all layers during both cold and warm periods. Dust intrusions from Northeast Africa and the Middle East were highlighted by episodes of PM10 crustal species and were most evident in Layer 1 during cold seasons. Therefore, this study reveals the spatial and vertical distribution of air masses transporting the main aerosol components at the Eastern Mediterranean.

Mega Asian dust event over China on 27–31 March 2021 observed with space-borne instruments and ground-based polarization lidar

Under the background of global climate change, the frequency of extreme weather events is ever-increasing. Climatic anomaly in dust source areas and intensive Mongolian cyclones jointly cause the reoccurrence of super dust storms in spring 2021. A mega dust storm broke out in southeast Mongolia on 27 March and significantly impacted air quality in China until 31 March. However, neither intensity nor affected areas are well depicted by the forecasting model. Here we report the horizontal distribution, transport path, dissipation, and influence on surface air quality of this event with the combination of space-borne and ground-based observations. Prudent analysis indicates a longer duration (5 days) and farther-south affected areas (25°N) than the previous report. Dust plumes were first transported to North and Northeast China, the Bohai Sea, and the Yellow Sea (28 March), and then respectively moved eastward to Korea-Japan regions and southward over mainland China (29 March), with PM10 values > 500 μg m−3 in 64 cities. Dust aerosols over the Yellow Sea retraced to the coastal areas of East China on 29 March, resulting in severe degradation of air quality over mainland China (25–40°N and 105–120°E) during 29–31 March. On 31 March, dust particles in this region were gradually removed, attributed to the wet removal and the enhanced south branch of westerlies. In central China, ground-based polarization lidar observed dust ∼26 h earlier (1600 LT, 27 March) than surface PM10 measurements. The dust plume showed a triple-layer vertical structure. The bottom layer mixed with local anthropogenic aerosols, concentrating most dust particles (>100 μg m−3); the upper two were composed of pure dust particles with lower mass concentration (<50 μg m−3). This study reveals the capacity of comprehensive observations with multiple approaches from both space and ground in real-time monitoring of mega dust storms and validating the sand storm forecasting.

Aerosol mass and size-resolved metal content in urban Bangkok, Thailand

Inhalable particulate matter (PM) is a health concern, and people living in large cities such as Bangkok are exposed to high concentrations. This exposure has been linked to respiratory and cardiac diseases and cancers of the lung and brain. Throughout 2018, PM was measured in northern Bangkok near a toll road (13.87°N, 100.58°E) covering all three seasons (cool, hot and rainy). PM10 was measured in 24- and 72-h samples. On selected dates aerodynamic size and mass distribution were measured as 3-day samples from a fixed 5th floor inlet. Particle number concentration was measured from the 5th floor inlet and in roadside survey measurements. There was a large fraction of particle number concentration in the sub-micron range, which showed the greatest variability compared with larger fractions. Metals associated with combustion sources were most found on the smaller size fraction of particles, which may have implications for associated adverse health outcomes because of the likely location of aerosol deposition in the distal airways of the lung. PM10 samples varied between 30 and 100 μg m−3, with highest concentrations in the cool season. The largest metal fractions present in the PM10 measurements were calcium, iron and magnesium during the hot season with average airborne concentrations of 13.2, 3.6 and 2.0 μg m−3, respectively. Copper, zinc, arsenic, selenium, molybdenum, cadmium, antimony and lead had large non-crustal sources. Principal component analysis (PCA) identified likely sources of the metals as crustal minerals, tailpipe exhaust and non-combustion traffic. A health risk analysis showed a higher risk of both carcinogenic and non-carcinogenic health effects in the drier seasons than the wet season due to ingestion of nickel, arsenic, cadmium and lead.Graphical abstract

Laboratory Chamber Evaluation of Flow Air Quality Sensor PM2.5 and PM10 Measurements.

The emergence of low-cost air quality sensors as viable tools for the monitoring of air quality at population and individual levels necessitates the evaluation of these instruments. The Flow air quality tracker, a product of Plume Labs, is one such sensor. To evaluate these sensors, we assessed 34 of them in a controlled laboratory setting by exposing them to PM10 and PM2.5 and compared the response with Plantower A003 measurements. The overall coefficient of determination (R2) of measured PM2.5 was 0.76 and of PM10 it was 0.73, but the Flows’ accuracy improved after each introduction of incense. Overall, these findings suggest that the Flow can be a useful air quality monitoring tool in air pollution areas with higher concentrations, when incorporated into other monitoring frameworks and when used in aggregate. The broader environmental implications of this work are that it is possible for individuals and groups to monitor their individual exposure to particulate matter pollution.

Invited Perspective: HEPA Filters-An Effective Way to Prevent Adverse Air Pollution Effects on Neurodevelopment?

Invited Perspective: HEPA Filters-An Effective Way to Prevent Adverse Air Pollution Effects on Neurodevelopment?

Statistical analysis of the temporal change of PM10 levels in the city of Sivas (Turkey).

The objective of this study is to statistically examine the variation of PM10 values measured at three stations in the center of Sivas between the years 2016 and 2020. Hourly PM10 measurement values were taken from three different stations (İstasyon Kavşağı, Meteoroloji, and Başöğretmen AQMSs) in the city center. Then the mean values of the measurements obtained between 2016 and 2020 were compared according to the years and the stations, as well as with the limit values given in the Regulation on Air Quality Assessment and Management(RAQAM). Analyses of variance were conducted to determine any differences between PM10 levels and 24-h limit values of PM10 for Turkey and between PM10 values of stations over the years. Considering the 5-year mean values, the mean value of all PM10 concentrations measured in the city center was calculated as 56.36 µg/m3. No statistical difference was found between the PM10 values measured in 2017 and 2018 at the İstasyon Kavşağı AQMS, and the comparisons of PM10 between stations over the years showed no difference between the Meteoroloji AQMS and the Başöğretmen AQMS in 2019 and 2020. The Spearman’s rank-order correlation results of PM10 over the years among the stations in the city showed that the strongest relationship was a moderate one between the years 2019 and 2020 with regard to the İstasyon Kavşağı AQMS. Probable dust transports were examined for the days when PM10 was at its highest, and the conclusion was that desert dust coming from the continent of Africa (south) to the center of Sivas had been effective.

Indoor air quality, thermal comfort and ventilation in deep energy retrofitted Irish dwellings

Residential building stock energy retrofits will play a key role in EU climate actions. The impact of these retrofits on indoor air quality (IAQ) and occupant comfort needs to be assessed to inform future renovation programmes. This study evaluated IAQ and occupant satisfaction in a sample (n = 14) of deep energy retrofitted Irish residences, at least 12 months post retrofit. Measurements of PM2.5, formaldehyde, total volatile organic compounds (TVOCs), carbon monoxide, radon, and carbon dioxide were made in the main bedroom and living area over a period of two days to three months (depending on the pollutant). Temperature and humidity in most homes were within design comfort limits. Higher concentrations of all pollutants were measured in bedrooms. Only 30% of bedroom data met EN16798 Category I limits for CO2 (within 380 ppm of outdoor concentrations), suggesting that bedrooms maybe under ventilated. Median formaldehyde concentrations of 25.4 and 20.7 μg/m3 were detected in bedroom and living rooms respectively, with building materials likely being the major source. All radon data (apart from one home located in a high radon area) was less than the national reference value of 200 Bq/m3. Measured ventilation extract flow rates in most participating homes would not meet the minimum performance requirements in Irish Regulations of 2019 – introduced post completion of the retrofits in this study. Greater compliance with the ventilation requirements of retrofits and the promotion and use of low emitting construction materials are recommended.

Analysis of Particulate Matter (PM10) Behavior in the Caribbean Area Using a Coupled SARIMA-GARCH Model

The aim of this study was to model the behavior of particles with aerodynamic diameter lower or equal to 10μm (PM10) in the Caribbean area according to African dust seasonality. To carry out this study, PM10 measurement from Guadeloupe (GPE) and Puerto Rico (PR) between 2006 and 2010 were used. Firstly, the missing data issues were addressed using algorithms that we elaborated. Thereafter, the coupled SARIMA-GARCH (Seasonal Autoregressive Integrated Moving Average and Generalized Autoregressive Conditional Heteroscedastic) model was developed and compared to PM10 empirical data. The SARIMA process is representative of the main PM10 sources, while the heteroskedasticity is also taken into account by the GARCH process. In this framework, PM10 data from GPE and PR are decomposed into the sum of the background atmosphere (Bt = anthropogenic activities + marine aerosol), African dust seasonality (St = mineral dust), and extreme events processes (Ct). Akaike’s information criterion (AIC) helped us to choose the best model. Forecast evaluation indexes such as the Mean Absolute Percentage Error (MAPE), the Mean Absolute Scale Error (MASE), and Theil’s U statistic provided significant results. Specifically, the MASE and U values were found to be almost zero. Thus, these indexes validated the forecasts of the coupled SARIMA-GARCH model. To sum up, the SARIMA-GARCH combination is an efficient tool to forecast PM10 behavior in the Caribbean area.

Historical reconstruction of background air pollution over France for 2000–2015

Abstract. This paper describes a 16-year dataset of air pollution concentrations and air quality indicators over France. Using a kriging method that combines background air quality measurements and modeling with the CHIMERE chemistry transport model, hourly concentrations of NO2, O3, PM10 and PM2.5 are produced with a spatial resolution of about 4 km. Regulatory indicators (annual average, SOMO35 (sum of ozone means over 35 ppb), AOT40 (accumulated ozone exposure over a threshold of 40 ppb), etc.) are also calculated from these hourly data. The NO2 and O3 datasets cover the period 2000–2015, as well as the annual PM10 data. Hourly PM10 concentrations are not available from 2000 to 2007 due to known artifacts in PM10 measurements. PM2.5 data are only available from 2009 onwards due to the limited number of measuring stations available before this date. The overall dataset was evaluated over all years by a cross-validation process against background stations (rural, sub-urban and urban) to take into account the data fusion between measurement and models in the method. The results are very good for PM10, PM2.5 and O3. They show an overestimation of NO2 concentrations in rural areas, while NO2 background values in urban areas are well represented. Maps of the main indicators are presented over several years, and trends are calculated. Finally, exposure and trends are calculated for the three main health-related indicators: annual averages of PM2.5, NO2 and SOMO35. The DOI link for the dataset is https://doi.org/10.5281/zenodo.5043645 (Real et al., 2021). We hope that the publication of this open dataset will facilitate further studies on the impacts of air pollution.

Analyses of a Lake Dust Source in the Middle East through Models Performance

Drying lakes have become a new source of dust, causing severe problems in surrounding areas. From 2000 to 2017, a statistical study was conducted on Lake Urmia in Iran in the Middle East. The results indicated a significant increase in the annual number of dusty days in stations around the lake and the mean annual aerosol optical depth (AOD) at 550 nm. The sharp decrease in annual snowfall rate over the Lake Urmia area since 2007 has been linked to the lake’s decreasing water level and drying. During a dust storm event from 27 October to 31 October 2017, a local dust storm originated from Lake Urmia before another large-scale dust storm originated from the An-Nafud desert. According to MODIS true-color images, dust particles were lifted from Lake Urmia and transported eastward to the Caspian Sea and the HYSPLIT model. The comparison of the four models under the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) revealed that the models overestimated surface dust concentrations compared to ground-based PM10 measurements. Nevertheless, the NOAA/WRF-Chem and DREAMABOL models simulated higher dust concentrations during the dust period. More emphasis should be placed on the development of dust models for SDS-WAS models in Lake Urmia.

The impact of the COVID-19 pandemic on air pollution: A global assessment using machine learning techniques

In response to the COVID-19 pandemic, most countries implemented public health ordinances that resulted in restricted mobility and a resultant change in air quality. This has provided an opportunity to quantify the extent to which carbon-based transport and industrial activity affect air quality. However, quantification of these complex effects has proven to be difficult, depending on the stringency of restrictions, country-specific emission source profiles, long-term trends and meteorological effects on atmospheric chemistry, emission levels and in-flow from nearby countries. In this study, confounding factors were disentangled for a direct comparison of pandemic-related reductions in absolute pollutions levels, globally. The non-linear relationships between atmospheric processes and daily ground-level NO2, PM10, PM2.5 and O3 measurements were captured in city- and pollutant-specific XGBoost models for over 700 cities, adjusting for weather, seasonality and trends. City-level modelling allowed adaptation to the distinct topography, urban morphology, climate and atmospheric conditions for each city, individually, as the weather variables that were most predictive varied across cities. Pollution forecasts for 2020 in absence of a pandemic were generated based on weather and formed an ensemble for country-level pollution reductions. Findings were robust to modelling assumptions and consistent with various published case studies. NO2 reduced most in China, Europe and India, following severe government restrictions as part of the initial lockdowns. Reductions were highly correlated with changes in mobility levels, especially trips to transit stations, workplaces, retail and recreation venues. Further, NO2 did not fully revert to pre-pandemic levels in 2020. Ambient PM2.5 pollution, which has severe adverse health consequences, reduced most in China and India. Since positive health effects could be offset to some extent by prolonged exposure to indoor pollution, alternative transport initiatives could prove to be an important pathway towards better health outcomes in these countries. Increased O3 levels during initial lockdowns have been documented widely. However, our analyses also found a subsequent reduction in O3 for many countries below what was expected based on meteorological conditions during summer months (e.g., China, United Kingdom, France, Germany, Poland, Turkey). The effects in periods with high O3 levels are especially important for the development of effective mitigation strategies to improve health outcomes.

Significance of Meteorological Feature Selection and Seasonal Variation on Performance and Calibration of a Low-Cost Particle Sensor

Poor air quality is a major environmental concern worldwide, but people living in low- and middle-income countries are disproportionately affected. Measurement of PM2.5 is essential for establishing regulatory standards and developing policy frameworks. Low-cost sensors (LCS) can construct a high spatiotemporal resolution PM2.5 network, but the calibration dependencies and subject to biases of LCS due to variable meteorological parameters limit their deployment for air-quality measurements. This study used data collected from June 2019 to April 2021 from a PurpleAir Monitor and Met One Instruments’ Model BAM 1020 as a reference instrument at Alberta, Canada. The objective of this study is to identify the relevant meteorological parameters for each season that significantly affect the performance of LCS. The meteorological features considered are relative humidity (RH), temperature (T), wind speed (WS) and wind direction (WD). This study applied Multiple Linear Regression (MLR), k-Nearest Neighbor (kNN), Random Forest (RF) and Gradient Boosting (GB) models with varying features in a stepwise manner across all the seasons, and only the best results are presented in this study. Improvement in the performance of calibration models is observed by incorporating different features for different seasons. The best performance is achieved when RF is applied but with different features for different seasons. The significant meteorological features are PM2.5_LCS in Summer, PM2.5_LCS, RH and T in Autumn, PM2.5_LCS, T and WS in Winter and PM2.5_LCS, RH, T and WS in Spring. The improvement in R2 for each season (values in parentheses) is Summer (0.66–0.94), Autumn (0.73–0.96), Winter (0.70–0.95) and Spring (0.70–0.94). This study signifies selecting the right combination of models and features to attain the best results for LCS calibration.

Organic aerosol source apportionment by using rolling positive matrix factorization: Application to a Mediterranean coastal city

We investigated the contributions and the evolution of organic aerosol (OA) sources at the Marseille-Longchamp supersite (MRS-LCP, France) based on Time-of-flight Aerosol Chemical Speciation Monitor (ToF-ACSM) measurements of non-refractory PM 1 over a fourteen-month period (1 February – 3 April 2018). The OA source apportionment was performed by positive matrix factorization (PMF) using the novel “rolling window” approach implemented in the Source Finder Professional (SoFi Pro). Here, PMF is performed over a 14-days window moving over the entire OA dataset, in order to account for the temporal variability of the source profiles. Six factors were resolved, including hydrocarbon-like organic aerosol (HOA) which is related to traffic exhausts, cooking-like organic aerosol (COA), biomass burning aerosol (BBOA), less oxidized organic aerosol (LOOA), more oxidized organic aerosol (MOOA) and a new defined source related to the mix between shipping and industrial plumes (Sh-IndOA). While HOA and COA consistently contribute to the total OA with on average 11.2% (ranging between 9.2 and 12.1% over the seasons) and 11.5% (11–12.1%), respectively, BBOA (11.7% on average) shows a larger seasonal variability with 18% in winter and no contribution in summer. BBOA profiles during winter were attributed to fresh biomass burning emissions from domestic heating, and more oxygenated profiles were assigned to regional land and agricultural waste burning for spring and early autumn. Sh-IndOA fraction is estimated to 4.5% (3.7–6.1%) and contributes to the total OA mass concentrations to a minor extent. The secondary organic aerosol (SOA) fraction includes both LOOA with 21.5% (18.8–27.2%) and MOOA with 39.6% (36.8–42.6%). Based on the f44/f43 analysis these sources appeared to be more linked to biogenic influences in summer, whereas the concentrations were associated with oxidized anthropogenic sources (biomass burning and road traffic) for the rest of the year. The investigation of MOOA geographical origins suggests some influence of air masses transported from the Rhône Valley and the west basin of the Mediterranean Sea. • OA source apportionment over 14 months at an urban background site. • Positive matrix factorization using the novel rolling window approach. • A new defined source of OA related to shipping and industrial plumes. • Highest contribution of SOA during every season.

The Relationship between PM2.5 and PM10 in Central Italy: Application of Machine Learning Model to Segregate Anthropogenic from Natural Sources

Particular Matter (PM) data are the most used for the assessment of air quality, but it is also useful to monitor VOC and CO. The health impact of PM increases with decreasing aerodynamic dimensions, therefore most of the monitoring is aimed at PM10 (fraction of PM with aerodynamic dimensions smaller than 10 µm) and PM2.5 (fraction with aerodynamic dimensions lower than 2.5 µm). Generally, anthropogenic emissions contribute mainly to PM2.5 levels, whereas natural sources can largely affect PM10 concentrations. PM2.5/PM10 ratio can be used as a proxy of the origin (anthropogenic vs natural) of the PM, providing a useful indication about the main sources of PM that characterizes a specific geographical or urban setting. This paper presents the results of the analysis of continuous measurements of PM10 and PM2.5 concentrations at eight stations of the regional air quality monitoring network in Abruzzo (Central Italy), in the period 2017–2018. The application of models based on machine learning technique shows that PM2.5/PM10 ratio can be used to classify PM emissions and to know the nature of the emission source (natural and anthropogenic), under determinate conditions, and properly taking into account the meteorological parameters.

Impact of Residential Real-World Wood Stove Operation on Air Quality concerning PM2.5 Immission

In Germany, the number of small wood-burning combustion plants was around 11 million in 2020. The PM2.5 immissions caused by the operation of these combustion plants are already about as high as those from traffic exhaust gases. Thus, particulate matter immissions occur not only on busy roads but also in residential areas. Since there are few official measuring stations for PM2.5 in residential areas and suburbs, this study determined PM2.5 concentrations from November 2020 to June 2021 at three stations (urban, suburban, and residential) in the Karlsruhe area. Simultaneous measurements of PM2.5 at the three locations have been implemented to determine short-term (peaks), medium-term, and long-term particulate matter levels and to assign them to sources by observation, considering wind direction. Illustratively, PM2.5 immission levels in January and May 2021 were compared in this paper. The comparison of the particulate matter immissions measured in the urban and residential area in January revealed that PM2.5 concentration peaks of up to 60 µg/m3 occurred for short periods in the residential area, especially on Fridays and in the evenings, which could be assigned to wood stove operation. In the urban and suburban areas, the number of the immission peaks was lower by 70–80%, and the peak concentrations were also lower by an average of 13–18%. However, the high short-term peaks have no significant impact when calculating the PM2.5 annual average according to the current limit value regulation (39. BImSchV).