Episodes Of High Concentrations Research Articles

An Application of Mist Generator as a Way to Reduce Particulate Matter during High Concentration Episodes in Urban Forests

Previous conventional mist devices can induce a detrimental effect of leaf burn by intense, focused sunlight in summer. A mist generator is designed to prevent particulate matter (PM) damage to trees by combining mist with PM during high PM episodes. We measured changes in microclimate conditions and the concentration of PM before, during, and after mist spraying in urban parks (Yangjae Citizen Forest, YCF; Cheongdam Road Park, CRP) from May 6 to 8, 2020. PM changes in YCF and CRP were observed immediately after mist spraying and were found to return to the previous concentrations. Mist spraying had no significant effects on the meteorological traits of air temperature, humidity, and wind speed but had significant effects on the concentration of PMx and the ratio of PM during a short time. Also, the ratio of PMx was partially affected by mist spraying. During the morning rush hour and lunch, mist, high wind speed, and low relative humidity conditions were related to the increase in mist movement, resulting in increasing PM (2.5–10 μm) and the deposition of these PM. During the evening rush hour, high relative humidity and low wind speed affected PM concentrations more than mist. This prototype of mist spraying could effectively condense and deposit the PM during high PM episodes.

Annual Variations of PM10, PM2.5 and PM1.0 Fraction at an Urban Site in Ansan, Korea

This study examines particulate matter (PM) concentrations, specifically PM10, PM2.5, and PM1.0, at an urban site in Ansan, South Korea. Three BAM-1020 continuous monitors measured PM10, PM2.5, and PM1.0 hourly from January to December 2021, with data acquisition rates of 97.1%, 97.3%, and 93.4%, respectively. Average annual concentrations were 45.7±48.4 µg/m³ (PM10), 26.4±22.7 µg/m³ (PM2.5), and 18.5±14.5 µg/m³ (PM1.0). PM10 was 91% of the annual air quality standard, while PM2.5 exceeded this standard by 176%, indicating severe pollution. Statistical analyses using probability density function (PDF) and cumulative distribution function (CDF) showed 98.5% of data converged at 150 µg/m³ (PM10), 75 µg/m³ (PM2.5), and 50 µg/m³ (PM1.0), with the remaining 1.5% corresponding to approximately 131 hours, annually. In 2021, all six observed yellow dust events occurred in spring (March to May). PM10, PM2.5, and PM1.0 concentrations increased from January to March, peaked in March, decreased until September, and rose again. The coarse fraction ratio increased stepwise from February to May, likely due to yellow dust and episodes of high PM concentration in the spring. The PM1.0 concentration was higher in summer and fall compared with that in spring and winter. The PM1.0/PM2.5 ratio was highest in summer and fall even though the annual PM1.0concentration was lowest, suggesting a higher secondary formation rate of fine particles during these periods. This underscores the need for further scientific investigation on PM1.0. The study provides insights into the simultaneous observation and distribution characteristics of PM10, PM2.5, and PM1.0, highlighting the mass concentration, distribution, and ratio of PM1.0. These findings are crucial for understanding PM distribution in Ansan, an industrial complex, and can serve as essential data for chemical composition comparisons, data validation, public health strategies, and cost-benefit analyses for regional PM improvement.

Exploring the long-term variations and high concentration episodes of peroxyacetyl nitrate in Megacity Seoul

Over the past few years, peroxyacetyl nitrate (PAN) has drawn significant attention as a key indicator of photochemical pollution owing to its intimate relationship with ozone and associated health effects. This study presents measurements conducted at the Korea University campus in Seoul during the high-ozone seasons from 2018 to 2021. PAN concentration was measured using fast gas chromatography with luminol chemiluminescence detection (GC-LCD), alongside measurements of O3, volatile organic compounds (VOCs), NO, NO2, and meteorological variables, including boundary layer height (BLH).The mean concentrations of PAN and O3 over the years were 0.56 ppbv and 35 ppbv in 2018, 1.29 ppbv and 58 ppbv in 2019, 0.21 ppbv and 50 ppbv 2020, and 0.53 ppbv and 46 ppbv in 2021, respectively. The annual variation observed in Seoul is consistent with trends seen in major cities worldwide during the COVID19 pandemic, reflecting a substantial reduction in urban emissions. Notably, the mean concentration of NOx and VOCs decreased significantly by more than 50 % and 25%, respectively, from 2019 to 2021.At temperatures above 30 °C, PAN decomposition was accelerated, decoupling a consistent positive relationship between PAN and O3 in 2020 and 2021. The results of a 0-D photochemical model (F0AM) calculation demonstrated that PAN formation primarily stems from anthropogenic VOCs, particularly > C2 alkenes. Elevated PAN concentrations during nighttime were attributed to boundary layer expansion and upper-air entrainment. Instances where PAN concentrations surged to at least 3 ppbv or higher in 2019 were attributed to biomass burning impacted air, as evidenced by concurrent elevations in K+ and OC in PM2.5, and O3.This study underscores the complex interplay of factors influencing PAN and ozone enhancements under decreased precursor levels, with an emphasis on dynamic change in the boundary layer, and long-distance transport of non-fossil sources during agricultural burning seasons.

Influence of meteorological conditions on the variability of indoor and outdoor particulate matter concentrations in a selected Polish health resort

The article evaluates air pollution by particulate matter (PM) in indoor and outdoor air in one of the Polish health resorts, where children and adults with respiratory diseases are treated. The highest indoor PM concentrations were recorded during the winter season. Therefore, the maximum average daily concentration values in indoor air for the PM10, PM2.5, and PM1 fractions were 50, 42 and 23 µg/m3, respectively. In the case of outdoor air, the highest average daily concentrations of PM2.5 reached a value of 40 µg/m3. The analyses and backward trajectories of episodes of high PM concentrations showed the impact of supra-regional sources and the influx of pollutants from North Africa on the variability of PM concentrations. The correlation between selected meteorological parameters and PM concentrations shows the relationship between PM concentrations and wind speed. For example, the correlation coefficients between PM1(I) and PM1(O) concentrations and wind speed were − 0.8 and − 0.7 respectively. These factors determined episodes of high PM concentrations during winter periods in the outdoor air, which were then transferred to the indoor air. Elevated concentrations in indoor air during summer were also influenced by chimney/gravity ventilation and the appearance of reverse chimney effect.

Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future

Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers. The algorithm has been used to identify ozone episodes in Europe from April to September over the last twenty years (2003–2022) in the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as well as in the historical simulation (1950–2014) and four shared socio-economic pathways (SSPs, spanning 2015–2100) of three Earth system models (UKESM1-0-LL, EC-Earth3-AerChem and GFDL-ESM4). While the total number of episodes has increased in recent years, the frequency of large episodes has decreased following European precursor emission reductions. The analysis of the 100 largest episodes shows that they tend to occur in Northern Europe during spring and in the center and south of the continent from June onwards. Most of the top 10 episodes occurred in the first years of the century and were associated with high temperatures, enhanced solar radiation, and anticyclonic conditions. Despite the decrease in large episodes in recent years, there is uncertainty regarding future European episodes. Episodes of reduced size are found for SSPs with weak greenhouse forcing and low precursor emissions, whereas episode sizes increase in scenarios with high methane concentrations and enhanced radiative forcing, even exceeding the maximum historical size. However, the three models project episodes of different sizes for any given scenario, probably associated with their differing warming trends and the varying level of complexity in the implementation of processes. These results point to the need to implement both effective climate and air quality policies to address the ozone air pollution problem in Europe in a warming climate.

Nitrate formation mechanisms causing high concentration of PM2.5 in a residential city with low anthropogenic emissions during cold season

During the cold season in South Korea, NO3− concentrations are known to significantly increase, often causing PM2.5 to exceed air quality standards. This study investigated the formation mechanisms of NO3− in a suburban area with low anthropogenic emissions. The average PM2.5 was 25.3 μg m−3, with NO3− identified as the largest contributor. Ammonium-rich conditions prevailed throughout the study period, coupled with low atmospheric temperature facilitating the transfer of gaseous HNO3 into the particulate phase. This result indicates that the formation of HNO3 played a crucial role in determining particulate NO3− concentration. Nocturnal increases in NO3− were observed alongside increasing ozone (O3) and relative humidity (RH), emphasizing the significance of heterogeneous reactions involving N2O5. NO3− concentrations at the study site were notably higher than in Seoul, the upwind metropolitan area, during a high concentration episode. This difference could potentially attributed to lower local NO concentrations, which enhanced the reaction between O3 and NO2, to produce NO3 radicals. High concentrations of Cl− and dust were also identified as contributors to the elevated NO3− concentrations.

Application of Doppler sodar in short-term forecasting of PM10 concentration in the air in Krakow (Poland)

Abstract. This paper describes an attempt to use data obtained from sodar (sound detection and ranging) for short-term forecasting of PM10 concentration levels in Krakow. Krakow is one of the most polluted cities in central Europe (CE) in terms of PM10 concentration. This is due to the high municipal emissions. Thanks to intensive corrective actions taken by the city authorities, these are being effectively eliminated, but the unfavourable topographic location of the city limits natural ventilation. The article describes all these conditions, focusing on presenting the method of short-term correction of air quality for the time needed to take quick corrective actions by the city authorities in the event of anticipated exceedances of the permissible values. Based on several years of measurements of the physical properties of the atmosphere with sodar, the authors of the paper suggest that sodar data could be considered for operational use to generate short-term predictions. The proposed method is based on the use of the spectrum, i.e. the set of amplitudes of signals returning to the sodar receiver from the reflection of a single-frequency sound transmission and its characteristic properties depending on the physical state of the atmosphere. Similar spectra were parameterised with a single numerical value using statistical methods. It was found that, in some cases preceding high concentrations of PM10, the spectral parameters had similar values. This made it possible to develop a forecasting method for such concentrations by using data mining to search for conditions in historical data closest to the state of the atmosphere at the time of forecasting. In this part of the study, data from 2017–2018 were used. In the next step, three methods of using the sodar data developed for PM10 prediction were proposed, comparing them with the method without sodar use. The study results were tested on independent material using data concerning the episodes of high concentrations of pollutants from October 2021 to March 2022 in Krakow. The findings were considered to be encouraging, also taking into account the speed and low cost of preparing the forecast.

Ozone alters the chemical signal required for plant – insect pollination: The case of the Mediterranean fig tree and its specific pollinator

Tropospheric ozone (O3) is likely to affect the chemical signal emitted by flowers to attract their pollinators through its effects on the emission of volatile organic compounds (VOCs) and its high reactivity with these compounds in the atmosphere. We investigated these possible effects using a plant-pollinator interaction where the VOCs responsible for pollinator attraction are known and which is commonly exposed to high O3 concentration episodes: the Mediterranean fig tree (Ficus carica) and its unique pollinator, the fig wasp (Blastophaga psenes). In controlled conditions, we exposed fig trees bearing receptive figs to a high-O3 episode (5 h) of 200 ppb and analyzed VOC emission. In addition, we investigated the chemical reactions occurring in the atmosphere between O3 and pollinator-attractive VOCs using real-time monitoring. Finally, we tested the response of fig wasps to the chemical signal when exposed to increasing O3 mixing ratios (0, 40, 80, 120 and 200 ppb). The exposure of the fig tree to high O3 levels induced a significant decrease in leaf stomatal conductance, a limited change in the emission by receptive figs of VOCs not involved in pollinator attraction, but a major change in the relative abundances of the compounds among pollinator-attractive VOCs in O3-enriched atmosphere. Fig VOCs reacted with O3 in the atmosphere even at the lowest level tested (40 ppb) and the resulting changes in VOC composition significantly disrupted the attraction of the specific pollinator. These results strongly suggest that current O3 episodes are probably already affecting the interaction between the fig tree and its specific pollinator.

Постпроцессинг численных прогнозов концентраций приземного озона с использованием машинного обучения

Monitoring the quality of air pollutant concentration forecasts based on chemical transport models (CTMs) currently used in the technology of the Hydrometcentre of Russia indicates the feasibility of the post-processing procedure application. For the first time, artificial neural networks (ANNs) were used to correct ground-level ozone model calculations. Retrospective hourly CTM CHIMERE forecasts for 2019‒2023 formed the training dataset. Experiments were carried out to select the optimal ANN settings. Results of the experimental testing of the best ANN on a week-long summer period with an episode of high ozone concentrations and a spring period with an episode of high ozone concentrations due to active tropospheric mixing are presented. The effectiveness of using ANNs to improve model forecasts of ground-level ozone and its daily dynamics is shown. Keywords: artificial neural networks, ground-level ozone, numerical forecast of pollution, chemical transport model, CHIMERE

Evaluation of different machine learning approaches for predicting high concentration episodes of ground-level ozone: A case study in Catalonia, Spain

Ground-level ozone (O3) is a pollutant with a great impact on human health and the environment. As a secondary air contaminant of photochemical origin, those areas with greater exposure to solar radiation, such as Spain and other Mediterranean countries, are considerably affected. With the aggravation of O3 pollution, it is important to provide reliable forecasting tools to help stakeholders implement more effective policies to mitigate the negative impact associated with this problem. In this regard, Machine Learning-based models have emerged in recent years, since they are able to identify complex relationships between ozone levels and relevant variables. However, their application to capture the most extreme events remains difficult. In this work, different ML approaches for predicting daily maximum 8-h average ozone (O3,MDA8) were compared, investigating their ability to forecast the highest concentration levels recorded. Two variants of the Random Forest algorithm (regression and classification) were applied to a specific area of Catalonia, Spain, with a special interest due to the high number of episodes of exceedance of O3 concentration levels. The predictive models were built with a 1 day time horizon, using datasets from 2002 to 2020. The variables used as inputs were other air pollutants concentrations and meteorological processes, monitored the day before to the target day to be predicted, and time information. Although results showed reasonable overall performances, low accuracy was achieved when forecasting the highest episodes of O3,MDA8. To improve the capacity of the models in predicting high-O3,MDA8 concentration levels, a methodology was proposed to fine-tuning the original predictions of the ML models according to a classification metric, G-Mean, which allows adjusting the balance between the correct predictions of different classes. Using the Sensitivity and Specificity metrics, the classical approaches were compared with the original ones proposed in the present study. The results obtained, for all the cases analysed, showed a mean increase in Sensitivity of 0.28, associated with a greater number of True Positives (correct predictions of high O3-episodes). On the other hand, the average Specificity value decreased, due to the appearance of a greater number of False Positives, although this reduction was only 0.05. The proposed criteria showed promising results, better balancing classification metrics and increasing the ratio of correct predictions linked to the higher ranges of O3.

Variability of ambient particulate matter loading at Henties Bay, Namibia

The Namibian coast is one of the areas of international interest for aerosol studies. This is due to the region’s importance for the global radiation budget because of the presence of a semi-permanent stratocumulus cloud along the coast. Aerosol particles may scatter/absorb radiation and directly influence how long clouds last by modifying their properties. This is all dependent on the particles’ chemical and physical properties because of the sources they were emitted from. In this study, we identified and investigated episodes of high (HAE) and low (LAE) PM concentrations and the meteorology that may favour their occurrence. Here, we investigated PM2.5 (particles with an aerodynamic diameter of 2.5 µm or less) and PM10 (particles with an aerodynamic diameter of 10 µm or less) at Henties Bay, Namibia. Daily aerosol measurements were taken with E-samplers between 15 and 29 July 2019. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used to investigate the long-range atmospheric transport of air masses that reached Henties Bay. The study found that during HAEs, the average PM2.5 concentration was 28.40 ± 18.10 µg/m3 and the average PM10 concentration was 68.20 ± 44.3 µg/m3. In contrast, during LAEs, the average PM2.5 concentration was 13.3 ± 9.52 µg/m3 and the average PM10 concentration was 30.00 ± 23.00 µg/m3. In both fractions, there was an observed dominant contribution from marine sources.

Hybrid machine learning techniques in the management of harmful algal blooms impact

Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high concentrations of marine biotoxins in their tissues. To avoid the risk to human consumption, harvesting is prohibited when toxicity is detected. At present, the closure of production areas is based on expert knowledge and the existence of a predictive model would help when conditions are complex and sampling is not possible. Although the concentration of toxin in meat is the method most commonly used by experts in the control of shellfish production areas, it is rarely used as a target by automatic prediction models. This is largely due to the irregularity of the data due to the established sampling programs. As an alternative, the activity status of production areas has been proposed as a target variable based on whether mollusc meat has a toxicity level below or above the legal limit. This new option is the most similar to the actual functioning of the control of shellfish production areas. For this purpose, we have made a comparison between hybrid machine learning models like Neural-Network-Adding Bootstrap (BAGNET) and Discriminative Nearest Neighbor Classification (SVM-KNN) when estimating the state of production areas. The study has been carried out in several estuaries with different levels of complexity in the episodes of algal blooms to demonstrate the generalization capacity of the models in bloom detection. As a result, we could observe that, with an average recall value of 93.41% and without dropping below 90% in any of the estuaries, BAGNET outperforms the other models both in terms of results and robustness.

Pollen long-distance transport associated with symptoms in pollen allergics on the German Alps: An old story with a new ending?

Pollen grains are among the main causes of respiratory allergies worldwide and hence they are routinely monitored in urban environments. However, their sources can be located farther, outside cities' borders. So, the fundamental question remains as to how frequent longer-range pollen transport incidents are and if they may actually comprise high-risk allergy cases. The aim was to study the pollen exposure on a high-altitude location where only scarce vegetation exists, by biomonitoring airborne pollen and symptoms of grass pollen allergic individuals, locally.The research was carried out in 2016 in the alpine research station UFS, located at 2650 m height, on the Zugspitze Mountain in Bavaria, Germany. Airborne pollen was monitored by use of portable Hirst-type volumetric traps. As a case study, grass pollen-allergic human volunteers were registering their symptoms daily during the peak of the grass pollen season in 2016, during a 2-week stay on Zugspitze, 13–24 June. The possible origin of some pollen types was identified using back trajectory model HYSPLIT for 27 air mass backward trajectories up to 24 h.We found that episodes of high aeroallergen concentrations may occur even at such a high-altitude location. More than 1000 pollen grains m−3 of air were measured on the UFS within only 4 days. It was confirmed that the locally detected bioaerosols originated from at least Switzerland, and up to northwest France, even eastern American Continent, because of frequent long-distance transport. Such far-transported pollen may explain the observed allergic symptoms in sensitized individuals at a remarkable rate of 87 % during the study period.Long-distance transport of aeroallergens can cause allergic symptoms in sensitized individuals, as evidenced in a sparse-vegetation, low-exposure, ‘low-risk’ alpine environment. We strongly suggest that we need cross-border pollen monitoring to investigate long-distance pollen transport, as its occurrence seems both frequent and clinically relevant.

Role of vertical advection and diffusion in long-range PM2.5 transport in Northeast Asia

This study quantitatively analyzed the role of vertical mixing in long-range transport (LRT) of PM2.5 during its high concentration episode in Northeast Asia toward the end of February 2014. The PM2.5 transport process from an upwind to downwind area was examined using the Community Multi-scale Air Quality (CMAQ) modeling system with its instrumented tool and certain code modifications. We identified serial distinctive roles of vertical advection (ZADV) and diffusion (VDIF) processes. The surface PM2.5 in an upwind area became aloft by VDIF— during daytime—to the planetary boundary layer (PBL) altitude of 1 km or lower. In contrast, ZADV updraft effectively transported PM2.5 vertically to an altitude of 2–3 km above the PBL. Furthermore, we found that the VDIF and ZADV in the upwind area synergistically promoted the vertical mixing of air pollutants up to an altitude of 1 km and higher. The aloft PM2.5 in the upwind area was then transported to the downwind area by horizontal advection (HADV), which was faster than HADV at the surface layer. Additionally, VDIF and ZADV over the downwind area mixed down the aloft PM2.5 on the surface. During this period, the VDIF and ZADV increased the PM2.5 concentrations in the downwind area by up to 15 μg·m−3 (15%) and 101 μg·m−3 (60%), respectively. This study highlights the importance of vertical mixing on long-range PM2.5 transport and warrants more in-depth model analysis with three-dimensional observations to enhance its comprehensive understanding.

PM2.5-bound Inorganic and Nonpolar Organic Compounds in Chuncheon, Korea

In this study, major chemical components of PM2.5 including nitrate, sulfate, organic carbon (OC), and elemental carbon (EC) were measured in Chuncheon, South Korea in May–June, 2021. Average PM2.5 concentration was 16.4±9.7 µg m−3, and OC was the largest contributor of PM2.5 mass concentration. High concentration episodes (HCEs), defined when PM2.5 concentration exceeded 30 µg m−3, were caused by Asian dust, secondary inorganic aerosol (SIA) formation, and primary OC emission. NH4+ was determined to be a limiting factor for SIA formation based on neutralization ratio. There was statistically significant correlation between n-alkanes and PM2.5, and odd alkanes including C27, C29, and C31, which are generally emitted from biogenic sources, were abundant species, suggesting the importance of natural sources over fossil fuel combustion. Polycyclic aromatic hydrocarbons (PAHs) concentrations were significantly lower than those measured at the same sampling site in 2014–2015. Based on the diagnostic ratios of PAHs, vehicular emission, rather than solid fuel emission, were significant for PAHs. Detailed characterization of chemical composition of PM2.5 reported in this study can be of great help in establishing an appropriate abatement policy to reduce PM2.5 concentrations.

The risk of respiratory incidents in response to the increase of ozone concentration in the ambient air, in the Silesian Voivodeship, in 2016-2017.

Due to the high level of urbanization and industrialization, Silesian Voivodeship remains a region with the poorest quality ambient air, especially in the winter season in which alarm levels are constantly being exceeded. However, in the summertime, there are observed short-term episodes of high ozone concentrations for which their impact on the population health is poorly documented. The aim of the study was to assess the risk of daily respiratory health problems related to an increased pollutants concentration typical for photochemical smog. In the ecological type of study, secondary epidemiological data were used. They were obtained from the National Health Fund (NFZ) in Katowice and included the number of outpatient visits in primary health care and hospitalizations due to respiratory diseases (J00-J99) and selected acute respiratory incidents registered between 01/01/2016 and 31/08/2017 in the Silesian Voivodeship. In the summertime of both years (2016 and 2017), there were observed short-term episodes of photochemical smog in the study region. Obtained results show a significant increase in the risk of outpatient visits due to total respiratory diseases, and also due to acute pharyngitis, acute laryngotracheitis, bronchitis, and asthma in response to the increase in ozone concentration. Similarly, a significant increase in the risk of hospitalization for all respiratory diseases was found, however, it appeared with a delay of at least two to three weeks. In the case of hospitalization due to bronchitis statistically significant risk was observed 2-4 days after the increase in exposure. The occurrence of registered respiratory incidents was confirmed in response to the increase in ozone concentration, characteristic of the summertime in the Silesian Voivodeship.

Chemical characteristics and sources of PM2.5 in the urban environment of Seoul, Korea

In Seoul, Korea, episodes of high concentrations of PM2.5 (particulate matter ≤2.5 μm) continue to occur despite the enforcement of air quality standards. Seoul has several sources of pollution owing to its high population density. Therefore, this study evaluated the chemical characteristics of PM2.5, including inorganic ions and various gases, focusing on those with concentrations exceeding the standard limit. The study was conducted from June 1 to August 22, 2018, in Seongbuk, and from March 29 to May 31, 2019, in Jungnang. Water-soluble inorganic ions of PM2.5 were measured every 30 min using a particle-into-liquid sampler (PILS) combined with two ion chromatographs. In Seongbuk, ammonium sulfate was mainly caused by ammonium-poor conditions. In Jungnang, ammonium nitrate production occurred mainly due to ammonium-rich conditions. In both regions, during the occurrence of concentrations of PM2.5 exceeding the standard limit, the proportion of nitrate among inorganic ions was the highest. In Jungnang, two emission sources located in the west and southwest were identified using a conditional probability function. Contaminants from the southwest had high concentrations of nitrates, presumably due to atmospheric stagnation and nitrate mixing in lower planetary boundary layer height due to an decrease in surface temperatures at dawn. Pollutants from the west had high concentrations of sulfates. These are generated through photochemical reactions from industrial complexes. Cluster analysis confirmed that 27.2% of the air was stagnant and flowed from the south. In most cases, air pollutants originated from western Korea and coastal China.

Chemically speciated mass size distribution, particle density, shape and origin of non-refractory PM<sub>1</sub> measured at a rural background site in central Europe

Abstract. Seasonal variability of non-refractory PM1 (NR-PM1) was studied at a rural background site (National Atmospheric Observatory Košetice – NAOK) in the Czech Republic to investigate the effect of regional and long-range atmospheric transport in central Europe. NR-PM1 measurements were performed by compact time-of-flight aerosol mass spectrometry (C-ToF-AMS), and the chemically speciated mass size distributions, density, shape, and origin were discussed. Average PM1 concentrations, calculated as the sum of the NR-PM1 and the equivalent black carbon (eBC) concentrations measured by an aethalometer (AE), were 8.58 ± 3.70 µg m−3 in summer and 10.08 ± 8.04 µg m−3 in winter. Organics were dominant during both campaigns (summer/winter: 4.97 ± 2.92/4.55 ± 4.40 µg m−3), followed by SO42-in summer (1.68 ± 0.81/1.36 ± 1.38 µg m−3) and NO3- in winter (0.67 ± 0.38/2.03 ± 1.71 µg m−3). The accumulation mode dominated the average mass size distribution during both seasons, with larger particles of all species measured in winter (mode diameters: Org: 334/413 nm, NO3-: 377/501 nm, SO42-: 400/547 nm, and NH4+: 489/515 nm) indicating regional and long-range transport. However, since the winter aerosols were less oxidized than the summer aerosols (comparing fragments f44 and f43), the importance of local sources in the cold part of the year was still enough to be considered. Although aged continental air masses from the south-east (SE) were rare in summer (7 %), they were related to the highest concentrations of PM1, eBC, and all NR-PM1 species, especially SO42- and NH4+. In winter, slow continental air masses from the south-west (SW) (44 %) were linked to inversion conditions over central Europe and were associated with the highest concentrations among all NR-PM1 species as well as PM1 and eBC. Average PM1 material density (ρm) corresponded to higher inorganic contents in both seasons (summer: ∼ 1.30 g cm−3 and winter: ∼ 1.40 g cm−3). During episodes of higher mass concentrations ρm ranged from 1.30–1.40 g cm−3 in summer and from 1.30–1.50 g cm−3 in winter. The dynamic shape factors (χ) decreased slightly with particle mobility diameter (Dm) in both seasons. This study provides insights into the seasonal effects and air mass variability on aerosol particles, focusing on episodes of high mass and number concentrations measured at a central European rural background site.

Characterization of PM2.5 Mass in Relation to PM1.0 and PM10 in Megacity Seoul

This study examines the PM2.5 characteristics in Seoul in relation to those of PM1.0 and PM10. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM1.0, PM2.5, and PM10 were 19.7 μg/m3, 26.0 μg/m3, and 48.2 μg/m3, respectively, and PM2.5 was reasonably correlated with PM1.0 (γ=0.79) and PM10 (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM1.0 with PM10 and high OC and NO3−; Group 2 (17%): PM10 considerably higher than PM1.0 and high Ca2+ and SO42−; Group 3 (52%): PM1.0 relatively more enhanced than PM10 and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM2.5 concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM2.5 mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM2.5 mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.

CMAQ-CNN: A new-generation of post-processing techniques for chemical transport models using deep neural networks

Chemical transport models simulate ambient air pollution concentrations by considering emission, transport and deposition mechanism, and other physical processes. Despite their advancements over the past decades, the models exhibit significant model-measurement errors, especially during high concentration episodes. Furthermore, biases within meteorological predictions, emission estimates, initial and boundary conditions, and other model inputs can propagate into the final predictions. This research introduces a new generation of post-processing application for real-time air quality forecasts that employ the physical process of a numerical model in conjunction with an advanced deep learning algorithm. We use a deep convolutional neural network (CNN) to map ozone precursors from Community Multiscale Air Quality (CMAQ) and meteorological parameters from the weather research and forecasting (WRF) model (as inputs variables) to observed hourly ozone concentrations at a monitoring station (as a target). Our results show that the CMAQ-CNN model significantly improves the performance of the CMAQ model in both accuracy and bias. The absolute correlation coefficient is improved by 0.16 on average. The CMAQ-CNN model improves simulated ozone peaks for almost all cases and reduces the bias of CMAQ predictions by an average of more than 20 ppb (or 40%). Systematic improvements in CMAQ-CNN simulations suggest that the deep learning model is effective at reproducing accurate estimates of ground-level air quality concentrations. While this study focuses on ozone in the United States and outputs of CMAQ, the proposed approach can be applied to any measured air pollution parameters or numerical model in a mesoscale resolution.