Seasonal and spatial variability of secondary inorganic aerosols in PM2.5 at Agra: Source apportionment through receptor models

Source apportionment of fine aerosol particles of water-soluble and carbonaceous species measured in semi-urban (Kharagpur) and megacity (Kolkata) atmospheres over the eastern Indo-Gangetic plain: Chemical characterisation, relative abundance and anthropogenic contributions

We conducted the source apportionment of fine aerosol particles (aerodynamic diameter ≤1.6μm) collected with the indigenously designed-fabricated submicron aerosol sampler (SAS) in the eastern Indo-Gangetic plain (IGP) semi-urban (Kharagpur, KGP) and megacity (Kolkata, KOL) atmospheres, examining the chemical characteristics at KGP (January 2015-February 2016), and accentuating their abundance and the sources of anthropogenic pollution relative to KOL. The fine water-soluble inorganic ions (WSII) at KGP predominantly constituted Ca2+ (52 %) and equivalent amounts (12 % each) of Cl-, Mg2+ and secondary inorganic aerosols (sum of SO42-, NO3- and NH4+). The annual mean of SO42- at KGP was twice (thrice) larger than NO3- (NH4+); this of organic carbon (OC) was thrice elemental carbon (EC), with secondary OC being 37 % of the total OC. The concordance in peaks of OC with K+ concentrations was identified during the seasonal open biomass burning at KGP (November and May). While the annual mean of OC (EC) concentration at KGP was slightly lower than (nearly equivalent to) KOL; K+, NO3-, NH4+ and F- concentrations at KOL were twice larger than KGP. Source quantification using Positive Matrix Factorization (PMF) revealed the regional dust with crustal elements marked as clean (polluted) at KGP (KOL) constituted the largest fractional contribution among the six identified factors at both KGP and KOL. The combustion-derived anthropogenic pollution comprising about 60 % (50 %) of fine particles at KOL (KGP) was predominantly from the transportation sector (in vehicular emissions and regional dust), coal combustion (industries) and open biomass burning at KOL; it was from brick kilns, residential biofuel combustion, and open biomass burning at KGP. The source-wide distribution of measured aerosol species showed their emergence from largely different sources at KGP and KOL; thereby suggesting a prioritised strategy for sustainable emissions mitigation considering the prominent sources of combustion-derived anthropogenic pollution and aerosol species for megacity and semi-urban atmospheres.

The main strategies for soil pollution apportionment: A review of the numerical methods

Nowadays, a large number of compounds with different physical and chemical properties have been determined in soil. Environmental behaviors and source identification of pollutants in soil are the foundation of soil pollution control. Identification and quantitative analysis of potential pollution sources are the prerequisites for its prevention and control. Many efforts have made to develop methods for identifying the sources of soil pollutants. These efforts have involved the measurement of source and receptor parameters and the analysis of their relationships via numerical statistics methods. We have comprehensively reviewed the progress made in the development of source apportionment methodologies to date and present our synthesis. The numerical methods, such as spatial geostatistics analysis, receptor models, and machine learning methods are addressed in depth. In most cases, however, the effectiveness of any single approach for source apportionment remains limited. Combining multiple methods to address soil quality problems can reduce uncertainty about the sources of soil pollution. This review also constructively highlights the key strategies of combining mathematical models with the assessment of chemical profiles to provide more accurate source attribution. This review intends to provide a comprehensive summary of source apportionment methodologies to help promote further development.

Characterization and sources of water-soluble inorganic ions during sulfate-driven and nitrate-driven haze on the largest loess accumulation plateau

With the rapid reduction of anthropogenic SO2 emissions, the critical driver of haze in China has shifted from being dominated by sulfate to alternating sulfate and nitrate. Haze induced by different driver species may differ in the chemical forms of water-soluble inorganic ions (WSIIs). The unique topography and high-emission industrial agglomeration of the Loess Plateau determine its severe local PM2.5 pollution and influence global weather patterns through the outward export of pollutants. PM2.5 samples were conducted in Pingyao, on the eastern Loess Plateau of China, in autumn and winter. The average mass of PM2.5 was 88.82 ± 57.37 μg/m3; sulfate, nitrate, and ammonium were the dominant component. The chemical form of the ion was dominated by (NH4)2SO4, NH4NO3, NaNO3 and KNO3 during the nitrate-driven (ND) haze, while (NH4)2SO4, NH4HSO4, NH4NO3, NaNO3 and KNO3 were predominant species during the sulfate-driven (SD) haze. Heterogeneous oxidation reactions dominated the mechanism of sulfate formation. Primary sulfate emissions or other generation pathways contributed to sulfate formation during the SD haze. The gas-phase homogeneous reaction of NO2 and NH3 dominates the nitrate generation during the ND haze. The heterogeneous reactions also played an essential role during the SD haze. Nitrate aerosol (42.30%) and coal and biomass combustion (23.23%) were the dominant sources of WSIIs during the ND haze. In comparison, nitrate aerosol (31.80%) and sulfate aerosol (25.08%) were considered the primary control direction during the SD haze. The chemical characteristics and sources of aerosols under various types of haze differ significantly, and knowledge gained from this investigation provides insight into the causes of heavy haze.

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Scattering visiometers are widely used to measure atmospheric visibility; however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predicting visibility on basins and plains. In eastern Taiwan, the SVM model is most suitable for predicting visibility on alluvial plains and rift valleys. Thus, the optimal prediction model should be identified according to the conditions in each area. These results can inform decision-making processes or improve visibility predicting in specific areas.

Global PM2.5 and secondary organic aerosols (SOA) levels with sectorial contribution to anthropogenic and biogenic SOA formation

This study estimates global PM_2.5 and anthropogenic and biogenic Secondary Organic Aerosols (a-SOA and b-SOA) and sources contributing to their formation. The global landscape was divided into eleven domains (North America (NAM); South America (SAM); Europe (EUR); North Africa and Middle East (NAF); Equatorial Africa (EAF); South of Africa (SAF); Russia and Central Asia (RUS); Eastern Asia (EAS); South Asia (SAS); Southeast Asia (SEA) and Australia (AUS)) and 46 cities based on varying populations. Three inventories for global emissions were considered: Community Emissions Data System, Model of Emission of Gases and Aerosol, and Global Fire Emissions Database. WRF-Chem model coupled with atmospheric reactions and the secondary organic aerosol model was employed for estimating PM_2.5, a-SOA, and b-SOA for 2018. No city attained WHO's annual PM_2.5 guideline of 5 μg/m³. Delhi, Dhaka, and Kolkata (63-92 μg/m³) in south Asia were the most polluted, and seven cities (mostly in EUR and NAM) met the WHO target IV (10 μg/m³). The highest SOA levels (2-9 μg/m³) were in the cities of SAS and Africa, but with a low SOA contribution to PM_2.5 (3-22%). However, the low levels of SOA (1-3 μg/m³) in EUR and NAM had a higher contribution of SOA to PM_2.5 (20-33%). b-SOA were consistent with the region's vegetation and forest cover. The SOA contribution was dominated by residential emissions in all domains (except in the NAF and AUS) (maximum in SAS). The non-coal industry was the second highest contributor (except in EAF, NAF, and AUS) and EUR had the maximum contribution from agriculture and transport. Globally, residential and industry (non-coal and coal) sectors showed the maximum contribution to SOA, with a-SOA and b-SOA being nearly equal. Ridding of biomass and residential burning of solid fuel is the single most action benefiting the PM_2.5 and SOA concerns.

Precipitation frequency controls nitrogenous aerosol in a tropical coastal city and its implications for plant carbon sequestration

The concentration of nitrogenous aerosols is influenced by air mass transition, local meteorological conditions, local emissions, and the wet removal effect driven by precipitation. Deposited nitrogenous aerosols influence nitrogen availability in the canopy, affecting the amount of plant carbon sequestration. However, the factors controlling nitrogenous aerosol concentrations and their implications for plant carbon sequestration remain unclear. In this study, multiple stable nitrogen isotopes in atmospheric aerosols (δ¹⁵N-TN, δ¹⁵N-NO₃^-, and δ¹⁵N-NH₄⁺) and rainwater (rainwater δ¹⁵N-NO₃^- and rainwater δ¹⁵N-NH₄⁺) in one-year observations were analyzed to explore the main factors controlling nitrogenous aerosol concentrations. The results showed that NO₃^- and NH₄⁺ were the major components of TN, and their concentrations in seasonal patterns were sensitive to frequent rainfall rather than local emissions or external contributions. The concentrations of nitrogenous aerosols were negatively correlated with precipitation frequency, indicating that increased precipitation frequency induced low concentrations of nitrogenous aerosols. Moreover, the positive matrix factorization (PMF) analysis showed that coarse mode NO₃^- was generated in the wet season but not in the dry season, reflecting the removal of precipitation. With the increased precipitation frequency from May to July, 42.4% of aerosol NO₃^- was scavenged into rainwater, indicated by the variations in the δ¹⁵N values of nitrogenous aerosols and rainwater. This result prompted us to calculate the loss of 12.1 ± 3.9 Gg carbon/yr plant carbon sequestration. Our study suggests that nitrogenous aerosols are captured by the high precipitation frequency in tropical areas, decreasing nitrogen availability in the canopy, which might decrease plant carbon sequestration.

Characterization of fine particulate matter water-soluble inorganic ions and estimation of aerosol acidity at three COALESCE network sites - Mysuru, Bhopal, and Mesra - in India

The study was carried out to understand the chemical, spatiotemporal characteristics of water-soluble inorganic ions (WSIIs), their association with PM_2.5 mass, and aerosol acidity in three COALESCE (carbonaceous aerosol emissions, source apportionment, and climate impacts) network sites of India (Mesra - Eastern India, Bhopal - Central India and Mysuru - Southern India). Alternate-day 24-h integrated bulk PM_2.5 samples were collected during 2019 along with on-site meteorological parameters. Annual average PM_2.5 concentrations were 67 ± 46 µg m^-3, 54 ± 47 µg m^-3, and 30 ± 24 µg m^-3 at Mesra, Bhopal, and Mysuru, respectively. PM_2.5 concentrations exceeded the annual mean (40 µg m^-3) recommended by the National Ambient Air Quality Standards (NAAQS) at Mesra and Bhopal. WSIIs existed in PM_2.5 mass at Mesra (50.5%), Bhopal (39.6%), and Mysuru (29.2%). SO₄^2-, NO₃^-, and NH₄⁺ (SNA) were major secondary inorganic ions in total WSIIs, with an annual average of 88.4% in Mesra and 82.0% in Bhopal 78.4% in Mysuru. Low NO₃^-/SO₄^2- ratios annually at Mesra (0.41), Bhopal (0.44), and Mysuru (0.24) indicated that stationary sources dominated vehicular emissions (1.0). Aerosol acidity varied from region to region and season to season depending on the presence of NH₄⁺, the dominant counter-ion to neutralize anions. Aerosols were near-neutral or alkaline at all three sites, except during the pre-monsoon season in Mysuru. An assessment of neutralization pathways for major anions [SO₄^2-  + NO₃^-] suggests that they mainly existed as sulfate and nitrate salts such as ammonium sulfate ((NH₄)₂SO₄) and ammonium bisulfate (NH₄HSO₄) in conjunction with ammonium nitrate (NH₄NO₃).

Variations in the concentration, source activity, and atmospheric processing of PM2.5-associated water-soluble ionic species over Jammu, India

Concentrations, sources, and atmospheric processing of water-soluble ionic species associated with PM_2.5 collected from 2015 to 2017 were studied in Jammu, an urban location in the North-Western Himalayan Region (NWHR). Being ecologically sensitive and sparsely studied for dynamics in PM_2.5 and associated WSIS, the present study is important for developing robust air pollution abatement strategies for the air-shed of NWHR. Twenty-four hourly PM_2.5 samples were collected on weekly basis at a receptor site and analyzed for WSIS using ion chromatography system. On annual basis, total sum of WSIS (ΣWSIS) contributed about 28.5% of PM_2.5, where the contribution of sulfate-nitrate-ammonium, a proxy for secondary inorganic aerosols (SIA), was found to be 18.7% of PM_2.5. The ΣWSIS and PM_2.5 concentration showed a seasonal cycle with the maximum concentration during winters and the minimum in summers. Mass fraction of ΣWSIS in PM_2.5 showed an anti-phase seasonal pattern indicating more source activity during summers. Season-wise, dominant WSIS constituting PM_2.5 were NO₃^-, SO₄^2-, NH₄⁺, and K⁺ during winters; whereas summer was marked with dominant contributions from SO₄^2-, NH₄⁺, Ca²⁺, and K⁺. Seasonal variability exhibited among SIA constituents underscored the crucial role of air temperature and relative humidity regime. It was observed that nss-K⁺ + NH₄⁺ were sufficient to neutralize most of the acidic species arising from precursor gases (NO_x and SO_x). Using principal component analysis, five major sources and processes, viz. (a) biomass burning activities, (b) secondary inorganic aerosol formation, (c) input from re-suspended dust, (d) transported dust, and (e) fertilizer residue, were identified for the emissions of PM_2.5-associated WSIS over Jammu. In future studies, impacts of dry and/or wet deposition of aerosol-associated WSIS on the crop productivity in the region should be studied.

Chemical characterization of PM2.5 from region highly impacted by hailstorms in South America

The chemical composition of particulate material plays an important role in the atmosphere, providing cloud and ice nuclei for storm development. This study aims to evaluate and infer the sources of ions, metals, and metalloids in the fine atmospheric particulate matter (PM_2.5) from triple border Paraná, Santa Catarina (Brazil), and northeastern Argentina, which is among those with the highest hail incidence in the world. Among the ions, the concentrations presented the following sequence in decreasing order: [Formula: see text]> K⁺> [Formula: see text]> [Formula: see text]> Ca²⁺> Cl^-> Na⁺> Mg²⁺. Regarding the metals and metalloid concentrations, the order was of S > Si > Al > Fe > P > Ti, Cr, Cu, and Zn > Br > Mn, and Ni. The main sources, supported by positive matrix factorization results, are soil and agricultural activities, as well as vehicular emissions due to the agricultural machinery and the displacement of residents. Besides, the influence of aerosols from biomass burning and industrial activities was observed, possibly come from long-distance transport. The composition of PM_2.5 presents one or more elements considered present ice nuclei (IN) activity, such as Al, Mn, Cu, Co, Ni, and V (in form of oxides), corroborating with other studies, also, with high hail incidence. However, further studies are needed to verify the role of aerosol characteristics in the formation of IN and, consequently, hail.

The role of particulate matter in reduced visibility and anionic composition of winter fog: a case study for Amritsar city

Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases. The present paper is an attempt to study the twenty fog samples collected from the rooftop of an academic building of Guru Nanak Dev University, Amritsar, India from November 2017 to January 2018. Fog samples were analysed for various parameters viz. pH, electrical conductivity (EC), chloride (Cl⁻), nitrate (NO₃⁻) and sulphate (SO₄²⁻) levels. The pH, EC, and Cl⁻, NO₃⁻ and SO₄²⁻ levels in the fog samples were estimated as 6.3–7.9, 240–790 μS cm⁻¹, 108–2025 μeq L⁻¹, 105–836 μeq L⁻¹ and 822–5642 μeq L⁻¹, respectively. It was noticed that sulphate was the dominant anion in fog samples. The SO₄²⁻ to NO₃⁻ molar ratio in the fog was estimated as 7.6 which suggests the burning of fossil fuel as the major pollutant from vehicular exhausts. Multiple regression analysis was performed to evaluate the effect of PM_2.5/PM₁₀ ratio and relative humidity (RH) on visibility. A box-cox plot of power transformation produced better model fitting, employing a square root transformation of the visibility which indicated that the PM_2.5/PM₁₀ and RH have an exponential effect on visibility.

Severe fog events during winter months in India are a serious concern due to the higher incidence of road accidents, flight delays and increased occurrence of respiratory diseases.

Role of ammonia in secondary inorganic aerosols formation at an ammonia-rich city in winter in north China: A comparative study among industry, urban, and rural sites

Ammonia is essential for the generation of secondary inorganic aerosols (SIA) in particulate matter, which affects severely the air quality in north China. In this study, PM_2.5 sampling was conducted as well as gaseous pollutant concentration and meteorological parameters were measured from November 2017 to January 2018. PM_2.5 concentration was highest in the industrial site (94.8 ± 41.7 μg m^-3), followed by urban (40.9 ± 24.1 μg m^-3) and rural (35.6 ± 20.3 μg m^-3) sites. The mass ratio of NO₃^-/SO₄^2- exhibited clear site variations, with the highest average value of 1.2 was found at the urban site, likely due to the dense traffic volume resulting in higher emissions of NO₂, and the lowest value of 0.9 at the industry site. The presence of Excess-NHx (E-NHx), raising the pH 24 by 1.4, 1.3, and 1.4 units in industry, urban, and rural sites, respectively, might be vital for raising the aerosol pH. Correlation coefficients of Nitrogen oxidation rate (NOR, NOR = [NO₃^-]/[NO₃^-] + [NO₂]) vs. Photochemical oxidants (O_x, NO₂ +O₃ in our study) and NOR vs. aerosol water content (AWC) at three sites were implied that both homogeneous and heterogeneous reactions occurred for nitrate formation in industry site, while heterogeneous reactions were dominant in urban and rural sites. Oxidation rates were most sensitive to the variation of E-NHx concentration at rural site, followed by the urban and industry sites, which was shown by the fact that the increase in E-NHx concentration by 1.0 μg m^-3 increased the SIA concentration by 1.21, 1.02, and 0.37 μg m^-3 at rural, urban, and industry sites, respectively. With the increase in NH_x emissions at present, the role of NH_x in SIA formation at ammonia-rich atmosphere requires more attention, especially in the less-noticed rural areas.

Chemical Composition and Source Apportionment of Total Suspended Particulate in the Central Himalayan Region

The present study analyzes data from total suspended particulate (TSP) samples collected during 3 years (2005–2008) at Nainital, central Himalayas, India and analyzed for carbonaceous aerosols (organic carbon (OC) and elemental carbon (EC)) and inorganic species, focusing on the assessment of primary and secondary organic carbon contributions (POC, SOC, respectively) and on source apportionment by positive matrix factorization (PMF). An average TSP concentration of 69.6 ± 51.8 µg m−3 was found, exhibiting a pre-monsoon (March–May) maximum (92.9 ± 48.5 µg m−3) due to dust transport and forest fires and a monsoon (June–August) minimum due to atmospheric washout, while carbonaceous aerosols and inorganic species expressed a similar seasonality. The mean OC/EC ratio (8.0 ± 3.3) and the good correlations between OC, EC, and nss-K+ suggested that biomass burning (BB) was one of the major contributing factors to aerosols in Nainital. Using the EC tracer method, along with several approaches for the determination of the (OC/EC)pri ratio, the estimated SOC component accounted for ~25% (19.3–29.7%). Furthermore, TSP source apportionment via PMF allowed for a better understanding of the aerosol sources in the Central Himalayan region. The key aerosol sources over Nainital were BB (27%), secondary sulfate (20%), secondary nitrate (9%), mineral dust (34%), and long-range transported mixed marine aerosol (10%). The potential source contribution function (PSCF) and concentration weighted trajectory (CWT) analyses were also used to identify the probable regional source areas of resolved aerosol sources. The main source regions for aerosols in Nainital were the plains in northwest India and Pakistan, polluted cities like Delhi, the Thar Desert, and the Arabian Sea area. The outcomes of the present study are expected to elucidate the atmospheric chemistry, emission source origins, and transport pathways of aerosols over the central Himalayan region.

Variation characteristics of fine particulate matter and its components in diesel vehicle emission plumes

A rapid reaction occurs near the exhaust nozzle when vehicle emissions contact the air. Twenty diesel vehicles were studied using a new multipoint sampling system that is suitable for studying the exhaust plume near the exhaust nozzle. The variation characteristics of fine particle matter (PM_2.5) and its components in diesel vehicle exhaust plumes were analyzed. The PM_2.5 emissions gradually increased with increasing distance from the nozzle in the plume. Elemental carbon emissions remained basically unchanged, organic carbon and total carbon (TC) increased with increasing distance. The concentrations of SO₄^2-, NO₃^- and NH₄⁺ (SNA) directly emitted by the vehicles were very low but increased rapidly in the exhaust plume. The selective catalytic reduction (SCR) reduced 42.7% TC, 40% NO₃^- emissions, but increased 104% SO₄^2- and 36% NH₄⁺ emissions, respectively. In summary, the SCR reduced 29% primary PM_2.5 emissions for the tested diesel vehicles. The NH₄NO₃ particle formation maybe more important in the plume, and there maybe other forms of formation of NH₄⁺ (eg. NH₄Cl). The generation of secondary organic carbon (SOC) plays a leading role in the generation of secondary PM_2.5. The SCR enhanced the formation of SOC and SNA in the plume, but comprehensive analysis shows that the SCR more enhanced the SNA formation in the plume, which is mainly new particles formation process. The inconsistency between secondary organic aerosol (SOA) and primary organic aerosol definitions is one of the important reasons for the difference between SOA simulation and observation.

Particulate air pollution and respiratory Haemophilus influenzae infection in Mianyang, southwest China

Particulate air pollution is correlated with many respiratory diseases. However, few studies have focused on the relationship between air particulate exposure and respiratory Heamophilus influenzae infection. Therefore, we detected respiratory Heamophilus influenzae infection by bacterial culture of sputum of patients, and we collected particulate air pollution data (including PM2.5 and PM10) from a national real-time urban air quality platform to analyze the relationship between particulate air pollution and respiratory Heamophilus influenzae infection. The mean concentrations of PM2.5 and PM10 were 37.58 μg/m3 and 58.44 μg/m3, respectively, showing particulate air pollution remains a severe issue in Mianyang. A total of 828 strains of Heamophilus influenzae were detected in sputum by bacterial culture. Multiple correspondence analysis suggested the heaviest particulate air pollution and the highest Heamophilus influenzae infection rates were all in winter, while the lowest particulate air pollution and the lowest Heamophilus influenzae infection rates were all in summer. In a single-pollutant model, each elevation of 10 μg/m3 of PM2.5, PM10, and PM2.5/10 (combined exposure level) increased the risk of respiratory Heamophilus influenzae infection by 34%, 23%, and 29%, respectively. Additionally, in the multiple-pollutant model, only PM2.5 was significantly associated with respiratory Heamophilus influenzae infection (B, 0.46; 95% confidence interval, 0.05-0.87), showing PM2.5 is an independent risk factor for respiratory Heamophilus influenzae infection. In summary, this study highlights air particulate exposure could increase the risk of respiratory Heamophilus influenzae infection, implying that stronger measures need to be taken to protect against respiratory infection induced by particulate air pollution.

Evaluating the ammonia emission from in-use vehicles using on-road remote sensing test

The on-road remote sensing test was conducted in Zhengzhou to obtain a large dataset of ammonia emissions from in-use vehicles. The ammonia emission characteristics and high-emitter vehicles of different manufacture years, vehicles with different emission standards, and vehicles with different types of other fuel vehicles were analysed. The results show that the average ammonia emission concentration obtained through remote sensing tests fluctuated after the initial reduction. The ammonia emission factors generally range from 0.30 to 0.47 g/kg, 0.34-0.50 g/kg and 0.29-0.60 g/kg for gasoline vehicles, diesel vehicles and other fuel vehicles respectively. Improving the emission standards of new vehicles has a direct role in reducing exhaust pollution from in-use vehicles. However, after the China III emission standard, the ammonia emission level showed a stable trend and no obvious downward trend. The distributions of ammonia emission rates were highly skewed as the dirtiest 10% of vehicles emitted much higher emissions than other vehicles. In the group with the highest emissions, the emissions from other fuel vehicles were lower than those from gasoline and diesel vehicles. However, the percentage of high-emitters decreased with newer emission standards for vehicles. The results indicate that remote sensing test technology will be very effective in screening vehicles with high ammonia emissions. However, some clean vehicles can be exempted from annual inspection through remote sensing test technology. Finally, based on the comprehensive analysis of big data from remote sensing, the ammonia emissions of diesel vehicles and other fuel vehicles cannot be ignored.

Source appointment of PM2.5 in Qingdao Port, East of China

Field measurements were conducted near Qingdao Port to characterize the particulate air pollutants, assess the spatial and seasonal characteristics of the pollutants, and identify the contribution from ship traffic emissions. By utilizing multiple statistical methods and data collected at two sites in Qingdao, we comprehensively explored the PM_2.5 seasonal characteristics and source apportionments of different PM_2.5 constituents, especially those originating from ship emissions, and identified potential source regions for samples collected in Qingdao. In this study, 118 concurrent daily PM_2.5 samples were collected from August 2018 to May 2019 at a port site (QH) and a coastal background site (BG). Vanadium (V) and Nickel (Ni) are the dominant metal elements from crude oil and crude oil combustion emissions. The significant correlations between V and Ni at both sampling sites, indicating that shipping emissions have a significant impact on the port and background area. Additionally, Ni and other metals showed significant correlations at the BG site, implying Ni also emission from the land-based oil at this site. The positive matrix factorization (PMF) model identified six main sources for the PM_2.5 samples in Qingdao, and they are coal combustion, industrial emissions/mineral dust, marine vessel emissions, secondary aerosols/biomass burning, sea salt/crustal emissions, and vehicle exhaust, respectively. Marine vessel emissions were the dominant contributor to PM_2.5 in Qingdao during the sampling periods (25.05%). The potential source contribution function (PSCF) analysis suggested that the Yellow Sea and Jiaodong Peninsula were the major sources regions for PM_2.5 in Qingdao. The Yellow Sea and Bohai Sea were the potential source regions for shipping emissions in Qingdao. Therefore, efforts to control shipping emissions should be strengthened not only at the Qingdao Port but also in surrounding ports.

Multi-city comparative PM2.5 source apportionment for fifteen sites in Europe: The ICARUS project

PM_2.5 is an air pollution metric widely used to assess air quality, with the European Union having set targets for reduction in PM_2.5 levels and population exposure. A major challenge for the scientific community is to identify, quantify and characterize the sources of atmospheric particles in the aspect of proposing effective control strategies. In the frame of ICARUS EU2020 project, a comprehensive database including PM_2.5 concentration and chemical composition (ions, metals, organic/elemental carbon, Polycyclic Aromatic Hydrocarbons) from three sites (traffic, urban background, rural) of five European cities (Athens, Brno, Ljubljana, Madrid, Thessaloniki) was created. The common and synchronous sampling (two seasons involved) and analysis procedure offered the prospect of a harmonized Positive Matrix Factorization model approach, with the scope of identifying the similarities and differences of PM_2.5 key-source chemical fingerprints across the sampling sites. The results indicated that the average contribution of traffic exhausts to PM_2.5 concentration was 23.3% (traffic sites), 13.3% (urban background sites) and 8.8% (rural sites). The average contribution of traffic non-exhausts was 12.6% (traffic), 13.5% (urban background) and 6.1% (rural sites). The contribution of fuel oil combustion was 3.8% at traffic, 11.6% at urban background and 18.7% at rural sites. Biomass burning contribution was 22% at traffic sites, 30% at urban background sites and 28% at rural sites. Regarding soil dust, the average contribution was 5% and 8% at traffic and urban background sites respectively and 16% at rural sites. Sea salt contribution was low (1-4%) while secondary aerosols corresponded to the 16-34% of PM_2.5. The homogeneity of the chemical profiles as well as their relationship with prevailing meteorological parameters were investigated. The results showed that fuel oil combustion, traffic non-exhausts and soil dust profiles are considered as dissimilar while biomass burning, sea salt and traffic exhaust can be characterized as relatively homogenous among the sites.

Biogenic secondary organic aerosols: A review on formation mechanism, analytical challenges and environmental impacts

The review initiates with current state of information on the atmospheric reaction mechanism of biogenic volatile organic compounds (BVOCs) and its fate in the atmosphere. The plants release BVOCs, i.e., isoprene, monoterpenes, and sesquiterpenes, which form secondary organic aerosols (SOA) upon oxidation. These oxidation reactions are primarily influenced by solar radiations along with other meteorological parameters viz.; temperature and relative humidity, therefore, the chemistry behind SOA formation is different during day than the night time. The review throws light upon the day and nighttime formation mechanism of SOA, recent advancements in the analytical techniques available for the measurements, and its impact on the environment. Studies have revealed that day time SOA formation is dominated by OH and O₃, however, NOx initiated SOA production is dominated during night. The formation mechanism addresses that the gaseous products of VOCs are firstly formed and then partitioned over the pre-existing particles. New particle formation and biomass-derived aerosols are found to be responsible for enhanced SOA formation. 2-Dimensional gas chromatography-mass spectrometer (2D-GC/MS) is observed to be best for the analysis of organic aerosols. Radiative forcing (RF) SOA is observed to be a useful parameter to evaluate the environmental impacts of SOA and reviewed studies have shown mean RF in the ranges of -0.27 to +0.20 W m^-2.

Investigating the seasonal variability in source contribution to PM2.5 and PM10 using different receptor models during 2013-2016 in Delhi, India

The present work deals with the seasonal variations in the contribution of sources to PM_2.5 and PM₁₀ in Delhi, India. Samples of PM_2.5 and PM₁₀ were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 and 238 ± 106 μg m^-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM_2.5 and PM₁₀ were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM_2.5 and PM₁₀ as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM_2.5 and PM₁₀. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

Farnesoid X receptor mediates hepatic steatosis induced by PM2.5

Ambient particulate matter (PM) newly has been regarded as a conceivable hazard for public health. A large number of studies have described that PM, exceptionally PM2.5, is correlated with respiratory, cardiovascular, and metabolic diseases, etc. PM2.5-induced hepatocyte steatosis previously has been uncovered both in cellular and murine models. Nevertheless, less is known about the underlying mechanism. Here, we found that PM2.5 could cause the downregulation of farnesoid X receptor (FXR), a key transcription factor for lipid metabolism. FXR could regulate the accumulation of lipid droplets induced by PM2.5 in vitro. Moreover, FXR-/- mice were exposed to PM2.5 for 2 months to investigate the role of FXR in pathogenesis of PM2.5-induced hepatic steatosis in vivo. The results showed that exposure of wild-type (WT) mice to PM2.5 caused mild liver steatosis compared with the mice exposure to filtered air (FA). Furthermore, the content of triglyceride (TG) and total cholesterol (TC) was elevated in WT mice liver triggered by the inhalation of PM2.5. However, there was no statistical difference in TG and TC content between FXR-/- mice with and without PM2.5 exposure. Overall, our finding suggested FXR mediated PM2.5-induced hepatic steatosis.

Key Factors in Measuring Ammonia Emissions with Dynamic Flux Chamber in Barns

In this study, measurement methods for estimating the NH3 emissions in barns and the development of different emission factors were reviewed, and the factors to be considered when applying a dynamic flux chamber approach were analyzed. First, one of the factors to be considered when applying the dynamic flux chamber was determined as the stabilization time in the chamber. As a result of the experiment, it was confirmed that the concentration in the chamber stabilized after 45 min. This is considered to take longer than the stabilization time of 20 min suggested in the previous study. The second is the choice of the measurement method. This method includes real-time measurement and the indophenol method. As a result of the experiment in both methods, the ammonia flux showed a difference of about 10%, so both methods are considered to be considered. Therefore, it is judged that the methodology should be selected according to the situation, such as weather or electric power secured at the barn site. In the future, if studies on whether the stabilization time in the chamber can be changed according to seasonal factors and ambient temperature, and based on a sufficiently large sample size, the results will contribute to improving the reliability of the estimated ammonia(NH3) emissions and the development of an emissions factor for use in the livestock sector in Korea.

Ammonia Emission Sources Characteristics and Emission Factor Uncertainty at Liquefied Natural Gas Power Plants

This study developed the NH₃ emission factor for Liquefied Natural Gas (LNG) power facilities in Korea by analyzing the emission characteristics from two LNG power plants using methods such as uncertainty analysis. Also, comparing the differences in NH₃ emission levels between the developed emission factors, which reflect the characteristics in Korea, and the U.S. Environmental Protection Agency (EPA) values currently applied in Korea. The estimation showed that the NH₃ emission factor for the LNG power plants was 0.0054 ton NH₃/10⁶Nm³, which is approximately nine times less than the EPA NH₃ emission factor of 0.051 ton NH₃/10⁶Nm³ for LNG fuels of the industrial energy combustion sector currently applied in national statistics in Korea. The Selective Catalytic Reduction (SCR) emission factor for LNG power plants was 0.0010 ton NH₃/10⁶Nm³, which is considerably lower than the EPA NH₃ emission factor of 0.146 ton NH₃/10⁶Nm³ currently applied in national statistics in Korea for the LNG fuels of the industrial process sector. This indicated the need for developing an emission factor that incorporates the unique characteristics in Korea. The uncertainty range of the LNG stack NH₃ emission factor developed in this study was ±10.91% at a 95% confidence level, while that of the SCR NH₃ emission factor was -10% to +20% at a 95% confidence level, indicating a slightly higher uncertainty range than the LNG stack. At present, quantitative analysis of air pollutants is difficult because numerical values of the uncertainty are not available. However, quantitative analysis might be possible using the methods applied in this study to estimate uncertainty.

Ammonia Emission Factors and Uncertainties of Coke Oven Gases in Iron and Steel Industries

In this study, uncertainties related to NH3 concentration, emission factor, and emission factor estimation in the exhaust gas of the steel sintering furnace using COG (coke oven gas) among the by-product gas generated in steel production was estimated to identify the missing source. By measuring the NH3 concentration in the exhaust gas of steel sintering furnace using COG, a concentration between 0.02 and 0.12 ppm was found, with an average concentration of 0.06 ppm, confirming the emissions of NH3. Using this measurement of the NH3 concentration, an NH3 emission factor of 0.0061 kg NH3/ton was derived. The uncertainty of the developed NH3 emission factor of the sintering furnace using COG was analyzed using a Monte Carlo simulation. Consequently, the uncertainty range of NH3 emission factor of the sintering furnace using COG was derived to be −11.4% to +12.89% at the 95% confidence level. According to the results of this study, NH3 emissions occur from the use of COG, yielding values higher than the NH3 emission factor resulting from the use of LNG(liquefied natural gas) in combustion facilities. It should be recognized that it is a missing emission source and the corresponding emission should be calculated.

Assessing atmospheric dry deposition via water-soluble ionic composition of roadside leaves

This study focuses on the water-soluble ion concentrations in the washing solution of leaves of different roadside tree species at three sites in Iran to estimate the ionic composition of the dry deposition of ambient air particulates. All considered water-soluble ion concentrations were significantly higher next to the roads with high traffic density compared to the reference site with low traffic density. The PCA results showed that Ca²⁺, Mg²⁺, [Formula: see text] and [Formula: see text] originated mainly from traffic activities and geological sources, and Na⁺, Cl^-, K⁺ and F^- from sea salts. In addition to sea salt, K⁺ and F^- were also originated from anthropogenic sources i.e. industrial activities, biomass burning and fluorite mining. Moreover, the concentration of the water-soluble ions depended on species and site. C. lawsoniana had significantly higher ion concentrations in its leaf washing solution compared to L. japonicum and P. brutia which indicates C. lawsoniana is the most suitable species for accumulating of atmospheric dry deposition. From our results, it can be concluded that sites with similar traffic density can have different particle loads and water-soluble ion species, and that concentrations in leaf-washing solutions depend on site conditions and species-specific leaf surface characteristics.

Emission Characteristics of Ammonia at Bituminous Coal Power Plant

This study developed a NH3 emission factor for bituminous coal power plants in South Korea in order to investigate the NH3 emission characteristics. The NH3 concentration analysis results showed that emissions from the selected bituminous coal power plants were in the range of 0.21–0.99 ppm, and that the difference in NH3 concentration was affected by NOx concentration. The NH3 emission factor was found to be 0.0029 kg NH3/ton, which demonstrated that the difference in the values obtained from the research conducted in South Korea was lower than the difference in the emission factor provided by the U.S. EPA, which is currently applied in the statistics of South Korea. NH3 emissions were compared by using the NH3 emission factor developed in this study alongside the EPA’s NH3 emission factor that is currently applied in South Korea’s statistics; the difference was found to be 206 NH3 ton/year. This implies that an emission factor that reflects the national characteristics of South Korea needs to be developed. The uncertainty range of the NH3 emission factor developed in this study was between −6.9% and +10.34% at a 95% confidence level.