Using an improved Source Directional Apportionment method to quantify the PM(2.5) source contributions from various directions in a megacity in China

Identification of potential source regions and long-range transport routes/channels of marine PM2.5 at remote sites in East Asia

Long-range transport (LRT) of air masses in East Asia and their impacts on marine PM2.5 were explored. Situated in the leeward region of East Asia, Taiwan Island marked by its elevated Central Mountain Range (CMR) separates air masses into two distinct air currents. This study aims to investigate the transport of PM2.5 from the north to the leeward region. Six transport routes (A-F) were identified and further classified them into three main channels (i.e. East, West, and South Channels) based on their transport routes and potential sources. Green Island (Site GR) and Hengchun Peninsula (Site HC) exhibited similarities in their transport routes, with Central China, North China, and Korean Peninsula being the major source regions of PM2.5, particularly during the Asian Northeastern Monsoons (ANMs). Dongsha Island (Site DS) was influenced by both Central China and coastal regions of East China, indicating Asian continental outflow (ACO) as the major source of PM2.5. The positive matrix factorization (PMF) analysis of PM2.5 resolved that soil dust, sea salts, biomass burning, ship emissions, and secondary aerosols were the major sources. Northerly Channels (i.e. East and West Channels) were primarily influenced by ship emissions and secondary aerosols, while South Channel was dominated by oceanic spray and soil dust. The results of W-PSCF and W-CWT analysis indicated that three remote sites experienced significant contributions from Central China in the highest PM2.5 concentration range (75-100%). In contrast, PM2.5 in the 0-25% and 25-50% ranges primarily originated from the open seas, with ship emissions being the prominent source. It suggested that northern regions with heavy industrialization and urbanization have impacts on high PM2.5 concentrations, while open seas are the main sources of low PM2.5 concentrations.

Contributors to reductions of PM2.5-bound heavy metal concentrations and health risks in a Chinese megacity during 2013, 2016 and 2019: An advanced method to quantify source-specific risks from various directions

PM_2.5-bound heavy metals were measured in a Chinese megacity (Tianjin) in 2013, 2016 and 2019, and analyzed by a new RSDA method (source directional apportionment of risks). Through combining the receptor model, cluster analysis of back trajectories, and risk assessment, the RSDA was developed in this work to quantify source-specific risks from each direction. Concentrations of PM_2.5 and most species (especially for heavy metals) underwent various reductions, and the incremental lifetime cancer risk (ILCR) and non-cancer risk (HQ) declined by more than 80% from 2013 to 2019. Pb was the highest contributor to the reduction of HMs mass concentration (58.6%), while Cr (85.5% for cancer risk) and As (26.0% for non-cancer risk) were more prominent for the reduction of HM risks. The coal combustion and industrial emissions were vital contributors to the reduction of both PM_2.5 mass concentrations (contributed 34.0% and 7.8% to the reduction respectively) and health risks (contributed 36.1% and 25.7% to the cancer risk reduction respectively). Although the percentage mass contribution of traffic emissions increased (7.7% in 2013 and 21.9% in 2019), the associated risks decreased (contributed 26.8% to the cancer risk reduction). Furthermore, the results of RSDA consistently implied that coal combustion, industrial emissions and traffic emissions controls in the northeast/north-northeast, south and southwest of the studied area played important roles in the risk reductions, which mainly due to the risk reduction of air masses from NE/NNE, S and SW, and their strong influence to Tianjin. The RSDA method can quantify the health risks from different sources and directions, and the evaluation of contributors to the reductions of risks in this work would provide a meaningful reference for policy maker to control PM_2.5 emissions and protect population health.

Determination and assessment of elemental concentration in the atmospheric particulate matter: a comprehensive review

The elemental concentrations of atmospheric particulate matter (PM) have a detrimental effect on human health in which some elemental species have carcinogenic nature. In India, significant variations have found in the practices adapted from sampling to analysis for the determination and assessment of the elemental concentration in PM. Therefore, Indian studies (2011-2020) on the related domain are summarized to impart consistency in the field and laboratory practices. Further, a comparative analysis with other countries has also been mentioned in the relevant sections to evaluate its likeness with Indian studies. To prepare this study, literature has been procured from reputed journals. Subsequently, each step from sampling to analysis has thoroughly discussed with quality assurance and control (QA/QC) compliance. In addition, a framework has been proposed that showed field and laboratory analysis in an organized manner. Consequently, this study will provide benefit to novice researcher and improve their understanding about the related subject. Also, it will assist other peoples/bodies in framing the necessary decisions to carry out this study.

Potential Risks of PM2.5-Bound Polycyclic Aromatic Hydrocarbons and Heavy Metals from Inland and Marine Directions for a Marine Background Site in North China

Ambient PM_2.5-bound ions, OC, EC, heavy metals (HMs), 18 polycyclic aromatic hydrocarbons (PAHs), 7 hopanes, and 29 n-alkanes were detected at Tuoji Island (TI), the only marine background atmospheric monitoring station in North China. The annual PM_2.5 average concentration was 47 ± 31 μg m^-3, and the average concentrations of the compositions in PM_2.5 were higher in cold seasons than in warm seasons. The cancer and non-cancer risks of HMs and PAHs in cold seasons were also higher than in warm seasons. BaP, Ni, and As dominated the ∑HQ (hazard quotient) in cold seasons, while the non-carcinogenic risk in warm seasons was mainly dominated by Ni, Mn, and As. The ILCR (incremental lifetime cancer risk) values associated with Cr and As were higher in the cold season, while ILCR-Ni values were higher in the warm season. The backward trajectory was calculated to identify the potential directions of air mass at TI. Through the diagnostic ratios of organic and inorganic tracers, the sources of particulate matter in different directions were judged. It was found that ship emissions and sea salt were the main sources from marine directions, while coal combustion, vehicles emissions, industrial process, and secondary aerosols were the main source categories for inland directions. In addition, potential HM and PAH risks from inland and marine directions were explored. The non-cancerous effects of TI were mainly affected by inland transport, especially from the southeast, northwest, and west-northwest. The cancerous effects of TI were mainly simultaneously affected by the inland direction and marine direction of transport.

The performance of a modified EWMA control chart for monitoring autocorrelated PM2.5 and carbon monoxide air pollution data

PM2.5 (particulate matter less than or equal to 2.5 micron) is found in the air and comprises dust, dirt, soot, smoke, and liquid droplets. PM2.5 and carbon monoxide emissions can have a negative impact on humans and animals throughout the world. In this paper, we present the performance of a modified exponentially weighted moving average (modified EWMA) control chart to detect small changes when the observations are autocorrelated with exponential white noise through the average run length evaluated (ARLs) by explicit formulas. The accuracy of the solution was verified with a numerical integral equation method. The efficacy of the modified EWMA control chart to monitor PM2.5 and carbon monoxide air pollution data and compare its performance with the standard EWMA control chart. The results suggest that the modified EWMA control chart is far superior to the standard one.

Using High-Temporal-Resolution Ambient Data to Investigate Gas-Particle Partitioning of Ammonium over Different Seasons

Ammonium is one of the dominant inorganic water-soluble ions in fine particulate matter (PM_2.5). In this study, source apportionment and thermodynamic equilibrium models were used to analyze the relationship between pH and the partitioning of ammonium (ε(NH₄⁺)) using hourly ambient samples collected from Tianjin, China. We found a "Reversed-S curve" between pH and ε(NH₄⁺) from the ambient hourly aerosol dataset when the theoretical ε(NO₃^-)^* (an index identified in this work) was within specific ranges. A Boltzmann function was then used to fit the Reversed-S curve. For the summer data set, when ε(NO₃^-)^* was between 0.7 and 0.8, the fitted R² was 0.88. Through thermodynamic analysis, we found that the values of k[H⁺]² (k = 3.08 × 10⁴ L² mol^-2) and ε(NO₃^-)^* can influence the pH-ε(NH₄⁺) curve. Under certain situations, the values of k[H⁺]² and ε(NO₃^-)^* are similar to each other, and ε(NH₄⁺) is sensitive to pH, suggesting that ε(NO₃^-)^* plays an important role in affecting the ε(NH₄⁺). During summer, winter, and spring seasons, when the relative humidity was greater than 0.36 and ε(NO₃^-)^* was between 0.8 and 0.95, there was an obvious Reversed-S curve, with R² = 0.60. The theoretical k[H⁺]² and ε(NO₃^-)^* developed in this work can be used to analyze the gas-particle partitioning of ammonia-ammonium and nitrate-nitric acid in the ambient atmosphere. Also, it is the first time that we created the joint source-NH₃/HNO₃ maps to integrate sources, aerosol pH and liquid water content, and ions (altogether in one map), which can provide useful information for designing effective strategies to control particulate matter pollution.

Chemical characteristics and source apportionment of PM2.5 using PMF modelling coupled with 1-hr resolution online air pollutant dataset for Linfen, China

The chemical species in PM_2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed for multiple temporal patterns, and PM_2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM_2.5 was 124 μg/m³ during the heating period, and NO₃^- and organic carbon (OC) were the dominant species. The concentrations and percentages of NO₃^-, SO₄^2-, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM_2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.

Different cardiorespiratory effects of indoor air pollution intervention with ionization air purifier: Findings from a randomized, double-blind crossover study among school children in Beijing

Indoor air pollution is associated with numerous adverse health outcomes. Air purifiers are widely used to reduce indoor air pollutants. Ionization air purifiers are becoming increasingly popular for their low power consumption and noise, yet its health effects remain unclear. This randomized, double-blind crossover study is conducted to explore the cardiorespiratory effects of ionization air purification among 44 children in Beijing. Real or sham purification was performed in classrooms for 5 weekdays. Size-fractionated particulate matter (PM), black carbon (BC), ozone (O₃), and negative air ions (NAI) were monitored, and cardiorespiratory functions were measured. Mixed-effect models were used to establish associations between exposures and health parameters. Real purification significantly decreased PM and BC, e.g. PM_0.5, PM_2.5, PM₁₀ and BC were decreased by 48%, 44%, 34% and 50%, respectively. O₃ levels were unchanged, while NAI was increased from 12 cm^-3 to 12,997 cm^-3. Real purification was associated with a 4.4% increase in forced exhaled volume in 1 s (FEV₁) and a 14.7% decrease in fractional exhaled nitrogen oxide (FeNO). However, heart rate variability (HRV) was altered negatively. Interaction effects of NAI and PM were observed only on HRV, and alterations in HRV were greater with high NAI. Ionization air purifier could bring substantial respiratory benefits, however, the potential negative effects on HRV need further investigation.

Temporal Variation and Chemical Components of Rural Ambient PM2.5 during Main Agricultural Activity Periods in the Black Soil Region of Northeast China

Agricultural emissions are crucial to regional air quality in the autumn and spring due to the intense agricultural activities in Northeast China. However, information on rural ambient particulate matter (PM) in Northeast China is rare, limiting the accurate estimation of agricultural atmospheric particulate matter emissions. In this study, we monitored hourly ambient PM2.5 (PM with a diameter of less than 2.5 μm) concentrations and analyzed daily chemical components (i.e., water-soluble ions, trace elements, organic carbon, and element carbon) at a rural site in Northeast China during the autumn and spring and assessed the impact of agricultural activities on atmospheric PM2.5 concentrations. The results showed that the daily average concentrations of PM2.5 were 143 ± 109 (range: 39–539) μg m−3 from 19 October to 23 November 2017 (i.e., typical harvesting month) and 241 ± 189 (range: 97–976) μg m−3 from 1 April to 13 May 2018 (i.e., typical tilling month). In autumn, the ambient PM2.5 concentrations were high with a Southwest wind, while a Southeast wind caused high PM2.5 concentrations during spring in the rural site. The concentrations of selected water-soluble ions, trace elements, and carbonaceous fractions accounted for 33%, 4%, and 26% of PM2.5 mass concentrations, respectively, in autumn and for 10%, 5%, and 3% of PM2.5 mass concentrations, respectively, in spring. On the basis of the component analysis, straw burning, agricultural machinery, and soil dust driven by wind and tilling were the main contributors to high rural PM2.5 concentrations. In addition, the increasing coal combustion around the rural site was another important source of PM2.5.

High-Resolution Data Sets Unravel the Effects of Sources and Meteorological Conditions on Nitrate and Its Gas-Particle Partitioning

Nitrate is one of the most abundant inorganic water-soluble ions in fine particulate matter (PM_2.5). However, the formation mechanism of nitrate in the ambient atmosphere, especially the impacts of its semivolatility and the various existing forms of nitrogen, remain under-investigated. In this study, hourly ambient observations of speciated PM_2.5 components (NO₃^-, SO₄^2-, etc.) were collected in Tianjin, China. Source contributions were analyzed by PMF/ME2 (Positive Matrix Factorization using the Multilinear Engine 2) program, and pH were estimated by ISORROPIA-II, to investigate the relationship between pH and nitrate. Five sources (factors) were resolved: secondary sulfate (SS), secondary nitrate (SN), dust, vehicle and coal combustion. SN and pH showed a triangle-shaped relationship. When SS was high, the fraction of nitrate partitioning into the aerosol phase exhibits a characteristic "S-curve" relationship with pH for different seasons. An index ( I_TL) is developed and combined with pH to explore the sensitive regions of "S-curve". Controlling the emissions of anions (SO₄^2-, Cl^-), cations (Ca²⁺, Mg²⁺, etc.) and gases (NO _x, NH₃, SO₂, etc.) will change pH, potentially reducing or increasing SN. The findings of this work provide an effective approach for exploring the formation mechanisms of nitrate under different influencing factors (sources, pH, and I_RL).

An advanced three-way factor analysis model (SDABB model) for size-resolved PM source apportionment constrained by size distribution of chemical species in source profiles

Source samples including crustal dust, cement dust, coal combustion were sampled and ambient samples of PM_2.5 and PM₁₀ were synchronously collected in Hefei from April to December 2014. The size distributions of the markers in the measured source profiles were incorporated into ME-2 solution to develop a new method, called the SDABB model (an advanced ABB three-way factor analysis model incorporating size distribution information). The performance of this model was investigated using three-way synthetic and ambient dataset. For the synthetic tests, the size distributions of markers estimated by the SDABB model were more consistent with true condition. The AAEs between estimated and observed contributions of the SDABB ranged from 15.2% to 29.0% for PM₁₀ and 19.9%-31.6% for PM_2.5, which is lower than those of PMF2. For the ambient PM, six source categories were identified by SDABB for both sizes, although the profiles were different. The source contributions were sulphate (33.33% and 24.53%), nitrate and SOC (22.33% and 18.16%), coal combustion (19.01% and 18.23%), vehicular exhaust (12.99% and 12.07%), crustal dust (10.69% and 19.40%) and cement dust (1.65% and 5.39%) for PM_2.5 and PM₁₀ respectively. In addition, the estimated ratios of Al, Si, Ti and Fe in CRD were 0.76, 0.84, 1.10 and 0.85; those of Al and Si in CC were 0.42 and 0.66; Ca and Si in CD were 0.95 and 1.10; NO₃^- and NH₄⁺ in nitrate were 1.11 and 1.01; and SO₄^2- and NH₄⁺ in sulphate were 0.96 and 1.16. These modeled ratios were consistent with the measured ratios. The size distribution of contributions also came close to reality. Thus, the advanced SDABB three-way model can better capture the characteristics of sources between sizes by effectively incorporating the size distributions of the markers as physical constraints.

Sources of particulate matter in China: Insights from source apportionment studies published in 1987-2017

Particulate matter (PM) in the atmosphere has adverse effects on human health, ecosystems, and visibility. It also plays an important role in meteorology and climate change. A good understanding of its sources is essential for effective emission controls to reduce PM and to protect public health. In this study, a total of 239 PM source apportionment studies in China published during 1987-2017 were reviewed. The documents studied include peer-reviewed papers in international and Chinese journals, as well as degree dissertations. The methods applied in these studies were summarized and the main sources in various regions of China were identified. The trends of source contributions at two major cities with abundant studies over long-time periods were analyzed. The most frequently used methods for PM source apportionment in China are receptor models, including chemical mass balance (CMB), positive matrix factorization (PMF), and principle component analysis (PCA). Dust, fossil fuel combustion, transportation, biomass burning, industrial emission, secondary inorganic aerosol (SIA) and secondary organic aerosol (SOA) are the main source categories of fine PM identified in China. Even though the sources of PM vary among seven different geographical areas of China, SIA, industrial, and dust emissions are generally found to be the top three source categories in 2007-2016. A number of studies investigated the sources of SIA and SOA in China using air quality models and indicated that fossil fuel combustion and industrial emissions were the most important sources of SIA (total contributing 63.5%-88.1% of SO₄^2-, and 47.3%-70% NO₃^-), and agriculture emissions were the dominant source of NH₄⁺ (contributing 53.9%-90%). Biogenic emissions were the most important source of SOA in China in summer, while residential and industrial emissions were important in winter. Long-term changes of PM sources at two megacities of Beijing and Nanjing indicated that the contributions of fossil fuel and industrial sources have been declining after stricter emission controls in recent years. In general, dust and industrial contributions decreased and transportation contributions increased after 2000. PM_2.5 emissions are predicted to decline in most regions during 2005-2030, even though the energy consumptions except biomass burning are predicted to continue to increase. Industrial, residential, and biomass burning sources will become more important in the future in the businuess-as-usual senarios. This review provides valuable information about main sources of PM and their trends in China. A few recommendations are suggested to further improve our understanding the sources and to develop effective PM control strategies in various regions of China.

Spatial, seasonal and diurnal patterns in physicochemical characteristics and sources of PM2.5 in both inland and coastal regions within a megacity in China

Day and night PM_2.5 samples were collected at coastal and inland stations in a megacity in China. Temporal, spatial, and directional characteristics of PM_2.5 concentrations and compositions were investigated. Average PM_2.5 concentration was higher at inland (153.28μg/m³) than at coastal (114.46μg/m³). PM_2.5 were significantly influenced by season and site but insignificantly by diurnal pattern. Sources were quantified by a two-way and a newly developed three-way receptor models conducted using ME2. Secondary sulfate and SOC (SS&SOC, 25% and 23%), coal and biomass burning (CC&BB, 20% and 21%), crustal and cement dust (CRD&CED, 19% and 21%), secondary nitrate (SN, 13% and 18%), vehicular exhaust (VE, 14% and 17%), and sea salt (SEA, 6% and 2%) were major sources for coastal and inland. Different mechanisms of heavy pollution were observed: heavy PM_2.5 caused by primary sources and secondary sources showed similar frequency at coast, while most of heavy pollutions at inland site might be associated with the elevation of secondary particles. For spatial characteristics, SS&SOC, CRD&CED contributions were higher at coastal; SN and VE presented higher fractions at inland. Higher SS&SOC, SN and CC&BB in winter might be attributed to intensive coal combustion for residential warming and to stable meteorological conditions.

Size distribution, directional source contributions and pollution status of PM from Chengdu, China during a long-term sampling campaign

Long-term and synchronous monitoring of PM₁₀ and PM_2.5 was conducted in Chengdu in China from 2007 to 2013. The levels, variations, compositions and size distributions were investigated. The sources were quantified by two-way and three-way receptor models (PMF2, ME2-2way and ME2-3way). Consistent results were found: the primary source categories contributed 63.4% (PMF2), 64.8% (ME2-2way) and 66.8% (ME2-3way) to PM₁₀, and contributed 60.9% (PMF2), 65.5% (ME2-2way) and 61.0% (ME2-3way) to PM_2.5. Secondary sources contributed 31.8% (PMF2), 32.9% (ME2-2way) and 31.7% (ME2-3way) to PM₁₀, and 35.0% (PMF2), 33.8% (ME2-2way) and 36.0% (ME2-3way) to PM_2.5. The size distribution of source categories was estimated better by the ME2-3way method. The three-way model can simultaneously consider chemical species, temporal variability and PM sizes, while a two-way model independently computes datasets of different sizes. A method called source directional apportionment (SDA) was employed to quantify the contributions from various directions for each source category. Crustal dust from east-north-east (ENE) contributed the highest to both PM₁₀ (12.7%) and PM_2.5 (9.7%) in Chengdu, followed by the crustal dust from south-east (SE) for PM₁₀ (9.8%) and secondary nitrate & secondary organic carbon from ENE for PM_2.5 (9.6%). Source contributions from different directions are associated with meteorological conditions, source locations and emission patterns during the sampling period. These findings and methods provide useful tools to better understand PM pollution status and to develop effective pollution control strategies.

A review of AirQ Models and their applications for forecasting the air pollution health outcomes

Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.

Pollution status of polycyclic aromatic hydrocarbons in surface sediments from the Yangtze River Estuary and its adjacent coastal zone

Polycyclic aromatic hydrocarbons (PAHs) are mainly produced by incomplete combustion and are used as indicators of anthropogenic activities on the environment. This study analyses the PAHs level in the Yangtze River Estuary (YRE), an important component of Yangtze River and a developed and populated region in China. Surface sediments were collected from 77 sites at the YRE and its adjacent coastal zone (IACZ) for a comprehensive study of PAHs. Kriging interpolation technology and Positive matrix factorization (PMF) model were applied to explore the spatial distribution and sources of PAHs. Concentrations of 16 PAHs (ΣPAHs) varied from 27.2 ng g(-1) to 621.6 ng g(-1) dry weight, with an average value of 158.2 ng g(-1). Spatially, ΣPAHs exhibited wide fluctuation and exhibited an increasing tendency from north to south. In addition, ΣPAHs exhibited a decreasing trend with increasing distance between the estuary and IACZ. The deposition flux of PAHs indicated that more than 107.8 t a(-1) PAHs was deposited in the study area annually. The results of the PMF model revealed that anthropogenic activities were the main sources of PAHs in the study area. Vehicle emissions and marine engines were the most important sources and accounted for 40.9% of the pollution. Coal combustion, petrogenic sources, and wood combustion were other sources that contributed 23.9%, 23.6%, and 11.5%, respectively. The distribution patterns of PAHs in the YRE and IACZ were influenced by many complicated factors such as sediment grain size, hydrodynamics and so on.

Development and application of three-dimensional potential source contribution function (3D-PSCF)

A potential source contribution function (PSCF) can indicate the source areas of high air pollutant concentrations using backward trajectories. However, the conventional two-dimensional PSCF (2D-PSCF) cannot consider the emission and transport height of air pollutants. That missing information might be critical because injection height varies depending on the source type, such as with biomass burning. We developed a simple algorithm to account for the height of trajectories with high concentrations and combined it with the conventional PSCF to devise 3D-PSCF. We demonstrate the applicability of the 3D-PSCF by applying it to particulate PAH data collected from September 2006 to August 2007 in Seoul. We found variation in the results from 3D-PSCF with threshold heights from 3,000 to 1,500 m. Applying 2,000 m as the threshold height in the PSCF calculation most clearly determined the possible source areas of air pollutants from biomass fuel burning that were affecting the air quality in Seoul.

Seasonal and regional variations of source contributions for PM10 and PM2.5 in urban environment

To characterize the sources of to PM10 and PM2.5, a long-term, speciate and simultaneous dataset was sampled in a megacity in China during the period of 2006-2014. The PM concentrations and PM2.5/PM10 were higher in the winter. Higher percentages of Al, Si, Ca and Fe were observed in the summer, and higher concentrations of OC, NO3(-) and SO4(2-) occurred in the winter. Then, the sources were quantified by an advanced three-way model (defined as an ABB three-way model), which estimates different profiles for different sizes. A higher percentage of cement and crustal dust was present in the summer; higher fractions of coal combustion and nitrate+SOC were observed in the winter. Crustal and cement contributed larger portion to coarse part of PM10, whereas vehicular and secondary source categories were enriched in PM2.5. Finally, potential source contribution function (PSCF) and source regional apportionment (SRA) methods were combined with the three-way model to estimate geographical origins. During the sampling period, the southeast region (R4) was an important region for most source categories (0.6%-11.5%); the R1 (centre region) also played a vital role (0.3-6.9%).

Influence of quarry mining dust on PM2.5 in a city adjacent to a limestone quarry: Seasonal characteristics and source contributions

To understand the influence of quarry mining dust on particulate matter, ambient PM2.5 and quarry mining dust source samples were collected in a city near quarry facilities during 2013-2014. Samples were subject to chemical analysis for dust-related species (Al, Si, Ca, Fe, Ti), tracer metals, carbon components and water-soluble ions. Seasonal variations of PM2.5 and its main chemical components were investigated. Distinctive seasonal variations of PM2.5 were observed, with the highest PM2.5 concentrations (112.42μgm(-3)) in fall and lowest concentrations in summer (45.64μgm(-3)). For dust-related species, mass fractions of Si and Al did not show obvious seasonal variations, whereas Ca presented higher fractions in spring and summer and lower fractions in fall and winter. A combined receptor model (PMF-CMB) was applied to quantify the quarry mining dust contribution to PM2.5. Seven sources were identified, including quarry mining dust, soil dust, cement dust, coal combustion vehicles, secondary sulfate and secondary nitrate. On a yearly average basis, the contribution of quarry mining dust to PM2.5 was 6%. The contribution of soil dust to PM2.5 was comparable with cement dust (13% and 13%, respectively). Other identified sources included vehicle, secondary sulfate, secondary nitrate and coal combustion, which contributed 23, 15, 9 and 18% of the total mass, respectively. Air mass residence time (AMRT) analysis showed that northeast and southeast regions might be the major PM2.5 source during the sampling campaign. The findings of this study can be used to understand the characteristics of quarry mining dust and control strategies for PM2.5.

Source regional contributions to PM2.5 in a megacity in China using an advanced source regional apportionment method

To quantify contributions of individual source categories from diverse regions to PM2.5, PM2.5 samples were collected in a megacity in China and analyzed through a newly developed source regional apportionment (SRA) method. Levels, compositions and seasonal variations of speciated PM2.5 dataset were investigated. Sources were determined by Multilinear Engine 2 (ME2) model, and results showed that the PM2.5 in Tianjin was mainly influenced by secondary sulphate & secondary organic carbon SOC (percent contribution of 26.2%), coal combustion (24.6%), crustal dust & cement dust (20.3%), secondary nitrate (14.9%) and traffic emissions (14.0%). The SRA method showed that northwest region R2 was the highest regional contributor to secondary sources, with percent contributions to PM2.5 being 9.7% for secondary sulphate & SOC and 6.0% for secondary nitrates; the highest coal combustion was from local region R1 (6.2%) and northwest R2 (8.0%); the maximum contributing region to crustal & cement dust was southeast region R4 (5.0%); and contributions of traffic emissions were relatively spatial homogeneous. The seasonal variation of regional source contributions was observed: in spring, the crustal and cement dust contributed a higher percentage and the R4 was an important contributor; the secondary process attributed an increase fraction in summer; the mixed coal combustion from southwest R5 enhanced in autumn.

Insights into the chemical characterization and sources of PM(2.5) in Beijing at a 1-h time resolution

As the widespread application of online instruments penetrates the environmental fields, it is interesting to investigate the sources of fine particulate matter (PM2.5) based on the data monitored by online instruments. In this study, online analyzers with 1-h time resolution were employed to observe PM2.5 composition data, including carbon components, inorganic ions, heavy metals and gas pollutants, during a summer in Beijing. Chemical characteristics, temporal patterns and sources of PM2.5 are discussed. On the basis of hourly data, the mean concentration value of PM2.5 was 62.16±39.37 μg m(-3) (ranging from 6.69 to 183.67 μg m(-3)). The average concentrations of NO3(-), SO4(2-), NH4(+), OC and EC, the major chemical species, were 15.18±13.12, 14.80±14.53, 8.90±9.51, 9.32±4.16 and 3.08±1.43 μg m(-3), respectively. The concentration of PM2.5 varied during the online-sampling period, initially increasing and then subsequently decreasing. Three factor analysis models, including principal component analysis (PCA), positive matrix factorization (PMF) and Multilinear Engine 2 (ME2), were applied to apportion the PM2.5 sources. Source apportionment results obtained by the three different models were in agreement. Four sources were identified in Beijing during the sampling campaign, including secondary sources (38-39%), crustal dust (17-22%), vehicle exhaust (25-28%) and coal combustion (15-16%). Similar source profiles and contributions of PM2.5 were derived from ME2 and PMF, indicating the results of the two models are reasonable. The finding provides information that could be exploited for regular air control strategies.

Review on recent progress in observations, source identifications and countermeasures of PM2.5

Recently, PM2.5 (atmospheric fine particulate matter with aerodynamic diameter ≤ 2.5 μm) have received so much attention that the observations, source appointment and countermeasures of it have been widely studied due to its harmful impacts on visibility, mood (mental health), physical health, traffic safety, construction, economy and nature, as well as its complex interaction with climate. A review on the PM2.5 related research is necessary. We start with summary of chemical composition and characteristics of PM2.5 that contains both macro and micro observation results and analysis, wherein the temporal variability of concentrations of PM2.5 and major components in many recent reports is embraced. This is closely followed by an overview of source appointment, including the composition and sources of PM2.5 in different countries in the six inhabitable continents based on the best available results. Besides summarizing PM2.5 pollution countermeasures by policy, planning, technology and ideology, the World Air Day is proposed to be established to inspire and promote the crucial social action in energy-saving and emission-reduction. Some updated knowledge of the important topics (such as formation and evolution mechanisms of hazes, secondary aerosols, aerosol mass spectrometer, organic tracers, radiocarbon, emissions, solutions for air pollution problems, etc.) is also included in the present review by logically synthesizing the studies. In addition, the key research challenges and future directions are put forward. Despite our efforts, our understanding of the recent reported observations, source identifications and countermeasures of PM2.5 is limited, and subsequent efforts both of the authors and readers are needed.