Long-term variation of the levels, compositions and sources of size-resolved particulate matter in a megacity in China

A comprehensive review on endocrine toxicity of gaseous components and particulate matter in smog

Smog is a form of extreme air pollution which comprises of gases such as ozone, sulfur dioxide, nitrogen and carbon oxides, and solid particles including particulate matter (PM2.5 and PM10). Different types of smog include acidic, photochemical, and Polish. Smog and its constituents are hazardaous to human, animals, and plants. Smog leads to plethora of morbidities such as cancer, endocrine disruption, and respiratory and cardiovascular disorders. Smog components alter the activity of various hormones including thyroid, pituitary, gonads and adrenal hormones by altering regulatory genes, oxidation status and the hypothalamus-pituitary axis. Furthermore, these toxicants are responsible for the development of metabolic disorders, teratogenicity, insulin resistance, infertility, and carcinogenicity of endocrine glands. Avoiding fossil fuel, using renewable sources of energy, and limiting gaseous discharge from industries can be helpful to avoid endocrine disruption and other toxicities of smog. This review focuses on the toxic implications of smog and its constituents on endocrine system, their toxicodynamics and preventive measures to avoid hazardous health effects.

Evaluating the performance of chemical transport models for PM2.5 source apportionment: An integrated application of spectral analysis and grey incidence analysis

Evaluating the performance of source apportionment (SA) models is difficult due to the non-observable nature of source contribution in reality. Here we propose a new approach to assess the performance of Chemical Transport Models (CTMs) for SA based on wavelet time-frequency spectral analysis and Grey Incidence Analysis (GIA). For each source category, certain species that better reflect the periodic characteristics of the emission sources were selected as the chemical tracers. The consistency of the time series between the simulated source contributions and the observed source-specific chemical tracers was then examined using a GIA model based on the perspective of similarity, and characterized by the GIA scores. By applying this method to six typical pollution episodes, we evaluated the performance of the Comprehensive Air Quality Model with Extensions-Particle Source Apportionment Technology (CAMx-PSAT) model for PM_2.5 SA from different temporal and spatial scales. The source- and episode-dependent optimal average time and main source regions were obtained. This approach is robust for facilitating a relatively meticulous evaluation of the performance of CTMs for PM_2.5 SA, and provides additional insight for decision-making for heavy pollution emergencies.

Overviewing the air quality models on air pollution in Sichuan Basin, China

Most developing countries in the world face the common challenges of reducing air pollution and advancing the process of sustainable development, especially in China. Air pollution research is a complex system and one of the main methods is through numerical simulation. The air quality model is an important technical method, it allows researchers to better analyze air pollutants in different regions. In addition, the SCB is a high-humidity and foggy area, and the concentration of atmospheric pollutants is always high. However, research on this region, one of the four most polluted regions in China, is still lacking. Reviewing the application of air quality models in the SCB air pollution has not been reported thoroughly. To fill these gaps, this review provides a comprehensive narration about i) The status of air pollution in SCB; ii) The application of air quality models in SCB; iii) The problems and application prospects of air quality models in the research of air pollution. This paper may provide a theoretical reference for the prevention and control of air pollution in the SCB and other heavily polluted areas in China and give some¹inspirations for air pollution forecast in other countries with complex terrain.

Durational effect of particulate matter air pollution wave on hospital admissions for schizophrenia

BACKGROUND

Short-term exposure to high level of ambient particulate matters (PM) concentrations has been linked with increased hospital admissions (HA) for schizophrenia. However, evidence is inconclusive about the added effect of multi-day exposure to high-level PM concentration on schizophrenia. This study aims to evaluate the durational effect of PM air pollution wave on schizophrenia.

METHOD

Data on daily HA for schizophrenia, PM (PM_2.5 and PM₁₀) and meteorological variables over the period of 2014-2017 was collected in Jining, Shandong, China. Air pollution wave of PM was defined as ≥2 or ≥3 or ≥4 consecutive days with PM concentration ≥90th or ≥92.5th or ≥95th or ≥97.5th percentiles, respectively. A time-series Poisson regression model with duration as the variable of interest was used to evaluate the associations of PM air pollution wave with HA for schizophrenia.

RESULTS

A total of 14650 hospital admissions for schizophrenia were identified. Under various air pollution wave definitions, both PM_2.5 and PM₁₀ had significant adverse effects on schizophrenia HA. PM_2.5 wave defined as ≥2 consecutive days with concentration ≥90th, ≥92.5th, ≥95th and ≥97.5th percentile was associated with 4.8% (2.0%-7.6%), 4.9% (1.9%-7.9%), 5.5% (2.0%-9.2%), and 7.6% (2.9%-12.6%) increase of HA for schizophrenia at lag 6. PM_2.5 waves defined as ≥3 consecutive days with concentration ≥90th, ≥92.5th, ≥95th and ≥97.5th percentile respectively corresponded to 5.0% (2.3%-7.8%), 5.1% (1.9%-8.4%), 6.9% (3.0%-10.8%) and 12.0% (5.3%-19.1%) increases in HA for schizophrenia at lag 6. The most significant associations were observed on the sixth day in different lag models.

CONCLUSIONS

PM air pollution wave was associated with increased risk of hospital admissions for schizophrenia, with stronger associations among married and female patients.

Meteorological Variables and Synoptic Patterns Associated with Air Pollutions in Eastern China during 2013-2018

Steady meteorological conditions are important external factors affecting air pollution. In order to analyze how adverse meteorological variables affect air pollution, surface synoptic situation patterns and meteorological conditions during heavy pollution episodes are discussed. The results showed that there were 78 RPHPDs (regional PM_2.5 pollution days) in Jiangsu, with a decreasing trend year by year. Winter had the most stable meteorological conditions, thus most RPHPDs appeared in winter, followed by autumn and summer, with the least days in spring. RPHPDs were classified into three patterns, respectively, as equalized pressure (EQP), advancing edge of a cold front (ACF) and inverted trough of low pressure (INT) according to the SLP (sea level pressure). RPHPDs under EQP were the most (51%), followed by ACF (37%); INT was the minimum (12%). Using statistical methods and meteorological condition data on RPHPDs from 2013 to 2017 to deduce the thresholds and 2018 as an independent dataset to validate the proposed thresholds, the threshold values of meteorological elements are summarized as follows. The probability of RPHPDs without rain was above 92% with the daily and hourly precipitation of all RPHPDs below 2.1 mm and 0.8 mm. Wind speed, RHs, inversion intensity(ITI), height difference in the temperature inversion(ITK), the lower height of temperature inversion (LHTI) and mixed-layer height (MLH) in terms of 25%–75% high probability range were respectively within 0.5–3.6 m s⁻¹, 55%–92%, 0.7–4.0 °C 100 m ⁻¹, 42–576 m, 3–570 m, 200–1200 m. Two conditions should be considered: whether the pattern was EQP, ACF or INT and whether the eight meteorological elements are within the thresholds. If both criteria are met, PM_2.5 particles tend to accumulate and air pollution diffusion conditions are poor. Unfavorable meteorological conditions are the necessary, but not sufficient condition for RPHPDs.

Characterization of aerosol particles during the most polluted season (winter) in urban Chengdu (China) by single-particle analysis

Chengdu, the capital city of Sichuan Province, is one of the most polluted cities in China. We used single-particle aerosol mass spectrometer to monitor particulate matter pollution in an urban area of Chengdu from December 9, 2015 to January 4, 2016 to determine the characteristics of air pollution during the winter months. The mass concentrations of particulate matter were high during the whole observation period, with mean values for PM_2.5 and PM₁₀ of 101 ± 60 and 162 ± 99 μg m^-3, respectively. The particles were clustered into nine distinct particle types: dust (3%), potassium-elemental carbon (KEC) (24%), organic carbon (OC) (12%), combined OC and EC (OCEC) (6%), K-organic nitrogen (KCN) (10%), K-nitrate (KNO₃) (12%), K-sulfate (KSO₄) (18%), K-sulfate and nitrate (KSN) (12%), and metal (3%) particles. Analysis on different types of day showed that: (1) from "excellent" (days with PM_2.5 lower than 35 μg m^-3) to "light pollution" (PM_2.5 between 75 and 115 μg m^-3) days, local/regional combustion was the major contributor, whereas the aggravation of pollution from light pollution to "heavy pollution" (PM_2.5 higher than 150 μg m^-3) days was mainly determined by the combined effect of local/regional combustion and long-distance transport; (2) as the air quality deteriorated, the mixing of sulfate and nitrate in particles increased sharply, especially sulfate; and (3) the relative aerosols acidity increased from excellent to light pollution days, while decreased significantly from light pollution to heavy pollution days. Backward trajectory analysis showed that there were significant differences in PM_2.5 concentrations and particle compositions between clusters of trajectories, which affected the level and evolution of PM_2.5 pollution in Chengdu. These results give a deeper understanding of PM_2.5 pollution in Chengdu and the Sichuan Basin.

Aerosols in an arid environment: The role of aerosol water content, particulate acidity, precursors, and relative humidity on secondary inorganic aerosols

Meteorological conditions, gas-phase precursors, and aerosol acidity (pH) can influence the formation of secondary inorganic aerosols (SIA) in fine particulate matter (PM_2.5). Most works related to the influence of pH and gas-phase precursors on SIA have been laboratory research, but field observation research is very scarce, especially in arid environments. The relationship among SIA, pH, gas-phase precursors, and meteorological conditions are investigated in Hohhot, a major city in China with an arid environment. Secondary inorganic species, e.g., SO₄^2-, NO₃^-, were typically found at low levels, reflecting the low level of secondary aerosol. It is interesting to note that the level of SO₂ in Hohhot was higher than in other cities while SO₄^2- was relatively lower than in other cities. Multiple receptor models were used to explore the contributions to the SIA and quantify the source impacts on the SIA. Annual average aerosol pH in Hohhot was 5.6 (range 1.1-8.4) which was estimated by a thermodynamic equilibrium model. Additionally, a statistical method was used to evaluate the influence of SIA sources on ambient aerosol concentrations. Aerosol water content and particulate acidity were found to be positively associated with secondary SO₄^2-, while NO₂ and RH had a significant impact on secondary NO₃^- in an arid atmosphere. The findings explain the relationship between gaseous precursors, relative humidity, aerosol pH and temperature in the arid city of Hohhot.

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.

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models

PM_2.5 is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM_2.5. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM_2.5 were collected at Tianjin, a megacity in northern China. The ambient PM_2.5 dataset, source information, and gas-to-particle ratios (such as SO₂/PM_2.5, CO/PM_2.5, and NOx/PM_2.5 ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM_2.5 was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

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.

A Global Perspective of Fine Particulate Matter Pollution and Its Health Effects

Fine particulate matter (PM) in the ambient air is implicated in a variety of human health issues throughout the globe. Regulation of fine PM in the atmosphere requires information on the dimension of the problem with respect to variations in concentrations and sources. To understand the current status of fine particles in the atmosphere and their potential harmful health effects in different regions of the world this review article was prepared based on peer-reviewed scientific papers, scientific reports, and database from government organizations published after the year 2000 to evaluate the global scenario of the PM_2.5 (particles <2.5 μm in aerodynamic diameter), its exceedance of national and international standards, sources, mechanism of toxicity, and harmful health effects of PM_2.5 and its components. PM_2.5 levels and exceedances of national and international standards were several times higher in Asian countries, while levels in Europe and USA were mostly well below the respective standards. Vehicular traffic has a significant influence on PM_2.5 levels in urban areas; followed by combustion activities (biomass, industrial, and waste burning) and road dust. In urban atmosphere, fine particles are mostly associated with different health effects with old aged people, pregnant women, and more so children being the most susceptible ones. Fine PM chemical constituents severely effect health due to their carcinogenic or mutagenic nature. Most of the research indicated an exceedance of fine PM level of the standards with a diverse array of health effects based on PM_2.5 chemical constituents. Emission reduction policies with epidemiological studies are needed to understand the benefits of sustainable control measures for fine PM mitigation.

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.

Evaluation of air quality in Chengdu, Sichuan Basin, China: are China's air quality standards sufficient yet?

Air quality evaluation is important in order to inform the public about the risk level of air pollution to human health. To better assess air quality, China released its new national ambient air quality standards (NAAQS-2012) and the new method to classify air quality level (AQL) in 2012. In this study, we examined the performance of China's NAAQS-2012 and AQL classification method through applying them, the World Health Organization (WHO) guidelines, and the US AQL classification method to evaluate air quality in Chengdu, the largest city in southwestern China. The results show that annual mean concentrations of PM₁₀, PM₂.₅, SO₂, NO₂, and O₃ at the seven urban sites were in the ranges of 138-161, 87-98, 18-32, 54-70, and 42-57 μg/m(3), respectively, and the annual mean concentrations of CO were in the range of 1.09-1.28 mg/m(3). Chengdu is located in one of the four largest regions affected by haze in China, and PM₁₀ and PM₂.₅ were the top air pollutants, with annual concentrations over 2 times of their standards in NAAQS-2012 and over 7 times of the WHO guidelines. Annual mean concentrations of the pollutants were much lower at the background site (LYS) than at the urban sites, but the annual mean concentrations of PM₁₀ and PM₂.₅ at LYS were 3.5 and 5.7 times of the WHO guidelines, respectively. These suggest that severe air pollution in Chengdu was largely associated with local emissions but also related to regional air pollution. The compliance rates of PM₁₀ , PM₂.₅, SO₂, and O₃ met China's NAAQS-2012 standards four times more frequently than they met the WHO guidelines, as NAAQS-2012 uses the loosest interim target (IT) standards of WHO for these four pollutants. Air pollution in Chengdu was estimated and stated to be less severe using China's classification than using the US classification, as China uses weaker concentration breakpoints and benign descriptions of AQL. Furthermore, China's AQL classification method does not capture the cumulative effects of multiple pollutants, and the risk assessment is mainly based on the exposure-response relationship between air pollutant and human health quantified in the North America and West Europe; these can bring some uncertainties into evaluating the risk to human health in China. In summary, although China greatly improved its NAAQS and AQL classification method in 2012, further improvements are still needed.

Further insights into the composition, source, and toxicity of PAHs in size-resolved particulate matter in a megacity in China

Concentrations of particulate matter with an aerodynamic diameter less than 10 μm (PM10 ) and PM with an aerodynamic diameter less than 2.5 μm (PM2.5 ), and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The average concentrations of PM10 and PM2.5 reached 209.75 μg/m(3) and 141.87 μg/m(3) , respectively, and those of ΣPAHs were 41.46 ng/m(3) for PM10 and 36.77 ng/m(3) for PM2.5 . The mass ratio concentrations were 219.23 μg/g and 311.01 μg/g in PM10 and PM2.5 , respectively. Three sources and their contributions for PAHs were obtained. For individual input mode, diesel exhaust contributed 46.77% (PM10 ) and 41.12% (PM2.5 ) for mass concentration and 48.69% (PM10 ) and 39.47% (PM2.5 ) for mass ratio concentration; gasoline exhaust contributed 31.02% (PM10 ) and 39.47% (PM2.5 ) for mass concentration and 28.95% (PM10 ) and 36.46% (PM2.5 ) for mass ratio concentration; and coal combustion contributed 22.22% (PM10 ) and 19.41% (PM2.5 ) for mass concentration and 22.36% (PM10 ) and 15.89% (PM2.5 ) for mass ratio concentration. For combined input mode, the same source categories were obtained. Source contributions to PM10 and PM2.5 were diesel exhaust (40.70% and 36.64%, respectively, for mass concentration; 49.19% and 38.47%, respectively, for mass ratio concentration), gasoline exhaust (35.09% and 38.47%, respectively, for mass concentration; 32.50% and 33.43%, respectively, for mass ratio concentration), and coal combustion (24.21% and 24.89%, respectively, for mass concentration; 18.31% and18.17%, respectively, for mass ratio concentration). Source risk assessment showed that vehicle emission was a significant contributor. The findings can help elucidate sources of PAHs and provide evidence supporting further applications of the Unmix model and additional studies about PAHs. Environ Toxicol Chem 2015;34:480-487. © 2014 SETAC.

Source apportionment of synchronously size segregated fine and coarse particulate matter, using an improved three-way factor analysis model

Samples of PM₁₀ and PM₂.₅ were synchronously collected from a megacity in China (Chengdu) during the 2011 sampling campaign and then analyzed by an improved three-way factor analysis method based on ME2 (multilinear engine 2), to investigate the contributions and size distributions of the source categories for size segregated particulate matter (PM). Firstly, the synthetic test was performed to evaluate the accuracy of the improved three-way model. The same five source categories with slightly different source profiles were caught. The low AAE (average absolute error) values between the estimated and the synthetic source contributions (<15%) and the approachable estimated PM₂.₅/PM₁₀ ratios with the simulated ratios might indicate that the results of the improved three-way factor analysis might be satisfactory. Then, for the ambient PM samples, the mean levels were 206.65 ± 69.90 μg/m(3) (PM₁₀) and 130.47 ± 43.67 μg/m(3) (PM₂.₅). The average ratio of PM₂.₅/PM₁₀ was 0.63. PM₁₀ and PM₂.₅ in Chengdu were influenced by the same source categories and their percentage contributions were in the same order: crustal dust & coal combustion presented the highest percentage contributions, accounting for 58.20% (PM₁₀) and 53.73% (PM2.5); followed by vehicle exhaust & secondary organic carbon (18.45% for PM₁₀ and 21.63% for PM₂.₅), secondary sulfate and nitrate (17.06% for PM₁₀ and 20.91% for PM₂.₅) and cement dust (6.30% for PM₁₀ and 3.73% for PM₂.₅). The source profiles and contributions presented slightly different distributions for PM₁₀ and PM₂.₅, which could better reflect the actual situation. The findings based on the improved three-way factor analysis method may provide clear and deep insights into the sources of synchronously size-resolved PM.

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

The transport of particulate matter (PM) and chemical species is an essential mechanism for determining the fate of PM pollutants and their effects. To determine source transport quantitatively, an ambient PM2.5 dataset from a megacity in China was analysed using a novel method called "Source Directional Apportionment" (SDA). The SDA method is developed in this work to quantify contributions of each source category from various directions. The three steps of SDA are (1) to estimate source categories and time series of source contributions to PM with a factor analysis model, (2) to identify directions by trajectory cluster analysis and (3) to quantify source directional contributions for each source category by combining the time series of source contributions to the back trajectories in each direction. For PM2.5 in Chengdu, crustal dust, vehicular exhaust, coal combustion and secondary sulphate are all important contributors to PM; secondary nitrate and cement dust are relatively less influential. Four potential source directions were identified in Chengdu during the sampling period from 2009 to 2011. The percentages of source directional contributions from Directions 1-4 (northeast, southwest to south, southwest and west) were estimated as follows: crustal dust (7.9%, 9.1%, 6.4% and 6.2%, respectively), cement dust (1.0%, 1.2%, 1.3% and 1.1%, respectively), vehicular exhaust (6.4%, 6.0%, 5.6% and 7.0%, respectively), secondary sulphate (5.1%, 5.2%, 5.6% and 8.6%, respectively) and secondary nitrate (2.0%, 2.4%, 2.5% and 2.3%, respectively). Finally, the source directional contributions to important chemical species were quantified to determine their transport from sources to receptor.

Chemical characteristic and toxicity assessment of particle associated PAHs for the short-term anthropogenic activity event: During the Chinese New Year's Festival in 2013

PM10 and PM2.5 samples were simultaneously collected during a period which covered the Chinese New Year's (CNY) Festival. The concentrations of particulate matter (PM) and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The possible source contributions and toxicity risks were estimated for Festival and non-Festival periods. According to the diagnostic ratios and Multilinear Engine 2 (ME2), three sources were identified and their contributions were calculated: vehicle emission (48.97% for PM10, 53.56% for PM2.5), biomass & coal combustion (36.83% for PM10, 28.76% for PM2.5), and cook emission (22.29% for PM10, 27.23% for PM2.5). An interesting result was found: although the PAHs are not directly from the fireworks display, they were still indirectly influenced by biomass combustion which is affiliated with the fireworks display. Additionally, toxicity risks of different sources were estimated by Multilinear Engine 2-BaP equivalent (ME2-BaPE): vehicle emission (54.01% for PM10, 55.42% for PM2.5), cook emission (25.59% for PM10, 29.05% for PM2.5), and biomass & coal combustion source (20.90% for PM10, 14.28% for PM2.5). It is worth to be noticed that the toxicity contribution of cook emission was considerable in Festival period. The findings can provide useful information to protect the urban human health, as well as develop the effective air control strategies in special short-term anthropogenic activity event.