[Characteristics, Sources Apportionment, and Health Risks of PM2.5-bound PAHs and Their Derivatives Before and After Heating in Zibo City].

Atmospheric polycyclic aromatic hydrocarbons (PAHs) and their derivatives are a global problem that influences the environment and threatens human health. To investigate the characteristics, sources, and health risk assessment of PM2.5-bound PAHs and their derivatives, PM2.5 were collected at an urban site in Zibo from November 5 to December 26, 2020, and the concentrations of 16 conventional PAHs, nine NPAHs, and five OPAHs in PM2.5 were analyzed using gas chromatography-mass spectrometry. Source apportionment of PAHs and their derivatives was conducted using diagnostic ratios and a PMF model, and the health risks of PAHs and their derivatives to adult men and women were evaluated using the source-dependent incremental lifetime cancer risk (ILCR) model. The results showed that the average concentrations of ∑16pPAHs, ∑9NPAHs, and ∑5OPAHs in PM2.5 of Zibo City during the sampling period were (41.61 ± 13.40), (6.38 ± 5.70), and (53.20 ± 53.47) ng·m-3, respectively. The concentrations of the three PAHs increased significantly after heating, which were 1.31, 2.04, and 5.24 times larger than those before heating. During the sampling period, Chr, BaP, and BaA were the dominant components of pPAHs; 9N-Ant and 2N-Flt + 3N-Flt were the dominant components of NPAHs; and ATQ and BZO were the dominant components of OPAHs. Source apportionment results showed that motor vehicles were the main source of PAHs and their derivatives in PM2.5 before heating, whereas after heating, the main sources were the mixed source of coal and biomass combustion and secondary formation. The total BaP equivalent (TEQ) was 14.5 ng·m-3 during the sampling period, and the TEQ increased significantly after heating, which was approximately 1.2 times of that before heating. Assisted by the individual PAH source apportionment results, the ILCR of PM2.5-boundPAHs and NPAHs in Zibo City had a certain potential carcinogenic risk for adult males (1.06 × 10-5) and females (9.32 × 10-6). Among them, the health risks of PAHs from gasoline vehicles, diesel vehicles, and coal/biomass combustion were significantly higher than those from other emission sources.

[Agricultural Ammonia Emission Inventory and Its Distribution in Xining City].

Based on the field research activity level, the emission inventory of agricultural ammonia in Xining City in 2018 was established using the emission factor method. The emission characteristics of agricultural ammonia in Xining City were analyzed, a spatial grid distribution of 3 km×3 km was carried out by ArcGIS, and an uncertainty analysis of the ammonia emission inventory of livestock and poultry breeding and nitrogen fertilizer application was carried out via Monte Carlo simulation. The results showed that the total amount of ammonia emissions from agricultural sources in Xining City in 2018 was 4644.58 t. Among them, the total annual ammonia emissions from nitrogen fertilizer application and livestock and poultry farming were 1664.84 t and 2979.75 t, respectively, accounting for 35.84% and 64.16% of the total ammonia emissions from agricultural sources in Xining City. The spatial distribution results showed that the agricultural source ammonia emissions of Xining City mainly came from Datong County, Huangyuan County, and Huangzhong County, which accounted for 40.10%, 30.66%, and 28.05% of the total agricultural source ammonia emissions of Xining City, respectively. From the perspective of monthly distribution, no monthly difference in ammonia emissions was found in livestock and poultry farming. The ammonia emissions from nitrogen fertilizer application mainly existed in June (799.96 t) and July (768.48 t), accounting for 48.05% and 46.16% of the annual emissions. Monte Carlo simulation results showed that under a 95% confidence interval, the uncertainties of ammonia emissions from nitrogen fertilizer application agricultural source ammonia were low, ranging from -24.38% to 26.71%.

[Characteristics and Source Apportionment of Carbon Components in Road Dust in Anshan].

In order to study the characteristics and sources of carbon fractions in PM2.5 in road dust in Anshan, road dust samples were collected from nine roads in Anshan in October 2014 and re-suspended on filters using a NK-ZXF sampler to obtain PM2.5 samples. A thermal optical carbon analyzer (IMPROVE-TOR) was employed to measure the mass fraction of organic carbon (OC) and elemental carbon (EC) in PM2.5. The results showed that ω(TC) in PM2.5 in road dust was 9.78% (outer loop)-14.00% (Qianshan West Road), ω(OC) was 8.15% (outer loop)-10.84% (Qianshan West Road), and ω(EC) was 1.63% (outer loop)-2.85% (Qianshan West Road). ω(OC) was much higher than ω(EC), indicating that road dust contained a large amount of organic carbon. All OC/EC values were greater than 2.0 during the sampling period, suggesting that there was secondary pollution. Spearman correlation analysis and linear fitting indicated that the sources of OC and EC were basically the same. Cluster analysis results showed that carbon components in PM2.5 in road dust in Anshan mainly originated from vehicle exhaust, biomass burning, and coal combustion emissions.

[Characteristics and Source Apportionment of Carbon Components in Road Dust PM2.5 and PM10 During Spring in Tianjin Derived by Using the Quadrat Sampling Method].

In order to study the characteristics and sources of carbon fractions in PM2.5 and PM10 of road dust in Tianjin, samples of road dust were collected by the quadrat sampling method in April 2015 in Tianjin, and samples were re-suspended on filters by using a NK-ZXF sampler. A Thermal Optical Carbon Analyzer (IMPROVE-TOR) was employed to measure the concentrations of organic carbon (OC) and elemental carbon (EC), and the pollution characteristics and sources were investigated by non-parametric tests and OC/EC ratio, correlation, and cluster analyses. The results showed that ω(total carbon, TC) in PM2.5 of road dust amounted to 4.89% (secondary road) -18.83% (expressway), ω(OC) amounted to 3.57% (secondary road) -15.39% (expressway), and ω(EC) amounted to 1.32% (secondary road) -3.44% (expressway); meanwhile, ω(TC) in PM10 of road dust was 8.14% (secondary road) -19.71% (expressway), ω(OC) was 5.91% (secondary road) -16.28% (expressway), and ω(EC) was 1.96% (main road) -3.43% (expressway). The mass fraction of each carbon component for the expressway was relatively high, and that for the secondary trunk road was relatively low, which may have been due to the large traffic volume on the expressway and corresponding large amounts of exhaust emissions from motor vehicles, whereas there were fewer vehicles on the secondary trunk road. Additionally, ω(OC) was significantly larger than ω(EC) for all types of roads, and ω(EC) did not vary much among the different road types. The non-parametric tests of two related samples showed that there was no significant difference in the mass fraction of each carbon component between PM2.5 and PM10. The correlation analysis showed that the sources of OC and EC in road dust were roughly the same. The OC/EC ratio analysis and cluster analysis showed that the main sources of the carbon components in the dust of roads in Tianjin in spring were coal combustion, motor vehicle exhaust, and biomass burning.

[Characteristics and Sources of Carbon Components in PM2.5 During Autumn and Winter in Panjin City].

In order to study the pollution characteristics and sources of carbon in Panjin City during autumn and winter, PM2.5 samples were collected at three monitoring points in October 2016 and January 2017. Pollution characteristics and sources of carbon components in PM2.5 were analyzed by the OC/EC ratio method, EC tracer method, and principal component analysis method. The results showed that the PM2.5 concentration exceeded the ambient air quality standard (GB 3095-2012) and that the average concentrations of OC and EC were 10.02 µg·m-3 and 3.91 µg·m-3 in autumn, respectively, and 16.04 µg·m-3 and 5.62 µg·m-3 in winter. According to the OC/EC ratio method, the OC/EC ratios were more than 2.0 during the sampling periods, indicating that there was secondary pollution in autumn and winter. Spearman correlation analysis and linear fitting indicated that the OC and EC sources were complex in development zones and that secondary school and cultural park areas may have similar sources during autumn and winter. SOC was quantitatively estimated by the EC tracer method, with concentrations of 7.21 µg·m-3 and 23.07 µg·m-3 in autumn and winter, respectively. The absolute and relative errors of SOC uncertainty were 0.98 µg·m-3 and 14.00% in autumn, respectively, and 1.87 µg·m-3 and 8.21% in winter. Based on the method of principal component analysis, the carbon components in autumn and winter were mainly derived from coal combustion, biomass burning, and vehicle exhaust.

[Characteristics of Elements in PM2.5 and PM10 in Road Dust Fall During Spring in Tianjin].

To explore the element pollution characteristics and sources of road dust fall, road dust fall samples were collected during spring in Tianjin and suspended to obtain PM2.5 and PM10, 16 elements were analyzed, and then their characteristics, sources, and similarities were studied by nonparametric tests, the coefficient of divergence, and enrichment factor. The results showed that the mass fraction (1%-20%) in sequential order of the elements in PM2.5 and PM10 in Tianjin road dust fall were Si > Al > Ca > Fe > Mg > K > Na. The coefficient of divergence of elements in PM10 and PM2.5 was 0.06, indicating that the elements profiles of PM10 might be similar to PM2.5. The elements of Cd and Cr in PM10 and PM2.5 were at a high level of enrichment degree, and Zn, Cu, Pb, and As showed significant enrichment. The main sources of the elements in PM10 and PM2.5 road dust fall in Tianjin were soil dust, construction dust, traffic dust (motor vehicle exhaust, tire wear, and brake wear), and coal combustion.

[Characteristics and Source Apportionment of Water-soluble Inorganic Ions in Road Dust PM2.5 During Spring in Tianjin Using the Quadrat Sampling Method].

In order to collect comprehensive information regarding the characteristics and sources of the water-soluble inorganic ions in road dust (RD) PM2.5 in Tianjin, samples of road dust were collected in April 2015 in Tianjin, and then re-suspended on filters using a NK-ZXF sampler. The concentrations of the major water-soluble inorganic ions were analyzed by ion chromatography. A correlation analysis, ratio method, and principal component analysis were used to analyze the sources of RD PM2.5. The results showed that the total water-soluble inorganic ion concentration in Tianjin accounted for 6.13%±2.32%, varying with different road types. Na+, K+, Mg2+, and Ca2+ showed high homology. NO3-/SO42- revealed that the main source of PM2.5 was mostly attributed to fixed sources. The principal component analysis showed that the main sources of RD PM2.5 in Tianjin were coal combustion, mobile sources, biomass burning, and construction dust.

[Characteristics and Source Apportionment of Water-soluble Ions in PM2.5 During Winter in Panjin].

In order to study the characteristics and source of water-soluble ions in PM2.5 in Panjin, PM2.5 samples were collected at three sites in January of 2017, and the levels of eight ions (Na+, Mg2+, Ca2+, K+, NH4+, SO42-, Cl-, and NO3-) were determined by ICS-900 ion chromatograph. The characteristics of PM2.5, the water-soluble ion concentration, SOR and NOR calculation, and ion balance calculation were investigated, and a principal component analysis was conducted. The results showed that the concentration of PM2.5 and water-soluble ions followed a descending sequence of park > development zone > the second middle school. SO42-, NO3-, and NH4+ were the major components of the water-soluble ions at the three sampling sites. SOR and NOR values were all higher than 0.10, indicating that SO42- and NO3- were mainly converted from SO2 and NOx. The ion balance calculation demonstrated that the cationic and anion equivalents in Panjin had great correlation in winter. Further study showed that the atmosphere in the development zone presented as neutral and that in the cultural park and the second middle school presented as partially alkaline. The principal component analysis further indicated that the water-soluble ions were mainly derived from fuel combustion, biomass burning, secondary particles, and re-suspended dust in winter.

[Characteristics and Source Apportionment of Water-soluble Inorganic Ions in Road Dust PM2.5 in Selected Cities in Liaoning Province].

In order to collect comprehensive information on the characteristics and sources of water-soluble inorganic ions in road dust (RD) PM2.5 in Liaoning Province, samples of road dust were collected in 2014 and 2016 in Anshan and Panjin and then re-suspended on filters using an NK-ZXF sampler. The concentrations of the major water-soluble inorganic ions were analyzed by ion chromatography. A correlation analysis, ratios, and a principal component analysis were used to analyze the sources of RD. The results showed that the total water-soluble inorganic ions in Panjin and Anshan accounted for 5.83%±3.34% and 5.84%±1.15% in RD PM2.5, respectively. NH4+, SO42-, and NO3- in RD PM2.5 coexisted in the forms of (NH4)2SO4, and NH4NO3 in Panjin, and NH4HSO4 and NH4NO3 in Anshan. The average values of NO3-/SO42- were 0.52±0.55 and 0.46±0.13 for Panjin and Anshan, respectively, indicating that the effects of stationary sources (e.g., coal combustion) on PM2.5 were more significant. The main sources of RD PM2.5 in Panjin were biomass burning, sea salt particles, construction dust, and mobile sources, while the main sources of RD PM2.5 in Anshan were coal combustion, biomass burning, sea salt particles, and steel smelting dust.

[Determining the concentration of coating solution attaching to honeycomb denuder in summer in Tianjin].

The study on determining the concentration of coating solution attaching to honeycomb denuder was conducted, from 1 July to 31 August, 2013, at the roof of Lihua building at Nankai University in Tianjin. The results of experiment showed that the optimized concentration of sodium carbonate coated on the honeycomb denuder was 3%, and the optimized concentration of citric acid was 6%. The contrast sampling results of PM2.5 between honeycomb denuder system and conventional method showed that 86% of the concentration of PM2.5 samples obtained by honeycomb denuder system were less than those obtained by conventional method, the main reasons may include that: (1) the majority of acid/alkaline gases were removed, so they could neither react with the enriched particles on the sampling membrane nor be adsorbed on particles; (2) parts of the particles were captured by the denuder during sampling; (3) the removal of acid/alkaline gases disturbed the state of equilibrium between gas- and particle-phases which may lead to the volatilization of some particles.

[Carbon source apportionment of PM2.5 in Chongqing based on local carbon profiles].

PM2.5 was sampled from commercial, industrial and residential areas in Chongqing urban city from 2nd May to 10th May 2012 in order to find out characteristics and sources of carbon in PM2.5. Eight kinds of carbons were analyzed by the TOR method. Characteristics of carbon pollution in PM2.5 from three kinds of functional areas and six kinds of sources, including coal-combustion, exhausts (vehicle, boat and construction machine), biomass burning, cooking smoke, were analyzed. Based on carbon source profiles, local indicating components of carbon sources in PM2.5 were obtained used the chemical mass balance (CMB) model. Contribution rate of different sources to PM2.5 carbon were parsed out by factor analysis. The results showed the OC/EC of coal-combustion, vehicle exhausts, boat exhausts, construction machine exhausts, biomass burning and cooking smoke were 6.3, 3.0, 1.9, 1.4, 12.7 and 31.3, respectively. High loads of EC2 and EC3 indicated diesel vehicle exhaust emissions, high loads of OC2, OC3, OC4 and OPC indicated coal-combustion emissions, OC1, OC2, OC3, OC4 and EC1 indicated gasoline vehicle exhaust emissions, OC3 indicated cooking emissions, and OPC indicated biomass burning emissions. OC/PM2.5, EC/PM2.5, secondary organic carbon (SOC)/OC in the commercial area were 17.4%, 6.9% and 40.0%, respectively. OC/PM2.5, EC/PM2.5 and SOC/OC in the industrial area were 15.5%, 6.6% and 37.4%, respectively. OC/PM2.5, EC/PM2.5 and SOC/OC in the residential area were 14.6% 5.6% and 42.8%, respectively. In the industrial area, the main sources of carbon in PM2.5 were coal combustion, gasoline vehicle exhausts and diesel exhaust. In the commercial area, the main sources of carbon were gasoline vehicle exhausts, diesel exhausts and cooking. In the residential area, the main sources of carbon were gasoline vehicle exhausts, cooking smoke and diesel exhausts.

[Estimation of the effect derived from wind erosion of soil and dust emission in Tianjin suburbs on the central district based on WEPS model].

Due to the lack of a prediction model for current wind erosion in China and the slow development for such models, this study aims to predict the wind erosion of soil and the dust emission and develop a prediction model for wind erosion in Tianjin by investigating the structure, parameter systems and the relationships among the parameter systems of the prediction models for wind erosion in typical areas, using the U.S. wind erosion prediction system (WEPS) as reference. Based on the remote sensing technique and the test data, a parameter system was established for the prediction model of wind erosion and dust emission, and a model was developed that was suitable for the prediction of wind erosion and dust emission in Tianjin. Tianjin was divided into 11 080 blocks with a resolution of 1 x 1 km2, among which 7 778 dust emitting blocks were selected. The parameters of the blocks were localized, including longitude, latitude, elevation and direction, etc.. The database files of blocks were localized, including wind file, climate file, soil file and management file. The weps. run file was edited. Based on Microsoft Visualstudio 2008, secondary development was done using C + + language, and the dust fluxes of 7 778 blocks were estimated, including creep and saltation fluxes, suspension fluxes and PM10 fluxes. Based on the parameters of wind tunnel experiments in Inner Mongolia, the soil measurement data and climate data in suburbs of Tianjin, the wind erosion module, wind erosion fluxes, dust emission release modulus and dust release fluxes were calculated for the four seasons and the whole year in suburbs of Tianjin. In 2009, the total creep and saltation fluxes, suspension fluxes and PM10 fluxes in the suburbs of Tianjin were 2.54 x 10(6) t, 1.25 x 10(7) t and 9.04 x 10(5) t, respectively, among which, the parts pointing to the central district were 5.61 x 10(5) t, 2.89 x 10(6) t and 2.03 x 10(5) t, respectively.

[Estimation of the effect derived from Tianjin suburban wind erosion open source on central district].

In some Chinese cities air pollution is serious, and the major pollutant is still PM10. The source apportionment results show: wind erosion dust is an important source of particulate matter. So the contribution of this source should be calculated. The suburban land of Tianjin was classified based on Landsat TM satellite image, and samples of different land use types were taken. The soil textures were determined with the analysis of soil samples by the laser particle analyzer and GIS-based interpolation computation. The wind erosion modulus, wind erosion fluxes, dust release modulus and dust release fluxes were calculated in four seasons in suburban of Tianjin (Beichen, Xiqing, Jinnan and Dongli). The wind erosion fluxes in spring, summer, autumn and winter were 21236.31, 4435.21, 7272.13 and 17204.4 t in 2009; the wind erosion fluxes in Beichen, Xiqing, Jinnan and Dongli were 6380.23, 32 881.13, 8 340.67 and 2 546.02 t in 2009; the wind erosion fluxes from cultivated land and forest grassland were 49 599.2 and 548.85 t, respectively. The soil dust release fluxes in Tianjin suburban were 9 352.92 t (particle size < 10 microm), 20 587.99 t (particle size 10-15 microm)and 13 873.74 t (particle size 15-20 microm). In 2009, there were 20 592.91 t soil dusts with particulate diameter of 20 microm or less that were transmitted from suburban to central district, and 4 395.89 t with particulate diameter of 10 microm or less, about 21.35%.