[Size Distribution and Source Appointment of Road Particles During Winter in Tianjin].

The significant role of traffic emissions mixed from various sources in urban air pollution has been widely recognized. However, the corresponding contributions to the roadside particle distribution are poorly understood due to the mixed impacts of various sources. Particle number concentrations of different sizes at the roadside in Nankai District of Tianjin were continuously monitored using a portable aerosol particle spectrometer during the morning rush hour (07:30-09:20) from Nov. 9, 2018 to Jan. 6, 2019. Characteristic and influencing factors of particle size distributions were discussed combined with temperature and relative humidity data, while potential sources of particles at the roadside were identified based on size distribution analysis. The results showed that the average total particle number concentrations were 502 cm-3, and the concentrations of the accumulation mode and coarse mode were 500 cm-3 and 2 cm-3, respectively. The distribution of number concentrations at the roadside was unimodal and primarily concentrated at 0.25-0.50 µm, with peak sizes at 0.28-0.30 µm. The same distribution trend of particle number concentration and difference in the concentration in the same segment size were observed at different periods. Vehicle activity level was the main influencing factor of road particulate matter concentration on different weekdays; the probability of the high value of road particulate matter concentration was reduced by a reasonable combination of the vehicle tail numbers. Temperature and relative humidity were both found to be positively correlated with the number concentration of particles. With the increase in temperature and relative humidity, the total and peak particle number concentration showed an overall upward trend. In addition, the peak particle size increased from 0.28-0.30 µm to 0.35-0.40 µm when relative humidity was higher than 80%. Three sources, including road dust, brake and tire wear, and the aging particles from vehicle exhaust, were identified using positive matrix factorization in this study. Road dust contributed 8.6% of the total number concentration, which mainly consisted of particles with sizes above 5.00 µm. Brake and tire wear contributed 2.8% of the total number concentration of particles with a size range of 0.80-4.00 µm. The aging particles from vehicle exhaust contributed the most (88.5%), with a peak at 0.25-0.65 µm. The sources of roadside particles were mainly related to vehicle activity, whereas temperature and relative humidity also affected the particle number size distribution.

[Distribution Characteristics and Source Apportionment of Elements Bonded with PM2.5 and PM10 in Linyi].

To investigate the pollution characteristics and sources of elements bonded with PM2.5 and PM10 in Linyi, PM2.5 and PM10 sample collections were carried out simultaneously in Linyi from December 2016 to October 2017, and 23 elements were determined by inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES), with the enrichment factor method and PMF employed to determine the source apportionment. The results indicated that the dominant elements in PM2.5 and PM10 were recognized as Si, Ca, Al, Fe, K, Na, and Mg, accounting for 92.93% and 94.61% of the total measured elements, respectively. The concentrations of 18 elements (excluding Ti, Ni, Mo, Cd, and Mg) were highest in winter and spring, and lowest in summer and autumn. Si, Al, Ca, K, and Na showed the highest levels in spring, mainly distributed in coarse particles; Cu, Zn, Pb, and Sb showed the highest levels in winter, mainly distributed in fine particles. The enrichment factor results showed that the enrichment of Cd, Sb, and Bi was significant, mainly originated by anthropogenic sources such as coal combustion, industrial production, and waste incineration. Based on the analysis results of PMF, coal, and copper smelting, municipal waste incineration, fugitive dust, vehicle emissions, and industrial sources were the main sources of elements in PM2.5, accounting for 22.64%, 7.49%, 41.22%, 14.71%, and 13.94%, respectively. For PM10, fugitive dust, coal and copper smelting, vehicle emissions, and industrial sources were the main sources of elements, contributing 55.47%, 19.80%, 7.48%, and 12.83%, respectively. Our results suggested that the main contributors to particulate matter pollution in Linyi during the sampling period were fugitive dust, and coal and copper smelting.

[Characterization of PM10 and PM2.5 Source Profiles for Emissions from Nonmetal Mineral Products Manufacturing Processes].

In view of the insufficient source profiles for emissions from nonmetal mineral products manufacturing processes in China, a dilution sampling system was used to collect PM10 and PM2.5 samples from glassmaking, ceramics, and firebrick manufacturing sources between February and June of 2017. The characteristics of 50 chemical components in the samples were studied to identify source profiles. The results showed that the dominant composition of particulate matter in glassmaking plant profiles was Na, with percentages ranging from 9.2% to 18.5%. Ceramics profiles were enriched in Al, Si, Ca, and Fe, with percentages ranging from 1.7% to 8.7%. Refractory brick and shale manufacturing process profiles were characterized by high abundances of SO42- (36.9%-48.1%) and NH4+ (7.7%-17.0%). Chemical components in the source profiles varied with the different fuel types and desulfurization, denitrification, and dedusting methods. The coefficients of divergence (CD) between PM2.5 and PM10 from the same process were similar except for the results from the shale manufacturing process (CD values>0.3), thus indicating that the elements profiles of PM2.5 might be similar to those in PM10. Profiles of the same particle size from different processes were significantly different from one another, with CD values ranging from 0.42 to 0.76. The CD values for float glass and medicinal glass, and the CD values for the two ceramic enterprises were relatively small. The distributions of weighted differences (R/U ratios) were used to compare the differences of components between the source profiles, and results showed that the identified components for glass manufacturing, ceramic manufacturing, fireproof bricks, and page rock bricks were Na and As, Al and Ti, NO3- and NH4+, and SO42- and NH4+, respectively.

[Emission Characteristics of Exhaust PM and Its Carbonaceous Components from China â ¢ to China â £ Diesel Vehicles in Shenyang].

Diesel vehicles were the primary source of atmospheric particulate matter (PM) emitted by motor vehicles. To study the emission factors and carbon components of PM2.5 and PM10 from diesel vehicles in Shenyang, exhaust PM samples were collected from 15 diesel vehicles including small, medium, and large passenger vehicles, and light, medium, and heavy-duty trucks under China Ⅲ and China Ⅳ emission standards. This was undertaken using a dilution channel sampling system, and the carbon components were also analyzed. The results showed that the average distance-based PM2.5 and PM10 emission factors for diesel vehicles under China Ⅲ were (0.193±0.092) g·km-1 and (0.338±0.305) g·km-1, respectively, and for China Ⅳ were (0.085±0.038) g·km-1 and (0.100±0.042) g·km-1, respectively. This shows that the PM emission factors decreased significantly with the improvement of emission standards. Under the same emission standards, emission factors increased with the increase of vehicle passenger volume or cargo capacity. TC (total carbon) was the main component of the emissions from diesel vehicles. The mass fraction of TC under China Ⅳ (23%-48%) was significantly lower than under China Ⅲ (29%-70%). The mass fraction of elemental carbon (EC) for all types of diesel vehicles was greater than organic carbon (OC). The OC/EC value was 0.70±0.29, and the OC/EC value for diesel vehicles under China Ⅳ was lower than under China Ⅲ. The total mileage of passenger vehicles was significantly higher than that of trucks, resulting in higher fuel consumption. The mass fraction of OC and EC in passenger vehicles was higher than for trucks under the same emission standards. EC2 (elemental carbon which was measured at temperatures of 700℃) was the highest carbon content of diesel vehicles under China Ⅲ and China Ⅳ emission standards, which can be used in the identification of diesel vehicles in source apportionment studies.

[Effect of astragalus injection on U937 leukemia cells proliferation and apoptosis and relevant molecular mechanisms].

OBJECTIVE: To study the effect of astragalus injection on U937 leukemia cells proliferation and apoptosis and relevant molecular mechanisms. METHODS: Leukemia cell line U937 cells were treated with different concentrations of astragalus (62.5, 125, 250, 500, 1 000 µg/mL). The U937 cells without astragalus treatment were used as the control group. The ability of cell proliferation was measured by MTT method. Flow cytometry was used to explore cell apoptosis. The cell morphology changes were observed under a fluorescent microscope by dyeing Hoechst33258. mRNA expression of c-myc and p27 in U937 cells which was exposed in 1 000 µg/mL astragalus after 0, 12, 24 and 48 hours was detected by reverse transcription polymerase chain reaction (RT-PCR). RESULTS: Various concentrations of astragalus injection inhibited U937 cell proliferation effectively compared with the control group (P<0.05). They also induced U937 cells apoptosis and the apoptosis rate reached to (63 ± 4)% in the 1 000 µg/mL astragalus treatment group. mRNA expression level of c-myc was gradually declined and p27 mRNA expression was gradually increased with astragalus treatment time (P<0.01). CONCLUSIONS: Astragalus injection may inhibit proliferation and induce apoptosis of leukemia cell line U937 in vitro. This contributes to down-regulation of c-myc expression and up-regulation of p27 expression.