[Analysis on the pollution characteristics and influence factors of PM_(2. 5) in Shenzhen City in 2016].

OBJECTIVE: To explore the pollution characteristics and possible influence factors of PM_(2. 5)in Shenzhen in 2016. METHODS: The dynamic characters of PM_(2. 5)mass concentration were analyzed using air quality monitoring and meteorology observation data. The correlations between PM_(2. 5)and other affecting factors, e. g. gas pollutions and meteorology factors, were discussed. A multiple linear regression model was built to reveal the impacts of those factors on PM_(2. 5)concentrations in Shenzhen City using the air quality monitoring and meteorology observation data. RESULTS: The mass concentration of PM_(2. 5)was(27. 02±13. 88)µg/m~3, and the ratios of PM_(2. 5)/PM_(10)was(0. 62±0. 08)in Shenzhen City. Significant positive correlations existed between PM_(2. 5)concentration and main airpollutants(NO_2, SO_2, CO and O_3)(P<0. 05). A significant negative correlation existed between PM_(2. 5) concentration and temperature, relative humidity(P<0. 05), and also a significant positive correlation between atmospheric pressure and PM_(2. 5)concentration. The multiple linear regression showed that air pollutants(NO_2, SO_2, CO, and O_3)and atmospheric pressure could explain 86. 7% of the variations of PM_(2. 5)concentration. The standard regression coefficient of CO in the model was the highest(0. 439). CONCLUSION: PM_(2. 5)pollution was relatively light in Shenzhen City, and PM_(2. 5)was the main component of inhalable particulate matter. Atmospheric pressure and other pollutants in the atmosphere were the main influencing factors of PM_(2. 5)concentration, and CO was the major contributor to PM_(2. 5)concentration rising in Shenzhen City.

[Temporal and spatial distribution of air pollution in Shenzhen City during 2014-2016].

OBJECTIVE: To investigate spatial-temporal distribution characteristics of air quality indexes( AQI) in Shenzhen City and provide scientific basis for control of air pollution. METHODS: The monitoring data of AQI collected at the 19 monitoring posts in Shenzhen City from 2014 to 2016 were pooled and analyzed, and daily, seasonal and annual means of AQI at each monitoring post were forked out. The theory of spatial autocorrelation analysis was adopted in analyzing strength of the spatial autocorrelation and spatial clustering patterns of AQI. RESULTS: The median of AQI of Shenzhen City was50. 0, and the air quality level of Shenzhen City was level 2. PM_(2. 5) was the primary air pollutant in 2014, and O_3 was the primary air pollutant in 2015 and 2016 respectively. From the time distribution, the air quality in Shenzhen City was the worst in the winter but the best in the summer, and the air quality in night was worse than the day. Spatial autocorrelation analysis showed that there was positive spatial autocorrelation of AQI in Shenzhen. The global moran's I of AQI was 0. 167( Z = 2. 000, P < 0. 05), and the greary coefficient was 0. 801( Z = 1. 711, P < 0. 05). The local moran's I of AQI was 1. 027( P < 0. 05) in Guangming New District. The moran scatter plot of AQI in Guangming New District was in the first quadrant, and appeared to be in a high-high clustering pattern. The moran scatter plot of AQI in Yantian District was in the third quadrant, and appeared to be in a low-low clustering pattern. CONCLUSION: The air quality of Shenzhen City was better. PM_(2. 5) and O_3 were the primary air pollutant. It is necessary to emphasize and strengthen the prevention and control of air pollution in Winter, especially in several administrative districts of the northwest of Shenzhen City.

[Analysis of pulmonary function of primary students living in air pollution areas].

OBJECTIVE: To explore the changes of lung function of primary students living in the regions of different air pollution. METHODS: Nanshan District and Longgang District were decided respectively as a relatively heavy air pollution region and as a relatively light air pollution region in Shenzhen according to their air pollutant concentrations. The daily monitoring values on atmospheric SO2, NO2, PM10 and CO from 2006 to 2008 in the two districts were collected. PM2.5 monthly monitoring was started in Jan. 2008. Pulmonary functions of primary students were performed in the two schools within the scope of the surrounding 3km of the PM2.5 monitoring points. RESULTS: Average concentrations of atmospheric SO2, NO2, CO, PM10 and PM2.5 in Nanshan District were more high than those in Longgang District. Their concentrations were 0.0285 mg/m3 vs 0.0227 mg/m3, 0.0649 mg/m3 vs 0.0473 mg/m3, 2.278 mg/m3 vs 1.478 mg/m3, 0.0724 mg/m3 vs 0.0713 mg/m3 and 0.0665 mg/m3 vs 0.0524 mg/m3 (P < 0.05) respectively except PM10. Levels of the key indicators of lung function (VC, VC%, FVC, FVC%, MVV, MVV%, FEV1.0, FEV1.0/FVC, PEF, PEF%, FEF25, FEF50 and FEF75) increased with ages in the primary students. Means of the most indicators in Nanshan District were higher than those in primary students in Longgang District. Significant difference existed in the same grade of students on lung function in the two regions (P < 0.05). CONCLUSION: Air pollution could affect pulmonary function of primary students.

[Investigation on the carrier for Haemophilus influenzae in healthy population in Shenzhen].

OBJECTIVE: To Investigate the rate of carrying virus of Haemophilus influenzae in population of Shenzhen. METHODS: Cross-sectional study was applied and 380 samples were recruited by cluster sampling in April to July 2008. The identification of H. influenzae strains were done according to the loboratory standard methodology described by Manual of Clinical Microbiology (American). All strains were biotyped according to Kilian's classification with the API NH system. And serotyped by a slide agglutination assay with type to a specific antiserum as described by Pittman. PCR method for identification of H. infiuenzae were performed as described by Falla. RESULTS: The rate of carrying Haemophilus influenzae in population was 31.84% (121/380). The rate of 6-year groups was the highest (54.90%). The rate of 50-year groups was the lowest (8.51%). The rate of carrying Haemophilus influenzae had significant difference by age groups (chi2 = 30.172 P < 0.005). Eight biotypes were found among the 139 H. influenzae isolates. 7.91% (11/139) of all isolates belonged to biotype I, 37.41% (52/139) were biotype II, 34.53% (48/139) were biotype III, 4.32% (6/139) were biotype IV, 7.91% (11/139) were biotype V, 0.72% (1/139) were biotype VI, 6.47% (9/139) were biotype VII and 0.72% (1/139) were biotype VIII respectively. 97.14% (170/175) were nontypeable. There were five isolates were typeable. Two were serotype b. Three were serotype e. The rate of producing beta-lactamase was 14.38% (20/139). CONCLUSION: The rate of carrying Haemophilus influenzae in healthy population of Shenzhen was high. The main isolates were type II and III. And the isolates were nontypeable H. influenzae primarily.