Objective To explore effects of atmospheric nitrogen dioxide （NO₂） pollution on mortality in cities of Guangzhou, Foshan and Zhuhai in Guangdong Province. Methods Air pollutant data, meteorological data and the daily case of death in the three cities were collected from 2013 to 2016. Basic characteristics of the data were statistically described and the correlation was analyzed by Spearman. Then, generalized additive model （GAM） was used to explore the relationship between the daily average concentration of NO₂ and daily mortality in the three cities. Results The average daily concentrations of NO₂ in Guangzhou, Foshan and Zhuhai from 2013 to 2016 were 46.4, 48.4 and 33.1 μg/m³, respectively, meeting the National Standards at Level 2（80 μg/m³）. The daily average concentration of NO₂ in the atmosphere of Guangzhou had a statistically significant effect on the daily total number of deaths and the number of deaths from circulatory diseases at the same day, lag1 and lag2 days （P<0.05 for all）. There was a statistically significant effect of the daily average concentration of NO₂ in Foshan City at lag1 and lag2 days on the daily total number of deaths of residents and the daily number of deaths from circulatory diseases （both P<0.05）. Both Guangzhou and Foshan showed the greatest effect on lag1 day. The daily average concentration of NO₂ had an impact on the daily death toll of respiratory diseases of residents in Guangzhou （ER=1.38） on lag2 day. Conclusions The incrase in the concentratienof The increase in the cocentration of NO₂ air pollutantion can affect the risk of daily mortality of residents.

Objective To understand the pollution level of water-soluble heavy metals in atmospheric PM_2.5 in different urban areas of Guangzhou in 2017 and assess health risks to the population. Methods According to the regional characteristics of air pollution in Guangzhou, sampling sites were set up in districts of Yuexiu, Panyu, and Conghua. PM_2.5 samples were collected monthly from January to December 2017. The mass of the main metals （Al, As, Cd, Pb, Mn） in PM_2.5 was determined by inductively coupled plasma mass spectrometry. The health risk assessment model recommended by USEPA was used to evaluate the health risks, and the non-carcinogenic risk （hazard quotient, HQ） was used to evaluate the non-cancer risk of a single pollutant. The cancer risk of a single pollutant was evaluated, setting 10^-6 as an acceptable risk level. Results The median PM_2.5 concentrations in the three districts in Guangzhou were 0.048 mg/m³ in Yuexiu, 0.048 mg/m³ in Panyu, and 0.040 mg/m³ in Conghua（P>0.05）. The concentration of Al, which was the highest among the five heavy metal elements, was 105.0 ng/m³ in Yuexiu, 63.9 ng/m³ in Panyu, and 78.6 ng/m³ in Conghua （ P<0.01）.The concentration of Mn was 32.2 ng/m³ in Yuexiu, 24.2 ng/m³ in Panyu, and 15.0 ng/m³ in Conghua, respectively （P<0.01 for all）. The concentration of As was 6.57 ng/m³ in Yuexiu, 5.85 ng/m³ in Panyu, and 5.04 ng/m³ in Conghua. The concentration of Cd was 1.15 ng/m³ in Yuexiu, 0.95 ng/m³ in Panyu, and 0.91 ng/m³ in Conghua. The concentration of Pb was 33.0 ng/m³ in Yuexiu, 28.0 ng/m³ in Panyu, and 29.8 ng/m³ in Conghua. The differences of concentrations of As, Cd, and Pb among the three regions were not significant （P>0.05 for all）. The non-carcinogenic risk of Mn was the highest in Yuexiu and Panyu （HQ>1.0）, but it posed only health risks to children. There was no non-carcinogenic risk for heavy metals in PM_2.5 in Conghua. The carcinogenic risk values ??of As and Cd in three districts ranged from 3.7×10^-6 to 4.9×10^-5 which was higher than the acceptable risk level 10^-6 and had potential risk. Conclusion PM_2.5 pollution in Guangzhou urban area was relatively light, but heavy metal pollution can not be ignored. The combined effects of heavy metals on children's non-carcinogenic health risks should be highly valued.

Objective To investigate the impact of fine particle matter （PM_2.5） and ozone on outpatient visits for circulatory system diseases and explore their interactions. Methods Time series research method was used in this study. Data on daily outpatient visits for circulatory system diseases of three Level-3 Class-A hospitals in cities of Guangzhou, Foshan, and Zhuhai were obtained from Guangdong Provincial Center for Disease Control and Prevention between 2015 and 2017. Daily ambient PM_2.5 and ozone concentration data from Guangdong Environmental Monitoring Center and daily meteorological data from Guangdong Provincial Meteorological Bureau were collected in the same period. Generalized additive model （GAM） was used to estimate the excess risk （ER） for each 10 μg/m³ increment in PM_2.5 and ozone_, concentrations and further to explore the potential interactions between PM_2.5 and ozone. Additionally, a meta-analysis was conducted to quantitatively combine the ERs of the three cities. Results During 2015- 2017, a 10 μg/m³ increment in PM_2.5 concentration was associated with an increment in the daily outpatient visits for circulatory system diseases by 2.45%, 0.64%, and 0.95% of ER in Guangzhou, Foshan, and Zhuhai, respectively. And a 10 μg/m³ increment in PM_2.5 and ozone concentrations was associated with an increment in the combined ER of three cities by 1.34% （95%CI：0.25%-2.43%） and -0.17%（95%CI：-0.47%-0.14%）, respectively. Additionally, ozone modified the association between PM_2.5 and the daily outpatient visits for circulatory system diseases. Specifically, when ozone concentration was lowest, the ER for PM_2.5 on daily outpatient visits for circulatory system diseases was highest （ER=4.19%,95%CI：1.82%-6.56%）. However, the modification effect of PM_2.5 on the association between ozone and outpatient visits for circulatory system diseases was not statistically significant. Conclusion Atmospheric PM_2.5 could increase the risk of outpatient visits for circulatory system diseases in the Pearl River Delta urban area, and ozone had a modification effect on it.

Objective To understand effect of air pollutant concentration on first aid visits of residents in Guangzhou, so as to improve the ability and control of early prevention of air pollution related diseases. Methods A semi-parametric generalized additive model （GAM） was used to examine the relationship between the concentrations of SO₂, NO₂, CO, PM_2.5, PM₁₀, O_3-1h and O_3-8h in the atmosphere and emergency department visits due to the first aid from January 1 to December 31, 2017 in Guangzhou after controlling long time trend, the “day of the week” effect and confounding meteorological factors. Results In 2017, the main pollutants in the air was CO in Guangzhou. The concentrations of PM_2.5, PM₁₀, NO₂ and CO in the air were highest in December and lowest in June, concentrations of O₃ （O_3-1h and O_3-8h） were highest in August and lowest in January, and concentration of SO₂ was highest in December and lowest in January. In 2017, the daily average number of first aid cases was 405.08, and the daily number of first aid cases was 26.57 for respiratory diseases and 38.44 for cardiovascular diseases. GAM analysis showed that each 10-unit increment in SO₂, NO₂, and CO concentrations was associated with an increment in the total number of first aid cases at the same day and within lag3 days. The increase in SO₂ concentration at the same day and three-day lag time had the greatest impact on the number of emergency department visits due to first aid for respiratory diseases, for every 10-unit increment in SO₂ concentration, the number of first aid cases for respiratory diseases increased by 8.01% and 9.62%, respectively. Conclusion Air pollution was associated with increased risk of emergency visits due to the first aid, especially for the respiratory diseases in Guangzhou. The health protection for the sensitive population should be emphasized in moderate and heavy pollution weather.

Objective To examine the effect of ambient PM_2.5 on daily outpatient visits of children with respiratory diseases in Panyu District, Guangzhou. Methods Data on daily outpatient visits of pediatric respiratory diseases from 2015 to 2017 were collected from a hospital in Panyu District though the hospital information system. Meanwhile, environmental quality data were collected from the Guangzhou Municipal Ecological Environment Bureau and meteorological data were collected from the China Meteorological Data Service Center. The Spearman correlation analysis and the time-series analysis with generalized additive model （GAM） were applied to analyze the association between the ambient PM_2.5 concentration and outpatient visits of children with respiratory diseases during the same period. Results The Spearman correlation analysis revealed a significant association between PM_2.5 concentration and the outpatient visits of children with respiratory diseases （r=0.16, P<0.05）.The GAM model indicated that the effect of PM_2.5 on daily outpatient visits of children with respiratory diseases was observed in lag 3 to 5 days（P<0.05）, and the effect was greatest in lag4. An increment of 10 μg/m³ in concentration of PM_2.5 was associated with an increment in the hospital outpatient visits of children with respiratory diseases by 0.72%（ 95% CI： 0.22%-1.23%） . Conclusion Ambient PM_2.5 pollution affected the daily outpatient visits of children with respiratory diseases significantly, with a lag effect in Panyu District from 2015 to 2017. The increase of PM_2.5 concentration would lead to the increase of outpatient visits of children with respiratory diseases.

Objective To compare effects of GM（1,1） gray model, Markov chain model, the gray and Markov chain combined model, and SARIMA model on predicting monthly reported cases of hepatitis A. Methods Using data of monthly reported cases of hepatitis A in Jiangxi Province from 2010 to 2014, GM （1,1） gray model, Markov chain model, combined model of gray and Markov chain, and SARIMA model were fitted respectively. Four models were used to predict the monthly reported cases of hepatitis A from January to December 2015 and compare with actual number of cases. The mean absolute percent error （MAPE）, mean error rate （MER）, mean squared error （MSE） and mean absolute error （MAE） were used to evaluate the model prediction effect. Results A total of 2 939 cases of hepatitis A were reported in Jiangxi Province during this period, and showed a downtrend year by year（r_s= -0.838,P<0.01）.SARIMA（0,1,1）（1,0,0）₁₂ was the optimal SARIMA model; the fitting accuracy of GM（1,1） gray model was qualified. The model predicted MAPE from low to high were the gray Markov chain combined model （23.894%）, SARIMA model （25.529%）, GM （1,1） gray model （28.429%）, and Markov chain model （39.426%） ）.MER from low to high were SARIMA model （21.303%）, gray Markov chain combined model （25.574%）, gray model （30.717%）, and Markov chain model （35.203%）.MSE and MAE from low to high were the SARIMA model （45.293, 4.918）, gray Markov chain combined model （47.122, 5.903）, gray model （67.738, 7.091）, and Markov chain model （85.252, 8.126）. Conclusions The grey Markov chain combined model and SARIMA model have better prediction results, and can be used to predict the number of hepatitis A cases.

Objective To investigate and analyze the degree and source of working stress in a power supply enterprise. Methods Using stratified random sampling method, questionnaire survey was conducted among employees in four departments of Shenzhen Power Supply Bureau： substation, transmission, district power supply bureau, and functional department. The contents of the investigation included demographic characteristics, degree and source of occupational stress and so on. Chi-square test and ordered multiple classification logistic regression were used to explore occupational stress and related factors in the power supply enterprise. Results A total of 1 508 people were investigated, of whom 1 383 questionnaires were included in the statistical analysis, accounting for 27.54% of the total number of employees in Shenzhen Power Supply Bureau （1 383/5 022）.Their average age was 36 years old and the male to female sex ratio was 3.17∶1. Of the respondents, 62.11% had undergraduate degree, 70.35% were married, and 45.12% worked for less than 10 years. The average length of working age was 14 years. Of 1309 employees, 94.6% had different degrees of occupational stress. Sources of occupational stress were overload （65.5%）, low pay （50.1%）, fast pace of work （39.0%）, customer complaints （25.9%）, poor working environment （19.0%）, and tense interpersonal relationship among colleagues （7.5%）. The ordered multi-classification logistic regression analysis showed that overload （OR=4.05）, fast pace of work （OR=4.24）, customer complaints （OR=2.41）, poor working environment （OR=1.50）, low pay （OR=1.67）, and 10 to 29 years of working age （OR=2.01,2.09） were risk factors for the severity of occupational stress （P < 0.05 or P< 0.01）.Conclusion Occupational stress existed in most employees in Shenzhen power supply enterprises. It is suggested to formulate accurate stress relief strategies according to different sources of occupational stress.

Objective To study the characteristics of ambient PM_2.5 pollution in different seasons and its impact on the daily deaths of residents in a city of North China. Methods Ambient PM_2.5 samples were collected in the central urban area of the city from November 2013 to May 2016. Concentrations of twelve metals （Pb, Mn, Al, Cd, Cr, Sb, As, Be, Hg, Ni, Se, Ti）, four inorganic water-soluble ions （NO₃^-, SO₄^2-, NH₄⁺, Cl^-）, and sixteen polycyclic aromatic hydrocarbons （PAHs） were analyzed. Data of average daily concentration of PM_2.5 in the atmosphere and daily non-accidental deaths of residents in the city were collected from 2013 to 2015. Generalized additive model （GAM） was used to analyze the relationship between average daily concentration of PM_2.5 and the deaths of residents. Results Obvious seasonal changes were observed in the PM_2.5 concentration, the highest of which was [（223.87 ± 176.13） µg/m³] in winter, followed by （137.81 ± 83.26）µg/m³ in spring and （135.41 ± 89.42）µg/m³ in autumn, and the lowest was （112.88 ± 50.46） µg/m³ in summer（P<0.01）. The concentration of inorganic water-soluble ions in PM_2.5 accounted for the largest proportion （41%-49%）, of which the average SO₄^2- concentration （34.60 µg/m³） was the highest. The metals accounted for 0.19%-0.51%, of which the Al concentration （546.03 ng/m³） was the highest. The PAHs accounted for 0.04%-0.23%, of which the benzo（g,h,i） perylene concentration （190.86 ng/m³） was the highest. Time series analysis showed that for every 10 μg/m³ of increased PM_2.5 concentration, the risks of total non-accidental deaths （lag0-5）, deaths from cardiovascular diseases （lag0-5） and deaths from respiratory diseases （lag1） rose by 0.73% （95%CI： 0.42%-1.04%）, 1.04% （95%CI： 0.64%-1.46%） and 0.63% （95%CI： 0.07%-1.19%）, respectively. Conclusion The proportion of inorganic water-soluble ions such as SO₄^2- and NO₃^- in PM_2.5 was relatively high in this city. The increase of ambient PM_2.5 concentrations may significantly associate with increased risks of non-accidental deaths, particularly the deaths from cardiovascular diseases.