杭州市主要空气污染物浓度与呼吸系统疾病的关系研究

发布时间：2018-06-01 10:32

本文选题：空气污染 + 呼吸系统疾病　；参考：《浙江农林大学》2017年硕士论文

【摘要】：本文利用杭州市2013年10月28日至2016年8月31日主要空气污染物浓度的日均变化数据与呼吸系统疾病每日门诊人数,旨在建立杭州市主要空气污染物对当地居民健康影响的关系模型,定量评价该区域主要空气污染物对居民呼吸系统疾病的影响并进行短期门诊人数预测。研究中收集了杭州市2013年10月28日至2016年8月31日主要空气污染物PM_(2.5)、PM_(10)、NO_2、SO_2的日均浓度变化数据和每日最高、最低和日均气温,以及杭州市居民呼吸系统疾病的日门诊人数资料。本文采用时间序列的半参数广义相加泊松回归模型(GAM),模型中引入的发病人数的长期趋势、气温、周期效应等混杂因素,通过平滑样条函数进行排除,分析杭州市主要空气污染物与日呼吸系统疾病门诊人数的关系及滞后效应;同时分别建立了三种预测模型对杭州市日呼吸系统疾病看病人数进行预测,选择最佳预测模型。研究分析结果如下:(1)2013年10月28日至2016年8月31日杭州市主要空气污染物PM_(2.5)、PM_(10)、NO_2、SO_2的日均浓度分别为62.25μg/m3、94.94μg/m3、46.59μg/m3、18.97μg/m3。其中,PM_(2.5)、PM_(10)、NO_2年均浓度均低于国家空气质量二级标准,SO_2年均浓度符合国家一级空气质量标准。各主要空气污染物之间存在较强的正相关关系,平均温度和各主要空气污染物之间存在较强的负相关关系。(2)PM_(2.5)、PM_(10)、NO_2和SO_2每增加一个IQR(污染物浓度四分位间距)时,即0.039μg/m3、0.063μg/m3、0.023μg/m3、0.014μg/m3时,对呼吸系统疾病发病人数的相对危险度(RR)分别为1.030(95%CI:1.016-1.045)、1.063(95%CI:1.043-1.084)、1.053(95%CI:1.016-1.091)和1.025(95%CI:1.003-1.048)。(3)杭州市主要空气污染物对日呼吸系统疾病的门诊人数总体上存在滞后性,不同的空气污染物对居民呼吸系统疾病的健康效应影响不同。主要空气污染物PM_(2.5)、PM_(10)、NO_2、SO_2对呼吸系统疾病的最佳滞后天数分别为3天、2天、4天、3天。(4)PM_(2.5)、PM_(10)、NO_2和SO_2每增加一个IQR(污染物浓度四分位间距)时,即0.039μg/m3、0.063μg/m3、0.023μg/m3、0.014μg/m3时,相应的呼吸系统疾病门诊人数增加的百分比分别为3%(95%CI:1.016-1.045)、6.3%(95%CI:1.043-1.084)、5.3%(95%CI:1.016-1.091)和2.5%(95%CI:1.003-1.048)。(5)本文建立三种预测模型对杭州市呼吸系统疾病的门诊人数进行了预测,这三种预测模型分别为非线性拟合预测模型,广义相加预测模型和BP神经网络预测模型。这三种模型分别对2016年8月1日至2016年8月31日杭州市呼吸系统疾病的门诊人数进行预测,预测值的平均相对误差分别为38.81%、14.91%和13.821%,均方误差分别为11.89,5.066和4.721。因此,建立BP神经网络预测模型对呼吸系统疾病门诊人数预测效果较好。因此,杭州市空气质量还有待提高,其中各主要空气污染物PM_(2.5)、PM_(10)、NO_2、SO_2的浓度对杭州市居民的健康效应存在不同的相关性,随着主要空气污染物浓度的增加,相应的杭州市居民敏感性呼吸系统疾病的门诊人数也会有增长的趋势。对此提出相关建议,杭州市政府相关部门可以制定空气污染环保规章制度,加强空气污染质量监测与管理,加强宣传保护环境力度,投入更多资金建设环保事业。
[Abstract]:In this paper, the daily change data of the main air pollutant concentration in Hangzhou city from October 28, 2013 to August 31, 2016 and the daily outpatient number of respiratory diseases were used to establish the relationship model between the main air pollutants in Hangzhou and the health effects of the local residents, and the quantitative assessment of the respiratory system diseases of the main air pollutants in the region The influence and short-term outpatient number forecast. The study collected the daily average concentration change data of the main air pollutants PM_ (2.5), PM_ (10), NO_2, SO_2, the daily maximum, the minimum daily temperature, and the daily outpatient data of the respiratory system diseases in Hangzhou city from October 28, 2013 to August 31, 2016. The semi parametric generalized additive Poisson regression model (GAM), the long-term trend of the incidence of the disease, the temperature, the periodic effect and other confounding factors which were introduced in the model, were eliminated by the smooth spline function, and the relationship between the number of main air pollutants in Hangzhou and the outpatients in the daily respiratory system and the lag effect were analyzed. At the same time, three kinds of factors were established respectively. The prediction model was used to predict the number of daily respiratory diseases in Hangzhou. The results were as follows: (1) the main air pollutants in Hangzhou from October 28, 2013 to August 31, 2016 were PM_ (2.5), PM_ (10), NO_2, and SO_2 were divided into 62.25 mu g/m3,94.94 mu g/m3,46.59 mu g/m3,18.97 Mu g/m3., PM_ (2) .5), PM_ (10), NO_2 average annual concentration is lower than national air quality two standard, SO_2 annual concentration conforms to national first class air quality standard. There is a strong positive correlation between the main air pollutants, and there is a strong negative correlation between the average temperature and the main air pollutants. (2) PM_ (2.5), PM_ (10), NO_2 and SO_2 every increase The relative risk (RR) for the number of respiratory diseases (RR) was 1.030 (95%CI:1.016-1.045), 1.063 (95%CI:1.043-1.084), 1.053 (95%CI: 1.016-1.091) and 1.025 (95%CI:1.003-1.048). (3) the main air pollutants in Hangzhou City, Hangzhou City, when 0.039 mu g/m3,0.023 mu g/m3,0.014 mu g/m3. The number of outpatients in the daily respiratory system was generally lagged, and the effects of different air pollutants on the health of respiratory diseases were different. The optimal lag days of the main air pollutants PM_ (2.5), PM_ (10), NO_2, and SO_2 for respiratory diseases were 3 days, 2 days, 4 days, 3 days. (4) PM_ (2.5), PM_ (10), NO_2 and SO_2 per increase. When adding a IQR (four point spacing of pollutant concentration), that is, 0.039 mu g/m3,0.023 mu g/m3,0.014 mu g/m3, the percentage of the corresponding respiratory disease outpatients increased by 3% (95%CI:1.016-1.045), 6.3% (95%CI:1.043-1.084), 5.3% (95%CI: 1.016-1.091) and 2.5% (95%CI:1.003-1.048). (5) three prediction models were established in this paper. The out-patient number of respiratory diseases in Hangzhou was predicted. The three models were nonlinear fitting prediction model, generalized additive prediction model and BP neural network prediction model. These three models predicted the number of outpatients of respiratory system disease in Hangzhou from August 1, 2016 to August 31, 2016, respectively. The average relative error is 38.81%, 14.91% and 13.821% respectively. The mean square error is 11.89,5.066 and 4.721., respectively. The prediction model of BP neural network is better to predict the number of outpatients in the respiratory system. Therefore, the air quality in Hangzhou still needs to be improved, including the main air pollutants PM_ (2.5), PM_ (10), NO_2, SO_2 concentration to Hangzhou. The health effect of the residents in the city has different correlation. With the increase of the concentration of the main air pollutants, the corresponding out-patient number of Hangzhou residents' sensitive respiratory diseases will also have a growing trend. The relevant suggestions are put forward. The relevant departments of the Hangzhou municipal government can set up the air pollution environmental regulations and regulations and strengthen the air pollution. Quality monitoring and management, strengthen publicity and protection of the environment, invest more funds to build environmental protection business.
【学位授予单位】：浙江农林大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R12;X51

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