本文選題：空氣污染 + 呼吸系統疾病��； 參考：《浙江農林大學》2017年碩士論文

【摘要】：本文利用杭州市2013年10月28日至2016年8月31日主要空氣污染物濃度的日均變化數據與呼吸系統疾病每日門診人數,旨在建立杭州市主要空氣污染物對當地居民健康影響的關系模型,定量評價該區(qū)域主要空氣污染物對居民呼吸系統疾病的影響并進行短期門診人數預測。研究中收集了杭州市2013年10月28日至2016年8月31日主要空氣污染物PM_(2.5)、PM_(10)、NO_2、SO_2的日均濃度變化數據和每日最高、最低和日均氣溫,以及杭州市居民呼吸系統疾病的日門診人數資料。本文采用時間序列的半參數廣義相加泊松回歸模型(GAM),模型中引入的發(fā)病人數的長期趨勢、氣溫、周期效應等混雜因素,通過平滑樣條函數進行排除,分析杭州市主要空氣污染物與日呼吸系統疾病門診人數的關系及滯后效應;同時分別建立了三種預測模型對杭州市日呼吸系統疾病看病人數進行預測,選擇最佳預測模型。研究分析結果如下:(1)2013年10月28日至2016年8月31日杭州市主要空氣污染物PM_(2.5)、PM_(10)、NO_2、SO_2的日均濃度分別為62.25μg/m3、94.94μg/m3、46.59μg/m3、18.97μg/m3。其中,PM_(2.5)、PM_(10)、NO_2年均濃度均低于國家空氣質量二級標準,SO_2年均濃度符合國家一級空氣質量標準。各主要空氣污染物之間存在較強的正相關關系,平均溫度和各主要空氣污染物之間存在較強的負相關關系。(2)PM_(2.5)、PM_(10)、NO_2和SO_2每增加一個IQR(污染物濃度四分位間距)時,即0.039μg/m3、0.063μg/m3、0.023μg/m3、0.014μg/m3時,對呼吸系統疾病發(fā)病人數的相對危險度(RR)分別為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)杭州市主要空氣污染物對日呼吸系統疾病的門診人數總體上存在滯后性,不同的空氣污染物對居民呼吸系統疾病的健康效應影響不同。主要空氣污染物PM_(2.5)、PM_(10)、NO_2、SO_2對呼吸系統疾病的最佳滯后天數分別為3天、2天、4天、3天。(4)PM_(2.5)、PM_(10)、NO_2和SO_2每增加一個IQR(污染物濃度四分位間距)時,即0.039μg/m3、0.063μg/m3、0.023μg/m3、0.014μg/m3時,相應的呼吸系統疾病門診人數增加的百分比分別為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)本文建立三種預測模型對杭州市呼吸系統疾病的門診人數進行了預測,這三種預測模型分別為非線性擬合預測模型,廣義相加預測模型和BP神經網絡預測模型。這三種模型分別對2016年8月1日至2016年8月31日杭州市呼吸系統疾病的門診人數進行預測,預測值的平均相對誤差分別為38.81%、14.91%和13.821%,均方誤差分別為11.89,5.066和4.721。因此,建立BP神經網絡預測模型對呼吸系統疾病門診人數預測效果較好。因此,杭州市空氣質量還有待提高,其中各主要空氣污染物PM_(2.5)、PM_(10)、NO_2、SO_2的濃度對杭州市居民的健康效應存在不同的相關性,隨著主要空氣污染物濃度的增加,相應的杭州市居民敏感性呼吸系統疾病的門診人數也會有增長的趨勢。對此提出相關建議,杭州市政府相關部門可以制定空氣污染環(huán)保規(guī)章制度,加強空氣污染質量監(jiān)測與管理,加強宣傳保護環(huán)境力度,投入更多資金建設環(huán)保事業(yè)。
[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.
【學位授予單位】：浙江農林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R12;X51

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