本文選題：空氣污染 + 呼吸系統(tǒng)疾病��； 參考：《浙江農(nóng)林大學(xué)》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(shù)據(jù)與呼吸系統(tǒng)疾病每日門診人數(shù),旨在建立杭州市主要空氣污染物對當(dāng)?shù)鼐用窠】涤绊懙年P(guān)系模型,定量評價(jià)該區(qū)域主要空氣污染物對居民呼吸系統(tǒng)疾病的影響并進(jìn)行短期門診人數(shù)預(yù)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(shù)據(jù)和每日最高、最低和日均氣溫,以及杭州市居民呼吸系統(tǒng)疾病的日門診人數(shù)資料。本文采用時(shí)間序列的半?yún)?shù)廣義相加泊松回歸模型(GAM),模型中引入的發(fā)病人數(shù)的長期趨勢、氣溫、周期效應(yīng)等混雜因素,通過平滑樣條函數(shù)進(jìn)行排除,分析杭州市主要空氣污染物與日呼吸系統(tǒng)疾病門診人數(shù)的關(guān)系及滯后效應(yīng);同時(shí)分別建立了三種預(yù)測模型對杭州市日呼吸系統(tǒng)疾病看病人數(shù)進(jìn)行預(yù)測,選擇最佳預(yù)測模型。研究分析結(jié)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(zhì)量二級標(biāo)準(zhǔn),SO_2年均濃度符合國家一級空氣質(zhì)量標(biāo)準(zhǔn)。各主要空氣污染物之間存在較強(qiáng)的正相關(guān)關(guān)系,平均溫度和各主要空氣污染物之間存在較強(qiáng)的負(fù)相關(guān)關(guān)系。(2)PM_(2.5)、PM_(10)、NO_2和SO_2每增加一個(gè)IQR(污染物濃度四分位間距)時(shí),即0.039μg/m3、0.063μg/m3、0.023μg/m3、0.014μg/m3時(shí),對呼吸系統(tǒng)疾病發(fā)病人數(shù)的相對危險(xiǎn)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(tǒng)疾病的門診人數(shù)總體上存在滯后性,不同的空氣污染物對居民呼吸系統(tǒng)疾病的健康效應(yīng)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(tǒng)疾病的最佳滯后天數(shù)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(gè)IQR(污染物濃度四分位間距)時(shí),即0.039μg/m3、0.063μg/m3、0.023μg/m3、0.014μg/m3時(shí),相應(yīng)的呼吸系統(tǒng)疾病門診人數(shù)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(yù)測模型對杭州市呼吸系統(tǒng)疾病的門診人數(shù)進(jìn)行了預(yù)測,這三種預(yù)測模型分別為非線性擬合預(yù)測模型,廣義相加預(yù)測模型和BP神經(jīng)網(wǎng)絡(luò)預(yù)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(tǒng)疾病的門診人數(shù)進(jìn)行預(yù)測,預(yù)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(jīng)網(wǎng)絡(luò)預(yù)測模型對呼吸系統(tǒng)疾病門診人數(shù)預(yù)測效果較好。因此,杭州市空氣質(zhì)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(yīng)存在不同的相關(guān)性,隨著主要空氣污染物濃度的增加,相應(yīng)的杭州市居民敏感性呼吸系統(tǒng)疾病的門診人數(shù)也會有增長的趨勢。對此提出相關(guān)建議,杭州市政府相關(guān)部門可以制定空氣污染環(huán)保規(guī)章制度,加強(qiáng)空氣污染質(zhì)量監(jiān)測與管理,加強(qiáng)宣傳保護(hù)環(huán)境力度,投入更多資金建設(shè)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(xué)位授予單位】：浙江農(nóng)林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R12;X51

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 師楠;周蘇荃;李一丹;;基于多模型組合優(yōu)化算法的風(fēng)速預(yù)測方法研究[J];電力科學(xué)與技術(shù)學(xué)報(bào);2015年03期

2 張曉凱;周玉靜;王超;梁靜霞;霍宏斐;;濟(jì)南地區(qū)春季大氣中顆粒物粒徑分布狀況及形態(tài)的掃描電鏡實(shí)驗(yàn)研究[J];山東師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年03期

3 ZHANG Ying;WANG Shi Gong;MA Yu Xia;SHANG Ke Zheng;CHENG Yi Fan;LI Xu;NING Gui Cai;ZHAO Wen Jing;LI Nai Rong;;Association between Ambient Air Pollution and Hospital Emergency Admissions for Respiratory and Cardiovascular Diseases in Beijing: a Time Series Study[J];Biomedical and Environmental Sciences;2015年05期

4 翟文慧;黃志剛;路晶凱;王偉;李勇;;北京市大氣細(xì)顆粒污染物與老年呼吸系統(tǒng)疾病急診就醫(yī)關(guān)系的病例交叉研究[J];現(xiàn)代生物醫(yī)學(xué)進(jìn)展;2015年08期

5 樊冬梅;黃素勤;封軍莉;劉紅建;徐興;丁華萍;;2012年泰興市居民全死因分析[J];江蘇預(yù)防醫(yī)學(xué);2015年01期

6 張瑩;邵毅;王式功;尚可政;王金艷;李旭;康延臻;;北京市空氣污染物對呼吸系統(tǒng)疾病門診人數(shù)的影響[J];中國環(huán)境科學(xué);2014年09期

7 姚東;呂慧洋;李輝章;杜靈彬;;浙江省腫瘤登記地區(qū)鼻咽癌發(fā)病與死亡現(xiàn)狀及趨勢分析[J];中國耳鼻咽喉頭頸外科;2014年08期

8 王德征;江國虹;顧清;張輝;徐忠良;宋桂德;張穎;沈成鳳;;采用時(shí)間序列泊松回歸分析天津市大氣污染物對心腦血管疾病死亡的急性影響[J];中國循環(huán)雜志;2014年06期

9 祁棟林;李曉東;肖宏斌;肖蓮桂;魏鴻業(yè);馬明亮;孔維強(qiáng);金惠瑛;;青海省城市大氣污染物變化特征及與氣象因子的灰色關(guān)聯(lián)分析[J];安徽農(nóng)業(yè)科學(xué);2014年16期

10 曉開提·依不拉音;鮑玉星;吳文華;劉秀麗;;烏魯木齊大氣污染與循環(huán)系統(tǒng)疾病日住院人數(shù)的時(shí)間序列分析[J];衛(wèi)生研究;2013年04期

相關(guān)博士學(xué)位論文 前1條

1 竇晨彬;空氣污染健康效應(yīng)的經(jīng)濟(jì)學(xué)分析[D];西南財(cái)經(jīng)大學(xué);2012年

相關(guān)碩士學(xué)位論文 前5條

1 周曉瑋;BP神經(jīng)網(wǎng)絡(luò)技術(shù)在壽險(xiǎn)營銷系統(tǒng)中的應(yīng)用研究[D];上海交通大學(xué);2014年

2 張姣艷;氣溫及大氣污染物對上海市兒童健康的影響[D];復(fù)旦大學(xué);2013年

3 劉欣宇;人工神經(jīng)網(wǎng)絡(luò)在泥石流預(yù)測中的應(yīng)用研究[D];西安科技大學(xué);2011年

4 石磊;遺傳算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)在股市預(yù)測中的應(yīng)用[D];安徽大學(xué);2011年

5 嚴(yán)俊;蘭州城區(qū)大氣污染與居民呼吸、循環(huán)系統(tǒng)疾病日住院人數(shù)關(guān)系的時(shí)間序列研究[D];蘭州大學(xué);2010年

，

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