應(yīng)用空間分析技術(shù)對(duì)浙江省飲用水水質(zhì)狀況進(jìn)行分析
發(fā)布時(shí)間：2018-09-19 15:55
【摘要】：研究目的 飲用水衛(wèi)生安全問(wèn)題一直是政府和人民群眾關(guān)注的熱點(diǎn),水質(zhì)的優(yōu)劣直接影響人民的身體健康。據(jù)統(tǒng)計(jì),80%的流行病學(xué)資料具有空間屬性,同樣飲用水?dāng)?shù)據(jù)也有著明顯的地理空間分布特征。運(yùn)用空間分析繼續(xù)能夠利用傳統(tǒng)的統(tǒng)計(jì)學(xué)方法尚未利用的空間信息,為研究者提供一種全新、可靠、科學(xué)合理的處理空間信息的方法。本次研究結(jié)果,展現(xiàn)了浙江省飲用水水質(zhì)狀況,同時(shí)也為政府制定有關(guān)飲用水政策提供理論依據(jù)。 資料來(lái)源與方法 本文的資料來(lái)源于2010年浙江省飲用水水質(zhì)監(jiān)督監(jiān)測(cè)數(shù)據(jù),和國(guó)家基礎(chǔ)地理信息網(wǎng)數(shù)據(jù)庫(kù)中獲取的1：400萬(wàn)中國(guó)縣界電子地圖。本次監(jiān)測(cè)的飲用水水樣為浙江省范圍內(nèi)的取得有效衛(wèi)生許可證水廠的出廠水、管網(wǎng)末梢水。出廠水枯水期或豐水期監(jiān)測(cè)一次；管網(wǎng)末梢水為供水區(qū)域內(nèi)按供水人口每2萬(wàn)人設(shè)1個(gè)采樣點(diǎn),每個(gè)采樣點(diǎn)每季度監(jiān)測(cè)1次。按《生活飲用水衛(wèi)生標(biāo)準(zhǔn)》(GB5749-2006)和《生活飲用水標(biāo)準(zhǔn)檢驗(yàn)方法》(GB/T5750-2006)進(jìn)行檢驗(yàn)評(píng)價(jià)。各地飲用水水質(zhì)監(jiān)督監(jiān)測(cè)數(shù)據(jù)匯總、合格率計(jì)算和數(shù)據(jù)變換在EXCEL2003中完成,空間分析在ARCGIS10.0和GS+9.0中完成。 結(jié)果 1.飲用水水質(zhì)情況地區(qū)分布圖：根據(jù)2010年浙江省飲用水水質(zhì)監(jiān)督監(jiān)測(cè)數(shù)據(jù)和浙江省縣界電子地圖制作成出廠水合格率地區(qū)分布圖和管網(wǎng)末梢水合格率地區(qū)分布圖。 2.三維趨勢(shì)分析：出廠水和管網(wǎng)末梢水在東西和南北方向在均存在趨勢(shì),其中管網(wǎng)末梢水在南北方向上浙中高,浙南、浙北低的趨勢(shì)更顯著。 3.變異函數(shù)擬合：出廠水塊金值C0為0.0095、基臺(tái)值Co+C為0.2040、塊金基臺(tái)比為0.047、自相關(guān)a為0.297、擬合優(yōu)度r2為0.616,擬合模型較好；管網(wǎng)末梢水塊金值C0為0.0799、基臺(tái)值Co+C為0.1608、塊金基臺(tái)比為0.497、自相關(guān)a為2.38、擬合優(yōu)度r2為0.370,擬合模型一般。 4.kriging插值：出廠水的合格率較高的地區(qū)主要在浙西南,較低地區(qū)主要在浙東、南沿海地區(qū),插值效果評(píng)價(jià)指標(biāo)分別為,估計(jì)偏差均數(shù)(M-PE)為0.005024、估計(jì)偏差標(biāo)準(zhǔn)化均數(shù)(MS-PE)為0.01058,估計(jì)偏差標(biāo)化均方根(RMSS-PE)為0.9694,估計(jì)偏差均方根(RMS-PE)為0.4477,估計(jì)偏差平均標(biāo)準(zhǔn)誤(ASE-PE)為0.4624；管網(wǎng)末梢水的合格率較高的地區(qū)主要在浙西南和杭州灣附近,較低地區(qū)主要在浙南、東沿海地區(qū)和浙北地區(qū),插值效果評(píng)價(jià)指標(biāo)分別為,估計(jì)偏差均數(shù)(M-PE)為0.01816、估計(jì)偏差標(biāo)準(zhǔn)化均數(shù)(MS-PE)為0.04646,估計(jì)偏差標(biāo)化均方根(RMSS-PE)為1.0107,估計(jì)偏差均方根(RMS-PE)為0.3187,估計(jì)偏差平均標(biāo)準(zhǔn)誤(ASE-PE)為0.3152。這說(shuō)明kriging插值預(yù)測(cè)是無(wú)偏、最優(yōu)插值。 5.空間自相關(guān)分析：經(jīng)過(guò)出廠水和管網(wǎng)末梢水全域Moran'sⅠ和全域G系數(shù)分析,只有管網(wǎng)末梢水合格率的Moran'sⅠ系數(shù)為0.2865,P0.05,其余均無(wú)統(tǒng)計(jì)學(xué)意義,說(shuō)明管網(wǎng)末梢水在整個(gè)浙江省區(qū)域內(nèi)存在的正向空間自相關(guān),呈聚集性分布。局域Moran'sⅠ系數(shù)和局域Getis系數(shù)的Z值檢驗(yàn)結(jié)果中,出廠水和管網(wǎng)末梢水水質(zhì)的聚集性表現(xiàn)有非常強(qiáng)的相似性,水質(zhì)“好”的聚集區(qū)在浙西南,遂昌縣、龍游縣附近區(qū)域,水質(zhì)“差”的聚集區(qū)在浙東南沿海,瑞安市、平陽(yáng)縣、蒼南縣附近區(qū)域。 結(jié)論 本文應(yīng)用空間分析技術(shù),直觀地顯示了浙江省飲用水水質(zhì)的地理分布,明確了出廠水和管網(wǎng)末梢水水質(zhì)的聚集性表現(xiàn)有非常強(qiáng)的相似性,水質(zhì)“好”的聚集區(qū)在浙西南,遂昌縣、龍游縣附近區(qū)域,水質(zhì)“差”的聚集區(qū)在浙東南沿海,瑞安市、平陽(yáng)縣、蒼南縣附近區(qū)域,這為政府部門制定相關(guān)政策和措施提供了參考信息。
[Abstract]:research objective
According to statistics, 80% of the epidemiological data have spatial attributes, and the drinking water data also have obvious geographical and spatial distribution characteristics. Unused spatial information provides researchers with a new, reliable, scientific and reasonable way to deal with spatial information. The results of this study show the quality of drinking water in Zhejiang Province, but also provide a theoretical basis for the government to formulate policies on drinking water.
Sources and methods of data
The data in this paper come from the monitoring data of drinking water quality in Zhejiang Province in 2010 and the electronic map of 14 million counties in China obtained from the database of National Basic Geographic Information Network. In the water supply area, one sampling point is set up for every 20 000 people, and each sampling point is monitored quarterly. The inspection and evaluation are carried out according to the Sanitary Standard of Drinking Water (GB5749-2006) and the Inspection Method of Drinking Water Standard (GB/T5750-2006). The pass rate calculation and data transformation are completed in EXCEL2003, and spatial analysis is completed in ARCGIS10.0 and GS+9.0.
Result
1. Distribution map of drinking water quality: According to the monitoring data of drinking water quality in Zhejiang Province in 2010 and the electronic map of County Boundaries in Zhejiang Province, the distribution map of qualified rate of factory water and the distribution map of qualified rate of pipe network end water were made.
2. Three-dimensional trend analysis shows that both the outlet water and the end water of the pipe network have a trend in the direction of East-West and north-south. The trend of the end water of the pipe network is higher in the South-North than in the middle of Zhejiang, and lower in the South and north of Zhejiang.
3. Variation function fitting: Gold value C0 is 0.0095, base value Co+C is 0.2040, block gold base-station ratio is 0.047, autocorrelation A is 0.297, goodness of fit R2 is 0.616, fitting model is better; gold value C0 is 0.0799, base value Co+C is 0.1608, block gold base-station ratio is 0.497, autocorrelation A is 2.38, goodness of fit R2 is 0.370, fitting model is better. General.
4. Kriging interpolation: the higher qualified rate of factory water is mainly in southwestern Zhejiang, the lower is mainly in eastern Zhejiang and southern coastal areas. The evaluation indexes of interpolation effect are: the mean of estimated deviation (M-PE) is 0.005024, the standard mean of estimated deviation (MS-PE) is 0.01058, the standard mean square root of estimated deviation (RMSS-PE) is 0.9694, and the mean square root of estimated deviation is 0.005024. (RMS-PE) was 0.4477, and ASE-PE was 0.4624. The areas with higher qualified rate of pipe network end water were mainly in southwest Zhejiang and near Hangzhou Bay, while the areas with lower qualified rate were mainly in South Zhejiang, east coastal areas and North Zhejiang. The evaluation indexes of interpolation effect were respectively, the mean of estimated deviation (M-PE) was 0.01816, and the standardized mean of estimated deviation was 0.01816. (MS-PE) is 0.04646, RMSS-PE is 1.0107, RMS-PE is 0.3187, ASE-PE is 0.3152. This shows that Kriging interpolation prediction is unbiased and optimal interpolation.
5. Spatial autocorrelation analysis: After the analysis of Moran's I and G coefficients, only Moran's I coefficients of the qualified rate of the end water of the pipe network were 0.2865, P 0.05, and the rest were not statistically significant, indicating that there was positive spatial autocorrelation of the end water of the pipe network in the whole region of Zhejiang Province. In the Z value test results of n's I coefficient and local Getis coefficient, the water quality of the factory water and the end water of the pipe network is very similar. The water quality of the "good" gathering area is in the southwest of Zhejiang, Suichang and Longyou counties, and the water quality of the "poor" gathering area is in the southeastern coastal areas of Zhejiang, Ruian, Pingyang and Cangnan counties.
conclusion
In this paper, the geographical distribution of drinking water quality in Zhejiang Province is visually displayed by using the spatial analysis technique. It is clear that the aggregation of the effluent water and the end water of the pipe network is very similar. The aggregation area of "good" water quality is in the southwest of Zhejiang, Suichang and Longyou counties, and the aggregation area of "poor" water quality is in the southeast coast of Zhejiang, Ruian. City, Pingyang County, Cangnan County, the vicinity of the region, which provides a reference for government departments to formulate relevant policies and measures.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R123.1
本文編號(hào)：2250588
[Abstract]:research objective
According to statistics, 80% of the epidemiological data have spatial attributes, and the drinking water data also have obvious geographical and spatial distribution characteristics. Unused spatial information provides researchers with a new, reliable, scientific and reasonable way to deal with spatial information. The results of this study show the quality of drinking water in Zhejiang Province, but also provide a theoretical basis for the government to formulate policies on drinking water.
Sources and methods of data
The data in this paper come from the monitoring data of drinking water quality in Zhejiang Province in 2010 and the electronic map of 14 million counties in China obtained from the database of National Basic Geographic Information Network. In the water supply area, one sampling point is set up for every 20 000 people, and each sampling point is monitored quarterly. The inspection and evaluation are carried out according to the Sanitary Standard of Drinking Water (GB5749-2006) and the Inspection Method of Drinking Water Standard (GB/T5750-2006). The pass rate calculation and data transformation are completed in EXCEL2003, and spatial analysis is completed in ARCGIS10.0 and GS+9.0.
Result
1. Distribution map of drinking water quality: According to the monitoring data of drinking water quality in Zhejiang Province in 2010 and the electronic map of County Boundaries in Zhejiang Province, the distribution map of qualified rate of factory water and the distribution map of qualified rate of pipe network end water were made.
2. Three-dimensional trend analysis shows that both the outlet water and the end water of the pipe network have a trend in the direction of East-West and north-south. The trend of the end water of the pipe network is higher in the South-North than in the middle of Zhejiang, and lower in the South and north of Zhejiang.
3. Variation function fitting: Gold value C0 is 0.0095, base value Co+C is 0.2040, block gold base-station ratio is 0.047, autocorrelation A is 0.297, goodness of fit R2 is 0.616, fitting model is better; gold value C0 is 0.0799, base value Co+C is 0.1608, block gold base-station ratio is 0.497, autocorrelation A is 2.38, goodness of fit R2 is 0.370, fitting model is better. General.
4. Kriging interpolation: the higher qualified rate of factory water is mainly in southwestern Zhejiang, the lower is mainly in eastern Zhejiang and southern coastal areas. The evaluation indexes of interpolation effect are: the mean of estimated deviation (M-PE) is 0.005024, the standard mean of estimated deviation (MS-PE) is 0.01058, the standard mean square root of estimated deviation (RMSS-PE) is 0.9694, and the mean square root of estimated deviation is 0.005024. (RMS-PE) was 0.4477, and ASE-PE was 0.4624. The areas with higher qualified rate of pipe network end water were mainly in southwest Zhejiang and near Hangzhou Bay, while the areas with lower qualified rate were mainly in South Zhejiang, east coastal areas and North Zhejiang. The evaluation indexes of interpolation effect were respectively, the mean of estimated deviation (M-PE) was 0.01816, and the standardized mean of estimated deviation was 0.01816. (MS-PE) is 0.04646, RMSS-PE is 1.0107, RMS-PE is 0.3187, ASE-PE is 0.3152. This shows that Kriging interpolation prediction is unbiased and optimal interpolation.
5. Spatial autocorrelation analysis: After the analysis of Moran's I and G coefficients, only Moran's I coefficients of the qualified rate of the end water of the pipe network were 0.2865, P 0.05, and the rest were not statistically significant, indicating that there was positive spatial autocorrelation of the end water of the pipe network in the whole region of Zhejiang Province. In the Z value test results of n's I coefficient and local Getis coefficient, the water quality of the factory water and the end water of the pipe network is very similar. The water quality of the "good" gathering area is in the southwest of Zhejiang, Suichang and Longyou counties, and the water quality of the "poor" gathering area is in the southeastern coastal areas of Zhejiang, Ruian, Pingyang and Cangnan counties.
conclusion
In this paper, the geographical distribution of drinking water quality in Zhejiang Province is visually displayed by using the spatial analysis technique. It is clear that the aggregation of the effluent water and the end water of the pipe network is very similar. The aggregation area of "good" water quality is in the southwest of Zhejiang, Suichang and Longyou counties, and the aggregation area of "poor" water quality is in the southeast coast of Zhejiang, Ruian. City, Pingyang County, Cangnan County, the vicinity of the region, which provides a reference for government departments to formulate relevant policies and measures.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：R123.1
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