应用空间分析技术对浙江省饮用水水质状况进行分析
发布时间：2018-09-19 15:55
【摘要】：研究目的 饮用水卫生安全问题一直是政府和人民群众关注的热点,水质的优劣直接影响人民的身体健康。据统计,80%的流行病学资料具有空间属性,同样饮用水数据也有着明显的地理空间分布特征。运用空间分析继续能够利用传统的统计学方法尚未利用的空间信息,为研究者提供一种全新、可靠、科学合理的处理空间信息的方法。本次研究结果,展现了浙江省饮用水水质状况,同时也为政府制定有关饮用水政策提供理论依据。 资料来源与方法 本文的资料来源于2010年浙江省饮用水水质监督监测数据,和国家基础地理信息网数据库中获取的1：400万中国县界电子地图。本次监测的饮用水水样为浙江省范围内的取得有效卫生许可证水厂的出厂水、管网末梢水。出厂水枯水期或丰水期监测一次；管网末梢水为供水区域内按供水人口每2万人设1个采样点,每个采样点每季度监测1次。按《生活饮用水卫生标准》(GB5749-2006)和《生活饮用水标准检验方法》(GB/T5750-2006)进行检验评价。各地饮用水水质监督监测数据汇总、合格率计算和数据变换在EXCEL2003中完成,空间分析在ARCGIS10.0和GS+9.0中完成。 结果 1.饮用水水质情况地区分布图：根据2010年浙江省饮用水水质监督监测数据和浙江省县界电子地图制作成出厂水合格率地区分布图和管网末梢水合格率地区分布图。 2.三维趋势分析：出厂水和管网末梢水在东西和南北方向在均存在趋势,其中管网末梢水在南北方向上浙中高,浙南、浙北低的趋势更显著。 3.变异函数拟合：出厂水块金值C0为0.0095、基台值Co+C为0.2040、块金基台比为0.047、自相关a为0.297、拟合优度r2为0.616,拟合模型较好；管网末梢水块金值C0为0.0799、基台值Co+C为0.1608、块金基台比为0.497、自相关a为2.38、拟合优度r2为0.370,拟合模型一般。 4.kriging插值：出厂水的合格率较高的地区主要在浙西南,较低地区主要在浙东、南沿海地区,插值效果评价指标分别为,估计偏差均数(M-PE)为0.005024、估计偏差标准化均数(MS-PE)为0.01058,估计偏差标化均方根(RMSS-PE)为0.9694,估计偏差均方根(RMS-PE)为0.4477,估计偏差平均标准误(ASE-PE)为0.4624；管网末梢水的合格率较高的地区主要在浙西南和杭州湾附近,较低地区主要在浙南、东沿海地区和浙北地区,插值效果评价指标分别为,估计偏差均数(M-PE)为0.01816、估计偏差标准化均数(MS-PE)为0.04646,估计偏差标化均方根(RMSS-PE)为1.0107,估计偏差均方根(RMS-PE)为0.3187,估计偏差平均标准误(ASE-PE)为0.3152。这说明kriging插值预测是无偏、最优插值。 5.空间自相关分析：经过出厂水和管网末梢水全域Moran'sⅠ和全域G系数分析,只有管网末梢水合格率的Moran'sⅠ系数为0.2865,P0.05,其余均无统计学意义,说明管网末梢水在整个浙江省区域内存在的正向空间自相关,呈聚集性分布。局域Moran'sⅠ系数和局域Getis系数的Z值检验结果中,出厂水和管网末梢水水质的聚集性表现有非常强的相似性,水质“好”的聚集区在浙西南,遂昌县、龙游县附近区域,水质“差”的聚集区在浙东南沿海,瑞安市、平阳县、苍南县附近区域。 结论 本文应用空间分析技术,直观地显示了浙江省饮用水水质的地理分布,明确了出厂水和管网末梢水水质的聚集性表现有非常强的相似性,水质“好”的聚集区在浙西南,遂昌县、龙游县附近区域,水质“差”的聚集区在浙东南沿海,瑞安市、平阳县、苍南县附近区域,这为政府部门制定相关政策和措施提供了参考信息。
[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.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：R123.1
本文编号：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.
【学位授予单位】：浙江大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：R123.1
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