Relationship between the actual test results of groundwater arsenic level and the predictions of a geographic information system model in four provinces of Huaihe River Basin

Q3 Medicine
Liu Yu-yan, Li Yongfang, Wang Da, Yang BoYi, Fang Shujun, Zhi XueYuan, Zheng QuanMei, Sun Gui-fan
{"title":"Relationship between the actual test results of groundwater arsenic level and the predictions of a geographic information system model in four provinces of Huaihe River Basin","authors":"Liu Yu-yan, Li Yongfang, Wang Da, Yang BoYi, Fang Shujun, Zhi XueYuan, Zheng QuanMei, Sun Gui-fan","doi":"10.3760/CMA.J.ISSN.2095-4255.2014.01.010","DOIUrl":null,"url":null,"abstract":"Objective To test the accuracy of predicted results by a geographic information system(GIS) model with the actual distribution of groundwater arsenic concentration in four provinces including Shandong,Henan,Anhui and Jiangsu of Huaihe River Basin.Methods The results of groundwater arsenic level of the four provinces in Huaihe River Basin were cited as the data resource; after stratified by arsenic in water,water arsenic > 0.01 mg/L as statistical standards,the number of villages and counties(districts) with arsenic level higher than the standards was calculated.Meanwhile,locations of counties(districts) sampled on the map of GIS predictive model were marked; the gray level of arsenic contaminated probability 0.3 was regarded as the criterion and the consistence of both results predicted by GIS model and detected actually in each province was analyzed.Results A total of 61 824 wells distributed in 2 781 villages around the four provinces of Huaihe River Basin were sampled,and 474 of the 2 781 villages were confirmed as high arsenic villages(arsenic > 0.01 mg/L),with an average detection rate of 17.04%(474/2 781).The detection rates of high arsenic villages in Shandong,Henan,Anhui and Jiangsu were 13.19% (79/599),23.82% (101/424),74.25% (199/268) and 6.38% (95/1 490),respectively,and the differences of detection rates among the provinces were statistically significant(x2 =820.84,P < 0.05).County(district) as a unit,among all the 103 counties(districts),the number of counties where the probability of high arsenic concentration in groundwater predicted by GIS model that greater than 0.3 was 72,accounting for 69.90%(72/103) of total counties,in which Shandong,Henan,Anhui and Jiangsu were 67.86%(19/28),61.36% (27/44),85.71% (12/14) and 82.35% (14/17),respectively.Among all 103 counties (districts),the number of counties(districts) where the detection rates of high arsenic villages beyond the standards was 62,and among these 62 counties,there were 42 counties where the probability of high arsenic concentration in groundwater predicted by GIS model was greater than 0.3,and the overall sensibility was 67.74%(42/62); among all the 41 counties where the detection rates of high arsenic villages were not beyond the standards,11 counties where the probability of high arsenic concentration in groundwater predicted by GIS model was lower than 0.3,and the overall specificity was 26.83%(11/41); the sensitivities of GIS model in Shandong,Henan,Anhui and Jiangsu were 57.89% (11/19),59.09% (13/22),84.62% (11/13) and 87.50% (7/8),respectively.Conclusion The results of groundwater arsenic investigated in the four provinces of Huaihe River Basin and predicted by GIS model are consistent,and we have proved that the results of GIS predictive model are accurate. \n \nKey words: \nHuaihe river basin;  Groundwater;  Arsenic;  Geographic information system; Predictive model","PeriodicalId":55880,"journal":{"name":"中华地方病学杂志","volume":"33 1","pages":"30-33"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中华地方病学杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3760/CMA.J.ISSN.2095-4255.2014.01.010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 2

Abstract

Objective To test the accuracy of predicted results by a geographic information system(GIS) model with the actual distribution of groundwater arsenic concentration in four provinces including Shandong,Henan,Anhui and Jiangsu of Huaihe River Basin.Methods The results of groundwater arsenic level of the four provinces in Huaihe River Basin were cited as the data resource; after stratified by arsenic in water,water arsenic > 0.01 mg/L as statistical standards,the number of villages and counties(districts) with arsenic level higher than the standards was calculated.Meanwhile,locations of counties(districts) sampled on the map of GIS predictive model were marked; the gray level of arsenic contaminated probability 0.3 was regarded as the criterion and the consistence of both results predicted by GIS model and detected actually in each province was analyzed.Results A total of 61 824 wells distributed in 2 781 villages around the four provinces of Huaihe River Basin were sampled,and 474 of the 2 781 villages were confirmed as high arsenic villages(arsenic > 0.01 mg/L),with an average detection rate of 17.04%(474/2 781).The detection rates of high arsenic villages in Shandong,Henan,Anhui and Jiangsu were 13.19% (79/599),23.82% (101/424),74.25% (199/268) and 6.38% (95/1 490),respectively,and the differences of detection rates among the provinces were statistically significant(x2 =820.84,P < 0.05).County(district) as a unit,among all the 103 counties(districts),the number of counties where the probability of high arsenic concentration in groundwater predicted by GIS model that greater than 0.3 was 72,accounting for 69.90%(72/103) of total counties,in which Shandong,Henan,Anhui and Jiangsu were 67.86%(19/28),61.36% (27/44),85.71% (12/14) and 82.35% (14/17),respectively.Among all 103 counties (districts),the number of counties(districts) where the detection rates of high arsenic villages beyond the standards was 62,and among these 62 counties,there were 42 counties where the probability of high arsenic concentration in groundwater predicted by GIS model was greater than 0.3,and the overall sensibility was 67.74%(42/62); among all the 41 counties where the detection rates of high arsenic villages were not beyond the standards,11 counties where the probability of high arsenic concentration in groundwater predicted by GIS model was lower than 0.3,and the overall specificity was 26.83%(11/41); the sensitivities of GIS model in Shandong,Henan,Anhui and Jiangsu were 57.89% (11/19),59.09% (13/22),84.62% (11/13) and 87.50% (7/8),respectively.Conclusion The results of groundwater arsenic investigated in the four provinces of Huaihe River Basin and predicted by GIS model are consistent,and we have proved that the results of GIS predictive model are accurate. Key words: Huaihe river basin;  Groundwater;  Arsenic;  Geographic information system; Predictive model
淮河流域四省地下水砷含量实测结果与地理信息系统模型预测的关系
目的利用淮河流域山东、河南、安徽、江苏四省地下水砷浓度的实际分布,验证地理信息系统(GIS)模型预测结果的准确性。方法以淮河流域四省地下水砷含量监测结果为数据源;以水中砷含量分层后,以水中砷含量> 0.01 mg/L为统计标准,计算砷含量高于该标准的村、县(区)个数。同时,在GIS预测模型地图上对采样县(区)的位置进行标记;以砷污染概率灰度值0.3为判据,分析了GIS模型预测结果与各省实际检测结果的一致性。结果在淮河流域四省分布的2 781个村共取样61 824口井,其中474个村为高砷村(砷> 0.01 mg/L),平均检出率为17.04%(474/2 781)。检出率高砷村庄山东、河南、安徽和江苏分别为13.19%(79/599),23.82%(101/424),74.25%(199/268)和6.38%(95/1 490),分别和检测率的差异具有统计学意义(x2 = 820.84, P < 0.05) .County(区)作为一个单元,在所有的103个县(区)、县的数量的概率高砷地下水浓度预测的GIS模型,大于0.3是在72年,会计占总数的69.90%(72/103),其中山东67.86%(19/28)、河南61.36%(27/44)、安徽85.71%(12/14)、江苏82.35%(14/17)。103个县(区)中,高砷村超标检出率的县(区)有62个,其中有42个县GIS模型预测地下水高砷浓度的概率大于0.3,总体敏感性为67.74%(42/62);在高砷村检出率未超标的41个县中,有11个县的GIS模型预测地下水高砷的概率小于0.3,总体特异性为26.83%(11/41);GIS模型在山东、河南、安徽和江苏的敏感性分别为57.89%(11/19)、59.09%(13/22)、84.62%(11/13)和87.50%(7/8)。结论淮河流域四省地下水砷调查结果与GIS模型预测结果一致,证明了GIS预测模型结果的准确性。关键词:淮河流域;地下水;砷;地理信息系统;预测模型
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
中华地方病学杂志
中华地方病学杂志 我国对人类健康危害特别严重的地方性疾病:克山病、大骨节病、碘缺乏病、地方性氟中毒、地方性砷中毒、鼠疫、布鲁氏菌病、寄生虫、新冠肺炎等疾病,同时还报道多发性自然疫源性疾病。
CiteScore
1.60
自引率
0.00%
发文量
8714
期刊介绍: The Chinese Journal of Endemiology covers predominantly endemic diseases threatening health of the people in the areas affected by the diseases including Keshan disease, Kaschin-Beck Disease, iodine deficiency disorders, endemic fluorosis, endemic arsenism, plague, epidemic hemorrhagic fever, brucellosis, parasite diseases and the diseases related to local natural and socioeconomic conditions; and reports researches in the basic science, etiology, epidemiology, clinical practice, control as well as multidisciplinary studies on the diseases.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信