河流水质采样策略与频率对氮磷负荷估算的影响

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1332 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要为探究适合的流域水质监测频率与采样策略,提高氮磷污染物负荷估算的精度,基于云南省洱源县凤羽河流域2011~2013年间逐日流量与总氮、总磷浓度的每日数据,在7d/次,14d/次,21d/次,28d/次4种采样频率下设定了3种不同的采样情景(情景a:普通随机取样、情景b:随机取样的基础上增加降水量超过10mm的水样样品采集,情景c:随机取样的基础上增加降水量超过25mm的水样样品采集).基于采样频率与采样情景的组合,利用LOADEST模型估算的总氮与总磷负荷,并与逐日连续水量水质数据计算的实测值进行比较,评估了不同取样频率和取样情景下LOADEST模型的负荷估算精度.结果表明:在LOADEST模型的估算过程中,同种采样频率下,增加降雨时期的浓度数据将会提高LOADEST模型的模拟精度;在降雨事件期间进行加密采样,不仅加大了工作量,且增加的采样次数过多(如情景b)使总氮的RMSE值由1.92增至2.26,总磷的RMSE值由0.08增至0.19, 高估氮磷负荷,模型模拟精度降低.在四种不同的采样频率下,情景c中总氮的RMSE值范围为1.92~2.07,总磷的RMSE值范围为0.06~0.13,均取得了该频率下最低的RMSE值,模型估算精度最高;从不同频率与采样情景对模型估算结果的影响来看,对总氮负荷估算结果(RMSE值范围为1.92~2.52)影响要大于总磷(RMSE值范围为0.06~0.18).其中,情景a与情景b的负荷估算结果精度随着采样频率降低而降低.情景c的估算结果受现有取样频率降低的影响不明显.本研究表明,在常规固定频率水质监测基础上结合气象预报适量增加降雨时的采样可在28d/次的较低取样频率下得到较高的氮磷负荷估算精度,符合我国各地区环境水质采样频率,可为相关研究的水质采样策略提供一定的参考作用.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	贾临东
	胡宏祥
	杜新忠
	雷秋良
	张天鹏
	刘宏斌
	蒋跃林

关键词 ： LOADEST模型, 总氮, 总磷, 采样情景, 负荷估算

Abstract：In order to explore suitable sampling frequency and strategy for water quality monitoring in the basin and improve the accuracy of nitrogen and phosphorus pollutant load estimation, nutrient loads were estimated based on daily data of daily discharge and total nitrogen and total phosphorus concentrations in Fengyu River Basin, Eryuan County, Yunnan Province from 2011 to 2013, under different scenarios. Specifically, three different sampling scenarios were set up (scenario a: general random sampling, scenario b: sampling water samples with more than 10mm of precipitation added on the basis of random sampling, and scenario c: sampling water samples with more than 25mm of precipitation added on the basis of random sampling) with sampling frequency ranging from weekly to nearly monthly. Based on the combination of different sampling frequencies and sampling scenarios, the accuracy of load estimations by LOADEST model is evaluated by comparing the total nitrogen and total phosphorus load estimated by LOADEST with the measured value calculated by daily continuous water quality and streamflow data. The results show that:adding the concentration data during the rainfall period will improve the simulation accuracy of LOADEST model under the same sampling frequency; sampling during rainfall events increases the frequency of sampling (such as scenario b), resulting in the RMSE of total nitrogen increasing from 1.92 to 2.26, and the RMSE value of total phosphorus increased from 0.08 to 0.19, which will affect the simulation accuracy of the model by overestimating the nitrogen and phosphorus load. Under four different sampling frequencies, the RMSE values of total nitrogen and total phosphorus in scenario c ranged from 1.92 to 2.07 and 0.06 to 0.13, obtained the lowest RMSE value and the highest accuracy of model estimation; different frequencies and sampling scenarios have greater impact on total nitrogen load estimation results (RMSE ranging from 1.92 to 2.52) than total phosphorus load estimation (RMSE ranging from 0.06 to 0.18). The accuracy of load estimation results for scenario a and scenario b decreases as the sampling frequency declines. The estimated results of scenario c are not significantly affected by the reduction of the existing sampling frequency. The results of this study show that the estimation accuracy of nitrogen and phosphorus load can be obtained at a low sampling frequency of 28d/ time by adding samplings during rainfall events based on weather forecast, which is consistent with the sampling frequency of environmental water quality in various regions of China. This study could provide reference for the water quality sampling strategy and frequency.

Key words： LOADEST model total nitrogen total phosphorus sampling scenario load estimation

收稿日期: 2023-09-03

PACS:

X522

基金资助:宁夏联合基金资助项目(U20A20114);国家重点研发计划(2022YFC3204001);国家自然科学基金(U20A20114,42107076)

通讯作者: 杜新忠,研究员,duxinzhong@caas.cn E-mail: duxinzhong@caas.cn

引用本文:

贾临东, 胡宏祥, 杜新忠, 雷秋良, 张天鹏, 刘宏斌, 蒋跃林. 河流水质采样策略与频率对氮磷负荷估算的影响[J]. 中国环境科学, 2024, 44(4): 2111-2118. JIA Lin-dong, HU Hong-xiang, DU Xin-zhong, LEI Qiu-liang, ZHANG Tian-peng, LIU Hong-bin, JIANG Yue-lin. Impact of sampling strategy and frequency of river water quality on nitrogen and phosphorus load estimation. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(4): 2111-2118.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2024/V44/I4/2111

[1] Bu H M, Meng W, Zhang Y. Nitrogen pollution and source identification in the Haicheng River basin in Northeast China[J]. Science of the Total Environment, 2011,409(18):3394-3402.
[2] 陈小华,钱晓雍,李小平,等.洱海富营养化时间演变特征(1988~2013年)及社会经济驱动分析[J]. 湖泊科学, 2018,30(1):70-78. Chen X H, Qian X Y, Li X P, et al. Long-term trend of eutrophication state of Lake Erhai in 1988~2013 and analyses of its socio-economic drivers[J]. Journal of Lake Sciences, 2018,30(1):70-78.
[3] 中华人民共和国环境保护部.2018年中国生态环境状况公报[EB/OL]. 2019-05-29. Ministry of Environmental Protection of the People's Republic of China. 2018 China ecological environment status bulletin[EB/OL]. 2019-05-29.
[4] 宋岸,肖举强.洱海流域富营养化成因及教训[J]. 广东化工, 2010,37(8):133-134. Song A, Xiao J Q. On the reasons and lessons of eutrophication in the Erhai Lake[J]. Guangdong Chemical Industry, 2010,37(8):133-134.
[5] Bengtsson L, Herschy R W, Fairbridge R W. Encyclopedia of lakes and reservoirs[M]. Netherlands:Springer, 2012:258-270.
[6] 许力文,卞建民,孙晓庆,等.灌区退水对区域地下水质影响与健康风险评估[J]. 中国环境科学, 2023,43(4):1688-1695. Xu L W, Bian J M, Sun X Q et al. Impact and health risk assessment of water withdrawal on regional groundwater quality[J]. China Environmental Science, 2023,43(4):1688-1695.
[7] Neilson B J, Cronin L E. Estuaries and nutrients[M]. New Jerrey:Humana Press, 1981:643.
[8] Tippie V K. Chesapeake bay:A framework for action[M]. Philadelphia:US.Evironmental.Protection.Agency, Chesapeake Bay Program, 1983.
[9] Lee C J, Hirsch R M, Schwarz G E, et al. An evaluation of methods for estimating decadal stream loads[J]. Journal of Hydrology, 2016,542:185-203.
[10] 苏静君,李叙勇,吴震.利用实测值估算断面泥沙年负荷的方法比较[J]. 中国环境科学, 2017,37(1):218-228. Su J J, Li X Y, Wu Z. Comparison of methods for estimating annual sediment load in sectional area using measured values[J]. China Environmental Science, 2017,37(1):218-228.
[11] 富国.河流污染物通量估算方法分析(I)——时段通量估算方法比较分析[J]. 环境科学研究, 2012,16(1):1-4. Fu G, Analysis of river pollutant flux estimation methods (I)——Comparative analysis of time period flux estimation methods[J]. Research of Environmental Sciences, 2012,16(1):1-4.
[12] 李娜,盛虎,何成杰,等.基于统计模型LOADEST的宝象河污染物通量估算[J]. 应用基础与工程科学学报, 2012,20(3):355-366. Li N, Sheng H, He C J, et al. Estimation of pollutant flux in Baoxiang River based on statistical model LOADEST[J]. Journal of Applied Basic and Engineering Sciences, 2012,20(3):355-366.
[13] Webb B W,Phillips J M,Walling D E,et al. Load estimation methodologies for British rivers and their relevance to the Loisracs(r) Programme[J]. The Science of the Total Environment, 1997,(194):378-389.
[14] Li H E, Lee J H W, Cai M. Nutrient load estimation methods for rivers[J]. International Journal of Sediment Research, 2003.
[15] 谢润婷.非点源污染河流的水环境容量动态分析与定量研究[D]. 杭州:浙江大学, 2017. Xie R T. Dynamic analysis and quantitative study on water environment capacity of non-point source polluted river[D]. Hangzhou:Zhejiang University, 2017.
[16] 张柏发.基于LOADEST和PAG/RNN模型的非点源污染河流营养物负荷量模拟与预测研究[D]. 杭州:浙江大学, 2015. Zhang B F. Simulation and prediction of nutrient load in non-point source polluted rivers based on LOADEST and PAG/RNN models[D]. Hangzhou:Zhejiang University, 2015.
[17] 郝晨林,邓义祥,汪永辉,等.河流污染物通量估算方法筛选及误差分析.环境科学学报, 2012,32(7):1670-1676. Hao C L, Deng Y X, Wang Y H, et al. Screening and error analysis of river pollutant flux estimation methods. Journal of Environmental Sciences, 2012,32(7):1670-1676.
[18] Runkel R, Crawford G C, Cohn T. Load estimator (LOADEST) is a FORTRAN program for estimating constituent loads in streams and rivers[R]. Techniques and Methods Book 4,chapter A5.
[19] Crawford C G. Estimating mean constituent loads in rivers by therating-curveandflow-duration, rating-urvemethods[D]. Bloomington, Ind, Indiana University, PhDdissertation, 1996,245.
[20] Cohn T A. Adjusted maximum likelihood estimation of the moments of lognormal populations from type I censored samples[R]. U S Geological Survey Open-File Report 88-350, 1988:34.
[21] Qian Y, Migliaccio K W, Wan Y, et al. Trend analysis of nutrient concentrations and loads in selected canals of the southern Indian River Lagoon, Florida[J]. Air & Soil Pollution, 2007,186:195-208.
[22] Harmel R D, King K W, Slade R. Automated storm water sampling on small watersheds[J]. Appl Eng Agric, 2003,19:667-674.
[23] Harmel R D, King K W, Haggard B E, et al. Practical guidance for discharge and water quality data collection on small. watersheds[J]. Transactions.of.the ASABE, 2006,49(4):937-948.
[24] Park Y S, Engel B A. Analysis for regression model behavior by sampling strategy for annual pollutant load estimation[J]. Journal of environmental quality, 2015,44(6):1843-1851.
[25] 高信娟.九龙江营养盐输出的水文调控及LOADEST模型应用研究[D]. 厦门:厦门大学, 2018. Gao X J. Study on hydrologic regulation of nutrient salt output in Jiulong River and application of LOADEST model[D]. Xiamen:Xiamen University, 2018.
[26] 李晓虹,雷秋良,周脚根,等.降雨强度对洱海流域凤羽河氮磷排放的影响[J]. 环境科学, 2019,40(12):5375-5383. Li X H, Lei Q L, Zhou F G, et al. Effects of rainfall intensity on nitrogen and phosphorus emissions from Fengyu River in Erhai.Lake.Basin.[J]. Environmental Science, 2019,40(12):5375-5383.
[27] Hanrahan B R, King K W, Williams M R. Controls on subsurface nitrate and dissolved reactive phosphorus losses from agricultural fields during precipitation-driven events[J]. Science of the Total Environment, 2021,754:142047.
[28] Lee C J, Hirsch R M, Schwarz G E, et al. An evaluation of methods for estimating decadal stream loads[J]. Journal of Hydrology, 2016,542:185-203.
[29] Aulenbach B T. Improving regression-model-based streamwater constituent load estimates derived from serially correlated data[J]. Journal of Hydrology, 2013,503:55-66.
[30] Appling A P, Leon M C, McDowell W H. Reducing bias and quantifying uncertainty in watershed flux estimates:the R package loadflex[J]. Ecosphere, 2015,6(12):1-25.
[31] Park Y S, Engel B A. Identifying the correlation between water quality data and LOADEST model behavior in annual sediment load estimations[J]. Water, 2016,8(9):368.
[32] Johnes P J. Uncertainties in annual riverine phosphorus load estimation:Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density[J]. Journal of Hydrology, 2007,332(1/2):241-258.
[33] Carey R O, Migliaccio K W, Brown M T. Nutrient discharges to Biscayne Bay, Florida:trends, loads, and a pollutant index[J]. Science of the Total Environment, 2011,409(3):530-539.
[34] Gao X, Chen N, Yu D, et al. Hydrological controls on nitrogen (ammonium versus nitrate) fluxes from river to coast in a subtropical region:Observation and modeling[J]. Journal of environmental management, 2018,213:382-391.
[35] Kerr J G, Eimers M C, Yao H. Estimating stream solute loads from fixed frequency sampling regimes:the importance of considering multiple solutes and seasonal fluxes in the design of long-term stream monitoring networks[J]. Hydrological Processes, 2016,30(10):1521-1535.
[36] Park Y S, Engel B A. Use of pollutant load regression models with various sampling frequencies for annual load estimation[J]. Water, 2014,6(6):1685-1697.
[37] Halliday S J, Skeffington R A, Bowes M J, et al. The water quality of the River Enborne, UK:Observations from high-frequency monitoring in a rural, lowland river system[J]. Water, 2014,6(1):150-180.
[38] Valero E. Characterization of the water quality status on a stretch of River Lérez around a small hydroelectric power station[J]. Water, 2012,4(4):815-834.
[39] Shen Z, Liao Q, Hong Q, et al. An overview of research on agricultural non-point source pollution modelling in China[J]. Separation and Purification Technology, 2012,84:104.