Trend assessment

1. Trend assessment First Name, Last name : Ruxandra BĂLĂEŢ, Lăcramioara COARNĂ Dumitru NEAGU, Elvira MARCHIDAN Organizations: Ministry of Environment, Water and Forests National Administration “ApeleRomane” – National Institute for Hydrology and Water Management - Groundwater Section April 2015, 28th meeting of WG Groundwater ROMANIA – Ministry of Environment, Water and Forests www.mmediu.ro

2. Trend assessment in ROMANIA for the GWD • Substances and GWB • Data used • Statistical method used • Environment significance • From monitoring point to • GWB level • 143gwb

3. Substances and GWB Trends have been asessedin ROMANIA for the second cycle: 1. First stage 2011-2012 • Data sets wereanalysed in order to detectsignificant trends in the concentrations of pollutants in all gwbfound in poorstatus for the 1st management cycle (19 gwb), for the substances causing the poorstatus (Nitrates, Nitrites, Ammonia). • Length of time series : 2000 - 2010 • From 19 gwbanalized, 13 gwb had 10 years time series, so trends analysis could be performed. • Gwstat software was used, based on ANOVA liniar regression generalized test for trend assessment and on the model of the 2 sections for trend reversal assessment. • Results : - In case of 5 gwb upward trends were detected; • - In case of 3 gwb downward trends were detected • - In 1 gwb trend reversal was detected • -

4. Substances and GWB 2. Second stage 2013-2014 • Trend and trend reversal analysiswereperformed in all gwbhavingcontinous data sets, for the nitrogen compounds (NO3, NO2 and NH4 ), in order to have sufficient time to allow mitigation and preventionmeasures to beimplemented, according to pt. 2.a.(ii) of GWD Annex 4. • Length of time series : 2000 – 2013 (14 years) • From 143 gwbanalized, 64 gwb had adequate time series, so trend and trend reversal analysis could be performed. For the whole process , data from approx. 1500 chemical monitoring points (wells and springs), sampled 1-3 times/year have been considered. The arithmetic average of the concentrations recorded in the year 2000 was considered as baseline level. Concentration values ​​below the quantification limit were replaced with half of the quantification limit occurring in the respective time series. • -

5. National Methodology • The methodologies used in Romania for trend and trends reversal assessment were conceived in the National Institute for Hydrology and Water Management based on: • Technical report nº 1 WFD CIS: Statistical aspects of the identification of groundwater pollution trends, and aggregation of monitoring results, 2001 • Existing available data • . According to this report reccommendations, the length of time series depends on the aggregation period and it is for annual aggregations 8 years and for half-annual and quarterly aggregations 6 years. • Trend analysis was carried on for more 8 years, in case of annual aggregation data.

6. Statistical methods used • Gwstat software– Trend Assessment • ANOVA liniar regression generalized test • - data adjustment (regularization): calculation of the annual average of available concentration values for each point of the monitoring network (well, spring); • - input of annual average concentrations / annual review of the trend option ( because a large number of monitoring sites had only one analysis per year); • - spatial aggregation of data for the whole gwb surface was necessary:GIS matrix through GISView Program was used;

8. Statistical methods used • Gwstat software– Trend Reversal Assessment • Model of the 2 sections • Based on the assumption that time seriescan be characterized by 2 linear trends with a slope direction change within the analysedtime perioad. • Applying 95% quantile of the distribution, reversal canbedetected if in the first section the slopeisascending ( pozitive) and in the second section the slopeisdescending ( negative). • Method steps: 1. optimization of time sections selection; • 2. analyse of slope change significance for the model of simple linearregression; • 3. statistical test to verifythat the model of the 2 sections is more significantthat the model of simple linearregression. • Practically, processing the introduced data series, the program can indicate an inversion in the trend slope, thus a trend reversal.

10. From monitoring point to GWB level • Spatial data agregation • For each groundwater body, depending on its surface, it was determined the GIS matrix through GISView program. Depending on the number of rows and columns of the GIS matrix which every side has 250 m., the relative coordinates were assigned to each monitoring point. • The GIS Matrix and the data files, type Excel 5.0/95, with annual average NO3, NO2 or NH4 concentrations in the monitoring points of the gwb are Gwstat input files. The computer program also estimates the network representativeness - Ru (%) it is achieved the spatial data aggregation. • In case of a homogeneous network (Ru ≥ 80%), the spatial aggregation of data is based on the arithmetic average (AM) and of the confidence limit > 95% (CL (AM)). • In case of a heterogeneously network (Ru < 80%), the spatial aggregation of the data is based on the Kriging average (KM) and on the confidence limit > 95% (CL (KM)). • Trend and trend reversal assessment are based on the data adjustment and aggregation for the entire groundwater body.

11. CONCLUSIONS General Results Analysis was done on 64 gwb for NO3, NO2 and NH4 (for the rest of 79 gwb times series were incomplete) identifying: - 20 significantupward trends; - 48 significantdownward trends; - 10 trend reversals; - «not detected» trend or trend reversals in the rest of cases. Wecanconcludethat the methodsused have limitations, they are not able to accuratedetect all the trends and trend reversals. Wewouldlike to have methods for trend and trend reversalscalculationestablishedatEuropeanlevel.

12. Thank you ! ROMANIA – Ministry of Environment, Water and Forests www.mmediu.ro