Physicochemical characterization of the agriculturally impacted springs
In addition to potential contaminations, the pH, oxygen content, water temperature (T) and temperature amplitude over the course of the year (ΔT) as well as the electrical conductivity (EC) are essential for the geoecological characterization of the spring waters. A total of 55 springs were studied between 2002 and 2018. Twenty-two springs with farmland accounting for more than 25% of land use are considered potentially influenced by agriculture. Table 2 lists selected physicochemical parameters from the 2018 monitoring period.
The pH of the spring waters influenced by agriculture are in the neutral range, between 6 and 8, and therefore do not exhibit any extreme values. In consonance with the lithology of the catchment areas, all the sites in the Muschelkalk and two springs each in the Rotliegend and the Bunter sandstone exhibit rather basic conditions, while all other spring waters are slightly acidic on average. The springs in the Muschelkalk exhibit significantly higher conductivity values than almost all the other sites; this is due to the high solubility of the minerals of the carbonate parent rock.
With the exception of a single heavily modified site (b06) the mean oxygen contents are well above the critical range of 3 mg/L for fish and many other aquatic organisms. However, the orientation value for good ecological status according to the German Surface Water Ordinance [11] of 8 mg/L is undercut by the annual average at seven locations and at least once over the course of the year at 13 locations.
Table 2
Median values of selected physicochemical parameters and land use of 22 springs influenced by agriculture
Spring ID
|
NO3−
|
NH4+
|
EC
|
T
|
ΔT
|
pH
|
O2
|
O2 min
|
Farmland
|
Cropland
|
Forest
|
Area
|
b06
|
2.20
|
0.09
|
892
|
8.7
|
15.8
|
7.3
|
1.8
|
0.0
|
25.5%
|
0.0%
|
73.2%
|
14.0
|
b07
|
13.45
|
0.12
|
330
|
11.0
|
11.9
|
6.7
|
7.4
|
1.4
|
79.2%
|
1.9%
|
19.7%
|
14.8
|
b08
|
29.55
|
0.08
|
353
|
11.4
|
12.1
|
7.1
|
9.2
|
8.3
|
81.9%
|
25.5%
|
6.9%
|
9.9
|
k05
|
32.10
|
0.08
|
360
|
11.3
|
7.6
|
6.6
|
9.5
|
8.4
|
91.1%
|
59.5%
|
2.9%
|
1.5
|
k06
|
18.00
|
0.10
|
287
|
9.0
|
8.9
|
6.1
|
8.5
|
5.9
|
57.8%
|
11.9%
|
32.1%
|
7.3
|
k08
|
38.05
|
0.03
|
182
|
11.2
|
7.2
|
6.3
|
9.2
|
7.8
|
76.3%
|
54.1%
|
22.1%
|
58.0
|
k09
|
13.80
|
0.03
|
237
|
8.7
|
13.1
|
6.7
|
10.7
|
8.0
|
85.6%
|
15.4%
|
14.4%
|
3.8
|
k10
|
52.60
|
0.03
|
342
|
11.1
|
5.1
|
6.7
|
7.5
|
6.4
|
82.9%
|
73.0%
|
17.1%
|
16.8
|
m02
|
12.95
|
0.15
|
595
|
7.7
|
11.8
|
7.3
|
8.5
|
2.3
|
89.3%
|
38.6%
|
3.8%
|
17.4
|
m03
|
21.30
|
0.05
|
722
|
12.0
|
5.5
|
7.4
|
8.4
|
7.8
|
93.0%
|
79.4%
|
2.0%
|
32.9
|
m07
|
12.30
|
0.13
|
705
|
11.9
|
7.3
|
7.3
|
8.6
|
5.4
|
26.9%
|
6.7%
|
73.1%
|
62.2
|
m08
|
52.40
|
0.11
|
692
|
11.2
|
6.2
|
7.4
|
9.8
|
8.0
|
82.5%
|
72.3%
|
15.7%
|
53.9
|
m09
|
13.95
|
0.14
|
630
|
11.5
|
12
|
7.9
|
10.5
|
8.0
|
93.6%
|
51.2%
|
6.4%
|
39.1
|
m10
|
49.45
|
0.05
|
698
|
10.6
|
2.1
|
7.3
|
10.4
|
10.1
|
93.1%
|
76.3%
|
0.7%
|
92.4
|
r01
|
39.15
|
0.03
|
283
|
10.6
|
3.2
|
6.7
|
9.6
|
9.4
|
72.1%
|
72.1%
|
27.9%
|
3.8
|
r02
|
21.50
|
0.11
|
225
|
11.2
|
4.9
|
6.3
|
7.5
|
6.5
|
46.4%
|
38.4%
|
53.6%
|
7.9
|
r03
|
24.90
|
0.03
|
447
|
10.4
|
2.8
|
7.7
|
7.9
|
6.5
|
64.0%
|
45.6%
|
34.7%
|
55.3
|
r09
|
34.15
|
0.11
|
244
|
11.3
|
11.3
|
7.2
|
9.7
|
8.3
|
89.7%
|
89.7%
|
3.6%
|
2.5
|
r12
|
30.80
|
0.06
|
417
|
11.7
|
3.3
|
6.6
|
9.7
|
9.0
|
82.9%
|
32.6%
|
5.3%
|
10.7
|
r13
|
32.80
|
0.09
|
223
|
10.7
|
6
|
6.3
|
9.7
|
9.3
|
51.6%
|
51.6%
|
48.4%
|
5.1
|
r14a
|
27.40
|
0.26
|
202
|
8.3
|
19.3
|
6.0
|
6.9
|
3.8
|
56.6%
|
56.6%
|
43.4%
|
1.5
|
r14b
|
41.05
|
0.20
|
248
|
10.6
|
14.2
|
6.6
|
6.7
|
3.0
|
90.5%
|
90.5%
|
9.5%
|
3.7
|
Median of physicochemical parameters calculated from 12 monthly measurements in 2018 |
NO3-, NH4+ and O2 in mg/L, EC in µS/cm, T and ΔT in °C, area in ha
The correlations of selected physicochemical parameters using the correlation matrix in Table 3 demonstrate a clear correlation of nitrate concentrations with the shares of farmland and cropland. In contrast, electrical conductivity (EC) exhibits a much weaker correlation with farmlands and croplands (r <0.5). The low correlation coefficient of r = 0.29 between NO3− and EC indicates that EC is likely dominated more by the solubility of the components of the geogenic parent substrate and other material inputs than by nitrate. The weak positive correlation of ammonium values with temperature amplitude ΔT and the weak negative correlation with oxygen content (O2 median and O2 min) indicate the influence of surface runoff at some springs. Springs with elevated amounts of surface runoff, relative to groundwater and interflow, exhibit greater temperature amplitudes over the course of the year. Increased temperatures lead to lower oxygen levels during the summer months. This is indicated by the negative correlation of ΔT and O2 min. The relationship to NH4+ suggests that ammonium enters predominantly via surface runoff, for example at livestock watering sites or from freshly fertilized land. Such relationships are found at springs r14a, r14b, and m02 (see Table 2). However, more than half of the agriculturally impacted springs exhibit very low ammonium levels (<0.1 mg/L) at significantly elevated NO3- concentrations. As expected, the principal component of nitrogen inputs is nitrate, which enters surface waters primarily via interflow.
Table 3
Correlation matrix between selected parameters based on Pearson's correlation coefficient r
R
|
NO3-
|
NH4+
|
EC
|
Δ T
|
O2 median
|
O2 min
|
Farmland [%]
|
Cropland [%]
|
NO3-
|
1.00
|
|
|
|
|
|
|
|
NH4+
|
0.00
|
1.00
|
|
|
|
|
|
|
EC
|
0.29
|
0.14
|
1.00
|
|
|
|
|
|
Δ T
|
-0.18
|
0.49
|
-0.11
|
1.00
|
|
|
|
|
O2 median
|
-0.03
|
-0.44
|
-0.27
|
-0.27
|
1.00
|
|
|
|
O2 min
|
0.07
|
-0.49
|
-0.20
|
-0.51
|
0.82
|
1.00
|
|
|
Farmland
|
0.80
|
0.09
|
0.47
|
0.00
|
-0.07
|
-0.09
|
1.00
|
|
Cropland
|
0.88
|
0.10
|
0.36
|
-0.07
|
-0.04
|
0.03
|
0.86
|
1.00
|
Bold = p <0.05 |
Interflow and groundwater
In order to determine the seepage and transport behavior of the near-surface groundwater, piezometers were installed at seven springs in the Rotliegend and in the Bunter sandstone and added to the monitoring sites in the program. In several random samples, the nitrate content of the springs and groundwater were measured at depths of approximately 1 m and 2 m. While the springs in areas where the predominant use is cropland exhibit elevated nitrate concentrations, as expected (11 - 33 mg/L in the Rotliegend and 47.5 mg/L and 59.8 mg/L in the Bunter sandstone), these concentrations are significantly reduced in the near-surface groundwater samples (see Table 4). At a depth of approximately 2 m, roughly 20% of the concentrations of the spring samples are still detected at the Bunter sandstone locations, while significantly lower values are detected in the upper horizons at depths of 1.2 - 1.4 (see Table 5).
Table 4
Nitrate concentration (mean) in piezometers and springs in Rotliegend / Lower Permian (numbers in sample ID indicate the sampling depth in m; 0 = spring)
Sample ID
|
Below ground [m]
|
Nitrate [mg/L)
|
% of spring concentration
|
ra_0
|
0
|
24.2
|
|
ra_2
|
2
|
0.3
|
1%
|
rb_0
|
0
|
29.4
|
|
rb_1
|
1
|
1.1
|
4%
|
rb_2
|
2
|
0.6
|
2%
|
rc_0
|
0
|
48.8
|
|
rc_1
|
1
|
0.6
|
1%
|
rc_2
|
2
|
0.3
|
1%
|
Numbers in sample-ID indicate the sampling depth in m; 0 = spring |
Table 5
Nitrate concentration (mean) in piezometers and springs in Bunter sandstone / Lower Triassic (numbers in sample-ID indicate the sampling depth in m; 0 = spring)
Sample ID
|
Below ground [m]
|
Nitrate [mg/L)
|
% of spring concentration
|
ba_0
|
0
|
59.8
|
|
ba_1.4
|
1.4
|
1.7
|
3%
|
ba_2
|
2
|
12.9
|
22%
|
bb_0
|
0
|
47.5
|
|
bb_1.2
|
1.2
|
2.0
|
4%
|
bb_1.9
|
1.9
|
9.8
|
20%
|
Numbers in sample-ID indicate the sampling depth in m; 0 = spring |
The piezometer samples from the Rotliegend consistently exhibit nitrate concentrations of <2 mg/L, whereby the values at a depth of 1 m are regularly somewhat higher than at 2 m. This demonstrates that in both the clayey-loamy soils of the Rotliegend and the sandy sites, the vertical transport of nitrate via infiltration is negligible in terms of quantity. The elevated nitrate concentrations of the piezometer samples from sites ba and bb at a depth of 2 m indicate that there is a connection to the respective groundwater body in both areas. The pollutions levels in both areas are clearly evident at approximately 10 mg/L. There is no such groundwater body in the Rotliegend, which means that elevated nitrate levels cannot be detected even at greater depths.
Nitrate levels in spring waters from 2002 to 2018
The focal point of this study was nitrate pollution in springs with an agricultural catchment area (agricultural area ≥25%). The measured nitrate concentrations at these 22 springs are, for the most part, clearly above 10 mg/L for all three measuring periods (see Fig. 2). An exception is site b06, which can be considered largely uncontaminated with nitrate levels of below 5 mg/L. The share of agricultural usage in the catchment area here is only 25% and consists exclusively of permanent grassland. In addition, there is a 100 m wide area of forest and field copses between the grassland area and the spring outcrop; this can further reduce the relatively low discharge of nitrates expected from the grassland area.
All other springs under agricultural influence exhibit mean nitrate concentrations that are, at times, significantly above 10 mg/L (median from 12 monthly measurements). The marine ecology target value of 14.2 mg/L is exceeded at all springs during at least one of the three monitoring periods. Nitrate levels above 30 mg/L were detected at ten springs during at least one monitoring period. The annual average exceeded the 50-mg/L limit value from the Nitrate Directive at three springs.
A comparison of the mean nitrate levels (medians) for the three monitoring periods reveals no clear trend for 2002, 2011-12 and 2018. On the one hand, nitrate levels from the three springs, m08, m10, and k10, which had peak nitrate levels above 70 mg/L in 2002, leveled off relatively consistently to approximately 50 mg/L in 2011-12 and 2018. On the other hand, a trend towards increasing nitrate levels can be observed in numerous springs with moderate pollution levels. The limit values from the Nitrate Directive are exceeded in individual measurements at these springs; however, the mean and median values over twelve months are significantly lower.
In particular, the springs in the Rotliegend (r01 to r14), with mean nitrate concentrations of between 25 and 40 mg/L over the two decades considered, exhibit no improvement. In some cases, a significant increase in nitrate pollution was observed between 2002 and 2018. The reason for this is likely the intensification of agricultural use and the lack of regulatory requirements for nitrate emissions below the legal limit. At the three springs with peak values that had exceeded the limit value of 50 mg/L in the past, appropriate measures had apparently been implemented to reduce emissions to the level just permissible.
Nitrate in springs without agricultural influence
In order to determine the background pollution that cannot be attributed to the influence of agriculture, the monitoring program also included springs with predominantly forested catchment areas (forest share >95%). Agricultural land use accounts for <1% of usage in the catchment areas of 24 springs; these springs can therefore be classified as largely unaffected by agriculture.
Fig. 3 shows the mean nitrate concentrations for the three monitoring periods and the medians of all measurements at the individual sites. The vast majority of these springs have consistently low nitrate levels of well below 10 mg/L. The determination limit for the analytical method was consistently undershot in 13 springs during 2018. Individual outliers, such as t04, t05, b12 and k12 were due to silvicultural activities such as clearing and/or the deposition of cuttings. The area around k01 is also strongly influenced by the activities of the former coal mining industry. In addition to local fills and excavations, there are also large-scale clearings and accumulations of residual wood.
The median of all measured values from the three periods is 3.26 mg/L (N = 769; see gray line in Fig. 3). The mean nitrate values from the three monitoring periods for the 24 forest springs (2002: 3.45 mg/L; 2011-12: 2.2 mg/L; 2018: 2.75 mg/L) are shown as comparison values in Fig. 2. Taking into account the ±2.3 mg/L measurement inaccuracy of the analytical method specified by the manufacturer, this results in a mean concentration of 5.6 mg/L nitrate as the threshold value for anthropogenic pollution in the study area. Nitrate levels that are clearly above this level therefore indicate increased anthropogenic inputs.
Seasonality of the nitrate content in comparison of the three monitoring periods
Due to its high degree of solubility, the release of nitrate from the soils of the agricultural areas into the spring waters depends, to a large extent, on the hydroclimatic conditions during the periods under consideration. During the growth phase, vegetation can absorb and thus retain a major part of the nitrate, even during heavier precipitation. Retention decreases sharply after the harvest and toward the end of the growing season in autumn, and the nitrate in the soil, which is released from fertilizer residues and now increasingly from decomposed, dead plant parts, can be freely washed out with the seepage. Considerable pollution peaks can particularly be expected after longer dry phases during late summer and autumn.
The three monitoring periods exhibit clear differences with regard to precipitation and temperature conditions. While annual precipitation for the 2011-12 and 2018 periods is slightly below the multi-year average for the 1981-2010 normal period, it exceeds the average by more than one-fifth in 2002 (see Table 1). The annual average temperatures for the first two periods are only slightly above the multi-year average. However, at almost 2°C above the multi-year mean, 2018 was significantly warmer than the other monitoring periods.
Differences in the monthly precipitation conditions over the course of the year can also be observed (see bars in Fig. 4). The highest amounts of precipitation generally occurred during the winter months for all three periods. However, the maximum for 2002 was in February, and the values for October and November clearly exceed those of December and January. During 2011-12 and 2018, on the other hand, a much drier autumn was followed by maximums in December and January. The 2011-12 phase is also characterized by a summer with relatively high precipitation.
To compare the trend of nitrate contents for the three monitoring periods during the course of the year, the monthly measured values are presented along with the monthly precipitation totals. Fig. 4 shows the annual trends of nitrate concentrations for two representative springs along with the monthly precipitation totals. As the measurements for the 2011-12 period were taken between July 2011 and June 2012, the monthly data for the two six-month periods, 2/2011 and 1/2012, are presented in reverse order for better comparability with the other monitoring periods. Thus, the period begins with January 2012 and ends with December 2011 and is referred to as the 2012-11 period for this review.
Nitrate pollution is relatively uniform without major peaks over the course of the year for the 2002 and 2018 monitoring periods at both sites, with the minimum in spring (March-April) and a continuous increase from May-August to the maximum in autumn/winter (beginning in October). This occurs largely in parallel with the monthly precipitation levels. At Bunter sandstone site b08, this was much more pronounced during 2018, which was a very warm, dry year, than in 2002, which was wet. At this site, the temporary maximums and minimums follow the precipitation curve somewhat, but there is some interference due to retention by vegetation during the growth phase (March-April), fertilization (increase May-June) and leaching after harvest (beginning in August). Heavy precipitation is less noticeable during the growing season (June) than in autumn/winter. At site m08, on the other hand, with the exception of the period from 2012-11, a very uniform course of nitrate pollution can be observed. This can be interpreted as a consequence of the different nitrate retention capacities of the two catchment areas. The predominantly sandy substrate in the Bunter sandstone at b08 has a good percolation capacity and a low retention capacity, whereby free nitrate in the soil solution, which comes from fertilizer applications or the decomposition of biomass, is discharged without delay to the spring via interflow during precipitation events. The clayey soils with shell limestone have greater water retention capacities and can retain significantly larger amounts of nitrate. With a well-equilibrated water balance and an existing nitrate surplus, leaching here is relatively continuous. The strong fluctuations at site b08 during the 2012-11 monitoring period can be explained by the intermittent drying up of the spring during the summer months.
Dependence of nitrate content on land use
The relationships between nitrate levels and some selected metrics have already been discussed above for the 2018 monitoring period. The strongest dependence is on the type of agricultural land use. There is a highly significant positive correlation with the share of cropland in the catchment area of the spring (r = 0.88; p <0.01). Grassland usage, on the other hand, has a much lesser impact on nitrate emissions in the catchment area. The correlation between the share of grassland and the mean nitrate concentration in the spring water is only slightly positive with r = 0.24 and not significant (p = 0.07) (see Fig. 5). This demonstrates that the highest nitrate emissions come from croplands, whereas grassland usage tends to have a neutral or even reducing effect on water pollution through dilution.
A comparison of the 50 springs for which measured values are available from all three monitoring periods reveals a highly significant correlation between the nitrate content and the share of cropland (p <0.01). Fig. 6 shows the regression lines for the three monitoring periods. The correlation coefficient increased slightly from 2002 (r = 0.82) to 2011-12 and 2018 (r = 0.87). When considering only those springs with a significant agricultural influence in terms of area (agricultural area >25%), the 2002 and 2018 correlation is highly significant, and 2011-12 is significant (p <0.05). Again, the significance of the correlation increased from 2002 to 2018.
A comparison of the regression lines for the three monitoring periods in Fig. 6 shows a very similar trend with almost the same slope. This correlation can be used to develop a regression model that estimates the mean nitrate concentrations attributable to non-point-source inputs, particularly from agriculture, based on the share of cropland in the catchment area.
Using the 150 median values of the twelve monthly measurements from the three periods, a coefficient of determination of R² = 0.72 yields a regression coefficient of m = 43.7 and a y-intercept b of 5.7.
The corresponding regression equation for predicting the potential nitrate content as a function of the share of cropland is:
where, Cnps stands for the predicted nitrate content in the watercourse caused by non-point sources in the selected catchment area i, and PCL represents the share of cropland in the selected catchment area i in terms of area.
As already shown above, the investigated spring catchment areas are to be regarded as representative in terms of topography, geology and land use structure for the rural regions of Saarland. Springs dominated by forest or grassland as well as sites with predominantly cropland areas were included. The relatively low nitrate emissions from the forest and grassland areas are included as a constant in the regression model via the y-axis intercept. Thus, the determined concentration Cnps at the monitoring site i of any watercourse in the study area represents the amount that is not caused by settlements or other point sources. Consequently, the share of point-source inputs can be estimated by taking the difference between the measured nitrate concentration at monitoring site i and the predicted value Cnps of the regression model.
where Cps represents the nitrate content attributable to point-source inputs to catchment area i, and Cm indicates the measured nitrate concentration at the outlet of catchment area i.
Thus, the nitrate concentrations measured at the outlet of catchment area i can be used to estimate the nitrate content attributable to point sources on the basis of the areal share of cropland in the catchment area:
The regression model assumes a close correlation between concentrations and areal proportions of the land use types in the catchment area. If reliable runoff values are available at the outlet of the catchment area, they can also be used to quantify the components of the nitrate load, each of which originates from point and non-point sources.
Pesticide contamination and nitrate levels
Pesticides are another pollution factor that can impair the ecological quality of surface waters right at their source; therefore, water samples from 25 springs in the monitoring program that are characterized as predominantly agricultural were analyzed for pesticide content and the content of pesticide degradation products. The samples were collected in April, June and October of 2019. Among the sampled springs were three forest springs (percentage of agricultural land <5%), which served as potentially uncontaminated reference sites (m01, b06).
A total of 26 pesticides and metabolites were detected. Atrazine, which has been banned in Germany since 1991 and in the EU since 2003, and its degradation product desethylatrazine were detected at four sites. However, the concentrations were below the limit values or orientation values of the surface water and drinking water ordinances. Worth noting is the detection of the neonicotinoid insecticide clothianidin at one spring (m08) during sampling in April 2019. As of February 2019, the application of this insecticide is no longer permitted. Atrazine was also detected at this site, and the spring exhibits the highest nitrate pollution of the entire study.
Fig. 7 shows the number of pesticidal agents detected along with the mean nitrate levels in 2018. A comparison of pesticide occurrences with nitrate contamination demonstrates a highly significant correlation between the number of detected substances and nitrate contamination (r = 0.77; p <0.01). The number of pesticides detected is also highly significantly positively correlated with the share of cropland (r = 0.64; p <0.01). In particular, the first relationship mentioned indicates a clear link between the intensity of conventional agricultural usage and water pollution. The absence of detected pesticides at sites m03 and k09, which each have nitrate levels around 20 mg/L with an agricultural land share of >85%, confirms the influence of management practices on emissions. Grasslands predominate at k09, particularly in the immediate vicinity of the spring, while the cropland and grassland areas at m03 are managed according to the principles of organic farming.