Ultraviolet‐visual spectroscopy estimation of nitrate concentrations in surface waters via machine learning

Author(s):  
Timothy J. Maguire ◽  
Karlynne R. Dominato ◽  
R. Paul Weidman ◽  
Scott O. C. Mundle
2021 ◽  
Author(s):  
Tainan Fonseca ◽  
Roberta Bittencourt Peixoto ◽  
Luana Pinho ◽  
Leticia Cotrim da Cunha ◽  
Ricardo Pollery ◽  
...  

<p>Eutrophication in coastal waters caused by non-treated urban discharges has been considered one of the most important effects of global change. At tropical latitudes, nutrient dynamics may be especially intense due to increased metabolic responses supported by high temperatures and solar incidence throughout the year. In addition, short-term variations, such as in rainfall and the tidal regime, may determine important changes in nutrient concentrations and the subsequent trophic status of coastal waters, which are still neglected especially during nocturnal periods due to common logistical constraints. Here, we assessed 24-h variations of water quality during the winter season in a tropical eutrophic bay that receives large inputs of nutrients from non-treated urban effluents (Guanabara Bay, RJ, Brazil). We measured concentrations of dissolved forms of nutrients (nitrate, nitrite, N-ammoniacal, phosphate, and silicate) and carbon (DOC), and oxygen (DO) associated with temperature, salinity, and pH in surface waters each 2h over two daily cycles (July and August 2018). Water samples for nutrients and DOC were preserved for later analysis, while other variables were measured in the field. A biomonitoring system with a submersible pump was used to collect surface coastal waters without bubbling, and along a 70 m pipe from the beach to the field lab. In turn, meteorological data were obtained from a city weather station located ~6 Km from the sampling area. The monthly accumulated precipitation with respect to the 24-h cycle in July was ~70% lower than in August (58 and 16 mm, respectively), although only that in July has showed a rainfall event during the sampling period. As a result, average DOC and N-ammoniacal concentrations in surface waters were ~50% lower, while nitrate, silicate and DO concentrations ~56, 164 and 50 % higher, respectively, during the 24-h cycle in August compared to July. Also, waters were slightly more basic and less saltier in August, contrasting with similar average values of phosphate concentrations and temperature between both sampling periods. Finally, DO concentrations indicated an intense metabolism, varying from a peak of supersaturation with high solar incidence to net autotrophy (2 pm) to undersaturation values as a proxy of net heterotrophy after the nocturnal period (6 am). In conclusion, this short-term study showed that higher monthly accumulated precipitation may dilute high DOC and N-ammoniacal concentrations in coastal aquatic ecosystems undergoing anthropogenic eutrophication. On the other hand, silicate and nitrate concentrations might be related to higher runoff inputs from the watershed. The event of precipitation in July also confirmed a drastic increase in nitrate concentrations, likely due to inputs from the watershed. Therefore, our findings reveal the complexity of accumulated and immediate effects of rainfall on nutrient levels in tropical coastal waters, which  highlight the importance of biomonitoring studies specially in urban areas.</p>


Author(s):  
Ana Maria OIŞTE ◽  
Iuliana Gabriela

Nitrates are natural components of the surface water, the amounts in surface waters is very important, usually being insignificant, but in urban area and along agricultural areas situated inside the river basin, their concentration is increased, being non-point sources and point sources located in the city. The nitrate concentrations is influenced both by the seasonal changes of rainfall and temperature as well as by land-use transformations, the variation being closely related to them. This paper presents the nitrate trends during seasons, the samples was taken in December, April and June from 76 points, the influence of the season characteristics being obvious. Obtained data indicates an upward trend, so that nitrate concentrations increase from December to June, higher values registered on tributaries of Bahlui river, caused by of the land-use, smaller flow and seasons variation of other physic-chemical parameters, otherwise the synergistic action of the natural and anthropic sources and parameters. Arc GIS software was used for illustrate the results followed by a statistics software were used to establish some correlations based on multivariate analysis. The analysis shows that non-point and anthropic sources of nitrates, influence nitrate regime during the succession of the seasons, nitrate levels almost doubled in surface water, or even higher in Bahlui river and its tributaries.


2001 ◽  
Vol 5 (3) ◽  
pp. 299-310 ◽  
Author(s):  
R. F. Wright ◽  
C. Alewell ◽  
J. M. Cullen ◽  
C. D. Evans ◽  
A. Marchetto ◽  
...  

Abstract. Long-term records of nitrogen in deposition and streamwater were analysed at 30 sites covering major acid sensitive regions in Europe. Large regions of Europe have received high inputs of inorganic nitrogen for the past 20 - 30 years, with an approximate 20% decline in central and northern Europe during the late 1990s. Nitrate concentrations in streamwaters are related to the amount of N deposition. All sites with less than 10 kgN ha-1 yr-1 deposition have low concentrations of nitrate in streamwater, whereas all sites receiving > 25 kgN ha-1 yr-1 have elevated concentrations. Very few of the sites exhibit significant trends in nitrate concentrations; similar analyses on other datasets also show few significant trends. Nitrogen saturation is thus a process requiring many decades, at least at levels of N deposition typical for Europe. Declines in nitrate concentrations at a few sites may reflect recent declines in N deposition. The overall lack of significant trends in nitrate concentrations in streams in Europe may be the result of two opposing factors. Continued high deposition of nitrogen (above the 10 kgN ha-1 yr-1 threshold) should tend to increase N saturation and give increased nitrate concentrations in run-off, whereas the decline in N deposition over the past 5 – 10 years in large parts of Europe should give decreased nitrate concentrations in run-off. Short and long-term variations in climate affect nitrate concentrations in streamwater and, thus, contribute "noise" which masks long-term trends. Empirical data for geographic pattern and long-term trends in response of surface waters to changes in N deposition set the premises for predicting future contributions of nitrate to acidification of soils and surface waters. Quantification of processes governing nitrogen retention and loss in semi-natural terrestrial ecosystems is a scientific challenge of increasing importance. Keywords: Europe, acid deposition, nitrogen, saturation, recovery, water


2004 ◽  
Vol 49 (3) ◽  
pp. 101-108 ◽  
Author(s):  
O. Oenema ◽  
L. van Liere ◽  
S. Plette ◽  
T. Prins ◽  
H. van Zeijts ◽  
...  

This study explores the effects of manure policy options for agricultural land in The Netherlands on nitrate leaching to groundwater, ammonia and nitrous oxide emissions to the atmosphere and on eutrophication of surface waters. The implementation of the farm gate balance MINAS at farm level, with levy-free N surpluses in the range of 300 to 40 kg per ha per year, and levy-free P surpluses in the range of 17.5 to 0.4 kg of P per ha per year, have been examined. Results indicate that nitrate concentrations in the upper groundwater are related to N surplus, land use, soil type and groundwater level. On dry sandy soils, the N surplus has to be below 60 to 140 kg of N per ha per year, depending on land use, to decrease the nitrate concentrations in the upper groundwater to below 50 mg nitrate per litre. Decreases of N and P concentrations in surface waters, upon lowering levy-free surpluses appear relatively small. For improving the ecological state of surface waters, we recommend a combination of low levy-free N and P surpluses with dredging P rich sediments, flushing of ditches, and decreasing discharges from other sources.


2020 ◽  
Author(s):  
Martin J. Wells ◽  
Troy E. Gilmore ◽  
Natalie Nelson ◽  
Aaron Mittelstet ◽  
John Karl Böhlke

Abstract. In this study, we explored the use of statistical machine learning and long-term groundwater nitrate monitoring data to estimate vadose-zone and saturated-zone lag times in an irrigated alluvial agricultural setting. Unlike most previous statistical machine learning studies that sought to predict groundwater nitrate concentrations within aquifers, the focus of this study was to leverage available groundwater nitrate concentrations and other environmental variable data to determine mean vertical velocities (transport rates) of water and solutes in the vadose zone and saturated zone (3.50 m/year and 3.75 m/year, respectively). Although a saturated-zone velocity that is greater than a vadose-zone velocity would be counterintuitive in most aquifer settings, the statistical machine learning results are consistent with two contrasting primary recharge processes in this aquifer: (1) diffuse recharge from irrigation and precipitation across the landscape, and (2) focused recharge from leaking irrigation conveyance canals. The vadose-zone mean velocity yielded a mean recharge rate (0.46 m/year) consistent with previous estimates from groundwater age-dating in shallow wells (0.38 m/year). The saturated zone mean velocity yielded a recharge rate (1.31 m/year) that was more consistent with focused recharge from leaky irrigation canals, as indicated by previous results of groundwater age-dating in intermediate-depth wells (1.22 m/year). Collectively, the statistical machine-learning model results are consistent with previous observations of relatively high-water fluxes and short transit times for water and nitrate in the aquifer. Partial dependence plots from the model indicate a sharp threshold where high groundwater nitrate concentrations are mostly associated with total travel times of seven years or less, possibly reflecting some combination of recent management practices and a tendency for nitrate concentrations to be higher in diffuse infiltration recharge than in canal leakage water. Limitations to the machine learning approach include potential non-uniqueness when comparing model performance for different transport rate combinations and highlight the need to corroborate statistical model results with a robust conceptual model and complementary information such as groundwater age.


2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Mary H. Ward ◽  
David C Wheeler ◽  
Bernard T. Nolan ◽  
Kyle Messier ◽  
Rena R Jones ◽  
...  

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