Magnus Persson

Low-cost capacitive (LCC) soil moisture sensors have considerable potential for precision agriculture due to their low cost, low energy consumption, and suitability for IoT-based systems. However, their field performance and the transferability of laboratory calibration to field conditions remain insufficiently documented. The objective of this study was to evaluate the field performance of LCC sensors calibrated in the laboratory and to assess practical calibration strategies for field application. Laboratory calibration was performed for eight soil types, and field performance was evaluated in four experiments under different soil and climatic conditions. The sensors showed high accuracy under laboratory conditions, with RMSE values of 0.002–0.022 m³/m³ for soil-specific calibration, but substantially larger errors when laboratory-derived calibrations were applied directly in the field (RMSE 0.055–0.191 m³/m³). Soil-specific field calibration gave the highest accuracy (RMSE 0.005–0.036 m³/m³), whereas a simplified one-point calibration also improved performance considerably (RMSE 0.006–0.078 m³/m³) while requiring much less effort. Sensor performance declined at high water contents and under saline conditions. The results show that low-cost capacitive sensors can be used for reliable field soil moisture monitoring.

Automated soil moisture systems are commonly used in precision agriculture and environmental monitoring. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 under lab and field conditions. The laboratory tests aimed at evaluating the response speed, best supply voltage, temperature dependency, calibration, and applicability for controlled infiltration column tests (one meter high). Laboratory tests indicated that the sensor is temperature and voltage-sensitive. The use of 5.5 V as supply voltage for the sensors drastically reduced the correlation between output and degree of soil saturation, thus we suggest the use of 3.3V. Soil temperature had a negligible impact on the sensor output: 0.27% of soil saturation degree per degree Celsius. For field implementation, a low-cost monitoring station was built using Arduino as a microcontroller and tested during three months. The sensors could represent daily and seasonal oscillation in soil moisture resulting from solar heating and precipitation. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor's response in dry soil. The monitored wetting front was compared to the one estimated by Hydrus model and had a high correlation. The low-cost sensor performance was later compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors promote soil moisture measurements with high accuracy at a high acquisition cost. On the other hand, low-cost sensors, such as the SKU:SEN0193, provide data with medium accuracy at a very low acquisition cost, enabling spatial monitoring through multiple-point measurements. Thus, the use of the SKU:SEN0193 sensor is suggested in projects with budget limitations with short duration where there is a medium requirement accuracy or when the spatial variability of soil water content is considerable. Despite the physical fragility of the hardware used (sensors and monitoring station) and the lower accuracy when compared to other commercial sensors, this work demonstrates through the case study of the SKU:SEN0193 sensor the possibility of using low-cost technologies for monitoring environmental variables.

Automated soil moisture systems are commonly used in precision agriculture. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 tested under lab and field conditions. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor response in dry soil. During the second stage of testing, the sensors were coupled to a low-cost monitoring station and installed in the field. The sensors were capable of measuring daily and seasonal oscillations in soil moisture resulting from solar radiation and precipitation. The low-cost sensor performance was compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors provide single-point information with high reliability but at a high acquisition cost, while low-cost sensors can be acquired in larger numbers at a lower cost, allowing for more detailed spatial and temporal observations, but with medium accuracy. The use of SKU sensors is then indicated for short-term and limited-budget projects in which high accuracy of the collected data is not required.

Current sustainability assessment (SA) tools to help deal with contaminated groundwater sites are inherently subjective and hardly applied. One reason may be lack of proper tools for addressing contaminant spread which are basically objective. To fill this gap, there is a need for contaminant transport models that provide site managers with needed room for applying their judgments and considerations about the efficiency of each remediation method based on their experiences in similar cases. INSIDE-T uses trend analysis and inverse modeling to estimate transport parameters. It then simulates contaminant transport both with and without the inclusion of remedial actions in a transparent way. The sustainability of each remedy measure can then be quantified based on the underlying SA tool (INSIDE). INSIDE-T was applied to a site in south Sweden, contaminated with pentachlorophenol. Simulation scenarios were developed to enable comparison between various remediation strategies and combinations of these. The application indicated that natural attenuation was not a viable option within the timeframe of interest. Although pump-and-treat combined with a permeable reactive barrier was found to be just as effective as bioremediation after five years, it received a much lower sustainability score overall. INSIDE-T outcomes enable site managers to test and evaluate different scenarios, a necessity in participatory decision-making practices such as remediation projects.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Capacitance sensors are widely used in agriculture for irrigation and soil management purposes. However, their use under saline conditions is a major challenge, especially for sensors operating with low frequency. Their dielectric readings are often biased by high soil electrical conductivity. New calculation approaches for soil water content (θ) and pore water electrical conductivity (ECp), in which apparent soil electrical conductivity (ECa) is included, have been suggested in recent research. However, these methods have neither been tested with low-cost capacitance probes such as the 5TE (70 MHz, Decagon Devices, Pullman, WA, USA) nor for field conditions. Thus, it is important to determine the performance of these approaches and to test the application range using the 5TE sensor for irrigated soils. For this purpose, sandy soil was collected from the Jemna oasis in southern Tunisia and four 5TE sensors were installed in the field at four soil depths. Measurements of apparent dielectric permittivity (Ka), ECa, and soil temperature were taken under different electrical conductivity of soil moisture solutions. Results show that, under field conditions, 5TE accuracy for θ estimation increased when considering the ECa effect. Field calibrated models gave better θ estimation (root mean square error (RMSE) = 0.03 m³ m⁻³) as compared to laboratory experiments (RMSE = 0.06 m³ m⁻³). For ECp prediction, two corrections of the Hilhorst model were investigated. The first approach, which considers the ECa effect on K’ reading, failed to improve the Hilhorst model for ECp > 3 dS m⁻¹ for both laboratory and field conditions. However, the second approach, which considers the effect of ECa on the soil parameter K₀, increased the performance of the Hilhorst model and gave accurate measurements of ECp using the 5TE sensor for irrigated soil.

The groundwater contamination risk in future climates was investigated at three locations in Sweden. Solute transport penetration depths were simulated using the HYDRUS-1D model using historical data and an ensemble of climate projections including two global climate models (GCMs), three emission scenarios and one regional climate model. Most projections indicated increasing precipitation and evapotranspiration until mid-century with a further increase at end-century. Results showed both increasing and decreasing groundwater contamination risks depending on emission scenario and GCM. Generally, the groundwater contamination risk is likely to be unchanged until mid-century, but higher at the end of the century. Soil and site specific relationships between Δ(P – PET) (i.e. change in the difference between precipitation, P, and potential evapotranspiration, PET) and changes in solute transport depths were determined. Using this, changes in solute transport depths for other climate projections can be assessed.

The present article is an assessment of wastewater treatment processes in the oil refinery sector in Kazakhstan by comparing relevant experience of developed and developing countries. The legislation in this sphere, the treatment methods, the discharge process and the effect on the environment were evaluated following international and national regulations. In our study, the wastewater systems in three factories in Kazakhstan were assessed. Results show that, even though the environmental regulation in Kazakhstan promotes the polluter pays principle and follows the World Health Organization (WHO) recommendations, the oil refinery plants in Kazakhstan still contain exceeding concentrations of pollutants in their effluents. One issue is that the local legislation allows disposal of wastewater to natural or artificial ponds as long as the concentrations of pollutants in effluents are less than the already existing concentrations in the pond. Consequently, the factories can use ponds with an initially high concentration of contaminants. The high initial concentration of pollutants in the pond water is due to wastewater discharged before the implementation of current environmental regulations. This issue in the current legislation leads to the situation where there is no incentive for efficient wastewater treatment. The national law also lacks regulations regarding which methodology should be used to assess the pollutants in the wastewater. Thus, the control by national environmental office for each enterprise is negotiated separately between the factory and the governmental body. This gives the factory a strong position to define the parameters assessing the effluents. This has led to none of the factories measuring, e.g., heavy metals in discharged wastewater. Total petroleum hydrocarbons (TPH) concentration in wastewater is often exceeded at each factory and there is no analysis done for different hydrocarbon fraction. To overcome the issues described in the present study, we strongly recommended a unified and transparent methodology for the country’s oil refinery industry to assess important pollutants in discharged wastewater.

Numerical simulations of unsaturated solute transport from point sources were carried out using HYDRUS-1D. Three different soil types cropped with spring wheat were considered at three different locations in Sweden: Malmö, Norrköping and Petisträsk. Two types of rainfall data were used, point-scale raingauge measurements and a gauge-adjusted weather radar product at four spatial resolutions, 2 × 2, 6 × 6, 10 × 10 and 14 × 14 km². The results showed that differences in the mean solute transport depths were small and not significant, with the exception of Petisträsk. Maximum transport depths were in most cases significantly larger using raingauge data compared to radar data. The results showed that using areal-averaged rainfall input will give solute transport estimations close to those using point-measured data. This shows the great potential in using radar-measured rainfall data in small-scale hydrological applications.