The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Assuring the quantity and quality of groundwater resources is essential for the well-being of human and ecological health, society, and the economy. For the last few decades, groundwater vulnerability modeling techniques have become essential for groundwater protection and management. Groundwater contamination is highly dynamic due to its dependency on recharge, which is a function of time-dependent parameters such as precipitation and evapotranspiration. Therefore, it is necessary to consider the time-series analysis in the “approximation” process to model the dynamic vulnerability of groundwater contamination. This systematic literature review (SLR) aims to critically review the methods used to evaluate the spatiotemporal assessment of groundwater vulnerability. The PRISMA method was employed to search web platforms and refine the collected research articles by applying certain inclusion and exclusion criteria. Despite the enormous growth in this field in recent years, spatiotemporal variations in precipitation and evapotranspiration were not considered considerably. Groundwater contamination vulnerability assessment needs to integrate the multicriteria decision support tools for better analysis of the subsurface flow, residence time, and groundwater recharge. Holistic approaches need to be formulated to evaluate the groundwater contamination in changing climatic scenarios and uncertainties, which can provide knowledge and tools with which to prepare sustainable groundwater management strategies.

Regional-scale groundwater contamination estimation is crucial for sustainable water management. The primary obstacles in evaluating groundwater include limited data availability, small sample sizes, and difficulties in linking concentration levels to land use patterns. Linear regression identifies the relationship between measured concentrations and both natural and human-influenced factors. However, the primary difficulty with this method lies in choosing a group of regressors that meet all necessary criteria for the model when multiple potential regressors exist. This study introduces a buffer-based land-use linear regression method to develop a catchment-scale model for predicting nitrate concentrations in groundwater. The model successfully captures 85 % of the spatial variability in nitrate across the study area, as indicated by the validation results from 32 training sites. The model's prediction capability and ability to capture the spatial variability of nitrate concentration were found to be good in the model development (R² = 0.89) and validation (R² = 0.80) steps. The model performed well in the accuracy assessment and error estimation processes (RMSE = 0.025 and MAE = 0.020). In future, this LUR model can be reparameterised with the latest available time series datasets to capture climate change scenarios. While this study focused on a small sub-catchment of the Bonet River, the methodology has the potential to be applied in a border study area. Future studies with a more robust methodology and more accurate predictor variables to explain the influence of the contamination sources, transport and attenuation processes can improve the buffer-based LUR technique for better model adaptation and applicability to other study areas.