This review article discusses the challenges and opportunities in calibrating low-cost environmental sensors for air and water quality monitoring. Low-cost sensors are increasingly used due to their affordability and ability to provide real-time data, enabling more localized and granular environmental monitoring. However, their accuracy and reliability can be compromised without proper calibration. Calibration is challenging due to variability in sensing materials, transducer designs, and environmental conditions. Standardized calibration protocols are necessary to ensure data accuracy and reliability. The review addresses four key questions related to low-cost sensor calibration and accuracy. It discusses why low-cost sensors are used as an alternative to high-cost sensors, the importance of selectivity and sensitivity in generating accurate data, the impact of calibration functions on improved accuracy, and various approaches to enhance sensor accuracy, such as incorporating advanced data analysis techniques and improving sensing materials and transducer design. The use of reference-grade sensors for calibration and validation can also improve data accuracy and reliability. The review also highlights the importance of self-calibration techniques, which allow sensors to automatically calibrate themselves without external reference standards or manual intervention. These techniques are particularly valuable in situations where traditional calibration methods are impractical, time-consuming, or costly. Self-calibration methods vary depending on the sensor type and the specific factors being addressed. For example, zero calibration involves placing the sensor in a solution with zero target concentration to establish a baseline reading, while span calibration involves immersing the sensor in a known concentration of the target substance. Calibration techniques include methods such as offset and gain calibration, which define the relationship between independent and dependent variables. Various calibration models, including linear regression, multivariate linear regression, and machine learning algorithms, are used to validate calibrations obtained from low-cost sensors. However, linear regression may not be sufficient to model the relationship due to the influence of non-linear and environmental variables. Machine learning techniques may prove useful in accounting for these variables. The review also discusses the challenges and opportunities in calibrating low-cost sensors for water quality monitoring. Water quality sensors are often affected by biofouling, which can lead to uncertainty in measurements. Sensors with self-cleaning mechanisms can improve calibration results. Additionally, the need for low-cost sensor devices with antifouling characteristics should be investigated and developed for commercial use. Standard algorithms for sensor drift should also be developed. 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. Standardized calibration protocols and innovative approaches to enhance sensing materials and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.This review article discusses the challenges and opportunities in calibrating low-cost environmental sensors for air and water quality monitoring. Low-cost sensors are increasingly used due to their affordability and ability to provide real-time data, enabling more localized and granular environmental monitoring. However, their accuracy and reliability can be compromised without proper calibration. Calibration is challenging due to variability in sensing materials, transducer designs, and environmental conditions. Standardized calibration protocols are necessary to ensure data accuracy and reliability. The review addresses four key questions related to low-cost sensor calibration and accuracy. It discusses why low-cost sensors are used as an alternative to high-cost sensors, the importance of selectivity and sensitivity in generating accurate data, the impact of calibration functions on improved accuracy, and various approaches to enhance sensor accuracy, such as incorporating advanced data analysis techniques and improving sensing materials and transducer design. The use of reference-grade sensors for calibration and validation can also improve data accuracy and reliability. The review also highlights the importance of self-calibration techniques, which allow sensors to automatically calibrate themselves without external reference standards or manual intervention. These techniques are particularly valuable in situations where traditional calibration methods are impractical, time-consuming, or costly. Self-calibration methods vary depending on the sensor type and the specific factors being addressed. For example, zero calibration involves placing the sensor in a solution with zero target concentration to establish a baseline reading, while span calibration involves immersing the sensor in a known concentration of the target substance. Calibration techniques include methods such as offset and gain calibration, which define the relationship between independent and dependent variables. Various calibration models, including linear regression, multivariate linear regression, and machine learning algorithms, are used to validate calibrations obtained from low-cost sensors. However, linear regression may not be sufficient to model the relationship due to the influence of non-linear and environmental variables. Machine learning techniques may prove useful in accounting for these variables. The review also discusses the challenges and opportunities in calibrating low-cost sensors for water quality monitoring. Water quality sensors are often affected by biofouling, which can lead to uncertainty in measurements. Sensors with self-cleaning mechanisms can improve calibration results. Additionally, the need for low-cost sensor devices with antifouling characteristics should be investigated and developed for commercial use. Standard algorithms for sensor drift should also be developed. 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. Standardized calibration protocols and innovative approaches to enhance sensing materials and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.