Low-cost sensors for atmospheric NO₂ measurement: A review

Nitrogen dioxide (NO₂) is a major air pollutant in urban areas, prompting the development of numerous analytical methods for its monitoring. Among these, the chemiluminescence method stands out as the most commonly used and is widely regarded as a reference method. In recent years, the development of low-cost sensor (LCS) technology has facilitated the outdoor measurement of NO₂ using different analytical methods. Nevertheless, the performance of these methods needs to be evaluated against reference methods. This review aims to identify studies that utilize both LCS and reference methods for measuring NO₂ in urban environments and to assess the performance of LCS. For this purpose, we conducted a search across four different scientific databases (Scopus, Web of Science, PubMed, and ScienceDirect). For detailed analysis, 65 primary studies were selected based on criteria such as real-case applications using measured data and the requirement for reference instruments and sensors to be measured together outdoors. The results clearly indicate that the majority of studies were conducted in the USA (n = 14), the UK (n = 7), and China (n = 5). Electrochemical (EC) LCS were used in 95 % of the studies, while metal oxide semiconductor (MOS) LCS were utilized in only 17 %, with EC LCS outperforming MOS sensors. Among sensor performance evaluation methods, machine learning techniques were the most commonly employed (68 applications), followed by linear regression and multiple linear regression methods (38 and 36 applications, respectively). Additionally, 79 % of studies measured NO₂ alongside ozone. Ambient temperature and humidity were found to influence LCS measurements significantly. Enhancing LCS to minimize external interference and interaction with other pollutants could improve the performance and reliability of NO₂ measurements, facilitating higher-performance applications. The adoption of LCS can offer policymakers detailed insights for source identification, pollution hotspot detection, and trend analysis.