The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To characterize the air quality (AQ), measurements must be fast (in real time), ascendible, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions.
Alternatively, the high accurate AQ measuring equipment still has relatively high price levels and is very difficult to use for non-professionals. On the global scale this results as a huge gap on the market for products which are easy to use e.g. user-friendly visualization tools, readability and smart system, which is able to send commands and alerts to the users.
Protecting populations from exposure to poor AQ is one of the greatest public health challenges affecting all nations on earth (WHO, 2014). For the past half century, developed countries have made an effort to measure concentrations of major pollutants known to degrade health or damage plants and physical structures. Generally, the focus has been on the most populated areas, with the goal to estimate it daily, monthly, or annual concentrations on a regional basis.
While greater spatial and temporal resolution has been desired, the costs of purchasing and operating instruments sufficiently robust, accurate, and free of interferences to generate reliable data has been prohibitive – an instrument to assess a single pollutant at ambient levels can cost many tens to hundreds of thousands of US dollars.
While electrochemical (EC) sensors have formed the basis for workplace and hazardous leak detection applications for many decades, their transition from workplace to ambient air is accompanied by much lower target concentration ranges over which the sensors must accurately measure the analyte species of interest. It is needed to fully understand and model the influence of the interferences resulting from changing temperature (T), relative humidity (RH), pressure (P), or other gas molecules that may compete with the oxidation– reduction reactions occurring at the working electrode (WE) of a given EC sensor.
InsightAir’s air quality sensors are not as accurate as the high-end scientific measuring devices. However, the availability of data, generated from InsightAir’s sensor network (multiple measurement locations log in 4D), outperforms the data generated by a handheld measuring device, which only generates data for a single point in place and in time.
More valuable and accurate insights can be gained from multiple location logging via InsightAir’s sensor network. Ideally, the sensor network can be considered as a primary source of data, assessing air quality. In particular cases the data can be double checked with highly accurate measurement devices on a certain intervals of time (eg. annually, monthly).