Model-based reconstruction of the time response of electrochemical air pollutant monitors to rapidly varying concentrations

Abstract

Electrochemical sensors are commonly used to measure concentrations of gaseous air pollutants in real time, especially for personal exposure investigations. The monitors are small, portable, and have suitable response times for estimating time-averaged concentrations. However, for transient exposures to air pollutants lasting only seconds to minutes, a non-instantaneous time response can cause measured values to diverge from actual input concentrations, especially when the pollutant fluctuations are pronounced and rapid. Using 38 Langan carbon monoxide (CO) monitors, which can be set to log data every 2 s, we found electrochemical sensor response times of 30-50 s. We derived a simple model based on Fick's Law to reconstruct a close to accurate time series from logged data. Starting with experimentally measured data for repetitive step input signals of alternating high and low CO concentrations, we were able to reconstruct a much improved 2-s concentration time series using the model. We also utilized the model to examine errors in monitor measurements for different averaging times. By selecting the averaging time based on the response time of the monitor, the error between actual and measured pollutant levels can be minimized. The methodology presented in this study is useful when aiming to accurately determine a time series of rapidly time-varying concentrations, such as for locations close to an active point source or near moving traffic.