Evaluation and Field Calibration of a Low-Cost Ozone Monitor at a Regulatory Urban Monitoring Station

在城市监管监测站对低成本臭氧监测仪进行评估和现场校准

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Abstract

The performance of a low cost ozone monitor (Aeroqual Series 500 portable gas monitors using a metal oxide sensor for ozone; model OZL) was assessed under field conditions. Ten ozone monitors were calibrated under clean-air laboratory conditions and controlled ozone concentrations of 5 to 100 ppb. Good linearity and response were obtained relative to a research-grade ozone monitor. One monitor was co-located at a regulatory air quality monitoring station that uses a U.S. federal equivalent method (FEM) ozone analyzer. Raw data from the Aeroqual monitor collected over 4 months (June-October) at a 10-minute time-resolution, showed good agreement (r(2)=0.83) with the FEM values but with an overestimation of ~12%. Data were averaged to different time resolutions; 1 h time averaged concentrations showed the best fit with the FEM results (r(2)=0.87). Data analyses suggested the potential of interferences due to temperature, relative humidity, nitrogen oxides, and volatile organic compounds. Correction models using temperature, humidity, and nitrogen dioxide (NO(2)) were tested to relate the monitor concentrations to the FEM values. Temperature and humidity were two readily available variables. The model (#3) that added NO(2) did not provide a substantial improvement in the fit. Thus, the models with only temperature and humidity can be easily developed by any user. The best model explained 91% of the variance and showed statistically significant improvement of the goodness of fits as well as decreased influence of the interfering variables on the diurnal and weekly patterns. The correction models were also able to lower the effect of seasonal temperature changes, allowing the use of the monitors over long-term sampling campaigns. Thus, the Aeroqual ozone monitor can return "FEM-like" concentrations after appropriate corrections. Data provided by a network of monitors could provide intra-urban spatial variations in ozone concentrations and provide more accurate human exposure assessments by reducing exposure misclassification.

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