Abstract
Abstract. CO2 emission estimates from urban areas can be obtained with a network of in situ instruments measuring atmospheric CO2 combined with high-resolution (inverse) transport modelling. Because the distribution of CO2 emissions is highly heterogeneous in space and variable in time in urban areas, gradients of atmospheric CO2 (here, dry air mole fractions) need to be measured by numerous instruments placed at multiple locations around and possibly within these urban areas. This calls for the development of lower-cost medium-precision sensors to allow a deployment at required densities. Medium precision is here set to be a random error (uncertainty) on hourly measurements of ±1 ppm or less, a precision requirement based on previous studies of network design in urban areas. Here we present tests of newly developed non-dispersive infrared (NDIR) sensors manufactured by Senseair AB performed in the laboratory and at actual field stations, the latter for CO2 dry air mole fractions in the Paris area. The lower-cost medium-precision sensors are shown to be sensitive to atmospheric pressure and temperature conditions. The sensors respond linearly to CO2 when measuring calibration tanks, but the regression slope between measured and assigned CO2 differs between individual sensors and changes with time. In addition to pressure and temperature variations, humidity impacts the measurement of CO2, with all of these factors resulting in systematic errors. In the field, an empirical calibration strategy is proposed based on parallel measurements with the lower-cost medium-precision sensors and a high-precision instrument cavity ring-down instrument for 6 months. The empirical calibration method consists of using a multivariable regression approach, based on predictors of air temperature, pressure and humidity. This error model shows good performances to explain the observed drifts of the lower-cost medium-precision sensors on timescales of up to 1–2 months when trained against 1–2 weeks of high-precision instrument time series. Residual errors are contained within the ±1 ppm target, showing the feasibility of using networks of HPP3 instruments for urban CO2 networks. Provided that they could be regularly calibrated against one anchor reference high-precision instrument these sensors could thus collect the CO2 (dry air) mole fraction data required as for top-down CO2 flux estimates.
Highlights
Urban areas cover only a small portion (< 3 %) of the land surface but account for about 70 % of fossil fuel CO2 emissions (Liu et al, 2014; Seto et al, 2014)
Despite the built-in pressure compensation algorithm developed for HPP3.2, reported {CO2} and p can still co-vary with a positive (S2.1 and S2.2) or a negative (S2.3) correlation, indicating that an additional correction is required when aiming to achieve the best possible results
The laboratory tests reveal a strong sensitivity of reported CO2 dry air mole fractions to ambient air pressure for the HPP3.1 series and a significantly decreased, yet noticeable, sensitivity to pressure, for the upgraded HPP3.2 sensors equipped with the built-in manufacturer p correction
Summary
Urban areas cover only a small portion (< 3 %) of the land surface but account for about 70 % of fossil fuel CO2 emissions (Liu et al, 2014; Seto et al, 2014). Uncertainties of fossil fuel CO2 emissions from inventories based on statistics of fuel amounts and/or energy consumption are on the order of 5 % for OECD countries and up to 20 % in other countries (Andres et al, 2014) but they are larger in the case of cities (Bréon et al, 2015; Wu et al, 2016). Sampling strategies and measurement accuracies differ between these approaches, the commonly used principle is to measure atmospheric CO2 dry air mole fraction gradients at stations between the upwind and downwind vicinity of an emitting area and infer the emissions that are consistent with those CO2 gradients and their uncertainties, using an atmospheric transport model. This approach is known as atmospheric CO2 inversion or as a “top-down” estimate
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