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<p><span class="inline-formula">CO₂</span> emission estimates from urban areas can be obtained with a network of in situ instruments measuring atmospheric <span class="inline-formula">CO₂</span> combined with high-resolution (inverse) transport modelling. Because the distribution of <span class="inline-formula">CO₂</span> emissions is highly heterogeneous in space and variable in time in urban areas, gradients of atmospheric <span class="inline-formula">CO₂</span> (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 <span class="inline-formula">±1</span>&thinsp;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 <span class="inline-formula">CO₂</span> 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 <span class="inline-formula">CO₂</span> when measuring calibration tanks, but the regression slope between measured and assigned <span class="inline-formula">CO₂</span> differs between individual sensors and changes with time. In addition to pressure and temperature variations, humidity impacts the measurement of <span class="inline-formula">CO₂</span>, 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 <span class="inline-formula">±1</span>&thinsp;ppm target, showing the feasibility of using networks of HPP3 instruments for urban <span class="inline-formula">CO₂</span> networks. Provided that they could be regularly calibrated against one anchor reference high-precision instrument these sensors could thus collect the <span class="inline-formula">CO₂</span> (dry air) mole fraction data required as for top-down <span class="inline-formula">CO₂</span> flux estimates.</p>