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. Atmospheric aerosol microphysical processes are asignificant source of uncertainty in predicting climate change.Specifically, aerosol nucleation, emissions, and growth rates, which aresimulated in chemical transport models to predict the particle sizedistribution, are not understood well. However, long-term size distributionmeasurements made at several ground-based sites across Europe implicitlycontain information about the processes that created those sizedistributions. This work aims to extract that information by developing andapplying an inverse technique to constrain aerosol emissions as well asnucleation and growth rates based on hourly size distribution measurements.We developed an inverse method based upon process control theory into anonline estimation technique to scale aerosol nucleation, emissions, andgrowth so that the model–measurement bias in three measured aerosolproperties exponentially decays. The properties, which are calculated fromthe measured and predicted size distributions, used to constrain aerosolnucleation, emission, and growth rates are the number of particles witha diameter between 3 and 6 nm, the number with a diameter greater than 10 nm,and the total dry volume of aerosol (N3–6, N10, Vdry),respectively. In this paper, we focus on developing and applying theestimation methodology in a zero-dimensional “box” model as aproof of concept before applying it to a three-dimensional simulation insubsequent work. The methodology is first tested on a dataset of syntheticand perfect measurements that span diverse environments in which the trueparticle emissions, growth, and nucleation rates are known. The inversetechnique accurately estimates the aerosol microphysical process rates withan average and maximum error of 2 % and 13 %, respectively. Next, weinvestigate the effect that measurement noise has on the estimated rates.The method is robust to typical instrument noise in the aerosol propertiesas there is a negligible increase in the bias of the estimated process rates.Finally, the methodology is applied to long-term datasets of in situ sizedistribution measurements in western Europe from May 2006 through June 2007.At Melpitz, Germany, and Hyytiälä, Finland, the average diurnalprofiles of estimated 3 nm particle formation rates are reasonable, havingpeaks near noon local time with average peak values of 1 and 0.15 cm−3 s−1, respectively. Thenormalized absolute error in estimated N3–6, N10, and Vdry atthree European measurement sites is less than 15 %, showing that theestimation framework developed here has potential to decreasemodel–measurement bias while constraining uncertain aerosol microphysicalprocesses.

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0