A simple linear model to estimate ozone AOT40 at forest sites from raw passive sampling data

A rapid, empirical method is described to derive weekly estimates of AOT40 from passive sampling ozone measurement at forest sites. The method is based on a linear regression: it has been developed after three-years of measurements in Trentino (Northern Italy) and tested on an independent dataset from passive sampling sites across Italy. It provides good estimates on a weekly basis when compared with values measured by conventional monitors (0.85<R2<0.970; 97<RMSE<302), although the regression coefficients differ between urban and forest sites. When considering passive sampling at forest sites, estimates are comparable with those obtained by another estimation method based on modeling of hourly concentrations (R2=0.94; 131<RMSE<351). In addition, regression coefficients are similar to those found for conventional monitors at forest sites. A test on an independent passive sampling dataset provided similar results (0.86<R2<0.99; 65<RMSE<478). When weekly AOT40 estimates are summed up to obtain AOT40 over the May-July period, errors tend to accumulate, and the median deviation between the two passive sampling estimation methods is 11%. The suggested method does not need any assumption, complex calculations or modeling techniques, and it can be useful when other estimation approaches are not feasible either in principle or in practice. However, the method is not useful when estimates of hourly concentration are of interest