Abstract. Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM 10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM 10 trends ranged between −0.09 and −1.16 µg m −3 yr −1 with urban traffic sites experiencing the greatest mean decrease in PM 10 concentrations at −0.77 µg m −3 yr −1 . Similar magnitudes have been reported for normalised PM 10 trends for earlier time periods in Switzerland which indicates PM 10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM 10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM 10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

In this paper, we analyze the relationship between economic complexity and environmental performance using annual data on 88 developed and developing countries for the period of 2002–2012. We use the Economic Complexity Index, which links a country’s productive structure with the amount of knowledge and know-how embodied in the goods it produces, and the Environmental Performance Index as a measure of environmental performance. We show that moving to higher levels of economic complexity leads to better overall environmental performance, which means that sophistication of exported products does not induce environmental degradation. Nevertheless, we find that the effect of economic complexity on air quality is negative, i.e., exposure to PM2.5, CO$$_2$$ 2 , methane and nitrous oxide emissions increases, and these findings are robust across alternative econometric specifications.

Abstract.Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series.Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, 5 synoptic scale, boundary layer height, and time variables to explain daily PM 10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM 10 concentrations.Notably, two regimes were suggested by the models which cause elevated PM 10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, 15 and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

Regulation for ozone (O 3 ) precursor emissions began in Europe around 1990 with control measures primarily targeting industries and traffic. To understand how these measures have affected air quality, it is important to investigate the temporal evolution of tropospheric O 3 concentrations in different types of environments. In this study, we analyze long-term trends of the concentrations