Random forest meteorological normalisation models for Swiss PM10 trend analysis

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 PM10 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 PM10 trends ranged between −0.09 and −1.16 μg m−3 year−1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at −0.77 μg m−3 year−1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 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 PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 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

Economic complexity and environmental performance: Evidence from a world sample

This paper analyzes 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, highlighting that a country’s productive structure is associated with the amount of knowledge and know-how embodied in the goods it produces. Measuring environmental performance through the Environmental Performance Index, we show that moving to higher levels of economic complexity leads to better environmental performance and therefore, that product sophistication does not induce environmental degradation. Nevertheless, the effect of economic complexity on air quality is negative, i.e., exposure to PM2.5 and CO2 emissions increases. These findings remain robust across alternative econometric specifications. Furthermore, we highlight the link between the complexity of products and environmental performance at the micro-level. We build two product-level indexes that link a product to the average level of (a) environmental performance and (b) air pollution (CO2 emissions) in the countries that export it. With these indexes, we illustrate how the development of more sophisticated products is associated with changes in environmental quality and show that the complexity of an economy captures information about the country’s level of pollution

Economic complexity and environmental performance: Evidence from a world sample

This paper analyzes 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, highlighting that a country’s productive structure is associated with the amount of knowledge and know-how embodied in the goods it produces. Measuring environmental performance through the Environmental Performance Index, we show that moving to higher levels of economic complexity leads to better environmental performance and therefore, that product sophistication does not induce environmental degradation. Nevertheless, the effect of economic complexity on air quality is negative, i.e., exposure to PM2.5 and CO2 emissions increases. These findings remain robust across alternative econometric specifications. Furthermore, we highlight the link between the complexity of products and environmental performance at the micro-level. We build two product-level indexes that link a product to the average level of (a) environmental performance and (b) air pollution (CO2 emissions) in the countries that export it. With these indexes, we illustrate how the development of more sophisticated products is associated with changes in environmental quality and show that the complexity of an economy captures information about the country’s level of pollution

Statistical methods for accounting and understanding ozone trends derived from observations

Emissions of ozone precursors have been regulated in Europe since around 1990 with air quality control measures, which resulted in reductions of nitrogen oxides and volatile organic compounds concentrations. In order to understand how these measures have affected tropospheric ozone, it is important to investigate its long-term temporal evolution in different types of environments and various geographic regions. Uncertainties in ozone long-term trends are associated to variations originating from meteorological influence on ozone. Also, ozone temporal evolution can vary significantly among different regions and types of environment. In this PhD thesis we used sophisticated statistical tools, and developed robust statistical approaches to study long-term trends of tropospheric ozone that reflect emissions reductions. First, we focus on a meteorological adjustment of ozone observations in order to derive long-term trends with lower uncertainties compared to common practices. In addition, a classification scheme for stations in Europe is needed to interpret response of ozone in site groups with similar spatio-temporal characteristics. A detailed long-term trend analysis was performed based on decomposition of the mean ozone observations in the time domain. The different time-dependent variations of ozone were extracted, namely the long-term trend, seasonal and short-term variability. This allows subtraction of the meteorologically driven seasonal variation from the observations and estimation of long-term trends on de-seasonalized concentrations. In addition, ozone peak concentrations were investigated using a localized regression approach, which corrects for meteorological influence. The meteorological adjustment of the mean and peak ozone allows estimation of long-term trends with lower uncertainty. A site grouping in Europe was developed using the long-term and seasonal variations of ozone. The implemented clustering approach based on the long-term variation resulted in a site type classification while a geographical classification was achieved on the seasonal variation. We observed that, despite the implementation of regulations, mean ozone has been increasing in most of the sites until mid-2000s, although, afterwards, a decline or a leveling off was observed. The point when the trend changes from increasing to decreasing depends on the site type; the closer a site locates to emission sources the later the change occurred. Also, it was concluded that with time urban and rural environments become more similar in terms of ozone concentrations. On the other hand, peak ozone has been reducing in most stations, while in sites close to emissions it increased until mid-2000s when it started to level off. The influence of air pollutants hemispheric transport is depicted in remote sites, where ozone increased until beginning of 2000s and decreased afterwards. A two-dimensional classification scheme, reflecting site type and region, has showed that mainly the site type influences ozone long-term trends, while the location is more important for temporal changes in ozone inter-annual cycle and relationship to temperature

Trends of surface maximum ozone concentrations in Switzerland based on meteorological adjustment for the period 1990-2014

We investigate the temporal trends of peak ozone in Switzerland for the 1990-2014 time period. The meteorological conditions have a large influence on ozone formation and drive a large part of the variability in ozone observations. Therefore, the influence of meteorology on ozone was estimated using generalized additive models and removed from the ozone observations. A variable selection method was used for model building allowing the detection of the meteorological variables that have the largest effect on the variability of daily maximum ozone at each considered station. It was found that peak concentrations of ozone have been reducing in most of the stations, indicating a positive effect of implemented air pollution control measures on locally produced ozone. In the remote, high alpine site of Jungfraujoch a small upward trend of peak ozone was observed, most likely due to influence of hemispheric background ozone. In the most polluted traffic sites, peak ozone has for a different reason also been increasing until around 2003, when this trend started to level off. In traffic sites the increasing ozone concentrations due to reduced titration by nitrogen monoxide was the dominating process. One of the advantages of meteorological correction of ozone observations for trend estimation is that the uncertainty in the calculated trends is reduced. In addition, trend estimation based on meteorologically corrected ozone is less influenced by exceptional meteorological events during a specific time period, such as heat waves or by temporal changes in meteorological variables

Economic Complexity and Environmental Performance: Evidence from a World Sample

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, CO2, methane and nitrous oxide emissions increases, and these findings are robust across alternative econometric specifications.ISSN:1420-2026ISSN:1573-296

Temporal and spatial analysis of ozone concentrations in Europe based on timescale decomposition and a multi-clustering approach

Air quality measures that were implemented in Europe in the 1990s resulted in reductions of ozone precursor concentrations. In this study, the effect of these reductions on ozone is investigated by analyzing surface measurements of this pollutant for the time period between 2000 and 2015. Using a nonparametric timescale decomposition methodology, the long-term, seasonal and short-term variation in ozone observations were extracted. A clustering algorithm was applied to the different timescale variations, leading to a classification of sites across Europe based on the temporal characteristics of ozone. The clustering based on the long-term variation resulted in a site-type classification, while a regional classification was obtained based on the seasonal and short-term variations. Long-term trends of desea-sonalized mean and meteo-adjusted peak ozone concentrations were calculated across large parts of Europe for the time period 2000-2015. A multidimensional scheme was used for a detailed trend analysis, based on the identified clusters, which reflect precursor emissions and meteorological influence either on the inter-annual or the short-term timescale. Decreasing mean ozone concentrations at rural sites and increasing or stabilizing at urban sites were observed. At the same time, downward trends for peak ozone concentrations were detected for all site types. In addition, a reduction of the amplitude in the seasonal cycle of ozone and a shift in the occurrence of the seasonal maximum towards earlier time of the year were observed. Finally, a reduced sensitivity of ozone to temperature was identified. It was concluded that long-term trends of mean and peak ozone concentrations are mostly controlled by precursor emissions changes, while seasonal cycle trends and changes in the sensitivity of ozone to temperature are among other factors driven by regional climatic conditions