Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land–sea and mountain–valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on.

Outliers are frequent in CO₂ and CH₄ observations at rural sites. The aim of this paper is to establish a procedure based on the lag-1 autocorrelation to form measurement groups, some of which include outliers, and the rest include regular measurements. Once observations are classified, a second objective is to determine the number of harmonics in order to suitably describe the annual evolution of both gases. Monthly CO₂ and CH₄ percentiles were calculated over a six-year period. Linear trends for most of the percentiles were around 2.24 and 0.0097 ppm year⁻¹, and the interquartile ranges of residuals calculated from detrended concentrations were 6 and 0.02 ppm for CO₂ and CH₄, respectively. Five concentration groups were proposed for CO₂ and six were proposed for CH₄ from the lag-1 autocorrelation applied to detrended observations. Monthly medians were calculated in each group, and combinations of harmonics were applied in an effort to fit the annual cycle. Finally, adding annual and semi-annual harmonics successfully described the cycle where one step was observed in the concentration decrease in spring, not only for high CO₂ percentiles but also for low CH₄ percentiles.

The main objective of this paper is to implement different methods to assess the salient features of the data trend for a CO₂ and CH₄ data series. Said series was obtained at the Low Atmosphere Research Centre (41°48′49″ N, 4°55′59″ W) using a Picarro analyser (G1301). Different functions were employed to determine and quantify the data trend. The first was a harmonic function based on a third-degree polynomial. An increasing trend, below 2.30 ppm year⁻¹ for CO₂ and below 11.90 ppb year⁻¹ for CH₄, was reported. Epanechnikov, Gaussian, biweight, tricubic, rectangular and triangle kernels, were applied with a 500-day bandwidth for the trend. The best fit was obtained by the biweight kernel (r > 0.20), with an increasing trend around 1.80 ppm year⁻¹ for CO₂ and around 7.15 ppb year⁻¹ for CH₄. The final analysis, which included local linear regression functions also applying a 500-day bandwidth, revealed increasing trends for both CO₂, around 1.98 ppm year⁻¹, and CH₄, around 10.85 ppb year⁻¹. Trend values were far more accelerated in the latter years of the series, regardless of the chosen function. This paper demonstrated the usefulness of the mathematical functions, allowing for an accurate determination of the data trend.