Search

Surface ozone concentrations increased in many regions of China from 2015 to 2019. While the central role of meteorology in modulating ozone pollution is widely acknowledged, its quantitative contribution remains highly uncertain. Here, we use a data-driven machine learning approach to assess the impacts of meteorology on surface ozone variations in China for the period 2015–2019, considering the months of highest ozone pollution from April to October. To quantify the importance of various meteorological driver variables, we apply nonlinear random forest regression (RFR) and linear ridge regression (RR) to learn about the relationship between meteorological variability and surface ozone in China, and contrast the results to those obtained with the widely used multiple linear regression (MLR) and stepwise MLR. We show that RFR outperforms the three linear methods when predicting ozone using local meteorological predictor variables, as evident from its higher coefficients of determination (R2) with observations (0.5–0.6 across China) when compared to the linear methods (typically R2 = 0.4–0.5). This refers to the importance of nonlinear relationships between local meteorological factors and ozone, which are not captured by linear regression algorithms. In addition, we find that including nonlocal meteorological predictors can further improve the modelling skill of RR, particularly for southern China where the averaged R2 increases from 0.47 to 0.6. Moreover, this improved RR shows a higher averaged meteorological contribution to the increased trend of ozone pollution in that region, pointing towards an elevated importance of large-scale meteorological phenomena for ozone pollution in southern China. Overall, RFR and RR are in close agreement concerning the leading meteorological drivers behind regional ozone pollution. In line with expectations, our analysis underlines that hot and dry weather conditions with high sunlight intensity are strongly related to high ozone pollution across China, thus further validating our novel approach. In contrast to previous studies, we also highlight surface solar radiation as a key meteorological variable to be considered in future analyses. By comparing our meteorology based predictions with observed ozone values between 2015 and 2019, we estimate that almost half of the 2015–2019 ozone trends across China might have been caused by meteorological variability. These insights are of particular importance given possible increases in the frequency and intensity of weather extremes such as heatwaves under climate change.

The vertical mixing state of the atmosphere, as well as the atmospheric boundary layer (ABL) height, are important atmospheric transport model parameters for the accurate simulation of greenhouse gas concentrations. In order to use tall tower greenhouse gas measurements to quantify regional scale emissions (top-down, inverse estimates) an estimate of the atmospheric transport model uncertainty across the time series of study is needed. Several methods have been used to estimate this, often relying on arbitrary thresholds or a combination of parameters (such as vertical gradients if a gas is measured at multiple points). Here we study if radon has potential as an independent measurement to assess model uncertainty.Radon is a radioactive noble gas present in our atmosphere and is a good tracer of mixing processes in the ABL due to its properties. Hence, measurements of atmospheric radon concentration can provide useful insights into the vertical mixing state of the atmosphere, and in turn may help to calibrate and validate atmospheric dispersion models.In this study, we use high temporal resolution atmospheric measurements of radon and CH4 from four tall tower sites in the UK, which are part of the Deriving Emissions linked to Climate Change (DECC) network: Heathfield (HFD), Ridge Hill (RGH), Tacolneston (TAC) and Weybourne (WAO). At each site, CH4 is measured at two or three different heights, while radon is measured at one height.To determine a metric whereby single-height measurements of radon can provide a proxy for vertical mixing states, we compare the diurnal cycle of the measured radon concentration with the modelled radon (calculated by the Met Office Numerical Atmospheric Modelling Environment (NAME) dispersion model and radon flux maps). The largest uncertainties are shown to be before sunrise and after sunset right before the inversion layer was formed/destroyed. The diurnal CH4 vertical gradient at these times is also compared with the modelled CH4 vertical gradient.

Long term records are vital for understanding the way in which our environment is changing. A significant rise in atmospheric methane began in 2007 and has accelerated thereafter, particularly since 2014. This trend was observed globally and was coupled with a sustained isotopic shift to values more depleted in 13C (more negative δ13C-CH4). Currently, there is no consensus as to why these observations have occurred. However, long term methane isotopic measurements can provide information about changes in the source mix of this important greenhouse gas. Here, long term records of both methane mole fraction and δ13C-CH4 from 5 sites across the UK are presented, showing an increase in CH4 and a decrease in 13C from 2007, similar to those recorded globally, but at the regional-local scale. The approximately weekly in-situ measurements offer an insight into both suburban and background regions of the UK. A method for isotopic discrimination from longer term atmospheric measurements of CO2 and δ13C-CH4 as outlined by Miller and Tans (2003) is utilised in this work. Miller-Tans analysis allows for the explicit specification of background values, vital when dealing with long term records due to both seasonal, local, regional and global background variations in atmospheric CH4 and δ13C-CH4. When applying the Miller-Tans method to the long-term data from UK sites, as expected, the heaviest δ13C-CH4 source signatures, which are associated with industrial sources such as gas leaks, are observed for the suburban sites, and biogenic, lighter, sources for the background sites. The methane source distribution is compared to results from mobile measurements carried out at Royal Holloway, University of London and to the UK National Atmospheric Emissions Inventory (NAEI). From the initial results, it seems that there is a larger proportion of thermogenic/pyrogenic emissions in this data compared to the NAEI inventory. At all sites, there has been a post 2006 decline in δ13C-CH4. Using the Miller Tans method, it is possible to calculate bulk regional source signatures which highlight a distinction between rural and suburban emissions, in general agreement with the NAEI.

We present a novel high-resolution inverse modelling system ("FLEXVAR") based on FLEXPART-COSMO back trajectories driven by COSMO meteorological fields at 7 km × 7 km resolution over the European COSMO-7 domain and the four-dimensional variational (4DVAR) data assimilation technique. FLEXVAR is coupled offline with the global inverse modelling system TM5-4DVAR to provide background mole fractions ("baselines") consistent with the global observations assimilated in TM5-4DVAR. We have applied the FLEXVAR system for the inverse modelling of European emissions in 2018 using 24 stations with in situ measurements, complemented with data from five stations with discrete air sampling (and additional stations outside the European COSMO-7 domain used for the global TM5-4DVAR inversions). The sensitivity of the FLEXVAR inversions to different approaches to calculate the baselines, different parameterizations of the model representation error, different settings of the prior error covariance parameters, different prior inventories and different observation data sets are investigated in detail. Furthermore, the FLEXVAR inversions are compared to inversions with the FLEXPART extended Kalman filter ("FLExKF") system and with TM5-4DVAR inversions at 1° × 1° resolution over Europe. The three inverse modelling systems show overall good consistency of the major spatial patterns of the derived inversion increments and in general only relatively small differences in the derived annual total emissions of larger country regions. At the same time, the FLEXVAR inversions at 7 km × 7 km resolution allow to better reproduce the observations than the TM5 4DVAR simulations at 1° × 1°. The three inverse models derive higher annual total CH4 emissions in 2018 for Germany, France and BENELUX compared to the sum of anthropogenic emissions reported to UNFCCC and natural emissions estimated from the Global Carbon Project CH4 inventory, but the uncertainty ranges of top-down and bottom-up total emission estimates overlap for all three country regions. In contrast, the top-down estimates for the sum of emissions from the United Kingdom and Ireland agree relatively well with the total of anthropogenic and natural bottom-up inventories.

Surface ozone concentrations have been increasing in many regions of China for the past few years, in contrast to policy-driven declines in other key air pollutants such as particulate matter. While the central role of meteorology in modulating ozone pollution is widely acknowledged, its quantitative contribution remains highly uncertain. Here, we use a data-driven machine learning approach to assess the impacts of meteorology on surface ozone variations in China for the years 2015 to 2019, considering the months of highest ozone pollution from April to October. To quantify the importance of various meteorological driver variables, we apply non-linear random forest regression (RFR) and linear ridge regression (RR) to learn relationships between meteorological variability and surface ozone in China, and contrast the results to those obtained with the widely used multiple linear regression (MLR) and stepwise MLR. We show that RFR outperforms the three linear methods when predicting ozone using only local meteorological predictor variables. This implies the importance of non-linear relationships between local meteorological factors and ozone, which are not captured by linear regression algorithms. In addition, we find that including non-local meteorological predictors can further improve the modelling skill of RR, particularly for Southern China, highlighting the importance of large-scale meteorological phenomena for ozone pollution in that region. Overall, RFR and RR are in close agreement concerning the leading meteorological drivers behind regional ozone pollution. For example, we find that temperature variations are the dominant meteorological driver for ozone pollution in Northern China (e.g., Beijing Tianjin Hebei region), whereas variations in relative humidity are the most important factor in Southern China (e.g., Pearl River Delta). Variability in surface solar radiation modulates photochemistry but was not considered as such in previous controlling factor analyses, and is found to be the most important predictor in the Yangtze River Delta and Sichuan Basin regions. In general, our analysis underlines that hot and dry weather conditions with high sunlight intensity are strongly related to high ozone pollution across China. This further validates our novel approach to quantify the central role of meteorology. By contrasting our meteorological ozone predictions with ozone measurements between 2015 and 2019, we estimate that almost half of the observed ozone trends across China might have been caused by meteorological variabilities on average. We highlight that these insights are of particular importance given possible increases in the frequency and intensity of weather extremes such as heatwaves under climate change.