Air pollution reaching hazardous levels in many Chinese cities has been a major concern in China over the past decades. New policies have been applied to regulate anthropogenic pollutant emissions, leading to changes in atmospheric composition and in particulate matter (PM) production. Increasing levels of atmospheric ammonia columns have been observed by satellite during recent years. In particular, observations from the Infrared Atmospheric Sounding Interferometer (IASI) reveal an increase of these columns by 15 % and 65 % from 2011 to 2013 and 2015, respectively, over eastern China. In this paper we performed model simulations for 2011, 2013 and 2015 in order to understand the origin of this increase and to quantify the link between ammonia and the inorganic components of particles: ${\mathrm{NH}}_{\mathrm{4}\left(\mathrm{p}\right)}^{+}/{\mathrm{SO}}_{\mathrm{4}\left(\mathrm{p}\right)}^{\mathrm{2}-}/{\mathrm{NO}}_{\mathrm{3}\left(\mathrm{p}\right)}^{-}$ . Interannual change of meteorology can be excluded as a reason: year 2015 meteorology leads to enhanced sulfate production over eastern China, which increases the ammonium and decreases the ammonia content, which is contrary to satellite observations. Reductions in SO₂ and NO_x emissions from 2011 to 2015 of 37.5 % and 21 % respectively, as constrained from satellite data, lead to decreased inorganic matter (by 14 % for ${\mathrm{NH}}_{\mathrm{4}\left(\mathrm{p}\right)}^{+}+{\mathrm{SO}}_{\mathrm{4}\left(\mathrm{p}\right)}^{\mathrm{2}-}+{\mathrm{NO}}_{\mathrm{3}\left(\mathrm{p}\right)}^{-}$ ). This in turn leads to increased gaseous NH_3(g) tropospheric columns by as much as 24 % and 49 % (sampled corresponding to IASI data availability) from 2011 to 2013 and 2015 respectively and thus can explain most of the observed increase.

China is a highly polluted region, particularly the North China Plain (NCP). However, emission reductions have been occurring in China for about the last 10 years; these reduction measures have been in effect since 2006 for SO₂ emissions and since 2010 for NO_x emissions. Recent studies have shown a decrease in the NO₂ tropospheric column since 2013 that has been attributed to the reduction in NO_x emissions. Quantifying how these emission reductions translate regarding ozone concentrations remains unclear due to apparent inconsistencies between surface and satellite observations. In this study, we use the lower tropospheric (LT) columns (surface – 6 km a.s.l. – above sea level) derived from the IASI-A satellite instrument to describe the variability and trend in LT ozone over the NCP for the 2008–2016 period. First, we investigate the IASI retrieval stability and robustness based on the influence of atmospheric conditions (thermal conditions and aerosol loading) and retrieval sensitivity changes. We compare IASI-A observations with the independent IASI-B instrument aboard the Metop-B satellite as well as comparing them with surface and ozonesonde measurements. The conclusion from this evaluation is that the LT ozone columns retrieved from IASI-A are reliable for deriving a trend representative of the lower/free troposphere (3–5 km). Deseasonalized monthly time series of LT ozone show two distinct periods: the first period (2008–2012) with no significant trend (< − 0.1 % yr⁻¹) and a second period (2013–2016) with a highly significant negative trend of −1.2 % yr⁻¹, which leads to an overall significant trend of −0.77 % yr⁻¹ for the 2008–2016 period. We explore the dynamical and chemical factors that could explain these negative trends using a multivariate linear regression model and chemistry transport model simulations to evaluate the sensitivity of ozone to the reduction in NO_x emissions. The results show that the negative trend observed from IASI for the 2013–2016 period is almost equally attributed to large-scale dynamical processes and emissions reduction, with the large El Niño event in 2015–2016 and the reduction of NO_x emissions being the main contributors. For the entire 2008–2016 period, large-scale dynamical processes explain more than half of the observed trend, with a possible reduction of the stratosphere–troposphere exchanges being the main contributor. Large-scale transport and advection, evaluated using CO as a proxy, only contributes to a small part of the trends ( ∼ 10 %). However, a residual significant negative trend remains; this shows the limitation of linear regression models regarding their ability to account for nonlinear processes such as ozone chemistry and stresses the need for a detailed evaluation of changes in chemical regimes with the altitude.

Excessive numerical diffusion is one of the major limitations in the representation of long-range transport by chemistry transport models. In the present study, we focus on excessive diffusion in the vertical direction, which has been shown to be a major issue, and we explore three possible ways of addressing this problem: increasing the vertical resolution, using an advection scheme with anti-diffusive properties and more accurately representing the vertical wind. This study was carried out using the CHIMERE chemistry transport model for the 18 March 2012 eruption of Mount Etna, which released about 3 kt of sulfur dioxide into the atmosphere in a plume that was observed by satellite instruments (the Infrared Atmospheric Sounding Interferometer instrument, IASI, and the Ozone Monitoring Instrument, OMI) for several days. The change from the classical Van Leer (1977) scheme to the Després and Lagoutière (1999) anti-diffusive scheme in the vertical direction was shown to provide the largest improvement to model outputs in terms of preserving the thin plume emitted by the volcano. To a lesser extent, the improved representation of the vertical wind field was also shown to reduce plume dispersion. Both of these changes helped to reduce vertical diffusion in the model as much as a brute-force approach (increasing vertical resolution).

Air pollution in Chinese megacities has reached extremely hazardous levels, and human activities are responsible for the emission or production of large amounts of particulate matter (PM). In addition to PM from anthropogenic sources, natural phenomena, such as dust storms over Asian deserts, may also emit large amounts of PM, which lead episodically to poor air quality over Chinese megacities. In this paper, we quantify the degradation of air quality by dust over Beijing, Chengdu and Shanghai megacities using the three dimensions (3D) chemistry transport model CHIMERE, which simulates dust emission and transport online. In the first part of our work, we evaluate dust emissions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Infrared Atmospheric Sounding Interferometer (IASI) satellite observations of aerosol optical depth, respectively, in the visible and the thermal infrared over source areas. PM simulations were also evaluated compared to surface monitoring stations. Then, mineral dust emissions and their impacts on particle composition of several Chinese megacities were analyzed. Dust emissions and transport over China were simulated during three years (2011, 2013 and 2015). Annual dust contributions to the PM ${}_{10}$ budget over Beijing, Chengdu and Shanghai were evaluated respectively as 6.6%, 9.5% and 9.3%. Dust outbreaks largely contribute to poor air quality events during springtime. Indeed it was found that dust significantly contribute for 22%, 52% and 43% of spring PM ${}_{10}$ events (for Beijing, Chengdu and Shanghai respectively).

Volcanic activity is an important source of atmospheric sulfur dioxide (SO₂), which, after conversion into sulfuric acid, induces impacts on rain acidity, human health, meteorology and the radiative balance of the atmosphere, among others. This work focuses on the conversion of SO₂ into sulfates (${\mathrm{SO}}_{\mathrm{4}\left(\mathrm{p}\right)}^{\mathrm{2}-}$ , S(+VI)) in the mid-tropospheric volcanic plume emitted by the explosive eruption of Mount Etna (Italy) on 12 April 2012, using the CHIMERE chemistry transport model. As the volcanic plume location and composition depend on several often poorly constrained parameters, using a chemistry transport model allows us to study the sensitivity of SO₂ oxidation to multiple aspects, such as volcanic water emissions, transition metal emissions, plume diffusion and plume altitude. Our results show that two pathways contribute to sulfate production in the mid-troposphere: (1) the oxidation of SO₂ by OH in the gaseous phase (70 %) and (2) aqueous oxidation by O₂ catalysed by Mn²⁺ and Fe³⁺ ions (25 %). Oxidation in the aqueous phase is the faster process, but liquid water is scarce in the mid-troposphere; therefore, the relative share of gaseous oxidation can be important. After 1 d in the mid-troposphere, about 0.5 % of the volcanic SO₂ was converted to sulfates via the gaseous process. Because of the nonlinear dependency of the kinetics in the aqueous phase on the amount of volcanic water emitted and on the availability of transition metals in the aqueous phase, several experiments have been designed to determine the prominence of different parameters. Our simulations show that, during the short time that liquid water remains in the plume, around 0.4 % of sulfates manage to quickly enter the liquid phase. Sensitivity tests regarding the advection scheme have shown that this scheme must be chosen wisely, as dispersion will impact both of the oxidation pathways explained above.

We present a supervised machine learning (ML) approach to improve the accuracy of the regional horizontal distribution of the aerosol optical depth (AOD) simulated by the CHIMERE chemistry transport model over North Africa and the Arabian Peninsula using Moderate Resolution Imaging Spectroradiometer (MODIS) AOD satellite observations. Our method produces daily AOD maps with enhanced precision and full spatial domain coverage, which is particularly relevant for regions with a high aerosol abundance, such as the Sahara Desert, where there is a dramatic lack of ground-based measurements for validating chemistry transport simulations. We use satellite observations and some geophysical variables to train four popular regression models, namely multiple linear regression (MLR), random forests (RF), gradient boosting (XGB), and artificial neural networks (NN). We evaluate their performances against satellite and independent ground-based AOD observations. The results indicate that all models perform similarly, with RF exhibiting fewer spatial artifacts. While the regression slightly overcorrects extreme AODs, it remarkably reduces biases and absolute errors and significantly improves linear correlations with respect to the independent observations. We analyze a case study to illustrate the importance of the geophysical input variables and demonstrate the regional significance of some of them.