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Reactive nitrogen emissions into the atmosphere are increasing due to human activities, affecting nitrogen deposition to the surface and impacting the productivity of terrestrial and marine ecosystems. An atmospheric chemistry-transport model (TM4-ECPL) is here used to calculate the global distribution of total nitrogen deposition, accounting for the first time for both its inorganic and organic fractions in gaseous and particulate phases, and past and projected changes due to anthropogenic activities. The anthropogenic and biomass burning ACCMIP historical and RCP6.0 and RCP8.5 emissions scenarios are used. Accounting for organic nitrogen (ON) primary emissions, the present-day global nitrogen atmospheric source is about 60% anthropogenic, while total N deposition increases by about 20% relative to simulations without ON primary emissions. About 20-25% of total deposited N is ON. About 10% of the emitted nitrogen oxides are deposited as ON instead of inorganic nitrogen (IN) as is considered in most global models. Almost a 3-fold increase over land (2-fold over the ocean) has been calculated for soluble N deposition due to human activities from 1850 to present. The investigated projections indicate significant changes in the regional distribution of N deposition and chemical composition, with reduced compounds gaining importance relative to oxidized ones, but very small changes in the global total flux. Sensitivity simulations quantify uncertainties due to the investigated model parameterizations of IN partitioning onto aerosols and of N chemically fixed on organics to be within 10% for the total soluble N deposition and between 25-35% for the dissolved ON deposition. Larger uncertainties are associated with N emissions.

The mixing layer is an important meteorological factor that affects air pollution. In this study, the atmospheric mixing layer height (MLH) was observed in Beijing from July 2009 to December 2012 using a ceilometer. By comparison with radiosonde data, we found that the ceilometer underestimates the MLH under conditions of neutral stratification caused by strong winds, whereas it overestimates the MLH when sand-dust is crossing. Using meteorological, PM2.5, and PM10 observational data, we screened the observed MLH automatically; the ceilometer observations were fairly consistent with the radiosondes, with a correlation coefficient greater than 0.9. Further analysis indicated that the MLH is low in autumn and winter and high in spring and summer in Beijing. There is a significant correlation between the sensible heat flux and MLH, and the diurnal cycle of the MLH in summer is also affected by the circulation of mountainous plain winds. Using visibility as an index to classify the degree of air pollution, we found that the variation in the sensible heat and buoyancy term in turbulent kinetic energy (TKE) is insignificant when visibility decreases from 10 to 5 km, but the reduction of shear term in TKE is near 70 %. When visibility decreases from 5 to 1 km, the variation of the shear term in TKE is insignificant, but the decrease in the sensible heat and buoyancy term in TKE is approximately 60 %. Although the correlation between the daily variation of the MLH and visibility is very poor, the correlation between them is significantly enhanced when the relative humidity increases beyond 80 %. This indicates that humidity-related physicochemical processes is the primary source of atmospheric particles under heavy pollution and that the dissipation of atmospheric particles mainly depends on the MLH. The presented results of the atmospheric mixing layer provide useful empirical information for improving meteorological and atmospheric chemistry models and the forecasting and warning of air pollution.

Isoprene is a significant source of atmospheric organic aerosol; however, the oxidation pathways that lead to secondary organic aerosol (SOA) have remained elusive. Here, we identify the role of two key reactive intermediates, epoxydiols of isoprene (IEPOX = β-IEPOX + δ-IEPOX) and methacryloylperoxynitrate (MPAN), which are formed during isoprene oxidation under low- and high-NOx conditions, respectively. Isoprene low-NOx SOA is enhanced in the presence of acidified sulfate seed aerosol (mass yield 28.6%) over that in the presence of neutral aerosol (mass yield 1.3%). Increased uptake of IEPOX by acid-catalyzed particle-phase reactions is shown to explain this enhancement. Under high-NOx conditions, isoprene SOA formation occurs through oxidation of its second-generation product, MPAN. The similarity of the composition of SOA formed from the photooxidation of MPAN to that formed from isoprene and methacrolein demonstrates the role of MPAN in the formation of isoprene high-NOx SOA. Reactions of IEPOX and MPAN in the presence of anthropogenic pollutants (i.e., acidic aerosol produced from the oxidation of SO₂ and NO₂, respectively) could be a substantial source of \"missing urban SOA\" not included in current atmospheric models.

New particle formation in the atmosphere is an important parameter in governing the radiative forcing of atmospheric aerosols. However, detailed nucleation mechanisms remain ambiguous, as laboratory data have so far not been successful in explaining atmospheric nucleation. We investigated the formation of new particles in a smog chamber simulating the photochemical formation of H₂SO₄ and organic condensable species. Nucleation occurs at H₂SO₄ concentrations similar to those found in the ambient atmosphere during nucleation events. The measured particle formation rates are proportional to the product of the concentrations of H₂SO₄ and an organic molecule. This suggests that only one H₂SO₄ molecule and one organic molecule are involved in the rate-limiting step of the observed nucleation process. Parameterizing this process in a global aerosol model results in substantially better agreement with ambient observations compared to control runs.

Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

Tropospheric ozone (O ) is a key component of air pollution and an important anthropogenic greenhouse gas . During the twentieth century, the proliferation of the internal combustion engine, rapid industrialization and land-use change led to a global-scale increase in O concentrations ; however, the magnitude of this increase is uncertain. Atmospheric chemistry models typically predict an increase in the tropospheric O burden of between 25 and 50 per cent since 1900, whereas direct measurements made in the late nineteenth century indicate that surface O mixing ratios increased by up to 300 per cent over that time period. However, the accuracy and diagnostic power of these measurements remains controversial . Here we use a record of the clumped-isotope composition of molecular oxygen ( O O in O ) trapped in polar firn and ice from 1590 to 2016 AD, as well as atmospheric chemistry model simulations, to constrain changes in tropospheric O concentrations. We find that during the second half of the twentieth century, the proportion of O O in O decreased by 0.03 ± 0.02 parts per thousand (95 per cent confidence interval) below its 1590-1958 AD mean, which implies that tropospheric O increased by less than 40 per cent during that time. These results corroborate model predictions of global-scale increases in surface pollution and vegetative stress caused by increasing anthropogenic emissions of O precursors . We also estimate that the radiative forcing of tropospheric O since 1850 AD is probably less than +0.4 watts per square metre, consistent with results from recent climate modelling studies .

The explosive growth of PM2.5 mass usually results in extreme PM2.5 levels and severe haze pollution in eastern China, and is generally underestimated by current atmospheric chemistry models. Based on one such model, GRAPES_CUACE, three sensitivity experiments – a “background” experiment (EXP1), an “online aerosol feedback” experiment (EXP2), and an “80 % decrease in the turbulent diffusion coefficient of chemical tracers” experiment, based on EXP2 (EXP3) – were designed to study the contributions of the aerosol–radiation feedback (AF) and the decrease in the turbulent diffusion coefficient to the explosive growth of PM2.5 during a “red alert” heavy haze event in China's Jing–Jin–Ji (Beijing–Tianjin–Hebei) region. The results showed that the turbulent diffusion coefficient calculated by EXP1 was about 60–70 m-2 s-1 on a clear day and 30–35 m-2 s-1 on a haze day. This difference in the diffusion coefficient was not enough to distinguish between the unstable atmosphere on the clear day and the extremely stable atmosphere during the PM2.5 explosive growth stage. Furthermore, the inversion calculated by EXP1 was obviously weaker than the actual inversion from sounding observations on the haze day. This led to a 40 %–51 % underestimation of PM2.5 by EXP1; the AF decreased the diffusion coefficient by about 43 %–57 % during the PM2.5 explosive growth stage, which obviously strengthened the local inversion. In addition, the local inversion indicated by EXP2 was much closer to the sounding observations than that indicated by EXP1. This resulted in a 20 %–25 % reduction of PM2.5 negative errors in the model, with errors as low as -16 % to -11 % in EXP2. However, the inversion produced by EXP2 was still weaker than the actual observations, and the AF alone could not completely explain the PM2.5 underestimation. Based on EXP2, the 80 % decrease in the turbulent diffusion coefficient of chemical tracers in EXP3 resulted in near-zero turbulent diffusion, referred to as a “turbulent intermittence” atmospheric state, which subsequently resulted in a further 14 %–20 % reduction of the PM2.5 underestimation; moreover, the negative PM2.5 errors were reduced to -11 % to 2 %. The combined effects of the AF and the decrease in the turbulent diffusion coefficient explained over 79 % of the underestimation of the explosive growth of PM2.5 in this study. The results show that online calculation of the AF is essential for the prediction of PM2.5 explosive growth and peaks during severe haze in China's Jing–Jin–Ji region. Furthermore, an improvement in the planetary boundary layer scheme with respect to extremely stable atmospheric stratification is essential for a reasonable description of local “turbulent intermittence” and a more accurate prediction of PM2.5 explosive growth during severe haze in this region of China.