A convolutional neural networks method for tropospheric ozone vertical distribution retrieval from Multi-AXis Differential Optical Absorption Spectroscopy measurements

The vertical distribution of tropospheric ozone (O3) is crucial for understanding atmospheric physicochemical processes. A Convolutional Neural Networks (CNN) method for the retrieval of tropospheric O3 vertical distribution from ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements to tackle the issue of stratospheric O3 absorption interference faced by MAX-DOAS in obtaining tropospheric O3 profiles. Firstly, a hybrid model, named PCA-F_Regression-SVR, is developed to screen features sensitive to O3 inversion based on the MAX-DOAS spectra and EAC4 reanalysis O3 profiles, which incorporates Principal Component Analysis (PCA), F_Regression function, and Support Vector Regression (SVR) algorithm. Thus, these screened features for ancillary inversion include the profiles of temperature, specific humidity, fraction of cloud coverage, eastward and northward wind, the profiles of SO2, NO2, and HCHO, as well as season and time features to serve as sensitive factors. Secondly, the preprocessed MAX-DOAS spectra dataset and the sensitive factor dataset are utilized as input, while the O3 profiles of the EAC4 reanalysis dataset incorporating the surface O3 concentrations are employed as output for constructing the CNN model. The Mean Absolute Percentage Error (MAPE) decreases from 26 % to approximately 19 %. Finally, the CNN model is applied for inversion and comparison of tropospheric O3 profiles using independent input data. The CNN model effectively reproduces the O3 profiles of the EAC4 dataset, showing a Gaussian-like spatial distribution with peaks primarily around 950 hPa (550 m). Since the reanalysis data used for model training has been smoothed, the CNN model is insensitive to extreme values. This behavior can be attributed to the MAPE loss function, which evaluates Absolute Percentage Errors (APEs) of O₃ concentration at all altitudes, resulting in varying retrieval accuracy across different altitudes while maintaining overall MAPE control. Temporally, the CNN model tends to overestimate surface O3 in summer by around 20 μg/m3, primarily due to the influence of the temperature feature in the sensitivity factor dataset. In conclusion, leveraging MAX-DOAS spectra enables the retrieval of tropospheric O3 vertical distribution through the established CNN model.

Global air quality and pollution

The impact of global air pollution on climate and the environment is a new focus in atmospheric science. Intercontinental transport and hemispheric air pollution by ozone jeopardize agricultural and natural ecosystems worldwide and have a strong effect on climate. Aerosols, which are spread globally but have a strong regional imbalance, change global climate through their direct and indirect effects on radiative forcing. In the 1990s, nitrogen oxide emissions from Asia surpassed those from North America and Europe and should continue to exceed them for decades. International initiatives to mitigate global air pollution require participation from both developed and developing countries.

ENERGY AND MATERIAL FLOW THROUGH THE URBAN ECOSYSTEM

▪ Abstract  This paper reviews the available data and models on energy and material flows through the world's 25 largest cities. Throughput is categorized as stored, transformed, or passive for the major flow modes. The aggregate, fuel, food, water, and air cycles are all examined. Emphasis is placed on atmospheric pathways because the data are abundant. Relevant models of urban energy and material flows, demography, and atmospheric chemistry are discussed. Earth system–level loops from cities to neighboring ecosystems are identified. Megacities are somewhat independent of their immediate environment for food, fuel, and aggregate inputs, but all are constrained by their regional environment for supplying water and absorbing wastes. We elaborate on analogies with biological metabolism and ecosystem succession as useful conceptual frameworks for addressing urban ecological problems. We conclude that whereas data are numerous for some individual cities, cross-cutting compilations are lacking in biogeochemical analysis and modeling. Synthesis of the existing information will be a crucial first step. Cross-cutting field research and integrated, multidisciplinary simulations will be necessary.

Estimation of the amount of tropospheric ozone in a cloudy sky by ground-based Fourier-transform infrared emission spectroscopy

The problem of retrieving minor concentrations of constituents by ground-based Fourier-transform infrared emission spectroscopy is addressed by means of the concept of differential optical emission spectroscopy in analogy to the concept of differential optical absorption spectroscopy. Using the prominent nu3 ozone feature at 1043 cm(-1), we show that the strength of the spectral signature depends not only on the amount of ozone but also on the atmospheric thermal structure. This dependence can be described by a rather accurate approximation, which was used to construct a simple diagram to estimate the amount of column ozone between the instrument site and a cloud deck as well as to determine the detection limit. The detection limit is shown to depend on cloud base height. For a given thermal lapse rate it was found that the lower the detection limit, the higher the cloud base altitude. However, as shown in a case study with variable cloud base height, the concept fails for semitransparent clouds. Multiple scattering of the emitted radiation within the clouds yielded a path enhancement that simulated an enhanced amount of constituent. The path enhancement was estimated to be 2.4-4 km at 1000 cm(-1) for low-level clouds, equivalent to an enhancement factor of 6-21. The multiple scattering effect has considerable consequences for ground-based as well as for nadir satellite retrieval techniques in cloudy skies.