The Environmental Trace Gases Monitoring Instrument (EMI) is a high-spectral-resolution payload onboard the latest pathfinder mission GaoFen-5, designed specifically for the monitoring of global atmospheric trace gas compositions and trends. This study describes a comparative analysis of the tropospheric nitrogen dioxide (NO₂) columns over the North China Plain (NCP) from November 2018 to April 2019 based on EMI products. Validation of satellite products based on a cross-correlation analysis with data from four ground-based multi-axis differential optical absorption spectroscopy sites provided good correlation coefficients (r) ranging from 0.78 to 0.88. The distribution and monthly averaged tropospheric NO₂ columns revealed high pollution exposure levels during winter (November–January) and a decrease from February onward in the NCP. Moreover, a typical pollution event was analyzed in detail in combination with wind field statistics. The results indicated that variations of NO₂ concentrations in Beijing and Tianjin were highly correlated with the wind direction from 22.5–45.0 degrees west of south, especially during times of high NO₂ amounts. These findings highlight that the EMI payload on the GaoFen-5 (GF-5) satellite is useful for remote sensing of regional and global NO₂ detection.

Prolonged exposure to elevated ozone levels poses significant health risk. Accurate forecasting of ozone concentration is crucial for enabling timely interventions to mitigate their decremental effects on public health and environmental well-being. Long short-term memory (LSTM) networks have garnered substantial attention in ozone prediction due to their proficiency in capturing long-term temporal dependencies. Nonetheless, these models often overlook short-term non-stationarity resulting from periodic ozone trends, which remains a critical limitation. Existing studies predominantly address short-term ozone forecasting (within hours), thereby constraining early warning systems and impeding effective control measures. To overcome these limitations, we propose a hybrid deep learning model, DLinear-LSTM, tailored for 24-hour ozone concentration forecasting. The model leverages the moving average kernel decomposition module from DLinear to segregate time series data into trend and seasonal components. Subsequently, stacked LSTM units independently model these components, effectively capturing both long-term temporal patterns and periodic fluctuations in ozone levels. Empirical evaluations reveal that DLinear-LSTM achieves superior forecasting accuracy with a mean absolute error (MAE) of 19.15 µg/m³, root mean square error (RMSE) of 25.87 µg/m³, and coefficient of determination (R²) of 0.715, outperforming state-of-the-art forecasting models. Additionally, ablation studies underscore the essential role of time series decomposition in enhancing the performance of air quality prediction models. In summary, the proposed DLinear-LSTM framework offers a robust data-driven approach for accurate air pollutant levels forecasting, aiding atmospheric pollution control and safeguarding public health.

The short-wave infrared (SWIR) grating imaging spectrometer based on indium gallium arsenide (InGaAs) material inverts the atmospheric methane concentration by measuring the scattered light signals in the sky. This study proposes spectral and radiometric calibration methods for the characteristics of the spectrometer, such as the small-area array, high signal-to-noise ratio, and high spectral resolution. Four spectral response function models, namely, the Gauss, Lorentz, Voigt and super-Gaussian models, were compared during spectral calibration. With a fitting residual of 0.032, the Gauss model was found to be the most suitable spectral response function for the spectrometer. Based on the spectral response function, the spectral range and spectral resolution of the spectrometer were determined to be 1592.4–1677.2 and 0.1867 nm, respectively. In addition, radiometric calibration of the spectrometer was achieved by combining an integrating sphere and linear measuring instrument. Moreover, absolute and relative radiometric calibrations of the spectrometer were performed. The low signal response problem caused by the quantum efficiency of the detector at long wavelength was corrected, and the uncertainty and non-stability uncertainty of absolute radiometric calibration were calculated to be less than 0.2%. Finally, the calibrated spectrometer was used to accurately measure the solar scattering spectrum in the SWIR band, and the solar spectrum was simulated by the radiative transfer model for verification; the measurement error was found to be 5%. Concurrently, a methane sample gas experiment was performed using the integrating-sphere light source, and the measurement error was less than 4%. This fully proves the effectiveness of the spectral and radiometric calibrations of the SWIR spectrometer and strongly guarantees a subsequent, rapid and accurate inversion of atmospheric methane concentration.

This paper presents a space-based limb-imaging spectrometer (LIS) for detecting atmospheric O₂ airglow; it scans the atmosphere with a vertical range of 10–100 km and has a vertical resolution of 2 km. The LIS’s detection performance needs to be examined before launch. A forward radiative transfer model (RTM) of airglow is studied to determine the airglow emission intensity. Spectral and radiation calibration is conducted to obtain the response parameters. Based on the airglow emission intensity, calibration results, and airglow spectral lines, the LIS’s simulated spectra are obtained, and then an optimal estimation inversion method for the LIS is studied. The results show that the LIS’s spectral range is 498.1 nm–802.3 nm, with a spectral resolution of 1.38 nm. Simulation results show that the LIS can detect airglow emission spectral lines, which characterize their dependence on temperature. The digital number response value is 20% to 50% of the saturation value. An inversion error analysis shows that, when the signal-to-noise ratio (SNR) of the LIS is 1000 and the prior temperature error is 10%, the inversion errors are 6.2 and 3 K at 63 and 77 km, respectively. This study shows that the LIS can achieve good SNR detection for airglow.

High-spectral-resolution retrievals of nitrogen dioxide (NO₂) provide detailed atmospheric absorption information, but they usually involve large data volume, low computational efficiency, and complex instrument requirements. To address these limitations, we employ a low-spectral-information retrieval strategy for fast atmospheric monitoring. In this study, the Discrete-Wavelength Differential Optical Absorption Spectroscopy (DWDOAS) technique is applied by selecting 14 representative wavelength samples in the 420–450 nm window. Multiple wavelength–resolution configurations are constructed and quantitatively assessed using an entropy-weighting scheme to identify the optimal setup. Using TROPOspheric Monitoring Instrument (TROPOMI) and Environmental Trace Gases Monitoring Instrument (EMI-II) measurements as case studies, we show that at a spectral resolution of ~2 nm, DWDOAS-derived NO₂ vertical column density (VCD) are highly consistent with those from conventional DOAS retrievals (correlation coefficient R > 0.7) and exhibit relative differences of approximately ±30%. Monte Carlo simulations further demonstrate method robustness, yielding mean uncertainties below 2 × 10¹⁴ molecules·cm⁻². The results indicate that DWDOAS effectively suppresses high-frequency spectral noise while preserving key differential absorption structures, thereby achieving a favorable trade-off between information retention and noise robustness. Nevertheless, increased retrieval uncertainty is observed under low-NO₂ background conditions or strong aerosol loading, which reduces sensitivity to weak absorption features. Overall, this study confirms that reliable NO₂ retrieval performance can be maintained while substantially reducing spectral information requirements, offering practical implications for low-resolution spectrometer design, onboard data compression, and rapid, wide-area atmospheric trace-gas monitoring.