Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential land are conducive to achieving urban carbon neutrality. This study took 84 residential communities in Susong County, Anhui Province as its research object, exploring the nonlinear relationship between the urban built environment and CEs from residential land. By identifying CEs from residential land through building electricity consumption, 14 built environment indicators, including land area (LA), floor area ratio (FAR), greening ratio (GA), building density (BD), gross floor area (GFA), land use mix rate (Phh), and permanent population density (PPD), were selected to establish an interpretable machine learning (ML) model based on the XGBoost-SHAP attribution analysis framework. The research results show that, first, the goodness of fit of the XGBoost model reached 91.9%, and its prediction accuracy was better than that of gradient boosting decision tree (GBDT), random forest (RF), the Adaboost model, and the traditional logistic model. Second, compared with other ML models, the XGBoost-SHAP model explained the influencing factors of CEs from residential land more clearly. The SHAP attribution analysis results indicate that BD, FAR, and Phh were the most important factors affecting CEs. Third, there was a significant nonlinear relationship and threshold effect between built environment characteristic variables and CEs from residential land. Fourth, there was an interaction between different dimensions of environmental factors, and BD, FAR, and Phh played a dominant role in the interaction. Reducing FAR is considered to be an effective CE reduction strategy. This research provides practical suggestions for urban planners on reducing CEs from residential land, which has important policy implications and practical significance.

Raman spectroscopic detection is one of the suitable methods for the detection of chemical warfare agents (CWAs) and simulants. Since the 1980s, many researchers have been dedicated to the research of chemical characteristic of CWAs and simulants and instrumental improvement for their analysis and detection. The spatial heterodyne Raman spectrometer (SHRS) is a new developing instrument for Raman detection that appeared in 2011. It is already well-known that SHRS has the characteristics of high spectral resolution, a large field-of-view, and high throughput. Thus, it is inherently suitable for the analysis and detection of these toxic chemicals and simulants. The in situ and standoff detection of some typical simulants of CWAs, such as dimethyl methylphosphonate (DMMP), diisopropyl methylphosphonate (DIMP), triethylphosphate (TEP), diethyl malonate (DEM), methyl salicylate (MES), 2-chloroethyl ethyl sulfide (CEES), and malathion, were tried. The achieved results show that SHRS does have the ability of in situ analysis or standoff detection for simulants of CWAs. When the laser power was set to as low as 26 mW, the SHRS still has a signal-to-noise ratio higher than 5 in in situ detection. The standoff Raman spectra detection of CWAs simulants was realized at a distance of 11 m. The potential feasibility of standoff detection of SHRS for CWAs simulants has been proved.

The Greenhouse Gases Monitoring Instrument (GMI), on board the Chinese Gaofen-5 (GF-5) satellite, provides rich observation data for the global remote sensing of atmospheric CO₂. To meet the high-precision satellite retrieval needs of atmospheric CO₂, this paper designs a coupled bidirectional reflectance distribution function (BRDF) CO₂ retrieval (CBCR) method, which describes the surface reflectance characteristics by the BRDF, corrects for atmospheric scattering based on full physics retrieval theory, and ensures the stable retrieval of multiple parameters and atmospheric CO₂ by enriching prior constraints. Theoretical analysis shows that the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics is significantly related to the aerosol optical depth (AOD), solar zenith angle (SZA), and viewing zenith angle (VZA). The validation of GMI CO₂ retrievals shows that the CBCR method significantly reduced the influence of the surface bidirectional reflectance characteristics under high AOD and high SZA conditions, decreased the atmospheric CO₂ retrieval error from 0.58 ± 5.64 ppm to −1.33 ± 3.13 ppm, and increased the correlation with the temporal variation of actual atmospheric CO₂ from 34.7 to 76.8%. Our CBCR method can correct the influence of atmospheric scattering induced by the surface bidirectional reflectance characteristics on atmospheric CO₂ retrieval, and this work demonstrates that describing the surface reflectance characteristics by using BRDF is a promising idea in the field of satellite CO₂ retrievals.

The Greenhouse gases Monitoring Instrument on GaoFen-5 satellite-II (GMI-II) uses spatial heterodyne spectroscopy (SHS) for quantitative monitoring of atmospheric greenhouse gases (GHG). Unlike the traditional SHS, the interferometer component of the GMI-II was designed with zero optical path difference offset, effectively improving spectral resolution while maintaining the same detector specifications. The secondary imaging system with non-isometric scaling of spatial and spectral dimensions was designed to decrease the integration time of a frame image or improve the spectral signal-to-noise ratio (SNR) under the same integration time. This paper introduces the design, manufacture, adjustment methods, and test results of the main performance indexes of the GMI-II that indicate that the spectral resolution of the O₂ A-band detection channel is better than 0.6 cm⁻¹ and other channels are better than 0.27 cm⁻¹. Under the typical radiance of other carbon monitors’ on-orbit statistics, the spectral SNR of the GMI-II is more than 300. These test results demonstrate that the GMI-II can be well adapted to quantitative remote sensing monitoring of atmospheric GHG.

Spatial heterodyne Raman spectroscopy (SHRS) is a type of method for the detection of Raman spectra and can achieve a very high spectral resolution. SHRS has no moving parts and can be built with rugged, compact packages, making it extremely suitable for planetary exploration. However, if a high spectral resolution is needed, a traditional one-dimensional spatial heterodyne spectrometer cannot achieve a broad bandpass because it is limited by the number of pixels of the detector. In order to solve this, two-dimensional (2-D) SHRS can be used to broaden the bandpass. A breadboard of 2-D SHRS has been designed and built, and some artificial and natural targets have been tested to learn about the detection ability of 2-D SHRS. The results show that 2-D SHRS can be used to detect Raman signals scattered from liquid and solid targets. When the Raman scattered signal is strong, it can even detect targets in containers. The detection of anti-Stokes Raman shift for sulfur and carbon tetrachloride has also been tried, and the results show that 2-D SHRS has the ability to detect anti-Stokes Raman shift below 500  cm⁻¹. The research may have a general implication in chemical analysis and planetary exploration.