The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.

Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation of important air pollutants such as nitrogen dioxide (NO₂) over large areas. The South Korean instrument GEMS, launched in February 2020, is the first instrument in geostationary orbit and covers a large part of Asia. Our investigations show the observed diurnal evolution of NO₂ at different measurement sites.

This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.

In this study, we evaluated the GEMS glyoxal products by comparing them with TROPOMI and MAX-DOAS measurements. GEMS and TROPOMI VCDs present similar spatial distributions. Monthly variations in GEMS VCDs and TROPOMI and MAX-DOAS VCDs differ in northeastern Asia, which we attributed to a polluted reference spectrum and high NO₂ concentrations. GEMS glyoxal products with unparalleled temporal resolution would enrich our understanding of VOC emissions and diurnal variation.

Solar absorption spectra near 2967 cm⁻¹ recorded by a ground-based FTIR with a high spectral resolution of 0.0035 cm^-1 are applied to retrieve C₃H₈ columns for the first time in Xianghe, China, within the NDACC-IRWG. The mean and standard deviation of the C₃H₈ columns are 1.80 ± 0.81 (1σ) × 10¹⁵ molec. cm^-2. Good correlations are found between C₃H₈ and other non-methane hydrocarbons, such as C₂H₆ (R = 0.84) and C₂H₂ (R = 0.79), as well as between C₃H₈ and CO (R = 0.72). 

New sorbent materials are needed to preconcentrate atmospheric oxidized mercury for analysis by developing mass spectrometry methods. Chitosan, α-Al₂O₃, and γ-Al₂O₃ were tested for quantitative gaseous oxidized mercury sorption in ambient air under laboratory and field conditions. Although these materials sorbed gaseous oxidized mercury without sorbing elemental mercury in the laboratory, less oxidized mercury was recovered from these materials compared to cation exchange membranes in the field.

We present a sensitive DART-MS/MS method for the fast and accurate quantification of semi-volatile organic compounds (SVOCs) in organic films without the need for pre-treatment. This method offers greatly improved repeatability in the absence of internal standards. By utilizing MS/MS analysis, the separation of isomeric components within films becomes possible. These developments increase the feasibility of the DART-MS approach for studying the dynamics of SVOCs in indoor surface films.

We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. Gaps in monitoring efforts include the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We conclude with a summary of cross-network unification and quality control efforts.

CHORA, an advanced cloud convective differential technique, enhances the accuracy of tropospheric-ozone retrievals. Unlike the traditional Pacific cloud reference sector scheme, CHORA introduces a local-cloud reference sector and an alternative approach (CLCT) for precision. Analysing monthly averaged TROPOMI data from 2018 to 2022 and validating with SHADOZ ozonesonde data, CLCT outperforms other methods and so is the preferred choice, especially in future geostationary satellite missions.

This paper presents a new machine learning model that allows us to compute NO₂ concentration profiles from satellite observations. A neural network was trained on synthetic data from the regional chemistry and transport model WRF-Chem. This is the first model of its kind. We present a thorough model validation study, covering various seasons and regions of the world.