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● A system of environmental optical monitoring technology has been established. ● New optical monitoring techniques and stereoscopic system should be established. ● The focus on interdisciplinarity should be increased. ● Pay more attention on greenhouse gases monitoring and atmospheric chemistry. The achievement of the targets of coordinated control of PM 2.5 and O 3 and the carbon peaking and carbon neutrality depend on the development of pollution and greenhouse gas monitoring technologies. Optical monitoring technology, based on its technical characteristics of high scalability, high sensitivity and wide-targets detection, has obvious advantages in pollution/greenhouse gases monitoring and has become an important direction in the development of environmental monitoring technology. At present, a system of environmental optical monitoring technology with differential optical absorption spectroscopy (DOAS), cavity ring-down spectroscopy (CRDS), light detection and ranging (LIDAR), laser heterodyne spectroscopy (LHS), tunable diode laser absorption spectroscopy (TDLAS), fourier transform infrared spectroscopy (FTIR) and fluorescence assay by gas expansion (FAGE) as the main body has been established. However, with the promotion of \"reduction of pollution and carbon emissions\" strategy, there have been significant changes in the sources of pollution/greenhouse gases, emission components and emission concentrations, which have put forward new and higher requirements for the development of monitoring technologies. In the future, we should pay more attention to the development of new optical monitoring techniques and the construction of stereoscopic monitoring system, the interdisciplinarity (among mathematics, physics, chemistry and biology, etc.), and the monitoring of greenhouse gases and research on atmospheric chemistry.

The Environmental Trace Gases Monitoring Instrument (EMI) is the first Chinese satellite-borne UV–Vis spectrometer aiming to measure the distribution of atmospheric trace gases on a global scale. The EMI instrument onboard the GaoFen-5 satellite was launched on 9 May 2018. In this paper, we present the tropospheric nitrogen dioxide (NO2) vertical column density (VCD) retrieval algorithm dedicated to EMI measurement. We report the first successful retrieval of tropospheric NO2 VCD from the EMI instrument. Our retrieval improved the original EMI NO2 prototype algorithm by modifying the settings of the spectral fit and air mass factor calculations to account for the on-orbit instrumental performance changes. The retrieved EMI NO2 VCDs generally show good spatiotemporal agreement with the satellite-borne Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument (correlation coefficient R of ~0.9, bias < 50%). A comparison with ground-based MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) observations also shows good correlation with an R of 0.82. The results indicate that the EMI NO2 retrieval algorithm derives reliable and precise results, and this algorithm can feasibly produce stable operational products that can contribute to global air pollution monitoring.Remote sensing: Tuned-up satellite identifies pollutant hot spotsImprovements to a high-resolution spectrometer onboard China’s recently-launched GaoFen-5 (GF-5) satellite can give researchers a new tool to assess atmospheric trace gases. Cheng Liu from the University of Science and Technology of China and his colleagues reported the first successful retrieval of nitrogen dioxide from the GF-5 orbiter thanks to crucial adjustments to calibration algorithms. The satellite’s payload Environmental Trace Gases Monitoring Instrument (EMI) uses visible spectroscopic measurements to detect nitrogen dioxide in the atmosphere, which can be used to identify the spatial distribution of pollution emissions on the Earth. To overcome on-orbit issues including detector saturation, the team has optimized the spectral retrieval settings by spectral precalibration, fitting wavelength adjustment, reference spectrum selection, etc. Comparisons to other satellite and ground measurements revealed the optimization effort yielded reliable results for monitoring pollution levels.

All-day monitoring of ozone and its precursors is crucial for understanding the chemical processes driving ozone pollution and improving urban air quality. Previously, the absence of cost-effective instruments with high temporal resolution, wide coverage, and fine spatial detail made comprehensive simultaneous observations of ozone and its precursors impossible, limiting our ability to study complex atmospheric chemical changes at night. Our study addresses this challenge by combining computed tomography and Differential optical absorption spectroscopy algorithms to achieve high-spatial-resolution, multi-constituent detection throughout the day. We tested multiple Light emitting diodes with varying wavelengths to enhance cost-effectiveness and incorporated high-precision tracking technology to enable accurate signal return from a drone-carried reflector array. Here, we show an affordable instrument that provides rapid response, precise spatial details, and extensive coverage for all-day detection of ozone and key precursors like formaldehyde and nitrogen dioxide, offering valuable insights into ozone pollution dynamics and aiding pollution source identification. The study demonstrates a cost-effective hyperspectral tomography system for continuous monitoring of ozone and its precursors, using drones and light-emitting diodes. It provides high-resolution, all-day data, aiding in understanding pollution dynamics and identifying pollution sources.

As laser chaos has been proven to be a robust tool to solve the multi-armed bandit (MAB) problem, this study investigates the problem of multiuser dynamic channel assignment using laser chaos in cognitive radio networks with K -orthogonal channels and M secondary users. A novel dynamic channel assignment algorithm with laser chaos series for multiple users, named parallel processing learning with laser chaos (PPL-LC) algorithm, is proposed to efficiently address two main objectives: stable channel assignment and fuzzy stable channel assignment. The latter objective accounts for the realistic scenario where users have fuzzy preferences and do not necessarily pursue the best preference. The PPL-LC algorithm uses the randomness properties of laser chaos to learn the assignment of channels to multiple users without any limitations on the number of channels, which has not been considered in existing laser chaos algorithms. Moreover, the PPL-LC is equipped with parallel processing channel selections, resulting in higher throughput and stronger adaptability with environmental changes over time than comparison algorithms, such as distributed stable strategy learning and coordinated stable marriage MAB algorithms. Finally, numerical examples are presented to demonstrate the performance of the PPL-LC algorithm.

Ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) and lidar measurements were performed in Shanghai, China, during May 2016 to investigate the vertical distribution of summertime atmospheric pollutants. In this study, vertical profiles of aerosol extinction coefficient, nitrogen dioxide (NO2) and formaldehyde (HCHO) concentrations were retrieved from MAX-DOAS measurements using the Heidelberg Profile (HEIPRO) algorithm, while vertical distribution of ozone (O3) was obtained from an ozone lidar. Sensitivity study of the MAX-DOAS aerosol profile retrieval shows that the a priori aerosol profile shape has significant influences on the aerosol profile retrieval. Aerosol profiles retrieved from MAX-DOAS measurements with Gaussian a priori profile demonstrate the best agreements with simultaneous lidar measurements and vehicle-based tethered-balloon observations among all a priori aerosol profiles. Tropospheric NO2 vertical column densities (VCDs) measured with MAX-DOAS show a good agreement with OMI satellite observations with a Pearson correlation coefficient (R) of 0.95. In addition, measurements of the O3 vertical distribution indicate that the ozone productions do not only occur at surface level but also at higher altitudes (about 1.1 km). Planetary boundary layer (PBL) height and horizontal and vertical wind field information were integrated to discuss the ozone formation at upper altitudes. The results reveal that enhanced ozone concentrations at ground level and upper altitudes are not directly related to horizontal and vertical transportation. Similar patterns of O3 and HCHO vertical distributions were observed during this campaign, which implies that the ozone productions near the surface and at higher altitudes are mainly influenced by the abundance of volatile organic compounds (VOCs) in the lower troposphere.

This study examined air quality data collected from 2015 to 2023 across Shenyang, Dalian, Changchun, and Harbin to assess interannual and monthly variations in PM2.5, PM10, SO2, NO2, and O3, along with their correlations, seasonal meteorological influences, and potential source regions. Annual mean concentrations of PM2.5, PM10, SO2, and NO2 declined substantially (by 39.9–79.3%), whereas O3 showed a fluctuating pattern, remaining persistently high in the coastal city of Dalian. Seasonally, PM2.5, PM10, SO2, and NO2 concentrations peaked in winter and decreased in summer, while O3 displayed the opposite trend. Particulate levels in Liaoning rebounded earlier in spring than in Jilin and Heilongjiang. Correlation analysis revealed strong positive relationships among particulate and gaseous pollutants, but O3 generally exhibited negative correlations with other species. Haze events occurred mainly in winter, whereas complex pollution episodes were more frequent in summer. Meteorological analysis indicated that relative humidity was negatively correlated with PM2.5, PM10, SO2, and NO2 in summer but positively correlated in winter. Elevated temperatures outside the winter months promoted NO2 dispersion and enhanced O3 formation. Strong winds in spring and winter markedly reduced PM2.5 and SO2 levels, though this effect was less evident in Shenyang. WPSCF results identified significant cross-regional transport from the southwest contributing to PM2.5, PM10, and NO2 during spring and winter, while O3 was primarily affected by long-range transport in spring and only marginally in winter. In Dalian, sea–land breeze circulation further intensified transport processes in summer and autumn. Overall, this work provides an integrated, multi-year, and multi-city assessment of pollution dynamics, meteorological drivers, and transboundary transport in Northeast China, offering new insights into regional air quality improvement and its spatial heterogeneity relative to other regions of China.

The vertical profile of PM2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes; it also provides important data support for studying the causes and sources of PM2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction—as well as the vertical profiles of precursors such as NO2 and SO2—to evaluate the vertical distribution of PM2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM2.5 can diffuse to high altitudes between 500 and 1000 m; in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM2.5 mass concentration in Beijing, a high-altitude PM2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM2.5 every hour.

Persistent heavy haze episodes have repeatedly shrouded North China in recent years. Besides anthropogenic emissions, natural dust also contributes to the aerosols in this region. Through continuous observation by a dual-wavelength Raman lidar, the primary aerosol types and their contributions to air pollution in North China were determined. The following three aerosol types can be classified: natural dust, anthropogenic aerosols, and the mixture of anthropogenic aerosols and dust (polluted dust). The classification results are basically consistent with the classification results from the cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) satellite measurements. The relative bias of the lidar ratio between the Raman lidar and CALIPSO is less than 25% over 90% of the cases, indicating that the CALIPSO lidar ratio selection algorithm is reasonable. The classification results show that approximately 45% of aerosols below 1.8 km are contributed by polluted dust during our one year observations. The contribution of dust increased with height, from 6% at 500 m to 28% at 1,800 m, while the contribution of anthropogenic aerosols decreased from 49% to 25%. In addition, polluted dust is the major aerosol subtype below 1.0 km in spring (over 60%) and autumn (over 70%). Anthropogenic aerosols contribute more than 75% of air pollution in summer. In winter, anthropogenic aerosols prevailed (over 80%) in the lower layer, while polluted dust (around 60%) dominated the upper layer. Our results identified the primarily aerosol types to assess the contributions of anthropogenic and natural sources to air pollution in North China, and highlight that natural dust plays a crucial role in lower-layer air pollution in spring and autumn, while controlling anthropogenic aerosols will significantly improve air quality in winter.

Marine vessels play a vital role in the global economy; however, their negative impact on the marine atmospheric environment is a growing concern. Quantifying marine vessel emissions is an essential prerequisite for controlling these emissions and improving the marine atmospheric environment. Optical imaging remote sensing is a vital technique for quantifying marine vessel emissions. However, the available imaging techniques have suffered from insufficient detection accuracy and inadequate spatiotemporal resolution. Herein, we propose a fast-hyperspectral imaging remote sensing technique that achieved precise imaging of nitrogen dioxide (NO 2 ) and sulfur dioxide (SO 2 ) from marine vessels. Several key techniques are developed, including the coaxial design of three camera systems (hyperspectral camera, visible camera, and multiwavelength filters) and a high-precision temperature control system for a spectrometer (20 °C ± 0.5 °C). Moreover, based on the variation of O 4 within them, plumes are categorized as aerosol-present and aerosol-absent, with different air mass factor (AMF) calculation schemes developed accordingly. Multiwavelength filters combined with spectral analysis enable precise identification of the plume outline and a detailed observation of the trace gas distribution inside the plume emitted from marine vessels. In addition, we focuse on the emission characteristics of NO 2 and SO 2 from large ocean cargo ships and small offshore cargo ships. Although there are still many emerging issues, such as measurement of cross-sections of trace gases at different temperature, nighttime imaging, and greenhouse gas imaging, this study opens a gate for synergies in pollution and carbon reductions and the continuous improvement of the marine atmospheric environment. A fast-hyperspectral imaging remote sensing technique was proposed to quantify nitrogen dioxide and sulfur dioxide emissions from marine vessels.

Air pollutant transport plays an important role in local air quality, but field observations of transport fluxes, especially their vertical distributions, are very limited. We characterized the vertical structures of transport fluxes in central Luoyang, Fen-Wei Plain, China, in winter based on observations of vertical air pollutant and wind profiles using multi-axis differential optical absorption spectroscopy (MAX-DOAS) and Doppler wind lidar, respectively. The northwest and the northeast are the two privileged wind directions. The wind direction and total transport scenarios were dominantly the northwest during clear days, turning to the northeast during the polluted days. Increased transport flux intensities of aerosol were found at altitudes below 400 m on heavily polluted days from the northeast to the southwest over the city. Considering pollution dependence on wind directions and speeds, surface-dominated northeast transport may contribute to local haze events. Northwest winds transporting clean air masses were dominant during clean periods and flux profiles characterized by high altitudes between 200 and 600 m in Luoyang. During the COVID-19 lockdown period in late January and February, clear reductions in transport flux were found for NO2 from the northeast and for HCHO from the northwest, while the corresponding main transport altitude remained unchanged. Our findings provide better understandings of regional transport characteristics, especially at different altitudes.