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Southwestern North America (SWNA) continuously experienced megadroughts and large wildfires in 2020 and 2021. Here, we quantified their impact on the terrestrial carbon budget using net biome production (NBP) estimates from an ensemble of atmospheric inversions assimilating in-situ CO2 and Carbon Observatory–2 (OCO-2) satellite XCO2 retrievals (OCO-2 v10 MIP Extension), two satellite-based gross primary production (GPP) datasets, and two fire CO2 emission datasets. We found that the 2020–2021 drought and associated wildfires in SWNA led to a large CO2 loss, an ensemble mean of 95.07 TgC estimated by the satellite inversions using both nadir and glint XCO2 retrievals (LNLG) within the OCO-2 v10 MIP, greater than 80% of SWNA’s annual total carbon sink. Moreover, the carbon loss in 2020 was mainly contributed by fire emissions while in 2021 mainly contributed by drought impacts on terrestrial carbon uptake. In addition, the satellite inversions indicated the huge carbon loss was mainly contributed by fire emissions from forests and grasslands along with carbon uptake reductions due to drought impacts on grasslands and shrublands. This study provides a process understanding of how some droughts and following wildfires affect the terrestrial carbon budget on a regional scale.

Despite the rapid and well-documented surge in global atmospheric CO 2 levels, predominantly driven by fossil fuel combustion and industrialization, the characterization of CO 2 variations at regional scales remains notably sparse. This study integrates satellite remote sensing (RS) and ground-based measurements to examine the spatiotemporal distributions and drivers of CO 2 in China’s Shaanxi Province from 2013 to 2022. Although Shaanxi has experienced rapid development, its CO 2 trends have remained unclear. By integrating CO 2 observations from satellite sources, specifically the Orbiting Carbon Observatory-2 (OCO-2) and Fourier Transform Spectrometer (FTS), with data from the World Data Centre for Greenhouse Gases (WDCGG) Hong Kong ground station, we have synthesized a uniquely comprehensive dataset that enables enhanced resolution in exploring intra-annual, interannual, and spatial CO 2 variations across the province. The results reveal pronounced seasonal CO 2 cycles and a consistent upward trend over the past decade. The monthly concentrations exhibited a sinusoidal pattern, oscillating between a minimum of 399.68 ± 6.58 ppm in August and peaking at 407.48 ± 6.58 ppm in April. High CO 2 regions within Shaanxi are predominantly found in its southern subtropical and temperate areas, reaching 418.4 ppm in 2022. From 2013 to 2022, the annual average CO 2 increased by 4.12% from 396 to 412.34 ppm, with a higher growth rate in southern compared to northern Shaanxi. This study elucidates the distinct spatiotemporal variations in CO 2 levels across Shaanxi Province, revealing prominent seasonal cycles and a discernible upward trend over the past decade. The results offer new insights into CO 2 characteristics and dynamics in this rapidly developing region of China, and further investigation into the factors underlying the observed variations is warranted.

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO2 column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO2 column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO2 column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO2 column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10−5 mol/m2 and lowest in 2022 at 2.85 × 10−5 mol/m2. Spatially, NO2 column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO2 column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO2 column concentration.

The recent rapid economic development in the Yangtze River Delta (YRD) has led to atmospheric destruction; therefore, it is imperative to solve the issue of atmospheric environmental pollution to ensure stable long-term development. Based on the NO2 column concentration observed by the TROPOMI (a tropospheric monitoring instrument) on the Sentinel-5P, the spatial–temporal distribution characteristics of the NO2 column concentration in the YRD from 2019 to 2020 were analyzed using the Google Earth Engine (GEE) platform, and the Geographical Detector (Geodetector) model was used to determine the driving factors of the NO2 column concentration. The results show that the correlation between the NO2 column concentration and the ground-monitored NO2 concentrations reached 70%. The annual variation trend of the NO2 column concentration exhibited a ‘U’-shaped curve, with the characteristics of ‘high in winter and low in summer, with a transition between spring and autumn’. It exhibited obvious agglomeration characteristics in terms of the spatial distribution, with a high-value agglomeration in the central region of the YRD, followed by the northern region, and a low-value agglomeration in the southern region, with higher altitudes. The change in the NO2 column concentration in the YRD was affected by both physical geographical factors and socio-economic factors; it is clear that the influence of socio-economic factors has increased.

Monitoring of CO2 column concentrations is valuable for atmospheric research. A mobile open-path system was developed based on tunable diode laser absorption spectroscopy (TDLAS) to measure atmospheric CO2 column concentrations. A laser beam was emitted downward from a distributed feedback diode laser at 2 μm and then reflected by the retroreflector array on the ground. We measured the CO2 column concentrations over the 20 and 110 m long vertical path. Several single-point sensors were distributed at different heights to provide comparative measurements for the open-path TDLAS system. The results showed that the minimum detection limit of system was 0.52 ppm. Some similarities were observed in trends from the open-path TDLAS system and these sensors, but the average of these sensors was more consistent with the open-path TDLAS system values than the single-point measurement. These field measurements demonstrate the feasibility of open-path TDLAS for measuring the CO2 column concentration and monitoring carbon emission over large areas.

Global climate change is one of the major environmental challenges faced today, and carbon dioxide (CO 2 ) emissions is the primary cause of global warming. Although many countries have committed to reducing carbon emissions and achieving carbon neutrality, progress in reducing emissions across various industries remains uncertain. To address this issue, this study employs advanced machine learning methods such as random forest, extreme gradient boosting (XGBoost), and stacked regression to construct a dynamic correlation model for exploring the relationship between sectoral electricity consumption and urban average CO 2 column concentration (XCO 2 ). By combining time-rolling window techniques, the model dynamically reveals temporal correlations between sector-specific electricity consumption and urban XCO 2 . The performance of the dynamic correlation model was validated with sectoral electricity consumption and urban XCO 2 data from 16 cities between 2017 and 2021. The model achieved a coefficient of determination ( R 2 ) of up to 0.864 and an root mean square error (RMSE) of 1.350. The study revealed that the model significantly outperformed the traditional random forest and XGBoost models in terms of prediction accuracy, effectively capturing the complex relationship between sectoral electricity consumption and carbon concentration. The correlation between electricity consumption in different industries and XCO 2 exhibited significant temporal fluctuations across cities. Through time-rolling analysis, the study revealed sector-specific influences, providing valuable insights for developing more precise and industry-targeted carbon reduction policies.

Understanding the spatial and temporal variations of CO 2 column concentration (CO 2 -CCs) is crucial for tackling climate change and promoting sustainable human development. This study provides an in-depth analysis of CO 2 dynamics in the Yellow River Basin, an area significantly affected by both natural and anthropogenic factors. Using data from the Orbiting Carbon Observatory 2 (OCO-2) and the Fourier transformation spectrometer (FTS) of the GOSAT satellite remote sensing sensors, supplemented with ground station data from the Waliguan station, we scrutinized the CO 2 levels in the region from 2013 to 2022. The regional CO 2 -CC displayed a 12-month cyclical variation and a continuous upward trend, escalating by approximately 4.26% over the 10-year period. Spatiotemporal differences were evident in the monthly variation of CO 2 -CC, with peak and minimum values occurring in May and August respectively. Geographically, the highest CO 2 -CC was found in the central part of the basin, while the lowest was in the northern part of Inner Mongolia. This study underscores the increased significance of the region’s CO 2 -CC, which showed an increase from 17.0 ppm at the start of the period to 21.0 ppm by the end, representing an overall growth of between 4.35 and 5.25%. The findings highlight the urgency of targeted measures to mitigate CO 2 emissions and adapt to their consequences in the Yellow River Basin, contributing to the global efforts against climate change and towards sustainable development.

This study uses ozone in the atmosphere column collected by the Aura satellite’s ozone monitoring instrument (OMI), to evaluate the ozone pollution status of Japan. Mann–Kendall and slope trend analysis, Hurst index analysis, the potential source contribution factor algorithm, GTWR (geographically and temporally weighted regression model), and the random forest regression model were used in this paper to investigate ozone column concentrations in Japan from 2010 through 2021. The results showed that ozone column concentrations had a significant latitudinal trend over the past 12 years, i.e., concentrations increased with increasing latitude. And the slope has showed a small upward trend over the years (slope = 0.01). The interannual maximum and minimum ozone column concentrations are in 2021 (387.57 DU) and 2015 (241.27 DU), respectively. The monthly maximum and minimum values occur in March (361.60 DU) and October (286.96 DU), with seasonal concentrations in the order of Spring (352.94 DU) > Winter (336.01 DU) > Summer (306.78 DU) > Autumn (296.30 DU). Column ozone concentrations have increased in 69.82% of Japan over the past 12 years. In 2022, the maximum, minimum, and mean ozone column concentrations based on random forest forecasts are 372DU, 278DU, and 334DU, respectively. Pollution sources in Japan are largely derived from pollutants that are transported across borders from the seas around the country or from other countries in the world, with the largest potential source areas being located in the Kanto region. Relative humidity, lift index, and air temperature (in all three seasons except winter) have a negative effect on ozone column concentrations. The positive effects of precipitable water and nitrogen dioxide on ozone column concentrations in the Hokkaido locality and southern Japan were more significant. The contribution of population density and GDP to ozone is small.

This study uses the daily product data of the concentration of ozone in the atmospheric column (ozone column concentration) collected by the Aura satellite’s Ozone Monitoring Instrument (OMI), to evaluate the ozone pollution status of the Fenwei Plain in east-central China, by employing pixel-based spatial analysis, an θ slope trend index, a Hurst index, and grey correlation. The following results were found. (1) The spatial distribution of ozone in the atmosphere of the Fenwei Plain was higher in the north and lower in the south, with high values appearing in Jinzhong, Lvliang, and other cities. (2) The changes in ozone column concentration periodically and seasonally in the Fenwei Plain. Seasonally, the ozone column concentration was highest in spring, followed by summer, winter, and autumn. (3) The pixel-based trend change of the ozone slope ( θ slope ) indicated that the ozone concentration in the region was in a downward trend, while the long-term correlation of the time series trend Hurst index found that the region should expect to see a weak rebound in the ozone column concentration in the future, so that routine monitoring should be strengthened. (4) The present study on the factors influencing the ozone concentration found that the concentration is relatively closely related to temperature, air pressure, humidity, grain sowing area, highway mileage, and secondary industry.

The tropospheric vertical column concentration (VCDtrop) of NO2, SO2, and HCHO was retrieved, respectively, by employing the geometric method (Geomtry), simplified model method (Model), and look-up table method (Table) with the observation spectra of the multi-axis differential absorption spectroscopy instrument (MAX-DOAS). The correlation and relative differences of the inversion results obtained by these three algorithms, as well as the changes in quantiles, were explored. The comparative analysis reveals that the more concentrated the vertical distribution height of gas components is in the near-surface layer, the better the conformity of the VCDtrop retrieved by different algorithms. However, the increase in relative differences is also related to the diurnal variation of gas components. The influence of aerosols on the inversion of the VCDtrop is greater than the change in the vertical distribution height of the gas component itself. The near-surface concentration and distribution height of gas components are the internal factors that give rise to relative differences in the inversion of the VCDtrop by different algorithms, while aerosols are one of the extremely important external reasons. The VCDtrop inverted by Geomtry without considering the influence of aerosols is generally larger except for NO2. Model sets up aerosols in accordance with the height and meteorological conditions of the atmospheric environment. Table can invert the aerosol profile in real time. Compared with Model, it shows a significant improvement in the refined setting of aerosols. Moreover, while obtaining the vertical distribution of aerosols, it can invert the diurnal variation of the VCDtrop. The VCDtrop inverted by Table is the smallest, and the relative difference with Model is on average about 10% smaller. The relative difference of the VCDtrop for the same height (aerosol optical thickness) quantile is 7–15% (about 25% lower on average). When comparing the inversion results of Table with the Ozone Monitoring Instrument (OMI) satellite product, the MAX-DOAS inversion results of NO2, SO2, and HCHO are all larger than the OMI product. This is related to the different observation methods of the MAX-DOAS and OMI and the configuration between the aerosol layer and the distribution height of gas components.