Search Results (20)

Pollutants information can be retrieved from visible (VIS) and ultraviolet (UV) diffuse solar spectra exploiting Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. In May 2021, the Italian research institute CNR-ISAC acquired and deployed a MAX-DOAS system SkySpec-2D. It is located in the “Giorgio Fea” observatory in San Pietro Capofiume (SPC), in the middle of the Po Valley, where it has constantly acquired zenith and off-axis diffuse solar spectra since the 1st October 2021. This work presents the retrieved tropospheric NO₂ and aerosol extinction profiles (and their columns) derived from the MAX-DOAS measurements using the newly developed DEAP retrieval code. The code has been validated both using synthetic differential Slant Column Densities (dSCDs) from the Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations (FRM4DOAS) project and real measured data. For this purpose, DEAP results are compared with the ones obtained with three state-of-the-art retrieval codes. In addition, an inter-comparison with satellite products from Sentinel-5P TROPOMI, for the tropospheric NO₂ Vertical Column Densities (VCDs), and MODIS-MAIAC for the tropospheric Aerosol Optical Depth (AOD), is performed. We find a bias of −0.6 × 10¹⁵ molec/cm² with a standard deviation of 1.8 × 10¹⁵ molec/cm² with respect to Sentinel-5P TROPOMI for NO₂ tropospheric VCDs and of 0.04 ± 0.08 for AOD with respect to MODIS-MAIAC data. The retrieved data show that the SPC measurement site is representative of the background pollution conditions of the Po Valley. For this reason, it is a good candidate for satellite validation and scientific studies over the Po Valley. Full article

Estimating the number of trees within a forest stand, i.e., the forest stand density (FSD), is challenging at large scales. Recently, researchers have turned to a combination of remote sensing and machine learning techniques to derive these estimates. However, in most cases, the developed models rely heavily upon additional data such as LiDAR-based elevations or multispectral information and are mostly applied to managed environments rather than natural/mixed forests. Furthermore, they often require the time-consuming manual digitization or masking of target features, or an annotation using a bounding box rather than a simple point annotation. Here, we introduce the Tree Convolutional Row Neural Network (Tree-CRowNN), an alternative model for tree counting inspired by Multiple-Column Neural Network architecture to estimate the FSD over 12.8 m × 12.8 m plots from high-resolution RGB aerial imagery. Our model predicts the FSD with very high accuracy (MAE: ±2.1 stems/12.8 m², RMSE: 3.0) over a range of forest conditions and shows promise in linking to Sentinel-2 imagery for broad-scale mapping (R²: 0.43, RMSE: 3.9 stems/12.8 m²). We believe that the satellite imagery linkage will be strengthened with future efforts, and transfer learning will enable the Tree-CRowNN model to predict the FSD accurately in other ecozones. Full article

Formaldehyde (HCHO) plays an important role in atmospheric photochemical reactions. Comparative studies between ground-based and satellite observations are necessary to assess and promote the potential use of column HCHO as a proxy for surface HCHO and volatile organic compound (VOC) oxidation. Previous studies have only validated temporal and vertical profile variations at one point, with limited studies comparing horizontal spatial variations due to sparse monitoring sites. The photochemistry-active Chinese Greater Bay Area (GBA) is a typical megacity cluster as well as a large hotspot of HCHO globally, which recorded a high incidence of ozone (O₃) pollution. Here, we conducted the first comparative study of ground-gridded (HCHO_gg) and satellite-derived (HCHO_sd) HCHO during typical O₃ episodes in the GBA. Our results revealed a good correlation between HCHO_gg and HCHO_sd, with a correlation coefficient higher than 0.5. Cloud coverage and ground pixel sizes were found to be the dominant factors affecting the quality of HCHO_sd and contributing to the varying satellite pixel density. Daily averages of HCHO_sd effectively improved the HCHO_sd accuracy, except in areas with low satellite pixel density. Furthermore, a new quality control procedure was established to improve HCHO_sd from Level 2 to Level 3, which demonstrated good application performance in O₃ sensitivity analysis. Our findings indicate that the correlation between satellite observations and surface air quality can be optimized by spatiotemporal averaging of hourly HCHO_sd, given the advent of geostationary satellites. Considering the representative range of sampling sites in this comparative study, we recommend establishing VOC monitoring stations within a 50 km radius in the GBA to further analyze and control photochemical pollution. Full article

Rfn (formaldehyde/nitrogen dioxide) is a common indicator based on satellite observations used to classify ozone formation sensitivity. However, it may underestimate anthropogenic volatile organic compounds (VOCs) in heavily polluted cities when only formaldehyde (HCHO) is used in Rfn to measure VOCs, since it is mainly derived from natural sources worldwide. In this study, we used multiaxis differential optical absorption spectroscopy to acquire tropospheric observations of nitrogen dioxide (NO₂), HCHO and glyoxal (CHOCHO) in Beijing from 1 April 2019 to 31 March 2020. Combined with VOCs detected simultaneously by gas chromatography—mass spectrometry and proton transfer reaction–time-of-flight/mass spectrometry near the ground, we evaluated the representativeness of HCHO column densities on total VOCs (TVOC) in equivalent propylene concentrations, which is called reactivity. The results showed that there were significant seasonal differences in the response of HCHO to TVOC reactivity, with fitting slopes of 2.3 (spring), 2.6 (summer), 2.9 (autumn) and 1.0 (winter) in the four seasons, respectively. Since CHOCHO can be used to partly characterize the contribution of anthropogenic VOC emissions and demonstrated a better response to TVOC reactivity in winter, with fitting slopes of 0.2 (spring), 0.2 (summer), 0.2 (autumn) and 0.5 (winter) in the four seasons, respectively, we introduced CHOCHO to construct a new indicator (HCHO + 6 × CHOCHO). The fitting slopes of the four seasons were more similar, being 3.2 (spring), 3.6 (summer), 4.0 (autumn) and 4.0 (winter). The ratio of the new indicator to NO₂, Rmn ((HCHO + 6 × CHOCHO)/NO₂), was used to reclassify the ozone formation sensitivity of urban areas in North China, revealing that it is a transition regime before 1300 LST (LST = UST + 8) and an NOx-limited regime afterwards. Rmn improved the sensitivity from the VOC-limited regime to the NOx-limited regime, enhancing the sensitivity of NOx and providing new robust support for ozone pollution prevention and control. Full article

Satellites are an effective source of atmospheric carbon dioxide (CO₂) monitoring; however, city-scale monitoring of atmospheric CO₂ through space-borne observations is still a challenging task due to the trivial change in atmospheric CO₂ concentration compared to its natural variability and background concentration. In this study, we attempted to evaluate the potential of space-based observations to monitor atmospheric CO₂ changes at the city scale through simple data-driven analyses. We used the column-averaged dry-air mole fraction of CO₂ (XCO₂) from the Carbon Observatory 2 (OCO-2) and the anthropogenic CO₂ emissions provided by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) product to explain the scenario of CO₂ over 120 districts of Pakistan. To study the anthropogenic CO₂ through space-borne observations, XCO₂ anomalies (MXCO₂) were estimated from OCO-2 retrievals within the spatial boundary of each district, and then the overall spatial distribution pattern of the MXCO₂ was analyzed with several datasets including the ODIAC emissions, NO₂ tropospheric column, fire locations, cropland, nighttime lights and population density. All the datasets showed a similarity in the spatial distribution pattern. The satellite detected higher CO₂ concentrations over the cities located along the China–Pakistan Economic Corridor (CPEC) routes. The CPEC is a large-scale trading partnership between Pakistan and China and large-scale development has been carried out along the CPEC routes over the last decade. Furthermore, the cities were ranked based on mean ODIAC emissions and MXCO₂ estimates. The satellite-derived estimates showed a good consistency with the ODIAC emissions at higher values; however, deviations between the two datasets were observed at lower values. To further study the relationship of MXCO₂ and ODIAC emissions with each other and with some other datasets such as population density and NO₂ tropospheric column, statistical analyses were carried out among the datasets. Strong and significant correlations were observed among all the datasets. Full article

To mitigate tropospheric ozone (O₃) pollution with proper and effective emission regulations, diagnostics for the O₃-sensitive regime are critical. In this study, we analyzed the satellite-measured formaldehyde (HCHO) and nitrogen dioxide (NO₂) column densities and derived the HCHO to NO₂ ratio (FNR) from 2005 to 2019. Over China, there was a clear increase in the NO₂ column during the first 5-year period and a subsequent decrease after 2010. Over the Republic of Korea and Japan, there was a continuous decline in the NO₂ column over 15 years. Over the entire East Asia, a substantial increase in the HCHO column was identified during 2015–2019. Therefore, FNR increased over almost all of East Asia, especially during 2015–2019. This increasing trend in FNR indicated the gradual shift from a volatile organic compound (VOC)-sensitive to a nitrogen oxide (NO_x)-sensitive regime. The long-term changes in HCHO and NO₂ columns generally corresponded to anthropogenic non-methane VOC (NMVOC) and NO_x emissions trends; however, anthropogenic sources did not explain the increasing HCHO column during 2015–2019. Because of the reduction in anthropogenic sources, the relative importance of biogenic NMVOC sources has been increasing and could have a larger impact on changing the O₃-sensitive regime over East Asia. Full article

We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume and a stochastic model of the possible emissions. Satellite data are processed to give column loads where ash is detected and to give information on where we have high confidence that there is negligible ash. An atmospheric dispersion model is used to relate emissions and column loads. Gaussian distributions are assumed for the a priori emissions and for the errors in the satellite retrievals. The optimal emissions estimate is obtained by finding the peak of the a posteriori probability density under the constraint that the emissions are non-negative. We apply this inversion method within a framework designed for use during an eruption with the emission estimates (for any given emission time) being revised over time as more information becomes available. We demonstrate the approach for the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions. We apply the approach in two ways, using only the ash retrievals and using both the ash and clear sky retrievals. For Eyjafjallajökull we have compared with an independent dataset not used in the inversion and have found that the inversion-derived emissions lead to improved predictions. Full article

Ground-based zenith scattered light differential optical absorption spectroscopy (DOAS) measurements were performed in summer and autumn (27 May–30 November) 2020 at Golmud (94°54′ E, 36°25′ N; 2807.6 m altitude) to investigate the abundances and temporal variations of ozone (O₃) and its depleting substances over the northern Tibetan Plateau (TP). The differential slant column densities (dSCDs) of O₃, nitrogen dioxide (NO₂), bromine monoxide (BrO), and chlorine dioxide (OClO) were simultaneously retrieved from scattered solar spectra in the zenith direction during the twilight period. The O₃ vertical column densities (VCDs) were derived by applying the Langley plot method, for which we investigated the sensitivities to the chosen wavelength, the a-priori O₃ profile and the aerosol extinction profile used in O₃ air mass factor (AMF) simulation as well as the selected solar zenith angle (SZA) range. The mean O₃ VCDs from June to November 2020 are 7.21 × 10¹⁸ molec·cm⁻² and 7.18 × 10¹⁸ molec·cm⁻² at sunrise and sunset, respectively. The derived monthly variations of the O₃ VCDs, ranging from a minimum of 6.9 × 10¹⁸ molec·cm⁻² in October to 7.5 × 10¹⁸ molec·cm⁻² in November, well matched the OMI satellite product, with a correlation coefficient R = 0.98. The NO₂ VCDs at SZA = 90°, calculated by a modified Langley plot method, were systematically larger at sunset than at sunrise as expected with a pm/am ratio of ~1.56. The maximum of the monthly NO₂ VCDs, averaged between sunrise and sunset, was 3.40 × 10¹⁵ molec·cm⁻² in July. The overall trends of the NO₂ VCDs were gradually decreasing with the time and similarly observed by the ground-based zenith DOAS and OMI. The average level of the BrO dSCD_90°–80° (i.e., dSCD between 90° and 80° SZA) was 2.06 × 10¹⁴ molec·cm⁻² during the period of June–November 2020. The monthly BrO dSCD_90°–80° presented peaks in August and July for sunrise and sunset, respectively, and slowly increased after October. During the whole campaign period, the OClO abundance was lower than the detection limit of the instrument. This was to be expected because during that season the stratospheric temperatures were above the formation temperature of polar stratospheric clouds. Nevertheless, this finding is still of importance, because it indicates that the OClO analysis works well and is ready to be used during periods when enhanced OClO abundances can be expected. As a whole, ground-based zenith DOAS observations can serve as an effective way to measure the columns of O₃ and its depleting substances over the TP. The aforementioned results are helpful in investigating stratospheric O₃ chemistry over the third pole of the world. Full article

The continuing increase in atmospheric CO₂ concentration caused by anthropogenic CO₂ emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO₂ data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO₂ (XCO₂) data (Mapping-XCO₂), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO₂ to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO₂ growth rate from Mapping-XCO₂, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO₂ anomalies, derived from removing background from the Mapping-XCO₂ data, reveal XCO₂ enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO₂ clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO₂ fluxes, which help better understand and analyze regional XCO₂ changes that are associated with atmospheric transport. To quantify regional anomalies of CO₂ emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO₂ anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO₂ emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO₂. In addition, we compared the variations in regional XCO₂ anomalies and NO₂ columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO₂ anomalies have a positive correlation with the decline of NO₂ columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO₂, because of its high detectivity and co-emission with CO₂, to assist the analysis and verification of CO₂ emissions in future studies. Full article

Nitrogen dioxide (NO₂) has a great influence on atmospheric chemistry. Scientifically identifying the temporal-spatial characteristics of NO₂ distribution and their driving factors will be of realistic significance to atmospheric governance in the Yangtze River Economic Belt (YREB). Based on the NO₂ data derived from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 satellite (2017~present), spatial autocorrelation analysis, standard deviation ellipse (SDE), and geodetectors were used to systematically analyze the spatial-temporal evolution and driving factors of tropospheric NO₂ vertical column density (NO₂ VCD) in the YREB from 2019 to 2020. The results showed that the NO₂ VCD in the YREB was high in winter and autumn and low in spring and summer (temporal distribution), and high in the northeast and low in the southwest (spatial distribution), with significant spatial agglomeration. High-value agglomeration zones were collectively and stably distributed in the east region, while low-value zones were relatively dispersed. The explanatory power of each potential factor for the NO₂ VCD showed regional and seasonal variations. Surface pressure was found to be a core influencing factor. Synergistic effects of factors presented bivariate enhancement or nonlinear enhancement, and interaction between any two factors strengthened the explanatory power of a single factor for the NO₂ VCD. Full article

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods. Full article

Atmospheric cold front propagation across the northern Gulf of Mexico is characterized by elevated surface wind velocities and a ~10–15 °C drop in surface air temperatures. These meteorological conditions result in significant heat energy losses from the surface ocean to the overlying atmosphere. These seasonally recurring cold-air outbreak events may penetrate the southern portion of the West Florida continental shelf and initiate turbulent and convective overturn of the water column. Examination of true color images derived from ocean-viewing, satellite-based radiometer data reveals coincident and substantial surface water discolorations that are optically similar to smaller-scale “whiting events,” despite the regional-scale extent of the observed phenomenon (>25,000 km²). Coupled air–sea numerical simulations suggest the surface water discoloration occurs and is sustained where the entire water column is dynamically unstable. The simulation results indicate significant density (σ_t) inversions between the surface and bottom waters. Thus, the combined numerical model and remote sensing analysis suggest that convective turbulence may be contributing to the sustained ventilation of bottom waters containing a high concentration of suspended particulates. High-temporal resolution true color images rendered from the GOES-R Advanced Baseline Imager (ABI) data appear to support the surface water discoloration’s turbulent-driven nature. Full article

Crop residue burning is the major biomass burning activity in China, strongly influencing the regional air quality and climate. As the cultivation pattern in China is rather scattered and intricate, it is a challenge to derive an accurate emission inventory for crop residue burning. In this study, we proposed a remote sensing-based method to estimate nitrogen oxide (NO_x) emissions related to crop residue burning at the field level over Hubei, China. The new method considers differences in emission factors and the spatial distribution for different crop types. Fire radiative power (FRP) derived from moderate-resolution imaging spectroradiometer (MODIS) was used to quantify NO_x emissions related to agricultural biomass combustion. The spatial distribution of different crops classified by multisource remote sensing data was used as an a priori constraint. We derived a new NO_x emission database for Hubei from 2014 to 2016 with spatial resolution of 1 × 1 km. Significant seasonal patterns were observed from the NO_x emission database. Peak NO_x emission occurring in October was related to the residue burning in late autumn harvesting. Another peak was observed between January and April, which was due to the frequent burning of stubble before spring sowing. Our results were validated by comparing our emission inventory with geostationary satellite observations, previous studies, global fire emission database (GFED), NO₂ vertical column densities (VCDs) from ozone monitoring instrument (OMI) satellite observations, and measurements from environmental monitoring stations. The comparisons showed NO_x emission from GFED database was 47% lower than ours, while the evaluations from most of the statistical studies were significantly higher than our results. The discrepancies were likely related to the differences of methodology and data sources. The spatiotemporal variations of NO_x emission in this study showed strong correlations with NO₂ VCDs, which agreed well with geostationary satellite observations. A reasonable correlation between in situ NO₂ observations and our results in agricultural regions demonstrated that our method is reliable. We believe that the new NO_x emission database for crop residue burning derived in this study can potentially improve the understanding of pollution sources and can provide additional information for the design of pollution control measures. Full article

Previous studies have reported that intra-urban variability of NO₂ concentrations is even higher than inter-urban variability. In recent years, an increasing number of studies have developed satellite-derived land use regression (LUR) models to predict ground-level NO₂ concentrations, though only a few have been conducted at a city scale. In this study, we developed a satellite-derived LUR model to predict seasonal NO₂ concentrations at a city scale by including satellite-retrieved NO₂ tropospheric column density, population density, traffic indicators, and NO_x emission data. The R² of model fitting and 10-fold cross validation were 0.70 and 0.61 for the satellite-derived seasonal LUR model, respectively. The satellite-based LUR model captured seasonal patterns and fine gradients of NO₂ variations at a 100 m × 100 m resolution and demonstrated that NO₂ pollution in winter is 1.46 times higher than that in summer. NO₂ concentrations declined significantly with increasing distance from roads and with increasing distance from the city center. In Suzhou, 84% of the total population lived in areas with NO₂ concentrations exceeding the annual-mean standard at 40 μg/m³ in 2014. This study demonstrated that satellite-retrieved data could help increase the accuracy and temporal resolution of the traditional LUR models at a city scale. This application could support exposure assessment at a high resolution for future epidemiological studies and policy development pertaining to air quality control. Full article

Bathymetry is considered an important component in marine applications as several coastal erosion monitoring and engineering projects are carried out in this field. It is traditionally acquired via shipboard echo sounding, but nowadays, multispectral satellite imagery is also commonly applied using different remote sensing-based algorithms. Satellite-Derived Bathymetry (SDB) relates the surface reflectance of shallow coastal waters to the depth of the water column. The present study shows the results of the application of Stumpf and Lyzenga algorithms to derive the bathymetry for a small area using an Unmanned Aerial Vehicle (UAV), also known as a drone, equipped with a multispectral camera acquiring images in the same WorldView-2 satellite sensor spectral bands. A hydrographic Multibeam Echosounder survey was performed in the same period in order to validate the method’s results and accuracy. The study area was approximately 0.5 km² and located in Tuscany (Italy). Because of the high percentage of water in the images, a new methodology was also implemented for producing a georeferenced orthophoto mosaic. UAV multispectral images were processed to retrieve bathymetric data for testing different band combinations and evaluating the accuracy as a function of the density and quantity of sea bottom control points. Our results indicate that UAV-Derived Bathymetry (UDB) permits an accuracy of about 20 cm to be obtained in bathymetric mapping in shallow waters, minimizing operative expenses and giving the possibility to program a coastal monitoring surveying activity. The full sea bottom coverage obtained using this methodology permits detailed Digital Elevation Models (DEMs) comparable to a Multibeam Echosounder survey, and can also be applied in very shallow waters, where the traditional hydrographic approach requires hard fieldwork and presents operational limits. Full article