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.
View factors for sky, trees, and buildings are three important parameters of the urban outdoor environment that describe the geometrical relationship between different surfaces from the perspective ...of radiative energy transfer. This study develops an approach for accurately estimating sky view factor (SVF), tree view factor (TVF), and building view factor (BVF) of street canyons in the high-density urban environment of Hong Kong using publicly available Google Street View (GSV) images and a deep-learning algorithm for extraction of street features (sky, trees, and buildings). As a result, SVF, TVF, and BVF maps of street canyons are generated. Verification using reference data of hemispheric photography from field surveys in compact high-rise and low-rise areas shows that the GSV-based VF estimates have a satisfying agreement with the reference data (all with R2 > 0.95), suggesting the effectiveness and high accuracy of the developed method. This is the first reported use of hemispheric photography for direct verification in a GSV-based streetscape study. Furthermore, a comparison between GSV-based and 3D-GIS-based SVFs shows that the two SVF estimates are significantly correlated (R2 = 0.40, p < 0.01) and show better agreement in high-density areas. However, the latter overestimates SVF by 0.11 on average, and the differences between them are significantly correlated with street trees (R2 = 0.53): the more street trees, the larger the difference. This suggests that a lack of street trees in a 3D-GIS model of street environments is the dominant factor contributing to the large discrepancies between the two datasets.
•GSV-based view factor estimation method with a deep-learning algorithm is proposed.•Sky, tree, and building view factors of street canyons are accurately quantified.•Hemispheric photography is used for direct verification of view factor estimates.•Comparison analysis of GSV-based and 3D-GIS-based methods is conducted.•The impacts of tree canopy and building density on SVF estimates are investigated.
Legislation in the State of California mandates reductions in emissions of short‐lived climate pollutants of 40% from 2013 levels by 2030 for CH4. Identification of the sector(s) responsible for ...these emissions and their temporal and spatial variability is a key step in achieving these goals. Here, we determine the emissions of CH4 in Los Angeles from 2011–2017 using a mountaintop remote sensing mapping spectrometer. We show that the pattern of CH4 emissions contains both seasonal and nonseasonal contributions. We find that the seasonal component peaks in the winter and is correlated (R2 = 0.58) with utility natural gas consumption from the residential and commercial sectors and not from the industrial and gas‐fired power plant sectors. The nonseasonal component is (22.9 ± 1.4) Gg CH4/month. If the seasonal correlation is causal, about (1.4 ± 0.1)% of the commercial and residential natural gas consumption in Los Angeles is released into the atmosphere.
Plain Language Summary
CH4 is a desirable target for greenhouse gas emission reductions because emission controls will have a rapid impact on radiative forcing. However, its emission budget is highly uncertain and poorly quantified. This paper reports new results from a novel mountaintop remote sensing spectrometer overlooking the Los Angeles basin. The study shows that the megacity's methane emissions are strongly correlated with the consumption of natural gas by residential and commercial consumers, with a leakage rate of (1.4 ± 0.1)%, while the nonseasonal component is (22.9 ± 1.4) Gg CH4/month. By identifying a clear relationship between CH4 emissions and natural gas consumption, our results provide strong constraints on the pathways for fugitive CH4 emissions from the natural gas distribution system in Los Angeles.
Key Points
A mountaintop remote sensing spectrometer is used to derive the time series and spatial pattern of methane emissions in LA basin
The methane emissions in the LA basin are strongly correlated with the consumption of natural gas by residential and commercial consumers
About (1.4 ± 0.1)% of the residential and commercial natural gas consumption in LA is released into the atmosphere
When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In ...this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties.
This study presents a method for calculating solar irradiance of street canyons using Google Street View (GSV) images and investigates its spatiotemporal patterns in a high-density urban environment. ...In this method, GSV images provide a unique way to characterize the street morphology from which the diurnal solar path and solar radiation exposure can be estimated in a street canyon. Verifications of our developed method using free-horizon HKO observations and street-level field measurements show that both the calculated clear-sky and all-sky solar irradiance of street canyons well capture the diurnal and seasonal cycles. In the high-density urban areas of Hong Kong, we found that (1) the lowest monthly averaged solar irradiations in winter are 6.6 (December) and 4.6 (February) MJ/m2/day, and the highest values in summer are 17.3 (July) and 10.8 (June) MJ/m2/day for clear-sky and all-sky calculations, respectively; (2) The spatial variability of solar irradiation is closely related to sky view factor (SVF). In summer, the irradiation in a low-rise region (SVF≥0.7) on average is about three times that in a high-rise region (SVF≤0.3), and they differ by about five times in winter; (3) Street orientation has a significant impact on the solar radiation received in a high-density street canyon. In general, street canyons with West-East orientation receive higher solar irradiation during summer and lower during winter compared to those with South-North orientation. The generated maps of street-level solar irradiation may help researchers investigate the interactions between solar radiation, human health and urban thermal balance in high-density urban environments.
•A method to estimate street-level solar irradiance using Google Street View images is proposed.•Evaluation using free-horizon and street-level measurements is implemented.•Spatiotemporal patterns of street-level solar irradiation are accurately quantified.•Direct and diffuse components of solar irradiation are quantified separately.•Effect of street canyon geometries and morphologies on solar irradiation is analyzed.
This study attempts to infer aerosol vertical structure in the urban boundary layer using passive hyperspectral measurements. A spectral sorting technique is developed to retrieve total aerosol ...optical depth (AOD) and effective aerosol layer height (ALH) from hyperspectral measurements in the 1.27‐μm oxygen absorption band by the mountaintop Fourier Transform Spectrometer at the California Laboratory for Atmospheric Remote Sensing instrument (1,673 m above sea level) overlooking the LA basin. Comparison to AOD measurements from Aerosol Robotic Network and aerosol backscatter profile measurements from a Mini MicroPulse Lidar shows agreement, with coefficients of determination (r2) of 0.74 for AOD and 0.57 for effective ALH. On average, the AOD retrieval has an error of 24.9% and root‐mean‐square error of 0.013, while the effective ALH retrieval has an error of 7.8% and root‐mean‐square error of 67.01 m. The proposed method can potentially be applied to existing and future satellite missions with hyperspectral oxygen measurements to constrain aerosol vertical distribution on a global scale.
Plain Language Summary
Satellite and ground‐based measurements have enabled accurate and continuous monitoring of total aerosol loading. However, these measurements provide little or no information on the vertical distribution of aerosols. In particular, there is poor measurement of aerosols in the planetary boundary layer, the part of the atmosphere closest to the surface. In this study, we develop an algorithm to retrieve the vertical structure of aerosols in the boundary layer using remote sensing observations of oxygen absorption with high spectral resolution. The algorithm is applied to infer the vertical profile of air pollutants in the Los Angeles basin using measurements made by a mountaintop instrument overlooking the basin. The proposed retrieval algorithm can potentially be applied to existing and future satellite missions with hyperspectral oxygen measurements to constrain the aerosol vertical distribution on a global scale. This important piece of information on aerosol vertical structure will potentially address several key priorities in the 2017 U.S. National Research Council Earth Science Decadal Survey, from forecasting air pollution in cities, quantifying the aerosol impact on Earth's climate, and reducing biases in greenhouse gas retrievals.
Key Points
A method is developed to constrain aerosol vertical profiles in the boundary layer using hyperspectral measurements of oxygen absorption
The method is tested using hyperspectral measurement of reflected solar radiation from a mountaintop instrument to infer aerosol profiles
The method can potentially be applied to satellite observations to constrain aerosol vertical structure on a global scale
Satellite-based detection of methane (CH4) point sources is crucial in identifying and mitigating anthropogenic emissions of CH4, a potent greenhouse gas. Previous studies have indicated the presence ...of CH4 point source emissions from coal mines in Shanxi, China, which is an important source region with large CH4 emissions, but a comprehensive survey has remained elusive. This study aims to conduct a survey of CH4 point sources over Shanxi's coal mines based on observations of the Advanced Hyperspectral Imager (AHSI) on board the Gaofen-5B satellite (GF-5B/AHSI) between 2021 and 2023. The spectral shift in centre wavelength and change in full width at half-maximum (FWHM) from the nominal design values are estimated for all spectral channels, which are used as inputs for retrieving the enhancement of the column-averaged dry-air mole fraction of CH4 (ΔXCH4) using a matched-filter-based algorithm. Our results show that the spectral calibration on GF-5B/AHSI reduced estimation biases of the emission flux rate by up to 5.0 %. We applied the flood-fill algorithm to automatically extract emission plumes from ΔXCH4 maps. We adopted the integrated mass enhancement (IME) model to estimate the emission flux rate values from each CH4 point source. Consequently, we detected CH4 point sources in 32 coal mines with 93 plume events in Shanxi province. The estimated emission flux rate ranges from 761.78 ± 185.00 to 12 729.12 ± 4658.13 kg h−1. Our results show that wind speed is the dominant source of uncertainty contributing about 84.84 % to the total uncertainty in emission flux rate estimation. Interestingly, we found a number of false positive detections due to solar panels that are widely spread in Shanxi. This study also evaluates the accuracy of wind fields in ECMWF ERA5 reanalysis by comparing them with a ground-based meteorological station. We found a large discrepancy, especially in wind direction, suggesting that incorporating local meteorological measurements into the study CH4 point source are important to achieve high accuracy. The study demonstrates that GF-5B/AHSI possesses capabilities for monitoring large CH4 point sources over complex surface characteristics in Shanxi.
The Northern California Camp Fire that took place in November 2018 was one
of the most damaging environmental events in California history. Here, we
analyze ground-based station observations of ...airborne particulate matter
that has a diameter <2.5 µm (PM2.5) across Northern
California and conduct numerical simulations of the Camp Fire using the
Weather Research and Forecasting model online coupled with chemistry
(WRF-Chem). Simulations are evaluated against ground-based observations of
PM2.5, black carbon, and meteorology, as well as satellite
measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol
layer height and aerosol index. The Camp Fire led to an increase in Bay Area
PM2.5 to over 50 µg m−3 for nearly 2 weeks, with
localized peaks exceeding 300 µg m−3. Using the Visible Infrared
Imaging Radiometer Suite (VIIRS) high-resolution fire detection products,
the simulations reproduce the magnitude and evolution of surface PM2.5
concentrations, especially downwind of the wildfire. The overall spatial
patterns of simulated aerosol plumes and their heights are comparable with
the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations
are carried out to analyze uncertainties that arise from fire emissions,
meteorological conditions, feedback of aerosol radiative effects on
meteorology, and various physical parameterizations, including the planetary
boundary layer model and the plume rise model. Downwind PM2.5
concentrations are sensitive to both flaming and smoldering emissions over
the fire, so the uncertainty in the satellite-derived fire emission products
can directly affect the air pollution simulations downwind. Our analysis
also shows the importance of land surface and boundary layer
parameterization in the fire simulation, which can result in large
variations in magnitude and trend of surface PM2.5. Inclusion of
aerosol radiative feedback moderately improves PM2.5 simulations,
especially over the most polluted days. Results of this study can assist in
the development of data assimilation systems as well as air quality
forecasting of health exposures and economic impact studies.
The weekday-weekend effect of anthropogenic emissions in cities, driven by the associated weekly changes in human activities, provides a unique opportunity to assess the sensitivity of observation ...networks (e.g., ground-based and space-borne instruments) on urban emissions. In this study, we focus on the weekly cycle amplitudes of nitrogen dioxide (NO2), carbon monoxide (CO), and carbon dioxide (CO2) in the Los Angeles (LA) megacity, where a significant weekly cycle of human activities exists. In addition, abundant observations are being produced continuously from existing ground-based, mountaintop, and satellite platforms to monitor carbon emissions and air quality in LA. From our analysis, significant agreement can be found in observations from different platforms. For NO2, a 30%–35% Sunday decline relative to mid-week mixing ratios can be observed from both ground-based and satellite observations. For CO, the Sunday drops from ground-based, mountain-top and satellite observations are 13%–20%. The TROPOMI instrument with its high spatial resolution provides detailed spatial information on the reduction of tropospheric NO2 and CO columns on Sundays. The spatial pattern is in good agreement with traffic density in LA. Impact due to the prevailing winds from the coast in the afternoon can also be observed. For anthropogenic CO2, we show that the weekly cycle of XCO2 enhancement above background from OCO-2 observations has a Sunday decline (15%–20%) consistent with ground-based observations and TCCON. This weekly pattern of CO2 in a megacity directly detected by OCO-2 is reported for the first time. In addition, we also investigate the weekly cycles in the stable carbon isotopic composition of CO2 (δ13C) from ground-based observations, which demonstrates the weekly variation in fossil fuel usage in LA. Finally, using the COVID-19 lockdown period as an example of a short-term perturbation on anthropogenic emissions, we found that the weekly cycle amplitude became larger during the lockdown period primarily because of the traffic volume changes in light-duty vehicles. This study highlights the consistencies and effectiveness of existing observing platforms in monitoring the anthropogenic emissions of the LA megacity.
•Weekly cycles of observed atmospheric NO2, CO, CO2, and δ13C in Los Angeles megacity are assessed.•Weekly cycles from ground-based, mountaintop, and satellite measurements are comparable.•A weekly pattern for CO2 in a megacity directly detected by OCO-2 is reported for the first time.•Existing observing platforms are consistent in monitoring carbon emissions of LA.
PDF
