Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands).We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well.We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2 .

Weather-Climate Anomalies and Regional Transport Contribute to Air Pollution in Northern China During the COVID-19 Lockdown.

Two persistent and heavy haze episodes during the COVID-19 lockdown (from 20 Jan to 22 Feb 2020) still occur in northern China, when anthropogenic emissions, particularly from transportation sources, are greatly reduced. To investigate the underlying cause, this study comprehensively uses in-situ measurements for ambient surface pollutants, reanalysis meteorological data and the WRF-Chem model to calculate the contribution of NOx emission change and weather-climate change to the "unexpectedly heavy" haze. Results show that a substantial NOx reduction has slightly decreased PM2.5 concentration. By contrast, the weakest East Asian winter monsoon (EAWM) in the 2019-2020 winter relative to the past decade is particularly important for haze occurrence. A warmer and moister climate is also favorable. Model results suggest that climate anomalies lead to a 25-50 µg m-3 increase of PM2.5 concentration, and atmospheric transport is also an important contributor to two haze episodes. The first haze is closely related to the atmospheric transport of pollutants from NEC to the south, and fireworks emissions in NEC are a possible amplifying factor that warrants future studies. The second one is caused by the convergence of a southerly wind and a mountain wind, resulting in an intra-regional transport within BTH, with a maximal PM2.5 increment of 50-100 µg m-3. These results suggest that climate change and regional transport are of great importance to haze occurrence in China, even with significant emission reductions of pollutants.

Methane, carbon dioxide, hydrogen sulfide, and isotopic ratios of methane observations from the Permian Basin tower network

We describe the instrumentation, calibration, and uncertainty of the network of ground-based, in situ, cavity ring down spectroscopy (CRDS) greenhouse gas (GHG) measurements deployed in the Permian Basin. The primary goal of the network is to be used in conjunction with atmospheric transport modeling to determine methane emissions of the Delaware sub-basin of the Permian Basin oil and natural gas extraction area in Texas and New Mexico. Four of the measurements are based on tall communications towers, while one is on a building on a mountain ridge, with the recent addition of a small tower at that site. Although methane (CH4) is the primary species of interest, carbon dioxide (CO2), hydrogen sulfide (H2S), and the isotopic ratio of methane (δ13CH4) are also reported for a subset of the sites. Measurements were reported following the WMO X2004A scale for CH4 and the WMO X2019 scale for CO2. CRDS instruments were calibrated for CH4 andCO2 in the laboratory prior to deployment. For H2S, data were offset-corrected using the minimum 40 min running mean value of the day, and for δ13CH4, calibrations were based on laboratory data. We describe the characteristics of the dataset with a set of illustrative analyses. Methane and carbon dioxide showed strong seasonality, with a well-defined diurnal cycle during the summer, which was opposed to the winter, when a diurnal cycle was absent. CH4 enhancements to the background, during the winter, are up to twice the summer values, which is attributed to the changes in boundary layer depth and wind speed. The largest CH4 enhancements occurred when winds blow from the center of the Delaware sub-basin, where most of the methane emissions come from. The magnitude of enhancements of CO2 did not present seasonality. H2S enhancements indicated a potential source northeast of the tower (Hobbs, New Mexico) where the inlet is installed. Isotopic ratios of methane indicated that oil and natural gas extraction is the source of local methane in the region. The hourly-averaged data, starting on 1 March 2020 and described in this paper, are archived at The Pennsylvania State University Data Commons at 10.26208/98y5-t941 (Monteiro et al., 2021).

Atmospheric CO2 concentration and its influence factors during 2020 COVID-19 pandemic in Nanchang: Journal of Nanjing University of Information Science & Technology

The atmospheric CO2 concentrations are mainly influenced by regional sinks/sources and atmospheric transport processes, thus observations in urban area contain essential information of anthropogenic CO2 emissions. To investigate the effect of COVID-19 on atmospheric CO2 concentration and its anthropogenic emissions, this study chose Nanchang city as the study area and used a priori emission inventory with WRF-STILT (Stochastic Time-Inverted Lagrangian Transport) atmospheric transport model to simulate hourly CO2 concentrations from January 24th to April 30th, 2020. In accordance with the government measures to control COVID-19 epidemic, the whole study period was divided into two periods of Level 1 period (from January 24th to March 11th) and Level 2 period (from March 12th to April 30th). Results indicate the model can well capture hourly variations of CO2 concentration, but it overestimated nighttime concentrations due to the negligence of emission source height. During Level 1 period, the observed and simulated afternoon (12:00-18:00) CO2 mole fractions were 433. 63×10-6 and 438. 22×10-6, respectively,in which the anthropogenic emissions were 21.9% overestimated by simulation compared with observations. While during Level 2 period, the observation and simulation were very close as 432. 06×10-6 and 432. 24 × 10-6. The above comparisons indicate that the CO2 emissions can be represented by a priori CO2 emission inventory in Level 2 period, but was overestimated by 21.9% in Level 1 period, and the discrepancy was mainly due to government measures to control COVID-19 pandemic during this period. Besides, the average biological NEE enhancements were generally lower than 2×10-6, indicating a small contribution compared with anthropogenic emissions. The higher PBLH (Planetary Boundary Layer Height) in Level 2 period also offset the enhancement in CO2 emissions, which was also the main reason for the close observations during two periods. Our findings can provide scientific method supports for greenhouse gas emission inversions at urban scale.

PM-Bound Polycyclic Aromatic Hydrocarbons and Nitro-Polycyclic Aromatic Hydrocarbons in the Ambient Air of Vladivostok: Seasonal Variation, Sources, Health Risk Assessment and Long-Term Variability.

Total suspended particles (TSP) were collected in Vladivostok, Russia, which is a typical port city. This study investigated the concentration, potential sources, and long-term variation in particle PAHs and NPAHs in the atmosphere of Vladivostok. The PAH and NPAH concentrations were higher in winter than in summer (PAHs: winter: 18.6 ± 9.80 ng/m3 summer: 0.54 ± 0.21 ng/m3; NPAHs: winter: 143 ± 81.5 pg/m3 summer: 143 ± 81.5 pg/m3). The diagnostic ratios showed that PAHs and NPAHs mainly came from vehicle emissions in both seasons, while heating systems were the main source of air pollution in winter. The TEQ assessment values were 2.90 ng/m3 and 0.06 ng/m3 in winter and summer, respectively, suggesting a significant excess cancer risk in the general population in winter. The ILCR values conveyed a potential carcinogenic risk because the value was between 1 × 10-5 and 1 × 10-7 and ingestion was a main contributor in Vladivostok. However, it is worth noting that the concentrations of PAHs and NPAHs showed an overall downward trend from 1999 to 2020. An important reason for this is the cogenerations project implemented by the Far Eastern Center for Strategic Research on Fuel and Energy Complex Development in 2010. This research clarified the latest variations in PAHs and NPAHs to provide continuous observation data for future chemical reaction or model prediction research.

Unexpected rise of atmospheric secondary aerosols from biomass burning during the COVID-19 lockdown period in Hangzhou, China.

After the global outbreak of COVID-19, the Chinese government took many measures to control the spread of the virus. The measures led to a reduction in anthropogenic emissions nationwide. Data from a single particle aerosol mass spectrometer in an eastern Chinese megacity (Hangzhou) before, during, and after the COVID-19 lockdown (5 January to February 29, 2020) was used to understand the effect lockdown had on atmospheric particles. The collected single particle mass spectra were clustered into eight categories. Before the lockdown, the proportions of particles ranked in order of: EC (57.9%) < K-SN (13.6%) < Fe-rich (10.2%) < ECOC (6.7%) < K-Na (6.6%) < OC (3.4%) < K-Pb (1.0%) < K-Al (0.7%). During the lockdown period, the EC and Fe-rich particles decreased by 42.8% and 93.2% compared to before lockdown due to reduced vehicle exhaust and industrial activity. By contrast, the K-SN and K-Na particles containing biomass burning tracers increased by 155.2% and 45.2% during the same time, respectively. During the lockdown, the proportions of particles ranked in order of: K-SN (39.7%) < EC (38.1%) < K-Na (11.0%) < ECOC (7.7%) < OC (1.2%) < K-Pb (0.9%) < Fe-rich (0.8%) < K-Al (0.6%). Back trajectory analysis indicated that both inland (Anhui and Shandong provinces) and marine transported air masses may have contributed to the increase in K-SN and K-Na particles during the lockdown, and that increased number of fugitive combustion points (i.e., household fuel, biomass combustion) was a contributing factor. Therefore, the results imply that regional synergistic control measures on fugitive combustion emissions are needed to ensure good air quality.

Global to local impacts on atmospheric CO2from the COVID-19 lockdown, biosphere and weather variabilities

The worldwide lockdown in response to the COVID-19 pandemic in year 2020 led to an economic slowdown and a large reduction in fossil fuel CO2 emissions (Le Quéré 2020 Nat. Clim. Change 10 647-53, Liu 2020 Nat. Commun. 11);however, it is unclear how much it would slow the increasing trend of atmospheric CO2 concentration, the main driver of climate change, and whether this impact can be observed considering the large biosphere and weather variabilities. We used a state-of-the-art atmospheric transport model to simulate CO2, and the model was driven by a new daily fossil fuel emissions dataset and hourly biospheric fluxes from a carbon cycle model forced with observed climate variability. Our results show a 0.21 ppm decrease in the atmospheric column CO2 anomaly in the Northern Hemisphere latitude band 0-45 N in March 2020, and an average of 0.14 ppm for the period of February-April 2020, which is the largest decrease in the last 10 years. A similar decrease was observed by the carbon observing satellite GOSAT (Yokota et al 2009 Sola 5 160-3). Using model sensitivity experiments, we further found that the COVID and weather variability are the major contributors to this CO2 drawdown, and the biosphere showed a small positive anomaly. Measurements at marine boundary layer stations, such as Hawaii, exhibit 1-2 ppm anomalies, mostly due to weather and the biosphere. At the city scale, the on-road CO2 enhancement measured in Beijing shows a reduction by 20-30 ppm, which is consistent with the drastically reduced traffic during the COVID lockdown. A stepwise drop of 20 ppm during the city-wide lockdown was observed in the city of Chengdu. The ability of our current carbon monitoring systems in detecting the small and short-lasting COVID signals at different policy relevant scales (country and city) against the background of fossil fuel CO2 accumulated over the last two centuries is encouraging. The COVID-19 pandemic is an unintended experiment. Its impact suggests that to keep atmospheric CO2 at a climate-safe level will require sustained effort of similar magnitude and improved accuracy, as well as expanded spatiotemporal coverage of our monitoring systems. © 2021 The Author(s). Published by IOP Publishing Ltd.

An Evaluation of the Impact of Pandemic Driven Lockdown on the Phytoplankton Biomass Over the North Indian Ocean Using Observations and Model

The unprecedented nationwide lockdown due to the 'coronavirus disease 2019' (COVID-19) affected humans and the environment in different ways. It provided an opportunity to examine the effect of reduced transportation and other anthropogenic activities on the environment. In the current study, the impact of lockdown on chlorophyll-a (Chl-a) concentration, an index of primary productivity, over the northern Indian Ocean (IO), is investigated using the observations and a physical-biogeochemical model. The statistics of model validation against observations shows a correlation coefficient of 0.85 (0.89), index of agreement as 0.90 (0.91). Root mean square error of 0.45 degrees C (0.50 degrees C) for sea surface temperature over the Bay of Bengal (BoB) (Arabian Sea, AS) is observed. The model results are analyzed to understand the upper-oceanic physical and biological processes during the lockdown. A comparison of the observed and model-simulated data during the lockdown period (March-June, 2020) and pre-pandemic period (March-June, 2019) shows significant differences in the physical (temperature and salinity) and biogeochemical (Chl-a concentration, nutrient concentration, and dissolved oxygen) parameters over the western AS, western BoB, and regions of Sri Lanka. During the pandemic, the reduced anthropogenic activities lead to a decrease in Chl-a concentration in the coastal regions of western AS and BoB. The enhanced aerosol/dust transport due to stronger westerly winds enhanced phytoplankton biomass in the western Arabian Sea (WAS) in May-June of the pandemic period.